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+"keyword","repo_name","file_path","file_extension","file_size","line_count","content","language"
+"PBPK model","Open-Systems-Pharmacology/OSP-PBPK-Model-Library","Fluconazole/Fluconazole_evaluation_report.md",".md","36595","577","# Building and evaluation of a PBPK model for Fluconazole in healthy adults
+
+| Version | 2.0-OSP12.2 |
+| ----------------------------------------------- | ------------------------------------------------------------ |
+| based on *Model Snapshot* and *Evaluation Plan* | https://github.com/Open-Systems-Pharmacology/Fluconazole-Model/releases/tag/v2.0 |
+| OSP Version | 12.2 |
+| Qualification Framework Version | 3.5 |
+
+This evaluation report and the corresponding PK-Sim project file are filed at:
+
+https://github.com/Open-Systems-Pharmacology/OSP-PBPK-Model-Library/
+
+# Table of Contents
+
+ * [1 Introduction](#introduction)
+ * [2 Methods](#methods)
+ * [2.1 Modeling strategy](#modeling-strategy)
+ * [2.2 Data used](#data-used)
+ * [2.3 Model parameters and assumptions](#model-parameters-and-assumptions)
+ * [3 Results and Discussion](#results-and-discussion)
+ * [3.1 Fluconazole final input parameters](#fluconazole-final-input-parameters)
+ * [3.2 Fluconazole Diagnostics Plots](#fluconazole-diagnostics-plots)
+ * [3.3 Concentration-Time Profiles](#ct-profiles)
+ * [3.3.1 Model Building](#model-building)
+ * [3.3.2 Model Verification](#model-verification)
+ * [4 Conclusion](#conclusion)
+ * [5 References](#main-references)
+
+# 1 Introduction
+
+Fluconazole is an antifungal medication for the treatment of a number of different fungal infections. It is metabolized by UGT2B7 and renally excreted by glomerular filtration. About 70% of fluconazole is excreted unchanged in urine during 120 hours. Fluconazole is a moderate inhibitor of CYP3A4 and can therefore serve as a perpetrator for CYP3A4 in DDI studies.
+
+The herein presented PBPK model of fluconazole has been developed using published pharmacokinetic clinical data by Palkama et al. ([Palkama 1998](#5-references)), Ripa et al. ([Ripa 1993](#5-references)), Shiba et al. ([Shiba 1990](#5-references)), Ahonen et al. ([Ahonen 1997](#5-references)), Brammer et al. ([Brammer 1990](#5-references)), Brammer et al. ([Brammer 1991](#5-references)), Thorpe et al. ([Thorpe 1990](#5-references) and Varhe et al. ([Varhe 1996](#5-references)).
+The model has then been evaluated by comparing observed data to simulations of both intravenously and orally administered fluconazole covering a dose range of 25 mg to 400 mg. Both single dose and multiple dose administration are represented in the development and evaluation data sets.
+
+The presented model includes the following features:
+
+- metabolism by UGT2B7,
+- inhibition of CYP3A4,
+- renal clearance by glomerular filtration,
+- oral absorption with dissolution rate assigned to the Weibull function for low, medium and high doses.
+
+# 2 Methods
+
+## 2.1 Modeling strategy
+
+The general concept of building a PBPK model has previously been described by Kuepfer et al. ([Kuepfer 2016](#5-references)). Relevant information on anthropometric (height, weight) and physiological parameters (e.g. blood flows, organ volumes, binding protein concentrations, hematocrit, cardiac output) in adults was gathered from the literature and has been previously published ([Willmann 2007](#5-references)). The information was incorporated into PK-Sim® and was used as default values for the simulations in adults.
+
+The applied activity and variability of plasma proteins and active processes that are integrated into PK-Sim® are described in the publicly available PK-Sim® Ontogeny Database Version 7.3 ([PK-Sim Ontogeny Database Version 7.3](#5-references)) or otherwise referenced for the specific process.
+
+A mean model was built based on clinical data from studies with intravenous administration of fluconazole by Palkama et al. 1998 ([Palkama 1998](#5-references)), Ripa et al. 1993 ([Ripa 1993](#5-references)) and Shiba et al. 1990 ([Shiba 1990](#5-references)). The studies conducted by Palkama et al. and Ripa et al. reported plasma concentrations of fluconazole and the study by Shiba reported fraction of unchanged fluconazole excreted to urine in addition to plasma concentrations. The mean PBPK model was developed using a typical male European individual. The relative tissue-specific expressions of enzymes predominantly being involved in the metabolism of fluconazole (UGT2B7) were considered ([Meyer 2012](#5-references)).
+
+A specific set of parameters (see below) was optimized to describe the distribution of fluconazole after intravenous administration using the Parameter Identification module provided in PK-Sim®. Structural model selection was mainly guided by visual inspection and total error of the resulting description of data, 95% confidence interval of the identified parameter values and biological plausibility.
+
+Once the appropriate structural model was identified for the intravenous administration, the model was verified by simulating other clinical studies reporting pharmacokinetic concentration-time profiles after intravenous administration of fluconazole.
+
+Thereafter additional parameters for oral administration of non-dissolved formulations (i.e. capsules) were identified. Administered doses of fluconazole were ranging from single dose of 25 mg to multiple dosing of 400 mg, why the applied Weibull function to describe the dissolution of fluconazole were divided into low, medium and high dose. The dissolution shape were kept between the doses while the dissolution time were separated.
+
+The model was then verified by simulating further clinical studies reporting pharmacokinetic concentration-time profiles after oral administration of fluconazole.
+
+Details about input data (physicochemical, *in vitro* and clinical) can be found in [Section 2.2](#22-data-used).
+
+Details about the structural model and its parameters can be found in [Section 2.3](#23-model-parameters-and-assumptions).
+
+## 2.2 Data used
+
+### 2.2.1 In vitro and physicochemical data
+
+A literature search was performed to collect available information on physicochemical properties of fluconazole. The obtained information from literature is summarized in the table below, and is used for model building.
+
+| **Parameter** | **Unit** | **Value** | Source | **Description** |
+| :---------------------- | -------- | --------- | ------------------------------------ | ------------------------------------------------------------ |
+| MW | g/mol | 306.28 | [Debruyne 1993](#5-references) | Molecular weight |
+| pKa | | 2.03 | [Charoo 2014](#5-references) | acid dissociation constant of conjugate acid; compound type: weak base |
+| Solubility (pH) | mg/mL | 6.9 (7.4) | [Charoo 2014](#5-references) | Aqueous Solubility |
+| logP | | 0.5 | [Christofoletti 2016](#5-references) | Partition coefficient between octanol and water |
+| | | 0.5 | [Charoo 2014](#5-references) | Partition coefficient between octanol and water |
+| fu | % | 89 | Pfizer | Fraction unbound in plasma |
+| | % | 88 | [Christofoletti 2016](#5-references) | Fraction unbound in plasma |
+| CLint UGT2B7 | 1/min | 0.005 | [Watt 2018](#5-references) | First order intrinsic clearance UGT2B7 |
+| Peff | cm/s | 0.000213 | GastroPlus | Specific intestinal permeability |
+| Ki CYP3A4 | µmol/L | 13.1 | [Sakaeda 2005](#5-references) | Non-competitive inhibition of CYP3A4 |
+| GFR fraction | | 0.17 | [Watt 2018](#5-references) | glomerular filtration fraction |
+
+### 2.2.2 Clinical data
+
+A literature search was performed to collect available clinical data on fluconazole in adults.
+
+The following publications were found in adults for model building:
+
+| Publication | Arm / Treatment / Information used for model building |
+| :---------------------------- | :----------------------------------------------------------- |
+| [Ahonen 1997](#5-references) | Plasma PK profiles in healthy subjects after single dose oral administration of 400 mg fluconazole |
+| [Brammer 1990](#5-references) | Plasma PK profiles in healthy subjects after multiple oral administration of 200, 300 and 400 mg fluconazole |
+| [Brammer 1991](#5-references) | Plasma PK profiles in healthy subjects after single dose oral administration of 50 mg fluconazole |
+| [Shiba 1990](#5-references) | Plasma PK profiles and urine data in healthy subjects after single iv and oral administration of 25 and 50 mg fluconazole and single oral dose of 100 mg fluconazole |
+| [Palkama 1998](#5-references) | Plasma PK profiles in healthy subjects after single dose iv and oral administration of 400 mg fluconazole |
+| [Ripa 1993](#5-references) | Plasma PK profiles in healthy subjects after single dose iv administration of 100 mg fluconazole |
+| [Thorpe 1990](#5-references) | Plasma PK profiles in healthy subjects after single dose oral administration of 100 mg fluconazole |
+| [Varhe 1996](#5-references) | Plasma PK profiles in healthy subjects after multiple oral administration of 100 and 200 mg fluconazole |
+
+The following table shows the data from the excretion studies ([Shiba 1990](#5-references)) used for model building:
+
+| Observer | Value |
+| ------------------------------------------------------------ | ----- |
+| Fraction excreted to urine of unchanged fluconazole after iv administration 25 mg | 74% |
+| Fraction excreted to urine of unchanged fluconazole after iv administration 25 mg | 72% |
+| Fraction excreted to urine of unchanged fluconazole after oral administration 25 mg | 69% |
+| Fraction excreted to urine of unchanged fluconazole after oral administration 50 mg | 67% |
+| Fraction excreted to urine of unchanged fluconazole after oral administration 100 mg | 75% |
+
+The following dosing scenarios were simulated and compared to respective data for model verification:
+
+| Scenario | Data reference |
+| ------------------------------------------------------------ | ------------------------------------ |
+| iv 400 mg (60 min) | [Ahonen 1997](#5-references) |
+| iv 50 mg (2 min) | [Brammer 1990](#5-references) |
+| iv 25 mg (1 min) | [Shiba 1990](#5-references) |
+| iv 800 mg (240 min) once daily for 14 days | [Sobue 2004](#5-references) |
+| iv 100 mg (20 min) | [Yeates 1994](#5-references) |
+| po 400 mg | [Palkama 1998](#5-references) |
+| po 100 mg | [Shiba 1990](#5-references) |
+| po 50 mg once daily for 4 days | [Varhe 1996](#5-references) |
+
+## 2.3 Model parameters and assumptions
+
+### 2.3.1 Absorption
+
+The parameter value for `Specific intestinal permeability` was used as in GastroPlus. Although, the permeability didn't seem to be the rate-limiting step in oral absorption as this parameter could not be identified when attempting to estimate a value using Parameter Identification. The default solubility was assumed to be the measured value in phosphate buffer (see [Section 2.2.1](#221-in-vitro-and-physicochemical-data))
+
+The dissolution of capsules/tablets (not always specified in the literature which formulation for oral administration was used) were implemented via empirical Weibull dissolution. A different Weibull function was used for different dose categories, low, medium high and high dose to distinguish the dissolution time across the dose range; see results of optimization in [Section 2.3.4](#234-automated-parameter-identification).
+
+### 2.3.2 Distribution
+
+Fluconazole has a low protein bound (approx. 11 %) fraction in plasma (see [Section 2.2.1](#221-in-vitro-and-physicochemical-data)). A value of 89% was used in this PBPK model for `Fraction unbound (plasma, reference value)`. The major binding partner was set to alpha1- acid glycoprotein(see [Section 2.2.1](#221-in-vitro-and-physicochemical-data)).
+
+An important parameter influencing the resulting volume of distribution is lipophilicity. The reported experimental logP values are in the range of 0.5-1.0 (see [Section 2.2.1](#221-in-vitro-and-physicochemical-data)) which served as a starting value. Finally, the model parameters `Lipophilicity` was optimized to match best clinical data (see also [Section 2.3.4](#234-automated-parameter-identification)).
+
+After testing the available organ-plasma partition coefficient and cell permeability calculation methods built in PK-Sim, observed clinical data was best described by choosing the partition coefficient calculation by `Rodgers and Rowland` and cellular permeability calculation by `PK-Sim Standard`.
+
+### 2.3.3 Metabolism and Elimination
+
+One metabolic pathway was implement into the model via first order kinetics
+
+* UGT2B7
+
+The UGT2B7 expression profiles is based on high-sensitive real-time RT-PCR ([Nishimura 2003](#5-references)). Absolute tissue-specific expressions were obtained by considering the respective absolute concentration in the liver. The `Specific clearance ` of fluconazole accounted by UGT2B7 was identified to best describe observed clinical data after intravenous administration (see [Section 2.3.4](#234-automated-parameter-identification)).
+
+Additionally, renal clearance (which is the main elimination process for fluconazole) assumed to be mainly driven by glomerular filtration was implemented. The `GFR fraction` was identified to best describe the observed fraction of unchanged fluconazole that was renally excreted (see [Section 2.3.4](#234-automated-parameter-identification)).
+
+### 2.3.4 Automated Parameter Identification
+
+This is the result of the final parameter identification for the intravenous model:
+
+| Model Parameter | Optimized Value | Unit |
+| ------------------------------ | --------------- | --------- |
+| `Lipophilicity` | 0.83 | Log Units |
+| `GFR fraction` | 0.14 | |
+| `Specific clearance ` (UGT2B7) | 1.85E-3 | 1/min |
+
+This is the result of the final parameter identification for the dissolution parameters of a oral administered fluconazole:
+
+| Model Parameter | Optimized Value | Unit |
+| --------------------------------------------------- | --------------- | ---- |
+| `Dissolution time (50% dissolved) high dose` | 105.81 | min |
+| `Dissolution time (50% dissolved) medium high dose` | 75.14 | min |
+| `Dissolution time (50% dissolved) low dose` | 33.40 | min |
+| `Dissolution shape` | 2.14 | |
+
+# 3 Results and Discussion
+
+The PBPK model for fluconazole was developed and verified with clinical pharmacokinetic data.
+
+The model was built and evaluated covering data from studies including in particular
+
+* intravenous infusions and oral administrations (capsules).
+* a dose range of 25 to 800 mg.
+
+The model quantifies excretion via urine (glomerular filtration) and metabolism via UGT2B7.
+
+The next sections show:
+
+1. the final model input parameters for the building blocks: [Section 3.1](#31-fluconazole-final-input-parameters).
+2. the overall goodness of fit: [Section 3.2](#32-fluconazole-diagnostics-plots).
+3. simulated vs. observed concentration-time profiles for the clinical studies used for model building and for model verification: [Section 3.3](#33-concentration-time-profiles).
+
+## 3.1 Fluconazole final input parameters
+
+The compound parameter values of the final PBPK model are illustrated below.
+
+### Compound: Fluconazole
+
+#### Parameters
+
+Name | Value | Value Origin | Alternative | Default
+------------------------------------------------ | ---------------------- | --------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------- | -------
+Solubility at reference pH | 6.9 mg/ml | | Measurement_Solubility_Charoo2014 | True
+Reference pH | 7 | | Measurement_Solubility_Charoo2014 | True
+Lipophilicity | 0.8302390226 Log Units | Parameter Identification-Parameter Identification-Value updated from 'Parameter Identification 1' on 2021-07-13 15:29 | Optimized_Lipophilicity | True
+Fraction unbound (plasma, reference value) | 89 % | | Measurement_Fraction_Unbound_Pfizer | True
+Specific intestinal permeability (transcellular) | 0.000213 cm/s | | Measurement_Specific_Intestinal_Permeability_GastroPlus | True
+F | 2 | | |
+Is small molecule | Yes | | |
+Molecular weight | 306.328 g/mol | | |
+Plasma protein binding partner | α1-acid glycoprotein | | |
+
+#### Calculation methods
+
+Name | Value
+----------------------- | -------------------
+Partition coefficients | Rodgers and Rowland
+Cellular permeabilities | PK-Sim Standard
+
+#### Processes
+
+##### Metabolizing Enzyme: UGT2B7-Watt2018
+
+Species: Human
+
+Molecule: UGT2B7
+
+###### Parameters
+
+Name | Value | Value Origin
+------------------- | --------------------- | ---------------------------------------------------------------------------------------------------------------------
+Intrinsic clearance | 0.008 l/min |
+Specific clearance | 0.0018460294654 1/min | Parameter Identification-Parameter Identification-Value updated from 'Parameter Identification 1' on 2021-07-13 15:29
+
+##### Systemic Process: Glomerular Filtration-Watt2018
+
+Species: Human
+
+###### Parameters
+
+Name | Value | Value Origin
+------------ | ------------:| ---------------------------------------------------------------------------------------------------------------------
+GFR fraction | 0.1433240785 | Parameter Identification-Parameter Identification-Value updated from 'Parameter Identification 1' on 2021-07-13 15:29
+
+##### Inhibition: CYP3A4-Sakaeda2005
+
+Molecule: CYP3A4
+
+###### Parameters
+
+Name | Value | Value Origin
+---- | ----------- | ------------:
+Ki | 13.1 µmol/l |
+
+### Formulation: Fluconazole_Weibull_high dose
+
+Type: Weibull
+
+#### Parameters
+
+Name | Value | Value Origin
+-------------------------------- | ------------------ | ---------------------------------------------------------------------------------------------------------------------
+Dissolution time (50% dissolved) | 105.8071997888 min | Parameter Identification-Parameter Identification-Value updated from 'Parameter Identification 2' on 2021-07-13 18:01
+Lag time | 0 min |
+Dissolution shape | 2.1410681544 | Parameter Identification-Parameter Identification-Value updated from 'Parameter Identification 2' on 2021-07-13 18:01
+Use as suspension | Yes |
+
+### Formulation: Fluconazole_Weibull_low dose
+
+Type: Weibull
+
+#### Parameters
+
+Name | Value | Value Origin
+-------------------------------- | ----------------- | ---------------------------------------------------------------------------------------------------------------------
+Dissolution time (50% dissolved) | 33.3965005792 min | Parameter Identification-Parameter Identification-Value updated from 'Parameter Identification 2' on 2021-07-13 18:01
+Lag time | 0 min |
+Dissolution shape | 2.1410681544 | Parameter Identification-Parameter Identification-Value updated from 'Parameter Identification 2' on 2021-07-13 18:01
+Use as suspension | Yes |
+
+### Formulation: Fluconazole_Weibull_medium high dose
+
+Type: Weibull
+
+#### Parameters
+
+Name | Value | Value Origin
+-------------------------------- | ----------------- | ---------------------------------------------------------------------------------------------------------------------
+Dissolution time (50% dissolved) | 75.1395611322 min | Parameter Identification-Parameter Identification-Value updated from 'Parameter Identification 2' on 2021-07-13 18:01
+Lag time | 0 min |
+Dissolution shape | 2.1410681544 | Parameter Identification-Parameter Identification-Value updated from 'Parameter Identification 2' on 2021-07-13 18:01
+Use as suspension | Yes |
+
+## 3.2 Fluconazole Diagnostics Plots
+
+Below you find the goodness-of-fit visual diagnostic plots for the PBPK model performance of all data used presented in [Section 2.2.2](#222-clinical-data).
+
+The first plot shows observed versus simulated plasma concentration, the second weighted residuals versus time.
+
+
+
+**Table 3-1: GMFE for Goodness of fit plot for concentration in plasma.**
+
+|Group |GMFE |
+|:----------------------------------------------------|:----|
+|Fluconazole iv (model building) |1.09 |
+|Fluconazole iv (model verification) |1.14 |
+|Fluconazole oral administration (model building) |1.20 |
+|Fluconazole oral administration (model verification) |1.26 |
+|All |1.18 |
+
+
+
+
+
+
+
+
+**Figure 3-1: Goodness of fit plot for concentration in plasma.**
+
+
+
+
+
+
+
+
+**Figure 3-2: Goodness of fit plot for concentration in plasma.**
+
+
+
+
+## 3.3 Concentration-Time Profiles
+
+Simulated versus observed concentration-time profiles of all data listed in [Section 2.2.2](#222-clinical-data) are presented below.
+
+### 3.3.1 Model Building
+
+
+
+
+
+**Figure 3-3: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-4: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-5: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-6: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-7: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-8: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-9: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-10: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-11: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-12: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-13: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-14: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-15: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-16: Time Profile Analysis**
+
+
+
+
+### 3.3.2 Model Verification
+
+
+
+
+
+**Figure 3-17: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-18: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-19: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-20: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-21: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-22: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-23: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-24: Time Profile Analysis**
+
+
+
+
+# 4 Conclusion
+
+The presented PBPK model adequately describes the intravenous and oral pharmacokinetics of fluconazole in adults.
+
+# 5 References
+
+**Ahonen 1997** Ahonen, Jouni, Klaus T. Olkkola, and Pertti J. Neuvonen. (1997). Effect of route of administration of fluconazole on the interaction between fluconazole and midazolam. *European journal of clinical pharmacology* 51.5, 415-419.
+
+**Brammer 1990** Brammer, K. W., P. R. Farrow, and J. K. Faulkner. (1990). Pharmacokinetics and tissue penetration of fluconazole in humans. *Reviews of infectious diseases* 12.Supplement_3, S318-S326.
+
+**Brammer 1991** Brammer, K. W., Coakley, A. J., Jezequel, S. G., & Tarbit, M. H. (1991). The disposition and metabolism of [14C] fluconazole in humans. *Drug metabolism and disposition* 19.4, 764-767.
+
+**Charoo 2014** Charoo, N., Cristofoletti, R., Graham, A., Lartey, P., Abrahamsson, B., Groot, D. W., ... & Dressman, J. (2014). Biowaiver monograph for immediate-release solid oral dosage forms: fluconazole. *Journal of pharmaceutical sciences*, *103*(12), 3843-3858.
+
+**Cristofoletti 2016** Cristofoletti, R., Charoo, N. A., & Dressman, J. B. (2016). Exploratory investigation of the limiting steps of oral absorption of fluconazole and ketoconazole in children using an in silico pediatric absorption model. *Journal of pharmaceutical sciences*, *105*(9), 2794-2803.
+
+**Debruyne 1993** Debruyne, D., & Ryckelynck, J. P. (1993). Clinical pharmacokinetics of fluconazole. *Clinical pharmacokinetics*, *24*(1), 10-27.
+
+**Kuepfer 2016** Kuepfer L, Niederalt C, Wendl T, Schlender JF, Willmann S, Lippert J, Block M, Eissing T, Teutonico D. (2016). Applied Concepts in PBPK Modeling: How to Build a PBPK/PD Model. *CPT Pharmacometrics Syst Pharmacol*. Oct;5(10), 516-531.
+
+**Meyer 2012** Meyer M, Schneckener S, Ludewig B, Kuepfer L, Lippert J. (2012). Using expression data for quantification of active processes in physiologically based pharmacokinetic modeling. *Drug Metab Dispos*. May;40(5), 892-901.
+
+**Nishimura 2003** Nishimura M, Yaguti H, Yoshitsugu H, Naito S, Satoh T. (2003). Tissue distribution of mRNA expression of human cytochrome P450 isoforms assessed by high-sensitivity real-time reverse transcription PCR. *Yakugaku Zasshi.* May;123(5), 369-75.
+
+**Palkama 1998** Palkama, V. J., Isohanni, M. H., Neuvonen, P. J., & Olkkola, K. T. (1998). The effect of intravenous and oral fluconazole on the pharmacokinetics and pharmacodynamics of intravenous alfentanil. *Anesthesia & Analgesia*, *87*(1), 190-194.
+
+**Ripa 1993** Ripa, S., Ferrante, L., & Prenna, M. (1993). Pharmacokinetics of fluconazole in normal volunteers. *Chemotherapy*, *39*(1), 6-12.
+
+**Sakaeda 2005** Sakaeda, T., Iwaki, K., Kakumoto, M., Nishikawa, M., Niwa, T., Jin, J. (2005). Effect of micafungin on cytochrome P450 3A4 and multidrug resistance protein 1 activities, and its comparison with azole antifungal drugs. *J Pharm Pharmacol*, *57*, 759-764.
+
+**Shiba 1990** Shiba, K., Saito, A., & Miyahara, T. (1990). Safety and pharmacokinetics of single oral and intravenous doses of fluconazole in healthy subjects. *Clinical therapeutics*, *12*(3), 206-215.
+
+**Sobue 2004** Sobue, S., Tan, K., Layton, G., Eve, M., & Sanderson, J. B. (2004). Pharmacokinetics of fosfluconazole and fluconazole following multiple intravenous administration of fosfluconazole in healthy male volunteers. *British journal of clinical pharmacology*, *58*(1), 20-25.
+
+**Thorpe 1990** Thorpe, J. E., Baker, N., & Bromet-Petit, M. (1990). Effect of oral antacid administration on the pharmacokinetics of oral fluconazole. *Antimicrobial agents and chemotherapy*, *34*(10), 2032-2033.
+
+**Varhe 1996** Varhe, A., Olkkola, K. T., & Neuvonen, P. J. (1996). Effect of fluconazole dose on the extent of fluconazole‐triazolam interaction. *British journal of clinical pharmacology*, *42*(4), 465-470.
+
+**Watt 2018** Watt, K. M., Cohen‐Wolkowiez, M., Barrett, J. S., Sevestre, M., Zhao, P., Brouwer, K. L., & Edginton, A. N. (2018). Physiologically based pharmacokinetic approach to determine dosing on extracorporeal life support: fluconazole in children on ECMO. *CPT: pharmacometrics & systems pharmacology*, *7*(10), 629-637.
+
+**Willmann 2007** Willmann S, Höhn K, Edginton A, Sevestre M, Solodenko J, Weiss W, Lippert J, Schmitt W. (2007). Development of a physiology-based whole-body population model for assessing the influence of individual variability on the pharmacokinetics of drugs. *J Pharmacokinet Pharmacodyn.* 34(3), 401-431.
+
+**Yeates 1994** Yeates, R. A., Ruhnke, M., Pfaff, G., Hartmann, A., Trautmann, M., & Sarnow, E. (1994). The pharmacokinetics of fluconazole after a single intravenous dose in AIDS patients. *British journal of clinical pharmacology*, *38*(1), 77-79.
+
+","Markdown"
+"PBPK model","Open-Systems-Pharmacology/OSP-PBPK-Model-Library","dAb2/dAb2_evaluation_report.md",".md","19725","337","# Building and evaluation of a PBPK model for domain antibody dAb2 in mice
+
+| Version | 1.0-OSP12.2 |
+| ----------------------------------------------- | ------------------------------------------------------------ |
+| based on *Model Snapshot* and *Evaluation Plan* | https://github.com/Open-Systems-Pharmacology/dAb2-Model/releases/tag/v1.0 |
+| OSP Version | 12.2 |
+| Qualification Framework Version | 3.5 |
+
+This evaluation report and the corresponding PK-Sim project file are filed at:
+
+https://github.com/Open-Systems-Pharmacology/OSP-PBPK-Model-Library/
+
+# Table of Contents
+
+ * [1 Introduction](#introduction)
+ * [2 Methods](#methods)
+ * [2.1 Modeling Strategy](#modeling-strategy)
+ * [2.2 Data](#methods-data)
+ * [2.2.1 In vitro / physico-chemical Data ](#invitro-and-physico-chemical-data)
+ * [2.2.2 PK Data ](#PK-data)
+ * [2.3 Model Parameters and Assumptions](#model-parameters-and-assumptions)
+ * [2.3.1 Absorption ](#model-parameters-and-assumptions-absorption)
+ * [2.3.2 Distribution ](#model-parameters-and-assumptions-distribution)
+ * [2.3.3 Metabolism and Elimination ](#model-parameters-and-assumptions-metabolism-and-elimination)
+ * [2.3.4 Tissue Concentrations ](#model-parameters-and-assumptions-tissue-concentrations)
+ * [2.3.5 Automated Parameter Identification ](#model-parameters-and-assumptions-parameter-identification)
+ * [3 Results and Discussion](#results-and-discussion)
+ * [3.1 Final input parameters](#final-input-parameters)
+ * [3.2 Diagnostics Plots](#diagnostics-plots)
+ * [3.3 Concentration-Time Profiles](#ct-profiles)
+ * [4 Conclusion](#conclusion)
+ * [5 References](#main-references)
+
+# 1 Introduction
+
+The dAb2 domain antibody is a fusion protein consisting of a VH (heavy chain) and a Vk (light chain) antibody fragment without known binding affinity which was used to a develop a PBPK model ([Sepp2015](#5-references)).
+
+Since the dAb2 is smaller than antibodies, the PK data (blood and tissue concentration–time profiles in mice) ([Sepp2015](#5-references)) were also used together with pharmacokinetic (PK) data from 5 other compounds to identify unknown parameters during the development of the generic large molecule physiologically based pharmacokinetic (PBPK) model in PK-Sim ([Niederalt 2018](#5-references)).
+
+The herein presented evaluation report evaluates the performance of the PBPK model for dAb2 in mice for the PK data used for the development of the generic large molecule model in PK-Sim.
+
+The presented dAb2 PBPK model as well as the respective evaluation plan and evaluation report are provided open-source (https://github.com/Open-Systems-Pharmacology/dAb2-Model)
+
+# 2 Methods
+
+## 2.1 Modeling Strategy
+
+The development of the large molecule PBPK model in PK-Sim® has previously been described by Niederalt et al. ([Niederalt 2018](#5-references)). In short, the model was built as an extension of the PK-Sim® model for small molecules incorporating (i) the two-pore formalism for drug extravasation from blood plasma to interstitial space, (ii) lymph flow, (iii) endosomal clearance and (iv) protection from endosomal clearance by neonatal Fc receptor (FcRn) mediated recycling.
+
+For model development and evaluation, PK data were used from compounds with a wide range of solute radii and from different species. The PK data used for parameter estimation were from the following compounds: antibody–drug conjugate BAY 79-4620 in mice (Bayer in house data), antibody 7E3 in wild-type and FcRn knockout mice ([Garg 2007](#5-references), [Garg2009](#5-references)), domain antibody dAb2 in mice ([Sepp 2015](#5-references)), antibodies MEDI-524 and MEDI-524-YTE in monkeys ([Dall'Acqua 2006](#5-references)), and antibody CDA1 in humans ([Taylor 2008](#5-references)). The PK data used for model evaluation were from inulin in rats ([Tsuji1983](#5-references)) and tefibazumab in humans ([Reilly 2005](#5-references)).
+
+The PBPK model including the estimated physiological parameters as described by Niederalt et al. ([Niederalt 2018](#5-references)) is available in the Open Systems Pharmacology Suite from version 7.1 onwards.
+
+This evaluation report focuses on the PBPK model for the domain antibody dAb2.
+
+Details about input data (physicochemical, *in vitro* and PK) can be found in [Section 2.2](#22-data).
+
+Details about the structural model and its parameters can be found in [Section 2.3](#23-model-parameters-and-assumptions).
+
+## 2.2 Data
+
+### 2.2.1 In vitro / physico-chemical Data
+
+A literature search was performed to collect available information on physicochemical properties of dAb2. The obtained information from literature is summarized in the table below.
+
+| **Parameter** | **Unit** | **Value** | Source | **Description** |
+| :------------ | -------- | --------- | ------------------------- | ------------------------------------------------------------ |
+| MW | g/mol | 25600 | [Sepp2015](#5-references) | Molecular weight |
+| r | nm | 2.43 | calculated from MW | Hydrodynamic solute radius. Calculated by empirical equation given in [Niederalt2018](#5-references), supplemental material |
+| Kd (FcRn) | µM | 999,999 | | high value representing no FcRn binding |
+
+### 2.2.2 PK Data
+
+Published plasma and tissue PK data on dAb2 in mice were used.
+
+| Publication | Description |
+| :------------------------ | :----------------------------------------------------------- |
+| [Sepp2015](#5-references) | Plasma and tissue PK data after an intravenous dose of dose of 10 mg/kg in mice. Tissue concentrations were analyzed using quantitative whole-body autoradiography. The concentrations were reported as percentage of injected dose / g tissue. These values were converted to concentrations in µg/ml assuming a density of 1 g/ml for all tissues except for bone for which a density of 1.5 g/ml was assumed (as in Ref. [Baxter 1994](#5-references)). Furthermore, a body weight of 29 g (i.e. a dose of 290 µg) was assumed for unit conversion of the experimental concentrations (body weight range reported: 26-33 g). |
+
+## 2.3 Model Parameters and Assumptions
+
+### 2.3.1 Absorption
+
+There is no absorption process since dAb2 was administered intravenously.
+
+### 2.3.2 Distribution
+
+The standard lymph and fluid recirculation flow rates and the standard vascular properties of the different tissues (hydraulic conductivity, pore radii, fraction of flow via large pores) from PK-Sim were used. dAb2, among other compounds, has been used to identify these lymph and fluid recirculation flow rates used in PK-Sim ([Niederalt 2018](#5-references)).
+
+### 2.3.3 Metabolism and Elimination
+
+dAb2 is predominantly renally eliminated by glomerular filtration ([Sepp 2015](#5-references)). Due to the molecular size of dAb2 the glomerular filtration is hindered and the glomerular filtration fraction was fitted. While being only of minor importance, the endosomal clearance process is present. The standard physiological parameters related to endosomal clearance were used (assuming no binding to FcRn).
+
+### 2.3.4 Tissue Concentrations
+
+For the comparison with experimental data, the parameters `Fraction of blood for sampling` used in the Observer for the tissue concentrations were set for all organs to 0.42 for comparison with autoradiography data according to the fit results (across compounds) in Ref. ([Niederalt 2018](#5-references)). (The parameter `Fraction of blood for sampling` specifies residual blood in tissue as ratio of blood volume contributing to the measured tissue concentration to the total in vivo capillary blood volume.)
+
+In the present evaluation report, the experimental gut concentrations were compared to simulated organ concentrations for small and large intestine separately in the goodness of fit plots as well as in the concentration-time profile plot.
+
+### 2.3.5 Automated Parameter Identification
+
+The table shows the parameter values that were specified in the model based on the parameter identification reported in Ref. ([Niederalt 2018](#5-references)), and which were not included in the PK-Sim database since version 7.1.
+
+| Model Parameter | Optimized Value | Unit |
+| ------------------------------------------------------------ | --------------- | ---- |
+| `GFR fraction` (glomerular filtration rate fraction) | 0.24 | - |
+| `Fraction of blood for sampling` (all organs) - for comparison with autoradiography data | 0.42 | |
+
+# 3 Results and Discussion
+
+The PBPK model for dAb2 was evaluated with blood and tissue PK data in mice.
+
+These PK data (except for kidney) have been used together with PK data from 5 other compounds to simultaneously identify parameters during the development of the generic model for proteins and large molecules in PK-Sim ([Niederalt 2018](#5-references)).
+
+As expected, the kidney concentrations are considerably underestimated by the PBPK simulations. In the present PBPK model, the kidney has the same organ model structure as other organs. Thus, drug within the tubular fluid does not account to total kidney concentrations. Drug in tubular fluid is relevant for small proteins which are renally cleared by glomerular filtration. For these proteins, the representation of the kidney has to be extended in order to describe total kidney concentrations, see e.g. Sepp et. al. 2015 ([Sepp 2015](#5-references)).
+
+The next sections show:
+
+1. the final model parameters for the building blocks: [Section 3.1](#final-input-parameters).
+2. the overall goodness of fit: [Section 3.2](#diagnostics-plots).
+3. simulated vs. observed concentration-time profiles for the clinical studies used for model building and for model verification: [Section 3.3](#ct-profiles).
+
+## 3.1 Final input parameters
+
+The compound parameter values of the final PBPK model are illustrated below.
+
+### Compound: dAb2
+
+#### Parameters
+
+Name | Value | Value Origin | Alternative | Default
+------------------------------------------ | ------------ | --------------------------------------------- | ----------- | -------
+Solubility at reference pH | 9999 mg/l | Other-/Dummy value not used in the simulation | Measurement | True
+Reference pH | 7 | Other-/Dummy value not used in the simulation | Measurement | True
+Lipophilicity | -5 Log Units | Other-/Dummy value not used in the simulation | Measurement | True
+Fraction unbound (plasma, reference value) | 1 | Other-Assumption | Measurement | True
+Is small molecule | No | | |
+Molecular weight | 25600 g/mol | Publication-Sepp2015 | |
+Plasma protein binding partner | Unknown | | |
+
+#### Calculation methods
+
+Name | Value
+----------------------- | ---------------
+Partition coefficients | PK-Sim Standard
+Cellular permeabilities | PK-Sim Standard
+
+#### Processes
+
+##### Systemic Process: Glomerular Filtration-GFR
+
+Species: Mouse
+
+###### Parameters
+
+Name | Value | Value Origin
+------------ | -----:| ------------------------
+GFR fraction | 0.24 | Parameter Identification
+
+## 3.2 Diagnostics Plots
+
+Below you find the goodness-of-fit visual diagnostic plots for the PBPK model performance of all data used presented in [Section 2.2.2](#PK-data).
+
+The first plot shows observed versus simulated plasma concentration, the second weighted residuals versus time.
+
+
+
+**Table 3-1: GMFE for Goodness of fit plot for concentration in blood and tissues**
+
+|Group |GMFE |
+|:-----------------------------------------------|:-----|
+|Blood and tissue concentrations - except kidney |1.89 |
+|Kidney tissue concentrations |23.23 |
+|All |2.29 |
+
+
+
+
+
+
+
+
+**Figure 3-1: Goodness of fit plot for concentration in blood and tissues**
+
+
+
+
+
+
+
+
+**Figure 3-2: Goodness of fit plot for concentration in blood and tissues**
+
+
+
+
+## 3.3 Concentration-Time Profiles
+
+Simulated versus observed concentration-time profiles of all data listed in [Section 2.2.2](#PK-data) are presented below.
+
+
+
+
+
+**Figure 3-3: Blood - lin scale**
+
+
+
+
+
+
+
+
+**Figure 3-4: Blood - log scale**
+
+
+
+
+
+
+
+
+**Figure 3-5: Lung**
+
+
+
+
+
+
+
+
+**Figure 3-6: Skin**
+
+
+
+
+
+
+
+
+**Figure 3-7: Muscle**
+
+
+
+
+
+
+
+
+**Figure 3-8: Spleen**
+
+
+
+
+
+
+
+
+**Figure 3-9: Liver**
+
+
+
+
+
+
+
+
+**Figure 3-10: Heart**
+
+
+
+
+
+
+
+
+**Figure 3-11: Bone**
+
+
+
+
+
+
+
+
+**Figure 3-12: Intestine**
+
+
+
+
+
+
+
+
+**Figure 3-13: Brain**
+
+
+
+
+
+
+
+
+**Figure 3-14: Kidney - log scale**
+
+
+
+
+# 4 Conclusion
+
+The herein presented PBPK model overall adequately describes the pharmacokinetics of a domain antibody dAb2 in mice - except for kidney concentrations. Total kidney concentrations cannot be described by the standard kidney representation of PK-Sim for renally excreted biologics, since drug within the tubular fluid is not represented in the organ concentration. Apart from kidney, the largest deviations between measured and simulated concentration-time profiles are observed for spleen for which the initial concentrations are overestimated by the model and bone for which the initial concentrations are underestimated.
+
+The PK data of dAb2 (except kidney concentrations) had been used during the development of the generic large molecule PBPK model in PK-Sim ([Niederalt 2018](#5-references)) together with PK data from 5 other compounds (7E3, BAY 79-4620, CDA1, MEDI-524 & MEDI-524-YTE).
+
+# 5 References
+
+**Dall'Acqua 2006** Dall’Acqua WF, Kiener PA, Wu H. Properties of human IgG1s engineered for enhanced binding to the neonatal Fc receptor (FcRn). J Biol Chem. 2006 Aug; 281(33):23514-23524. doi: 10.1074/jbc.M604292200.
+
+**Garg 2007** Garg A, Balthasar JP. Physiologically-based pharmacokinetic (PBPK) model to predict IgG tissue kinetics in wild-type and FcRn-knockout mice. J Pharmacokinet Pharmacodyn. 2007 Jul; 34(5):687-709. doi: 10.1007/s10928-007-9065-1.
+
+**Garg 2009** Garg A, Balthasar J. Investigation of the influence of FcRn on the distribution of IgG to the brain. AAPS J. 2009 July; 11(3):553-557. doi: 10.1208/s12248-009-9129-9.
+
+**Lobo 2004** Lobo ED, Hansen R J, Balthasar JP. Antibody pharmacokinetics and pharmacodynamics. J Pharm Sci. 2004 Nov;93(11):2645-2668. doi: 10.1002/jps.20178.
+
+**Niederalt 2018** Niederalt C, Kuepfer L, Solodenko J, Eissing T, Siegmund HU, Block M, Willmann S, Lippert J. A generic whole body physiologically based pharmacokinetic model for therapeutic proteins in PK-Sim. J Pharmacokinet Pharmacodyn. 2018 Apr;45(2):235-257. doi: 10.1007/s10928-017-9559-4.
+
+**Reilly 2005** Reilley S, Wenzel E, Reynolds L, Bennett B, Patti JM, Hetherington S. Open-label, dose escalation study of the safety and pharmacokinetic profile of tefibazumab in healthy volunteers. Antimicrob Agents Chemother. 2005 Mar;49(3):959–962. doi: 10.1128/AAC.49.3.959-962.2005.
+
+**Sepp 2015** Sepp A, Berges A, Sanderson A, Meno-Tetang G. Development of a physiologically based pharmacokinetic model for a domain antibody in mice using the two-pore theory. J Pharmacokinet Pharmacodyn. 2015 Jan;42(2):97-109. doi: 10.1007/s10928-014-9402-0.
+
+**Taylor 1984** Taylor AE, Granger DN. Exchange of macromolecules across the microcirculation. Handbook of Physiology - Cardiovascular System. Microcirculation (Eds. Renkin EM and Michel CC. Bethesda, MD, American Physiological Society). 1984; Vol. 4(Pt 2):467–520.
+
+**Taylor 2008** Taylor CP, Tummala S, Molrine D, Davidson L, Farrell RJ, Lembo A, Hibberd PL, Lowy I, Kelly CP. Open-label, dose escalation phase I study in healthy volunteers to evaluate the safety and pharmacokinetics of a human monoclonal antibody to Clostridium difficile toxin A. Vaccine. 2008 Jun;26(27-28):3404–3409. doi: 10.1016/j.vaccine.2008.04.042.
+
+**Tsuji 1983** Tsuji A, Yoshikawa T, Nishide K, Minami H, Kimura M, Nakashima E, Terasaki T, Miyamoto E, Nightingale CH, Yamana T. Physiologically based pharmacokinetic model for beta-lactam antibiotics I: tissue distribution and elimination in rats. J Pharm Sci. 1983 Nov;72(11):1239-1252. doi: 10.1002/jps.2600721103.
+
+","Markdown"
+"PBPK model","Open-Systems-Pharmacology/OSP-PBPK-Model-Library","Felodipine/Felodipine_evaluation_report.md",".md","41856","547","# Building and evaluation of a PBPK model for Felodipine in healthy adults
+
+| Version | 2.0-OSP12.2 |
+| ----------------------------------------------- | ------------------------------------------------------------ |
+| based on *Model Snapshot* and *Evaluation Plan* | https://github.com/Open-Systems-Pharmacology/Felodipine-Model/releases/tag/v2.0 |
+| OSP Version | 12.2 |
+| Qualification Framework Version | 3.5 |
+
+This evaluation report and the corresponding PK-Sim project file are filed at:
+
+https://github.com/Open-Systems-Pharmacology/OSP-PBPK-Model-Library/
+
+# Table of Contents
+
+ * [1 Introduction](#introduction)
+ * [2 Methods](#methods)
+ * [2.1 Modeling Strategy](#modeling-strategy)
+ * [2.2 Data](#data)
+ * [2.3 Model Parameters and Assumptions](#model-parameters-and-assumptions)
+ * [3 Results and Discussion](#results-and-discussion)
+ * [3.1 Final input parameters](#final-input-parameters)
+ * [3.2 Diagnostics Plots](#diagnostics-plots)
+ * [3.3 Concentration-Time Profiles](#ct-profiles)
+ * [3.3.1 Model Building](#model-building)
+ * [3.3.2 Model Verification](#model-verification)
+ * [4 Conclusion](#conclusion)
+ * [5 References](#main-references)
+
+# 1 Introduction
+
+Felodipine is a calcium-channel blocker, indicated for angina pectoris and arterial hypertension. It is mostly metabolized by CYP3A4 making it a sensitive probe and victim drug for the investigation of CYP3A4 activity *in vivo*. It is a BCS class II compound. Felodipine shows substantial first pass metabolism resulting in a bioavailability of 15%.
+
+The model has been developed and evaluated by comparing observed data to simulations of a large number of clinical studies covering a dose range of 1.5 mg to 10 mg after intravenous and oral administrations. Furthermore, it has been evaluated within a CYP3A4 DDI modeling network as a victim drug.
+
+Model features include:
+
+- metabolism by CYP3A4
+- metabolism by an unknown enzyme *via* unspecific hepatic clearance
+- a decrease in the permeability between the intracellular and interstitial space (model parameters `P (intracellular->interstitial)` and `P (interstitial->intracellular)`) in intestinal mucosa to optimize quantitatively the extent of gut wall metabolism
+
+# 2 Methods
+
+## 2.1 Modeling Strategy
+
+The general concept of building a PBPK model has previously been described by Kuepfer et al. ([Kuepfer 2016](#5-references)). Relevant information on anthropometric (height, weight) and physiological parameters (e.g. blood flows, organ volumes, binding protein concentrations, hematocrit, cardiac output) in adults was gathered from the literature and has been previously published ([Willmann 2007](#5-references)). The information was incorporated into PK-Sim® and was used as default values for the simulations in adults.
+
+The applied activity and variability of plasma proteins and active processes that are integrated into PK-Sim® are described in the publicly available PK-Sim® Ontogeny Database Version 7.3 ([PK-Sim Ontogeny Database Version 7.3](#5-references)) or otherwise referenced for the specific process.
+
+First, a mean model was built using clinical data from single dose and multiple doses studies with intravenous and oral administration of felodipine ([Edgar 1987](#5-references), [Bailey 1996](#5-references), [Gelal 2005](#5-references), [Bailey 2003](#5-references), [Goosen 2004](#5-references), [Jalava 1997](#5-references), [Blychert 1990](#5-references), [Lundahl 1998](#5-references), [Bailey 1995](#5-references), [Aberg 1997](#5-references), [Bailey 1993](#5-references), [Edgar 1992](#5-references), [Lundahl 1997](#5-references)). One DDI study ([Jalava 1997](#5-references)) was also used in the optimization to help the model describe DDI better. The mean PBPK model was developed using a typical male European individual. The relative tissue-specific expressions of enzymes predominantly being involved in the metabolism of felodipine (CYP3A4) were considered ([Meyer 2012](#5-references)).
+
+A specific selected set of parameters (see below) was optimized using the Parameter Identification module provided in PK-Sim®. Structural model selection was mainly guided by visual inspection of the resulting description of data and biological plausibility.
+
+Once the appropriate structural model was identified, dissolution kinetic parameters were optimized for immediate-release tablets.
+
+The model was then evaluated by simulating further clinical studies reporting pharmacokinetic concentration-time profiles of felodipine.
+
+Details about input data (physicochemical, *in vitro* and clinical) can be found in [Section 2.2](#22-data).
+
+Details about the structural model and its parameters can be found in [Section 2.3](#23-model-parameters-and-assumptions).
+
+## 2.2 Data
+
+### 2.2.1 In vitro and physicochemical data
+
+A literature search was performed to collect available information on physicochemical properties of felodipine. The obtained information from literature is summarized in the table below, and is used for model building.
+
+| **Parameter** | **Unit** | **Value** | Source | **Description** |
+| :------------------------------------ | -------------------------- | ---------------- | --------------------------------- | ------------------------------------------------------------ |
+| MW | g/mol | 384.254 | [DrugBank DB01023](#5-references) | Molecular weight |
+| pKa1 | | n.a. | [Alskar 2018](#5-references) | Acid dissociation constant of conjugate acid |
+| pKa1 | | 5.07 | [Pandey 2013](#5-references) | Acid dissociation constant of conjugate acid; compound type: acid |
+| Solubility (pH) | mg/L | 14.3
(7.1) | [Takano 2016](#5-references) | Aqueous Solubility |
+| | | 1 | [Scholz 2002](#5-references) | Aqueous Solubility |
+| | | 53 | [Söderlind 2010](#5-references) | Solubility in fasted state simulated intestinal fluid I |
+| | | 12 | [Söderlind 2010](#5-references) | Solubility in fasted state simulated intestinal fluid II |
+| | | 14 | [Söderlind 2010](#5-references) | Solubility in fasted human intestinal fluid |
+| | | 15
(7.5) | [Persson 2005](#5-references) | Solubility in fasted human intestinal fluid |
+| | | 413
(6.1) | [Persson 2005](#5-references) | Solubility in fed human intestinal fluid |
+| | | 191 | [Persson 2005](#5-references) | Solubility in fed state simulated intestinal fluid |
+| | | 77
(6.35) | [Scholz 2002](#5-references) | Solubility in fasted state chyme |
+| | | 56
(4.93) | [Scholz 2002](#5-references) | Solubility in fed state chyme |
+| logP | | 4.36 | [DrugBank DB01023](#5-references) | Partition coefficient between octanol and water |
+| | | 3.44 | [DrugBank DB01023](#5-references) | Partition coefficient between octanol and water |
+| | | 3.86 | [McPherson 2020](#5-references) | Partition coefficient between octanol and water |
+| | | 4.5 | [Scholz 2002](#5-references) | Partition coefficient between octanol and water |
+| | | 4.8 | [Bu 2006](#5-references) | Partition coefficient between octanol and water |
+| fu | % | 0.36 | [Soons 1993](#5-references) | Fraction unbound in plasma |
+| | % | 0.36 | [Ushimura 2010](#5-references) | Fraction unbound in plasma |
+| Vmax, Km CYP3A | pmol/mg/min,
µmol/L | 1630
2.81 | [Walsky 2004](#5-references) | CYP3A liver microsomes Michaelis-Menten kinetics |
+| Vmax, Km CYP3A | pmol/mg/min,
µmol/L | 240
6.9 | [Bu 2006](#5-references) | CYP3A liver microsomes Michaelis-Menten kinetics |
+| Vmax, Km CYP3A4 | pmol/mg/min,
µmol/L | 36.8
0.938 | [Walsky 2004](#5-references) | Recombinant CYP3A4 Michaelis-Menten kinetics |
+| Vmax, Km CYP3A5 | pmol/mg/min,
µmol/L | 24.2
1.41 | [Walsky 2004](#5-references) | Recombinant CYP3A5 Michaelis-Menten kinetics |
+
+### 2.2.2 Clinical data
+
+A literature search was performed to collect available clinical data on felodipine in adults.
+
+The following publications were found in adults for model building:
+
+| Publication | Arm / Treatment / Information used for model building |
+| :------------------------------------- | :---------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| [Lundahl 1997](#5-references) | Plasma PK profiles in healthy subjects with single dose administrations of a felodipine 1.5 mg intravenous infusion |
+| [Edgar 1987](#5-references) | Plasma PK profiles in healthy subjects with single dose administrations of a felodipine:
- 10 mg oral solution
- 10 mg extended release tablet
- 10 mg immediate release tablet |
+| [Blychert 1990](#5-references) | Plasma PK profiles in healthy subjects with multiple dose administrations of a felodipine:
- 10 mg oral solution
- 10 mg extended release tablet |
+| [Goosen 2004](#5-references) | Plasma PK profiles in healthy subjects with single dose administrations of a felodipine 5 mg extended release tablet |
+| [Jalava 1997](#5-references) | Plasma PK profiles in healthy subjects with single dose administrations of a felodipine 5 mg extended release tablet
- alone (control)
- with itraconazole (treatment)|
+| [Bailey 1996](#5-references) | Plasma PK profiles in healthy subjects with single dose administrations of a felodipine 10 mg extended release tablet |
+| [Gelal 2005](#5-references) | Plasma PK profiles in healthy subjects with single dose administrations of a felodipine 10 mg extended release tablet |
+| [Bailey 2003](#5-references) | Plasma PK profiles in healthy subjects with single dose administrations of a felodipine 10 mg extended release tablet |
+| [Bailey 1993](#5-references) | Plasma PK profiles in healthy subjects with single dose administrations of a felodipine 5 mg immediate release tablet |
+| [Edgar 1992](#5-references) | Plasma PK profiles in healthy subjects with single dose administrations of a felodipine 5 mg immediate release tablet |
+| [Blychert 1990](#5-references) | Plasma PK profiles in healthy subjects with multiple dose administrations of a felodipine:
- 10 mg oral solution
- 10 mg extended release tablet |
+| [Lundahl 1998](#5-references) | Plasma PK profiles in healthy subjects with multiple dose administrations of a felodipine 10 mg extended release tablet |
+| [Bailey 1995](#5-references) | Plasma PK profiles in healthy subjects with multiple dose administrations of a felodipine 10 mg extended release tablet |
+| [Aberg 1997](#5-references) | Plasma PK profiles in healthy subjects with multiple dose administrations of a felodipine 10 mg extended release tablet |
+
+The following dosing scenarios were simulated and compared to respective data for model verification:
+
+| Scenario | Data reference |
+| ------------------------------------------------------------ | ------------------------------------ |
+| po 5 mg single dose (extended release tablet) | [Dresser 2000](#5-references) |
+| po 10 mg single dose (extended release tablet) | [Dresser 2017](#5-references) |
+| | [Dresser 2002](#5-references) |
+| | [Bailey 2000](#5-references) |
+| | [Bailey 1998](#5-references) |
+| | [Lundahl 1997](#5-references) |
+| | [Madsen 1996](#5-references) |
+| po 2.5 / 5 mg once daily (extended release tablet) | [Dresser 2000](#5-references) |
+| po 10 mg once daily (immediate release tablet) | [Blychert 1990](#5-references) |
+| po 10 mg twice daily (immediate release tablet) | [Blychert 1990](#5-references) |
+| | [Smith 1987](#5-references) |
+| po 10 mg three times daily (immediate release tablet) | [Bratel 1989](#5-references) |
+
+
+## 2.3 Model Parameters and Assumptions
+
+### 2.3.1 Absorption
+
+The model parameter `Specific intestinal permeability` was was calculated by PK-Sim® and kept to that value since it was insensitive. The default solubility was assumed to be measured value in fasted state simulated intestinal fluid (see [Section 2.2.1](#221-in-vitro-and-physicochemical-data))
+
+The dissolution of both immediate and extended-release tables was implemented via two Weibull dissolution tablets, and the dissolution kinetic parameters were optimized (see [Section 2.3.4](#234-automated-parameter-identification)).
+
+### 2.3.2 Distribution
+
+Felodipine is highly bound to proteins in plasma (see [Section 2.2.1](#221-in-vitro-and-physicochemical-data)). A value of 0.36% was used in this PBPK model for `Fraction unbound (plasma, reference value)`.
+
+An important parameter influencing the resulting volume of distribution is lipophilicity. The reported experimental logP values are in the range of 4 (see [Section 2.2.1](#221-in-vitro-and-physicochemical-data)) which served as a starting value. Finally, the model parameter `Lipophilicity` was optimized to match best clinical data (see also [Section 2.3.4](#234-automated-parameter-identification)).
+
+After testing the available organ-plasma partition coefficient and cell permeability calculation methods built in PK-Sim, observed clinical data was best described by choosing the partition coefficient calculation by `Rodgers and Rowland` and cellular permeability calculation by `PK-Sim Standard`.
+
+### 2.3.3 Metabolism and Elimination
+
+Two metabolic pathways were implement into the model via Michaelis-Menten kinetics
+
+* CYP3A4
+* unknown hepatic enzyme *via* unspecific hepatic clearance
+
+The latter was preferred over renal clearance, since there is evidence that felodipine is fully metabolized and not found in urine ([Edgar 1987](#5-references)).
+CYP3A5 was not implemented since the fraction metabolized appeared to be minor compared to CYP3A4.
+
+The CYP3A4 expression profiles is based on high-sensitive real-time RT-PCR ([Nishimura 2003](#5-references)). Absolute tissue-specific expressions were obtained by considering the respective absolute concentration in the liver. The PK-Sim database provides a default value for CYP3A4 (compare [Rodrigues 1999](#5-references) and assume 40 mg protein per gram liver).
+
+The first model simulations showed that gut wall metabolism was underrepresented in the PBPK model. In order to increase gut wall metabolism, the “mucosa permeability on basolateral side” (jointly the model parameters in the mucosa: ``P (interstitial->intracellular)`` and ``P (intracellular->interstitial)``) was estimated. A decrease in this permeability may lead to higher gut wall concentrations and, in turn, to a higher gut wall elimination. This parameter was preferred over other parameters such as relative CYP3A4 expression or fraction unbound (fu) in the gut wall as it is technically not limited to a maximum value of 100%.
+
+### 2.3.4 Automated Parameter Identification
+
+This is the result of the final parameter identification for the base model:
+
+| Model Parameter | Optimized Value | Unit |
+| ------------------------------------------------------------ | ------------------------------------------------------------ | --------- |
+| `Lipophilicity` | 4.51 | Log Units |
+| Basolateral mucosa permeability
(``P (interstitial->intracellular)``, ``P (intracellular->interstitial)``) |0.09 | cm/min |
+| `kcat` (CYP3A4) | 204.70 | 1/min |
+| `Dissolution time` (extended release tablet) | 286.95 | min |
+| `Dissolution shape` (extended release tablet) | 0.76 | |
+| `Lag time` (extended release tablet) | 18.68 | min |
+| `Tablet time delay factor` (extended release tablet) | 0.07 | |
+| `Dissolution time` (immediate release tablet) | 46.50 | min |
+| `Dissolution shape` (immediate release tablet) | 0.89 | |
+
+# 3 Results and Discussion
+
+The PBPK model for felodipine was developed and verified with clinical pharmacokinetic data.
+
+The model was built and evaluated covering data from studies including in particular
+
+* intravenous (infusions) and oral administrations (solutions, immediate release and extended release tablets).
+* a dose range of 1.5 to 10 mg.
+
+The model quantifies metabolism via CYP3A4, and a second unknown hepatic enzyme.
+
+The next sections show:
+
+1. the final model input parameters for the building blocks: [Section 3.1](#31-final-input-parameters).
+2. the overall goodness of fit: [Section 3.2](#32-diagnostics-plots).
+3. simulated vs. observed concentration-time profiles for the clinical studies used for model building and for model verification: [Section 3.3](#33-concentration-time-profiles).
+
+## 3.1 Final input parameters
+
+The compound parameter values of the final PBPK model are illustrated below.
+
+### Compound: Felodipine
+
+#### Parameters
+
+Name | Value | Value Origin | Alternative | Default
+------------------------------------------ | ---------------------- | ------------------------------------------------------------------------------------------------------------------------------- | ----------- | -------
+Solubility at reference pH | 12 µg/ml | Publication-Soderlind, 2010 | FaSSIF II | True
+Reference pH | 6.5 | Publication-Soderlind, 2010 | FaSSIF II | True
+Lipophilicity | 4.5085714616 Log Units | Parameter Identification-Parameter Identification-Value updated from 'PI_IV+Solution+ER+DDI_additionalCL_3' on 2022-08-10 10:40 | Measurement | True
+Fraction unbound (plasma, reference value) | 0.0036 | Publication-Ushimura, 2010 | Measurement | True
+Cl | 2 | Internet-DrugBank DB01023 | |
+Is small molecule | Yes | | |
+Molecular weight | 384.254 g/mol | Internet-DrugBank DB01023 | |
+Plasma protein binding partner | Unknown | | |
+
+#### Calculation methods
+
+Name | Value
+----------------------- | -------------------
+Partition coefficients | Rodgers and Rowland
+Cellular permeabilities | PK-Sim Standard
+
+#### Processes
+
+##### Metabolizing Enzyme: CYP3A4-Walsky 2004
+
+Molecule: CYP3A4
+
+###### Parameters
+
+Name | Value | Value Origin
+---------------------------------- | ----------------------------- | -------------------------------------------------------------------------------------------------------------------------------
+In vitro Vmax for liver microsomes | 1630 pmol/min/mg mic. protein | Publication-Walsky 2004
+Km | 2.81 µmol/l | Publication-Walsky 2004
+kcat | 204.6995652687 1/min | Parameter Identification-Parameter Identification-Value updated from 'PI_IV+Solution+ER+DDI_additionalCL_3' on 2022-08-10 10:40
+
+##### Systemic Process: Total Hepatic Clearance-Unspecific hepatic clearance
+
+Species: Human
+
+###### Parameters
+
+Name | Value | Value Origin
+----------------------------- | ---------------------- | -------------------------------------------------------------------------------------------------------------------------------
+Fraction unbound (experiment) | 0.0036 |
+Lipophilicity (experiment) | 4.3407865958 Log Units |
+Plasma clearance | 0 ml/min/kg |
+Specific clearance | 12.8042083376 1/min | Parameter Identification-Parameter Identification-Value updated from 'PI_IV+Solution+ER+DDI_additionalCL_3' on 2022-08-10 10:41
+
+### Formulation: Felodipine_IR tablet
+
+Type: Weibull
+
+#### Parameters
+
+Name | Value | Value Origin
+-------------------------------- | ----------------- | -------------------------------------------------------------------------------------------------------------
+Dissolution time (50% dissolved) | 46.5005948338 min | Parameter Identification-Parameter Identification-Value updated from 'PI IR Tablet alone' on 2022-08-10 11:29
+Lag time | 0 min |
+Dissolution shape | 0.8876005929 | Parameter Identification-Parameter Identification-Value updated from 'PI IR Tablet alone' on 2022-08-10 11:29
+Use as suspension | Yes |
+
+### Formulation: Felodipine_ER tablet
+
+Type: Weibull
+
+#### Parameters
+
+Name | Value | Value Origin
+-------------------------------- | ------------------ | -------------------------------------------------------------------------------------------------------------------------------
+Dissolution time (50% dissolved) | 286.9463213309 min | Parameter Identification-Parameter Identification-Value updated from 'PI_IV+Solution+ER+DDI_additionalCL_3' on 2022-08-10 10:41
+Lag time | 18.6758448616 min | Parameter Identification-Parameter Identification-Value updated from 'PI_IV+Solution+ER+DDI_additionalCL_3' on 2022-08-10 10:41
+Dissolution shape | 0.7639975313 | Parameter Identification-Parameter Identification-Value updated from 'PI_IV+Solution+ER+DDI_additionalCL_3' on 2022-08-10 10:41
+Use as suspension | Yes |
+
+## 3.2 Diagnostics Plots
+
+Below you find the goodness-of-fit visual diagnostic plots for the PBPK model performance of all data used presented in [Section 2.2.2](#222-clinical-data).
+
+The first plot shows observed versus simulated plasma concentration, the second weighted residuals versus time.
+
+
+
+**Table 3-1: GMFE for Felodipine concentration in plasma**
+
+|Group |GMFE |
+|:---------------------------------------------------|:----|
+|Intravenous administration (model building) |1.33 |
+|Oral administration, ER tablet (model building) |1.30 |
+|Oral administration, ER tablet (model verification) |1.35 |
+|Oral administration, IR tablet (model building) |1.51 |
+|Oral administration, IR tablet (model verification) |1.35 |
+|Oral administration, solution (model building) |1.17 |
+|All |1.33 |
+
+
+
+
+
+
+
+
+**Figure 3-1: Felodipine concentration in plasma**
+
+
+
+
+
+
+
+
+**Figure 3-2: Felodipine concentration in plasma**
+
+
+
+
+## 3.3 Concentration-Time Profiles
+
+Simulated versus observed concentration-time profiles of all data listed in [Section 2.2.2](#222-clinical-data) are presented below.
+
+### 3.3.1 Model Building
+
+
+
+
+
+**Figure 3-3: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-4: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-5: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-6: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-7: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-8: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-9: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-10: Time Profile Analysis**
+
+
+
+
+### 3.3.2 Model Verification
+
+
+
+
+
+**Figure 3-11: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-12: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-13: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-14: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-15: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-16: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-17: Time Profile Analysis**
+
+
+
+
+# 4 Conclusion
+
+The herein presented PBPK model adequately describes the pharmacokinetics of felodipine in adults.
+
+In particular, it applies quantitative metabolism by CYP3A4, and a second unknown hepatic enzyme. Thus, the model is fit for purpose to be applied for the investigation of drug-drug interactions with regard to its CYP3A4 metabolism.
+
+# 5 References
+
+**Aberg 1997** Aberg J, Abrahamsson B, Grind M, Nyberg G, Olofsson B. Bioequivalence, pharmacokinetic and pharmacodynamic response to combined extended release formulations of felodipine and metoprolol in healthy volunteers. Eur J Clin Pharmacol. 1997;52(6):471-7.
+
+**Alskar 2018** Alskar LC, Keemink J, Johannesson J, Porter CJH, Bergstrom CAS. Impact of Drug Physicochemical Properties on Lipolysis-Triggered Drug Supersaturation and Precipitation from Lipid-Based Formulations. Mol Pharm. 2018;15(10):4733-44.
+
+**Bailey 1993** Bailey DG, Arnold JM, Munoz C, Spence JD. Grapefruit juice--felodipine interaction: mechanism, predictability, and effect of naringin. Clin Pharmacol Ther. 1993;53(6):637-42.
+
+**Bailey 1995** Bailey DG, Arnold JM, Bend JR, Tran LT, Spence JD. Grapefruit juice-felodipine interaction: reproducibility and characterization with the extended release drug formulation. Br J Clin Pharmacol. 1995;40(2):135-40.
+
+**Bailey 1996** Bailey DG, Bend JR, Arnold JM, Tran LT, Spence JD. Erythromycin-felodipine interaction: magnitude, mechanism, and comparison with grapefruit juice. Clin Pharmacol Ther. 1996;60(1):25-33.
+
+**Bailey 1998** Bailey DG, Kreeft JH, Munoz C, Freeman DJ, Bend JR. Grapefruit juice-felodipine interaction: effect of naringin and 6',7'-dihydroxybergamottin in humans. Clin Pharmacol Ther. 1998;64(3):248-56.
+
+**Bailey 2000** Bailey DG, Dresser GK, Kreeft JH, Munoz C, Freeman DJ, Bend JR. Grapefruit-felodipine interaction: effect of unprocessed fruit and probable active ingredients. Clin Pharmacol Ther. 2000;68(5):468-77.
+
+**Bailey 2003** Bailey D. Bergamottin, lime juice, and red wine as inhibitors of cytochrome P450 3a4 activity: comparison with grapefruit juice. Clinical Pharmacology & Therapeutics. 2003;73(6):529-37.
+
+**Blychert 1990** Blychert E, Wingstrand K, Edgar B, Lidman K. Plasma concentration profiles and antihypertensive effect of conventional and extended-release felodipine tablets. Br J Clin Pharmacol. 1990;29(1):39-45.
+
+**Bratel 1989** Bratel T, Billing B, Dahlqvist R. Felodipine reduces the absorption of theophylline in man. Eur J Clin Pharmacol. 1989;36(5):481-5.
+
+**Bu 2006** Bu HZ. A literature review of enzyme kinetic parameters for CYP3A4-mediated metabolic reactions of 113 drugs in human liver microsomes: structure-kinetics relationship assessment. Curr Drug Metab. 2006;7(3):231-49.
+
+**Dresser 2000** Dresser GK, Bailey DG, Carruthers SG. Grapefruit juice--felodipine interaction in the elderly. Clin Pharmacol Ther. 2000;68(1):28-34.
+
+**Dresser 2002** Dresser GK, Wacher V, Wong S, Wong HT, Bailey DG. Evaluation of peppermint oil and ascorbyl palmitate as inhibitors of cytochrome P4503A4 activity in vitro and in vivo. Clin Pharmacol Ther. 2002;72(3):247-55.
+
+**Dresser 2017** Dresser GK, Urquhart BL, Proniuk J, Tieu A, Freeman DJ, Arnold JM, et al. Coffee inhibition of CYP3A4 in vitro was not translated to a grapefruit-like pharmacokinetic interaction clinically. Pharmacol Res Perspect. 2017;5(5).
+
+**DrugBank DB01023** https://go.drugbank.com/drugs/DB01023
+
+**Edgar 1987** Edgar B, Lundborg P, Regårdh CG. Clinical pharmacokinetics of felodipine. A summary. Drugs. 1987;34 Suppl 3:16-27.
+
+**Edgar 1992** Edgar B, Bailey D, Bergstrand R, Johnsson G, Regårdh CG. Acute effects of drinking grapefruit juice on the pharmacokinetics and dynamics of felodipine--and its potential clinical relevance. Eur J Clin Pharmacol. 1992;42(3):313-7.
+
+**Gelal 2005** Gelal A, Balkan D, Ozzeybek D, Kaplan YC, Gurler S, Guven H, et al. Effect of menthol on the pharmacokinetics and pharmacodynamics of felodipine in healthy subjects. Eur J Clin Pharmacol. 2005;60(11):785-90.
+
+**Goosen 2004** Goosen TC, Cillie D, Bailey DG, Yu C, He K, Hollenberg PF, et al. Bergamottin contribution to the grapefruit juice-felodipine interaction and disposition in humans. Clin Pharmacol Ther. 2004;76(6):607-17.
+
+**Jalava 1997** Jalava KM, Olkkola KT, Neuvonen PJ. Itraconazole greatly increases plasma concentrations and effects of felodipine. Clin Pharmacol Ther. 1997;61(4):410-5.
+
+**Kuepfer 2016** Kuepfer L, Niederalt C, Wendl T, Schlender JF, Willmann S, Lippert J, et al. Applied Concepts in PBPK Modeling: How to Build a PBPK/PD Model. CPT Pharmacometrics Syst Pharmacol. 2016;5(10):516-31.
+
+**Lundahl 1997** Lundahl J, Regårdh CG, Edgar B, Johnsson G. Effects of grapefruit juice ingestion--pharmacokinetics and haemodynamics of intravenously and orally administered felodipine in healthy men. Eur J Clin Pharmacol. 1997;52(2):139-45.
+
+**Lundahl 1998** Lundahl JU, Regårdh CG, Edgar B, Johnsson G. The interaction effect of grapefruit juice is maximal after the first glass. Eur J Clin Pharmacol. 1998;54(1):75-81.
+
+**Madsen 1996** Madsen JK, Jensen JD, Jensen LW, Pedersen EB. Pharmacokinetic interaction between cyclosporine and the dihydropyridine calcium antagonist felodipine. Eur J Clin Pharmacol. 1996;50(3):203-8.
+
+**McPherson 2020** McPherson S, Perrier J, Dunn C, Khadra I, Davidson S, Ainousah B, et al. Small scale design of experiment investigation of equilibrium solubility in simulated fasted and fed intestinal fluid. Eur J Pharm Biopharm. 2020;150:14-23.
+
+**Meyer 2012** Meyer M, Schneckener S, Ludewig B, Kuepfer L, Lippert J. Using expression data for quantification of active processes in physiologically based pharmacokinetic modeling. Drug Metab Dispos. 2012;40(5):892-901.
+
+**Nishimura 2003** Nishimura M, Yaguti H, Yoshitsugu H, Naito S, Satoh T. Tissue distribution of mRNA expression of human cytochrome P450 isoforms assessed by high-sensitivity real-time reverse transcription PCR. Yakugaku Zasshi. 2003;123(5):369-75.
+
+**Pandey 2013** Pandey MM, Jaipal A, Kumar A, Malik R, Charde SY. Determination of pK(a) of felodipine using UV-Visible spectroscopy. Spectrochim Acta A Mol Biomol Spectrosc. 2013;115:887-90.
+
+**Persson 2005** Persson EM, Gustafsson AS, Carlsson AS, Nilsson RG, Knutson L, Forsell P, et al. The effects of food on the dissolution of poorly soluble drugs in human and in model small intestinal fluids. Pharm Res. 2005;22(12):2141-51.
+
+**PK-Sim Ontogeny Database Version 7.3** https://github.com/Open-Systems-Pharmacology/OSPSuite.Documentation/blob/38cf71b384cfc25cfa0ce4d2f3addfd32757e13b/PK-Sim%20Ontogeny%20Database%20Version%207.3.pdf
+
+**Rodrigues 2003** Rodrigues E, Vilarem MJ, Ribeiro V, Maurel P, Lechner MC. Two CCAAT/enhancer binding protein sites in the cytochrome P4503A1 locus. Potential role in the glucocorticoid response. Eur J Biochem. 2003;270(3):556-64.
+
+**Scholz 2002** Scholz A, Abrahamsson B, Diebold SM, Kostewicz E, Polentarutti BI, Ungell AL, et al. Influence of hydrodynamics and particle size on the absorption of felodipine in labradors. Pharm Res. 2002;19(1):42-6.
+
+**Smith 1987** Smith SR, Wilkins MR, Jack DB, Kendall MJ, Laugher S. Pharmacokinetic interactions between felodipine and metoprolol. Eur J Clin Pharmacol. 1987;31(5):575-8.
+
+**Söderlind 2010** Söderlind E, Karlsson E, Carlsson A, Kong R, Lenz A, Lindborg S, et al. Simulating fasted human intestinal fluids: understanding the roles of lecithin and bile acids. Mol Pharm. 2010;7(5):1498-507.
+
+**Soons 1993** Soons PA, Cohen AF, Breimer DD. Comparative effects of felodipine, nitrendipine and nifedipine in healthy subjects: concentration-effect relationships of racemic drugs and enantiomers. Eur J Clin Pharmacol. 1993;44(2):113-20.
+
+**Takano 2016**Takano J, Maeda K, Bolger MB, Sugiyama Y. The Prediction of the Relative Importance of CYP3A/P-glycoprotein to the Nonlinear Intestinal Absorption of Drugs by Advanced Compartmental Absorption and Transit Model. Drug Metab Dispos. 2016;44(11):1808-18.
+
+**Ushimura 2010** Uchimura T, Kato M, Saito T, Kinoshita H. Prediction of human blood-to-plasma drug concentration ratio. Biopharm Drug Dispos. 2010;31(5-6):286-97.
+
+**Walsky 2004** Walsky RL, Obach RS. Validated assays for human cytochrome P450 activities. Drug Metab Dispos. 2004;32(6):647-60.
+
+**Willmann 2007** Willmann S, Höhn K, Edginton A, Sevestre M, Solodenko J, Weiss W, et al. Development of a physiology-based whole-body population model for assessing the influence of individual variability on the pharmacokinetics of drugs. J Pharmacokinet Pharmacodyn. 2007;34(3):401-31.
+
+","Markdown"
+"PBPK model","Open-Systems-Pharmacology/OSP-PBPK-Model-Library","Montelukast/Montelukast_evaluation_report.md",".md","28793","537","# Building and evaluation of a PBPK model for montelukast in adults
+
+| Version | 2.0-OSP12.2 |
+| ----------------------------------------------- | ------------------------------------------------------------ |
+| based on *Model Snapshot* and *Evaluation Plan* | https://github.com/Open-Systems-Pharmacology/Montelukast-Model/releases/tag/v2.0 |
+| OSP Version | 12.2 |
+| Qualification Framework Version | 3.5 |
+
+This evaluation report and the corresponding PK-Sim project file are filed at:
+
+https://github.com/Open-Systems-Pharmacology/OSP-PBPK-Model-Library/
+
+# Table of Contents
+
+ * [1 Introduction](#introduction)
+ * [2 Methods](#methods)
+ * [2.1 Modeling strategy](#modeling-strategy)
+ * [2.2 Data used](#data-used)
+ * [2.3 Model parameters and assumptions](#model-parameters-and-assumptions)
+ * [3 Results and Discussion](#results-and-discussion)
+ * [3.1 Montelukast final input parameters](#final-input-parameters)
+ * [3.2 Montelukast Diagnostics Plots](#diagnostics-plots)
+ * [3.3 Montelukast Concentration-Time profiles](#ct-profiles)
+ * [4 Conclusion](#conclusion)
+ * [5 References](#main-references)
+
+# 1 Introduction
+
+The presented model building and evaluation report evaluates the performance of a physiology-based pharmacokinetic (PBPK) model for montelukast in adults.
+
+Montelukast is a selective and orally active leukotriene receptor antagonist that inhibits the cysteinyl leukotriene (CysLT) receptor 1, used in the maintenance treatment of asthma. Montelukast is mainly metabolized by CYP2C8 (72%) ([Marzolini 2017](#5-references)). Montelukast is a strongly lipophilic drug. The final lipophilicity was estimated to be lower than the reported values, as with lipophilicity values above 3-4 log units the drug already reached maximal permeability levels. The final montelukast model applies metabolism by CYP2C8, and to a minor extend involved clearance by the enzymes CYP3A4/5 (16% ), CYP2C9 (12%) and glomerular filtration ([Marzolini 2017, Filppula 2011, Zhou 2017](#5-references)) and adequately described the pharmacokinetics of montelukast in adults.
+
+The montelukast model is a whole-body PBPK model, allowing for dynamic translation between individuals. The montelukast report demonstrates the level of confidence in the montelukast PBPK model built with the OSP suite with regard to reliable predictions of montelukast pharmacokinetics (PK) in adults during model-informed drug development.
+
+# 2 Methods
+
+## 2.1 Modeling strategy
+
+The general concept of building a PBPK model has previously been described by Kuepfer et al. ([Kuepfer 2016](#5-references)). Relevant information on anthropometric (height, weight) and physiological parameters (e.g. blood flows, organ volumes, binding protein concentrations, hematocrit, cardiac output) in adults was gathered from the literature and has been previously published ([Schlender 2016](#5-references)). The information was incorporated into PK-Sim® and was used as default values for the simulations in adults.
+
+The applied activity and variability of plasma proteins and active processes that are integrated into PK-Sim® are described in the publicly available PK-Sim® Ontogeny Database Version 7.3 ([PK-Sim Ontogeny Database Version 7.3](#5-references)) or otherwise referenced for the specific process.
+
+First, a base mean model was built using data from the single dose escalation study to find an appropriate structure describing the PK of montelukast. The mean PK model was developed using a typical European individual. Unknown parameters were identified using the Parameter Identification module provided in PK-Sim®. Structural model selection was mainly guided by visual inspection of the resulting description of data and biological plausibility.
+
+Once the appropriate structural model was identified, additional parameters for different formulations were identified, if available.
+
+A final PBPK model was established and simulations were compared to the reported data to evaluate model appropriateness and to assess model verification, by means of diagnostics plots and predicted versus observed concentration-time profiles, of which the results support an adequate prediction of the PK in adults.
+
+During model building, uncertainties in data quality, as well as study differences may cause not being able to adequately describe the PK of all reported clinical studies.
+
+## 2.2 Data used
+
+### 2.2.1 In vitro / physicochemical data
+
+A literature search was performed to collect available information on physicochemical properties of montelukast. The obtained information from literature is summarized in the table below and is used for model building.
+
+| **Parameter** | **Unit** | **Value (reference)** | **Description** |
+| :-------------- | ----------- | ----------------------------------- | ------------------------------------------------ |
+| MW | g/mol | 586.2 ([Marzolini 2017](#5-references)) | Molecular weight |
+| pKa | | 4.4 ([Marzolini 2017](#5-references)) | Acid dissociation constant |
+| Solubility (pH) | mg/mL | 8.2E-06 (7) ([Drugbank](#5-references)) | Solubility |
+| logP | | 7.90 ([Marzolini 2017](#5-references)) | Partition coefficient between octanol and water |
+| fu | | 0.0018 ([Marzolini 2017](#5-references)) | Fraction unbound |
+| fe** | | <0.002 ([Marzolini 2017](#5-references)) | fraction of dose excreted unchanged in urine |
+| CYP3A4-CLint | µl/min/pmol | 1.8 ([Marzolini 2017](#5-references)) | Cytochrome-P450 3A4 mediated intrinsic clearance |
+| CYP3A5-CLint | µl/min/pmol | 1.8 ([Marzolini 2017](#5-references)) | Cytochrome-P450 3A5 mediated intrinsic clearance |
+| CYP2C8-CLint | µl/min/pmol | 3.6 ([Marzolini 2017](#5-references)) | Cytochrome-P450 2C8 mediated intrinsic clearance |
+| CYP2C9-CLint | µl/min/pmol | 0.48 ([Marzolini 2017](#5-references)) | Cytochrome-P450 2C9 mediated intrinsic clearance |
+
+** fe was matched by modeling unchanged renal excretion in PK-Sim as glomerular filtration (GF)
+
+### 2.2.2 Clinical data
+
+A literature search was performed to collect available clinical data on montelukast PK in adults.
+
+The following publications were found in adults for model building and evaluation:
+
+| Publication | Study description |
+| :-------------------------------- | :----------------------------------------------------------- |
+| [Cheng 1996](#5-references) | Pharmacokinetics, bioavailability, and safety of montelukast sodium (MK-0476) in healthy males and females |
+| [Fey 2014](#5-references) | Bioequivalence of two formulations of montelukast sodium 4 mg oral granules in healthy adults |
+| [Knorr 2000](#5-references) | Montelukast adult (10-mg film-coated tablet) and pediatric (5-mg chewable tablet) dose selections |
+| [Zhao 1997](#5-references) | Pharmacokinetics and bioavailability of montelukast sodium (MK-0476) in healthy young and elderly volunteers |
+
+## 2.3 Model parameters and assumptions
+
+### 2.3.1 Absorption
+
+Montelukast is a selective and orally active leukotriene receptor antagonist. For oral administration the following parameters play, amongst others, a role with regard to the absorption kinetics of a compound, which can be estimated with PBPK: solubility, lipophilicity and intestinal permeability. Montelukast is a strongly lipophilic drug. The final lipophilicity was estimated to be lower than the reported values, as with lipophilicity values above 3-4 log units the drug already reached maximal permeability levels.
+
+### 2.3.2 Distribution
+
+It has been determined that the protein binding of montelukast to plasma proteins exceeds 99% ([FDA drug label](#5-references)). The fraction unbound (fu) of montelukast is built-in as 0.0018 as also reported by Marzolini et al. ([Marzolini 2017](#5-references)).
+
+After testing the available organ-plasma partition coefficient and cell permeability calculation methods built-in in PK-Sim, observed clinical data was best described by choosing the partition coefficient calculation method by Rodgers and Rowland, and PK-Sim standard cell permeability calculation method. Specific organ permeability normalized to surface area was automatically calculated by PK-Sim.
+
+### 2.3.3 Metabolism and Elimination
+
+Montelukast is mainly metabolized by CYP2C8 (72%) ([Marzolini 2017](#5-references)). The final montelukast model applies metabolism by CYP2C8, and to a minor involved clearance by the enzymes CYP3A4/5 (16%), CYP2C9 (12%) and glomerular filtration ([Marzolini 2017, Filppula 2011, Zhou 2017](#5-references)) and adequately described the pharmacokinetics of montelukast in adults.
+
+# 3 Results and Discussion
+
+The PBPK model for montelukast was developed with clinical pharmacokinetic data covering intravenous as well as oral administration with a dose range of 2-10mg including single dose and multiple dose clinical data, for different types of tablet formulations.
+
+During the model fitting, the following parameters were estimated (all other parameters were fixed to reported values):
+
+* Lipophilicity
+* Specific intestinal permeability (transcellular)
+* Formulation kinetics : Weibull function parameters (Dissolution shape and Dissolution time) for
+ * Singular tablet
+ * Sandoz tablet
+ * Film-coated tablet
+ * Chewable tablet
+
+The fit resulted in an adequate description of the clinical data. Overall the model results show that the PBPK model of montelukast adequately described the data for intravenous administration for single dose. The estimated clearance values as a fraction of the reported clearance using only intravenous data resulted in a value close to 1, which allowed to fix the clearance parameters to the reported values. This was done to prevent the otherwise high correlation with the estimated absorption related parameters (dissolution kinetics for the different tablet formulations, lipophilicity and intestinal permeability during model building).
+
+## 3.1 Montelukast final input parameters
+
+The compound parameter values of the final montelukast PBPK model are illustrated below.
+
+### Compound: Montelukast
+
+#### Parameters
+
+Name | Value | Value Origin | Alternative | Default
+------------------------------------------------ | ---------------------- | ------------------------------------------------- | ------------------------- | -------
+Solubility at reference pH | 8.2E-06 mg/ml | Internet-source: Drugbank (ALOGPS) | Water Solubility (ALOGPS) | True
+Reference pH | 7 | Internet-source: Drugbank (ALOGPS) | Water Solubility (ALOGPS) | True
+Lipophilicity | 3.3153408097 Log Units | Parameter Identification-Parameter Identification | Fit | True
+Fraction unbound (plasma, reference value) | 0.0018 | Publication-Marzolini 2017 | Marzolini 2017 | True
+Specific intestinal permeability (transcellular) | 0.0819181318 cm/min | Parameter Identification-Parameter Identification | Fit | True
+Cl | 1 | Publication-Marzolini 2017 | |
+Is small molecule | Yes | | |
+Molecular weight | 586.2 g/mol | Publication-Marzolini 2017 | |
+Plasma protein binding partner | Albumin | | |
+
+#### Calculation methods
+
+Name | Value
+----------------------- | -------------------
+Partition coefficients | Rodgers and Rowland
+Cellular permeabilities | PK-Sim Standard
+
+#### Processes
+
+##### Metabolizing Enzyme: CYP2C8-Marzolini 2017
+
+Molecule: CYP2C8
+
+###### Parameters
+
+Name | Value | Value Origin
+------------------------------ | --------------------------- | --------------------------
+In vitro CL/recombinant enzyme | 3.6 µl/min/pmol rec. enzyme | Publication-Marzolini 2017
+
+##### Metabolizing Enzyme: CYP3A4-Marzolini 2017
+
+Molecule: CYP3A4
+
+###### Parameters
+
+Name | Value | Value Origin
+------------------------------ | --------------------------- | --------------------------
+In vitro CL/recombinant enzyme | 1.8 µl/min/pmol rec. enzyme | Publication-Marzolini 2017
+
+##### Metabolizing Enzyme: CYP2C9-Marzolini 2017
+
+Molecule: CYP2C9
+
+###### Parameters
+
+Name | Value | Value Origin
+------------------------------ | ---------------------------- | --------------------------
+In vitro CL/recombinant enzyme | 0.48 µl/min/pmol rec. enzyme | Publication-Marzolini 2017
+
+##### Metabolizing Enzyme: CYP3A5-Filppula 2011
+
+Molecule: CYP3A5
+
+###### Parameters
+
+Name | Value | Value Origin
+------------------------------ | ---------------------------- | --------------------------
+In vitro CL/recombinant enzyme | 0.16 µl/min/pmol rec. enzyme | Publication-Marzolini 2017
+
+##### Systemic Process: Glomerular Filtration-Marzolini 2017
+
+Species: Human
+
+###### Parameters
+
+Name | Value | Value Origin
+------------ | -----:| --------------------------
+GFR fraction | 1 | Publication-Marzolini 2017
+
+### Formulation: Filmcoated tablet
+
+Type: Weibull
+
+#### Parameters
+
+Name | Value | Value Origin
+-------------------------------- | ------------------ | ----------------------------------------------------------------------------------------------------------------------
+Dissolution time (50% dissolved) | 130.7856594083 min | Parameter Identification-Parameter Identification-Value updated from 'Parameter Identification 15' on 2019-03-21 11:13
+Lag time | 0 min |
+Dissolution shape | 1.309742335 | Parameter Identification-Parameter Identification-Value updated from 'Parameter Identification 15' on 2019-03-21 11:13
+Use as suspension | Yes |
+
+### Formulation: Chewable tablet
+
+Type: Weibull
+
+#### Parameters
+
+Name | Value | Value Origin
+-------------------------------- | ----------------- | ----------------------------------------------------------------------------------------------------------------------
+Dissolution time (50% dissolved) | 70.3249031902 min | Parameter Identification-Parameter Identification-Value updated from 'Parameter Identification 15' on 2019-03-21 11:13
+Lag time | 0 min |
+Dissolution shape | 1.2919957494 | Parameter Identification-Parameter Identification-Value updated from 'Parameter Identification 15' on 2019-03-21 11:13
+Use as suspension | Yes |
+
+### Formulation: Sandoz Oral granules
+
+Type: Weibull
+
+#### Parameters
+
+Name | Value | Value Origin
+-------------------------------- | ----------------- | ----------------------------------------------------------------------------------------------------------------------
+Dissolution time (50% dissolved) | 96.1730639663 min | Parameter Identification-Parameter Identification-Value updated from 'Parameter Identification 15' on 2019-03-21 11:13
+Lag time | 0 min |
+Dissolution shape | 1.9271553023 | Parameter Identification-Parameter Identification-Value updated from 'Parameter Identification 15' on 2019-03-21 11:13
+Use as suspension | Yes |
+
+### Formulation: Singulair mini Oral granules
+
+Type: Weibull
+
+#### Parameters
+
+Name | Value | Value Origin
+-------------------------------- | ------------------ | ----------------------------------------------------------------------------------------------------------------------
+Dissolution time (50% dissolved) | 133.9238802749 min | Parameter Identification-Parameter Identification-Value updated from 'Parameter Identification 15' on 2019-03-21 11:13
+Lag time | 0 min |
+Dissolution shape | 1.6357552071 | Parameter Identification-Parameter Identification-Value updated from 'Parameter Identification 15' on 2019-03-21 11:13
+Use as suspension | Yes |
+
+## 3.2 Montelukast Diagnostics Plots
+
+Below you find the goodness-of-fit visual diagnostic plots for montelukast PBPK model performance (Individually simulated versus observed plasma concentration and weighted residuals versus time, including the geometric mean fold error (GMFE)) of all data used for model building.
+
+
+
+**Table 3-1: GMFE for Goodness of fit plot for concentration in plasma.**
+
+|Group |GMFE |
+|:-----------------------------|:----|
+|Montelukast Chewable Tablet |1.17 |
+|Montelukast Filmcoated Tablet |1.38 |
+|Montelukast iv |1.32 |
+|Montelukast Oral Granules |1.31 |
+|All |1.31 |
+
+
+
+
+
+
+
+
+**Figure 3-1: Goodness of fit plot for concentration in plasma.**
+
+
+
+
+
+
+
+
+**Figure 3-2: Goodness of fit plot for concentration in plasma.**
+
+
+
+
+## 3.3 Montelukast Concentration-Time profiles
+
+Simulated versus observed plasma concentration-time profiles of all data are listed below.
+
+
+
+
+
+**Figure 3-3: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-4: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-5: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-6: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-7: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-8: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-9: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-10: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-11: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-12: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-13: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-14: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-15: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-16: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-17: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-18: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-19: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-20: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-21: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-22: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-23: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-24: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-25: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-26: Time Profile Analysis 1**
+
+
+
+
+# 4 Conclusion
+
+The final montelukast PBPK model applies elimination mainly by CYP2C8 and adequately describes the pharmacokinetics of montelukast in adults receiving intravenous and oral SD and MD of montelukast ranging from 2-10mg, for different types of tablet formulations.
+
+This model could be applied for the investigation of drug-drug interactions (DDI), and translation to special populations such as pediatrics with regard to CYP2C8 based elimination.
+
+# 5 References
+
+**Cheng 1996** Cheng H, Leff JA, Amin R, Gertz BJ, De Smet M, Noonan N, Rogers JD, Malbecq W, Meisner D, Somers G. Pharmacokinetics, bioavailability, and safety of montelukast sodium (MK-0476) in healthy males and females. Pharm Res. 1996 Mar;13(3):445-8.
+
+**Drugbank.ca** (https://www.drugbank.ca/drugs/DB00471 )
+
+**FDA drug label** (https://www.merck.com/product/usa/pi_circulars/s/singulair/singulair_pi.pdf)
+
+**Fey 2014** Fey C, Thyroff-Friesinger U, Jones S. Bioequivalence of two formulations of montelukast sodium 4 mg oral granules in healthy adults. Clin Transl Allergy. 2014 Sep 18;4:29. doi: 10.1186/2045-7022-4-29. eCollection 2014.
+
+**Filppula 2011** Filppula AM, Laitila J, Neuvonen PJ, Backman JT. Reevaluation of the microsomal metabolism of montelukast: major contribution by CYP2C8 at clinically relevant concentrations. Drug Metab Dispos. 2011 May;39(5):904-11. doi: 10.1124/dmd.110.037689. Epub 2011 Feb 2.
+
+**Knorr 2000** Knorr B, Holland S, Rogers JD, Nguyen HH, Reiss TF. Montelukast adult (10-mg film-coated tablet) and pediatric (5-mg chewable tablet) dose selections. J Allergy Clin Immunol. 2000 Sep;106(3 Suppl):S171-8
+
+**Kuepfer 2016** Kuepfer L, Niederalt C, Wendl T, Schlender JF, Willmann S, Lippert J, Block M, Eissing T, Teutonico D. Applied Concepts in PBPK Modeling: How to Build a PBPK/PD Model.CPT Pharmacometrics Syst Pharmacol. 2016 Oct;5(10):516-531. doi: 10.1002/psp4.12134. Epub 2016 Oct 19.
+
+**Marzolini 2017** Marzolini C, Rajoli R, Battegay M, Elzi L, Back D, Siccardi M. Efavirenz Involving Simultaneous Inducing and Inhibitory Effects on Cytochromes. Clin Pharmacokinet. 2017 Apr;56(4):409-420. doi: 10.1007/s40262-016-0447-7.
+
+**PK-Sim Ontogeny Database Version 7.3** (https://github.com/Open-Systems-Pharmacology/OSPSuite.Documentation/blob/38cf71b384cfc25cfa0ce4d2f3addfd32757e13b/PK-Sim%20Ontogeny%20Database%20Version%207.3.pdf)
+
+**Schlender 2016** Schlender JF, Meyer M, Thelen K, Krauss M, Willmann S, Eissing T, Jaehde U. Development of a Whole-Body Physiologically Based Pharmacokinetic Approach to Assess the Pharmacokinetics of Drugs in Elderly Individuals. Clin Pharmacokinet. 2016 Dec;55(12):1573-1589.
+
+**Zhao 1997** Zhao JJ, Rogers JD, Holland SD, Larson P, Amin RD, Haesen R, Freeman A, Seiberling M, Merz M, Cheng H. Pharmacokinetics and bioavailability of montelukast sodium (MK-0476) in healthy young and elderly volunteers.Biopharm Drug Dispos. 1997 Dec;18(9):769-77
+
+**Zhou 2017** Zhou W, Johnson TN, Bui KH, Cheung SYA, Li J, Xu H, Al-Huniti N, Zhou D. Predictive Performance of Physiologically Based Pharmacokinetic (PBPK) Modeling of Drugs Extensively Metabolized by Major Cytochrome P450s in Children. Clin Pharmacol Ther. 2018 Jul;104(1):188-200. doi: 10.1002/cpt.905. Epub 2017 Nov 20.
+
+","Markdown"
+"PBPK model","Open-Systems-Pharmacology/OSP-PBPK-Model-Library","Fluvoxamine/Fluvoxamine_evaluation_report.md",".md","39616","628","# Building and evaluation of a PBPK model for fluvoxamine in healthy adults
+
+| Version | 2.0-OSP12.2 |
+| ----------------------------------------------- | ------------------------------------------------------------ |
+| based on *Model Snapshot* and *Evaluation Plan* | https://github.com/Open-Systems-Pharmacology/Fluvoxamine-Model/releases/tag/v2.0 |
+| OSP Version | 12.2 |
+| Qualification Framework Version | 3.5 |
+
+This evaluation report and the corresponding PK-Sim project file are filed at:
+
+https://github.com/Open-Systems-Pharmacology/OSP-PBPK-Model-Library/
+
+# Table of Contents
+
+ * [1 Introduction](#introduction)
+ * [2 Methods](#methods)
+ * [2.1 Modeling Strategy](#modeling-strategy)
+ * [2.2 Data](#22)
+ * [2.3 Model Parameters and Assumptions](#23)
+ * [3 Results and Discussion](#3)
+ * [3.1 Final input parameters](#final-input-parameters)
+ * [3.2 Diagnostics Plots](#diagnostics-plots)
+ * [3.3 Concentration-Time Profiles](#concentration-time-profiles)
+ * [3.3.1 Model Building](#model-building)
+ * [3.3.2 Model Verification](#model-validation)
+ * [4 Conclusion](#4)
+ * [5 References](#5)
+
+# 1 Introduction
+
+Fluvoxamine is a selective serotonin reuptake inhibitor used to treat major depression and obsessive compulsive disorder ([Perucca 1994](#5-references), [ANI Pharmaceuticals Inc. 2008](#5-references)) . Recommended doses are 50 to 300 mg once daily. The pharmacokinetics of orally administered single doses are linear. Following multiple oral administration, the pharmacokinetics at steady-state become non-linear, due to saturable Michaelis-Menten kinetics of the metabolic pathways ([Spigset 1998](#5-references)). Metabolism of fluvoxamine includes hydroxylation via CYP1A2 and O-demethylation via the very polymorphic CYP2D6 ([Miura 2007](#5-references), [Spigset 2001](#5-references)). Following oral administration fluvoxamine is excreted via the urine as metabolites ([DeBree 1983](#5-references)). The U.S. Food and Drug Administration (FDA) recommends fluvoxamine as strong clinical CYP1A2 and CYP2C19 index inhibitor to evaluate the impact of CYP1A2/CYP2C19 inhibition on CYP1A2/CYP2C19 substrates ([FDA 2017](#5-references)). Furthermore, the FDA lists fluvoxamine as moderate CYP3A4 inhibitor.
+
+The aim of this project was to develop a PBPK model of fluvoxamine, mechanistically describing its metabolism by CYP1A2 and CYP2D6 and its inhibitory effect on CYP1A2 and CYP3A4, that can be used for drug-drug interaction (DDI) predictions.
+
+The presented model was developed and evaluated by Britz et al. ([Britz 2019](#5-references))
+
+# 2 Methods
+
+## 2.1 Modeling Strategy
+
+The general workflow for building an adult PBPK model has been described by Kuepfer et al. ([Kuepfer 2016](#5-references)). Relevant information on the anthropometry (height, weight) was gathered from the respective clinical study, if reported. Information on physiological parameters (e.g. blood flows, organ volumes, hematocrit) in adults was gathered from the literature and has been incorporated in PK-Sim® as described previously ([Willmann 2007](#5-references)). The applied activity and variability of plasma proteins and active processes that are integrated into PK-Sim® are described in the publicly available 'PK-Sim® Ontogeny Database Version 7.3' ([PK-Sim Ontogeny Database Version 7.3](#5-references)).
+
+The PBPK model was built based on healthy individuals, using the reported mean values for age, weight, height, and genetic background for each study protocol. If no information on these parameters could be found, a healthy male European individual, 30 years of age, with a body weight of 73 kg and a height of 176 cm was used. To model the specific metabolic clearance, CYP1A2 and CYP2D6 were implemented in accordance with literature, using the PK-Sim expression database RT-PCR profiles ([Meyer 2012](#5-references)) to define their relative expression in the different organs of the body. Glomerular filtration and enterohepatic cycling were enabled, as they are involved in fluvoxamine excretion.
+
+Unknown parameters (see below) were identified using the Parameter Identification module provided in PK-Sim®.
+
+The model was then verified by simulating:
+
+- single and multiple dose studies
+- the effect of smoking on CYP1A2 metabolism of fluvoxamine
+- plasma levels of fluvoxamine in CYP2D6 extensive (EM) and poor metabolizers (PM).
+
+Details about input data (physicochemical, *in vitro* and clinical) can be found in [Section 2.2](#22-data).
+
+Details about the structural model and its parameters can be found in [Section 2.3](#23-model-parameters-and-assumptions).
+
+## 2.2 Data
+
+### 2.2.1 In vitro / physico-chemical Data
+
+A literature search was performed to collect available information on physicochemical properties of fluvoxamine. The obtained information from literature is summarized in the table below.
+
+| **Parameter** | **Unit** | **Value** | Source | **Description** |
+| :--------------------- | -------- | ----------------------- | --------------------------------------------------- | ------------------------------------------------------------ |
+| MW | g/mol | 318.34 | [Drugbank](#5-references) | Molecular weight |
+| pKa | | 9.40 (base) | [Hallifax 2007](#5-references) | Acid dissociation constant |
+| Solubility (pH) | mg/mL | 14.66 (7.0) | [MSDS](#5-references) | Solubility |
+| logP | | 2.80 | [Drugbank](#5-references) (predicted by ChemAxon) | Partition coefficient between octanol and water |
+| | | 2.89 | [Drugbank](#5-references) (predicted by ALOGPS) | Partition coefficient between octanol and water |
+| | | 3.20 | [Drugbank](#5-references) (experimentally measured) | Partition coefficient between octanol and water |
+| fu | | 0.13 ± 0.01a | [Yao 2001](#5-references) | Fraction unbound in plasma |
+| | | 0.14 ± 0.02a | [Yao 2001](#5-references) | Fraction unbound in plasma |
+| | | 0.23 | [Claassen 1983](#5-references) | Fraction unbound in plasma |
+| fu,mic | | 0.20 ± 0.05a | [Yao 2001](#5-references) | Fraction unbound in human liver microsomes at a protein concentration of 1 mg/mL |
+| | | 0.31 ± 0.03a | [Yao 2001](#5-references) | Fraction unbound in human liver microsomes at a protein concentration of 0.5 mg/mL |
+| | | 0.70 ± 0.03a | [Yao 2001](#5-references) | Fraction unbound in supersomes at a protein concentration of 0.3 mg/mL |
+| CYP2D6 Km | µmol/L | 76.30 | [Miura 2007](#5-references) | Michaelis-Menten constant |
+| CYP2D6 kcat | 1/min | 0 | [Crews 2014](#5-references) | The number of substrate molecule each enzyme site converts to product per unit time, and in which the enzyme is working at maximum efficiency |
+| CYP1A2 Ki | µmol/L | 0.011 | [Karjalainen 2008](#5-references) | Competitive inhibition constant of the competitive inhibition model measured in human liver microsomes |
+| CYP1A2 Ki,u | nmol/L | 35 | [Yao 2001](#5-references) | Unbound competitive inhibition constant of the mixed inhibition model measured in human liver microsomes at a protein concentration of 1 mg/mL |
+| | nmol/L | 36 | [Yao 2001](#5-references) | Competitive inhibition constant of the mixed inhibition model measured in human liver microsomes at a protein concentration of 0.5 mg/mL |
+| | nmol/L | 36 | [Yao 2001](#5-references) | Competitive inhibition constant of the mixed inhibition model measured in supersomes at a protein concentration of 0.3 mg/mL |
+| CYP3A4 Ki | µmol/L | 1.60 | [Olesen 2000](#5-references) | Competitive inhibition constant of the competitive inhibition model measured in human liver microsomes |
+
+a denotes mean ± standard deviation
+
+### 2.2.2 Clinical Data
+
+A literature search was performed to collect available clinical data on fluvoxamine in healthy adults.
+
+The fluvoxamine PBPK model was developed using 26 different clinical studies with pharmacokinetic (PK) blood sampling. These studies include 1 study of 30 mg fluvoxamine administered intravenously (iv) as a single-dose, and 25 studies of fluvoxamine administered orally (po) in single- or multiple-doses. In the single-dose po studies fluvoxamine was administered in doses of 25 - 200 mg. In the multiple-dose po studies fluvoxamine was administered once (q.d.) or twice daily (b.i.d.), in doses of 10 - 150 mg per administration.
+
+#### 2.2.2.1 Model Building
+
+The following studies were used for model building (training data):
+
+| Publication | Arm / Treatment / Information used for model building |
+| :------------------------------------- | :----------------------------------------------------------- |
+| [Japanese Society 2015](#5-references) | Healthy Japanese adults with 30 mg as 60 min infusion or oral administration of 200 mg |
+| [de Vries 1993](#5-references) | Healthy adults with oral administration of 25-100 mg |
+| [Orlando 2010](#5-references) | Healthy adults with oral administration of 50 mg |
+| [Labellarte 2004](#5-references) | Healthy CYP2D6 EM with oral administration of 50 mg twice a day |
+| [Spigset 1998](#5-references) | Healthy CYP2D6 EM (80%) and PM (20%) with oral administration of doses between 12.5-100 mg twice a day |
+| [Fleishaker 1994](#5-references) | Healthy adults with oral administration of 50 mg or 100 mg once daily |
+
+#### 2.2.2.2 Model Verification
+
+The following studies were used for model verification:
+
+| Publication | Arm / Treatment / Information used for model building |
+| :------------------------------------- | :----------------------------------------------------------- |
+| [Christensen 2002](#5-references) | Healthy CYP2D6 EM with oral administration of 10 mg or 25 mg twice a day and healthy CYP2D6 PM with oral administration of 10 mg or 25 mg once daily |
+| [Fukasawa 2006](#5-references) | Healthy Japanese adults with single oral doses of 50 mg |
+| [Japanese Society 2015](#5-references) | Healthy Japanese adults with single oral doses of 25-100 mg |
+| [Kunii 2005](#5-references) | Healthy CYP2D6 EM with single oral doses of 50 mg |
+| [Spigset 1995](#5-references) | Healthy smokers or non-smokers with oral administration of 50 mg as single dose |
+| [Spigset 1997](#5-references) | Healthy CYP2D6 EM or PM with oral administration of 50 mg as single dose |
+| [van Harten 1991](#5-references) | Healthy adults with oral administration of 50 mg as single dose |
+| [de Vries 1992](#5-references) | Healthy adults with oral administration of 50 mg twice a day |
+| [Bahrami 2007](#5-references) | Healthy adults with oral administration of 100 mg as single dose |
+| [de Bree 1983](#5-references) | Healthy adults with oral administration of 100 mg as single dose |
+
+## 2.3 Model Parameters and Assumptions
+
+### 2.3.1 Absorption
+
+Since a rapid dissolution and absorption was assumed for tablet as well as capsule formulation, the drug formulation was implemented as solution.
+
+The specific intestinal permeability was identified during parameter identification.
+
+### 2.3.2 Distribution
+
+It is described in literature that fluvoxamine is moderately bound to plasma proteins (77%, [Claassen 1983](#5-references)). This value was implemented in PK-Sim®. The protein binding partner was set to unknown.
+
+An important parameter influencing the distribution of a compound is lipophilicity. To accurately describe the distribution of fluvoxamine, logP was optimized during parameter identification to match observed clinical data.
+
+After testing the available organ-plasma partition coefficient and cell permeability calculation methods built in PK-Sim, observed clinical data was best described by choosing the partition coefficient calculation by `Schmitt` and cellular permeability calculation by `PK-Sim Standard`.
+
+### 2.3.3 Metabolism and Elimination
+
+The final model applies metabolism by CYP1A2, CYP2D6 and glomerular filtration. The metabolic processes by CYP1A2 and CYP2D6 were described by Michaelis-Menten kinetics. The Michaelis-Menten constant Km for CYP2D6 metabolism was fixed according to literature values, other parameters were identified during parameter identification.
+
+To distinguish between fluvoxamine metabolism in CYP2D6 extensive metabolizers (EM) and poor metabolizers (PM), the CYP2D6 catalytic rate constant kcat of PMs was set to zero. This assumption was made because CYP2D6 PMs were characterized by absent CYP2D6 enzymatic activity [Crews 2014](#5-references), which results in a predicted 1.5-fold increase of the fluvoxamine AUC in CYP2D6 PMs compared with CYP2D6 EMs.
+
+Smoking is the strongest known inducer of CYP1A2 and results in higher metabolism of CYP1A2 substrates [Zhou 2009](#5-references). As no detailed information on the frequency, duration, and amount of smoking was available from literature, the induction of CYP1A2 was implemented as a static 1.38-fold increase in enzyme activity. This factor was optimized based on the study of Spigset et al. ([Spigset 1995](#5-references)) resulting in a 39% reduction of the fluvoxamine AUC in smokers.
+
+### 2.3.4 Enzyme inhibition
+
+To describe the inhibition of CYP1A2 by fluvoxamine, the reported Ki value of 11 nmol/L [Karjalainen 2008](#5-references) was corrected for fluvoxamine binding in the in vitro test system as recommended by [Yao 2001](#5-references) and a value of 10 nmol/L was then used for both `Ki_c` and `Ki_u` to describe mixed-type inhibition in the PBPK model.
+
+To describe the inhibition of CYP3A4 by fluvoxamine, the reported Ki value of 1.6 µmol/L ([Olesen 2000](#5-references)) was included in the model.
+
+### 2.3.5 Automated Parameter Identification
+
+This is the result of the final parameter identification.
+
+| Model Parameter | Optimized Value | Unit |
+| --------------------------------------------- | --------------- | --------- |
+| `logP` | 3.57 | log units |
+| `Km` (CYP1A2) | 7.35 | nmol/L |
+| `kcat` (CYP1A2) *non-smokers* | 0.016 | 1/min |
+| `kcat` (CYP1A2) *smokers* | 0.022 | 1/min |
+| `kcat` (CYP2D6) *extensive metabolizers (EM)* | 110.56 | 1/min |
+| `Specific intestinal permeability` | 2.74 E-6 | dm/min |
+
+# 3 Results and Discussion
+
+The PBPK model for fluvoxamine was developed and verified with clinical pharmacokinetic data.
+
+The model was evaluated covering data from studies including in particular
+
+* intravenous and oral administration
+* single and multiple doses
+* a dose range from 25 mg to 200 mg
+* subjects phenotyped as CYP2D6 *extensive metabolizers* (*EM*) and *poor metabolizers* (*PM*)
+* smokers and non-smokers
+
+The model quantifies metabolism via CYP1A2 and CYP2D6 and the effect of smoking and different CYP2D6 phenotypes on fluvoxamine metabolism.
+
+The next sections show:
+
+1. the final model parameters for the building blocks: [Section 3.1](#31-final-input-parameters).
+2. the overall goodness of fit: [Section 3.2](#32-diagnostics-plots).
+3. simulated vs. observed concentration-time profiles for the clinical studies used for model building and for model verification: [Section 3.3](#33-concentration-time-profiles).
+
+## 3.1 Final input parameters
+
+The compound parameter values of the final PBPK model are illustrated below.
+
+### Compound: Fluvoxamine
+
+#### Parameters
+
+Name | Value | Value Origin | Alternative | Default
+------------------------------------------------ | ----------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------- | ----------- | -------
+Solubility at reference pH | 14.66 mg/ml | | Measurement | True
+Reference pH | 7 | | Measurement | True
+Lipophilicity | 3.5726507829 Log Units | Parameter Identification | Measurement | True
+Fraction unbound (plasma, reference value) | 0.23 | Publication-Claassen et al., Review of the animal pharmacology and pharmacokinetics of fluvoxamine. Br. J. Clin. Pharmacol. 15, 349S-355S (1983). | Measurement | True
+Specific intestinal permeability (transcellular) | 2.7380788903E-06 dm/min | Parameter Identification | Fitted | True
+F | 3 | | |
+Is small molecule | Yes | | |
+Molecular weight | 318.335 g/mol | | |
+Plasma protein binding partner | Unknown | | |
+
+#### Calculation methods
+
+Name | Value
+----------------------- | ---------------
+Partition coefficients | Schmitt
+Cellular permeabilities | PK-Sim Standard
+
+#### Processes
+
+##### Metabolizing Enzyme: CYP1A2-Fit
+
+Molecule: CYP1A2
+
+###### Parameters
+
+Name | Value | Value Origin
+------------------------------------------- | -------------------------- | ------------
+In vitro Vmax for liver microsomes | 0 pmol/min/mg mic. protein |
+Content of CYP proteins in liver microsomes | 45 pmol/mg mic. protein | Unknown
+Km | 0.0073460807948 µmol/l |
+kcat | 0.0155447966 1/min | Unknown
+
+##### Metabolizing Enzyme: CYP2D6-Miura2007
+
+Molecule: CYP2D6
+
+###### Parameters
+
+Name | Value | Value Origin
+-------------------------------- | ------------------------------ | ------------------------
+In vitro Vmax/recombinant enzyme | 0.69 pmol/min/pmol rec. enzyme |
+Km | 76.3 µmol/l |
+kcat | 110.5561921693 1/min | Parameter Identification
+
+##### Systemic Process: Glomerular Filtration-4% Urine
+
+Species: Human
+
+###### Parameters
+
+Name | Value | Value Origin
+------------ | -----:| ------------:
+GFR fraction | 1 |
+
+##### Inhibition: CYP1A2-Karjalainen2008/Yao2001
+
+Molecule: CYP1A2
+
+###### Parameters
+
+Name | Value | Value Origin
+---- | --------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
+Ki_c | 10 nmol/l | Publication-In Vitro-Karjalainen et al. In vitro inhibition of CYP1A2 by model inhibitors, anti-inflammatory analgesics and female sex steroids: predictability of in vivo interactions. Basic Clin. Pharmacol. Toxicol. 103, 157–65 (2008) and Yao, C. et al. Fluvoxamine-theophylline interaction: gap between in vitro and in vivo inhibition constants toward cytochrome P4501A2. Clin. Pharmacol. Ther. 70, 415–24 (2001)
+Ki_u | 10 nmol/l | Publication-In Vitro-Karjalainen et al. In vitro inhibition of CYP1A2 by model inhibitors, anti-inflammatory analgesics and female sex steroids: predictability of in vivo interactions. Basic Clin. Pharmacol. Toxicol. 103, 157–65 (2008) and Yao, C. et al. Fluvoxamine-theophylline interaction: gap between in vitro and in vivo inhibition constants toward cytochrome P4501A2. Clin. Pharmacol. Ther. 70, 415–24 (2001)
+
+##### Inhibition: CYP3A4-Olesen2000/Yao2001
+
+Molecule: CYP3A4
+
+###### Parameters
+
+Name | Value | Value Origin
+---- | ---------- | -----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
+Ki | 1.6 µmol/l | Publication-In Vitro-Olesen et al. Fluvoxamine-Clozapine drug interaction: inhibition in vitro of five cytochrome P450 isoforms involved in clozapine metabolism. J. Clin. Psychopharmacol. 20, 35–42 (2000) and Yao, C. et al. Fluvoxamine-theophylline interaction: gap between in vitro and in vivo inhibition constants toward cytochrome P4501A2. Clin. Pharmacol. Ther. 70, 415–24 (2001)
+
+### Formulation: Solution
+
+Type: Dissolved
+
+## 3.2 Diagnostics Plots
+
+Below you find the goodness-of-fit visual diagnostic plots for the PBPK model performance of all data used presented in [Section 2.2.2](#222-clinical-data).
+
+The first plot shows observed versus simulated plasma concentration, the second weighted residuals versus time.
+
+
+
+**Table 3-1: GMFE for Goodness of fit plot for concentration in plasma**
+
+|Group |GMFE |
+|:----------------|:----|
+|test dataset |1.62 |
+|training dataset |1.21 |
+|All |1.40 |
+
+
+
+
+
+
+
+
+**Figure 3-1: Goodness of fit plot for concentration in plasma**
+
+
+
+
+
+
+
+
+**Figure 3-2: Goodness of fit plot for concentration in plasma**
+
+
+
+
+## 3.3 Concentration-Time Profiles
+
+Simulated versus observed concentration-time profiles of all data listed in [Section 2.2.2](#222-clinical-data) are presented below.
+
+### 3.3.1 Model Building
+
+
+
+
+
+**Figure 3-3: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-4: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-5: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-6: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-7: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-8: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-9: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-10: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-11: Time Profile Analysis**
+
+
+
+
+### 3.3.2 Model Verification
+
+
+
+
+
+**Figure 3-12: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-13: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-14: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-15: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-16: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-17: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-18: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-19: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-20: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-21: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-22: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-23: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-24: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-25: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-26: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-27: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-28: Time Profile Analysis**
+
+
+
+
+# 4 Conclusion
+
+The herein presented PBPK model adequately describes the pharmacokinetics of fluvoxamine in adults.
+
+In particular, it applies quantitative metabolism by CYP1A2 and CYP2D6. The inhibition of CYP1A2 and CYP3A4 are implemented and evaluated (shown elsewhere) in the current model as well. Thus, the model is fit for purpose to be applied for the prediction of drug-drug interaction
+
+# 5 References
+
+**ANI Pharmaceuticals Inc. 2008** ANI Pharmaceuticals Inc. Fluvoxamine maleate - prescribing information. (2008).
+
+**Bahrami 2007** Bahrami, G. & Mohammadi, B. Rapid and sensitive bioanalytical method for measurement of fluvoxamine in human serum using 4-chloro-7-nitrobenzofurazan as pre-column derivatization agent: application to a human pharmacokinetic study. J. Chromatogr. B. Analyt. Technol. Biomed. Life Sci. 857, 322–6 (2007).
+
+**Britz 2019** Physiologically-based pharmacokinetic models for CYP1A2 drug–drug interaction prediction: a modeling network of fluvoxamine, theophylline, caffeine, rifampicin, and midazolam. CPT Pharmacometrics Syst. Pharmacol. 8, 296-307 (2019)
+
+**Christensen 2002** Christensen, M. et al. Low daily 10-mg and 20-mg doses of fluvoxamine inhibit the metabolism of both caffeine (cytochrome P4501A2) and omeprazole (cytochrome P4502C19). Clin. Pharmacol. Ther. 71, 141–52 (2002).
+
+**Claassen 1983** Claassen, V. Review of the animal pharmacology and pharmacokinetics of fluvoxamine. Br. J. Clin. Pharmacol. 15, 349S–355S (1983).
+
+**Crews 2014** Crews, K.R. et al. Clinical Pharmacogenetics Implementation Consortium guidelines for cytochrome P450 2D6 genotype and codeine therapy: 2014 update. Clin. Pharmacol. Ther. 95, 376–82 (2014).
+
+**DeBree 1983** DeBree, H., VanderSchoot, J. & Post, L. Fluvoxamine maleate; Disposition in man. Eur. J. Drug Metab. Pharmacokinet. 8, 175–79 (1983).
+
+**DeVries 1992** DeVries, M., VanHarten, J., VanBemmel, P. & Raghoebar, M. Single and multiple oral dose fluvoxamine kinetics in young and elderly subjects. Ther. Drug Monit. 14, 493–98 (1992).
+
+**Drugbank** (https://www.drugbank.ca/drugs/DB00176), last view: 22 October 2018;
+
+**Fleishaker 1994** Fleishaker, J. & Hulst, L. A pharmacokinetic and pharmacodynamic evaluation of the combined administration of alprazolam and fluvoxamine. Eur. J. Clin. Pharmacol. 46, 35–9 (1994).
+
+**Fukasawa 2006** Fukasawa, T. et al. Effects of caffeine on the kinetics of fluvoxamine and its major metabolite in plasma after a single oral dose of the drug. Ther. Drug Monit. 28, 308–11 (2006).
+
+**Hallifax 2007** Hallifax, D. & Houston, J.B. Saturable uptake of lipophilic amine drugs into isolated hepatocytes: mechanisms and consequences for quantitative clearance prediction. Drug Metab. Dispos. 35, 1325–32 (2007).
+
+**Japanese Society 2015** Japanese Society of Hospital Pharmacists. 医薬品インタビューフォーム. (2015).
+
+**Karjalainen 2008** Karjalainen, M.J., Neuvonen, P.J. & Backman, J.T. In vitro inhibition of CYP1A2 by model inhibitors, anti-inflammatory analgesics and female sex steroids: predictability of in vivo interactions. Basic Clin. Pharmacol. Toxicol. 103, 157–65 (2008).
+
+**Kuepfer 2016** Kuepfer L, Niederalt C, Wendl T, Schlender JF, Willmann S, Lippert J, Block M, Eissing T, Teutonico D. Applied Concepts in PBPK Modeling: How to Build a PBPK/PD Model.CPT Pharmacometrics Syst Pharmacol. 2016 Oct;5(10):516-531. doi: 10.1002/psp4.12134. Epub 2016 Oct 19.
+
+**Kunii 2005** Kunii, T. et al. Interaction study between enoxacin and fluvoxamine. Ther. Drug Monit. 27, 349–53 (2005).
+
+**Labellarte 2004** Labellarte, M. et al. Multiple-dose pharmacokinetics of fluvoxamine in children and adolescents. J. Am. Acad. Child Adolesc. Psychiatry 43, 1497–505 (2004).
+
+**Meyer 2012** Meyer, M., Schneckener, S., Ludewig, B., Kuepfer, L. & Lippert, J. Using expression data for quantification of active processes in physiologically-based pharmacokinetic modeling. Drug Metab. Dispos. 40, 892–901 (2012).
+
+**Miura 2007** Miura, M. & Ohkubo, T. Identification of human cytochrome P450 enzymes involved in the major metabolic pathway of fluvoxamine. Xenobiotica. 37, 169–79 (2007).
+
+**MSDS** material safety data sheet of fluvoxamine
+
+**Olesen 2000** Olesen, O.V. & Linnet, K. Fluvoxamine-Clozapine drug interaction: inhibition in vitro of five cytochrome P450 isoforms involved in clozapine metabolism. J. Clin. Psychopharmacol. 20, 35–42 (2000).
+
+**Orlando 2010** Orlando, R., DeMartin, S., Andrighetto, L., Floreani, M. & Palatini, P. Fluvoxamine pharmacokinetics in healthy elderly subjects and elderly patients with chronic heart failure. Br. J. Clin. Pharmacol. 69, 279–86 (2010).
+
+**Perucca 1994** Perucca, E., Gatti, G. & Spina, E. Clinical pharmacokinetics of fluvoxamine. Clin. Pharmacokinet. 27, 175–90 (1994).
+
+**PK-Sim Ontogeny Database Version 7.3** (https://github.com/Open-Systems-Pharmacology/OSPSuite.Documentation/blob/38cf71b384cfc25cfa0ce4d2f3addfd32757e13b/PK-Sim%20Ontogeny%20Database%20Version%207.3.pdf)
+
+**Schlender 2016** Schlender JF, Meyer M, Thelen K, Krauss M, Willmann S, Eissing T, Jaehde U. Development of a Whole-Body Physiologically Based Pharmacokinetic Approach to Assess the Pharmacokinetics of Drugs in Elderly Individuals. Clin Pharmacokinet. 2016 Dec;55(12):1573-1589.
+
+**Spigset 1995** Spigset, O., Carleborg, L., Hedenmalm, K. & Dahlqvist, R. Effect of cigarette smoking on fluvoxamine pharmacokinetics in humans. Clin. Pharmacol. Ther. 58, 399–403 (1995).
+
+**Spigset 1997** Spigset, O., Granberg, K., Hägg, S., Norström, A. & Dahlqvist, R. Relationship between fluvoxamine pharmacokinetics and CYP2D6/CYP2C19 phenotype polymorphisms. Eur. J. Clin. Pharmacol. 52, 129–33 (1997).
+
+**Spigset 1998** Spigset, O., Granberg, K., Hägg, S., Söderström, E. & Dahlqvist, R. Non-linear fluvoxamine disposition. Br. J. Clin. Pharmacol. 45, 257–63 (1998).
+
+**Spigset 2001** Spigset, O., Axelsson, S., Norström, A., Hägg, S. & Dahlqvist, R. The major fluvoxamine metabolite in urine is formed by CYP2D6. Eur. J. Clin. Pharmacol. 57, 653–8 (2001).
+
+**FDA 2017** U.S. Food and Drug Administration. Clinical Drug Interaction Studies - Study Design, Data Analysis, Implications for Dosing, and Labeling Recommendations. Draft Guidance for Industry. (2017).
+
+**VanHarten 1991** VanHarten, J., VanBemmel, P., Dobrinska, M.R., Ferguson, R.K. & Raghoebar, M. Bioavailability of fluvoxamine given with and without food. Biopharm. Drug Dispos. 12, 571–6 (1991).
+
+**Yao 2001** Yao, C. et al. Fluvoxamine-theophylline interaction: gap between in vitro and in vivo inhibition constants toward cytochrome P4501A2. Clin. Pharmacol. Ther. 70, 415–24 (2001).
+
+**Zhou 2009** Zhou, S.F., Yang, L.P., Zhou, Z.W., Liu, Y.H. & Chan, E. Insights into the substrate specificity, inhibitors, regulation, and polymorphisms and the clinical impact of human cytochrome P450 1A2. AAPS J. 11, 481–494 (2009).
+
+","Markdown"
+"PBPK model","Open-Systems-Pharmacology/OSP-PBPK-Model-Library","S-Mephenytoin/S-Mephenytoin_evaluation_report.md",".md","21893","334","# Building and evaluation of a PBPK model for S-Mephenytoin in adults
+
+| Version | 2.0-OSP12.2 |
+| ----------------------------------------------- | ------------------------------------------------------------ |
+| based on *Model Snapshot* and *Evaluation Plan* | https://github.com/Open-Systems-Pharmacology/S-Mephenytoin-Model/releases/tag/v2.0 |
+| OSP Version | 12.2 |
+| Qualification Framework Version | 3.5 |
+
+This evaluation report and the corresponding PK-Sim project file are filed at:
+
+https://github.com/Open-Systems-Pharmacology/OSP-PBPK-Model-Library/
+
+# Table of Contents
+
+ * [1 Introduction](#introduction)
+ * [2 Methods](#methods)
+ * [2.1 Modeling Strategy](#modeling-strategy)
+ * [2.2 Data](#data)
+ * [2.3 Model Parameters and Assumptions](#model-parameters-and-assumptions)
+ * [3 Results and Discussion](#results-and-discussion)
+ * [3.1 Final input parameters](#final-input-parameters)
+ * [3.2 Diagnostics Plots](#diagnostics-plots)
+ * [3.3 Concentration-Time Profiles](#ct-profiles)
+ * [3.3.1 Model Verification](#model-verification)
+ * [4 Conclusion](#conclusion)
+ * [5 References](#main-references)
+ * [6 Glossary](#glossary)
+
+# 1 Introduction
+
+The presented PBPK model of S-mephenytoin has been developed to be used in a PBPK Drug-Drug-Interactions (DDI) network with S-mephenytoin as a substrate of CYP2C19.
+
+Mephenytoin is a hydantoin-derivative anticonvulsant used to control various partial seizures and was first used in the 1940s ([Troupin 1979](#5-references)).
+
+Only limited clinical PK and ADME data are available. Mephenytoin is soluble and rapidly absorbed with a Tmax of 1 hour. The mean half-life in human is 6.8 hours. No hints for dose non-linearity could be found in literature.
+
+Mephenytoin is the mixture of the two enantiomers S- and R-Mephenytoin. S-Mephenytoin is mainly metabolized via CYP2C19. Only a very minor part is metabolized by CYP2C9. The R-enantiomer is not metabolized by CYP2C19. The clearance of S-Mephenytoin in CYP2C19 EM is 40 to 100-fold higher than in PM.
+
+# 2 Methods
+
+## 2.1 Modeling Strategy
+
+The general workflow for building an adult PBPK model has been described by Kuepfer et al. ([Kuepfer 2016](#5-references)). Relevant information on the anthropometry (height, weight) was gathered from the respective clinical study, if reported. Information on physiological parameters (e.g. blood flows, organ volumes, hematocrit) in adults was gathered from the literature and has been incorporated in PK-Sim® as described previously ([Willmann 2007](#5-references)). The applied activity and variability of plasma proteins and active processes that are integrated into PK-Sim® are described in the publicly available 'PK-Sim® Ontogeny Database Version 7.3' ([PK-Sim Ontogeny Database Version 7.3](#5-references)).
+
+Only the S-enantiomer of mephenytoin is modeled. The modeling work flow can be summarized as following:
+
+| **Modelling step** | **Data used / comment** |
+| ------------------------------------------------------------ | ------------------------------------------------------------ |
+| **1.) Development of mean po model (no i.v. data available)** | IVIVE based on physico-chemistry and in vitro metabolization or recalculated in vivo clearance. Alternative middle out fits to in vivo data were tried but could not improve DDI prediction significantly. Additional limited data lead to identifiability problems. |
+| **2.) Evaluation of p.o. model with virtual PK-Sim population** | Range and mean plasma profiles after p.o. administration for the study population was in line with the PK-Sim in-built variability of CYP2C9 and CYP2C19. |
+
+The predefined “Standard European Male for DDI” individual was used (age = 30 y, weight = 73 kg, height = 176 cm, BMI = 23.57 kg/m2). CYP2C19 expression from the PK-Sim in-built RT-PCR database was added.
+
+Due to the limited data, an IVIVE approach has been selected. Parameters describing intrinsic CYP2C19 clearance were recalculated from CL/F ([Adedoyin 1998](#5-references)) and in vitro metabolism data from human microsomes CYP2C19 clearance and CYP2C9 clearance ([Steere 2015](#5-references)).
+
+A simulation of a population with 2000 virtual individuals according to the biometrics of the individuals (8 males and females, 32-78 years) used in a study by [Adedoyin 1998](#5-references) was carried out. Additional variability was included on CYP2C19 among the population, by using the geometric SD as derived from reported CL/F values in [Olivares-Morales 2016](#5-references).
+
+Details about input data (physicochemical, *in vitro* and clinical) can be found in [Section 2.2](#22-data).
+
+Details about the structural model and its parameters can be found in [Section 2.3](#23-model-parameters-and-assumptions).
+
+## 2.2 Data
+
+### 2.2.1 In vitro and physico-chemical data
+
+A literature search was performed to collect available information on physico-chemical properties of S-mephenytoin ([Table 1](#table-1)).
+
+| **Parameter** | **Unit** | **Value** | Source | **Description** |
+| :------------------------------ | -------- | ------------- | --------------------------------- | ---------------------------- |
+| MW+ | g/mol | 218.52 | [DrugBank DB00532](#5-references) | Molecular weight. |
+| pKa,acid+ | | 8.51 | [DrugBank DB00532](#5-references) | Acidic dissociation constant |
+| Solubility (pH)+ | mg/mL | 1.27
(7) | [DrugBank DB00532](#5-references) | Aqueous Solubility |
+| logP+ | | 1.69 | [DrugBank DB00532](#5-references) | Partition coefficient |
+| fu+ | % | 70.2 | [Steere 2015](#5-references) | Fraction unbound in plasma |
+
+**Table 1:** Physico-chemical and *in-vitro* metabolization properties of S-mephenytoin extracted from literature. *+: Value used in final model*
+
+### 2.2.2 Clinical data
+
+A literature search was performed to collect available clinical data on S-mephenytoin. Data used for model development and validation are listed in [Table 2](#table-2).
+
+| **Source** | **Route** | **Dose [mg]/** **Schedule** | **Pop.** | **Age [yrs] (mean) /range** | **Weight [kg] (mean) /range** | **Sex** | **N** | **Form.** | **Comment** |
+| -------------------- | --------- | ------------------------------- | ------------ | ------- | ----- | --------- | --------------------------------- | --------------------------------- | --------------------------------- |
+| [Adedoyin 1998](#5-references) | p.o. | 100 mg s.d. | HV, all EM | 54.7 / 32-73 | - | m/f | 8 | tablet | 50 g S-mephenytoin simulated |
+| [Jacqz 1986](#5-references) | p.o. | 100 mg s.d. | HV, 6 EM, 1 IM and 1 PM | 25-76 | - | m/f | 8 | tablet | 50 g S-mephenytoin simulated |
+| [Yao 2003](#5-references) | p.o. | 100 mg s.d. | HV | 23-49 | - | m/f | 12 | - | S-mephenytoin, with and without Fluvoxamine MD of 37.5, 62.5 and 87.5 mg/day |
+| [Iga 2016](#5-references) | p.o. | 100 mg s.d. | - | - | - | - | - | - | 50 g S-mephenytoin simulated |
+| | | | | | | | | | |
+| [Wedlund 1985](#5-references) | p.o. | 100 mg s.d. | HV | 21-7 | 54-108 | male | 8 | tablet | 50 g S-mephenytoin simulated |
+
+**Table 2:** Literature sources of clinical concentration data of S-mephenytoin used for model development and validation. *s.d.: single dose*
+
+## 2.3 Model Parameters and Assumptions
+
+### 2.3.1 Absorption
+
+Intestinal permeability is calculated by PK-Sim. No data are available to estimate the parameter.
+
+### 2.3.2 Distribution
+
+Partition coefficient calculation by `Rodgers and Rowland` and cellular permeability calculation by `PK-Sim Standard` have been assumed. Identification of the best suited calculation methods was not possible due to lack of i.v. data.
+
+### 2.3.3 Metabolism and Elimination
+
+Two linear metabolic pathways for S-mephenytoin were implement in the model:
+
+* CYP2C19
+* CYP2C9
+
+Additionally, glomerular filtration was implemented with assumed GFR fraction of 1. No active renal excretion described in literature. Renal clearance is minor compared to overall plasma clearance ([Jacqz 1986](#5-references)) and can be described by glomerular filtration.
+
+### 2.3.5 Automated Parameter Identification
+
+Performing parameter identification on lipophilicity, CYP2C19 intrinsic clearance, and intestinal permeability did not improve the performance of the model. Therefore, no parameters have been estimated.
+
+# 3 Results and Discussion
+
+The next sections show:
+
+1. Final model input parameters for the building blocks: [Section 3.1](#31-final-input-parameters).
+2. Overall goodness of fit: [Section 3.2](#32-diagnostics-plots).
+3. Simulated vs. observed concentration-time profiles for the clinical studies used for model building and for model verification: [Section 3.3](#33-concentration-time-profiles).
+
+## 3.1 Final input parameters
+
+The parameter values of the final PBPK model are illustrated below.
+
+### Compound: S-Mephenytoin
+
+#### Parameters
+
+Name | Value | Value Origin | Alternative | Default
+------------------------------------------ | -------------- | ------------------------- | ----------- | -------
+Solubility at reference pH | 1.27 mg/ml | Database-DrugBank DB00532 | Measurement | True
+Reference pH | 7 | Database-DrugBank DB00532 | Measurement | True
+Lipophilicity | 1.69 Log Units | Database-DrugBank DB00532 | Measurement | True
+Fraction unbound (plasma, reference value) | 70.2 % | Publication-Steere 2015 | Measurement | True
+Is small molecule | Yes | | |
+Molecular weight | 218.52 g/mol | Database-DrugBank DB00532 | |
+Plasma protein binding partner | Albumin | | |
+
+#### Calculation methods
+
+Name | Value
+----------------------- | -------------------
+Partition coefficients | Rodgers and Rowland
+Cellular permeabilities | PK-Sim Standard
+
+#### Processes
+
+##### Metabolizing Enzyme: CYP2C19-Adedoyin1998_Table1_CL_F
+
+Species: Human
+
+Molecule: CYP2C19
+
+###### Parameters
+
+Name | Value | Value Origin
+------------------- | ------------- | -------------------------
+Intrinsic clearance | 1986.9 ml/min | Publication-Adedoyin 1998
+
+##### Metabolizing Enzyme: CYP2C9-Steere2015_Clint_CYP2C9_Table1
+
+Molecule: CYP2C9
+
+###### Parameters
+
+Name | Value | Value Origin
+------------------------------------------- | --------------------------- | --------------------------
+In vitro CL for liver microsomes | 0.39 µl/min/mg mic. protein | Publication-Steere2015
+Content of CYP proteins in liver microsomes | 96 pmol/mg mic. protein | Publication-Rodrigues 1999
+
+##### Systemic Process: Glomerular Filtration-Assumption
+
+Species: Human
+
+###### Parameters
+
+Name | Value | Value Origin
+------------ | -----:| ----------------
+GFR fraction | 1 | Other-Assumption
+
+## 3.2 Diagnostics Plots
+
+The following section displays the goodness-of-fit visual diagnostic plots for the PBPK model performance of all data listed in [Section 2.2.2](#222-clinical-data).
+
+The first plot shows observed versus simulated plasma concentration, the second weighted residuals versus time.
+
+
+
+**Table 3-1: GMFE for S-mephenytoin concentration in plasma after 50 mg oral administration - mean data**
+
+|Group |GMFE |
+|:-------------|:----|
+|Adedoyin 1998 |2.13 |
+|Iga 2016 |1.45 |
+|Jacqz 1986 |1.81 |
+|Wedlund 1985 |1.85 |
+|All |1.65 |
+
+
+
+
+
+
+
+
+**Figure 3-1: S-mephenytoin concentration in plasma after 50 mg oral administration - mean data**
+
+
+
+
+
+
+
+
+**Figure 3-2: S-mephenytoin concentration in plasma after 50 mg oral administration - mean data**
+
+
+
+
+## 3.3 Concentration-Time Profiles
+
+Simulated versus observed concentration-time profiles of all data listed in [Section 2.2.2](#222-clinical-data) are presented below.
+
+### 3.3.1 Model Verification
+
+
+
+
+
+**Figure 3-3: S-Mephenytoin 50 mg p.o.**
+
+
+
+
+
+
+
+
+**Figure 3-4: Time Profile Analysis**
+
+
+
+
+# 4 Conclusion
+
+A PBPK model describing the limited set of in vivo p.o. data could be established.
+
+Population simulated data had a comparable variability with the observed data when included additional variability for CYP2C19 expression. Moreover, the mean data from other studies ([Jacqz 1986](#5-references), [Wedlund 1985](#5-references)) were in the range of expected variability, confirming the adequacy of the model.
+
+# 5 References
+
+**Adedoyin 1998** Adedoyin A, Arns PA, Richards WO, Wilkinson GR, Branch RA. Selective effect of liver disease on the activities of specific metabolizing enzymes: investigation of cytochromes P450 2C19 and 2D6. *Clin Pharmacol Ther*. 1998;64(1):8-17.
+
+**DrugBank DB00532** (https://www.drugbank.ca/drugs/DB00532)
+
+**Iga 2016** Iga K. Dynamic and Static Simulations of Fluvoxamine-Perpetrated Drug-Drug Interactions Using Multiple Cytochrome P450 Inhibition Modeling, and Determination of Perpetrator-Specific CYP Isoform Inhibition Constants and Fractional CYP Isoform Contributions to Vic. *J Pharm Sci*.Victim Clearance. Journal of Pharmaceutical Sciences. 2016 Mar;105(3):1307-1317–17.
+
+**Jacqz 1986** Jacqz E, Hall SD, Branch RA, Wilkinson GR. Polymorphic metabolism of mephenytoin in man: Pharmacokinetic interaction with a co‐regulated substrate, mephobarbital. *Clin Pharmacol Ther*. 1986;39(6):646-653.
+
+**Kuepfer 2016** Kuepfer L, Niederalt C, Wendl T, Schlender JF, Willmann S, Lippert J, Block M, Eissing T, Teutonico D. Applied Concepts in PBPK Modeling: How to Build a PBPK/PD Model.CPT Pharmacometrics Syst Pharmacol. 2016 Oct;5(10):516-531.
+
+**Olivares-Morales 2016** Olivares-Morales A, Ghosh A, Aarons L, Rostami-Hodjegan A. Development of a Novel Simplified PBPK Absorption Model to Explain the Higher Relative Bioavailability of the OROS(R) Formulation of Oxybutynin. *AAPS J*. 2016;18(6):1532-1549.
+
+**PK-Sim Ontogeny Database Version 7.3** (https://github.com/Open-Systems-Pharmacology/OSPSuite.Documentation/blob/38cf71b384cfc25cfa0ce4d2f3addfd32757e13b/PK-Sim%20Ontogeny%20Database%20Version%207.3.pdf)
+
+**Steere 2015** Steere B, Baker JAR, Hall SD, Guo Y. Prediction of in vivo clearance and associated variability of CYP2C19 substrates by genotypes in populations utilizing a pharmacogenetics-based mechanistic model. *Drug Metab Dispos*. 2015;43(6):870-883.
+
+**Troupin 1979** Troupin AS, Friel P, Lovely MP, Wilensky AJ. Clinical pharmacology of mephenytoin and ethotoin. *Ann Neurol*. 1979;6(5):410-414.
+
+**Wedlund 1985** Wedlund PJ, Aslanian WS, Jacqz E, McAllister CB, Branch RA, Wilkinson GR. Phenotypic differences in mephenytoin pharmacokinetics in normal subjects. *J Pharmacol Exp Ther*. 1985;234:662-669.
+
+**Willmann 2007** Willmann S, Höhn K, Edginton A, Sevestre M, Solodenko J, Weiss W, Lippert J, Schmitt W. Development of a physiology-based whole-body population model for assessing the influence of individual variability on the pharmacokinetics of drugs. *J Pharmacokinet Pharmacodyn* 2007, 34(3): 401-431.
+
+**Yao 2003** Yao C, Kunze KL, Trager WF, Kharasch ED, Levy RH. Comparison of in vitro and in vivo inhibition potencies of fluvoxamine toward CYP2C19. *Drug Metab Dispos*. 2003;31(5):565-571.
+
+# 6 Glossary
+
+| ADME | Absorption, Distribution, Metabolism, Excretion |
+| ------- | ------------------------------------------------------------ |
+| AUC | Area under the plasma concentration versus time curve |
+| AUCinf | AUC until infinity |
+| AUClast | AUC until last measurable sample |
+| AUCR | Area under the plasma concentration versus time curve Ratio |
+| b.i.d. | Twice daily (bis in diem) |
+| CL | Clearance |
+| Clint | Intrinsic liver clearance |
+| Cmax | Maximum concentration |
+| CmaxR | Maximum concentration Ratio |
+| CYP | Cytochrome P450 oxidase |
+| CYP1A2 | Cytochrome P450 1A2 oxidase |
+| CYP2C19 | Cytochrome P450 2C19 oxidase |
+| CYP3A4 | Cytochrome P450 3A4 oxidase |
+| DDI | Drug-drug interaction |
+| e.c. | Enteric coated |
+| EE | Ethinylestradiol |
+| EM | Extensive metabolizers |
+| fm | Fraction metabolized |
+| FMO | Flavin-containing monooxygenase |
+| fu | Fraction unbound |
+| FDA | Food and Drug administration |
+| GFR | Glomerular filtration rate |
+| HLM | Human liver microsomes |
+| hm | homozygous |
+| ht | heterozygous |
+| IM | Intermediate metabolizers |
+| i.v. | Intravenous |
+| IVIVE | In Vitro to In Vivo Extrapolation |
+| Ka | Absorption rate constant |
+| kcat | Catalyst rate constant |
+| Ki | Inhibitor constant |
+| Kinact | Rate of enzyme inactivation |
+| Km | Michaelis Menten constant |
+| m.d. | Multiple dose |
+| OSP | Open Systems Pharmacology |
+| PBPK | Physiologically-based pharmacokinetics |
+| PK | Pharmacokinetics |
+| PI | Parameter identification |
+| PM | Poor metabolizers |
+| RT-PCR | Reverse transcription polymerase chain reaction |
+| p.o. | Per os |
+| q.d. | Once daily (quaque diem) |
+| SD | Single Dose |
+| SE | Standard error |
+| s.d.SPC | Single dose Summary of Product Characteristics |
+| SD | Standard deviation |
+| TDI | Time dependent inhibition |
+| t.i.d | Three times a day (ter in die) |
+| UGT | Uridine 5'-diphospho-glucuronosyltransferase |
+| UM | Ultra-rapid metabolizers |
+
+","Markdown"
+"PBPK model","Open-Systems-Pharmacology/OSP-PBPK-Model-Library","7E3/7E3_evaluation_report.md",".md","21598","411","# Building and evaluation of a PBPK model for antibody 7E3 in wild-type and FcRn knockout mice
+
+| Version | 1.0-OSP12.2 |
+| ----------------------------------------------- | ------------------------------------------------------------ |
+| based on *Model Snapshot* and *Evaluation Plan* | https://github.com/Open-Systems-Pharmacology/7E3-Model/releases/tag/v1.0 |
+| OSP Version | 12.2 |
+| Qualification Framework Version | 3.5 |
+
+This evaluation report and the corresponding PK-Sim project file are filed at:
+
+https://github.com/Open-Systems-Pharmacology/OSP-PBPK-Model-Library/
+
+# Table of Contents
+
+ * [1 Introduction](#introduction)
+ * [2 Methods](#methods)
+ * [2.1 Modeling Strategy](#modeling-strategy)
+ * [2.2 Data](#methods-data)
+ * [2.2.1 In vitro / physico-chemical Data ](#invitro-and-physico-chemical-data)
+ * [2.2.2 PK Data ](#PK-data)
+ * [2.3 Model Parameters and Assumptions](#model-parameters-and-assumptions)
+ * [2.3.1 Absorption ](#model-parameters-and-assumptions-absorption)
+ * [2.3.2 Distribution ](#model-parameters-and-assumptions-distribution)
+ * [2.3.3 Metabolism and Elimination ](#model-parameters-and-assumptions-metabolism-and-elimination)
+ * [2.3.4 Tissue Concentrations ](#model-parameters-and-assumptions-tissue-concentrations)
+ * [2.3.5 Automated Parameter Identification ](#model-parameters-and-assumptions-parameter-identification)
+ * [3 Results and Discussion](#results-and-discussion)
+ * [3.1 Final input parameters](#final-input-parameters)
+ * [3.2 Diagnostics Plots](#diagnostics-plots)
+ * [3.3 Concentration-Time Profiles](#ct-profiles)
+ * [4 Conclusion](#conclusion)
+ * [5 References](#main-references)
+
+# 1 Introduction
+
+7E3 is a murine IgG1 antibody directed against human platelet glycoprotein IIb/IIIa.
+
+For 7E3, plasma and tissue concentration-time profiles for various tissues were measured in wild-type as well as FcRn knockout mice ([Garg2007, Garg2009](#5-references)). These data were used together with pharmacokinetic (PK) data from 5 other compounds to identify unknown parameters during the development of the generic large molecule physiologically based pharmacokinetic (PBPK) model in PK-Sim ([Niederalt 2018](#5-references)).
+
+The herein presented evaluation report evaluates the performance of a PBPK model for 7E3 in wild-type and FcRn knockout mice for the PK data used for the development of the generic large molecule model in PK-Sim.
+
+The presented 7E3 PBPK model as well as the respective evaluation plan and evaluation report are provided open-source (https://github.com/Open-Systems-Pharmacology/7E3-Model).
+
+# 2 Methods
+
+## 2.1 Modeling Strategy
+
+The development of the large molecule PBPK model in PK-Sim® has previously been described by Niederalt et al. ([Niederalt 2018](#5-references)). In short, the model was built as an extension of the PK-Sim® model for small molecules incorporating (i) the two-pore formalism for drug extravasation from blood plasma to interstitial space, (ii) lymph flow, (iii) endosomal clearance and (iv) protection from endosomal clearance by neonatal Fc receptor (FcRn) mediated recycling.
+
+For model development and evaluation, PK data were used from compounds with a wide range of solute radii and from different species. The PK data used for parameter estimation were from the following compounds: antibody–drug conjugate BAY 79-4620 in mice (Bayer in house data), antibody 7E3 in wild-type and FcRn knockout mice ([Garg 2007](#5-references), [Garg2009](#5-references)), domain antibody dAb2 in mice ([Sepp 2015](#5-references)), antibodies MEDI-524 and MEDI-524-YTE in monkeys ([Dall'Acqua 2006](#5-references)), and antibody CDA1 in humans ([Taylor 2008](#5-references)). The PK data used for model evaluation were from inulin in rats ([Tsuji1983](#5-references)) and tefibazumab in humans ([Reilly 2005](#5-references)).
+
+The PBPK model including the estimated physiological parameters as described by Niederalt et al. ([Niederalt 2018](#5-references)) is available in the Open Systems Pharmacology Suite from version 7.1 onwards.
+
+This evolution report focuses on the PBPK model for the antibody 7E3.
+
+Details about input data (physicochemical, *in vitro* and PK) can be found in [Section 2.2](#22-data).
+
+Details about the structural model and its parameters can be found in [Section 2.3](#23-model-parameters-and-assumptions).
+
+## 2.2 Data
+
+### 2.2.1 In vitro / physico-chemical Data
+
+A literature search was performed to collect available information on physicochemical properties of 7E3. The obtained information from literature is summarized in the table below.
+
+| **Parameter** | **Unit** | **Value** | Source | **Description** |
+| :------------ | -------- | --------- | ---------------------------- | ------------------------------------------------------------ |
+| MW | g/mol | 150000 | [Lobo 2004](#5-references) | Molecular weight |
+| r | nm | 5.34 | [Taylor 1984](#5-references) | Hydrodynamic solute radius |
+| Kd (FcRn) | µM | 0.75 | [Zhou 2003](#5-references) | Dissociation constant for binding of a murine IgG1 antibody to murine FcRn at pH 6 |
+
+### 2.2.2 PK Data
+
+Published plasma and tissue PK data on 7E3 in wild-type and FcRn knockout mice were used.
+
+| Publication | Description |
+| :------------------------ | :----------------------------------------------------------- |
+| [Garg2007](#5-references) | Experimental plasma and tissue concentrations after single 8 mg/kg IV bolus injection. Tissue concentrations of 125I-labeled 7E3 were determined from blotted dried tissues after sacrificing 3 mice per time point. |
+| [Garg2009](#5-references) | Experimental plasma and brain concentrations after single 8 mg/kg IV bolus injection. Brain concentrations were corrected for residual blood. |
+
+## 2.3 Model Parameters and Assumptions
+
+### 2.3.1 Absorption
+
+There is no absorption process since 7E3 was administered intravenously.
+
+### 2.3.2 Distribution
+
+The standard lymph and fluid recirculation flow rates and the standard vascular properties of the different tissues (hydraulic conductivity, pore radii, fraction of flow via large pores) from PK-Sim were used. The antibody 7E3, among other compounds, has been used to identify these lymph and fluid recirculation flow rates used in PK-Sim ([Niederalt 2018](#5-references)).
+
+### 2.3.3 Metabolism and Elimination
+
+The FcRn mediated clearance present in the standard PK-Sim model was used as only clearance process. The standard physiological parameters related to FcRn mediated clearance were used (rate constants for endosomal uptake and recycling, association rate constant for FcRn binding and concentration of FcRn in the endosomal space). For the FcRn knockout mice, the FcRn concentration was set to 0. The antibody 7E3, among other compounds, has been used to identify these parameters using literature values for the drug affinities to FcRn in the endosomal space ([Niederalt 2018](#5-references)).
+
+### 2.3.4 Tissue Concentrations
+
+For the comparison with experimental data, the parameters `Fraction of blood for sampling` used in the Observer for the tissue concentrations were set for all organs except brain to 0.18 for comparison with tissue dissection data according to the fit results (across compounds) in Ref. ([Niederalt 2018](#5-references)). Since the experimental brain concentrations of 7E3 were corrected for residual blood ([Garg 2009](#5-references)), no residual blood was assumed for brain. (The parameter `Fraction of blood for sampling` specifies residual blood in tissue as ratio of blood volume contributing to the measured tissue concentration to the total in vivo capillary blood volume.)
+In the present evaluation report, the experimental gut concentrations were compared to simulated organ concentrations for small and large intestine separately in the goodness of fit plots as well as in the concentration-time profile plot.
+
+### 2.3.5 Automated Parameter Identification
+
+No drug specific parameters were fitted. The antibody 7E3, among other compounds, has been used to develop the model for proteins and large molecules in PK-Sim ([Niederalt 2018](#5-references)).
+
+The table shows the tissue observer parameter that was specified in the model based on the parameter identification reported in Ref. ([Niederalt 2018](#5-references)), and which was not included in the PK-Sim database since version 7.1.
+
+| Model Parameter | Optimized Value | Unit |
+| ------------------------------------------------------------ | --------------- | ---- |
+| `Fraction of blood for sampling` (all organs) - for comparison with tissue dissection data. | 0.18 | |
+
+# 3 Results and Discussion
+
+The PBPK model for 7E3 was evaluated with plasma and tissue PK data in wild-type and FcRn knockout mice.
+
+These PK data have been used together with PK data from 5 other compounds to simultaneously identify parameters during the development of the generic model for proteins and large molecules in PK-Sim ([Niederalt 2018](#5-references)).
+
+The next sections show:
+
+1. the final model parameters for the building blocks: [Section 3.1](#final-input-parameters).
+2. the overall goodness of fit: [Section 3.2](#diagnostics-plots).
+3. simulated vs. observed concentration-time profiles for the clinical studies used for model building and for model verification: [Section 3.3](#ct-profiles).
+
+## 3.1 Final input parameters
+
+The compound parameter values of the final PBPK model are illustrated below.
+
+### Compound: 7E3
+
+#### Parameters
+
+Name | Value | Value Origin | Alternative | Default
+------------------------------------------ | ------------ | ----------------------------------------------- | ----------- | -------
+Solubility at reference pH | 9999 mg/l | Unknown-/Dummy value not used in the simulation | Measurement | True
+Reference pH | 7 | Unknown-/Dummy value not used in the simulation | Measurement | True
+Lipophilicity | -5 Log Units | Unknown-/Dummy value not used in the simulation | Measurement | True
+Fraction unbound (plasma, reference value) | 1 | Unknown-Assumption | Measurement | True
+Is small molecule | No | | |
+Molecular weight | 150000 g/mol | Publication-Lobo2004 | |
+Plasma protein binding partner | Unknown | | |
+Radius (solute) | 0.00534 µm | Publication-Taylor1984 | |
+Kd (FcRn) in endosomal space | 0.75 µmol/l | Publication-Zhou2003 | |
+
+#### Calculation methods
+
+Name | Value
+----------------------- | ---------------
+Partition coefficients | PK-Sim Standard
+Cellular permeabilities | PK-Sim Standard
+
+#### Processes
+
+## 3.2 Diagnostics Plots
+
+Below you find the goodness-of-fit visual diagnostic plots for the PBPK model performance of all data used presented in [Section 2.2.2](#PK-data).
+
+The first plot shows observed versus simulated plasma concentration, the second weighted residuals versus time.
+
+
+
+**Table 3-1: GMFE for Goodness of fit plot for concentration in plasma and tissues**
+
+|Group |GMFE |
+|:---------------|:----|
+|FcRn KO mouse |1.52 |
+|Wild-type mouse |1.29 |
+|All |1.39 |
+
+
+
+
+
+
+
+
+**Figure 3-1: Goodness of fit plot for concentration in plasma and tissues**
+
+
+
+
+
+
+
+
+**Figure 3-2: Goodness of fit plot for concentration in plasma and tissues**
+
+
+
+
+## 3.3 Concentration-Time Profiles
+
+Simulated versus observed concentration-time profiles of all data listed in [Section 2.2.2](#PK-data) are presented below.
+
+
+
+
+
+**Figure 3-3: Plasma - lin scale (wild-type mice)**
+
+
+
+
+
+
+
+
+**Figure 3-4: Plasma - log scale (wild-type mice)**
+
+
+
+
+
+
+
+
+**Figure 3-5: Lung (wild-type mice)**
+
+
+
+
+
+
+
+
+**Figure 3-6: Kidney (wild-type mice)**
+
+
+
+
+
+
+
+
+**Figure 3-7: Skin (wild-type mice)**
+
+
+
+
+
+
+
+
+**Figure 3-8: Muscle (wild-type mice)**
+
+
+
+
+
+
+
+
+**Figure 3-9: Spleen (wild-type mice)**
+
+
+
+
+
+
+
+
+**Figure 3-10: Liver (wild-type mice)**
+
+
+
+
+
+
+
+
+**Figure 3-11: Heart (wild-type mice)**
+
+
+
+
+
+
+
+
+**Figure 3-12: Gut (wild-type mice)**
+
+
+
+
+
+
+
+
+**Figure 3-13: Brain (wild-type mice)**
+
+
+
+
+
+
+
+
+**Figure 3-14: Plasma (FcRn KO mice)**
+
+
+
+
+
+
+
+
+**Figure 3-15: Lung (FcRn KO mice)**
+
+
+
+
+
+
+
+
+**Figure 3-16: Kidney (FcRn KO mice)**
+
+
+
+
+
+
+
+
+**Figure 3-17: Skin (FcRn KO mice)**
+
+
+
+
+
+
+
+
+**Figure 3-18: Muscle (FcRn KO mice)**
+
+
+
+
+
+
+
+
+**Figure 3-19: Spleen (FcRn KO mice)**
+
+
+
+
+
+
+
+
+**Figure 3-20: Liver (FcRn KO mice)**
+
+
+
+
+
+
+
+
+**Figure 3-21: Heart (FcRn KO mice)**
+
+
+
+
+
+
+
+
+**Figure 3-22: Gut (FcRn KO mice)**
+
+
+
+
+
+
+
+
+**Figure 3-23: Brain (FcRn KO mice)**
+
+
+
+
+# 4 Conclusion
+
+The herein presented PBPK model overall adequately describes the pharmacokinetics of 7E3 in mice. The higher antibody clearance in the FcRn KO mice compared to the clearance in the wild-type mice is well described by the model. The largest deviations between measured and simulated concentration time profiles were observed for skin concentrations in the FcRn KO mice and spleen concentrations in the wild-type mice which were overestimated by the model and for brain concentrations in the FcRn KO mice which were underestimated. The initial concentrations in muscle, especially for the FcRn KO mice, as well as in kidney and gut for the wild-type mice were underestimated.
+
+The PK data had been used during the development of the generic large molecule PBPK model in PK-Sim ([Niederalt 2018](#5-references)) together with PK data from 5 other compounds (BAY 79-4620, CDA1, dAb2, MEDI-524 & MEDI-524-YTE).
+
+# 5 References
+
+**Dall'Acqua 2006** Dall’Acqua WF, Kiener PA, Wu H. Properties of human IgG1s engineered for enhanced binding to the neonatal Fc receptor (FcRn). J Biol Chem. 2006 Aug; 281(33):23514-23524. doi: 10.1074/jbc.M604292200.
+
+**Garg 2007** Garg A, Balthasar JP. Physiologically-based pharmacokinetic (PBPK) model to predict IgG tissue kinetics in wild-type and FcRn-knockout mice. J Pharmacokinet Pharmacodyn. 2007 Jul; 34(5):687-709. doi: 10.1007/s10928-007-9065-1.
+
+**Garg 2009** Garg A, Balthasar J. Investigation of the influence of FcRn on the distribution of IgG to the brain. AAPS J. 2009 July; 11(3):553-557. doi: 10.1208/s12248-009-9129-9.
+
+**Lobo 2004** Lobo ED, Hansen R J, Balthasar JP. Antibody pharmacokinetics and pharmacodynamics. J Pharm Sci. 2004 Nov;93(11):2645-2668. doi: 10.1002/jps.20178.
+
+**Niederalt 2018** Niederalt C, Kuepfer L, Solodenko J, Eissing T, Siegmund HU, Block M, Willmann S, Lippert J. A generic whole body physiologically based pharmacokinetic model for therapeutic proteins in PK-Sim. J Pharmacokinet Pharmacodyn. 2018 Apr;45(2):235-257. doi: 10.1007/s10928-017-9559-4.
+
+**Reilly 2005** Reilley S, Wenzel E, Reynolds L, Bennett B, Patti JM, Hetherington S. Open-label, dose escalation study of the safety and pharmacokinetic profile of tefibazumab in healthy volunteers. Antimicrob Agents Chemother. 2005 Mar;49(3):959–962. doi: 10.1128/AAC.49.3.959-962.2005.
+
+**Sepp 2015** Sepp A, Berges A, Sanderson A, Meno-Tetang G. Development of a physiologically based pharmacokinetic model for a domain antibody in mice using the two-pore theory. J Pharmacokinet Pharmacodyn. 2015 Jan;42(2):97-109. doi: 10.1007/s10928-014-9402-0.
+
+**Taylor 1984** Taylor AE, Granger DN. Exchange of macromolecules across the microcirculation. Handbook of Physiology - Cardiovascular System. Microcirculation (Eds. Renkin EM and Michel CC. Bethesda, MD, American Physiological Society). 1984; Vol. 4(Pt 2):467–520.
+
+**Taylor 2008** Taylor CP, Tummala S, Molrine D, Davidson L, Farrell RJ, Lembo A, Hibberd PL, Lowy I, Kelly CP. Open-label, dose escalation phase I study in healthy volunteers to evaluate the safety and pharmacokinetics of a human monoclonal antibody to Clostridium difficile toxin A. Vaccine. 2008 Jun;26(27-28):3404–3409. doi: 10.1016/j.vaccine.2008.04.042.
+
+**Tsuji 1983** Tsuji A, Yoshikawa T, Nishide K, Minami H, Kimura M, Nakashima E, Terasaki T, Miyamoto E, Nightingale CH, Yamana T. Physiologically based pharmacokinetic model for beta-lactam antibiotics I: tissue distribution and elimination in rats. J Pharm Sci. 1983 Nov;72(11):1239-1252. doi: 10.1002/jps.2600721103.
+
+**Zhou 2003** Zhou J, Johnson JE, Ghetie V, Ober RJ, Ward ES. Generation of mutated variants of the human form of the MHC class I-related receptor, FcRn, with increased affinity for mouse immunoglobulin G. J Mol Biol. 2003 Sep;332(4):901-913. doi: 10.1016/s0022-2836(03)00952-5.
+
+","Markdown"
+"PBPK model","Open-Systems-Pharmacology/OSP-PBPK-Model-Library","Sufentanil/Sufentanil_evaluation_report.md",".md","17019","278","# Building and evaluation of a PBPK model for sufentanil in adults
+
+| Version | 2.0-OSP12.2 |
+| ----------------------------------------------- | ------------------------------------------------------------ |
+| based on *Model Snapshot* and *Evaluation Plan* | https://github.com/Open-Systems-Pharmacology/Sufentanil-Model/releases/tag/v2.0 |
+| OSP Version | 12.2 |
+| Qualification Framework Version | 3.5 |
+
+This evaluation report and the corresponding PK-Sim project file are filed at:
+
+https://github.com/Open-Systems-Pharmacology/OSP-PBPK-Model-Library/
+
+# Table of Contents
+
+ * [1 Introduction](#introduction)
+ * [2 Methods](#methods)
+ * [2.1 Modeling strategy](#modeling-strategy)
+ * [2.2 Data used](#data)
+ * [2.3 Model parameters and assumptions](#model-parameters-and-assumptions)
+ * [3 Results and Discussion](#results-and-discussion)
+ * [3.1 Sufentanil final input parameters](#final-input-parameters)
+ * [3.2 Sufentanil Diagnostics Plots](#diagnostics-plots)
+ * [3.3 Sufentanil Concentration-Time profiles](#ct-profiles)
+ * [4 Conclusion](#conclusion)
+ * [5 References](#main-references)
+
+# 1 Introduction
+
+The presented model building and evaluation report evaluates the performance of a PBPK model for sufentanil in adults.
+
+Sufentanil is a potent synthetic opioid. It has a high lipid solubility, which accounts for the fast onset when given intravenously. The commercial solution comes as preservative‐free sufentanil citrate, injectable with a pH of 4.5–7.0 (Jansen‐Cilag AB, Sweden). Sufentanil is solely metabolised by CYP3A4. Due to the high hepatic extraction ratio, for the overall clearance of sufentanil both CYP3A4 activity as well as liver blood flow rate play dominant roles in the elimination in adult populations. The final sufentanil model applies metabolism by CYP3A4 and glomerular filtration and adequately described the pharmacokinetics of sufentanil in adults.
+
+The sufentanil model is a whole-body PBPK model, allowing for dynamic translation between individuals. The sufentanil report demonstrates the level of confidence in the sufentanil PBPK model build with the OSP suite with regard to reliable predictions of sufentanil PK in adults during model-informed drug development.
+
+# 2 Methods
+
+## 2.1 Modeling strategy
+
+The general concept of building a PBPK model has previously been described by Kuepfer et al. ([Kuepfer 2016](#5-references)). Relevant information on anthropometric (height, weight) and physiological parameters (e.g. blood flows, organ volumes, binding protein concentrations, hematocrit, cardiac output) in adults was gathered from the literature and has been previously published ([Schlender 2016](#5-references)). The information was incorporated into PK-Sim® and was used as default values for the simulations in adults.
+
+The applied activity and variability of plasma proteins and active processes that are integrated into PK-Sim® are described in the publicly available PK-Sim® Ontogeny Database Version 7.3 ([PK-Sim Ontogeny Database Version 7.3](#5-references)) or otherwise referenced for the specific process.
+
+First, a base mean model was built using data from the single dose escalation study to find an appropriate structure describing the PK of sufentanil. The mean PK model was developed using a typical European individual. Unknown parameters were identified using the Parameter Identification module provided in PK-Sim®. Structural model selection was mainly guided by visual inspection of the resulting description of data and biological plausibility.
+
+Once the appropriate structural model was identified, additional parameters for different formulations were identified, if available.
+
+A final PBPK model was established and simulations were compared to the reported data to evaluate model appropriateness and to assess model qualification, by means of diagnostics plots and predicted versus observed concentration-time profiles, of which the results support an adequate prediction of the PK in adults.
+
+During model building, uncertainties in data quality, as well as study differences may cause not being able to adequately describe the PK of all reported clinical studies.
+
+## 2.2 Data used
+
+### 2.2.1 In vitro / physicochemical data
+
+A literature search was performed to collect available information on physicochemical properties of sufentanil. The obtained information from literature is summarized in the table below, and is used for model building.
+
+| **Parameter** | **Unit** | **Literature value (reference)** | **Description** |
+| :-------------- | ----------- | ----------------------------------- | ------------------------------------------------ |
+| MW | g/mol | 386.6 ([Zhou 2017](#5-references)) | Molecular weight |
+| pKa | | 8 ([Zhou 2017](#5-references)) | Base dissociation constant |
+| Solubility (pH) | mg/L | 0.076 (7) ([Roy 1988](#5-references)) | Solubility |
+| logP | | 3.95 ([Zhou 2017](#5-references)) | Partition coefficient between octanol and water |
+| fu | | 0.075 ([Zhou 2017](#5-references)) | Fraction unbound |
+| CLr* | L/h | 1 ([Zhou 2017](#5-references)) | Renal clearance |
+| CYP3A4 CLint* | µl/min/pmol | 20.74 ([Zhou 2017](#5-references)) | Cytochrome-P450 3A4 mediated intrinsic clearance |
+
+*CLr and CYP3A4int parameters are built in PK-Sim as glomerular filtration (GF) and CYP3A4 - first order intrinsic clearance, respectively.
+
+### 2.2.2 Clinical data
+
+A literature search was performed to collect available clinical data on sufentanil in adults.
+
+The following publications were found in adults for model building and evaluation:
+
+| Publication | Study description |
+| :-------------------------------- | :----------------------------------------------------------- |
+| [Bovill 1984](#5-references) | The pharmacokinetics of sufentanil in elective surgical patients, without hepatic or renal dysfunction. |
+| [Willsie 2015](#5-references) | Pharmacokinetic properties of single- and repeated-dose sufentanil sublingual tablets in healthy volunteers. |
+| [Taverne 1992](#5-references) | Comparative absorption and distribution pharmacokinetics of intravenous and epidural sufentanil in elective surgical patients, without hepatic or renal dysfunction. |
+
+## 2.3 Model parameters and assumptions
+
+### 2.3.1 Absorption
+
+Only intravenous data was available for model building.
+
+### 2.3.2 Distribution
+
+Plasma protein binding of sufentanil, related to the alpha acid glycoprotein concentration, is approximately 93% in healthy males as described in the drug-label for intravenous administration ([FDA drug label](#5-references)). The fraction unbound (fu) of sufentanil is built-in as 0.075 as also reported by Zhou et al. ([Zhou 2017](#5-references)).
+
+After testing the available organ-plasma partition coefficient and cell permeability calculation methods built in PK-Sim, observed clinical data was best described by choosing the partition coefficient calculation method by Schmitt, and PK-Sim standard cell permeability calculation method. Specific organ permeability normalized to surface area was automatically calculated by PK-Sim.
+
+### 2.3.3 Metabolism and Elimination
+
+Sufentanil is solely metabolised by CYP3A4. Due to the high hepatic extraction ratio, for the overall clearance of sufentanil both CYP3A4 values as well as liver blood flow rate play dominant roles in the elimination in adult populations. The final sufentanil model applies metabolism by CYP3A4 and glomerular filtration and adequately described the pharmacokinetics of sufentanil in adults.
+
+# 3 Results and Discussion
+
+The PBPK model for sufentanil was developed with clinical pharmacokinetic data covering intravenous administration with a dose range of 15 up to 355.5µg (based on a mean patient body weight of 71.5kg receiving 5µg/kg dose), including single dose (SD) clinical data.
+
+During the model-fitting, the following parameters were estimated (all other parameters were fixed to reported values):
+
+* Lipophilicity
+* CYP3A4 First order intrinsic clearance
+
+The fit resulted in an adequate description of the clinical data. In contrast to the PK data derived from Bovill et al. ([Bovill 1984](#5-references)) and Willsie et al. ([Willsie 2015](#5-references)), the extracted mean concentrations by Taverne et al. ([Taverne 1992](#5-references)) from the reported figure was slightly overpredicted. As the individual data in the reported figure could not be properly extracted and showed considerable variability, it may be that the mean values included outliers and therefore be biased. As only limited amount of data was available, more available data could be used to further evaluate the model performance.
+
+Overall the model results show that the PBPK model of sufentanil adequately described the data for intravenous administration for single dose.
+
+## 3.1 Sufentanil final input parameters
+
+The compound parameter values of the final sufentanil PBPK model are illustrated below.
+
+### Compound: Sufentanil
+
+#### Parameters
+
+Name | Value | Value Origin | Alternative | Default
+------------------------------------------ | -------------------- | ------------------------------------------------- | ------------------------ | -------
+Solubility at reference pH | 0.076 mg/l | Internet-Other-ROY,SD & FLYNN,GL (1988) | ROY,SD & FLYNN,GL (1988) | True
+Reference pH | 7 | Internet-Other-ROY,SD & FLYNN,GL (1988) | ROY,SD & FLYNN,GL (1988) | True
+Lipophilicity | 2.896 Log Units | Parameter Identification-Parameter Identification | Fitted LogP | True
+Fraction unbound (plasma, reference value) | 0.075 | Publication-Zhou et al. 2017 | Zhou et al. 2017 | True
+Is small molecule | Yes | | |
+Molecular weight | 386.6 g/mol | Publication-Zhou et al. 2017 | |
+Plasma protein binding partner | α1-acid glycoprotein | | |
+
+#### Calculation methods
+
+Name | Value
+----------------------- | ---------------
+Partition coefficients | Schmitt
+Cellular permeabilities | PK-Sim Standard
+
+#### Processes
+
+##### Systemic Process: Glomerular Filtration-GFR
+
+Species: Human
+
+###### Parameters
+
+Name | Value | Value Origin
+------------ | -----:| ----------------------------
+GFR fraction | 1 | Publication-Zhou et al. 2017
+
+##### Metabolizing Enzyme: CYP3A4-Zhou et al. 2017
+
+Species: Human
+
+Molecule: CYP3A4
+
+###### Parameters
+
+Name | Value | Value Origin
+------------------- | ------------------ | -------------------------------------------------
+Intrinsic clearance | 9.6138746106 l/min | Parameter Identification-Parameter Identification
+
+## 3.2 Sufentanil Diagnostics Plots
+
+Below you find the goodness-of-fit visual diagnostic plots for sufentanil PBPK model performance (Individually simulated versus observed plasma concentration and weighted residuals versus time, including the geometric mean fold error (GMFE)) of all data used for model building.
+
+
+
+**Table 3-1: GMFE for Goodness of fit plot for concentration in plasma.**
+
+|Group |GMFE |
+|:-------------|:----|
+|Sufentanil iv |1.40 |
+
+
+
+
+
+
+
+
+**Figure 3-1: Goodness of fit plot for concentration in plasma.**
+
+
+
+
+
+
+
+
+**Figure 3-2: Goodness of fit plot for concentration in plasma.**
+
+
+
+
+## 3.3 Sufentanil Concentration-Time profiles
+
+Simulated versus observed plasma concentration-time profiles of all data are listed below.
+
+
+
+
+
+**Figure 3-3: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-4: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-5: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-6: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-7: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-8: Time Profile Analysis 1**
+
+
+
+
+# 4 Conclusion
+
+The final sufentanil PBPK model applies elimination by CYP3A4 and glomerular filtration and adequately describes the pharmacokinetics of sufentanil in adults receiving SD of sufentanil ranging from 15µg to 355.5µg intravenously.
+
+This model could be applied for the investigation of drug-drug interactions (DDI), and translation to special populations such as pediatrics with regard to CYP3A4 based elimination.
+
+# 5 References
+
+**Bovill 1984** Bovill JG, Sebel PS, Blackburn CL, Oei-Lim V, Heykants JJ. The pharmacokinetics of sufentanil in surgical patients. Anesthesiology. 1984 Nov;61(5):502-6.
+
+**Drugbank.ca** (https://www.drugbank.ca/drugs/DB00512 )
+
+**FDA drug label** (https://www.accessdata.fda.gov/drugsatfda_docs/label/2014/019050s032lbl.pdf)
+
+**Kuepfer 2016** Kuepfer L, Niederalt C, Wendl T, Schlender JF, Willmann S, Lippert J, Block M, Eissing T, Teutonico D. Applied Concepts in PBPK Modeling: How to Build a PBPK/PD Model.CPT Pharmacometrics Syst Pharmacol. 2016 Oct;5(10):516-531. doi: 10.1002/psp4.12134. Epub 2016 Oct 19.
+
+**PK-Sim Ontogeny Database Version 7.3** (https://github.com/Open-Systems-Pharmacology/OSPSuite.Documentation/blob/38cf71b384cfc25cfa0ce4d2f3addfd32757e13b/PK-Sim%20Ontogeny%20Database%20Version%207.3.pdf)
+
+**Roy 1988** Roy SD, Flynn GL. Solubility and Related Physicochemical Properties of Narcotic Analgesics. Pharm Res. 1988 Sep;5(9):580-6. doi: 10.1023/a:1015994030251.
+
+**Schlender 2016** Schlender JF, Meyer M, Thelen K, Krauss M, Willmann S, Eissing T, Jaehde U. Development of a Whole-Body Physiologically Based Pharmacokinetic Approach to Assess the Pharmacokinetics of Drugs in Elderly Individuals. Clin Pharmacokinet. 2016 Dec;55(12):1573-1589.
+
+**Taverne 1992** Taverne RH, Ionescu TI , Nuyten ST. Comparative absorption and distribution pharmacokinetics of intravenous and epidural sufentanil for major abdominal surgery. Clin Pharmacokinet. 1992 Sep;23(3):231-7. doi: 10.2165/00003088-199223030-00005.
+
+**Willsie 2015** Willsie SK, Evashenk MA2, Hamel LG, Hwang SS, Chiang YK, Palmer PP. Pharmacokinetic properties of single- and repeated-dose sufentanil sublingual tablets in healthy volunteers. Clin Ther. 2015 Jan 1;37(1):145-55. doi: 10.1016/j.clinthera.2014.11.001. Epub 2014 Dec 24.
+
+**Zhou 2017** Zhou W, Johnson TN, Bui KH, Cheung SYA, Li J, Xu H, Al-Huniti N, Zhou D. Predictive Performance of Physiologically Based Pharmacokinetic (PBPK) Modeling of Drugs Extensively Metabolized by Major Cytochrome P450s in Children. Clin Pharmacol Ther. 2018 Jul;104(1):188-200. doi: 10.1002/cpt.905. Epub 2017 Nov 20.
+
+","Markdown"
+"PBPK model","Open-Systems-Pharmacology/OSP-PBPK-Model-Library","Vancomycin/Vancomycin_evaluation_report.md",".md","17264","281","# Building and evaluation of a PBPK model for vancomycin in adults
+
+| Version | 2.0-OSP12.2 |
+| ----------------------------------------------- | ------------------------------------------------------------ |
+| based on *Model Snapshot* and *Evaluation Plan* | https://github.com/Open-Systems-Pharmacology/Vancomycin-Model/releases/tag/v2.0 |
+| OSP Version | 12.2 |
+| Qualification Framework Version | 3.5 |
+
+This evaluation report and the corresponding PK-Sim project file are filed at:
+
+https://github.com/Open-Systems-Pharmacology/OSP-PBPK-Model-Library/
+
+# Table of Contents
+
+ * [1 Introduction](#introduction)
+ * [2 Methods](#methods)
+ * [2.1 Modeling strategy](#modeling-strategy)
+ * [2.2 Data used](#data)
+ * [2.3 Model parameters and assumptions](#model-parameters-and-assumptions)
+ * [3 Results and Discussion](#results-and-discussion)
+ * [3.1 Vancomycin final input parameters](#final-input-parameters)
+ * [3.2 Vancomycin Diagnostics Plots](#diagnostics-plots)
+ * [3.3 Vancomycin Concentration-Time profiles](#ct-profiles)
+ * [4 Conclusion](#conclusion)
+ * [5 References](#main-references)
+
+# 1 Introduction
+
+The presented model building and evaluation report evaluates the performance of a PBPK model for vancomycin in adults.
+
+Vancomycin is a glycopeptide antibiotic related to ristocetin that inhibits bacterial cell wall assembly and is used to treat a number of bacterial infections. It can be administered intravenously, as well as orally in case of diarrhea therapy. Vancomycin is mainly eliminated via glomerular filtration (GF). A previous PBPK model for vancomycin using PK-Sim was reported by Radke et al. ([Radke 2017](#5-references)), with the dose fraction excreted unchanged into urine in adults being 90% with 10% hepatic elimination. Our final vancomycin model was rebuilt that applies only GFR mediated clearance that adequately described the pharmacokinetics in adults. No further improvement of vancomycin pharmacokinetics could be determined after introducing hepatic clearance.
+
+The vancomycin model is a whole-body PBPK model, allowing for dynamic translation between individuals. The vancomycin report demonstrates the level of confidence in the vancomycin PBPK model built with the OSP suite with regard to reliable predictions of vancomycin PK in adults during model-informed drug development.
+
+# 2 Methods
+
+## 2.1 Modeling strategy
+
+The general concept of building a PBPK model has previously been described by Kuepfer et al. ([Kuepfer 2016](#5-references)). Relevant information on anthropometric (height, weight) and physiological parameters (e.g. blood flows, organ volumes, binding protein concentrations, hematocrit, cardiac output) in adults was gathered from the literature and has been previously published ([Schlender 2016](#5-references)). The information was incorporated into PK-Sim® and was used as default values for the simulations in adults.
+
+The applied activity and variability of plasma proteins and active processes that are integrated into PK-Sim® are described in the publicly available PK-Sim® Ontogeny Database Version 7.3 ([PK-Sim Ontogeny Database Version 7.3](#5-references)) or otherwise referenced for the specific process.
+
+First, a base mean model was built using data from the single dose escalation study to find an appropriate structure describing the PK of vancomycin. The mean PK model was developed using a typical European individual. Unknown parameters were identified using the Parameter Identification module provided in PK-Sim®. Structural model selection was mainly guided by visual inspection of the resulting description of data and biological plausibility.
+
+Once the appropriate structural model was identified, additional parameters for different formulations were identified, if available.
+
+A final PBPK model was established and simulations were compared to the reported data to evaluate model appropriateness and to assess model qualification, by means of diagnostics plots and predicted versus observed concentration-time profiles, of which the results support an adequate prediction of the PK in adults.
+
+During model building, uncertainties in data quality, as well as study differences may cause not being able to adequately describe the PK of all reported clinical studies.
+
+## 2.2 Data used
+
+### 2.2.1 In vitro / physicochemical data
+
+A literature search was performed to collect available information on physicochemical properties of vancomycin. The obtained information from literature is summarized in the table below, and is used for model building.
+
+| **Parameter** | **Unit** | **Literature value (reference)** | **Description** |
+| :----------------- | --------- | ------------------------------------------------------------ | ----------------------------------------------- |
+| MW | g/mol | 1449.3 ([Radke 2017](#5-references)) | Molecular weight |
+| pKa | | Acid 2.18, Base 7.75, Base 8.89 ([Radke 2017](#5-references)) | Acid/base dissociation constant |
+| Solubility (pH) | mg/L | 225 (7) ([Drugbank.ca](#5-references)) | Solubility |
+| logP | | -4.41 ([Zhou 2016](#5-references)), 1.11 ([Drugbank.ca](#5-references)), 2.45 ([Radke 2017](#5-references)) | Partition coefficient between octanol and water |
+| fu | | 0.48 ([Zhou 2016](#5-references)), 0.67 ([Radke 2017](#5-references)) | Fraction unbound |
+| GFR fraction | µM | 1 ([Zhou 2016](#5-references)) | fraction of Glomerular filtration rate |
+| Hepatic clearance* | mL/min/kg | 0.11 ([Radke 2017](#5-references)) | Hepatic clearance |
+| Renal clearance* | mL/min/kg | 0.95 ([Radke 2017](#5-references)) | Renal clearance |
+
+*Both Hepatic and Renal clearance reported by others have not been used in the final model.
+
+### 2.2.2 Clinical data
+
+A literature search was performed to collect available clinical data on vancomycin in adults.
+
+The following publications were found in adults for model building and evaluation:
+
+| Publication | Study description |
+| :-------------------------------- | :----------------------------------------------------------- |
+| [Boeckh 1988](#5-references) | Pharmacokinetics and serum bactericidal activity of vancomycin alone and in combination with ceftazidime in healthy volunteers |
+| [Healy 1987](#5-references) | Comparison of steady-state pharmacokinetics of two dosage regimens of vancomycin in normal volunteers |
+
+## 2.3 Model parameters and assumptions
+
+### 2.3.1 Absorption
+
+Only intravenous data was available for model building.
+
+### 2.3.2 Distribution
+
+Sun et al. ([Sun 1993](#5-references)) reported that albumin and immunoglobulin A are the dominant protein binding partners of vancomycin, and that vancomycin does not bind to alpha-1 acid glycoprotein (AAG). As in PK-Sim there is only the option to bind to albumin or AAG, vancomycin binding is built-in as bound to albumin only in the PBPK model. The fraction unbound (fu) of vancomycin is built-in as 0.67 as reported by Radke et al. ([Radke 2017](#5-references)).
+
+After testing the available organ-plasma partition coefficient and cell permeability calculation methods built in PK-Sim, observed clinical data was best described by choosing the partition coefficient calculation method by Schmitt, and cell permeability calculation method by Charge dependent Schmidt. Specific organ permeability normalized to surface area was automatically calculated by PK-Sim.
+
+### 2.3.3 Metabolism and Elimination
+
+A previous PBPK model for vancomycin using PK-Sim was reported by Radke et al. ([Radke 2017](#5-references)) , with the dose fraction excreted unchanged into urine in adults being 90% with 10% hepatic elimination. Zhou et al. ([Zhou 2016](#5-references)) also published a PBPK model for vancomycin introducing an unknown hepatic clearance process being roughly 20% of total elimination. Our final vancomycin model was rebuilt that applies only GFR mediated clearance that adequately described the pharmacokinetics in adults. No further improvement of vancomycin pharmacokinetics could be determined after introducing hepatic clearance.
+
+# 3 Results and Discussion
+
+The PBPK model vancomycin was developed with clinical pharmacokinetic data covering intravenous administration with a dose range of 500-1000mg, including single dose (SD) as well as multiple dose (MD) clinical data.
+
+During the model-fitting, the following parameter was estimated (all other parameters were fixed to reported values):
+
+* Lipophilicity
+
+The fit resulted in an adequate description of all data. As only limited amount of data was available, and the description of the data was adequate, an additional inclusion of hepatic clearance did not further improve the description of the data as proposed in other published PBPK models, and is therefore not included in the model. Further data were not available to further evaluate the model performance.
+
+The model results show that the PBPK model of vancomycin adequately described the data for intravenous administration for single and multiple dose.
+
+## 3.1 Vancomycin final input parameters
+
+The compound parameter values of the final vancomycin PBPK model are illustrated below.
+
+### Compound: Vancomycin
+
+#### Parameters
+
+Name | Value | Value Origin | Alternative | Default
+------------------------------------------ | ---------------------- | ------------------------------------------------- | ----------- | -------
+Solubility at reference pH | 225 mg/l | Internet-Drugbank | Drugbank | True
+Reference pH | 7 | Internet-Drugbank | Drugbank | True
+Lipophilicity | 2.2307891407 Log Units | Parameter Identification-Parameter Identification | LogP | True
+Fraction unbound (plasma, reference value) | 0.67 | Parameter Identification-Parameter Identification | Measurement | True
+Is small molecule | Yes | | |
+Molecular weight | 1449.3 g/mol | Publication-Other-Radke 2017 | |
+Plasma protein binding partner | Albumin | | |
+
+#### Calculation methods
+
+Name | Value
+----------------------- | ------------------------
+Partition coefficients | Schmitt
+Cellular permeabilities | Charge dependent Schmitt
+
+#### Processes
+
+##### Systemic Process: Glomerular Filtration-Zhou et al. 2016 GFR
+
+Species: Human
+
+###### Parameters
+
+Name | Value | Value Origin
+------------ | -----:| ---------------------------
+GFR fraction | 1 | Publication-Other-Zhou 2016
+
+## 3.2 Vancomycin Diagnostics Plots
+
+Below you find the goodness-of-fit visual diagnostic plots for vancomycin PBPK model performance (Individually simulated versus observed plasma concentration and weighted residuals versus time, including the geometric mean fold error (GMFE)) of all data used for model building.
+
+
+
+**Table 3-1: GMFE for Goodness of fit plot for concentration in plasma.**
+
+|Group |GMFE |
+|:-------------|:----|
+|Vancomycin iv |1.11 |
+
+
+
+
+
+
+
+
+**Figure 3-1: Goodness of fit plot for concentration in plasma.**
+
+
+
+
+
+
+
+
+**Figure 3-2: Goodness of fit plot for concentration in plasma.**
+
+
+
+
+## 3.3 Vancomycin Concentration-Time profiles
+
+Simulated versus observed plasma concentration-time profiles of all data are listed below.
+
+
+
+
+
+**Figure 3-3: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-4: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-5: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-6: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-7: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-8: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-9: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-10: Time Profile Analysis 1**
+
+
+
+
+# 4 Conclusion
+
+The final vancomycin PBPK model applies elimination by glomerular filtration and adequately describes the pharmacokinetics of vancomycin in adults receiving SD, MD of vancomycin ranging from 500mg to 1000mg intravenously.
+
+This model could be applied for the investigation of drug-drug interactions (DDI), and translation to special populations such as pediatrics with regard GFR based elimination.
+
+# 5 References
+
+**Boeck 1988** Boeckh M, Lode H, Borner K, Höffken G, Wagner J, Koeppe P. Pharmacokinetics and serum bactericidal activity of vancomycin alone and in combination with ceftazidime in healthy volunteers. Antimicrob Agents Chemother. 1988 Jan;32(1):92-5.
+
+**Drugbank.ca** (https://www.drugbank.ca/drugs/DB00512 )
+
+**Healy 1987** Healy DP, Polk RE, Garson ML, Rock DT, Comstock TJ. Comparison of steady-state pharmacokinetics of two dosage regimens of vancomycin in normal volunteers. Antimicrob Agents Chemother. 1987 Mar;31(3):393-7.
+
+**Kuepfer 2016** Kuepfer L, Niederalt C, Wendl T, Schlender JF, Willmann S, Lippert J, Block M, Eissing T, Teutonico D. Applied Concepts in PBPK Modeling: How to Build a PBPK/PD Model.CPT Pharmacometrics Syst Pharmacol. 2016 Oct;5(10):516-531. doi: 10.1002/psp4.12134. Epub 2016 Oct 19.
+
+**PK-Sim Ontogeny Database Version 7.3** (https://github.com/Open-Systems-Pharmacology/OSPSuite.Documentation/blob/38cf71b384cfc25cfa0ce4d2f3addfd32757e13b/PK-Sim%20Ontogeny%20Database%20Version%207.3.pdf)
+
+**Radke 2017** Radke C, Horn D, Lanckohr C, Ellger B, Meyer M, Eissing T, Hempel G. Development of a Physiologically Based Pharmacokinetic Modelling Approach to Predict the Pharmacokinetics of Vancomycin in Critically Ill Septic Patients. Clin Pharmacokinet. 2017 Jul;56(7):759-779. doi: 10.1007/s40262-016-0475-3.
+
+**Schlender 2016** Schlender JF, Meyer M, Thelen K, Krauss M, Willmann S, Eissing T, Jaehde U. Development of a Whole-Body Physiologically Based Pharmacokinetic Approach to Assess the Pharmacokinetics of Drugs in Elderly Individuals. Clin Pharmacokinet. 2016 Dec;55(12):1573-1589.
+
+**Sun 1993** Sun H, Maderazo EG, Krusell AR. Serum Protein-Binding Characteristics of Vancomycin. Antimicrob Agents Chemother. 1993 May;37(5):1132-6.
+
+**Zhou 2016** Zhou W, Johnson TN, Xu H, Cheung S, Bui KH, Li J, Al-Huniti N, Zhou D. Predictive Performance of Physiologically Based Pharmacokinetic and Population Pharmacokinetic Modeling of Renally Cleared Drugs in Children. CPT Pharmacometrics Syst Pharmacol. 2016 Sep;5(9):475-83.
+
+","Markdown"
+"PBPK model","Open-Systems-Pharmacology/OSP-PBPK-Model-Library","Alfentanil/Alfentanil_evaluation_report.md",".md","27495","493","# Building and Evaluation of a PBPK Model for alfentanil in Adults
+
+| Version | 3.0-OSP12.2 |
+| ----------------------------------------------- | ------------------------------------------------------------ |
+| based on *Model Snapshot* and *Evaluation Plan* | https://github.com/Open-Systems-Pharmacology/Alfentanil-Model/releases/tag/v3.0 |
+| OSP Version | 12.2 |
+| Qualification Framework Version | 3.5 |
+
+This evaluation report and the corresponding PK-Sim project file are filed at:
+
+https://github.com/Open-Systems-Pharmacology/OSP-PBPK-Model-Library/
+
+# Table of Contents
+
+ * [1 Introduction](#introduction)
+ * [2 Methods](#methods)
+ * [2.1 Modeling Strategy](#modeling-strategy)
+ * [2.2 Data](#data)
+ * [2.3 Model Parameters and Assumptions](#model-parameters-and-assumptions)
+ * [3 Results and Discussion](#results-and-discussion)
+ * [3.1 Final input parameters](#final-input-parameters)
+ * [3.2 Diagnostics Plots](#diagnostics-plots)
+ * [3.3 Concentration-Time Profiles](#ct-profiles)
+ * [3.3.1 Model Building](#model-building)
+ * [3.3.2 Model Verification](#model-verification)
+ * [4 Conclusion](#conclusion)
+ * [5 References](#main-references)
+
+# 1 Introduction
+
+Alfentanil is a potent analgesic synthetic opioid. It is fast but short-acting and used for anesthesia during surgery. Alfentanil is metabolized solely by CYP3A4 ([Phimmasone 2001](#5-references)). Like midazolam, alfentanil is not a substrate for P-gp ([Wandel 2002](#5-references)) and less than 1% of an alfentanil dose is excreted unchanged in urine ([Meuldermans 1988](#5-references)).
+
+Although in clinical use alfentanil is always administered intravenously (iv), some DDI studies published plasma concentration-time profiles of alfentanil following oral ingestion. The presented alfentanil model was established using clinical PK data of 8 publications, covering iv and oral (po) administration and a dosing range from 0.015 to 0.075 mg/kg as well as absolute doses of 1 mg iv and 4 mg po. The established model is based on the model developed by Hanke *et al.* ([Hanke 2018](#5-references)) and applies metabolism by CYP3A4 and glomerular filtration.
+
+# 2 Methods
+
+## 2.1 Modeling Strategy
+
+The general concept of building a PBPK model has previously been described by e.g. Kuepfer et al. ([Kuepfer 2016](#5-references)). The relevant anthropometric (height, weight) and physiological information (e.g. blood flows, organ volumes, binding protein concentrations, hematocrit, cardiac output) in adults was gathered from the literature and has been previously published ([Willmann 2007](#5-references)). This information was incorporated into PK-Sim® and was used as default values for the simulations in adults.
+
+Variability of plasma proteins and CYP3A4 are integrated into PK-Sim® and described in the publicly available PK-Sim® Ontogeny Database Version 7.3 ([PK-Sim Ontogeny Database Version 7.3](#5-references)) or otherwise referenced for the specific process.
+
+First, a base mean model was built using clinical data including selected single dose studies with intravenous and oral applications (solution) of alfentanil to find an appropriate structure to describe the pharmacokinetics in plasma. The mean PBPK model was developed using a typical European individual. The relative tissue specific expressions of enzymes predominantly being involved in the metabolism of alfentanil were included in the model as described elsewhere ([Meyer 2012](#5-references)).
+
+Unknown parameters (see below) were identified using the Parameter Identification module provided in PK-Sim®. Structural model selection was mainly guided by visual inspection of the resulting description of data and biological plausibility.
+
+Details about input data (physicochemical, *in vitro* and clinical) can be found in [Section 2.2](#22-data).
+
+Details about the structural model and its parameters can be found in [Section 2.3](#23-model-parameters-and-assumptions).
+
+## 2.2 Data
+
+### 2.2.1 In vitro / physicochemical Data
+
+A literature search was performed to collect available information on physicochemical properties of alfentanil. The obtained information from literature is summarized in the table below.
+
+| **Parameter** | **Unit** | **Value** | Source | **Description** |
+| :-------------- | -------- | ---------- | --------------------------------- | --------------------------------------------------------- |
+| MW | g/mol | 416.52 | [DrugBank DB00802](#5-references) | Molecular weight |
+| pKa | | 6.5 (base) | [Jansson 2008](#5-references) | Acid dissociation constant |
+| Solubility (pH) | mg/L | 992 (6.5) | [Baneyx 2014](#5-references) | Solubility |
+| logD | | 2.1 | [Baneyx 2014](#5-references) | Partition coefficient between octanol and water at pH 7.4 |
+| | | 2.2 | [Jansson 2008](#5-references) | Partition coefficient between octanol and water |
+| fu | % | 8.6 | [Gertz 2010](#5-references) | Fraction unbound in plasma |
+| | | 10.0 | [Edginton 2008](#5-references) | Fraction unbound in plasma |
+| | | 12.0 | [Almond 2016](#5-references) | Fraction unbound in plasma |
+
+### 2.2.2 Clinical Data
+
+A literature search was performed to collect available clinical data on alfentanil in healthy adults.
+
+#### 2.2.2.1 Model Building
+
+The following studies were used for model building:
+
+| Publication | Arm / Treatment / Information used for model building |
+| :------------------------------- | :----------------------------------------------------------- |
+| [Ferrier 1985](#5-references) | Healthy subjects with a single iv dose of 0.05 mg/kg |
+| [Kharasch 1997](#5-references) | Healthy subjects with a single iv dose of 0.02 mg/kg |
+| [Kharasch 2004](#5-references) | Healthy subjects with a single iv dose of 0.015 mg/kg, healthy subjects with a single oral dose of 0.06 mg/kg |
+| [Kharasch 2011](#5-references) | Healthy subjects with a single iv dose of 0.015 mg/kg, healthy subjects with a single oral dose of 0.075 mg/kg |
+| [Kharasch 2011b](#5-references) | Healthy subjects with an iv dose of 1 mg, healthy subjects with an oral dose of 1 mg. Publication compares sequential and simultaneous dosing of oral deuterated and intravenous unlabeled alfentanil. Furthermore, IV and oral administration of alfentanil is combined with grapefruit juice. Grapefruit juice is considered to have no effect on hepatic clearance, and, hence, no effect on IV administered alfentanil |
+| [Kharasch 2012](#5-references) | Healthy subjects with a single iv dose of 0.02 mg/kg, healthy subjects with a single oral dose of 0.043 mg/kg |
+| [Meistelman 1987](#5-references) | Healthy subjects with a single iv dose of 0.02 mg/kg |
+| [Phimmasone 2001](#5-references) | Healthy subjects with a single iv dose of 0.015 mg/kg |
+
+## 2.3 Model Parameters and Assumptions
+
+### 2.3.1 Absorption
+
+Absorption observed in clinical studies can be fully explained by passive absorption.
+
+### 2.3.2 Distribution
+
+After testing the available organ-plasma partition coefficient and cell permeability calculation methods built in PK-Sim, observed clinical data was best described by choosing the partition coefficient calculation by `Rodgers and Rowland` and cellular permeability calculation by `PK-Sim Standard`.
+
+### 2.3.3 Metabolism and Elimination
+
+Alfentanil is metabolized solely by CYP3A4. The tissue-specific CYP3A4 expression implemented in the model is based on high-sensitive real-time RT-PCR ([Nishimura 2013](#5-references)).
+
+The first model simulations showed that gut wall metabolization was too low in the PBPK model. In order to increase gut wall metabolization, the “mucosa permeability on basolateral side” (jointly the model parameters in the mucosa: ``P (interstitial->intracellular)`` and ``P (intracellular->interstitial)``) was estimated. This may lead to higher gut wall concentrations and, in turn, to a higher gut wall elimination.
+
+### 2.3.4 Automated Parameter Identification
+
+This is the result of the final parameter identification:
+
+| Model Parameter | Optimized Value | Unit |
+| -------------------------- | --------------- | ---- |
+| `Lipophilicity` | 1.846 | |
+| `Specific intestinal permeability` | 5.737E-4 | cm/min |
+| `Specific organ permeability` | 6.875E-3 | cm/min |
+| Basolateral mucosa permeability
(``P (interstitial->intracellular)``, ``P (intracellular->interstitial)``) | 5.415E-4 | cm/min |
+| `CYP3A4 - 1st order CL - intrinsic clearance` | 0.527 | l/min |
+
+# 3 Results and Discussion
+
+The PBPK model for alfentanil was developed and evaluated using publically available, clinical pharmacokinetic data from studies listed in [Section 2.2.2](#222-clinical-data).
+
+The next sections show:
+
+1. the final model parameters for the building blocks: [Section 3.1](#31-final-input-parameters).
+2. the overall goodness of fit: [Section 3.2](#32-diagnostics-plots).
+3. simulated vs. observed concentration-time profiles for the clinical studies used for model building and for model verification: [Section 3.3](#33-concentration-time-profiles).
+
+## 3.1 Final input parameters
+
+The compound parameter values of the final PBPK model are illustrated below.
+
+### Compound: Alfentanil
+
+#### Parameters
+
+Name | Value | Value Origin | Alternative | Default
+------------------------------------------------ | ----------------------- | --------------------------------------------------------------------------------------------------------------------- | ----------- | -------
+Solubility at reference pH | 992 mg/l | Publication-Hanke 2018 | Baneyx 2014 | True
+Reference pH | 6.5 | Publication-Hanke 2018 | Baneyx 2014 | True
+Lipophilicity | 1.8463211883 Log Units | Parameter Identification-Parameter Identification-Value updated from 'Parameter Identification 4' on 2019-09-06 11:28 | Fit | True
+Fraction unbound (plasma, reference value) | 0.1 | Parameter Identification-Parameter Identification-Value updated from 'Parameter Identification 4' on 2019-09-06 11:28 | Healthy | True
+Permeability | 0.0068752756625 cm/min | Parameter Identification-Parameter Identification-Value updated from 'Parameter Identification 4' on 2019-09-06 11:28 | Optimized | True
+Specific intestinal permeability (transcellular) | 0.00057373577138 cm/min | Parameter Identification-Parameter Identification-Value updated from 'Parameter Identification 4' on 2019-09-06 11:28 | Optimized | True
+Is small molecule | Yes | | |
+Molecular weight | 416.52 g/mol | Publication-Drugbank | |
+Plasma protein binding partner | α1-acid glycoprotein | | |
+
+#### Calculation methods
+
+Name | Value
+----------------------- | -------------------
+Partition coefficients | Rodgers and Rowland
+Cellular permeabilities | PK-Sim Standard
+
+#### Processes
+
+##### Metabolizing Enzyme: CYP3A4-1st order CL
+
+Species: Human
+
+Molecule: CYP3A4
+
+###### Parameters
+
+Name | Value | Value Origin
+------------------- | ------------------ | ---------------------------------------------------------------------------------------------------------------------
+Intrinsic clearance | 0.5272297928 l/min | Parameter Identification-Parameter Identification-Value updated from 'Parameter Identification 4' on 2019-09-06 11:28
+
+##### Systemic Process: Glomerular Filtration-GFR
+
+Species: Human
+
+###### Parameters
+
+Name | Value | Value Origin
+------------ | -----:| ----------------------
+GFR fraction | 0.06 | Publication-Hanke 2018
+
+### Formulation: Solution
+
+Type: Dissolved
+
+## 3.2 Diagnostics Plots
+
+Below you find the goodness-of-fit visual diagnostic plots for the PBPK model performance of all data used presented in [Section 2.2.2](#222-clinical-data).
+
+The first plot shows observed versus simulated plasma concentration, the second weighted residuals versus time.
+
+
+
+**Table 3-1: GMFE for Goodness of fit plot for concentration in plasma**
+
+|Group |GMFE |
+|:--------------------------|:----|
+|Intravenous administration |1.26 |
+|Oral administration |1.45 |
+|All |1.32 |
+
+
+
+
+
+
+
+
+**Figure 3-1: Goodness of fit plot for concentration in plasma**
+
+
+
+
+
+
+
+
+**Figure 3-2: Goodness of fit plot for concentration in plasma**
+
+
+
+
+## 3.3 Concentration-Time Profiles
+
+Simulated versus observed concentration-time profiles of all data listed in [Section 2.2.2](#222-clinical-data) are presented below.
+
+### 3.3.1 Model Building
+
+
+
+
+
+**Figure 3-3: Ferrier 1985, Alfentanil iv 0.05 mg/kg - log**
+
+
+
+
+
+
+
+
+**Figure 3-4: Ferrier 1985, Alfentanil iv 0.05 mg/kg - linear**
+
+
+
+
+
+
+
+
+**Figure 3-5: Kharasch 1997, Alfentanil iv 0.02 mg/kg - log**
+
+
+
+
+
+
+
+
+**Figure 3-6: Kharasch 1997, Alfentanil iv 0.02 mg/kg - lin**
+
+
+
+
+
+
+
+
+**Figure 3-7: Kharasch 2004, Alfentanil iv 0.015 mg/kg - log**
+
+
+
+
+
+
+
+
+**Figure 3-8: Kharasch 2004, Alfentanil iv 0.015 mg/kg - lin**
+
+
+
+
+
+
+
+
+**Figure 3-9: Kharasch 2004, Alfentanil po 0.06 mg/kg - log**
+
+
+
+
+
+
+
+
+**Figure 3-10: Kharasch 2004, Alfentanil po 0.06 mg/kg - lin**
+
+
+
+
+
+
+
+
+**Figure 3-11: Kharasch 2011, Alfentanil iv 0.015 mg/kg - log**
+
+
+
+
+
+
+
+
+**Figure 3-12: Kharasch 2011, Alfentanil iv 0.015 mg/kg - lin**
+
+
+
+
+
+
+
+
+**Figure 3-13: Kharasch 2011, Alfentanil po 0.075 mg/kg - log**
+
+
+
+
+
+
+
+
+**Figure 3-14: Kharasch 2011, Alfentanil po 0.075 mg/kg - lin**
+
+
+
+
+
+
+
+
+**Figure 3-15: Kharasch 2011b, Alfentanil IV 1 mg - log**
+
+
+
+
+
+
+
+
+**Figure 3-16: Kharasch 2011b, Alfentanil IV 1 mg - lin**
+
+
+
+
+
+
+
+
+**Figure 3-17: Kharasch 2011b, Alfentanil PO 4 mg - log**
+
+
+
+
+
+
+
+
+**Figure 3-18: Kharasch 2011b, Alfentanil PO 4 mg - lin**
+
+
+
+
+
+
+
+
+**Figure 3-19: Meistelman 1987, 20µg/kg, adult male individual - log**
+
+
+
+
+
+
+
+
+**Figure 3-20: Meistelman 1987, 20µg/kg, adult male individual - lin**
+
+
+
+
+
+
+
+
+**Figure 3-21: Phimmasone 2001, Alfentanil iv 0.015 mg/kg - log**
+
+
+
+
+
+
+
+
+**Figure 3-22: Phimmasone 2001, Alfentanil iv 0.015 mg/kg - lin**
+
+
+
+
+### 3.3.2 Model Verification
+
+
+
+
+
+**Figure 3-23: Kharasch 2012, Alfentanil iv 0.02 mg/kg - log**
+
+
+
+
+
+
+
+
+**Figure 3-24: Kharasch 2012, Alfentanil iv 0.02 mg/kg - lin**
+
+
+
+
+
+
+
+
+**Figure 3-25: Kharasch 2012, Alfentanil po 0.043 mg/kg - log**
+
+
+
+
+
+
+
+
+**Figure 3-26: Kharasch 2012, Alfentanil po 0.043 mg/kg - lin**
+
+
+
+
+# 4 Conclusion
+
+The herein presented PBPK model adequately describes the pharmacokinetics of alfentanil after iv and oral administration of a variety of doses to healthy adults. Parameters that were optimized during parameter identification are in a close range to the measured or calculated values and, consistent with literature, no additional active processes were needed to describe the PK of alfentanil.
+
+In conclusion, the presented alfentanil PBPK model is well-suited to be applied in drug-drug-interaction scenarios to predict the interaction potential.
+
+# 5 References
+
+**Almond 2016** Almond, L.M. et al. Prediction of drug-drug interactions arising from CYP3A induction using a physiologically based dynamic model. Drug Metab. Dispos. 44, 821–32 (2016).
+
+**Baneyx 2014** Baneyx, G., Parrott, N., Meille, C., Iliadis, A. & Lavé, T. Physiologically based pharmacokinetic modeling of CYP3A4 induction by rifampicin in human: influence of time between substrate and inducer administration. Eur. J. Pharm. Sci. 56, 1–15 (2014).
+
+**DrugBank DB00802** https://www.drugbank.ca/drugs/DB00802, accessed 05-15-2020.
+
+**Edginton 2008** Edginton, A.N. & Willmann, S. Physiology-based simulations of a pathological condition: prediction of pharmacokinetics in patients with liver cirrhosis. Clin. Pharmacokinet. 47, 743–52 (2008).
+
+**Ferrier 1985** Ferrier, C. et al. Alfentanil pharmacokinetics in patients with cirrhosis. Anesthesiology 62, 480–484 (1985).
+
+**Gertz 2010** Gertz, M., Harrison, A., Houston, J. B., & Galetin, A. (2010). Prediction of human intestinal first-pass metabolism of 25 CYP3A substrates from in vitro clearance and permeability data. Drug Metab. Dispos. 38, 1147-1158 (2010).
+
+**Hanke 2018** Hanke N, Frechen S, Moj D, Britz H, Eissing T, Wendl T, Lehr T. PBPK Models for CYP3A4 and P-gp DDI Prediction: A Modeling Network of Rifampicin, Itraconazole, Clarithromycin, Midazolam, Alfentanil, and Digoxin. CPT Pharmacometrics Syst Pharmacol. 2018 Oct;7(10):647-659. doi: 10.1002/psp4.12343. Epub 2018 Sep 7.
+
+**Jansson 2008** Jansson, R., Bredberg, U. & Ashton, M. Prediction of drug tissue to plasma concentration ratios using a measured volume of distribution in combination with lipophilicity. J. Pharm. Sci. 97, 2324–39 (2008).
+
+**Kharasch 1997** Kharasch, E.D. et al. The role of cytochrome P450 3A4 in alfentanil clearance. Implications for interindividual variability in disposition and perioperative drug interactions. Anesthesiology 87, 36–50 (1997).
+
+**Kharasch 2004** Kharasch, E.D., Walker, A., Hoffer, C. & Sheffels, P. Intravenous and oral alfentanil as in vivo probes for hepatic and first-pass cytochrome P450 3A activity: noninvasive assessment by use of pupillary miosis. Clin. Pharmacol. Ther. 76, 452–66 (2004).
+
+**Kharasch 2011** Kharasch, E.D. et al. Sensitivity of intravenous and oral alfentanil and pupillary miosis as minimal and noninvasive probes for hepatic and first-pass CYP3A induction. Clin. Pharmacol. Ther. 90, 100–8 (2011).
+
+**Kharasch 2011b** Kharasch ED, Vangveravong S, Buck N, London A, Kim T, Blood J, Mach RH. Concurrent assessment of hepatic and intestinal cytochrome P450 3A activities using deuterated alfentanil. Clin Pharmacol Ther. 2011 Apr;89(4):562-70. doi: 10.1038/clpt.2010.313. Epub 2011 Feb 23.
+
+**Kharasch 2012** Kharasch ED, Whittington D, Ensign D, Hoffer C, Bedynek PS, Campbell S, Stubbert K, Crafford A, London A, Kim T. Mechanism of efavirenz influence on methadone pharmacokinetics and pharmacodynamics. Clin Pharmacol Ther. 2012 Apr;91(4):673-84. doi: 10.1038/clpt.2011.276. Epub 2012 Mar 7.
+
+**Kuepfer 2016** Kuepfer L, Niederalt C, Wendl T, Schlender JF, Willmann S, Lippert J, Block M, Eissing T, Teutonico D. Applied Concepts in PBPK Modeling: How to Build a PBPK/PD Model.CPT Pharmacometrics Syst Pharmacol. 2016 Oct;5(10):516-531. doi: 10.1002/psp4.12134. Epub 2016 Oct 19.
+
+**Meistelman 1987** Meistelman C, Saint-Maurice C, Lepaul M, Levron JC, Loose JP, Mac Gee K. A comparison of alfentanil pharmacokinetics in children and adults. Anesthesiology. 1987 Jan;66(1):13-6. PubMed PMID: 3099603.
+
+**Meuldermans 1988** Meuldermans, W. et al. Alfentanil pharmacokinetics and metabolism in humans. Anesthesiology 69, 527–534 (1988).
+
+**Meyer 2012** Meyer M, Schneckener S, Ludewig B, Kuepfer L, Lippert J. Using expression data for quantification of active processes in physiologically based pharmacokinetic modeling. Drug Metab Dispos. 2012 May;40(5):892-901.
+
+**Nishimura 2013** Nishimura M, Yaguti H, Yoshitsugu H, Naito S, Satoh T. Tissue distribution of mRNA expression of human cytochrome P450 isoforms assessed by high-sensitivity real-time reverse transcription PCR. Yakugaku Zasshi. 2003 May;123(5):369-75.
+
+**Phimmasone 2001** Phimmasone, S. & Kharasch, E.D. A pilot evaluation of alfentanil-induced miosis as a noninvasive probe for hepatic cytochrome P450 3A4 (CYP3A4) activity in humans. Clin. Pharmacol. Ther. 70, 505–17 (2001).
+
+**PK-Sim Ontogeny Database Version 7.3** (https://github.com/Open-Systems-Pharmacology/OSPSuite.Documentation/blob/38cf71b384cfc25cfa0ce4d2f3addfd32757e13b/PK-Sim%20Ontogeny%20Database%20Version%207.3.pdf)
+
+**Wandel 2002** Wandel, C., Kim, R., Wood, M. & Wood, A. Interaction of morphine, fentanyl, sufentanil, alfentanil, and loperamide with the efflux drug transporter P-glycoprotein. Anesthesiology 96, 913–920 (2002).
+
+**Willmann 2007** Willmann S, Höhn K, Edginton A, Sevestre M, Solodenko J, Weiss W, Lippert J, Schmitt W. Development of a physiology-based whole-body population model for assessing the influence of individual variability on the pharmacokinetics of drugs. J Pharmacokinet Pharmacodyn. 2007, 34(3): 401-431.
+
+","Markdown"
+"PBPK model","Open-Systems-Pharmacology/OSP-PBPK-Model-Library","Tizanidine/Tizanidine_evaluation_report.md",".md","28178","463","# Building and evaluation of a PBPK model for tizanidine in adults
+
+| Version | 2.0-OSP12.2 |
+| ----------------------------------------------- | ------------------------------------------------------------ |
+| based on *Model Snapshot* and *Evaluation Plan* | https://github.com/Open-Systems-Pharmacology/Tizanidine-Model/releases/tag/v2.0 |
+| OSP Version | 12.2 |
+| Qualification Framework Version | 3.5 |
+
+This evaluation report and the corresponding PK-Sim project file are filed at:
+
+https://github.com/Open-Systems-Pharmacology/OSP-PBPK-Model-Library/
+
+# Table of Contents
+
+ * [1 Introduction](#introduction)
+ * [2 Methods](#methods)
+ * [2.1 Modeling Strategy](#modeling-strategy)
+ * [2.2 Data](#data)
+ * [2.3 Model Parameters and Assumptions](#model-parameters-and-assumptions)
+ * [3 Results and Discussion](#results-and-discussion)
+ * [3.1 Final input parameters](#final-input-parameters)
+ * [3.2 Diagnostics Plots](#diagnostics-plots)
+ * [3.3 Concentration-Time Profiles](#ct-profiles)
+ * [3.3.1 Model Building](#model-building)
+ * [3.3.2 Model Verification](#model-verification)
+ * [4 Conclusion](#conclusion)
+ * [5 References](#main-references)
+ * [6 Glossary](#glossary)
+
+# 1 Introduction
+
+The presented PBPK model of tizanidine has been developed to be used in a PBPK Drug-Drug-Interactions (DDI) network with tizanidine as a substrate of CYP1A2.
+
+Tizanidine is a centrally acting skeletal muscle relaxant generally used for the symptomatic treatment of acute painful muscle spasms and chronic spasticity resulting from diverse neurologic disorders ([Granfors 2004](#5-references)).
+
+**Absorption**: After oral administration of tizanidine-HCl it is absorbed fast and completely with peak plasma concentrations reached within 1 hour. Administration of tizanidine together with food increases plasma concentrations. The influence of food on the concentration-time profile is also dependent on the formulation. A capsule given with food leads to a prolonged Tmax, with a Cmax slightly lower than when the drug is given without food. In contrast, giving the tablet with food leads to higher peak concentrations while Tmax remains unchanged.
+
+**Distribution**: Approximately 30% is bound to plasma proteins. The concentration-time profile elicits a monophasic shape.
+
+**Metabolism**: Over 95% of a dose is metabolized. The main enzyme system involved in the metabolism of tizanidine is CYP1A2.
+
+**Excretion**: Only a minor part of the dose is recovered unchanged in the urine <5%.
+
+# 2 Methods
+
+## 2.1 Modeling Strategy
+
+The general workflow for building an adult PBPK model has been described by Kuepfer et al. ([Kuepfer 2016](#5-references)). Relevant information on the anthropometry (height, weight) was gathered from the respective clinical study, if reported. Information on physiological parameters (e.g. blood flows, organ volumes, hematocrit) in adults was gathered from the literature and has been incorporated in PK-Sim® as described previously ([Willmann 2007](#5-references)). The applied activity and variability of plasma proteins and active processes that are integrated into PK-Sim® are described in the publicly available 'PK-Sim® Ontogeny Database Version 7.3' ([PK-Sim Ontogeny Database Version 7.3](#5-references)).
+
+Since concentration-time profiles following intravenous administration are not publicly available, model building was based on data following oral administration. In general, the following step-wise workflow was followed:
+
+1. Fit intrinsic CL of CYP1A2, and Weibull absorption parameters (renal elimination fixed to GFR) using data from single dose studies where 4 or 8 mg tablets where given in the fasted state to healthy volunteers. The fitting was done for each of the five tissue distribution models available in PK-Sim.
+2. Predictions for fed-state. If adjustments are necessary, only absorption relevant parameters are fitted.
+ - Tablet-Fed (2, 4, 8 mg)
+ - Capsule-Fed (4, 8 mg)
+3. Model evaluation: Predict multiple dosing of 4 mg (tablet, fasted) using the best model and parameters from the previous step. If no adjustment of parameters is necessary, move on to next step.
+
+The predefined “Standard European Male for DDI” individual (age = 30 y, weight = 73 kg, height = 176 cm, BMI = 23.57 kg/m2) was used for simulations.
+
+Simulations of capsule administrations in fed state were carried out by adding the ""High-fat breakfast"" meal event at time = 0 in PK-Sim. Simulations of tablets administration in fed state were carried out without a food event but with adjusted dissolution profile.
+
+To judge the predictive variability of the model, a population simulation was carried out generating a virtual population of 2000 healthy European male subjects with the weight and age range according to [Granfors 2004](#5-references) (21 – 31 years, 65 – 83 kg).
+
+Details about input data (physicochemical, *in vitro* and clinical) can be found in [Section 2.2](#22-data).
+
+Details about the structural model and its parameters can be found in [Section 2.3](#23-model-parameters-and-assumptions).
+
+## 2.2 Data
+
+### 2.2.1 In vitro and physico-chemical data
+
+A literature search was performed to collect available information on physico-chemical properties of tizanidine ([Table 1](#table-1)).
+
+| **Parameter** | **Unit** | **Value** | Source | **Description** |
+| :------------------------------ | --------- | ---------------- | --------------------------------- | ------------------------------- |
+| MW+ | g/mol | 253.711 | [DrugBank DB00697](#5-references) | Molecular weight |
+| pKa,base+ | | 7.49 | [DrugBank DB00697](#5-references) | Acidic dissociation constant |
+| Solubility (pH)+ | mg/mL | 0.133
(7.4) | [DrugBank DB00697](#5-references) | Aqueous Solubility |
+| logP+ | | 1.4 | [DrugBank DB00697](#5-references) | Partition coefficient |
+| fu+ | % | 70 | [SmPC tizanidine](#5-references) | Fraction unbound in plasma |
+| Intrinsic CL | ml/min/kg | 17 | [Granfors 2004](#5-references) | Predicted from microsomal assay |
+
+**Table 1:** Physico-chemical and *in-vitro* metabolization properties of tizanidine extracted from literature. *+: Value used in final model*
+
+### 2.2.2 Clinical data
+
+A literature search was performed to collect available clinical data on tizanidine ([Table 2](#table-2)).
+
+| **Source** | **Dose [mg]/** **Schedule \*** | **Pop.** | Age [yrs] (mean or range) | Weight [kg] (mean or range) | **Sex** | **N** | **Form.** | Fasted or Fed | **Comment** |
+| -------------------- | ------------------------------- | ------------ | ------- | ----- | --------- | --------------------------------- | --------------------------------- | --------------------------------- | -------------------- |
+| [Momo 2010](#5-references)+ | 2 | HV | 29 | 70 | m | 12 | Tablet | Fed | |
+| [Granfors 2004](#5-references)+ | 4 | HV | 21-31 | 65-83 | m | 10 | Tablet | Fasted | |
+| [Schellenberger 1999](#5-references) | 4 t.i.d. | HV | 19-37 | 70-97 | m | 12 | Tablet | Fasted | |
+| [Henney 2007](#5-references)+ | 4 | HV | 26 | 71 | m12/f6 | 18 | Tablet /Capsule | Fed | |
+| [Backman 2008](#5-references)+ | 4 | HV | 23 | 78 | m | 38 | Tablet | Fasted | Male/female-non-smokers and male-smokers |
+| [Backman 2006](#5-references)+ | 4 | HV | 21 | 71 | m6/f4 | 10 | Tablet | Fasted | Only control group used |
+| [Shah 2006](#5-references) | 8 | HV | 18-52 | 46-102 | m54/f42 | 96 | Tablet / Capsule | Fed/ Fasted | |
+| [Henney 2008](#5-references) | 6 | HV | 18-39 | NA | m19/f9 | 27 | Capsule | Fasted | |
+| [Tse 1987](#5-references) | 4 t.i.d. | HV | 21-48 | 57-86 | m | 6 | Tablet | Fasted | |
+| [Al-Ghazawi 2013](#5-references)+ | 4 | HV | 28 | 75 | m | 36 | Tablet | Fasted | |
+
+**Table 2:** Literature sources of clinical concentration data of tizanidine used for model development and validation. *\*: single dose unless otherwise specified; EM: extensive metabolizers;+: Data used for final parameter identification*
+
+## 2.3 Model Parameters and Assumptions
+
+### 2.3.1 Absorption
+
+Release of tizanidine from the tablet is modelled using the Weibull-formulation, with parameter values identified by fitting the model to observed concentration profiles after po administration of 4 and 8 mg to healthy volunteers after an overnight fast.
+
+The same parameter values are used for the capsule and tablet formulations in fasted state. In the fed state, the capsule formulation is equal to that in the fasted state. For the tablet formulation, the parameter values differ from those in fasted state.
+
+### 2.3.2 Distribution
+
+Physico-chemical parameters were set to the reported values (see [Section 2.2.1](#221-in-vitro-and-physico-chemical-data)). It was assumed that the major binding partner in plasma is albumin.
+
+After testing the available organ-plasma partition coefficient and cell permeability calculation methods available in PK-Sim, observed clinical data were best described by choosing the partition coefficient calculation by `Berezhkovskiy` and cellular permeability calculation by `PK-Sim Standard`.
+
+### 2.3.3 Metabolism and Elimination
+
+Metabolization of tizanidine is modeled as CYP1A2 Intrinsic clearance. with parameter `Intrinsic clearance` being identified by fitting the model to concentration data after po administration.
+
+Glomerular filtration with `GFR fraction = 1` has been assumed.
+
+### 2.3.4 Automated Parameter Identification
+
+Following parameter values were estimated for the base model:
+
+- Intrinsic CL (CYP1A2)
+
+- Dissolution shape (Weibull formulation)
+
+- Dissolution time (50% dissolved) (Weibull formulation)
+
+# 3 Results and Discussion
+
+The next sections show:
+
+1. Final model input parameters for the building blocks: [Section 3.1](#31-final-input-parameters).
+2. Overall goodness of fit: [Section 3.2](#32-diagnostics-plots).
+3. Simulated vs. observed concentration-time profiles for the clinical studies used for model building and for model verification: [Section 3.3](#33-concentration-time-profiles).
+
+## 3.1 Final input parameters
+
+The parameter values of the final PBPK model are illustrated below.
+
+### Compound: Tizanidine
+
+#### Parameters
+
+Name | Value | Value Origin | Alternative | Default
+------------------------------------------ | ------------- | ------------------------- | ----------- | -------
+Solubility at reference pH | 0.133 mg/ml | Database-DrugBank DB00697 | S_aq | True
+Reference pH | 7.4 | Database-DrugBank DB00697 | S_aq | True
+Lipophilicity | 1.4 Log Units | Database-DrugBank DB00697 | LogP | True
+Fraction unbound (plasma, reference value) | 0.7 | Unknown-SmPC tizanidine | fu_plasma | True
+Cl | 1 | Database-DrugBank DB00697 | |
+Is small molecule | Yes | | |
+Molecular weight | 253.711 g/mol | Database-DrugBank DB00697 | |
+Plasma protein binding partner | Albumin | | |
+
+#### Calculation methods
+
+Name | Value
+----------------------- | ---------------
+Partition coefficients | Berezhkovskiy
+Cellular permeabilities | PK-Sim Standard
+
+#### Processes
+
+##### Metabolizing Enzyme: CYP1A2-CLint
+
+Species: Human
+
+Molecule: CYP1A2
+
+###### Parameters
+
+Name | Value | Value Origin
+------------------- | ------------ | ------------------------
+Intrinsic clearance | 7.2862 l/min | Parameter Identification
+
+##### Systemic Process: Glomerular Filtration-Assumption
+
+Species: Human
+
+###### Parameters
+
+Name | Value | Value Origin
+------------ | -----:| ----------------
+GFR fraction | 1 | Other-Assumption
+
+### Formulation: Tizanidine tablet fasted
+
+Type: Weibull
+
+#### Parameters
+
+Name | Value | Value Origin
+-------------------------------- | ----------- | ------------------------
+Dissolution time (50% dissolved) | 38.5287 min | Parameter Identification
+Lag time | 0 min |
+Dissolution shape | 0.963 | Parameter Identification
+Use as suspension | Yes |
+
+### Formulation: Tizanidine tablet fed
+
+Type: Weibull
+
+#### Parameters
+
+Name | Value | Value Origin
+-------------------------------- | ---------- | ------------------------
+Dissolution time (50% dissolved) | 3.7642 min | Parameter Identification
+Lag time | 0 min |
+Dissolution shape | 0.3881 | Parameter Identification
+Use as suspension | Yes |
+
+## 3.2 Diagnostics Plots
+
+The following section displays the goodness-of-fit visual diagnostic plots for the PBPK model performance of all data listed in [Section 2.2.2](#222-clinical-data).
+
+The first plot shows observed versus simulated plasma concentration, the second weighted residuals versus time.
+
+
+
+**Table 3-1: GMFE for Tizanidine concentration in plasma**
+
+|Group |GMFE |
+|:--------------------------------------|:----|
+|Oral administration (model building) |1.52 |
+|Oral administration (model validation) |1.77 |
+|All |1.65 |
+
+
+
+
+
+
+
+
+**Figure 3-1: Tizanidine concentration in plasma**
+
+
+
+
+
+
+
+
+**Figure 3-2: Tizanidine concentration in plasma**
+
+
+
+
+## 3.3 Concentration-Time Profiles
+
+Simulated versus observed concentration-time profiles of all data listed in [Section 2.2.2](#222-clinical-data) are presented below.
+
+### 3.3.1 Model Building
+
+
+
+
+
+**Figure 3-3: Tizanidine 2mg po tablet fed**
+
+
+
+
+
+
+
+
+**Figure 3-4: Tizanidine 4mg po tablet fasted**
+
+
+
+
+
+
+
+
+**Figure 3-5: Tizanidine 4mg po capsule fed**
+
+
+
+
+
+
+
+
+**Figure 3-6: Tizanidine 4mg po tablet fed**
+
+
+
+
+### 3.3.2 Model Verification
+
+
+
+
+
+**Figure 3-7: Tizanidine 4mg tid po tablet fasted**
+
+
+
+
+
+
+
+
+**Figure 3-8: Tizanidine 6mg po capsule fasted**
+
+
+
+
+
+
+
+
+**Figure 3-9: Tizanidine 8mg po capsule fasted**
+
+
+
+
+
+
+
+
+**Figure 3-10: Tizanidine 8mg po tablet fasted**
+
+
+
+
+
+
+
+
+**Figure 3-11: Tizanidine 8mg po capsule fed**
+
+
+
+
+
+
+
+
+**Figure 3-12: Tizanidine 8mg po tablet fed**
+
+
+
+
+
+
+
+
+**Figure 3-13: Time Profile Analysis**
+
+
+
+
+# 4 Conclusion
+
+The PBPK model developed for tizanidine accurately predicts the time-profiles following single and multiple dosing of tizanidine. Population simulations show that predicted PK variability is in accordance with the literature.
+
+The renal elimination is limited to glomerular filtration only. The predicted small fraction eliminated in Urine (~1%) is in line with literature reports (<5%). Given the minor contribution of the renal pathway no further exploration of active secretion was considered as the impact on DDI predictions is likely small.
+
+Observed data suggests that administration of food accelerates the dissolution of tizanidine tablets, which is modeled through a separate set of parameter values. The effect of food on the capsule could not be described well in terms of peak concentrations. DDI predictions should be restricted to the tablet formulation. The fa estimated by PK-Sim was 1.0 in both fed and fasted condition, indicating that fa was not the limiting factor in this model.
+
+As the model was developed in non-smoking subjects, it cannot be used to predict tizanidine concentrations in smokers.
+
+# 5 References
+
+**Al-Ghazawi 2013** Al-Ghazawi M, Alzoubi M, Faidi B. Pharmacokinetic comparison of two 4 mg tablet formulations of tizanidine. Int Journal of Clinical Pharmacology and Therapeutics. 2013 Mar 1;51(03):255–63.
+
+**Backman 2006** Backman JT, Granfors MT, Neuvonen PJ. Rifampicin is only a weak inducer of CYP1A2-mediated presystemic and systemic metabolism: Studies with tizanidine and caffeine. Eur J Clin Pharmacol. 2006;62(6):451-461.
+
+**Backman 2008** Backman JT, Schröder MT, Neuvonen PJ. Effects of gender and moderate smoking on the pharmacokinetics and effects of the CYP1A2 substrate tizanidine. Eur J Clin Pharmacol. 2008;64(1):17-24.
+
+**DrugBank DB00697** (https://www.drugbank.ca/drugs/DB00697)
+
+**Granfors 2004** Granfors MT, Backman JT, Laitila J, Neuvonen PJ. Tizanidine is mainly metabolized by cytochrome P450 1A2 in vitro: Tizanidine is mainly metabolized by cytochrome P450 IA2 in vitro. British Journal of Clinical Pharmacology. 2004 Jan 8;57(3):349–53.
+
+**Heazlewood 1983** Heazlewood V, Symoniw P, Maruff P, Eadie M. Tizanidine- Initial pharmacokinetic studies in patients with spasticity. Eur J Clin Pharmacol. 1983; 25:65-67.
+
+**Henney 2007** Henney HR, Shah J. Relative bioavailability of tizanidine 4-mg capsule and tablet formulations after a standardized high-fat meal: A single-dose, randomized, open-label, crossover study in healthy subjects. Clin Ther. 2007;29(4):661-669.
+
+**Henney 2008** Henney HR, Fitzpatrick A, Stewart J, Runyan JD. Relative bioavailability of tizanidine hydrochloride capsule formulation compared with capsule contents administered in applesauce: A single-dose, open-label, randomized, two-way, crossover study in fasted healthy adult subjects. Clinical Therapeutics. 2008 Dec;30(12):2263–71.
+
+**Kuepfer 2016** Kuepfer L, Niederalt C, Wendl T, Schlender JF, Willmann S, Lippert J, Block M, Eissing T, Teutonico D. Applied Concepts in PBPK Modeling: How to Build a PBPK/PD Model.CPT Pharmacometrics Syst Pharmacol. 2016 Oct;5(10):516-531.
+
+**Mathias 1989** Mathias C, Luckitt J, Desai P, Baker H, Masri W El, Hans I. Pharmacodynamics and pharmacokinetics of the oral antispastic agent tizanidine in patients with spinal cord injury. J Rehabil Res. 1989;26(4):9-16.
+
+**Momo 2010** Momo K, Homma M, Osaka Y, Inomata SI, Tanaka M, Kohda Y. Effects of mexiletine, a CYP1A2 inhibitor, on tizanidine pharmacokinetics and pharmacodynamics. J Clin Pharmacol. 2010;50(3):331-337.
+
+**PK-Sim Ontogeny Database Version 7.3** (https://github.com/Open-Systems-Pharmacology/OSPSuite.Documentation/blob/38cf71b384cfc25cfa0ce4d2f3addfd32757e13b/PK-Sim%20Ontogeny%20Database%20Version%207.3.pdf)
+
+**Schellenberger 1999** Schellenberger MK, Groves L, Shah J, Novack GD. A controlled pharmacokinetic evaluation of tizanidine and baclofen at steady state. Drug Metabolism and Disposition.1999;2:201–4.
+
+**Shah 2006** Shah J, Wesnes KA, Kovelesky RA, Henney HR. Effects of food on the single-dose pharmacokinetics/pharmacodynamics of tizanidine capsules and tablets in healthy volunteers. Clin Ther. 2006;28(9):1308-1317.
+
+**SmPC tizanidine** Zanaflex prescribing information. Website: https://www.accessdata.fda.gov/drugsatfda_docs/label/2006/020397s021,021447s002lbl.pdf , 2006, Acorda Therapeutics Inc
+
+**Tse 1987** Tse FLS, Jaffe JM, Bhuta S. Pharmacokinetics Of Orally Administered Tizanidine In Healthy Volunteers. Fundamental & Clinical Pharmacology. 1987 Nov 12;1(6):479–88.
+
+**Willmann 2007** Willmann S, Höhn K, Edginton A, Sevestre M, Solodenko J, Weiss W, Lippert J, Schmitt W. Development of a physiology-based whole-body population model for assessing the influence of individual variability on the pharmacokinetics of drugs. *J Pharmacokinet Pharmacodyn* 2007, 34(3): 401-431.
+
+# 6 Glossary
+
+| ADME | Absorption, Distribution, Metabolism, Excretion |
+| ------- | ------------------------------------------------------------ |
+| AUC | Area under the plasma concentration versus time curve |
+| AUCinf | AUC until infinity |
+| AUClast | AUC until last measurable sample |
+| AUCR | Area under the plasma concentration versus time curve Ratio |
+| b.i.d. | Twice daily (bis in diem) |
+| CL | Clearance |
+| Clint | Intrinsic liver clearance |
+| Cmax | Maximum concentration |
+| CmaxR | Maximum concentration Ratio |
+| CYP | Cytochrome P450 oxidase |
+| CYP1A2 | Cytochrome P450 1A2 oxidase |
+| CYP2C19 | Cytochrome P450 2C19 oxidase |
+| CYP3A4 | Cytochrome P450 3A4 oxidase |
+| DDI | Drug-drug interaction |
+| e.c. | Enteric coated |
+| EE | Ethinylestradiol |
+| EM | Extensive metabolizers |
+| fm | Fraction metabolized |
+| FMO | Flavin-containing monooxygenase |
+| fu | Fraction unbound |
+| FDA | Food and Drug administration |
+| GFR | Glomerular filtration rate |
+| HLM | Human liver microsomes |
+| hm | homozygous |
+| ht | heterozygous |
+| IM | Intermediate metabolizers |
+| i.v. | Intravenous |
+| IVIVE | In Vitro to In Vivo Extrapolation |
+| Ka | Absorption rate constant |
+| kcat | Catalyst rate constant |
+| Ki | Inhibitor constant |
+| Kinact | Rate of enzyme inactivation |
+| Km | Michaelis Menten constant |
+| m.d. | Multiple dose |
+| OSP | Open Systems Pharmacology |
+| PBPK | Physiologically-based pharmacokinetics |
+| PK | Pharmacokinetics |
+| PI | Parameter identification |
+| PM | Poor metabolizers |
+| RT-PCR | Reverse transcription polymerase chain reaction |
+| p.o. | Per os |
+| q.d. | Once daily (quaque diem) |
+| SD | Single Dose |
+| SE | Standard error |
+| s.d.SPC | Single dose Summary of Product Characteristics |
+| SD | Standard deviation |
+| TDI | Time dependent inhibition |
+| t.i.d | Three times a day (ter in die) |
+| UGT | Uridine 5'-diphospho-glucuronosyltransferase |
+| UM | Ultra-rapid metabolizers |
+
+","Markdown"
+"PBPK model","Open-Systems-Pharmacology/OSP-PBPK-Model-Library","Raltegravir/Raltegravir_evaluation_report.md",".md","36711","626","# Building and evaluation of a PBPK model for raltegravir in adults
+
+| Version | 2.0-OSP12.2 |
+| ----------------------------------------------- | ------------------------------------------------------------ |
+| based on *Model Snapshot* and *Evaluation Plan* | https://github.com/Open-Systems-Pharmacology/Raltegravir-Model/releases/tag/v2.0 |
+| OSP Version | 12.2 |
+| Qualification Framework Version | 3.5 |
+
+This evaluation report and the corresponding PK-Sim project file are filed at:
+
+https://github.com/Open-Systems-Pharmacology/OSP-PBPK-Model-Library/
+
+# Table of Contents
+
+ * [1 Introduction](#introduction)
+ * [2 Methods](#methods)
+ * [2.1 Modeling strategy](#modeling-strategy)
+ * [2.2 Data used](#data)
+ * [2.3 Model parameters and assumptions](#model-parameters-and-assumptions)
+ * [3 Results and Discussion](#results-and-discussion)
+ * [3.1 Raltegravir final input parameters](#final-input-parameters)
+ * [3.2 Raltegravir Diagnostics Plots](#diagnostics-plots)
+ * [3.3 Raltegravir Concentration-Time profiles](#ct-profiles)
+ * [4 Conclusion](#conclusion)
+ * [5 References](#main-references)
+
+# 1 Introduction
+
+The presented model building and evaluation report evaluates the performance of a PBPK model for raltegravir in adults.
+
+Raltegravir, sold under the brand name Isentress, is an antiretroviral medication used to treat HIV/AIDS by blocking the establishment of post-integration HIV latency. It is also used as part of post exposure prophylaxis to prevent HIV infection following potential exposure. Raltegravir is only taken orally and is mainly metabolized by UGT1A1 (~70%) [(Kassahun 2007](#5-references)). The final raltegravir model features metabolism by UGT1A1 and to a minor extent by UGT1A9. Additionally, there is excretion via glomerular filtration. The model adequately describes the pharmacokinetics of raltegravir in adults.
+
+The raltegravir model is a whole-body PBPK model, allowing for dynamic translation between individuals with organs expressing UGT1A1. The raltegravir report demonstrates the level of confidence in the raltegravir PBPK model build with the OSP suite with regard to reliable predictions of raltegravir PK adults during model-informed drug development.
+
+# 2 Methods
+
+## 2.1 Modeling strategy
+
+The general concept of building a PBPK model has previously been described by Kuepfer et al. ([Kuepfer 2016](#5-references)). Relevant information on anthropometric (height, weight) and physiological parameters (e.g. blood flows, organ volumes, binding protein concentrations, hematocrit, cardiac output) in adults was gathered from the literature and has been previously published ([PK-Sim Ontogeny Database Version 7.3](#5-references)). The information was incorporated into PK-Sim® and was used as default values for the simulations in adults.
+
+The applied activity and variability of plasma proteins and active processes that are integrated into PK-Sim® are described in the publicly available PK-Sim® Ontogeny Database Version 7.3 ([Schlender 2016](#5-references)) or otherwise referenced for the specific process.
+
+First, a base mean model was built using data from the single dose escalation study to find an appropriate structure describing the PK of Raltegravir. The mean PK model was developed using a typical European individual. Unknown parameters were identified using the Parameter Identification module provided in PK-Sim®. Structural model selection was mainly guided by visual inspection of the resulting description of data and biological plausibility.
+
+Once the appropriate structural model was identified, additional parameters for different formulations were identified.
+
+A final PBPK model was established and simulations were compared to the reported data to evaluate model appropriateness and to assess model qualification, by means of diagnostics plots and predicted versus observed concentration-time profiles, of which the results support an adequate prediction of the PK in adults.
+
+During model building, uncertainties in data quality, as well as study differences may cause not being able to adequately describe the PK of all reported clinical studies.
+
+## 2.2 Data used
+
+### 2.2.1 In vitro / physico-chemical data
+
+A literature search was performed to collect available information on physicochemical properties of raltegravir. The obtained information from literature is summarized in the table below, and is used for model building.
+
+| **Parameter** | **Unit** | **Raltegravir literature** | **Description** |
+| :-------------- | ----------- | ---------------------------------------------------------- | ----------------------------------------------- |
+| MW | g/mol | 444.4163 ([drugbank.ca](#5-references)) | Molecular weight |
+| pKa | | 6.67 ([Moss 2012](#5-references)) | Acid dissociation constant |
+| Solubility (pH) | mg/L | Reference pH-dependent table ([Moss 2013](#5-references)) | Solubility |
+| logD (pH 7) | | 0.58 ([Moss 2012](#5-references)) | Partition coefficient between octanol and water |
+| fu | | 0.17 ([Laufer 2009](#5-references)) | Fraction unbound |
+| Km UGT1A1 | µM | 99 ([Kassahun 2007](#5-references)) | Michaelis-Menten constant |
+| Vmax UGT1A1 | nmol/min/mg | 0.89 ([Kassahun 2007](#5-references)) | Maximum rate of reaction |
+| Km UGT1A9 | µM | 296 ([Kassahun 2007](#5-references)) | Michaelis-Menten constant |
+| Vmax UGT1A9 | nmol/min/mg | 0.53 ([Kassahun 2007](#5-references)) | Maximum rate of reaction |
+
+### 2.2.2 Clinical data
+
+A literature search was performed to collect available clinical data on Raltegravir in adults.
+
+The following publications were found in adults for model building and evaluation:
+
+| Publication | Study description |
+| :-------------------------------- | :----------------------------------------------------------- |
+| [Iwamoto 2008](#5-references) | Single- and multiple-dose escalation study in healthy subjects |
+| [Iwamoto 2009](#5-references) | Effects of ritonavir and efavirenz on safety, tolerability and pharmacokinetics of raltegravir |
+| [Markowitz 2006](#5-references) | Monotherapy, followed by a longer term combination therapy of raltegravir versus efavirenz |
+| [Kassahun 2007](#5-references) | Pharmacokinetics study in healthy adults |
+| [Rhee 2014](#5-references) | Pediatric formulation study in healthy adults |
+| [Wenning 2009](#5-references) | Effect of rifampin on the pharmacokinetics of raltegravir |
+
+## 2.3 Model parameters and assumptions
+
+### 2.3.1 Absorption
+
+As no intravenous data is currently available to study systemic clearance of raltegravir *in vivo*, only oral data was used for model building. For oral administration the following parameters play a role with regards to the absorption kinetics of a compound, which can be estimated with PBPK: solubility, lipophilicity and intestinal permeability. Moss et al. ([Moss 2013](#5-references)) published values for raltegravir solubility in population groups with very low-, low-, medium-, high-, and very high intestinal luminal pH, after a single 400 mg dose of raltegravir. For the raltegravir PBPK model we have applied the medium pH group for creating a pH dependent solubility profile throughout the intestinal tract. The lipophilicity as well as pKa of raltegravir was also published by Moss et al ([Moss 2012, Moss 2013](#5-references)) to be 0.58 (as log partition coefficient between octanol and water (pH 7) and 6.67 (acid)), respectively. These values were applied and fixed in the raltegravir PBPK model, without further optimization. Regarding intestinal transcellular permeability (Pint), Moss et al ([Moss 2012](#5-references)) reported a range of apical to basolateral apparent permeability in Caco-2 monolayer at different pH values. Using published functions Pint can be calculated from Caco-2 cell membrane permeability measurements (Parrot et al. ([Parrot 2002](#5-references))), Thelen et al. ([Thelen 2010](#5-references), Sun et al. ([Sun 2002](#5-references)) and Sjögren et al. ([Sjögren 2013](#5-references))). However as no reference/calibrator compound was available to correct for inter-study variability, these functions could not be applied, and it was decided to estimate the Pint from *in vivo* clinical data instead. Nevertheless, for plausibility check, a theoretical Pint was calculated using the aforementioned functions without correction, resulting in a range of Pint from 4.64E-04 to 1.47E-09 cm/min. The finally estimated (based on *in vivo* data) Pint falls within this range.
+
+**Table 2.** Reported Caco-permeability and calculated theoretical effective permeability (intestinal transcellular permeability, Peff) values for raltegravir via different reported functions, lacking a reference compound for correcting inter-study variability.
+
+| Reference publication of reported function | **pH apical to basolateral** | **Peff apical to basolateral (cm/min)** | **Reference compound available for correcting Inter study variability** |
+| --------------------------------- | ------------------------- | ----------------- | --------------------------------------------- |
+| Raltegravir Caco permeability (Moss 2012 ) | 7.4 |6.60E-6 | - |
+| Raltegravir Caco permeability (Moss 2012) | 6.5 | 9.20E-6 | - |
+| Parrot 2002 | 7.4 | 2.14E-04 | Not available |
+| Thelen 2010 | 7.4 | 1.47E-09 | Not available |
+| Sjögren 2013 | 7.4 | 1.03606E-6 | Not available |
+| Sun 2002 | 7.4 | 2.77E-4 | Not available |
+| Sun 2002 | 6.5 | 2.86E-4 | Not available |
+| Simcyp (*) | 6.5 | 4.62E-4 | Not available |
+
+*Not published as paper, Simcyp applied an adapted version of Sun et al 2002
+
+### 2.3.2 Distribution
+
+Laufer et al. ([Laufer 2009](#5-references)) published a fu in humans to be 0.17. [Barau et al 2013](#5-references) reported that raltegravir binds to serum albumin, and not alpha glycoprotein, which is built-in as such in the PBPK model.
+
+After testing the available organ-plasma partition coefficient and cell permeability calculation methods built in PK-Sim, observed clinical data was best described by choosing the partition coefficient calculation by Rodgers and Rowland, and cell permeability calculation by PK-Sim standard. Specific organ permeability normalized to surface area was automatically calculated by PK-Sim.
+
+### 2.3.3 Metabolism and Elimination
+
+Kassahun et al. ([Kassahun 2007](#5-references)) studied the absorption, metabolism, and excretion of raltegravir in healthy volunteers after a single oral dose of 200 mg (200Ci) of [14C] raltegravir. Human liver microsomal incubations confirmed the dominant role of UGT metabolism for raltegravir. Additionally, data from incubations using cDNA-expressed UGTs indicate that the major mechanism of metabolism of raltegravir in humans is UGT1A1-mediated glucuronidation. Raltegravir was in particular converted by UGT1A1 and 1A9. The apparent arithmetic mean Km values for the glucuronidation of raltegravir by UGT1A1 and UGT1A9 were 99 (standard deviation (SD): 16) and 296 (SD: 55) µM, respectively. The corresponding Vmax values (nmol/min/mg) were 0.89 (SD: 0.05) for UGT1A1, and 0.53 (SD: 0.06) for UGT1A9.
+
+Based on this information, the reported in vitro Km values for UGT1A1 and 1A9 were included in the model. Reported Vmax values were in units nmol/min/mg protein and thus not directly transferable into the PBPK model. Therefore, a joint scaling factor fUGT on the *in vitro* Vmax values was estimated to match observed *in vivo* data, and keeping the relative relationship between those *in vitro* values (0.89 and 0.53 nmol/min/mg) for UGT1A1 and UGT1A9 fixed according to:
+
+Vmax,UGT1A1 = fUGT * Vmax,in-vitro,UGT1A1
+
+Vmax,UGT1A9 = fUGT * Vmax,in-vitro,UGT1A9
+
+It is especially important to fix the relative contribution of both enzymes as a ratio to ensure that, when translating to other populations (e.g. children where both UGTs undergo a different ontogeny pattern, or patients who have differently reduced amounts of UGT1A1 vs 1A9) the relative contributions can be adequately scaled.
+Note that the estimated scaling factor fUGT will be directly implemented into the final *in vivo* Vmax values (only Vmax,UGT1A1 and Vmax,UGT1A9 will be reported in [section 3](#3-results-and-discussion))
+
+Finally, as ~9% of the dose is excreted in human urine as unchanged parent compound, GFR is introduced in the raltegravir PBPK model.
+
+# 3 Results and Discussion
+
+The PBPK model **raltegravir** was developed with clinical pharmacokinetic data covering 4 different oral formulation and a dose range of 10-1600mg, including single dose (SD) as well as multiple dose (MD) clinical data.
+
+As there were 4 different oral formulations available for model evaluation, all formulations require an estimation of the dissolution kinetics via a Weibull function. This function requires the estimation of 2 parameters, the dissolution time (time where 50% of the drug is dissolved), and dissolution shape (shape parameter of the Weibull function). Therefore, to minimize the amount of parameters for fitting, as a first step, the PK study data (lactose formulation) by Iwamoto et al. ([Iwamoto 2007](#5-references)) was fitted which includes SD escalation and hast a broad dose-range (10mg-1600mg) to capture dose (non-) linearity. During the model-fitting, the following parameters were estimated (all other parameters were fixed to reported values):
+
+* Vmax (as unique scaling factor fUGT , as described in [section 2.3.3](#233-metabolism-and-elimination))
+* Weibull function parameters: Dissolution time and dissolution shape
+* Specific intestinal permeability (transcellular)
+
+The fit resulted in an adequate description of all data. As there is no iv data available, it was not possible to clearly distinguish between clearance and absorption, resulting in a considerable correlation between Vmax and dissolution shape (Weibull). An attempt to fix Vmax to reported in vitro values, and only estimating absorption (lipophilicity and intestinal transcellular permeability) resulted in an underprediction of the clearance, and clearly indicated a need for increase in clearance. As described above, no reported intestinal permeability was found other than Caco2-permeability. Caco2-permeability could not be translated to effective intestinal permeability without a reference compound. Therefore it was decided to continue with the model where both Pint and Vmax were estimated.
+
+As a second step, clinical study data for all other formulations summarised in [section 2.2.2](#222-clinical-data) were included for model fitting, including film-coated tablets (100-400mg MD, 200-400mg SD), chewable tablets (400mg fasted + fed) and oral granules in suspension (400mg). In this step, only the Weibull functions were estimated with all other parameters fixed based on the first step. Finally, as the parameters of the Weibull functions were highly correlated (as expected), only dissolution shape was estimated as a last step. The model results show that the PBPK model of raltegravir adequately described the date for all formulations and doses available.
+
+## 3.1 Raltegravir final input parameters
+
+The compound parameter values of the final raltegravir PBPK model are illustrated below.
+
+### Compound: Raltegravir
+
+#### Parameters
+
+Name | Value | Value Origin | Alternative | Default
+------------------------------------------------ | --------------------- | -------------------------------------- | ----------- | -------
+Solubility table | 40 mg/l | Publication-In Vitro-Moss 2013 Table 2 | Moss 2013 | True
+Lipophilicity | 0.58 Log Units | Publication-Moss 2012 | Moss 2012 | True
+Fraction unbound (plasma, reference value) | 0.17 | Publication-In Vitro-Laufer 2009 | Measurement | True
+Specific intestinal permeability (transcellular) | 2.8481843854E-07 cm/s | Parameter Identification | Fit | True
+F | 1 | Publication-Other-Drugbank.ca | |
+Is small molecule | Yes | | |
+Molecular weight | 444.4163 g/mol | Publication-Other-Drugbank.ca | |
+Plasma protein binding partner | Albumin | | |
+
+#### Calculation methods
+
+Name | Value
+----------------------- | -------------------
+Partition coefficients | Rodgers and Rowland
+Cellular permeabilities | PK-Sim Standard
+
+#### Processes
+
+##### Systemic Process: Glomerular Filtration-Kassahun 2007
+
+Species: Human
+
+###### Parameters
+
+Name | Value | Value Origin
+------------ | -----:| ----------------------------------
+GFR fraction | 1 | Publication-In Vitro-Kassahun 2007
+
+##### Metabolizing Enzyme: UGT1A1-Kassahun 2007
+
+Molecule: UGT1A1
+
+###### Parameters
+
+Name | Value | Value Origin
+---------------------------------- | ------------------------------------- | ----------------------------------
+In vitro Vmax for liver microsomes | 2.7351231632 nmol/min/mg mic. protein | Parameter Identification
+Km | 99 µM | Publication-In Vitro-Kassahun 2007
+
+##### Metabolizing Enzyme: UGT1A9-Kassahun 2007
+
+Molecule: UGT1A9
+
+###### Parameters
+
+Name | Value | Value Origin
+---------------------------------- | ------------------------------------- | ----------------------------------
+In vitro Vmax for liver microsomes | 1.6287812095 nmol/min/mg mic. protein | Parameter Identification
+Km | 296 µM | Publication-In Vitro-Kassahun 2007
+
+### Formulation: filmcoated tablet (original Merck formulation)
+
+Type: Weibull
+
+#### Parameters
+
+Name | Value | Value Origin
+-------------------------------- | ---------- | ------------------------
+Dissolution time (50% dissolved) | 500 min | Parameter Identification
+Lag time | 0 min |
+Dissolution shape | 0.03536656 | Parameter Identification
+Use as suspension | Yes |
+
+### Formulation: chewable tablet
+
+Type: Weibull
+
+#### Parameters
+
+Name | Value | Value Origin
+-------------------------------- | -------------------- | ------------------------
+Dissolution time (50% dissolved) | 1.0000049774E-05 min | Parameter Identification
+Lag time | 0 min |
+Dissolution shape | 0.050078869 | Parameter Identification
+Use as suspension | Yes |
+
+### Formulation: Weibull (lactose formulation)
+
+Type: Weibull
+
+#### Parameters
+
+Name | Value | Value Origin
+-------------------------------- | ------------------ | ------------------------
+Dissolution time (50% dissolved) | 2.30152527E-10 min | Parameter Identification
+Lag time | 0 min |
+Dissolution shape | 0.0389537131 | Parameter Identification
+Use as suspension | Yes |
+
+### Formulation: Weibull (granules)
+
+Type: Weibull
+
+#### Parameters
+
+Name | Value | Value Origin
+-------------------------------- | -------------------- | ------------------------
+Dissolution time (50% dissolved) | 0.00010000047426 min | Parameter Identification
+Lag time | 0 min |
+Dissolution shape | 0.0654456264 | Parameter Identification
+Use as suspension | Yes |
+
+## 3.2 Raltegravir Diagnostics Plots
+
+Below you find the goodness-of-fit visual diagnostic plots for raltegravir PBPK model performance (observed versus individually simulated plasma concentration and weighted residuals versus time, including the geometric mean fold error (GMFE)) of all data used for model building.
+
+
+
+**Table 3-1: GMFE for Goodness of fit plot for concentration in plasma.**
+
+|Group |GMFE |
+|:----------------------------------|:----|
+|Raltegravir chewable tablet |1.37 |
+|Raltegravir filmcoated tablet |1.56 |
+|Raltegravir granules in suspension |1.42 |
+|Raltegravir lactose formulation |1.48 |
+|All |1.49 |
+
+
+
+
+
+
+
+
+**Figure 3-1: Goodness of fit plot for concentration in plasma.**
+
+
+
+
+
+
+
+
+**Figure 3-2: Goodness of fit plot for concentration in plasma.**
+
+
+
+
+## 3.3 Raltegravir Concentration-Time profiles
+
+Simulated versus observed plasma concentration-time profiles of all data are listed below.
+
+
+
+
+
+**Figure 3-3: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-4: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-5: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-6: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-7: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-8: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-9: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-10: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-11: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-12: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-13: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-14: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-15: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-16: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-17: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-18: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-19: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-20: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-21: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-22: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-23: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-24: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-25: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-26: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-27: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-28: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-29: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-30: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-31: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-32: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-33: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-34: Time Profile Analysis 1**
+
+
+
+
+# 4 Conclusion
+
+The final raltegravir PBPK model applies metabolism by UGT1A1, UGT1A9 and glomerular filtration and adequately describes the pharmacokinetics of raltegravir in adults receiving SD, MD of Raltegravir ranging from 10mg to 1600mg, including four different oral formulations.
+
+This model could be applied for the investigation of drug-drug interactions (DDI), and translation to special populations such as pediatrics with regard to UGT1A1 and 1A9 metabolism.
+
+# 5 References
+
+**Barau 2013** Caroline Barau, Valérie Furlan, Yazdan Yazdanpanah, Catherine Fagard, Jean-Michel Molina, Anne-Marie Taburet, and Aurélie Barrail-Tran. Characterization of Binding of Raltegravir to Plasma Proteins. Antimicrob Agents Chemother. 2013 Oct; 57(10): 5147–5150.
+doi: 10.1128/AAC.00625-13.
+
+**Drugbank.ca** (https://www.drugbank.ca/drugs/DB06817 )
+
+**Iwamoto 2008** Iwamoto M, Wenning LA, Petry AS, Laethem M, De Smet M, Kost JT, Merschman SA, Strohmaier KM, Ramael S, Lasseter KC, Stone JA, Gottesdiener KM, Wagner JA. Safety, tolerability, and pharmacokinetics of raltegravir after single and multiple doses in healthy subjects. Clin Pharmacol Ther. 2008 Feb;83(2):293-9. Epub 2007 Aug 22.
+
+**Iwamoto 2009** Iwamoto M, Wenning LA, Nguyen BY, Teppler H, Moreau AR, Rhodes RR, Hanley WD, Jin B, Harvey CM, Breidinger SA, Azrolan N, Farmer HF Jr, Isaacs RD, Chodakewitz JA, Stone JA, Wagner JA. Effects of omeprazole on plasma levels of raltegravir. Clin Infect Dis. 2009 Feb 15;48(4):489-92. doi: 10.1086/596503.
+
+**Kassahun 2007** Kassahun K, McIntosh I, Cui D, Hreniuk D, Merschman S, Lasseter K, Azrolan N, Iwamoto M, Wagner JA, Wenning LA. Metabolism and disposition in humans of raltegravir (MK-0518), an anti-AIDS drug targeting the human immunodeficiency virus 1 integrase enzyme. Drug Metab Dispos. 2007 Sep;35(9):1657-63. Epub 2007 Jun 25.
+
+**Kuepfer 2016** Kuepfer L, Niederalt C, Wendl T, Schlender JF, Willmann S, Lippert J, Block M, Eissing T, Teutonico D. Applied Concepts in PBPK Modeling: How to Build a PBPK/PD Model.CPT Pharmacometrics Syst Pharmacol. 2016 Oct;5(10):516-531. doi: 10.1002/psp4.12134. Epub 2016 Oct 19.
+
+**Laufer 2009** Laufer R, Paz OG, Di Marco A, Bonelli F, Monteagudo E, Summa V, Rowley M. Quantitative prediction of human clearance guiding the development of Raltegravir (MK-0518, isentress) and related HIV integrase inhibitors. Drug Metab Dispos. 2009 Apr;37(4):873-83. doi: 10.1124/dmd.108.023804. Epub 2009 Jan 14.
+
+**Markowitz 2006** Markowitz M, Morales-Ramirez JO, Nguyen BY, Kovacs CM, Steigbigel RT, Cooper DA, Liporace R, Schwartz R, Isaacs R, Gilde LR, Wenning L, Zhao J, Teppler H. Antiretroviral activity, pharmacokinetics, and tolerability of MK-0518, a novel inhibitor of HIV-1 integrase, dosed as monotherapy for 10 days in treatment-naive HIV-1-infected individuals. J Acquir Immune Defic Syndr. 2006 Dec 15;43(5):509-15.
+
+**Moss 2012** Moss DM, Siccardi M, Murphy M, Piperakis MM, Khoo SH, Back DJ, Owen A. Divalent metals and pH alter raltegravir disposition in vitro. Antimicrob Agents Chemother. 2012 Jun;56(6):3020-6. doi: 10.1128/AAC.06407-11. Epub 2012 Mar 26.
+
+**Moss 2013** Moss DM, Siccardi M, Back DJ, Owen A. Predicting intestinal absorption of raltegravir using a population-based ADME simulation. J Antimicrob Chemother. 2013 Jul;68(7):1627-34. doi: 10.1093/jac/dkt084. Epub 2013 Mar 20.
+
+**Parrott 2008** Parrott N, Lave T. Applications of physiologically based absorption models in drug discovery and development. Mol Pharm. 2008 Sep-Oct;5(5):760-75. doi: 10.1021/mp8000155. Epub 2008 Jun 12.
+
+**PK-Sim Ontogeny Database Version 7.3** (https://github.com/Open-Systems-Pharmacology/OSPSuite.Documentation/blob/38cf71b384cfc25cfa0ce4d2f3addfd32757e13b/PK-Sim%20Ontogeny%20Database%20Version%207.3.pdf)
+
+**Rhee 2014** Rhee EG, Rizk ML, Brainard DM, Gendrano IN 3rd, Jin B, Wenning LA, Wagner JA, Iwamoto M. A pharmacokinetic comparison of adult and paediatric formulations of raltegravir in healthy adults. Antivir Ther. 2014;19(6):619-24. doi: 10.3851/IMP2765. Epub 2014 Mar 7.
+
+**Schlender 2016** Schlender JF, Meyer M, Thelen K, Krauss M, Willmann S, Eissing T, Jaehde U. Development of a Whole-Body Physiologically Based Pharmacokinetic Approach to Assess the Pharmacokinetics of Drugs in Elderly Individuals. Clin Pharmacokinet. 2016 Dec;55(12):1573-1589.
+
+**Sjögren 2013** Sjögren E, Westergren J, Grant I, Hanisch G, Lindfors L, Lennernäs H, Abrahamsson B, Tannergren C. In silico predictions of gastrointestinal drug absorption in pharmaceutical product development: application of the mechanistic absorption model GI-Sim. Eur J Pharm Sci. 2013 Jul 16;49(4):679-98. doi: 10.1016/j.ejps.2013.05.019. Epub 2013 May 29.
+
+**Sun 2002** Sun D, Lennernas H, Welage LS, Barnett JL, Landowski CP, Foster D, Fleisher D, Lee KD, Amidon GL. Comparison of human duodenum and Caco-2 gene expression profiles for 12,000 gene sequences tags and correlation with permeability of 26 drugs. Pharm Res. 2002 Oct;19(10):1400-16.
+
+**Thelen 2011** Thelen K, Coboeken K, Willmann S, Burghaus R, Dressman JB, Lippert J. Evolution of a detailed physiological model to simulate the gastrointestinal transit and absorption process in humans, part 1: oral solutions. J Pharm Sci. 2011 Dec;100(12):5324-45. doi: 10.1002/jps.22726. Epub 2011 Oct 12
+
+**Wenning 2009** Larissa A. Wenning,, William D. Hanley, Diana M. Brainard, Amelia S. Petry, Kalyan Ghosh, Bo Jin, Eric Mangin, Thomas C. Marbury, Jolene K. Berg, Jeffrey A. Chodakewitz, Julie A. Stone,1 Keith M. Gottesdiener, John A. Wagner, and Marian Iwamoto. Effect of Rifampin, a Potent Inducer of Drug-Metabolizing Enzymes, on the Pharmacokinetics of Raltegravir. Antimicrob Agents Chemother. 2009 Jul; 53(7): 2852–2856.
+
+","Markdown"
+"PBPK model","Open-Systems-Pharmacology/OSP-PBPK-Model-Library","Cimetidine/Cimetidine_evaluation_report.md",".md","46350","780","# Building and evaluation of a PBPK model for cimetidine in healthy adults
+
+| Version | 2.0-OSP12.2 |
+| ----------------------------------------------- | ------------------------------------------------------------ |
+| based on *Model Snapshot* and *Evaluation Plan* | https://github.com/Open-Systems-Pharmacology/Cimetidine-Model/releases/tag/v2.0 |
+| OSP Version | 12.2 |
+| Qualification Framework Version | 3.5 |
+
+This evaluation report and the corresponding PK-Sim project file are stored at:
+
+https://github.com/Open-Systems-Pharmacology/OSP-PBPK-Model-Library/
+
+# Table of Contents
+
+ * [1 Introduction](#introduction)
+ * [2 Methods](#methods)
+ * [2.1 Modeling Strategy](#modeling-strategy)
+ * [2.2 Data](#data)
+ * [2.3 Model Parameters and Assumptions](#model-parameters-and-assumptions)
+ * [3 Results and Discussion](#results-and-discussion)
+ * [3.1 Final input parameters](#final-input-parameters)
+ * [3.2 Diagnostics Plots](#diagnostics-plots)
+ * [3.3 Concentration-Time Profiles](#ct-profiles)
+ * [3.3.1 Model Building](#model-building)
+ * [3.3.2 Model Validation](#model-validation)
+ * [4 Conclusion](#conclusion)
+ * [5 References](#main-references)
+
+# 1 Introduction
+
+Cimetidine is a histamine H2 receptor antagonist that inhibits stomach acid production. It is mainly used as an antacid for the treatment of gastric and duodenal ulcers, Zollinger-Ellison syndrome and esophageal reflux.
+
+The herein presented model was developed and published by Hanke et al. ([Hanke 2020](#5-references)) and adjusted later on to PK-Sim V10 by refitting CYP3A4 Ki and MATE1 Ki.
+
+Cimetidine is mainly excreted unchanged via the kidneys (40–80% of the dose) with a high renal clearance of 400 ml/min. Metabolism is reported to account for 25– 40% of of the total elimination of cimetidine, with less than 2% of the dose excreted unchanged with the bile. Cimetidine inhibits several transporters and CYP enzymes and it is recommended by the FDA as strong inhibitor of OCT2/MATE and as weak inhibitor of CYP3A4 and CYP2D6 for the use in clinical DDI studies and drug labeling.
+
+The cimetidine model was established using 27 clinical studies, covering a dosing range from 100 to 800 mg. The final model applies active uptake of cimetidine into the liver by OCT1,
+uptake into the kidney by OAT3 and secretion from the kidney into the urine by MATE1, as well
+as an unspecific hepatic clearance and passive renal glomerular filtration.
+
+The herein presented model building and evaluation report evaluates the performance of the PBPK model for cimetidine in (healthy) adults.
+
+# 2 Methods
+
+## 2.1 Modeling Strategy
+
+The general concept of building a PBPK model has previously been described by e.g. Kuepfer et al. ([Kuepfer 2016](#5-references)). The relevant anthropometric (height, weight) and physiological information (e.g. blood flows, organ volumes, binding protein concentrations, hematocrit, cardiac output) in adults was gathered from the literature and has been previously published ([Willmann 2007](#5-references)). This information was incorporated into PK-Sim® and was used as default values for the simulations in adults. Variability of plasma proteins and CYP enzymes are integrated into PK-Sim® and described in the publicly available PK-Sim® Ontogeny Database Version 7.3 ([PK-Sim Ontogeny Database Version 7.3](#5-references)) or otherwise referenced for the specific process.
+
+The final model applies active uptake of cimetidine into the liver by OCT1, uptake into the kidney by OAT3 and secretion from the kidney into the urine by MATE1, as well
+as an unspecific hepatic clearance and passive renal glomerular filtration. The transporters were integrated into the PBPK model using the ([PK-Sim Ontogeny Database Version 7.3](#5-references)) and is described in detail in [Hanke 2020](#5-references). For PK-Sim V10, CYP3A4 Ki and MATE1 Ki were adjusted to improve the performance in CYP3A4 and MATE1 interaction scenarios. For further details, see [Section 2.3](#23-model-parameters-and-assumptions).
+
+First, a base PBPK model was built using clinical data including single and multiple dose studies with intravenous and oral applications of cimetidine to find an appropriate structure to describe the pharmacokinetics in plasma. This PBPK model was developed using a typical European individual adjusted to the demography of the respective study population.
+
+Oral administration of cimetidine in the fasted state frequently produces two plasma concentrations peaks. These double peaks are probably caused by the phasic gastrointestinal motility that controls gastric emptying in the fasted state. To describe the very different shapes of the observed mean cimetidine plasma profiles, split dose administration protocols for all studies of cimetidine administered orally in the fasted state were optimized in a NONMEM analysis (see [Hanke 2020](#5-references)). The resulting split dose administration protocols were then implemented and used for the PBPK modeling of the respective cimetidine studies.
+
+Unknown parameters (see below) were identified using the Parameter Identification module provided in PK-Sim®. Structural model selection was mainly guided by visual inspection of the resulting description of data and biological plausibility.
+
+Details about input data (physicochemical, *in vitro* and clinical) can be found in [Section 2.2](#22-data).
+
+Details about the structural model and its parameters can be found in [Section 2.3](#23-model-parameters-and-assumptions).
+
+## 2.2 Data
+
+### 2.2.1 In vitro / physico-chemical Data
+
+A literature search was performed to collect available information on physiochemical properties of cimetidine. The obtained information from literature is summarized in the table below.
+
+| **Parameter** | **Unit** | **Value** | Source | **Description** |
+| :-------------- | -------- | --------------- | ------------------------------------------------------------ | ----------------------------------------------- |
+| MW | g/mol | 252.34 | [Wishart 2006](#5-references) | Molecular weight |
+| pKa1 | 6.93 | (base) | [Avdeef 2001](#5-references) | Acid dissociation constant |
+| pKa2 | 13.38 | (acid) | [Wishart 2006](#5-references) | Acid dissociation constant |
+| Solubility (pH) | mg/L | 24.00 (6.8) | [Avdeef 2001](#5-references) | Water solubility |
+| logP | | 0.48 | [Avdeef 2001](#5-references) | Partition coefficient between octanol and water |
+| fu | % | 78.00 | [Taylor 1978](#5-references) | Fraction unbound in plasma |
+| B/P ratio | | 0.98 | [Somogyi 1983](#5-references) | Blood to plasma ratio |
+| OCT1 Km | μmol/l | 2600 | [Umehara 2007](#5-references) | Michaelis-Menten constant |
+| OAT3 Km | µmol/l | 149 | [Tahara 2005](#5-references) | Michaelis-Menten constant |
+| MATE1 Km | µmol/l | 8.0 | [Ohta 2005](#5-references) | Michaelis-Menten constant |
+| OCT1 Ki | µmol/l | 104 | [Ito 2012](#5-references) | Inhibition constant for competitive inhibition |
+| OCT2 Ki | µmol/l | 124 | [Ito 2012](#5-references) | Inhibition constant for competitive inhibition |
+| MATE1 Ki (refitted in PK-Sim V10) | µmol/l | 3.8 (0.65) | [Ito 2012](#5-references) | Inhibition constant for competitive inhibition |
+| CYP3A4 Ki (refitted in PK-Sim V10) | µmol/l | 268 (30.51266) | [Wrighton 1994](#5-references) | Inhibition constant for competitive inhibition |
+
+### 2.2.2 Clinical Data
+
+A literature search was performed to collect available clinical data on efavirenz in healthy adults.
+
+#### 2.2.2.1 Model Building
+
+The following studies were used for model building:
+
+| Publication | Arm / Treatment / Information used for model building |
+| :-------------------------------- | :----------------------------------------------------------- |
+| [Bodemar 1981](#5-references) | Peptic ulcer patients receiving a single intravenous dose of 200 mg and oral doses of 200, 400 and 800 mg |
+| [Morgan 1983](#5-references) | Peptic ulcer patients receiving a single intravenous dose of 200 mg (5 min infusion) |
+| [Bodemar 1979](#5-references) | Healthy subjects receiving single oral doses of 200 and 400mg (tablet) |
+| [Walkenstein 1978](#5-references) | Healthy subjects receiving a single oral dose of 300mg (solution) |
+| [D'Angio 1986](#5-references) | Healthy subjects receiving a single oral dose of 300mg (tablet) |
+
+#### 2.2.2.2 Model verification
+
+The following studies were used for model verification:
+
+| Publication | Arm / Treatment / Information used for model verification |
+| :-------------------------------- | :----------------------------------------------------------- |
+| [Grahnen 1979](#5-references) | Healthy subjects receiving a single intravenous dose of 100 mg and a single oral dose of 400 mg (tablet) |
+| [Larsson 1982](#5-references) | Peptic ulcer patients receiving a single intravenous dose of 200 mg |
+| [Mihaly 1984](#5-references) | Peptic ulcer patients receiving a single intravenous and a single oral dose of 200 mg |
+| [Morgan 1983](#5-references) | Peptic ulcer patients receiving a single intravenous dose of 200 mg (30 min infusion) |
+| [Lebert 1981](#5-references) | Healthy subjects receiving a single intravenous dose of 300 mg (2 min infusion) |
+| [Walkenstein 1978](#5-references) | Healthy subjects receiving a single intravenous dose of 300 mg (2 min infusion) and a single oral dose of 300 mg (tablet) |
+| [Kanto 1981](#5-references) | Healthy subjects receiving a single oral dose of 200 mg |
+| [Burland 1975](#5-references) | Healthy subjects receiving single oral doses of 200 mg solution and capsule |
+| [Bodemar 1979](#5-references) | Peptic ulcer patients receiving a single oral dose of 200 mg (tablet) |
+| [Bodemar 1981](#5-references) | Peptic ulcer patients receiving single oral doses of 800 mg and multiple oral doses of 200 and 400 mg |
+| [Barbhaiya 1995](#5-references) | Healthy subjects receiving multiple oral doses of 300 mg (tablet) |
+| [Somogyi 1981](#5-references) | Healthy subjects receiving a single oral dose of 400 mg (tablet) |
+| [Tiseo 1998](#5-references) | Healthy subjects receiving multiple oral doses of 800 mg (tablet) |
+
+#### 2.2.2.3 Model update due to PK-Sim V10 conversion
+
+As a consequence of updating the cimetidine PBPK model to PK-Sim version 10, the CYP3A4 Ki value needed to be readjusted. For this purpose, AUC ratios of the following clinical DDI studies were used to inform Ki in an additional parameter identification:
+
+| Publication | Interaction of cimetidine with: |
+| :------------------------------------- | :------------------------------|
+| [Kienlen 1993](#5-references) | Alfentanil |
+| [Abernethy 1983](#5-references) | Alprazolam and triazolam |
+| [Elliott 1984](#5-references) | Midazolam |
+| [Fee 1987](#5-references) | Midazolam |
+| [Greenblatt 1986](#5-references) | Intravenous and oral midazolam |
+| [Martinez 1999](#5-references) | Midazolam |
+| [Salonen 1986](#5-references) | Midazolam |
+| [Pourbaix 1985](#5-references) | Triazolam. NOTE: The interaction of cimetidine with alprazolam of this publication was not used for parameterization due to very long simulation duration! |
+| [Cox 1986](#5-references) | Triazolam |
+| [Friedman 1988](#5-references) | Triazolam |
+
+Similarly, MATE1 Ki value was adjusted to reproduce the observed inhibition effect on metformin PK (https://github.com/Open-Systems-Pharmacology/Cimetidine-Metformin-DDI).
+
+## 2.3 Model Parameters and Assumptions
+
+### 2.3.1 Absorption
+
+Absorption observed in clinical studies can be fully explained by passive absorption.
+
+### 2.3.2 Distribution
+
+Cimetidine is reported to be actively taken up into the liver by OCT1 ([Umehara 2007](#5-references)), into the kidney by OAT3 ([Tahara 2005](#5-references)) and secreted from the kidney into the urine by MATE1 ([Ohta 2010](#5-references)).
+
+After testing the available organ-plasma partition coefficient and cell permeability calculation methods built in PK-Sim, observed clinical data was best described by choosing the partition coefficient calculation method by `Rodgers and Rowland` and cellular permeability calculation by `PK-Sim Standard`.
+
+A `Lipophilicity` of 1.66 was back-calculated from the blood-to-plasma ratio of 0.98 ([Somogyi 1983](#5-references), [Hanke 2020](#5-references)).
+
+### 2.3.3 Metabolism, Elimination and Inhibition
+
+Cimetidine is mainly excreted unchanged via the kidneys. Additionally, 25 to 40 % is hepatically metabolized via an unknown pathway.
+
+Cimetidine inhibits several enzymes such as CYP3A4 and CYP2D6 as well as transporters such as OCT2, OCT2 and MATE.
+
+### 2.3.4 Automated Parameter Identification
+
+The parameter identification tool in PK-Sim has been used to estimate selected model parameters by adjusting to PK data of the clinical studies that were used in the model building process (see [Section 2.2](#22-data)).
+
+Specific intestinal permeability, unspecific hepatic clearance (CLhep) and Kcat values for OCT1, OAT3 and MATE1 were reestimated in PK-Sim Version 10, and, therefore, do not correspond to the original values published by [Hanke 2020](#5-references). The result of the final parameter identification is shown in the table below:
+
+| Model Parameter | Optimized Value | Unit |
+| -------------------------- | --------------- | ---- |
+| Specific intestinal permeability| 5.26E-06 | cm/min |
+| CLhep| 0.12| 1/min |
+| kcat OCT1| 14098.32 | 1/min |
+| kcat OAT3| 2522831.10 | 1/min |
+| kcat MATE1| 159.47 | 1/min |
+
+As a result of updating the cimetidine PBPK model to PK-Sim V10, the interaction parameter CYP3A4 Ki was fitted in a second step to improve the performance in CYP3A4 interactions. In detail, CYP3A4 Ki was adjusted such that the error of the simulated AUC ratios of cimetidine with several CYP3A4 substrates vs. corresponding observed AUC ratios of the clinical studies (see [Section 2.2.2.3](#2223-model-update-due-to-pk-sim-v10-conversion)) was minimized.
+
+| Model Parameter | Optimized Value | Unit |
+| -------------------------- | --------------- | ---- |
+| CYP3A4 Ki| 30.51266 | µmol/l |
+
+# 3 Results and Discussion
+
+The PBPK model for cimetidine was developed and evaluated using publicly available clinical pharmacokinetic data from studies listed in [Section 2.2.2](#222-clinical-data).
+
+The next sections show:
+
+1. the final model parameters for the building blocks: [Section 3.1](#31-final-input-parameters).
+2. the overall goodness of fit: [Section 3.2](#32-diagnostics-plots).
+3. simulated vs. observed concentration-time profiles for the clinical studies used for model building and for model verification: [Section 3.3](#33-concentration-time-profiles).
+
+## 3.1 Final input parameters
+
+The compound parameter values of the final PBPK model are illustrated below.
+
+### Compound: Cimetidine
+
+#### Parameters
+
+Name | Value | Value Origin | Alternative | Default
+------------------------------------------------ | ----------------------- | ------------------------------------------------------------------------------------------------------------------- | ----------- | -------
+Solubility at reference pH | 24 mg/ml | Publication-Avdeef 2001 | Measurement | True
+Reference pH | 6.8 | Publication-Avdeef 2001 | Measurement | True
+Lipophilicity | 1.655 Log Units | Parameter Identification | Measurement | True
+Fraction unbound (plasma, reference value) | 0.78 | Publication-Taylor 1978 | Measurement | True
+Specific intestinal permeability (transcellular) | 5.2554004942E-06 cm/min | Parameter Identification-Parameter Identification-Value updated from 'Parameter Identification' on 2021-09-13 17:00 | Fit | True
+Is small molecule | Yes | | |
+Molecular weight | 252.34 g/mol | Database-Drugbank | |
+Plasma protein binding partner | Unknown | | |
+
+#### Calculation methods
+
+Name | Value
+----------------------- | -------------------
+Partition coefficients | Rodgers and Rowland
+Cellular permeabilities | PK-Sim Standard
+
+#### Processes
+
+##### Systemic Process: Total Hepatic Clearance-Somogyi 1983
+
+Species: Human
+
+###### Parameters
+
+Name | Value | Value Origin
+----------------------------- | ------------------ | -------------------------------------------------------------------------------------------------------------------
+Fraction unbound (experiment) | 0.78 |
+Lipophilicity (experiment) | 1.655 Log Units |
+Plasma clearance | 0 ml/min/kg |
+Specific clearance | 0.1209722937 1/min | Parameter Identification-Parameter Identification-Value updated from 'Parameter Identification' on 2021-09-13 17:00
+
+##### Transport Protein: MATE1-Paper
+
+Molecule: MATE1
+
+###### Parameters
+
+Name | Value | Value Origin
+------------------------- | -------------------- | -------------------------------------------------------------------------------------------------------------------
+Transporter concentration | 1 µmol/l |
+Vmax | 0 µmol/l/min |
+Km | 8 µmol/l | Parameter Identification
+kcat | 159.4749627996 1/min | Parameter Identification-Parameter Identification-Value updated from 'Parameter Identification' on 2021-09-13 17:00
+
+##### Transport Protein: OAT3-Paper
+
+Molecule: OAT3
+
+###### Parameters
+
+Name | Value | Value Origin
+------------------------- | ------------------ | -------------------------------------------------------------------------------------------------------------------
+Transporter concentration | 1 µmol/l |
+Vmax | 0 µmol/l/min |
+Km | 149 µmol/l | Publication-Tahara 2005
+kcat | 2522831.1016 1/min | Parameter Identification-Parameter Identification-Value updated from 'Parameter Identification' on 2021-09-13 17:00
+
+##### Transport Protein: OCT1-Paper
+
+Molecule: OCT1
+
+###### Parameters
+
+Name | Value | Value Origin
+------------------------- | ---------------------- | -------------------------------------------------------------------------------------------------------------------
+Transporter concentration | 1 µmol/l |
+Vmax | 0 µmol/l/min |
+Km | 2600 µmol/l | Publication-Umehara 2007
+kcat | 14098.3224931732 1/min | Parameter Identification-Parameter Identification-Value updated from 'Parameter Identification' on 2021-09-13 17:00
+
+##### Systemic Process: Glomerular Filtration-GFR
+
+Species: Human
+
+###### Parameters
+
+Name | Value | Value Origin
+------------ | -----:| ------------:
+GFR fraction | 1 |
+
+##### Inhibition: OCT1-Ito 2012
+
+Molecule: OCT1
+
+###### Parameters
+
+Name | Value | Value Origin
+---- | ---------- | --------------------
+Ki | 104 µmol/l | Publication-Ito 2012
+
+##### Inhibition: OCT2-Ito 2012
+
+Molecule: OCT2
+
+###### Parameters
+
+Name | Value | Value Origin
+---- | ---------- | --------------------
+Ki | 124 µmol/l | Publication-Ito 2012
+
+##### Inhibition: MATE1-Ito 2012
+
+Molecule: MATE1
+
+###### Parameters
+
+Name | Value | Value Origin
+---- | ----------- | -----------------------------------------------------------------------------------------------------------------------
+Ki | 0.65 µmol/l | Parameter Identification-Parameter Identification-https://github.com/Open-Systems-Pharmacology/Cimetidine-Metformin-DDI
+
+##### Inhibition: CYP3A4-Wrighton 1994
+
+Molecule: CYP3A4
+
+###### Parameters
+
+Name | Value | Value Origin
+---- | --------------- | ----------------------------------------------------------------------------------------------------------------------------
+Ki | 30.51266 µmol/l | Parameter Identification-Parameter Identification-Value adjusted in parameter identification outside of PK-Sim on 2023-11-14
+
+### Formulation: Tablet
+
+Type: Weibull
+
+#### Parameters
+
+Name | Value | Value Origin
+-------------------------------- | ----- | ------------:
+Dissolution time (50% dissolved) | 1 min |
+Lag time | 0 h |
+Dissolution shape | 10 |
+Use as suspension | Yes |
+
+## 3.2 Diagnostics Plots
+
+Below you find the goodness-of-fit visual diagnostic plots for the PBPK model performance of all data used presented in [Section 2.2.2](#222-clinical-data).
+
+The first plot shows simulated versus observed plasma concentration, the second weighted residuals versus time.
+
+
+
+**Table 3-1: GMFE for Goodness of fit plot for concentration in plasma**
+
+|Group |GMFE |
+|:----------------------------|:----|
+|iv administration |1.36 |
+|multiple oral administration |1.50 |
+|single oral administration |1.51 |
+|All |1.47 |
+
+
+
+
+
+
+
+
+**Figure 3-1: Goodness of fit plot for concentration in plasma**
+
+
+
+
+
+
+
+
+**Figure 3-2: Goodness of fit plot for concentration in plasma**
+
+
+
+
+## 3.3 Concentration-Time Profiles
+
+Simulated versus observed concentration-time profiles of all data listed in [Section 2.2.2](#222-clinical-data) are presented below.
+
+### 3.3.1 Model Building
+
+
+
+
+
+**Figure 3-3: iv 200 mg (5 min),Morgan 1983, n=6**
+
+
+
+
+
+
+
+
+**Figure 3-4: iv 200 mg, Bodemar 1981, n=10**
+
+
+
+
+
+
+
+
+**Figure 3-5: iv 200 mg, Bodemar 1981, n=10, urine**
+
+
+
+
+
+
+
+
+**Figure 3-6: po 200 mg, Bodemar 1981, n=10**
+
+
+
+
+
+
+
+
+**Figure 3-7: po 200 mg, Bodemar 1981, n=10, urine**
+
+
+
+
+
+
+
+
+**Figure 3-8: po 300 mg (sol), Walkenstein 1978, n=24**
+
+
+
+
+
+
+
+
+**Figure 3-9: po 300 mg (sol), Walkenstein 1978, n=24, urine**
+
+
+
+
+
+
+
+
+**Figure 3-10: po 300 mg (tab), D'Angio 1986, n=6**
+
+
+
+
+
+
+
+
+**Figure 3-11: po 400 mg, Bodemar 1981, n=9**
+
+
+
+
+
+
+
+
+**Figure 3-12: po 400 mg, Bodemar 1981, n=9, urine**
+
+
+
+
+
+
+
+
+**Figure 3-13: po 800 mg, Bodemar 1981, n=9**
+
+
+
+
+
+
+
+
+**Figure 3-14: po 800 mg, Bodemar 1981, n=9, urine**
+
+
+
+
+### 3.3.2 Model Validation
+
+
+
+
+
+**Figure 3-15: iv 100 mg (5 min), Grahnen 1979, n=3**
+
+
+
+
+
+
+
+
+**Figure 3-16: iv 100 mg (5 min), Grahnen 1979, n=3, urine**
+
+
+
+
+
+
+
+
+**Figure 3-17: iv 200 mg (30 min),Morgan 1983, n=4**
+
+
+
+
+
+
+
+
+**Figure 3-18: iv 200 mg, Larsson 1982, n=9**
+
+
+
+
+
+
+
+
+**Figure 3-19: iv 200 mg, Larsson 1982, n=9, urine**
+
+
+
+
+
+
+
+
+**Figure 3-20: iv 200 mg, Mihaly 1984, n=6**
+
+
+
+
+
+
+
+
+**Figure 3-21: iv 300 mg (2 min), Lebert 1981, n=1**
+
+
+
+
+
+
+
+
+**Figure 3-22: iv 300 mg (2 min), Walkenstein 1978, n=12**
+
+
+
+
+
+
+
+
+**Figure 3-23: iv 300 mg (2 min), Walkenstein 1978, n=12**
+
+
+
+
+
+
+
+
+**Figure 3-24: po 200 mg (tab), Bodemar 1979 (fasted)**
+
+
+
+
+
+
+
+
+**Figure 3-25: po 200 mg (tab), Bodemar 1979 (fed)**
+
+
+
+
+
+
+
+
+**Figure 3-26: po 200 mg, Burland 1975, caps**
+
+
+
+
+
+
+
+
+**Figure 3-27: po 200 mg, Burland 1975, sol**
+
+
+
+
+
+
+
+
+**Figure 3-28: po 200 mg, Kanto 1981, n=8**
+
+
+
+
+
+
+
+
+**Figure 3-29: po 200 mg, Mihaly 1984, n=8**
+
+
+
+
+
+
+
+
+**Figure 3-30: po 200/400 mg QID, Bodemar 1981 (fasted)**
+
+
+
+
+
+
+
+
+**Figure 3-31: po 300 mg (tabl), Walkenstein 1978, n=12**
+
+
+
+
+
+
+
+
+**Figure 3-32: po 300 mg QID (sol), Barbhaiya 1995, n=18**
+
+
+
+
+
+
+
+
+**Figure 3-33: po 400 mg (tab), Bodemar 1979, n=10**
+
+
+
+
+
+
+
+
+**Figure 3-34: po 400 mg (tab), Somogyi 1981, n=8**
+
+
+
+
+
+
+
+
+**Figure 3-35: po 400 mg (tab),Grahnen 1979, n=3**
+
+
+
+
+
+
+
+
+**Figure 3-36: po 400 mg (tab),Grahnen 1979, n=3, urine**
+
+
+
+
+
+
+
+
+**Figure 3-37: po 800 mg (tab) qd, Tiseo 1998, n=18**
+
+
+
+
+# 4 Conclusion
+
+The herein presented PBPK model adequately describes the pharmacokinetics of cimetidine after intravenous and oral administration of single and multiple doses to healthy adults and peptic ulcer patients covering a broad dosing range from 100 to 800 mg. The established cimetidine PBPK model is verified for the use as a mild inhibitor of CYP3A4 drug in drug-drug interaction simulations.
+
+# 5 References
+
+**Abernethy 1983** Abernethy DR, Greenblatt DJ, Divoll M, Moschitto LJ, Harmatz JS, Shader RI. Interaction of cimetidine with the triazolobenzodiazepines alprazolam and triazolam. Psychopharmacology (Berl). 1983;80(3):275-8. doi: 10.1007/BF00436169.
+
+**Avdeef 2001** Avdeef A, Berger CM. pH-metric solubility. 3. Dissolution titration template method for solubility determination. Eur J Pharm Sci. 2001 Dec;14(4):281-91. doi: 10.1016/s0928-0987(01)00190-7. PMID: 11684402.
+
+**Barbhaiya 1995** Barbhaiya RH, Shukla UA, Greene DS. Lack of interaction between nefazodone and cimetidine: a steady state pharmacokinetic study in humans. Br J Clin Pharmacol. 1995 Aug;40(2):161-5. doi: 10.1111/j.1365-2125.1995.tb05771.x. PMID: 8562300; PMCID: PMC1365177.
+
+**Bodemar 1979** Bodemar G, Norlander B, Fransson L, Walan A. The absorption of cimetidine before and during maintenance treatment with cimetidine and the influence of a meal on the absorption of cimetidine--studies in patients with peptic ulcer disease. Br J Clin Pharmacol. 1979 Jan;7(1):23-31. doi: 10.1111/j.1365-2125.1979.tb00892.x. PMID: 760739; PMCID: PMC1429608.
+
+**Bodemar 1981** Bodemar G, Norlander B, Walan A. Pharmacokinetics of cimetidine after single doses and during continuous treatment. Clin Pharmacokinet. 1981 Jul-Aug;6(4):306-15. doi: 10.2165/00003088-198106040-00005. PMID: 7249489.
+
+**Burland 1975** Burland WL, Duncan WA, Hesselbo T, Mills JG, Sharpe PC, Haggie SJ, Wyllie JH. Pharmacological evaluation of cimetidine, a new histamine H2-receptor antagonist, in healthy man. Br J Clin Pharmacol. 1975 Dec;2(6):481-6. doi: 10.1111/j.1365-2125.1975.tb00564.x. PMID: 9952; PMCID: PMC1402643.
+
+**Cox 1986** Cox SR, Kroboth PD, Anderson PH, Smith RB. Mechanism for the interaction between triazolam and cimetidine. Biopharm Drug Dispos. 1986 Nov-Dec;7(6):567-75.
+
+**D'Angio 1986** D'Angio R, Mayersohn M, Conrad KA, Bliss M. Cimetidine absorption in humans during sucralfate coadministration. Br J Clin Pharmacol. 1986 May;21(5):515-20. doi: 10.1111/j.1365-2125.1986.tb02834.x. PMID: 3755052; PMCID: PMC1401033.
+
+**Elliott 1984** Elliott P, Dundee JW, Elwood RJ, Collier PS. The influence of H2 receptor antagonists on the plasma concentrations of midazolam and temazepam. Eur J Anaesthesiol. 1984 Sep;1(3):245-51.
+
+**Fee 1987** Fee JP, Collier PS, Howard PJ, Dundee JW. Cimetidine and ranitidine increase midazolam bioavailability. Clin Pharmacol Ther. 1987 Jan;41(1):80-4. doi: 10.1038/clpt.1987.13. PMID: 3802710.
+
+**Friedman 1988** Friedman H, Greenblatt DJ, Burstein ES, Scavone JM, Harmatz JS, Shader RI. Triazolam kinetics: interaction with cimetidine, propranolol, and the combination. J Clin Pharmacol. 1988 Mar;28(3):228-33.
+
+**Grahnen 1979** Grahnén A, von Bahr C, Lindström B, Rosén A. Bioavailability and pharmacokinetics of cimetidine. Eur J Clin Pharmacol. 1979 Nov;16(5):335-40. doi: 10.1007/BF00605632. PMID: 520401.
+
+**Greenblatt 1986** Greenblatt DJ, Locniskar A, Scavone JM, Blyden GT, Ochs HR, Harmatz JS, Shader RI. Absence of interaction of cimetidine and ranitidine with intravenous and oral midazolam. Anesth Analg. 1986 Feb;65(2):176-80. PMID: 2935051.
+
+**Hanke 2020** Hanke N, Türk D, Selzer D, Ishiguro N, Ebner T, Wiebe S, Müller F, Stopfer P, Nock V, Lehr T. A Comprehensive Whole-Body Physiologically Based Pharmacokinetic Drug-Drug-Gene Interaction Model of Metformin and Cimetidine in Healthy Adults and Renally Impaired Individuals. Clin Pharmacokinet. 2020 Nov;59(11):1419-1431. doi: 10.1007/s40262-020-00896-w. PMID: 32449077; PMCID: PMC7658088.
+
+**Ito 2012** Ito S, Kusuhara H, Yokochi M, Toyoshima J, Inoue K, Yuasa H, Sugiyama Y. Competitive inhibition of the luminal efflux by multidrug and toxin extrusions, but not basolateral uptake by organic cation transporter 2, is the likely mechanism underlying the pharmacokinetic drug-drug interactions caused by cimetidine in the kidney. J Pharmacol Exp Ther. 2012 Feb;340(2):393-403. doi: 10.1124/jpet.111.184986. Epub 2011 Nov 9. PMID: 22072731.
+
+**Kanto 1981** Kanto J, Allonen H, Jalonen H, Mäntylä R. The effect of metoclopramide and propantheline on the gastrointestinal absorption of cimetidine. Br J Clin Pharmacol. 1981 Jun;11(6):629-31. doi: 10.1111/j.1365-2125.1981.tb01184.x. PMID: 7272182; PMCID: PMC1402204.
+
+**Kuepfer 2016** Kuepfer L, Niederalt C, Wendl T, Schlender JF, Willmann S, Lippert J, Block M, Eissing T, Teutonico D. Applied Concepts in PBPK Modeling: How to Build a PBPK/PD Model.CPT Pharmacometrics Syst Pharmacol. 2016 Oct;5(10):516-531.
+
+**Kienlen 1993** Kienlen, J., Levron, JC., Aubas, S. *et al.* Pharmacokinetics of Alfentanil in Patients Treated with Either Cimetidine or Ranitidine. Drug Invest **6,** 257–262 (1993).
+
+**Larsson 1982** Larsson R, Erlanson P, Bodemar G, Walan A, Bertler A, Fransson L, Norlander B. The pharmacokinetics of cimetidine and its sulphoxide metabolite in patients with normal and impaired renal function. Br J Clin Pharmacol. 1982 Feb;13(2):163-70. doi: 10.1111/j.1365-2125.1982.tb01351.x. PMID: 7059413; PMCID: PMC1402003.
+
+**Lebert 1981** Lebert PA, Mahon WA, MacLeod SM, Soldin SJ, Fenje P, Vandenberghe HM. Ranitidine kinetics and dynamics. II. Intravenous dose studies and comparison with cimetidine. Clin Pharmacol Ther. 1981 Oct;30(4):545-50. doi: 10.1038/clpt.1981.201. PMID: 6269789.
+
+**Martinez 1999** Martínez C, Albet C, Agúndez JA, Herrero E, Carrillo JA, Márquez M, Benítez J, Ortiz JA. Comparative in vitro and in vivo inhibition of cytochrome P450 CYP1A2, CYP2D6, and CYP3A by H2-receptor antagonists. Clin Pharmacol Ther. 1999 Apr;65(4):369-76. doi: 10.1016/S0009-9236(99)70129-3. PMID: 10223772.
+
+**Meyer 2012** Meyer M, Schneckener S, Ludewig B, Kuepfer L, Lippert J. Using expression data for quantification of active processes in physiologically based pharmacokinetic modeling. Drug Metab Dispos. 2012 May;40(5):892-901.
+
+**Mihaly 1984** Mihaly GW, Jones DB, Anderson JA, Smallwood RA, Louis WJ. Pharmacokinetic studies of cimetidine and ranitidine before and after treatment in peptic ulcer patients. Br J Clin Pharmacol. 1984 Jan;17(1):109-11. doi: 10.1111/j.1365-2125.1984.tb05010.x. PMID: 6318788; PMCID: PMC1463299.
+
+**Morgan 1983** Morgan DJ, Uccellini DA, Raymond K, Mihaly GW, Jones DB, Smallwood RA. The influence of duration of intravenous infusion of an acute dose on plasma concentrations of cimetidine. Eur J Clin Pharmacol. 1983;25(1):29-34. doi: 10.1007/BF00544010. PMID: 6617722.
+
+**Nishimura 2013** Nishimura M, Yaguti H, Yoshitsugu H, Naito S, Satoh T. Tissue distribution of mRNA expression of human cytochrome P450 isoforms assessed by high-sensitivity real-time reverse transcription PCR. Yakugaku Zasshi. 2003 May;123(5):369-75.
+
+**Ohta 2005** Ohta KY, Inoue K, Yasujima T, Ishimaru M, Yuasa H. Functional characteristics of two human MATE transporters: kinetics of cimetidine transport and profiles of inhibition by various compounds. J Pharm Pharm Sci. 2009;12(3):388-96. doi: 10.18433/j3r59x. PMID: 20067714.
+
+**PK-Sim Ontogeny Database Version 7.3** (https://github.com/Open-Systems-Pharmacology/OSPSuite.Documentation/blob/38cf71b384cfc25cfa0ce4d2f3addfd32757e13b/PK-Sim%20Ontogeny%20Database%20Version%207.3.pdf)
+
+**Pourbaix 1985** Pourbaix S, Desager JP, Hulhoven R, Smith RB, Harvengt C. Pharmacokinetic consequences of long term coadministration of cimetidine and triazolobenzodiazepines, alprazolam and triazolam, in healthy subjects. Int J Clin Pharmacol Ther Toxicol. 1985 Aug;23(8):447-51.
+
+**Salonen 1986** Salonen M, Aantaa E, Aaltonen L, Kanto J. Importance of the interaction of midazolam and cimetidine. Acta Pharmacol Toxicol (Copenh). 1986 Feb;58(2):91-5. doi: 10.1111/j.1600-0773.1986.tb00076.x. PMID: 2939688.
+
+**Smith 1984** Smith MS, Benyunes MC, Bjornsson TD, Shand DG, Pritchett EL. Influence of cimetidine on verapamil kinetics and dynamics. Clin Pharmacol Ther. 1984 Oct;36(4):551-4. doi: 10.1038/clpt.1984.218. PMID: 6478741.
+
+**Somogyi 1981** Somogyi A, Thielscher S, Gugler R. Influence of phenobarbital treatment on cimetidine kinetics. Eur J Clin Pharmacol. 1981;19(5):343-7. doi: 10.1007/BF00544584. PMID: 7238562.
+
+**Somogyi 1983** Somogyi A, Gugler R. Clinical pharmacokinetics of cimetidine. Clin Pharmacokinet. 1983 Nov-Dec;8(6):463-95. doi: 10.2165/00003088-198308060-00001. PMID: 6418428.
+
+**Tahara 2005** Tahara H, Kusuhara H, Endou H, Koepsell H, Imaoka T, Fuse E, Sugiyama Y. A species difference in the transport activities of H2 receptor antagonists by rat and human renal organic anion and cation transporters. J Pharmacol Exp Ther. 2005 Oct;315(1):337-45. doi: 10.1124/jpet.105.088104. Epub 2005 Jul 8. PMID: 16006492.
+
+**Taylor 1978** Taylor DC, Cresswell PR, Bartlett DC. The metabolism and elimination of cimetidine, a histamine H2-receptor antagonist, in the rat, dog, and man. Drug Metab Dispos. 1978 Jan-Feb;6(1):21-30. PMID: 23270.
+
+**Tiseo 1998** Tiseo PJ, Perdomo CA, Friedhoff LT. Concurrent administration of donepezil HCl and cimetidine: assessment of pharmacokinetic changes following single and multiple doses. Br J Clin Pharmacol. 1998 Nov;46 Suppl 1(Suppl 1):25-9. doi: 10.1046/j.1365-2125.1998.0460s1025.x. PMID: 9839762; PMCID: PMC1873814.
+
+**Umehara 2007** Umehara KI, Iwatsubo T, Noguchi K, Kamimura H. Functional involvement of organic cation transporter1 (OCT1/Oct1) in the hepatic uptake of organic cations in humans and rats. Xenobiotica. 2007 Aug;37(8):818-31. doi: 10.1080/00498250701546012. PMID: 17701831.
+
+**Walkenstein 1978** Walkenstein SS, Dubb JW, Randolph WC, Westlake WJ, Stote RM, Intoccia AP. Bioavailability of cimetidine in man. Gastroenterology. 1978 Feb;74(2 Pt 2):360-5. PMID: 620910.
+
+**Willmann 2007** Willmann S, Höhn K, Edginton A, Sevestre M, Solodenko J, Weiss W, Lippert J, Schmitt W. Development of a physiology-based whole-body population model for assessing the influence of individual variability on the pharmacokinetics of drugs. J Pharmacokinet Pharmacodyn. 2007, 34(3): 401-431.
+
+**Wishart 2006** Wishart DS, Knox C, Guo AC, Shrivastava S, Hassanali M, Stothard P, Chang Z, Woolsey J. DrugBank: a comprehensive resource for in silico drug discovery and exploration. Nucleic Acids Res. 2006 Jan 1;34(Database issue):D668-72. doi: 10.1093/nar/gkj067. PMID: 16381955; PMCID: PMC1347430.
+
+**Wrighton 1994** Wrighton SA, Ring BJ. Inhibition of human CYP3A catalyzed 1'-hydroxy midazolam formation by ketoconazole, nifedipine, erythromycin, cimetidine, and nizatidine. Pharm Res. 1994 Jun;11(6):921-4. doi: 10.1023/a:1018906614320. PMID: 7937537.
+
+","Markdown"
+"PBPK model","Open-Systems-Pharmacology/OSP-PBPK-Model-Library","Propofol/Propofol_evaluation_report.md",".md","28579","472","# Building and evaluation of a PBPK model for propofol in adults
+
+| Version | 1.0-OSP12.2 |
+| ----------------------------------------------- | ------------------------------------------------------------ |
+| based on *Model Snapshot* and *Evaluation Plan* | [https://github.com/Open-Systems-Pharmacology/Propofol-Model/releases/tag/v1.0](https://github.com/Open-Systems-Pharmacology/Propofol-Model/releases/tag/v1.0) |
+| OSP Version | 12.2 |
+| Qualification Framework Version | 3.5 |
+
+This evaluation report and the corresponding PK-Sim project file are filed at:
+
+[https://github.com/Open-Systems-Pharmacology/OSP-PBPK-Model-Library/](https://github.com/Open-Systems-Pharmacology/OSP-PBPK-Model-Library/)
+
+# Table of Contents
+
+ * [1 Introduction](#introduction)
+ * [2 Methods](#methods)
+ * [2.1 Modeling strategy](#modeling-strategy)
+ * [2.2 Data used](#data)
+ * [2.3 Model parameters and assumptions](#model-parameters-and-assumptions)
+ * [3 Results and Discussion](#results-and-discussion)
+ * [3.1 Propofol final input parameters](#final-input-parameters)
+ * [3.2 Propofol Diagnostics Plots](#diagnostics-plots)
+ * [3.3 Propofol Concentration-Time profiles](#ct-profiles-model-building)
+ * [4 Conclusion](#conclusion)
+ * [5 References](#references)
+
+# 1 Introduction
+
+The presented model building and evaluation report evaluates the performance of a PBPK model for propofol in adults.
+
+Propofol is an anaesthetic agent used for induction and maintenance of general anaesthesia. Propofol is only given intravenously and is mainly metabolized by Uridine 5'-diphospho-glucuronosyltransferase 1A9 (UGT1A9) (53-70%) [(Al-Jahdari 2006, Restrepo 2009](#5-references)). The final propofol model features metabolism by UGT1A9 and to a minor extent by Cytochrome P450 2B6 (CYP2B6) [(Al-Jahdari 2006, Oda 2009](#5-references)). Additionally, there is excretion via glomerular filtration. The model adequately describes the pharmacokinetics of propofol in adults.
+
+The propofol model is a whole-body PBPK model, allowing for dynamic translation between individuals with organs expressing UGT1A9. The propofol report demonstrates the level of confidence in the propofol PBPK model build with the OSP suite with regard to reliable predictions of propofol PK adults during model-informed drug development.
+
+# 2 Methods
+
+## 2.1 Modeling strategy
+
+The general concept of building a PBPK model has previously been described by Kuepfer et al. ([Kuepfer 2016](#5-references)). Relevant information on anthropometric (height, weight) and physiological parameters (e.g. blood flows, organ volumes, binding protein concentrations, hematocrit, cardiac output) in adults was gathered from the literature and has been previously published ([Schlender 2016](#5-references)). The information was incorporated into PK-Sim® and was used as default values for the simulations in adults.
+
+The applied activity and variability of plasma proteins and active processes that are integrated into PK-Sim® are described in the publicly available PK-Sim® Ontogeny Database Version 7.3 ([PK-Sim Ontogeny Database Version 7.3](#5-references)) or otherwise referenced for the specific process.
+
+First, a base mean model was built using data from the single dose escalation study to find an appropriate structure describing the PK of propofol. The mean PK model was developed using a typical European individual. Unknown parameters were identified using the Parameter Identification module provided in PK-Sim®. Structural model selection was mainly guided by visual inspection of the resulting description of data and biological plausibility.
+
+A final PBPK model was established and simulations were compared to the reported data to evaluate model appropriateness and to assess model qualification, by means of diagnostics plots and predicted versus observed concentration-time profiles, of which the results support an adequate prediction of the PK in adults.
+
+During model building, uncertainties in data quality, as well as study differences may cause not being able to adequately describe the PK of all reported clinical studies.
+
+## 2.2 Data used
+
+### 2.2.1 In vitro / physicochemical data
+
+A literature search was performed to collect available information on physicochemical properties of propofol. The obtained information from literature is summarized in the table below, and is used for model building.
+
+| **Parameter** | **Unit** | **Literature value (reference)** | **Description** |
+| :-------------- | ----------- | --------------------------------------- | ----------------------------------------------- |
+| MW | g/mol | 178.2707 ([Drugbank.ca](#5-references)) | Molecular weight |
+| pKa | | 10.1 ([Drugbank.ca](#5-references)) | Acid dissociation constant |
+| Solubility (pH) | mg/L | 124 (7) ([Drugbank.ca](#5-references)) | Solubility |
+| logP (pH 7) | | 0.58 ([Moss 2012](#5-references)) | Partition coefficient between octanol and water |
+| fu | | 0.024 ([Takizawa 2005](#5-references)) | Fraction unbound |
+| Km,u UGT1A9 | mM | 0.12 ([Al-Jahdari 2006](#5-references)) | Unbound Michaelis-Menten constant |
+| Vmax UGT1A9 | nmol/min/mg | 2.40 ([Al-Jahdari 2006](#5-references)) | Maximum rate of reaction |
+| Km,u CYP2B7 | mM | 0.03 ([Al-Jahdari 2006](#5-references)) | Unbound Michaelis-Menten constant |
+| Vmax CYP2B7 | nmol/min/mg | 1.08 ([Al-Jahdari 2006](#5-references)) | Maximum rate of reaction |
+
+### 2.2.2 Clinical data
+
+A literature search was performed to collect available clinical data on propofol in adults.
+
+The following publications were used in adults for model building and evaluation, of which individual patient data was available for download under [http://opentci.org/data/propofol](http://opentci.org/data/propofol):
+
+| Publication | Study description |
+| :-------------------------------- | :----------------------------------------------------------- |
+| [Gepts 1987](#5-references) | Disposition of propofol administered as constant rate intravenous infusion in humans |
+| [Schnider 1998](#5-references) | Influence of administration rate on propofol plasma-effect site equilibrium |
+| [Struys 2007](#5-references) | The influence of method of administration and covariates on the pharmacokinetics of propofol in adult volunteers |
+
+## 2.3 Model parameters and assumptions
+
+### 2.3.1 Absorption
+
+Propofol is only administered intravenously.
+
+### 2.3.2 Distribution
+
+Takizawa et al. ([Takizawa 2005](#5-references)) published a fu in humans to be 0.024. Mazoit et al. ([Mazoit 1999](#5-references)) reported that propofol binds to almost exclusively to serum albumin as plasma protein, which is built-in as such in the PBPK model.
+
+After testing the available organ-plasma partition coefficient and cell permeability calculation methods built in PK-Sim, observed clinical data was best described by choosing the partition coefficient calculation and cell permeability calculation by PK-Sim standard. Specific organ permeability normalized to surface area was automatically calculated by PK-Sim.
+
+### 2.3.3 Metabolism and Elimination
+
+Propofol undergoes fast biotransformation to different metabolites.([Restrepo 2009](#5-references)) *In vitro* studies show that particularly the UGT1A9 ([Court 2005](#5-references)) is involved, followed by CYP2B6 ([Oda 2001](#5-references)).
+
+Al-Jahdari et al. ([Al-Jahdari 2006](#5-references)) investigated the contribution of the liver and kidneys to propofol metabolism in humans using an *in vitro–in vivo* scale up approach. Human kidney and liver microsomal incubations confirmed the dominant role of UGT metabolism for propofol. Propofol was in particular metabolized by UGT1A9 and CYP2B6. The apparent arithmetic mean unbound Km (Km,u) values in the liver for the glucuronidation and hydroxylation of propofol by UGT1A9 and CYP2B6 were 0.12 (standard deviation (SD): 0.072) and 0.0072 (SD: 0.0) mM, respectively. ([Al-Jahdari 2006](#5-references)) The corresponding Vmax values (nmol/min/mg protein) were 2.40 (SD: 0.2) for UGT1A9, and 1.08 (SD: 0.1) for CYP2B6. In the kidney the Km,u and Vmax values for glucuronidation by UGT1A9 were 0.38 (SD: 0.19) mM, and 7.97 (4.5) (nmol/min/mg protein), respectively. The Km,u and Vmax for hydroxylation were reported to be negligible. ([Al-Jahdari 2006](#5-references))
+
+The abundance of proteins in different organs in PK-Sim is calculated from relative expression values. For each organ, the relative expression defines the concentration of the protein in whole organ as a fraction of a defined reference concentration value. The relative gene expressions for both UGT1A9 and CYP2B6 are derived from reported Reverse Transcription-Polymerase Chain Reaction (RT-PCR) values by Nishimura et al. ([Nishimura 2006 ](#5-references))
+
+The reported 25.9 pmol/mg UGT1A9 protein expression level in human liver microsomes (HLM) by Ohtsuki et al. ([Ohtsuki 2012](#5-references)) was used to calculate the reference concentration imputed in PK-Sim. The relationship between age and human microsomal protein (MPPGL) observed by Barter et al. ([Barter 2007](#5-references)) is estimated 40 mg/g for a 30 year old individual. As the expression of UGT1A9 is highest in the kidney and relatively 10% in the liver ([Nishimura 2006 ](#5-references)), this resulted in a reference concentration of 10.36 µmol/L liver tissue for UGT1A9 which is imputed in PK-Sim. For CYP2B6, which is mainly expressed in the liver, the reported CYP2B6 protein expression level of 1.56 µmol /L liver tissue by Rodriguez et al. ([Rodrigues 1999](#5-references)) is imputed as reference concentration in PK-Sim. The reported expression level in HLM for CYP2B6 is 39 pmol/mg microsomal protein. ([Rodrigues 1999](#5-references))
+
+For the estimation of propofol clearance in PK-Sim, Kcat is estimated, which is Vmax/protein expression level in HLM.
+
+Although UGT1A9 expression is highest in the kidney ([Nishimura 2006 ](#5-references)), as no measurement results were available for CYP2B6 mediated hydroxylation in the kidney, the reported liver *in vitro* Km,u and Vmax values for UGT1A9 and CYP2B6 were included in the model. Reported Vmax values were in units nmol/min/mg protein and thus not directly transferable into the PBPK model. Therefore, a joint scaling factor factivity on the *in vitro* Kcat values was estimated to match observed *in vivo* data, and keeping the relative relationship between those *in vitro* values (0.89 and 0.53 nmol/min/mg) for UGT1A1 and CYP2B6 fixed according to:
+
+Kcat, UGT1A9 = factivity * Kcat, in-vitro, UGT1A9
+
+Kcat, CYP2B6 = factivity * Kcat, in-vitro, CYP2B6
+
+It is especially important to fix the relative contribution of both enzymes as a ratio to ensure that, when translating to other populations (e.g. children where both enzymes may undergo a different ontogeny pattern, or patients who have differently reduced amounts of UGT1A1 vs CYP2B6) the relative contributions can be adequately scaled.
+Note that the estimated scaling factor factivity will be directly implemented into the final *in vivo* Vmax values (only Kcat, UGT1A9 and Kcat, CYP2B6 will be reported in [section 3](#3-results-and-discussion)).
+
+Finally, as ~0.3% of the dose is excreted in human urine as unchanged parent compound, GFR is introduced in the propofol PBPK model.
+
+# 3 Results and Discussion
+
+The PBPK model propofol was developed with clinical pharmacokinetic data after intravenous administration covering a dose range of 1-36mg/kg, including bolus infusion as well as continuous infusion clinical data.
+
+During the model-fitting, the following parameters were estimated (all other parameters were fixed to reported values):
+
+* Kcat (as unique scaling factor factivity , as described in [section 2.3.3](#233-metabolism-and-elimination))
+* Lipophilicity
+
+The mean model fit resulted in an adequate description of all data, that showed to be highly variable. The reported 2.5 mg/kg bolus infusion data by Struys et al. ([Struys 2007](#5-references)) only contained concentration time profiles over 5 minutes. Nevertheless this data was included in the analysis, and showed overprediction in the first 2 minutes by the model, compared to available 1 mg/kg and 2 mg/kg data reported by Schnider et al. ([Schnider 1998](#5-references)) that was well described by the propofol PBPK model. This discrepancy in propofol distribution was assumed to be inter-study variability related.
+
+Overall, the model results show that the PBPK model of propofol adequately described the data for all available doses.
+
+## 3.1 Propofol final input parameters
+
+The compound parameter values of the final propofol PBPK model are illustrated below.
+
+### Compound: Propofol
+
+#### Parameters
+
+Name | Value | Value Origin | Alternative | Default
+------------------------------------------ | ---------------------- | --------------------------------------------------------------- | ----------- | -------
+Solubility at reference pH | 124 mg/l | Internet-https://www.drugbank.ca/drugs/DB00818, in water @ 25°C | Measurement | True
+Reference pH | 7 | Internet-https://www.drugbank.ca/drugs/DB00818, in water @ 25°C | Measurement | True
+Lipophilicity | 3.5486243812 Log Units | Parameter Identification-Parameter Identification | Fit | True
+Fraction unbound (plasma, reference value) | 0.024 | Publication-Drugbank.ca | Measurement | True
+Is small molecule | Yes | | |
+Molecular weight | 178.2707 g/mol | Internet-Drugbank.ca | |
+Plasma protein binding partner | Albumin | | |
+
+#### Calculation methods
+
+Name | Value
+----------------------- | ---------------
+Partition coefficients | PK-Sim Standard
+Cellular permeabilities | PK-Sim Standard
+
+#### Processes
+
+##### Systemic Process: Glomerular Filtration-GFR
+
+Species: Human
+
+###### Parameters
+
+Name | Value | Value Origin
+------------ | -----:| ---------------------------
+GFR fraction | 1 | Publication-Al-Jahdari 2005
+
+##### Metabolizing Enzyme: UGT1A9-Al-Jahdari 2006 Liver
+
+Molecule: UGT1A9
+
+Metabolite: Propofol glucuronide
+
+###### Parameters
+
+Name | Value | Value Origin
+------------------------------------------- | ---------------------------- | ----------------------------------------------------------------
+In vitro Vmax for liver microsomes | 2.4 nmol/min/mg mic. protein | Publication-In Vitro-Al-Jahdari 2006 HLM
+Content of CYP proteins in liver microsomes | 25.9 pmol/mg mic. protein | Publication-Assumption-Ohtsuki 2012 (UGT1A9)
+Km | 0.12 mM | Publication-In Vitro-Al-Jahdari 2006 HLM (corrected for fu, mic)
+kcat | 471.8406263631 1/min | Parameter Identification
+
+##### Metabolizing Enzyme: CYP2B6-Cumulative CYP Action
+
+Molecule: CYP2B6
+
+###### Parameters
+
+Name | Value | Value Origin
+------------------------------------------- | ----------------------------- | ------------------------------------------------------
+In vitro Vmax for liver microsomes | 1.08 nmol/min/mg mic. protein | Publication-In Vitro-Al-Jahdari 2006 HLM
+Content of CYP proteins in liver microsomes | 39 pmol/mg mic. protein | Publication-Assumption-Rodriguez 1999 (CYP2B6)
+Km | 0.0072 mM | Publication-Al-Jahdari 2005 HLM (corrected for fu,mic)
+kcat | 141.007756417 1/min | Parameter Identification
+
+## 3.2 Propofol Diagnostics Plots
+
+Below you find the goodness-of-fit visual diagnostic plots for propofol PBPK model performance (individually simulated versus observed plasma concentration and weighted residuals versus time, including the geometric mean fold error (GMFE)) of all data used for model building.
+
+
+
+**Table 3-1: GMFE for Goodness of fit plot for concentration in plasma.**
+
+|Group |GMFE |
+|:---------------------------------------|:----|
+|Propofol bolus + continuous IV infusion |1.68 |
+|Propofol bolus IV infusion |2.33 |
+|Propofol continuous IV infusion |1.29 |
+|All |1.57 |
+
+
+
+
+
+
+
+
+**Figure 3-1: Goodness of fit plot for concentration in plasma.**
+
+
+
+
+
+
+
+
+**Figure 3-2: Goodness of fit plot for concentration in plasma.**
+
+
+
+
+## 3.3 Propofol Concentration-Time profiles
+
+Simulated versus observed plasma concentration-time profiles of all data are listed below.
+
+
+
+
+
+**Figure 3-3: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-4: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-5: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-6: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-7: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-8: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-9: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-10: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-11: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-12: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-13: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-14: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-15: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-16: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-17: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-18: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-19: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-20: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-21: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-22: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-23: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-24: Time Profile Analysis 1**
+
+
+
+
+# 4 Conclusion
+
+The final propofol PBPK model applies metabolism by UGT1A9, CYP2B6 and glomerular filtration and adequately describes the pharmacokinetics of propofol in adults receiving intravenous bolus and continuous infusion of propofol ranging from 1 mg/kg to 36 mg/kg.
+
+This model could be applied for the investigation of drug-drug interactions (DDI), and translation to special populations such as pediatrics with regard to UGT1A1 and CYP2B6 metabolism.
+
+# 5 References
+
+**Al-Jahdari 2006** Al-Jahdari WS, Yamamoto K, Hiraoka H, Nakamura K, Goto F, Horiuchi R. Prediction of total propofol clearance based on enzyme activities in microsomes from human kidney and liver. Eur J Clin Pharmacol. 2006 Jul;62(7):527-33. doi: 10.1007/s00228-006-0130-2. Epub 2006 Jun 9.
+
+**Barter 2007** Barter ZE, Bayliss MK, Beaune PH, Boobis AR, Carlile DJ, Edwards RJ, Houston JB, Lake BG, Lipscomb JC, Pelkonen OR, Tucker GR, Rostami-Hodjegan A. Scaling factors for the extrapolation of in vivo metabolic drug clearance from in vitro data: reaching a consensus on values of human microsomal protein and hepatocellularity per gram of liver.Curr Drug Metab. 2007 Jan;8(1):33-45. doi: 10.2174/138920007779315053.
+
+**Court 2005** Court M, Isoform‐Selective Probe Substrates for *In Vitro* Studies of Human UDP‐Glucuronosyltransferases. Methods Enzymol. 2005;400:104-16. doi: 10.1016/S0076-6879(05)00007-8.
+
+**Drugbank.ca** ([https://www.drugbank.ca/drugs/DB00818](https://www.drugbank.ca/drugs/DB00818))
+
+**Gepts 1987** Gepts E, Camu F, Cockshott ID, Douglas EJ. Disposition of propofol administered as constant rate intravenous infusions in humans. Anesth Analg. 1987 Dec;66(12):1256-63.
+
+**Kuepfer 2016** Kuepfer L, Niederalt C, Wendl T, Schlender JF, Willmann S, Lippert J, Block M, Eissing T, Teutonico D. Applied Concepts in PBPK Modeling: How to Build a PBPK/PD Model.CPT Pharmacometrics Syst Pharmacol. 2016 Oct;5(10):516-531. doi: 10.1002/psp4.12134. Epub 2016 Oct 19.
+
+**Mazoit 1999** Mazoit JX, Samii K. Binding of propofol to blood components: implications for pharmacokinetics and for pharmacodynamics. Br J Clin Pharmacol, 47 (1999), pp. 35-42.
+
+**Nishimura 2006** Nishimura M and Naito S. Tissue-specific mRNA expression profiles of human phase I metabolizing enzymes except for cytochrome P450 and phase II metabolizing enzymes. Drug Metab Pharmacokinet. 21(5). 357-74. 2006.
+
+**Oda 2001** Oda Y, Hamaoka N, Hiroi T, Imaoka S, Hase I, Tanaka K, Funae Y, Ishizaki T, Asada A. Involvement of human liver cytochrome P4502B6 in the metabolism of propofol. Br J Clin Pharmacol. 2001 Mar; 51(3): 281–285. doi: 10.1046/j.1365-2125.2001.00344.x
+
+**Ohtsuki 2012** Ohtsuki S, Schaefer O, Kawakami H, Inoue T, Liehner S, Saito A, Ishiguro N, Kishimoto W, Ludwig-Schwellinger E, Ebner T, Terasaki T. Simultaneous absolute protein quantification of transporters, cytochromes P450, and UDP-glucuronosyltransferases as a novel approach for the characterization of individual human liver: comparison with mRNA levels and activities. Drug Metab Dispos. 2012 Jan;40(1):83-92. doi: 10.1124/dmd.111.042259.
+
+**PK-Sim Ontogeny Database Version 7.3** ([https://github.com/Open-Systems-Pharmacology/OSPSuite.Documentation/blob/38cf71b384cfc25cfa0ce4d2f3addfd32757e13b/PK-Sim%20Ontogeny%20Database%20Version%207.3.pdf](https://github.com/Open-Systems-Pharmacology/OSPSuite.Documentation/blob/38cf71b384cfc25cfa0ce4d2f3addfd32757e13b/PK-Sim%20Ontogeny%20Database%20Version%207.3.pdf))
+
+**Restrepo 2009** Restrepo JG, Garcia-Martín E, Martínez C, Agúndez JAG. Polymorphic drug metabolism in anaesthesia. Curr Drug Metab. 2009 Mar;10(3):236-46. doi: 10.2174/138920009787846305.
+
+**Rodrigues 1999** Rodrigues AD. Integrated cytochrome P450 reaction phenotyping: attempting to bridge the gap between cDNA-expressed cytochromes P450 and native human liver microsomes. Biochem Pharmacol. 465-80. 57 (5). 1999.
+
+**Schlender 2016** Schlender JF, Meyer M, Thelen K, Krauss M, Willmann S, Eissing T, Jaehde U. Development of a Whole-Body Physiologically Based Pharmacokinetic Approach to Assess the Pharmacokinetics of Drugs in Elderly Individuals. Clin Pharmacokinet. 2016 Dec;55(12):1573-1589.
+
+**Schnider 1998** Schnider TW, Minto CF, Gambus PL, Andresen C, Goodale DB, Shafer SL, Youngs EJ. Anesthesiology. 1998 May;88(5):1170-82. doi: 10.1097/00000542-199805000-00006.
+
+**Struys 2007** Struys MMRF, Coppens MJ, De Neve N, Mortier EP, Doufas AG, Van Bocxlaer JFP, Shafer SL. Anesthesiology. 2007 Sep;107(3):386-96. doi: 10.1097/01.anes.0000278902.15505.f8.
+
+**Takizawa 2005** Takizawa D, Hiraoka H, Goto F, Yamamoto K, Horiuchi R. Human kidneys play an important role in the elimination of propofol. Anesthesiology. 2005 Feb;102(2):327-30. doi: 10.1097/00000542-200502000-00014.
+
+","Markdown"
+"PBPK model","Open-Systems-Pharmacology/OSP-PBPK-Model-Library","Carbamazepine/Carbamazepine_evaluation_report.md",".md","89642","1209","# Building and evaluation of a PBPK Model for carbamazepine in healthy adults
+
+| Version | 2.0-OSP12.2 |
+| ----------------------------------------------- | ------------------------------------------------------------ |
+| based on *Model Snapshot* and *Evaluation Plan* | https://github.com/Open-Systems-Pharmacology/Carbamazepine-Model/releases/tag/v2.0 |
+| OSP Version | 12.2 |
+| Qualification Framework Version | 3.5 |
+
+This evaluation report and the corresponding PK-Sim project file are stored at:
+
+[https://github.com/Open-Systems-Pharmacology/OSP-PBPK-Model-Library](https://github.com/Open-Systems-Pharmacology/OSP-PBPK-Model-Library)
+
+# Table of Contents
+
+ * [1 Introduction](#introduction)
+ * [2 Methods](#methods)
+ * [2.1 Modeling Strategy](#modeling-strategy)
+ * [2.2 Data](#data)
+ * [2.3 Model Parameters and Assumptions](#model-parameters-and-assumptions)
+ * [3 Results and Discussion](#results-and-discussion)
+ * [3.1 Final input parameters](#final-input-parameters)
+ * [3.2 Diagnostics Plots](#diagnostics-plots)
+ * [3.3 Concentration-Time Profiles](#ct-profiles)
+ * [4 Conclusion](#conclusion)
+ * [5 References](#main-references)
+
+# 1 Introduction
+
+Carbamazepine, sold under the trade name Tegretol® among others, is an anticonvulsant medication used primarily to treat epilepsy and neuropathic pain. Other indications include schizophrenia where it is used as an adjunctive treatment along with other medications, and bipolar disorder where it is used as a second-line agent. Carbamazepine is typically taken by mouth on empty stomach or together with meals, depending on the administered formulation.
+
+Carbamazepine is extensively metabolized by various enzymes including CYP2B6, 2C8, 3A4, and UGT2B7 ([Kerr 1994](#5-references), [Pelkonen 2001](#5-references), [Staines 2004](#5-references)). Following oral administration the major dose fraction is metabolized to carbamazepine-10,11-epoxide ([Eichelbaum 1985](#5-references), [Tomson 1983](#5-references)). This reaction is mainly catalyzed by CYP3A4, with some contribution from CYP2C8 ([Kerr 1994](#5-references)). After oral administration, a minor fraction of the dose (approximately 1 - 3%) is excreted unchanged in urine ([Bernus 1994](#5-references), [Morselli 1975](#5-references)), while approximately 1% of the dose can be recovered as unchanged drug in the bile ([Terhaag 1978](#5-references)).
+
+Carbamazepine is classified by the U.S. Food and Drug Administration (FDA) as a strong CYP3A4 and CYP2B6 inducer and hence induces its own metabolism.
+
+The herein presented model was developed independently of the model reported by Fuhr et al. ([Fuhr 2021](#5-references)). The main difference between the two models pertains to the metabolite carbamazepine-10,11-epoxide, which is included as separate compound in the model by Fuhr et al. ([Fuhr 2021](#5-references)), but not modeled in the herein presented model. Another structural model differences concerns the enzymatic elimination pathways of carbamazepine; the model by Fuhr et al. ([Fuhr 2021](#5-references)) includes five different metabolism pathways, whereas the herein presented model includes three different metabolism pathways. Additionally, the parameterization of CYP2B6 and 3A4 induction differs between the two models.
+
+# 2 Methods
+
+## 2.1 Modeling Strategy
+
+The general workflow for building an adult PBPK model has been described by Kuepfer et al. ([Kuepfer 2016](#5-references)). Relevant information on the anthropometry (height, weight) was gathered from the respective clinical study, if reported. Information on physiological parameters (e.g. blood flows, organ volumes, hematocrit) in adults was gathered from the literature and has been incorporated in PK-Sim®) as described previously ([Willmann 2007](#5-references)). The applied activity and variability of plasma proteins and active processes that are integrated into PK-Sim® are described in the publicly available 'PK-Sim® Ontogeny Database Version 7.3' ([PK-Sim Ontogeny Database Version 7.3](#5-references)).
+
+The PBPK model was developed based on publicly available pharmacokinetic data of adult healthy subjects covering a carbamazepine dose range from 10 to 800 mg following intravenous administration or oral administration as liquid oral dosage form, immediate release (IR) tablet or extended release (XR) formulations in the fasted state. The carbamazepine PBPK model includes metabolism by CYP2B6, CYP3A4, and UGT2B7, unchanged renal excretion, and induction of CYP2B6 and 3A4 by carbamazepine. Pharmacokinetics of carbamazepine following administration in the fed state was not considered in the herein presented model. Furthermore, the metabolite carbamazepine-10,11-epoxide was not modeled as separate compound.
+
+Unknown parameters (see below) were identified using the Parameter Identification module provided in PK-Sim®. Structural model selection was mainly guided by visual inspection of the resulting description of data and biological plausibility. Several parameter identifications were conducted to optimize unknown parameters. In a first step, lipophilicity and enzymatic clearances (catalyzed by CYP3A4, CYP2B6 and UGT2B7) were optimized using observed plasma concentration-time profile data following administration of carbamazepine intravenously or orally as syrup. In a second parameter identification, enzymatic clearances were refined and optimized together with the glomerular filtration rate fraction of carbamazepine and the dissolution kinetics of the IR tablet using observed plasma concentration-time profiles and the dose fraction excreted unchanged in urine after single dose administration of various doses as IR tablet. Subsequently, the EC50 value of CYP3A4 induction was optimized using observed plasma concentration-time profile data after multiple dose administration of carbamazepine. In a final parameter identification, the dissolution kinetics and carbamazepine solubility of XR formulations were optimized.
+
+Details about input data (physicochemical, *in vitro* and clinical) can be found in [Section 2.2](#22-data).
+
+Details about the structural model and its parameters can be found in [Section 2.3](#23-model-parameters-and-assumptions).
+
+## 2.2 Data
+
+### 2.2.1 In vitro / physicochemical Data
+
+A literature search was performed to collect available information on physicochemical properties of carbamazepine. The information is summarized in the table below.
+
+| **Parameter** | **Unit** | **Value** | Source | **Description** |
+|:---------------------------------------- | ------------------------ | ----------------------------------------- | --------------------------------- | ------------------------------------------------------- |
+| MW | g/mol | 236.27 | [DrugBank DB00564](#5-references) | Molecular weight |
+| logP (calculated) | | 1.54 | [Austin 2002](#5-references) | Partition coefficient between octanol and water |
+| logP (calculated) | | 2.1 | [DrugBank DB00564](#5-references) | Partition coefficient between octanol and water |
+| logP (calculated) | | 2.45 | [Fenet 2012](#5-references) | Partition coefficient between octanol and water |
+| logP (calculated) | | 2.77 | [DrugBank DB00564](#5-references) | Partition coefficient between octanol and water |
+| Solubility (pH) | µg/mL | 336 (6.2) | [Annaert 2010](#5-references) | Solubility in human intestinal fluid |
+| Solubility (pH) | µg/mL | 283 (7.0) | [Söderlind 2010](#5-references) | Solubility in human intestinal fluid |
+| Solubility (pH) | µg/mL | 306 (6.9) | [Clarysse 2011](#5-references) | Solubility in fasted human intestinal fluid |
+| fu | | 0.25 | [Pynnönen 1977](#5-references) | Fraction unbound in plasma of healthy subjects |
+| fu | | 0.243 ± 0.013 [0.225 - 0.258]a | [Morselli 1975](#5-references) | Fraction unbound in plasma of healthy male subjects |
+| fu | | 0.239 | [Di Salle 1974](#5-references) | Fraction unbound in plasma of normal subjects |
+| fu | | 0.237 ± 0.031b | [Vinçon 1987](#5-references) | Fraction unbound in plasma of epileptic patients |
+| fu | | 0.182 ± 0.05 [0.103 - 0.297]a | [Hooper 1975](#5-references) | Fraction unbound in plasma of normal subjects |
+| Km CYP2B6 | µM | 420 | [Pearce 2002](#5-references) | CYP2B6 Michaelis-Menten constant |
+| Vmax CYP2B6 | pmol/min/pmol rec enzyme | 0.429 | [Pearce 2002](#5-references) | in vitro metabolic rate constant for recombinant CYP2B6 |
+| Km CYP2C8 | µM | 757 | [Cazali 2003](#5-references) | CYP2C8 Michaelis-Menten constant |
+| Vmax CYP2C8 | pmol/min/pmol rec enzyme | 0.673 | [Cazali 2003](#5-references) | in vitro metabolic rate constant for recombinant CYP2C8 |
+| Km CYP3A4c | µM | 282 | [Pearce 2002](#5-references) | CYP3A4 Michaelis-Menten constant |
+| Km CYP3A4 (→CBZE)d | µM | 248 | [Huang 2004](#5-references) | CYP3A4 Michaelis-Menten constant |
+| Km UGT2B7 | µM | 214 | [Staines 2004](#5-references) | UGT2B7 Michaelis-Menten constant |
+| Vmax UGT2B7 | pmol/min/mg mic enzyme | 0.79 | [Staines 2004](#5-references) | in vitro metabolic rate constant for microsomal enzymes |
+| Microsomal UGT2B7 | pmol/mg mic protein | 82.9 | [Achour 2014](#5-references) | Content of UGT2B7 proteins in liver microsomes |
+| Intestinal permeability | cm/min | 0.0258 | [Lennernäs 2007](#5-references) | Transcellular intestinal permeability |
+
+a denotes mean ± standard deviation [range]
+
+b denotes mean ± standard deviation
+
+c refers to CYP3A4-mediated reaction forming other metabolites than carbamazepine-10,11-epoxide
+
+d refers to CYP3A4-mediated reaction forming carbamazepine-10,11-epoxide
+
+### 2.2.2 Clinical Data
+
+A literature search was conducted to collect available data on carbamazepine pharmacokinetics in healthy adult subjects after intravenous or oral administration in the fasted state.
+
+The following studies were used for model building:
+
+| Publication | Arm / Treatment / Information used for model building |
+|:------------------------------ |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| [Bernus 1994](#5-references) | Healthy subjects receiving two oral doses of 600 mg carbamazepine as IR tablet (only pharmacokinetic data following the first dose were used for model building) |
+| [Gérardin 1976](#5-references) | Healthy subjects receiving a single oral dose of 100 mg carbamazepine as IR tablet |
+| [Gérardin 1990](#5-references) | Healthy subjects receiving a single oral dose of 100 mg [15N]-carbamazepine as suspension concomitantly with a single intravenous dose of 10 mg carbamazepine |
+| [McLean 2001](#5-references) | Healthy subjects receiving a single oral dose of 400 mg carbamazepine as XR formulation in fasted state |
+| [Møller 2001](#5-references) | Healthy subjects receiving a multiple oral doses of carbamazepine, starting at 100 mg and escalating to 400 mg |
+| [Wada 1978](#5-references) | Healthy subjects receiving a single oral dose of 200 mg carbamazepine as syrup and IR tablet |
+
+The following studies were used for model evaluation:
+
+| Publication | Arm / Treatment / Information used for model building |
+|:----------------------------------------------------------- |:------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| [Barzaghi 1987](#5-references) | Healthy subjects receiving a single oral dose of 400 mg carbamazepine |
+| [Bedada 2015](#5-references) | Healthy subjects receiving a single oral dose of 200 mg carbamazepine |
+| [Bedada 2016](#5-references) | Healthy subjects receiving a single oral dose of 200 mg carbamazepine |
+| [Bernus 1994](#5-references) | Healthy subjects receiving two oral doses of 600 mg carbamazepine (only pharmacokinetic data following the second dose were used for model evaluation) |
+| [Bianchetti 1987](#5-references) | Healthy subjects receiving a single oral dose of 400 mg carbamazepine |
+| [Burstein 2000](#5-references) | Healthy subjects receiving a multiple oral doses of carbamazepine, starting at 100 mg and escalating to 400 mg |
+| [Caraco 1995](#5-references) | Healthy lean subjects receiving a single oral dose of 200 mg carbamazepine |
+| [Cawello 2000](#5-references) | Healthy subjects receiving a multiple oral doses of carbamazepine, starting at 100 mg and escalating to 200 mg |
+| [Cotter 1977](#5-references) | Healthy subject receiving a single oral dose of 800 mg carbamazepine |
+| [Dalton 1985a](#5-references) | Healthy subjects receiving a single oral dose of 600 mg carbamazepine |
+| [Dalton 1985b](#5-references) | Healthy subjects receiving a single oral dose of 600 mg carbamazepine |
+| [Eichelbaum 1985](#5-references) | Healthy subjects receiving a single oral dose of 200 mg carbamazepine |
+| [Elqidra 2004](#5-references) | Healthy subjects receiving a single oral dose of 200 mg carbamazepine |
+| [European Patent Application EP 1044681 A2](#5-references) | Healthy subjects receiving a single oral dose of 400 and 600 mg carbamazepine |
+| [Gérardin 1976](#5-references) | Healthy subjects receiving a single oral dose of 200, and 600 mg carbamazepine |
+| [Ji 2008](#5-references) | Healthy subjects receiving a multiple oral doses of carbamazepine, starting at 200 mg and escalating to 400 mg |
+| [Kayali 1994](#5-references) | Healthy subjects receiving a single oral dose of 200 mg carbamazepine |
+| [Kim 2005](#5-references) | Healthy subjects receiving a single oral dose of 200 mg carbamazepine |
+| [Kovacević 2009](#5-references) | Healthy subjects receiving a single oral dose of 400 mg carbamazepine |
+| [Levy 1975](#5-references) | Healthy subjects receiving a single oral carbamazepine dose of 6 mg/kg body weight |
+| [Meyer 1996](#5-references) | Healthy subjects receiving a single oral dose of 200 mg carbamazepine |
+| [Meyer 1998](#5-references) | Healthy subjects receiving a single oral dose of 200 mg carbamazepine |
+| [Miles 1989](#5-references) | Healthy subjects receiving a multiple oral doses of 300 and 400 mg carbamazepine |
+| [Morselli 1975](#5-references) | Healthy subjects receiving a single oral dose of 400 mg carbamazepine |
+| [Pynnönen 1977](#5-references) | Healthy subjects receiving a single oral dose of 400 mg carbamazepine |
+| [Rawlins 1975](#5-references) | Healthy subject receiving a single oral dose of 50, 100, and 200 mg carbamazepine |
+| [Saint-Salvi 1987](#5-references) | Healthy subjects receiving a single oral dose of 200 mg carbamazepine |
+| [Stevens 1998](#5-references) | Healthy subjects receiving multiple oral doses of 400 mg carbamazepine |
+| [Strandjord 1975](#5-references) | Healthy subjects receiving a single oral dose of 400 mg carbamazepine |
+| [Sumi 1987](#5-references) | Healthy subjects receiving a single oral dose of 200 mg carbamazepine |
+| [Tomson 1983](#5-references) | Healthy subject receiving a single oral doses of 200 mg carbamazepine |
+| [US Patent Application - US 2009/0169619 A1](#5-references) | Healthy subjects receiving a single oral dose of 300 mg carbamazepine |
+| [Wong 1983](#5-references) | Healthy subjects receiving a single oral dose of 400 mg carbamazepine |
+
+## 2.3 Model Parameters and Assumptions
+
+### 2.3.1 Absorption
+
+Absorption of carbamazepine from the gastrointestinal tract can be fully explained by passive diffusion; active uptake by drug transporters does not seem to play a role. Intestinal permeability was observed to be not a rate-limiting step in drug absorption. The solubility of carbamazepine following administration of the IR tablet was fixed to the mean value (308 mg/L at a pH of 6.7) reported by several studies in fasted human intestinal fluid ([Annaert 2010](#5-references), [Söderlind 2010](#5-references), [Clarysse 2011](#5-references)).
+
+### 2.3.2 Distribution
+
+Plasma protein binding of carbamazepine was fixed to 75.7% as reported by Morselli et al. for healthy subjects ([Morselli 1975](#5-references)). The distribution of carbamazepine throughout the body was found to be best described by the partition coefficient calculation by `Rodgers and Rowlands` and cellular permeability calculation by `PK-Sim Standard`.
+
+### 2.3.3 Metabolism, Excretion and Induction
+
+#### Metabolism
+
+Carbamazepine metabolism is complex involving multiple enzymes with more than 30 metabolites identified ([Lertratanangkoon 1982](#5-references)). Several *in vitro* studies suggest involvement of CYP1A2, 2A6, 2B6, 2C8, 2E1, 3A4, and UGT2B7 in carbamazepine metabolism ([Cazali 2003](#5-references), [Kerr 1994](#5-references), [Pearce 2002](#5-references), [Pelkonen 2001](#5-references), [Staines 2004](#5-references)).
+
+In various *in vitro* assays, the biotransformation to the main metabolite, carbamazepine-10,11-epoxide, appears to be mainly catalyzed by CYP3A4 with minimal contribution by CYP2C8 ([Cazali 2003](#5-references), [Egnell 2003](#5-references), [Kerr 1994](#5-references)). For example, Egnell et al. report that, at equimolar amounts of recombinantly expressed CYP enzymes, the activity of CYP3A4 towards carbamazepine was more than 20-fold higher than that of CYP2C8 ([Egnell 2003](#5-references)). Therefore, carbamazepine epoxidation was modeled via CYP3A4 only.
+
+Further oxidative metabolism pathways include 2- and 3-hydroxylation. The formation of 2-hydroxycarbamazepine is mediated by several CYP enzymes *in vitro* (including CYP1A2, 2A6, 2B6, 2E1, and 3A4); though, the contribution of any of these isoforms does not exceed 50% of the total formation ([Pearce 2002](#5-references)). In experiments with liver slices, 2-hydroxylation appears to be a minor elimination pathway (1-2 % of total clearance) as reported by Pelkonen et al. ([Pelkonen 2001](#5-references)). Hence, 2-hydroxylation was not accounted for in the PBPK model.
+
+The formation of 3-hydroxycarbamazepine also appears to constitute a minor metabolism pathway ([Pelkonen 2001](#5-references)); still, in human liver microsomes, 3-hydroxycarbamazepine was formed at rates ~25 times greater than those of 2-hydroxycarbamazepine ([Pearce 2002](#5-references)). The responsible enzyme for 3-hydroxylation *in vitro* seems to be CYP2B6, although a minor contribution by CYP1A2, 2A6, and 3A4 cannot be ruled out ([Pearce 2002](#5-references)). In the PBPK model, 3-hydroxylation was implemented as CYP2B6-mediated reaction.
+
+N-glucuronidation of carbamazepine in human liver microsomes and baculovirus-infected insect cells expressing human UGTs was also observed with UGT2B7 appearing to be the responsible enzyme for this reaction ([Staines 2004](#5-references)). Thus, the PBPK model also includes UGT2B7-mediated N-glucuronidation of carbamazepine.
+
+In summary, the following three metabolic pathways, each mediated by a specific enzyme, were implemented in the PBPK model:
+
+* 10,11-epoxidation via CYP3A4
+* 3-hydroxylation via CYP2B6
+* N-glucuronidation via UGT2B7
+
+Since no clinical mass balance data were found for these three pathways, the following clearance kinetics in human liver microsomes reported for each pathway were initially implemented in the PBPK model:
+
+| Biotransformation pathway | Km [µM] | Vmax [pmol/min/mg microsomal protein] | Source |
+| ------------------------- | ------------------ | ------------------------------------------------ | ------------------------------ |
+| 10,11-epoxidation | 808 | 726 | [Sakamoto 2013](#5-references) |
+| 3-hydroxylation | 235 | 49.0 | [Pearce 2002](#5-references) |
+| N-glucuronidation | 234 | 3.5 | [Staines 2004](#5-references) |
+
+The following enzymatic content in human liver microsomes was assumed:
+
+| Enzyme | Enzyme content [pmol/mg microsomal protein] | Source |
+| ------ | ------------------------------------------- | ------------------------------- |
+| CYP3A4 | 108 | [Rodrigues 1999](#5-references) |
+| CYP2B6 | 39 | [Rodrigues 1999](#5-references) |
+| UGT2B7 | 82.9 | [Achour 2014](#5-references) |
+
+The expression profiles for these enzymes were loaded from the 'PK-Sim® Ontogeny Database Version 7.3' ([PK-Sim Ontogeny Database Version 7.3](#5-references)) using RT-PCR as data source for each enzyme.
+
+Upon implementation of these enzyme clearance pathways, it was seen that total clearance was slightly overestimated in the PBPK model. Therefore, the kcat values of each enzyme were optimized during parameter identification; to respect the initial mass balance of these biotransformation reactions as reported in human liver microsomes, the kcat values were not fitted independently but were varied together by the same factor.
+
+#### Excretion
+
+A minor fraction of the carbamazepine dose (approximately 1%) is excreted unchanged in urine ([Bernus 1994](#5-references), [Morselli 1975](#5-references)). In the model, unchanged renal excretion was implemented as glomerular filtration with the parameter `GFR fraction` being fitted to the clinical excretion data reported by Bernus et al. ([Bernus 1994](#5-references)).
+
+#### Induction
+
+Carbamazepine induces CYP2B6 and 3A4 via the CAR- and PXR-pathway ([Faucette 2007](#5-references), [Williamson 2016](#5-references)). CYP2B6 induction was informed based on *in vitro* experiments conducted by Faucette et al. ([Faucette 2004](#5-references)). These authors reported the induction of CYP2B6 activity at various carbamazepine concentrations in three preparations of primary human hepatocytes. The reported data suggest linear induction in the tested carbamazepine concentration range. A linear-mixed effects model was fitted to the reported data; the fitted slope was 0.149. To implement a linear induction in the PBPK model, the EC50 value of the Emax model was set to an arbitrarily high value (1000 µM) and Emax was then calculated as product of the fitted slope value and EC50 resulting in a value of 149.
+
+CYP3A4 induction was initially parameterized based on internal *in vitro* experiments and calibrated with rifampicin induction data as described by Almond et al. ([Almond 2016](#5-references)). This resulted in an EC50 of 63.0 µM and an Emax of 5.39. Simulated carbamazepine plasma concentrations in steady-state indicated that the induction was underestimated; therefore, the calibrated EC50 value was optimized during parameter identification, while the calibrated Emax value was kept fixed.
+
+### 2.3.4 Automated Parameter Identification
+
+The parameter identification tool in PK-Sim® has been used to estimate the model parameters described above. The result of the parameter identifications is shown in the table below:
+
+| Model Parameter | Optimized Value | Unit |
+| -------------------------- | --------------- | ---- |
+| `Lipophilicity` | 2.01 | |
+| `kcat` (CYP3A4) | 5.01 | 1/min |
+| `kcat` (CYP2B6) | 0.936 | 1/min |
+| `kcat` (UGT2B7) | 0.0669 | 1/min |
+| `GFR fraction` | 0.0240 | |
+| `EC50` (CYP3A4) | 27.2 | µM |
+| `Dissolution time (50% dissolved)` (IR tablet, fasted) | 109 | min |
+| `Dissolution shape` (IR tablet, fasted) | 0.689 | |
+| `Dissolution time (50% dissolved)` (XR formulation, fasted) | 315 | min |
+| `Dissolution shape` (XR formulation, fasted) | 1.23 | |
+| `Solubility at ref pH` -- for XR formulations only | 546 | mg/L |
+
+# 3 Results and Discussion
+
+The PBPK model for carbamazepine was developed and evaluated using publicly available clinical pharmacokinetic data from studies listed in [Section 2.2.2](#222-clinical-data).
+
+The next sections show:
+
+1. the final model parameters for the building blocks: [Section 3.1](#31-final-input-parameters).
+2. the overall goodness of fit: [Section 3.2](#32-diagnostics-plots).
+3. simulated vs. observed concentration-time profiles for the clinical studies used for model building and for model verification: [Section 3.3](#33-concentration-time-profiles).
+
+## 3.1 Final input parameters
+
+The compound parameter values of the final PBPK model are illustrated below.
+
+### Compound: Carbamazepine
+
+#### Parameters
+
+Name | Value | Value Origin | Alternative | Default
+------------------------------------------ | ---------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------ | -------
+Solubility at reference pH | 308.3333 mg/l | Publication-Mean value of the following FaHIF solubility data reported in the literature: 336 µg/mL, pH 6.2 (Annaert 2010; DOI: 10.1016/j.ejps.2009.10.005); 283 µg/mL, pH 7.0 (Söderlind 2010; DOI: 10.1021/mp100144v); 306 mg/mL, pH 6.9 (Clarysse 2011; DOI: 10.1016/j.ejps.2011.04.016) | IR tablet (FaHIF) | True
+Reference pH | 6.7 | Publication-Mean value of the following FaHIF solubility data reported in the literature: 336 µg/mL, pH 6.2 (Annaert 2010; DOI: 10.1016/j.ejps.2009.10.005); 283 µg/mL, pH 7.0 (Söderlind 2010; DOI: 10.1021/mp100144v); 306 mg/mL, pH 6.9 (Clarysse 2011; DOI: 10.1016/j.ejps.2011.04.016) | IR tablet (FaHIF) | True
+Solubility at reference pH | 546.0199756643 mg/l | Parameter Identification-Parameter Identification-Value updated from '004-2_from-003-1_XRtablet_fasted_solubility_FINAL' on 2022-03-24 12:41 | XR tablet (fitted) | False
+Reference pH | 6.7 | Parameter Identification-Parameter Identification-Value updated from '004-2_from-003-1_XRtablet_fasted_solubility_FINAL' on 2022-03-16 18:25 | XR tablet (fitted) | False
+Lipophilicity | 2.0067753065 Log Units | Parameter Identification-Parameter Identification-Value updated from '001-5-3_CYP3A4_MM-kinetics_WithoutTablet' on 2022-02-21 16:49 | Optimized | True
+Fraction unbound (plasma, reference value) | 0.243 | Publication-Morselli 1975 (DOI: 10.1007/978-3-642-85921-2_16) | Morselli 1975 | True
+Is small molecule | Yes | | |
+Molecular weight | 236.2686 g/mol | Internet-DrugBank (https://go.drugbank.com/drugs/DB00564) | |
+Plasma protein binding partner | Albumin | | |
+
+#### Calculation methods
+
+Name | Value
+----------------------- | -------------------
+Partition coefficients | Rodgers and Rowland
+Cellular permeabilities | PK-Sim Standard
+
+#### Processes
+
+##### Induction: CYP3A4-DMPK
+
+Molecule: CYP3A4
+
+###### Parameters
+
+Name | Value | Value Origin
+---- | ------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
+EC50 | 27.193363407 µmol/l | Parameter Identification-Parameter Identification-Value updated from '003-1_from002-3-6_EC50' on 2022-02-24 10:23
+Emax | 5.3929777775 | Publication-In Vitro-DMPK measurement (internal data); the measured Emax was calibrated with rifampicin by using the Emax implemented in the rifampicin OSP model v1.2 according the the method described by Almond 2016 (DOI: 10.1124/dmd.115.066845)
+
+##### Systemic Process: Glomerular Filtration-Glomerular Filtration
+
+Species: Human
+
+###### Parameters
+
+Name | Value | Value Origin
+------------ | ------------:| -----------------------------------------------------------------------------------------------------------------------------------------
+GFR fraction | 0.0240108793 | Parameter Identification-Parameter Identification-Value updated from '002-3-6_from001-5-3_IRtablet-sd_Pint-FIX_FINAL' on 2022-02-23 17:18
+
+##### Metabolizing Enzyme: UGT2B7-N-Glucuronidation_Staines2004
+
+Molecule: UGT2B7
+
+###### Parameters
+
+Name | Value | Value Origin
+------------------------------------------- | ---------------------------- | -----------------------------------------------------------------------------------------------------------------------------------------
+In vitro Vmax for liver microsomes | 3.5 pmol/min/mg mic. protein | Publication-In Vitro-Staines 2004 (DOI: 10.1124/jpet.104.073114)
+Content of CYP proteins in liver microsomes | 82.9 pmol/mg mic. protein | Publication-In Vitro-Achour 2014 (DOI: 10.1124/dmd.113.055632)
+Km | 234 µmol/l | Publication-In Vitro-Staines 2004 (DOI: 10.1124/jpet.104.073114)
+kcat | 0.0668699322 1/min | Parameter Identification-Parameter Identification-Value updated from '002-3-6_from001-5-3_IRtablet-sd_Pint-FIX_FINAL' on 2022-02-23 17:18
+
+##### Metabolizing Enzyme: CYP2B6-3-Hydroxylation_Pearce2002
+
+Molecule: CYP2B6
+
+###### Parameters
+
+Name | Value | Value Origin
+------------------------------------------- | --------------------------- | -----------------------------------------------------------------------------------------------------------------------------------------
+In vitro Vmax for liver microsomes | 49 pmol/min/mg mic. protein | Publication-In Vitro-Pearce 2002 (DOI: 10.1124/dmd.30.11.1170)
+Content of CYP proteins in liver microsomes | 39 pmol/mg mic. protein | Publication-In Vitro-Rodrigues 1999 (DOI: 10.1016/s0006-2952(98)00268-8)
+Km | 235 µmol/l | Publication-Pearce 2002 (DOI: 10.1124/dmd.30.11.1170)
+kcat | 0.9361790504 1/min | Parameter Identification-Parameter Identification-Value updated from '002-3-6_from001-5-3_IRtablet-sd_Pint-FIX_FINAL' on 2022-02-23 17:18
+
+##### Metabolizing Enzyme: CYP3A4-Epoxidation_Sakamoto2013
+
+Molecule: CYP3A4
+
+###### Parameters
+
+Name | Value | Value Origin
+---------------------------------- | ---------------------------- | -----------------------------------------------------------------------------------------------------------------------------------------
+In vitro Vmax for liver microsomes | 726 pmol/min/mg mic. protein | Publication-In Vitro-Sakamoto 2013 (DOI: 10.1248/bpb.b13-00569)
+Km | 808 µmol/l | Publication-In Vitro-Sakamoto 2013 (DOI: 10.1248/bpb.b13-00569)
+kcat | 5.0088763476 1/min | Parameter Identification-Parameter Identification-Value updated from '002-3-6_from001-5-3_IRtablet-sd_Pint-FIX_FINAL' on 2022-02-23 17:18
+
+##### Induction: CYP2B6-Faucette2004
+
+Molecule: CYP2B6
+
+###### Parameters
+
+Name | Value | Value Origin
+---- | ----------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------
+EC50 | 1000 µmol/l | Publication-Set to an arbitrarily high value to enable linear induction as suggested by Faucette 2004 (DOI: 10.1124/dmd.32.3.348); see evaluation report for details
+Emax | 148.7284 | Publication-Linear-mixed effects model fitted to reported data by Faucette 2004 (DOI: 10.1124/dmd.32.3.348); see evaluation report for details
+
+### Compound: [15N]-Carbamazepine
+
+#### Parameters
+
+Name | Value | Value Origin | Alternative | Default
+------------------------------------------ | ---------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------ | -------
+Solubility at reference pH | 308.3333 mg/l | Publication-Mean value of the following FaHIF solubility data reported in the literature: 336 µg/mL, pH 6.2 (Annaert 2010; DOI: 10.1016/j.ejps.2009.10.005); 283 µg/mL, pH 7.0 (Söderlind 2010; DOI: 10.1021/mp100144v); 306 mg/mL, pH 6.9 (Clarysse 2011; DOI: 10.1016/j.ejps.2011.04.016) | IR tablet (FaHIF) | True
+Reference pH | 6.7 | Publication-Mean value of the following FaHIF solubility data reported in the literature: 336 µg/mL, pH 6.2 (Annaert 2010; DOI: 10.1016/j.ejps.2009.10.005); 283 µg/mL, pH 7.0 (Söderlind 2010; DOI: 10.1021/mp100144v); 306 mg/mL, pH 6.9 (Clarysse 2011; DOI: 10.1016/j.ejps.2011.04.016) | IR tablet (FaHIF) | True
+Solubility at reference pH | 546.0199756643 mg/l | Parameter Identification-Parameter Identification-Value updated from '004-2_from-003-1_XRtablet_fasted_solubility_FINAL' on 2022-03-24 12:41 | XR tablet (fitted) | False
+Reference pH | 6.7 | Parameter Identification-Parameter Identification-Value updated from '004-2_from-003-1_XRtablet_fasted_solubility_FINAL' on 2022-03-16 18:25 | XR tablet (fitted) | False
+Lipophilicity | 2.0067753065 Log Units | Parameter Identification-Parameter Identification-Value updated from '001-5-3_CYP3A4_MM-kinetics_WithoutTablet' on 2022-02-21 16:49 | Optimized | True
+Fraction unbound (plasma, reference value) | 0.243 | Publication-Morselli 1975 (DOI: 10.1007/978-3-642-85921-2_16) | Morselli 1975 | True
+Is small molecule | Yes | | |
+Molecular weight | 236.2686 g/mol | Internet-DrugBank (https://go.drugbank.com/drugs/DB00564) | |
+Plasma protein binding partner | Albumin | | |
+
+#### Calculation methods
+
+Name | Value
+----------------------- | -------------------
+Partition coefficients | Rodgers and Rowland
+Cellular permeabilities | PK-Sim Standard
+
+#### Processes
+
+##### Induction: CYP3A4-DMPK
+
+Molecule: CYP3A4
+
+###### Parameters
+
+Name | Value | Value Origin
+---- | ------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
+EC50 | 27.193363407 µmol/l | Parameter Identification-Parameter Identification-Value updated from '003-1_from002-3-6_EC50' on 2022-02-24 10:23
+Emax | 5.3929777775 | Publication-In Vitro-DMPK measurement (internal data); the measured Emax was calibrated with rifampicin by using the Emax implemented in the rifampicin OSP model v1.2 according the the method described by Almond 2016 (DOI: 10.1124/dmd.115.066845)
+
+##### Systemic Process: Glomerular Filtration-Glomerular Filtration
+
+Species: Human
+
+###### Parameters
+
+Name | Value | Value Origin
+------------ | ------------:| -----------------------------------------------------------------------------------------------------------------------------------------
+GFR fraction | 0.0240108793 | Parameter Identification-Parameter Identification-Value updated from '002-3-6_from001-5-3_IRtablet-sd_Pint-FIX_FINAL' on 2022-02-23 17:18
+
+##### Metabolizing Enzyme: UGT2B7-N-Glucuronidation_Staines2004
+
+Molecule: UGT2B7
+
+###### Parameters
+
+Name | Value | Value Origin
+------------------------------------------- | ---------------------------- | -----------------------------------------------------------------------------------------------------------------------------------------
+In vitro Vmax for liver microsomes | 3.5 pmol/min/mg mic. protein | Publication-In Vitro-Staines 2004 (DOI: 10.1124/jpet.104.073114)
+Content of CYP proteins in liver microsomes | 82.9 pmol/mg mic. protein | Publication-In Vitro-Achour 2014 (DOI: 10.1124/dmd.113.055632)
+Km | 234 µmol/l | Publication-In Vitro-Staines 2004 (DOI: 10.1124/jpet.104.073114)
+kcat | 0.0668699322 1/min | Parameter Identification-Parameter Identification-Value updated from '002-3-6_from001-5-3_IRtablet-sd_Pint-FIX_FINAL' on 2022-02-23 17:18
+
+##### Metabolizing Enzyme: CYP2B6-3-Hydroxylation_Pearce2002
+
+Molecule: CYP2B6
+
+###### Parameters
+
+Name | Value | Value Origin
+------------------------------------------- | --------------------------- | -----------------------------------------------------------------------------------------------------------------------------------------
+In vitro Vmax for liver microsomes | 49 pmol/min/mg mic. protein | Publication-In Vitro-Pearce 2002 (DOI: 10.1124/dmd.30.11.1170)
+Content of CYP proteins in liver microsomes | 39 pmol/mg mic. protein | Publication-In Vitro-Rodrigues 1999 (DOI: 10.1016/s0006-2952(98)00268-8)
+Km | 235 µmol/l | Publication-Pearce 2002 (DOI: 10.1124/dmd.30.11.1170)
+kcat | 0.9361790504 1/min | Parameter Identification-Parameter Identification-Value updated from '002-3-6_from001-5-3_IRtablet-sd_Pint-FIX_FINAL' on 2022-02-23 17:18
+
+##### Metabolizing Enzyme: CYP3A4-Epoxidation_Sakamoto2013
+
+Molecule: CYP3A4
+
+###### Parameters
+
+Name | Value | Value Origin
+---------------------------------- | ---------------------------- | -----------------------------------------------------------------------------------------------------------------------------------------
+In vitro Vmax for liver microsomes | 726 pmol/min/mg mic. protein | Publication-In Vitro-Sakamoto 2013 (DOI: 10.1248/bpb.b13-00569)
+Km | 808 µmol/l | Publication-In Vitro-Sakamoto 2013 (DOI: 10.1248/bpb.b13-00569)
+kcat | 5.0088763476 1/min | Parameter Identification-Parameter Identification-Value updated from '002-3-6_from001-5-3_IRtablet-sd_Pint-FIX_FINAL' on 2022-02-23 17:18
+
+##### Induction: CYP2B6-Faucette2004
+
+Molecule: CYP2B6
+
+###### Parameters
+
+Name | Value | Value Origin
+---- | ----------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------
+EC50 | 1000 µmol/l | Publication-Set to an arbitrarily high value to enable linear induction as suggested by Faucette 2004 (DOI: 10.1124/dmd.32.3.348); see evaluation report for details
+Emax | 148.7284 | Publication-Linear-mixed effects model fitted to reported data by Faucette 2004 (DOI: 10.1124/dmd.32.3.348); see evaluation report for details
+
+### Formulation: CBZ_tabletIR_fasted (Tegretol)
+
+Type: Weibull
+
+#### Parameters
+
+Name | Value | Value Origin
+-------------------------------- | ------------------ | -----------------------------------------------------------------------------------------------------------------------------------------
+Dissolution time (50% dissolved) | 109.3089775422 min | Parameter Identification-Parameter Identification-Value updated from '002-3-6_from001-5-3_IRtablet-sd_Pint-FIX_FINAL' on 2022-02-23 17:18
+Lag time | 0 min |
+Dissolution shape | 0.6890123758 | Parameter Identification-Parameter Identification-Value updated from '002-3-6_from001-5-3_IRtablet-sd_Pint-FIX_FINAL' on 2022-02-23 17:18
+Use as suspension | Yes |
+
+### Formulation: CBZ_capsuleXR_fasted (Carbatrol)
+
+Type: Weibull
+
+#### Parameters
+
+Name | Value | Value Origin
+-------------------------------- | ------------------ | --------------------------------------------------------------------------------------------------------------------------------------------
+Dissolution time (50% dissolved) | 315.2431776804 min | Parameter Identification-Parameter Identification-Value updated from '004-2_from-003-1_XRtablet_fasted_solubility_FINAL' on 2022-03-24 12:41
+Lag time | 0 min |
+Dissolution shape | 1.2290186648 | Parameter Identification-Parameter Identification-Value updated from '004-2_from-003-1_XRtablet_fasted_solubility_FINAL' on 2022-03-24 12:41
+Use as suspension | Yes |
+
+### Formulation: Solution
+
+Type: Dissolved
+
+## 3.2 Diagnostics Plots
+
+Below you find the goodness-of-fit visual diagnostic plots for the PBPK model performance of all data used presented in [Section 2.2.2](#222-clinical-data).
+
+The first plot shows simulated versus observed plasma concentration, the second weighted residuals versus time.
+
+
+
+**Table 3-1: GMFE for Goodness of fit plot for concentration in plasma**
+
+|Group |GMFE |
+|:----------------------------------------------------------------|:----|
+|Carbamazepine, IV administration |1.28 |
+|Carbamazepine, PO administration as extended release formulation |1.41 |
+|Carbamazepine, PO administration as immediate release tablet |1.40 |
+|Carbamazepine, PO administration as liquid oral dosage form |2.35 |
+|All |1.49 |
+
+
+
+
+
+
+
+
+**Figure 3-1: Goodness of fit plot for concentration in plasma**
+
+
+
+
+
+
+
+
+**Figure 3-2: Goodness of fit plot for concentration in plasma**
+
+
+
+
+## 3.3 Concentration-Time Profiles
+
+Simulated versus observed concentration-time profiles of all data listed in [Section 2.2.2](#222-clinical-data) are presented below.
+
+
+
+
+
+**Figure 3-3: Barzaghi1987_400mg_sd_tabIR**
+
+
+
+
+
+
+
+
+**Figure 3-4: Bedada2015_200mg_sd_tabIR**
+
+
+
+
+
+
+
+
+**Figure 3-5: Bedada2016_200mg_sd_tabIR**
+
+
+
+
+
+
+
+
+**Figure 3-6: Bernus1994_600mg_D1+D5_tabIR - plasma**
+
+
+
+
+
+
+
+
+**Figure 3-7: Bernus1994_600mg_D1+D5_tabIR - urine**
+
+
+
+
+
+
+
+
+**Figure 3-8: Bianchetti1987_400mg_sd_tabIR_fed**
+
+
+
+
+
+
+
+
+**Figure 3-9: Burstein2000_100-200-400mg_md_tabIR**
+
+
+
+
+
+
+
+
+**Figure 3-10: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-11: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-12: Cotter1977_individual_800mg_sd_tabIR**
+
+
+
+
+
+
+
+
+**Figure 3-13: Dalton1985_control_600mg_sd_tabIR**
+
+
+
+
+
+
+
+
+**Figure 3-14: Dalton1985a_600mg_sd_tabIR**
+
+
+
+
+
+
+
+
+**Figure 3-15: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-16: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-17: EUPatent2005_400mg_sd_TegretolXR**
+
+
+
+
+
+
+
+
+**Figure 3-18: EUPatent2005_600mg_sd_TegretolXR**
+
+
+
+
+
+
+
+
+**Figure 3-19: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-20: Geradin1976_200mg_sd_tabIR**
+
+
+
+
+
+
+
+
+**Figure 3-21: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-22: Geradin1976_mean_200mg_md_tabIR**
+
+
+
+
+
+
+
+
+**Figure 3-23: Geradin1976_mean_200mg_md_tabIR - first dose**
+
+
+
+
+
+
+
+
+**Figure 3-24: Geradin1976_mean_200mg_md_tabIR - last dose**
+
+
+
+
+
+
+
+
+**Figure 3-25: Geradin1990_Subject1_100mg-iv_100mg-po-sol**
+
+
+
+
+
+
+
+
+**Figure 3-26: Geradin1990_Subject2_100mg-iv_100mg-po-sol**
+
+
+
+
+
+
+
+
+**Figure 3-27: Ji2008_100-200-400mg_md_tabIR**
+
+
+
+
+
+
+
+
+**Figure 3-28: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-29: Kim2005_200mg_sd_tabIR**
+
+
+
+
+
+
+
+
+**Figure 3-30: Kovacevic2009_400mg_sd_TegretolIR**
+
+
+
+
+
+
+
+
+**Figure 3-31: Kovacevic2009_400mg_sd_TegretolXR**
+
+
+
+
+
+
+
+
+**Figure 3-32: Levy1975_6mg-kg_sd_solution_fasted**
+
+
+
+
+
+
+
+
+**Figure 3-33: Levy1975_6mg-kg_sd_tabIR_fasted**
+
+
+
+
+
+
+
+
+**Figure 3-34: McLean2001_400mg_sd_capXR_fasted**
+
+
+
+
+
+
+
+
+**Figure 3-35: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-36: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-37: Miles1989_control_357_md_tabIR**
+
+
+
+
+
+
+
+
+**Figure 3-38: Moller2001_100-200-400mg_md_tabIR**
+
+
+
+
+
+
+
+
+**Figure 3-39: Morselli1975_healthy_400mg_sd_tabIR_plasma**
+
+
+
+
+
+
+
+
+**Figure 3-40: Morselli1975_healthy_400mg_sd_tabIR_urine**
+
+
+
+
+
+
+
+
+**Figure 3-41: Pynnoenen1977_400mg_sd_tabIR_plasma**
+
+
+
+
+
+
+
+
+**Figure 3-42: Pynnoenen1977_400mg_sd_tabIR_saliva**
+
+
+
+
+
+
+
+
+**Figure 3-43: Rawlins1975_100mg_sd_sol**
+
+
+
+
+
+
+
+
+**Figure 3-44: Rawlins1975_200mg_sd_sol**
+
+
+
+
+
+
+
+
+**Figure 3-45: Rawlins1975_200mg_sd_sol**
+
+
+
+
+
+
+
+
+**Figure 3-46: Rawlins1975_50mg_sd_sol**
+
+
+
+
+
+
+
+
+**Figure 3-47: SaintSalvi1987_200mg_sd_tabIR**
+
+
+
+
+
+
+
+
+**Figure 3-48: Stevens1998_400mg_bid_CarbatrolXR**
+
+
+
+
+
+
+
+
+**Figure 3-49: Stevens1998_400mg_bid_CarbatrolXR - last dose**
+
+
+
+
+
+
+
+
+**Figure 3-50: Stevens1998_400mg_bid_TegretolXR**
+
+
+
+
+
+
+
+
+**Figure 3-51: Stevens1998_400mg_bid_TegretolXR - last dose**
+
+
+
+
+
+
+
+
+**Figure 3-52: Strandjord1975_mean_400mg_sd_tabIR - observed mean data**
+
+
+
+
+
+
+
+
+**Figure 3-53: Strandjord1975_mean_400mg_sd_tabIR - observed individual data**
+
+
+
+
+
+
+
+
+**Figure 3-54: Sumi1987_200mg_sd_solution**
+
+
+
+
+
+
+
+
+**Figure 3-55: Sumi1987_200mg_sd_tabletA**
+
+
+
+
+
+
+
+
+**Figure 3-56: Sumi1987_200mg_sd_tabletB**
+
+
+
+
+
+
+
+
+**Figure 3-57: Sumi1987_200mg_sd_tabletC**
+
+
+
+
+
+
+
+
+**Figure 3-58: Tomson1983_GB_200mg_sd_susp**
+
+
+
+
+
+
+
+
+**Figure 3-59: USPatent2009_300mg_sd_capXR**
+
+
+
+
+
+
+
+
+**Figure 3-60: Wada1987_200mg_sd_syrup_plasma**
+
+
+
+
+
+
+
+
+**Figure 3-61: Wada1987_200mg_sd_syrup_saliva**
+
+
+
+
+
+
+
+
+**Figure 3-62: Wada1978_200mg_sd_tabIR_plasma**
+
+
+
+
+
+
+
+
+**Figure 3-63: Wada1978_200mg_sd_tabIR_plasma 1**
+
+
+
+
+
+
+
+
+**Figure 3-64: Wong1983_control_400mg_sd_tabIR**
+
+
+
+
+# 4 Conclusion
+
+The herein presented PBPK model adequately describes the pharmacokinetics of carbamazepine after single and multiple oral administration of various doses to healthy adults.
+
+In conclusion, the presented carbamazepine PBPK model is well-suited to be applied in drug-drug-interaction scenarios.
+
+# 5 References
+
+**Achour 2014** Achour, B., Russell, M. R., Barber, J., & Rostami-Hodjegan, A. (2014). Simultaneous quantification of the abundance of several cytochrome P450 and uridine 5′-diphospho-glucuronosyltransferase enzymes in human liver microsomes using multiplexed targeted proteomics. *Drug metabolism and disposition*, *42*(4), 500-510.
+
+**Almond 2016** Almond, L. M., Mukadam, S., Gardner, I., Okialda, K., Wong, S., Hatley, O., ... & Kenny, J. R. (2016). Prediction of drug-drug interactions arising from CYP3A induction using a physiologically based dynamic model. *Drug Metabolism and Disposition*, *44*(6), 821-832.
+
+**Annaert 2010** Annaert, P., Brouwers, J., Bijnens, A., Lammert, F., Tack, J., & Augustijns, P. (2010). Ex vivo permeability experiments in excised rat intestinal tissue and in vitro solubility measurements in aspirated human intestinal fluids support age-dependent oral drug absorption. *European journal of pharmaceutical sciences*, *39*(1-3), 15-22.
+
+**Austin 2002** Austin, R. P., Barton, P., Cockroft, S. L., Wenlock, M. C., & Riley, R. J. (2002). The influence of nonspecific microsomal binding on apparent intrinsic clearance, and its prediction from physicochemical properties. *Drug Metabolism and Disposition*, *30*(12), 1497-1503.
+
+**Barzaghi 1987** Barzaghi, N., Gatti, G., Crema, F., Monteleone, M., Amione, C., Leone, L., & Perucca, E. (1987). Inhibition by erythromycin of the conversion of carbamazepine to its active 10, 11‐epoxide metabolite. *British journal of clinical pharmacology*, *24*(6), 836-838.
+
+**Bedada 2015** Bedada, S. K., & Nearati, P. (2015). Effect of resveratrol on the pharmacokinetics of carbamazepine in healthy human volunteers. *Phytotherapy Research*, *29*(5), 701-706.
+
+**Bedada 2016** Bedada, S. K., Appani, R., & Boga, P. K. (2017). Effect of piperine on the metabolism and pharmacokinetics of carbamazepine in healthy volunteers. *Drug research*, *67*(01), 46-51.
+
+**Bernus 1994** Bernus, I., Dickinson, R. G., Hooper, W. D., & Eadie, M. J. (1994). Early stage autoinduction of carbamazepine metabolism in humans. *European journal of clinical pharmacology*, *47*(4), 355-360.
+
+**Bianchetti 1987** Bianchetti, G., Padovani, P., Thenot, J. P., Thiercelin, J. F., & Morselli, P. L. (1987). Pharmacokinetic interactions of progabide with other antiepileptic drugs. *Epilepsia*, *28*(1), 68-73.
+
+**Burstein 2000** Burstein, A. H., Horton, R. L., Dunn, T., Alfaro, R. M., Piscitelli, S. C., & Theodore, W. (2000). Lack of effect of St John's Wort on carbamazepine pharmacokinetics in healthy volunteers. *Clinical Pharmacology & Therapeutics*, *68*(6), 605-612.
+
+**Caraco 1995** Caraco, Y., Zylber-Katz, E., Berry, E. M., & Levy, M. (1995). Carbamazepine pharmacokinetics in obese and lean subjects. *Annals of Pharmacotherapy*, *29*(9), 843-847.
+
+**Cawello 2010** Cawello, W., Nickel, B., & Eggert‐Formella, A. (2010). No pharmacokinetic interaction between lacosamide and carbamazepine in healthy volunteers. *The Journal of Clinical Pharmacology*, *50*(4), 459-471.
+
+**Cazali 2003** Cazali, N., Tran, A., Treluyer, J. M., Rey, E., d’Athis, P., Vincent, J., & Pons, G. (2003). Inhibitory effect of stiripentol on carbamazepine and saquinavir metabolism in human. *British journal of clinical pharmacology*, *56*(5), 526-536.
+
+**Clarysse 2011** Clarysse, S., Brouwers, J., Tack, J., Annaert, P., & Augustijns, P. (2011). Intestinal drug solubility estimation based on simulated intestinal fluids: comparison with solubility in human intestinal fluids. *European journal of pharmaceutical sciences*, *43*(4), 260-269.
+
+**Cotter 1977** Cotter, L. M., Eadie, M. J., Hooper, W. D., Lander, C. M., Smith, G. A., & Tyrer, J. H. (1977). The pharmacokinetics of carbamazepine. *European journal of clinical pharmacology*, *12*(6), 451-456.
+
+**Dalton 1985a** Dalton, M. J., Powell, J. R., & Messenheimer Jr, J. A. (1985). The Influence of Cimetidine on Single‐Dose Carbamazepine Pharmacokinetics. *Epilepsia*, *26*(2), 127-130.
+
+**Dalton 1985b** Dalton, M. J., Powell, J. R., Messenheimer Jr, J. A., Nazario, M., & Mallet, L. (1985). Ranitidine Does Not Alter Single-Dose Carbamazepin Pharmacokinetics in Healthy Adults. *Drug intelligence & clinical pharmacy*, *19*(12), 941-944.
+
+**Di Salle 1974** Di Salle, E., Pacifici, G. M., & Morselli, P. L. (1974). Studies on plasma protein binding of carbamazepine. *Pharmacological research communications*, *6*(2), 193-202.
+
+**Drugbank DB00564**. URL: https://www.drugbank.ca/drugs/DB00564, accessed on 12-14-2020.
+
+**Drugbank DBMET00291**. URL: https://www.drugbank.ca/metabolites/DBMET00291, accessed on 12-16-2020.
+
+**Egnell 2003** Egnell, A. C., Houston, B., & Boyer, S. (2003). In vivo CYP3A4 heteroactivation is a possible mechanism for the drug interaction between felbamate and carbamazepine. *Journal of Pharmacology and Experimental Therapeutics*, *305*(3), 1251-1262.
+
+**Eichelbaum 1985** Eichelbaum, M., Tomson, T., Tybring, G., & Bertilsson, L. (1985). Carbamazepine metabolism in man. *Clinical pharmacokinetics*, *10*(1), 80-90.
+
+**Elqidra 2004** Elqidra, R., Ünlü, N., Capan, Y., Sahin, G., Dalkara, T., & Hincal, A. A. (2004). Effect of polymorphism on in vitro-in vivo properties of carbamazepine conventional tablets. *Journal of Drug Delivery Science and Technology*, *14*(2), 147-153.
+
+**European Patent Application EP 1044681 A2** European Patent Application 2000, EP 1044681 A2, Application no. 00650026.8. URL: https://patentimages.storage.googleapis.com/0c/45/b7/d2be4fa9d24371/EP1044681A2.pdf, accessed on 12-01-2022.
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+
+","Markdown"
+"PBPK model","Open-Systems-Pharmacology/OSP-PBPK-Model-Library","Verapamil/Verapamil_evaluation_report.md",".md","69999","1251","# Building and Evaluation of a PBPK Model for Verapamil in Adults
+
+| Version | 2.1-OSP12.2 |
+| ----------------------------------------------- | ------------------------------------------------------------ |
+| based on *Model Snapshot* and *Evaluation Plan* | https://github.com/Open-Systems-Pharmacology/Verapamil-Model/releases/tag/v2.1 |
+| OSP Version | 12.2 |
+| Qualification Framework Version | 3.5 |
+
+This evaluation report and the corresponding PK-Sim project file are filed at:
+
+https://github.com/Open-Systems-Pharmacology/OSP-PBPK-Model-Library/
+
+# Table of Contents
+
+ * [1 Introduction](#introduction)
+ * [2 Methods](#methods)
+ * [2.1 Modeling Strategy](#modeling-strategy)
+ * [2.2 Data](#methods-data)
+ * [2.3 Model Parameters and Assumptions](#model-parameters-and-assumptions)
+ * [3 Results and Discussion](#results-and-discussion)
+ * [3.1 Final input parameters](#final-input-parameters)
+ * [3.2 Diagnostics Plots](#diagnostics-plots)
+ * [3.3 Concentration-Time Profiles](#ct-profiles)
+ * [4 Conclusion](#conclusion)
+ * [5 References](#main-references)
+
+# 1 Introduction
+
+Verapamil is used for the treatment of high blood pressure, angina (chest pain from not enough blood flow to the heart), and supraventricular tachycardia.
+
+Verapamil is administered as a 1:1 racemat of R- and S-verapamil which are metabolized mainly by CYP3A4 to S- and R-norverapamil. All four entities are mechanism-based inactivators of CYP3A4 and non-competitive inhibitors of P-gp.
+
+The presented verapamil model was established using observed concentration-time profiles of more than 45 clinical studies with doses from 0.1 mg to 250 mg in different verapamil dosing schedules including multiple doses and different routes of administration (intravenous, single and multiple oral administration). It includes enantioselective plasma protein binding, enantioselective metabolism by CYP3A4, non-stereospecific P-gp transport, and passive glomerular filtration
+The model building and application has been published by Hanke *et al.* 2020 ([Hanke 2020](#5-references)).
+
+The herein presented model building and evaluation report evaluates the performance of the PBPK model for verapamil in (healthy) adults.
+
+# 2 Methods
+
+## 2.1 Modeling Strategy
+
+The general concept of building a PBPK model has previously been described by e.g. Kuepfer et al. ([Kuepfer 2016](#5-references)). The relevant anthropometric (height, weight) and physiological information (e.g. blood flows, organ volumes, binding protein concentrations, hematocrit, cardiac output) in adults was gathered from the literature and has been previously published ([Schlender 2016](#5-references)). This information was incorporated into PK-Sim® and was used as default values for the simulations in adults.
+
+Variability of plasma proteins and CYP enzymes are integrated into PK-Sim® and described in the publicly available PK-Sim® Ontogeny Database Version 7.3 ([PK-Sim Ontogeny Database Version 7.3](#5-references), [Schlender 2016](#5-references)) or otherwise referenced for the specific process.
+
+First, a base mean model was built and adjusted to clinical data including single and multiple dose studies with intravenous (only single dose) and oral applications of verapamil to find an appropriate structure to describe the pharmacokinetics in plasma. The mean PBPK model was developed using a typical European individual adjusted to the demography of the respective study population.
+
+Unknown parameters (see below) were identified using the Parameter Identification module provided in PK-Sim®. Structural model selection was mainly guided by visual inspection of the resulting description of data and biological plausibility.
+
+Details about input data (physicochemical, *in vitro* and clinical) can be found in [Section 2.2](#22-data).
+
+Details about the structural model and its parameters can be found in [Section 2.3](#23-model-parameters-and-assumptions).
+
+## 2.2 Data
+
+### In vitro / physico-chemical Data
+
+A literature search was performed to collect available information on physiochemical properties of R- and S-verapamil and R- and S-norverapamil. The obtained information from literature is summarized in the tables below.
+
+#### R-verapamil
+
+| **Parameter** | **Unit** | **Value** | Source | **Description** |
+| :-------------- | -------- | --------------- | ------------------------------------------------------------ | ----------------------------------------------- |
+| MW | g/mol | 454.611 | [Wishart 2006](#5-references) | Molecular weight |
+| pKa (base) | - | 8.75 | [Hasegawa 1984](#5-references) | Acid dissociation constant |
+| Solubility (pH 6.54) | g/L | 46.0 | [Vogelpoel 2004](#5-references) | Water solubility |
+| logP | | 3.79 | [Hansch 1995](#5-references) | Partition coefficient between octanol and water |
+| fu | % | 5.1 | [Sanaee 2011](#5-references) | Fraction unbound in plasma |
+| CYP3A4 Km -> Norvera | µmol/L | 19.59 | [Wang 2013](#5-references) | CYP3A4 Michaelis-Menten constant for norverapamil formation |
+| CYP3A4 Km -> D617 | µmol/L | 35.34 | [Wang 2013](#5-references) | CYP3A4 Michaelis-Menten constant for D617 formation |
+| P-gp Km | µmol/L | 1.01 | [Shirasaka 2008](#5-references) | Pgp Michaelis-Menten constant |
+| CYP3A4 MBI KI | µmol/L | 27.63 | [Wang 2013](#5-references) | Conc. for half-maximal inactivation |
+| CYP3A4 MBI kinact | 1/min | 0.038 | [Wang 2013](#5-references) | Maximum inactivation rate |
+| Pgp non-competitive Ki | µmol/L | 0.31 | [Döppenschmitt 1999](#5-references) | Conc. for half-maximal inactivation |
+
+#### S-verapamil
+
+| **Parameter** | **Unit** | **Value** | Source | **Description** |
+| :-------------- | -------- | --------------- | ------------------------------------------------------------ | ----------------------------------------------- |
+| MW | g/mol | 454.611 |[Wishart 2006](#5-references) | Molecular weight |
+| pKa (base) | - | 8.75 | [Hasegawa 1984](#5-references) | Acid dissociation constant |
+| Solubility (pH 6.54) | g/L | 46.0 | [Vogelpoel 2004](#5-references) | Water solubility |
+| logP | | 3.79 | [Hansch 1995](#5-references) | Partition coefficient between octanol and water |
+| fu | % | 11 | [**Sanaee 2011**](#5-references) | Fraction unbound in plasma |
+| CYP3A4 Km -> Norvera | µmol/L | 9.72 | [Wang 2013](#5-references) | CYP3A4 Michaelis-Menten constant for norverapamil formation |
+| CYP3A4 Km -> D617 | µmol/L | 23.64 | [Wang 2013](#5-references) | CYP3A4 Michaelis-Menten constant for D617 formation |
+| P-gp Km | µmol/L | 1.01 | [Shirasaka 2008](#5-references) | Pgp Michaelis-Menten constant |
+| CYP3A4 MBI KI | µmol/L | 3.85 | [Wang 2013](#5-references) | Conc. for half-maximal inactivation |
+| CYP3A4 MBI kinact | 1/min | 0.034 | [Wang 2013](#5-references) | Maximum inactivation rate |
+| Pgp non-competitive Ki | µmol/L | 0.31 | Döppenschmitt 1999 (#5-references) | Conc. for half-maximal inactivation |
+
+#### R-norverapamil
+
+| **Parameter** | **Unit** | **Value** | Source | **Description** |
+| :-------------- | -------- | --------------- | ------------------------------------------------------------ | ----------------------------------------------- |
+| MW | g/mol | 440.584 | [Wishart 2006](#5-references) | Molecular weight |
+| pKa (base) | - | 8.6 - 8.9 | [Sigma-Aldrich 2013](#5-references) | Acid dissociation constant |
+| fu | % | 5.1 | assumed (from parent) | Fraction unbound in plasma |
+| CYP3A4 Km -> D620 | µmol/L | 144 | [Tracy 1999](#5-references) | CYP3A4 Michaelis-Menten constant for norverapamil degradation |
+| P-gp Km | µmol/L | 1.01 | assumed (from parent) | Pgp Michaelis-Menten constant |
+| CYP3A4 MBI KI | µmol/L | 6.1 | [Wang 2013](#5-references) | Conc. for half-maximal inactivation |
+| CYP3A4 MBI kinact | 1/min | 0.048 | [Wang 2013](#5-references) | Maximum inactivation rate |
+| Pgp non-competitive Ki | µmol/L | 0.30 | [Pauli-Magnus 2000](#5-references) | Conc. for half-maximal inactivation |
+
+#### S-norverapamil
+
+| **Parameter** | **Unit** | **Value** | Source | **Description** |
+| :-------------- | -------- | --------------- | ------------------------------------------------------------ | ----------------------------------------------- |
+| MW | g/mol | 440.584 | [Wishart 2006](#5-references) | Molecular weight |
+| pKa (base) | - | 8.6 - 8.9 | [Sigma-Aldrich 2013](#5-references) | Acid dissociation constant |
+| fu | % | 11 | assumed (from parent) | Fraction unbound in plasma |
+| CYP3A4 Km -> D620 | µmol/L | 36 | [Tracy 1999](#5-references) | CYP3A4 Michaelis-Menten constant for norverapamil degradation |
+| P-gp Km | µmol/L | 1.01 | assumed (from parent) | Pgp Michaelis-Menten constant |
+| CYP3A4 MBI KI | µmol/L | 2.90 | [Wang 2013](#5-references) | Conc. for half-maximal inactivation |
+| CYP3A4 MBI kinact | 1/min | 0.080 | [Wang 2013](#5-references) | Maximum inactivation rate |
+| Pgp non-competitive Ki | µmol/L | 0.30 | [Pauli-Magnus 2000](#5-references) | Conc. for half-maximal inactivation |
+
+### Clinical Data
+
+A literature search was performed to collect available clinical data on verapamil in healthy adults.
+
+#### Model Building and parameterizing of CYP3A4 interaction
+
+The following studies were used for model building and parameterization of CYP3A4 interaction:
+If not stated otherwise, the drug was given as a 1:1 racemat of S- and R-verapamil.
+
+| Publication | Arm / Treatment / Information used for model building |
+| :------------------------------- | :----------------------------------------------------------- |
+| [Eichelbaum 1984](#5-references) | Healthy subjects receiving single intravenous doses of 5, 25 and 50 mg of R-verapamil and 5, 7.5 and 10 mg of S-verapamil |
+| [Streit 2005](#5-references) | Healthy subjects receiving single intravenous doses of 5 mg |
+| [Barbarash 1988](#5-references) | Healthy subjects receiving single intravenous doses of 10 mg |
+| [Abernethy 1993](#5-references) | Healthy subjects receiving single intravenous doses of 20 mg |
+| [Maeda 2011](#5-references) | Healthy subjects receiving single oral doses of 0.1, 3 and 16 mg |
+| [Blume 1989](#5-references) | Healthy subjects receiving single oral doses of 80 mg |
+| [Ratiopharm 1988](#5-references) | Healthy subjects receiving single oral doses of 80 mg |
+| [Johnson 2001](#5-references) | Healthy subjects receiving multiple oral doses of 80 mg TID. |
+| [Härtter 2012](#5-references) | Healthy subjects receiving single oral doses of 120 mg and multiple oral doses of 120 mg BID |
+| [Hla 1987](#5-references) | Healthy subjects receiving multiple oral doses of 120 mg BID |
+
+#### Model verification
+
+The following studies were used for model verification:
+
+| Publication | Arm / Treatment / Information used for model building |
+| :------------------------------- | :----------------------------------------------------------- |
+| [Mooy 1985](#5-references) | Healthy subjects receiving single intravenous doses of 3 mg and single oral doses of 80 mg |
+| [Johnston 1981](#5-references) | Healthy subjects receiving single intravenous doses of 0.1 mg/kg and single oral doses of 120 mg |
+| [Abernethy 1985](#5-references) | Healthy subjects receiving single intravenous doses of 10 mg and single oral doses of 120 mg |
+| [Barbarash 1988](#5-references) | Healthy subjects receiving single oral doses of 120 mg |
+| [Wing 1985](#5-references) | Healthy subjects receiving single intravenous doses 10mg and single oral doses of 80 mg |
+| [McAllister 1982](#5-references) | Healthy subjects receiving single intravenous doses of 10 mg |
+| [Smith 1984](#5-references) | Healthy subjects receiving single intravenous doses of 10 mg and single oral doses of 120 mg |
+| [Freedman 1981](#5-references) | Healthy subjects receiving single intravenous doses of 13.1 mg |
+| [Vogelsang 1984](#5-references) | Healthy subjects receiving single oral doses of 250mg R-verapamil |
+| [Blume 1983](#5-references) | Healthy subjects receiving single oral doses of 40 mg |
+| [Blume 1990](#5-references) | Healthy subjects receiving single oral doses of 40 mg |
+| [John 1992](#5-references) | Healthy subjects receiving single oral doses of 40 mg |
+| [Sawicki 2002](#5-references) | Healthy subjects receiving single oral doses of 40 mg |
+| [Choi 2008](#5-references) | Healthy subjects receiving single oral doses of 60 mg |
+| [Wing 1985](#5-references) | Healthy subjects receiving single oral doses of 80 mg |
+| [Maeda 2011](#5-references) | Healthy subjects receiving single oral doses of 80 mg |
+| [Ratiopharm 1989](#5-references) | Healthy subjects receiving single oral doses of 80 mg |
+| [Boehringer 2018](#5-references) | Healthy subjects receiving single oral doses of 120 mg |
+| [Blume 1987](#5-references) | Healthy subjects receiving single oral doses of 120 mg |
+| [Johnston 1981](#5-references) | Healthy subjects receiving single oral doses of 120 mg |
+| [Mikus 1990](#5-references) | Healthy subjects receiving single oral doses of 160 mg |
+| [van Haarst 2009](#5-references) | Healthy subjects receiving multiple oral doses of 180 mg BID |
+| [Blume 1994](#5-references) | Healthy subjects receiving single oral doses of 240 mg QD |
+| [Joergenson 1988](#5-references) | Healthy subjects receiving multiple oral doses of 120 mg BID |
+| [Shand 1981](#5-references) | Healthy subjects receiving multiple oral doses of 120 mg TID |
+| [Karim 1995](#5-references) | Healthy subjects receiving single oral doses of 240 mg |
+
+## 2.3 Model Parameters and Assumptions
+
+### Absorption
+
+Verapamil is transported by P-gp. The model includes non-stereospecific P-gp transport.
+
+### Distribution
+
+After testing the available organ-plasma partition coefficient and cell permeability calculation methods built in PK-Sim, observed clinical data was best described by choosing the partition coefficient calculation by `Rodgers and Rowland` and cellular permeability calculation by `PK-Sim Standard`.
+
+### Metabolism, Elimination and Inhibition
+
+Verapamil is metabolized by CYP3A4 and transported by P-gp. The model includes enantioselective metabolism by CYP3A4, non-stereospecific P-gp transport. Additionally passive glomerular filtration was integrated.
+
+Mechanism-based inactivation of CYP3A4 and non-competitive inhibition of P-gp by all four entities (S-verapamil, R-verapamil, S-norverapamil and R-norverapamil) was taken into account. The CYP3A4 MBI KI and kinact values were taken from literature, the KI values for P-gp inhibition were optimized.
+
+### Automated Parameter Identification
+
+The parameter identification tool in PK-Sim has been used to estimate selected model parameters by adjusting to PK data of the clinical studies that were used in the model building process.
+
+The result of the final parameter identification is shown in the tables below:
+
+#### R-verapamil
+
+| Model Parameter | Optimized Value | Unit |
+| -------------------------- | --------------- | ---- |
+| logP | 2.84 ||
+| CYP3A4 kcat -> Norvera | 34.94 |1/min|
+| CYP3A4 kcat -> D617 | 43.98 |1/min|
+| P-gp kcat | 12.60 |1/min|
+| Pgp non-competitive Ki | 0.038 |µmol/L|
+| Cellular permeability | 9.94E-02 |cm/min|
+| Intestinal permeability | 3.54E-06 |cm/min|
+| SR tablet Weibull time | 155.24 |min|
+| SR tablet Weibull shape | 2.37 | |
+
+#### S-verapamil
+
+| Model Parameter | Optimized Value | Unit |
+| -------------------------- | --------------- | ---- |
+| logP | 2.84 ||
+| CYP3A4 kcat -> Norvera | 26.17 |1/min|
+| CYP3A4 kcat -> D617 | 56.42 |1/min|
+| P-gp kcat | 12.60 |1/min|
+| Pgp non-competitive Ki | 0.038 |µmol/L|
+| Cellular permeability | 9.94E-02 |cm/min|
+| Intestinal permeability | 3.54E-06 |cm/min|
+| SR tablet Weibull time | 155.24 |min|
+| SR tablet Weibull shape | 2.37 | |
+
+#### R-norverapamil
+
+| Model Parameter | Optimized Value | Unit |
+| -------------------------- | --------------- | ---- |
+| logP | 2.84 ||
+| CYP3A4 kcat -> D620 | 145.64 |1/min|
+| P-gp kcat | 3.39 |1/min|
+| Pgp non-competitive Ki | 0.038 |µmol/L|
+| Cellular permeability | 9.94E-02 |cm/min|
+| Intestinal permeability | 3.54E-06 |cm/min|
+
+#### S-norverapamil
+
+| Model Parameter | Optimized Value | Unit |
+| -------------------------- | --------------- | ---- |
+| logP | 2.84 ||
+| CYP3A4 kcat -> D620 | 41.10 |1/min|
+| P-gp kcat | 3.39 |1/min|
+| Pgp non-competitive Ki | 0.038 |µmol/L|
+| Cellular permeability | 9.94E-02 |cm/min|
+| Intestinal permeability | 3.54E-06 |cm/min|
+
+# 3 Results and Discussion
+
+The PBPK model for verapamil was developed and evaluated using publically available, clinical pharmacokinetic data from studies listed in [Section 2.2](#clinical-data).
+
+The next sections show:
+
+1. the final model parameters for the building blocks: [Section 3.1](#31-final-input-parameters).
+2. the overall goodness of fit: [Section 3.2](#32-diagnostics-plots).
+3. simulated vs. observed concentration-time profiles for the clinical studies used for model building and for model verification: [Section 3.3](#33-concentration-time-profiles).
+
+## 3.1 Final input parameters
+
+The compound parameter values of the final PBPK model are illustrated below.
+
+### Compound: R-Verapamil
+
+#### Parameters
+
+Name | Value | Value Origin | Alternative | Default
+------------------------------------------------ | ----------------------- | ------------------------------------------------------------------------------------------------------ | ----------- | -------
+Solubility at reference pH | 46 mg/ml | | Measurement | True
+Reference pH | 6.54 | | Measurement | True
+Lipophilicity | 2.8407448658 Log Units | Parameter Identification-Parameter Identification-Value updated from '30b - final' on 2019-12-30 13:23 | logP | True
+Fraction unbound (plasma, reference value) | 5.1 % | Publication-In Vivo | Measurement | True
+Permeability | 0.0994098912 cm/min | Parameter Identification-Parameter Identification-Value updated from '30b - final' on 2019-12-30 13:23 | Fitted | True
+Specific intestinal permeability (transcellular) | 3.5447164381E-06 cm/min | Parameter Identification-Parameter Identification-Value updated from '30b - final' on 2019-12-30 13:23 | Fitted | True
+Is small molecule | Yes | | |
+Molecular weight | 454.611 g/mol | | |
+Plasma protein binding partner | Unknown | | |
+
+#### Calculation methods
+
+Name | Value
+----------------------- | -------------------
+Partition coefficients | Rodgers and Rowland
+Cellular permeabilities | PK-Sim Standard
+
+#### Processes
+
+##### Systemic Process: Glomerular Filtration-GFR
+
+Species: Human
+
+###### Parameters
+
+Name | Value | Value Origin
+------------ | -----:| ------------:
+GFR fraction | 1 |
+
+##### Transport Protein: P-gp-Paper
+
+Molecule: P-gp
+
+###### Parameters
+
+Name | Value | Value Origin
+------------------------- | ------------------------------------ | ------------------------------------------------------------------------------------------------------
+In vitro Vmax/transporter | 0.00057724 pmol/min/pmol transporter |
+Km | 1.01 µmol/l |
+kcat | 12.5970868779 1/min | Parameter Identification-Parameter Identification-Value updated from '30b - final' on 2019-12-30 13:23
+
+##### Inhibition: P-gp-Non-competitive
+
+Molecule: P-gp
+
+###### Parameters
+
+Name | Value | Value Origin
+---- | ------------------- | ------------------------------------------------------------------------------------------------------
+Ki | 0.0383697779 µmol/l | Parameter Identification-Parameter Identification-Value updated from '30b - final' on 2019-12-30 13:23
+
+##### Inhibition: CYP3A4-MBI
+
+Molecule: CYP3A4
+
+###### Parameters
+
+Name | Value | Value Origin
+------------- | ------------ | ------------:
+kinact | 0.038 1/min |
+K_kinact_half | 27.63 µmol/l |
+
+##### Metabolizing Enzyme: CYP3A4-Norverapamil
+
+Molecule: CYP3A4
+
+Metabolite: R-Norverapamil
+
+###### Parameters
+
+Name | Value | Value Origin
+---------------------------------- | ----------------------------- | ------------------------------------------------------------------------------------------------------
+In vitro Vmax for liver microsomes | 1.27 nmol/min/mg mic. protein |
+Km | 19.59 µmol/l |
+kcat | 34.9352466212 1/min | Parameter Identification-Parameter Identification-Value updated from '30b - final' on 2019-12-30 13:23
+
+##### Metabolizing Enzyme: CYP3A4-D617
+
+Molecule: CYP3A4
+
+###### Parameters
+
+Name | Value | Value Origin
+---------------------------------- | ----------------------------- | ------------------------------------------------------------------------------------------------------
+In vitro Vmax for liver microsomes | 1.17 nmol/min/mg mic. protein |
+Km | 35.34 µmol/l |
+kcat | 43.9812289146 1/min | Parameter Identification-Parameter Identification-Value updated from '30b - final' on 2019-12-30 13:23
+
+### Compound: S-Verapamil
+
+#### Parameters
+
+Name | Value | Value Origin | Alternative | Default
+------------------------------------------------ | ----------------------- | ------------------------------------------------------------------------------------------------------ | ----------- | -------
+Solubility at reference pH | 46 mg/ml | | Measurement | True
+Reference pH | 6.54 | | Measurement | True
+Lipophilicity | 2.8407448658 Log Units | Parameter Identification-Parameter Identification-Value updated from '30b - final' on 2019-12-30 13:23 | logP | True
+Fraction unbound (plasma, reference value) | 11 % | Publication-In Vivo | Measurement | True
+Permeability | 0.0994098912 cm/min | Parameter Identification-Parameter Identification-Value updated from '30b - final' on 2019-12-30 13:23 | Fitted | True
+Specific intestinal permeability (transcellular) | 3.5447164381E-06 cm/min | Parameter Identification-Parameter Identification-Value updated from '30b - final' on 2019-12-30 13:23 | Fitted | True
+Is small molecule | Yes | | |
+Molecular weight | 454.611 g/mol | | |
+Plasma protein binding partner | Unknown | | |
+
+#### Calculation methods
+
+Name | Value
+----------------------- | -------------------
+Partition coefficients | Rodgers and Rowland
+Cellular permeabilities | PK-Sim Standard
+
+#### Processes
+
+##### Systemic Process: Glomerular Filtration-GFR
+
+Species: Human
+
+###### Parameters
+
+Name | Value | Value Origin
+------------ | -----:| ------------:
+GFR fraction | 1 |
+
+##### Transport Protein: P-gp-Paper
+
+Molecule: P-gp
+
+###### Parameters
+
+Name | Value | Value Origin
+------------------------- | ------------------------------------ | ------------------------------------------------------------------------------------------------------
+In vitro Vmax/transporter | 0.00057724 pmol/min/pmol transporter |
+Km | 1.01 µmol/l |
+kcat | 12.5970868779 1/min | Parameter Identification-Parameter Identification-Value updated from '30b - final' on 2019-12-30 13:23
+
+##### Inhibition: P-gp-Non-competitive
+
+Molecule: P-gp
+
+###### Parameters
+
+Name | Value | Value Origin
+---- | ------------------- | ------------------------------------------------------------------------------------------------------
+Ki | 0.0383697779 µmol/l | Parameter Identification-Parameter Identification-Value updated from '30b - final' on 2019-12-30 13:23
+
+##### Inhibition: CYP3A4-MBI
+
+Molecule: CYP3A4
+
+###### Parameters
+
+Name | Value | Value Origin
+------------- | ----------- | ------------:
+kinact | 0.034 1/min |
+K_kinact_half | 3.85 µmol/l |
+
+##### Metabolizing Enzyme: CYP3A4-Norverapamil
+
+Molecule: CYP3A4
+
+Metabolite: S-Norverapamil
+
+###### Parameters
+
+Name | Value | Value Origin
+---------------------------------- | ----------------------------- | ------------------------------------------------------------------------------------------------------
+In vitro Vmax for liver microsomes | 1.02 nmol/min/mg mic. protein |
+Km | 9.72 µmol/l |
+kcat | 26.1743386639 1/min | Parameter Identification-Parameter Identification-Value updated from '30b - final' on 2019-12-30 13:23
+
+##### Metabolizing Enzyme: CYP3A4-D617
+
+Molecule: CYP3A4
+
+###### Parameters
+
+Name | Value | Value Origin
+---------------------------------- | ----------------------------- | ------------------------------------------------------------------------------------------------------
+In vitro Vmax for liver microsomes | 0.86 nmol/min/mg mic. protein |
+Km | 23.64 µmol/l |
+kcat | 56.4245798193 1/min | Parameter Identification-Parameter Identification-Value updated from '30b - final' on 2019-12-30 13:23
+
+### Compound: R-Norverapamil
+
+#### Parameters
+
+Name | Value | Value Origin | Alternative | Default
+------------------------------------------------ | ----------------------- | ------------------------------------------------------------------------------------------------------ | ----------- | -------
+Solubility at reference pH | 46 mg/ml | | Measurement | True
+Reference pH | 6.54 | | Measurement | True
+Lipophilicity | 2.8407448658 Log Units | Parameter Identification-Parameter Identification-Value updated from '30b - final' on 2019-12-30 13:23 | logP | True
+Fraction unbound (plasma, reference value) | 5.1 % | Other-Assumption | Measurement | True
+Permeability | 0.0994098912 cm/min | Parameter Identification-Parameter Identification-Value updated from '30b - final' on 2019-12-30 13:23 | Fitted | True
+Specific intestinal permeability (transcellular) | 3.5447164381E-06 cm/min | Parameter Identification-Parameter Identification-Value updated from '30b - final' on 2019-12-30 13:23 | Fitted | True
+Is small molecule | Yes | | |
+Molecular weight | 440.584 g/mol | | |
+Plasma protein binding partner | Unknown | | |
+
+#### Calculation methods
+
+Name | Value
+----------------------- | -------------------
+Partition coefficients | Rodgers and Rowland
+Cellular permeabilities | PK-Sim Standard
+
+#### Processes
+
+##### Metabolizing Enzyme: CYP3A4-D620
+
+Molecule: CYP3A4
+
+###### Parameters
+
+Name | Value | Value Origin
+-------------------------------- | --------------------------- | ------------------------------------------------------------------------------------------------------
+In vitro Vmax/recombinant enzyme | 9 pmol/min/pmol rec. enzyme |
+Km | 144 µmol/l |
+kcat | 145.6385399671 1/min | Parameter Identification-Parameter Identification-Value updated from '30b - final' on 2019-12-30 13:23
+
+##### Systemic Process: Glomerular Filtration-GFR
+
+Species: Human
+
+###### Parameters
+
+Name | Value | Value Origin
+------------ | -----:| ------------:
+GFR fraction | 1 |
+
+##### Transport Protein: P-gp-Paper
+
+Molecule: P-gp
+
+###### Parameters
+
+Name | Value | Value Origin
+------------------------- | ------------------------------------ | ------------------------------------------------------------------------------------------------------
+In vitro Vmax/transporter | 0.00057724 pmol/min/pmol transporter |
+Km | 1.01 µmol/l |
+kcat | 3.3916609583 1/min | Parameter Identification-Parameter Identification-Value updated from '30b - final' on 2019-12-30 13:23
+
+##### Inhibition: P-gp-Non-competitive
+
+Molecule: P-gp
+
+###### Parameters
+
+Name | Value | Value Origin
+---- | ------------------- | ------------------------------------------------------------------------------------------------------
+Ki | 0.0383697779 µmol/l | Parameter Identification-Parameter Identification-Value updated from '30b - final' on 2019-12-30 13:23
+
+##### Inhibition: CYP3A4-MBI
+
+Molecule: CYP3A4
+
+###### Parameters
+
+Name | Value | Value Origin
+------------- | ----------- | ------------:
+kinact | 0.048 1/min |
+K_kinact_half | 6.1 µmol/l |
+
+### Compound: S-Norverapamil
+
+#### Parameters
+
+Name | Value | Value Origin | Alternative | Default
+------------------------------------------------ | ----------------------- | ------------------------------------------------------------------------------------------------------ | ----------- | -------
+Solubility at reference pH | 46 mg/ml | | Measurement | True
+Reference pH | 6.54 | | Measurement | True
+Lipophilicity | 2.8407448658 Log Units | Parameter Identification-Parameter Identification-Value updated from '30b - final' on 2019-12-30 13:23 | logP | True
+Fraction unbound (plasma, reference value) | 11 % | Other-Assumption | Measurement | True
+Permeability | 0.0994098912 cm/min | Parameter Identification-Parameter Identification-Value updated from '30b - final' on 2019-12-30 13:23 | Fitted | True
+Specific intestinal permeability (transcellular) | 3.5447164381E-06 cm/min | Parameter Identification-Parameter Identification-Value updated from '30b - final' on 2019-12-30 13:23 | Fitted | True
+Is small molecule | Yes | | |
+Molecular weight | 440.584 g/mol | | |
+Plasma protein binding partner | Unknown | | |
+
+#### Calculation methods
+
+Name | Value
+----------------------- | -------------------
+Partition coefficients | Rodgers and Rowland
+Cellular permeabilities | PK-Sim Standard
+
+#### Processes
+
+##### Metabolizing Enzyme: CYP3A4-D620
+
+Molecule: CYP3A4
+
+###### Parameters
+
+Name | Value | Value Origin
+-------------------------------- | ----------------------------- | ------------------------------------------------------------------------------------------------------
+In vitro Vmax/recombinant enzyme | 6.5 pmol/min/pmol rec. enzyme |
+Km | 36 µmol/l |
+kcat | 41.0994047535 1/min | Parameter Identification-Parameter Identification-Value updated from '30b - final' on 2019-12-30 13:23
+
+##### Systemic Process: Glomerular Filtration-GFR
+
+Species: Human
+
+###### Parameters
+
+Name | Value | Value Origin
+------------ | -----:| ------------:
+GFR fraction | 1 |
+
+##### Transport Protein: P-gp-Paper
+
+Molecule: P-gp
+
+###### Parameters
+
+Name | Value | Value Origin
+------------------------- | ------------------------------------ | ------------------------------------------------------------------------------------------------------
+In vitro Vmax/transporter | 0.00057724 pmol/min/pmol transporter |
+Km | 1.01 µmol/l |
+kcat | 3.3916609583 1/min | Parameter Identification-Parameter Identification-Value updated from '30b - final' on 2019-12-30 13:23
+
+##### Inhibition: P-gp-Non-competitive
+
+Molecule: P-gp
+
+###### Parameters
+
+Name | Value | Value Origin
+---- | ------------------- | ------------------------------------------------------------------------------------------------------
+Ki | 0.0383697779 µmol/l | Parameter Identification-Parameter Identification-Value updated from '30b - final' on 2019-12-30 13:23
+
+##### Inhibition: CYP3A4-MBI
+
+Molecule: CYP3A4
+
+###### Parameters
+
+Name | Value | Value Origin
+------------- | ---------- | ------------:
+kinact | 0.08 1/min |
+K_kinact_half | 2.9 µmol/l |
+
+### Formulation: Solution
+
+Type: Dissolved
+
+### Formulation: Retard Tablet Verapamil (Knoll)
+
+Type: Weibull
+
+#### Parameters
+
+Name | Value | Value Origin
+-------------------------------- | ------------------ | -------------------------------------------------------------------------------------------------------------------------------------------
+Dissolution time (50% dissolved) | 155.2445399403 min | Parameter Identification-Parameter Identification-Value updated from '240 mg retard (Isoptin RR) QD vs Verabeta 240 RR' on 2019-12-31 11:13
+Lag time | 0 min |
+Dissolution shape | 2.3662989419 | Parameter Identification-Parameter Identification-Value updated from '240 mg retard (Isoptin RR) QD vs Verabeta 240 RR' on 2019-12-31 11:13
+Use as suspension | Yes |
+
+## 3.2 Diagnostics Plots
+
+Below you find the goodness-of-fit visual diagnostic plots for the PBPK model performance of all data used presented in [Section 2.2](#clinical-data).
+
+The first plot shows observed versus simulated plasma concentration, the second weighted residuals versus time.
+
+
+
+**Table 3-1: GMFE for Goodness of fit plot for concentration in plasma**
+
+|Group |GMFE |
+|:-----------------------------------------|:----|
+|Intravenous administration - norverapamil |1.66 |
+|Intravenous administration - R-verapamil |1.25 |
+|Intravenous administration - S-verapamil |1.31 |
+|Intravenous administration - verapamil |1.45 |
+|Oral administration - norverapamil |1.29 |
+|Oral administration - R-norverapamil |1.14 |
+|Oral administration - R-verapamil |1.33 |
+|Oral administration - S-norverapamil |1.16 |
+|Oral administration - S-verapamil |1.31 |
+|Oral administration - verapamil |1.42 |
+|All |1.36 |
+
+
+
+
+
+
+
+
+**Figure 3-1: Goodness of fit plot for concentration in plasma**
+
+
+
+
+
+
+
+
+**Figure 3-2: Goodness of fit plot for concentration in plasma**
+
+
+
+
+## 3.3 Concentration-Time Profiles
+
+Simulated versus observed concentration-time profiles of all data listed in [Section 2.2](#clinical-data) are presented below.
+
+
+
+
+
+**Figure 3-3: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-4: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-5: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-6: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-7: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-8: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-9: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-10: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-11: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-12: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-13: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-14: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-15: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-16: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-17: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-18: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-19: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-20: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-21: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-22: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-23: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-24: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-25: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-26: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-27: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-28: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-29: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-30: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-31: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-32: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-33: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-34: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-35: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-36: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-37: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-38: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-39: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-40: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-41: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-42: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-43: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-44: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-45: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-46: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-47: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-48: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-49: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-50: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-51: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-52: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-53: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-54: Time Profile Analysis**
+
+
+
+
+# 4 Conclusion
+
+The herein presented PBPK model adequately describes the pharmacokinetics of R-verapamil, S-verapamil, R-norverapamil and S-norverapamil after single and multiple administration of a variety of doses to healthy adults. Furthermore, mechanism-based CYP3A4 (auto-) inactivation on verapamil itself can be described well with the optimized parameterization.
+
+# 5 References
+
+**Abernethy 1985** Abernethy DR, Schwartz JB, Todd EL. Lack of interaction between verapamil and cimetidine. Clin Pharmacol Ther. 1985 Sep;38(3):342-9. doi: 10.1038/clpt.1985.183. PMID: 4028631.
+
+**Abernethy 1993** Abernethy DR, Wainer IW, Longstreth JA, Andrawis NS (1993) Stereoselective verapamil disposition and dynamics in aging during racemic verapamil administration. The Journal of pharmacology and experimental therapeutics 266(2):904–11
+
+**Barbarash 1988** Barbarash RA, Bauman JL, Fischer JH, Kondos GT, Batenhorst RL. Near-total reduction in verapamil bioavailability by rifampin. Electrocardiographic correlates. Chest. 1988 Nov;94(5):954-9.
+
+**Blume 1983** Blume H, Mutschler E (1989) Bioäquivalenz: Qualitätsbewertung wirkstoffgleicher Fertigarzneimittel: Anleitung, Methoden, Materialien. Govi-Verlag
+
+**Blume 1994** Blume H, Mutschler E (1989) Bioäquivalenz: Qualitätsbewertung wirkstoffgleicher Fertigarzneimittel: Anleitung, Methoden, Materialien. Govi-Verlag
+
+**Blume 1987** Blume H, Mutschler E (1989) Bioäquivalenz: Qualitätsbewertung wirkstoffgleicher Fertigarzneimittel: Anleitung, Methoden, Materialien. Govi-Verlag
+
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+
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+
+","Markdown"
+"PBPK model","Open-Systems-Pharmacology/OSP-PBPK-Model-Library","Triazolam/Triazolam_evaluation_report.md",".md","28515","423","# Building and evaluation of a PBPK model for triazolam in healthy adults
+
+| Version | 2.0-OSP12.2 |
+| ----------------------------------------------- | ------------------------------------------------------------ |
+| based on *Model Snapshot* and *Evaluation Plan* | https://github.com/Open-Systems-Pharmacology/Triazolam-Model/releases/tag/v2.0 |
+| OSP Version | 12.2 |
+| Qualification Framework Version | 3.5 |
+
+This evaluation report and the corresponding PK-Sim project file are filed at:
+
+https://github.com/Open-Systems-Pharmacology/OSP-PBPK-Model-Library/
+
+# Table of Contents
+
+ * [1 Introduction](#introduction)
+ * [2 Methods](#methods)
+ * [2.1 Modeling Strategy](#modeling-strategy)
+ * [2.2 Data](#data)
+ * [2.3 Model Parameters and Assumptions](#model-parameters-and-assumptions)
+ * [3 Results and Discussion](#results-and-discussion)
+ * [3.1 Final input parameters](#final-input-parameters)
+ * [3.2 Diagnostics Plots](#diagnostics-plots)
+ * [3.3 Concentration-Time Profiles](#ct-profiles)
+ * [4 Conclusion](#conclusion)
+ * [5 References](#main-references)
+
+# 1 Introduction
+
+The presented model building and evaluation report evaluates the performance of a PBPK model for triazolam in healthy adults.
+
+Triazolam, sold under the trade name Halcion, among others, belongs to the group of benzodiazepines and is used for short-term treatment of insomnia and circadian rhythm sleep disorders. It is generally administered orally as immediate release tablet, but other forms of administrations, e.g. intravenously or as sublingual tablet, exist as well.
+
+Following oral administration, triazolam is rapidly absorbed with an absolute bioavailability of 44 ± 24% (mean ± standard deviation, [Kroboth 1995](#5-references)). Triazolam is widely distributed throughout the body. Its fraction unbound in human plasma averages around 17% and is, within the range of 20 to 1000 ng/mL, not influenced by total triazolam concentrations ([Eberts 1981](#5-references)). Triazolam is extensively metabolized via CYP3A4 to α-hydroxy-alprazolam and 4-hydroxy-alprazolam ([Eberts 1981](#5-references), [Kronbach 1989](#5-references)) and is therefore often used as victim compound in drug-drug interaction (DDI) studies.
+
+The presented triazolam PBPK model was developed for intravenous (IV) administration and oral (PO) administration of the immediate release tablet given in fasted state in healthy, non-obese adults.
+
+# 2 Methods
+
+
+
+## 2.1 Modeling Strategy
+
+The general workflow for building an adult PBPK model has been described by Kuepfer et al. ([Kuepfer 2016](#5-references)). Relevant information on the anthropometry (height, weight) was gathered from the respective clinical study, if reported. Information on physiological parameters (e.g. blood flows, organ volumes, hematocrit) in adults was gathered from the literature and has been incorporated in PK-Sim®) as described previously ([Willmann 2007](#5-references)). The applied activity and variability of plasma proteins and active processes that are integrated into PK-Sim® are described in the publicly available 'PK-Sim® Ontogeny Database Version 7.3' ([PK-Sim Ontogeny Database Version 7.3](#5-references)).
+
+The PBPK model was developed based on clinical data of healthy, non-obese, adult subjects obtained from the literature, covering different single doses of triazolam administered intravenously or orally as immediate release tablet in the fasted state.
+
+Unknown parameters were simultaneously optimized using all available PK data, in particular:
+
+- 6 data sets following single IV administration of 5 different doses of triazolam (0.125 mg, 0.25 mg, 0.5 mg, 0.75 mg, 1 mg)
+- 22 data sets following single PO administration of 3 different doses of triazolam as immediate release tablet (0.125 mg, 0.25 mg, 0.5 mg)
+
+Structural model selection was mainly guided by visual inspection of the resulting description of data and biological plausibility. The following parameters were identified using the Parameter Identification module provided in PK-Sim® and MoBi® ([Open Systems Pharmacology Documentation](#5-references)):
+
+- `Dissolution time (50% dissolved)`
+- `Dissolution shape`
+- `Specific intestinal permeability`
+- `Mucosa permeability (interstitial<->intracellular)`
+- `Lipophilicity`
+- `Metabolizing Enzyme - CYP3A4 - kcat`
+
+Details about input data (physicochemical, *in vitro* and clinical) can be found in [Section 2.2](#22-data).
+
+Details about the structural model and its parameters can be found in [Section 2.3](#23-model-parameters-and-assumptions).
+
+## 2.2 Data
+
+### 2.2.1 In vitro / physicochemical data
+
+A literature search was carried out to collect available information on physicochemical properties of triazolam. The obtained information from the literature is summarized in the table below and is used for model building.
+
+| **Parameter** | **Unit** | **Literature** | **Description** |
+| :--------------------- | -------- | ------------------------------------------------------------ | ------------------------------------------------------------ |
+| Molecular weight | g/mol | 343.21 ([drugbank.ca](#5-references)) | Molecular weight |
+| pKa (basic) | | 1.52 ([Konishi 1982](#5-references)) | Acid dissociation constant |
+| logP | | 2.42 ([drugbank.ca](#5-references)) | Partition coefficient between octanol and water |
+| logD | | 1.63 ([Greenblatt 1983a](#5-references)) | Partition coefficient between octanol and water at physiological pH |
+| fu | | 0.099 ± 0.015a ([Jochemsen 1983](#5-references)); 0.11 ([Eberts 1981](#5-references)); 0.174 ± 0.020a ([Friedman 1988](#5-references)); 0.188 ± 0.139a ([Ochs 1987](#5-references)); 0.213 [0.193 - 0.264]b ([Greenblatt 1983b](#5-references)); 0.229 [0.204 - 0.259]c ([Greenblatt 1983b](#5-references)) | Fraction unbound in human plasma of healthy adults |
+| Water solubility | mg/L | 4.53 ([drugbank.ca](#5-references)) | Estimated solubility in water |
+
+a mean ± standard deviation
+
+b mean [range] in young males
+
+c mean [range] in young females
+
+### 2.2.2 Clinical data
+
+A literature search was carried out to collect triazolam PK data in healthy adults.
+
+The following publications were found and used for model building and evaluation:
+
+| Publication | Study description |
+| :------------------------------------- | :----------------------------------------------------------- |
+| [Friedman 1986](#5-references) | PO single dose administration of 0.5 mg |
+| [Friedman 1988](#5-references) | PO single dose administration of 0.5 mg |
+| [Greenblatt 1989](#5-references) | PO single dose administration of 0.25 mg |
+| [Greenblatt 1991](#5-references) | PO single dose administration of 0.125 mg |
+| [Greenblatt 2000](#5-references) | PO single dose administration of 0.25 mg |
+| [Greenblatt 2004](#5-references) | PO single dose administration of 0.25 mg |
+| [Hukkinen 1995](#5-references) | PO single dose administration of 0.25 mg |
+| [Lilja 2000](#5-references) | PO single dose administration of 0.25 mg |
+| [Kroboth 1985](#5-references) | IV single dose administration of 0.25 mg and PO single dose administration of 0.25 mg |
+| [O'Connor-Semmes 2001](#5-references) | PO single dose administration of 0.25 mg |
+| [Ochs 1984](#5-references) | PO single dose administration of 0.5 mg |
+| [Phillips 1986](#5-references) | PO single dose administration of 0.5 mg |
+| [Smith 1987](#5-references) | IV single dose administration of 0.125 mg, 0.25 mg, 0.5 mg, 0.75 mg, and 1 mg |
+| [Varhe 1994](#5-references) | PO single dose administration of 0.25 mg |
+| [Varhe 1996a](#5-references) | PO single dose administration of 0.25 mg |
+| [Varhe 1996b](#5-references) | PO single dose administration of 0.25 mg |
+| [Varhe 1996c](#5-references) | PO single dose administration of 0.25 mg |
+| [Villikka 1997](#5-references) | PO single dose administration of 0.5 mg |
+| [Villikka 1998](#5-references) | PO single dose administration of 0.5 mg |
+| [von Moltke 1996](#5-references) | PO single dose administration of 0.125 mg |
+
+## 2.3 Model Parameters and Assumptions
+
+### 2.3.1 Dissolution and absorption
+
+Dissolution of the immediate release tablet of triazolam was described by a Weibull function with the two parameters `Dissolution shape` and `Dissolution time (50% dissolved)` being fitted, together with the other parameters listed in [Section 2.1](#21-modeling-strategy), to observed PK data to better match the observations. `Specific intestinal permeability (transcellular)` was also optimized together with the parameters listed in [Section 2.1](#21-modeling-strategy).
+### 2.3.2 Distribution
+
+In the model, the `fraction unbound (plasma, reference value)` was set to 0.174 which is the reported mean value measured in 19 healthy male and female volunteers aged 20 to 45 years ([Friedman 1988](#5-references)). This value is also the approximate average of all pooled values reported in several studies ([Jochemsen 1983](#5-references), [Eberts 1981](#5-references), [Greenblatt 1983](#5-references), [Friedman 1988](#5-references), [Ochs 1987](#5-references)). `Lipophilicity` was optimized together with the other parameters listed in [Section 2.1](#21-modeling-strategy) to better match observed PK data. The observed PK data were found to be best described using the model for estimating intracellular-to-plasma partition coefficients according to the method by `Rodgers and Rowland` ([Rodgers 2005](#5-references), [Rodgers 2006](#5-references)). Cellular permeabilities were automatically calculated using the method `PK-Sim Standard` ([Open Systems Pharmacology Documentation](#5-references)).
+
+### 2.3.3 Elimination
+
+Triazolam is extensively metabolized via CYP3A to the two metabolites α-hydroxy-triazolam and 4-hydroxy-triazolam. In the model, these two biotransformation pathways were separately described via Michaelis-Menten kinetics. The `Km` values for each pathway were fixed to reported literature values, namely 74.2 µmol/L for the α-OH pathway and 305 µmol/L for the 4-OH pathway ([von Moltke 1996](#5-references)). Together with the other parameters listed in [Section 2.1](#21-modeling-strategy), the `kcat` values were optimized while keeping the ratio between both values constant (by selecting the option `Use as Factor`). The gene expression profile of CYP3A4 was loaded from the internal PK-Sim® database using the expression data quantified by RT-PCR ([Open Systems Pharmacology Documentation](#5-references)).
+
+# 3 Results and Discussion
+
+The PBPK model for triazolam was developed and verified with clinical PK data.
+
+The next sections show:
+
+1. the final model parameters for the building blocks: [Section 3.1](#31-final-input-parameters).
+2. the overall goodness of fit: [Section 3.2](#32-diagnostics-plots).
+3. simulated vs. observed concentration-time profiles for the clinical studies used for model building: [Section 3.3](#33-concentration-time-profiles).
+
+## 3.1 Final input parameters
+
+The compound parameter values of the final PBPK model are illustrated below.
+
+### Compound: Triazolam
+
+#### Parameters
+
+Name | Value | Value Origin | Alternative | Default
+------------------------------------------------ | ----------------------- | ---------------------------------------------------------------------------------------------------- | ----------- | -------
+Solubility at reference pH | 4.53 mg/l | Unknown-drugbank.ca | Measurement | True
+Reference pH | 7 | Unknown-drugbank.ca | Measurement | True
+Lipophilicity | 1.897007419 Log Units | Parameter Identification-Parameter Identification-Value updated from 'IV + Oral' on 2018-11-13 16:52 | Optimized | True
+Fraction unbound (plasma, reference value) | 0.174 | Publication-In Vivo-PMID: 3360971 | Measurement | True
+Specific intestinal permeability (transcellular) | 7.0220146601E-05 cm/min | Parameter Identification-Parameter Identification-Value updated from 'IV + Oral' on 2018-11-13 16:52 | Optimized | True
+Cl | 2 | | |
+Is small molecule | Yes | | |
+Molecular weight | 343.21 g/mol | | |
+Plasma protein binding partner | Unknown | | |
+
+#### Calculation methods
+
+Name | Value
+----------------------- | -------------------
+Partition coefficients | Rodgers and Rowland
+Cellular permeabilities | PK-Sim Standard
+
+#### Processes
+
+##### Metabolizing Enzyme: CYP3A4-alpha-OH pathway
+
+Molecule: CYP3A4
+
+###### Parameters
+
+Name | Value | Value Origin
+---------------------------------- | ----------------------------- | ----------------------------------------------------------------------------------------------------
+In vitro Vmax for liver microsomes | 2.36 nmol/min/mg mic. protein | Publication-In Vitro-PMID: 8632299
+Km | 74.2 µmol/l | Publication-In Vitro-PMID: 8632299
+kcat | 4.0317206142 1/min | Parameter Identification-Parameter Identification-Value updated from 'IV + Oral' on 2018-11-13 16:52
+
+##### Metabolizing Enzyme: CYP3A4-4-OH pathway
+
+Molecule: CYP3A4
+
+###### Parameters
+
+Name | Value | Value Origin
+---------------------------------- | ------------------------------ | ----------------------------------------------------------------------------------------------------
+In vitro Vmax for liver microsomes | 10.27 nmol/min/mg mic. protein | Publication-In Vitro-PMID: 8632299
+Km | 305 µmol/l | Publication-In Vitro-PMID: 8632299
+kcat | 17.5448180963 1/min | Parameter Identification-Parameter Identification-Value updated from 'IV + Oral' on 2018-11-13 16:52
+
+### Formulation: Halcion
+
+Type: Weibull
+
+#### Parameters
+
+Name | Value | Value Origin
+-------------------------------- | ---------------- | ----------------------------------------------------------------------------------------------------
+Dissolution time (50% dissolved) | 1.7958147418 min | Parameter Identification-Parameter Identification-Value updated from 'IV + Oral' on 2018-11-13 16:52
+Lag time | 0 min |
+Dissolution shape | 2.5169993312 | Parameter Identification-Parameter Identification-Value updated from 'IV + Oral' on 2018-11-13 16:52
+Use as suspension | Yes |
+
+## 3.2 Diagnostics Plots
+
+Below you find the goodness-of-fit visual diagnostic plots for the PBPK model performance of all data used presented in [Section 2.2.2](#222-clinical-data).
+
+The first plot shows observed versus simulated plasma concentration, the second weighted residuals versus time.
+
+
+
+**Table 3-1: GMFE for Goodness of fit plot for concentration in plasma**
+
+|Group |GMFE |
+|:-----|:----|
+|IV |1.24 |
+|PO |1.28 |
+|All |1.27 |
+
+
+
+
+
+
+
+
+**Figure 3-1: Goodness of fit plot for concentration in plasma**
+
+
+
+
+
+
+
+
+**Figure 3-2: Goodness of fit plot for concentration in plasma**
+
+
+
+
+## 3.3 Concentration-Time Profiles
+
+Simulated versus observed concentration-time profiles of all data listed in [Section 2.2.2](#222-clinical-data) are presented below.
+
+
+
+
+
+**Figure 3-3: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-4: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-5: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-6: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-7: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-8: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-9: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-10: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-11: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-12: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-13: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-14: Time Profile Analysis 1**
+
+
+
+
+# 4 Conclusion
+
+The final triazolam PBPK model applies metabolism by CYP3A4, modelled as two separate pathways yielding α-hydroxy-triazolam and 4-hydroxy-triazolam as metabolites. Overall, the model adequately describes the observed PK of triazolam in healthy, non-obese adults receiving different single IV or PO doses of triazolam. The model is deemed fit for purpose to be applied as victim drug for the investigation of CYP3A4 drug-drug interactions.
+
+# 5 References
+
+**drugbank.ca**. (https://www.drugbank.ca/drugs/DB00897), accessed on 11-19-2019.
+
+**Eberts 1981** Eberts FS Jr, Philopoulos Y, Reineke LM, Vliek RW. Triazolam disposition. *Clin Pharmacol Ther* 1981, 29(1): 81-93.
+
+**Friedman 1986** Friedman H, Greenblatt DJ, Burstein ES, Harmatz JS, Shader RI. Population study of triazolam pharmacokinetics. *Br J Clin Pharmacol* 1986, 22(6): 639-642.
+
+**Friedman 1988** Friedman H, Greenblatt DJ, Burstein ES, Scavone JM, Harmatz JS, Shader RI. kinetics: interaction with cimetidine, propranolol, and the combination. *J Clin Pharmacol* 1988, 28(3): 228-233.
+
+**Greenblatt 1983a** Greenblatt DJ, Arendt RM, Abernethy DR, Giles HG, Sellers EM, Shader RI. In vitro quantitation of benzodiazepine lipophilicity: relation to in vivo distribution. *Br J Anaesth* 1983, 55(10): 985-989.
+
+**Greenblatt 1983** Greenblatt DJ, Divoll M, Abernethy DR, Moschitto LJ, Smith RB, Shader RI. Reduced clearance of triazolam in old age: relation to antipyrine oxidizing capacity. *Br J Clin Pharmacol* 1983, 15(3): 303-309.
+
+**Greenblatt 1989** Greenblatt DJ, Harmatz JS, Engelhardt N, Shader RI. Pharmacokinetic determinants of dynamic differences among three benzodiazepine hypnotics. Flurazepam, temazepam, and triazolam. *Arch Gen Psychiatry* 1989, 46(4): 326-332.
+
+**Greenblatt 1991** Greenblatt DJ, Harmatz JS, Shapiro L, Engelhardt N, Gouthro TA, Shader RI. Sensitivity to triazolam in the elderly. *N Engl J Med* 1991, 324(24): 1691-1698.
+
+**Greenblatt 2000** Greenblatt DJ, Harmatz JS, von Moltke LL, Wright CE, Durol AL, Harrel-Joseph LM, Shader RI. Comparative kinetics and response to the benzodiazepine agonists triazolam and zolpidem: evaluation of sex-dependent differences. *J Pharmacol Exp Ther* 2000, 293(2): 435-443.
+
+**Greenblatt 2004** Greenblatt DJ, Harmatz JS, von Moltke LL, Wright CE, Shader RI. Age and gender effects on the pharmacokinetics and pharmacodynamics of triazolam, a cytochrome P450 3A substrate. *Clin Pharmacol Ther* 2004, 76(5): 467-479.
+
+**Hukkinen 1995** Hukkinen SK, Varhe A, Olkkola KT, Neuvonen PJ. Plasma concentrations of triazolam are increased by concomitant ingestion of grapefruit juice. *Clin Pharmacol Ther* 1995, 58(2): 127-131.
+
+**Jochemsen 1983** Jochemsen R, Wesselman JG, van Boxtel CJ, Hermans J, Breimer DD. Comparative pharmacokinetics of brotizolam and triazolam in healthy subjects. *Br J Clin Pharmacol* 1983, 16 Suppl 2: 291S-297S.
+
+**Konishi 1982** Konishi M, Hirai K, Mori Y. Kinetics and mechanism of the equilibrium reaction of triazolam in aqueous solution. *J Pharm Sci* 1982, 71(12): 1328-1334.
+
+**Kroboth 1995** Kroboth PD, McAuley JW, Kroboth FJ, Bertz RJ, Smith RB. Triazolam pharmacokinetics after intravenous, oral, and sublingual administration. *J Clin Psychopharmacol* 1995, 15(4): 259-262.
+
+**Kronbach 1989** Kronbach T, Mathys D, Umeno M, Gonzalez FJ, Meyer UA. Oxidation of midazolam and triazolam by human liver cytochrome P450IIIA4. *Mol Pharmacol* 1989, 36(1): 89-96.
+
+**Kuepfer 2016** Kuepfer L, Niederalt C, Wendl T, Schlender JF, Willmann S, Lippert J, Block M, Eissing T, Teutonico D. Applied concepts in PBPK modeling: how to build a PBPK/PD model. *CPT Pharmacometrics Syst Pharmacol* 2016, 5(10): 516-531.
+
+**Lilja 2000** Lilja JJ, Kivistö KT, Backman JT, Neuvonen PJ. Effect of grapefruit juice dose on grapefruit juice-triazolam interaction: repeated consumption prolongs triazolam half-life. *Eur J Clin Pharmacol* 2000, 56(5): 411-415.
+
+**O'Connor-Semmes 2001** O'Connor-Semmes RL, Kersey K, Williams DH, Lam R, Koch KM. Effect of ranitidine on the pharmacokinetics of triazolam and alpha-hydroxytriazolam in both young (19-60 years) and older (61-78 years) people. *Clin Pharmacol Ther* 2001, 70(2): 126-131.
+
+**Ochs 1984** Ochs HR, Greenblatt DJ, Arendt RM, Hübbel W, Shader RI. Pharmacokinetic noninteraction of triazolam and ethanol. *J Clin Psychopharmacol* 1984, 4(2): 106-107.
+
+**Ochs 1987** Ochs HR, Greenblatt DJ, Burstein ES. Lack of influence of cigarette smoking on triazolam pharmacokinetics. *Br J Clin Pharmacol* 1987, 23(6): 759-763.
+
+**Open Systems Pharmacology Documentation**. (https://docs.open-systems-pharmacology.org/), accessed on 07-30-2019.
+
+**Phillips 1986** Phillips JP, Antal EJ, Smith RB. A pharmacokinetic drug interaction between erythromycin and triazolam. *J Clin Psychopharmacol* 1986, 6(5): 297-299.
+
+**PK-Sim Ontogeny Database Version 7.3**. (https://github.com/Open-Systems-Pharmacology/OSPSuite.Documentation/blob/38cf71b384cfc25cfa0ce4d2f3addfd32757e13b/PK-Sim%20Ontogeny%20Database%20Version%207.3.pdf), accessed on 07-30-2019.
+
+**Smith 1987** Smith RB, Kroboth PD, Varner PD. Pharmacodynamics of triazolam after intravenous administration. *J Clin Pharmacol* 1987, 27(12): 971-979.
+
+**Varhe 1994** Varhe A, Olkkola KT, Neuvonen PJ. Oral triazolam is potentially hazardous to patients receiving systemic antimycotics ketoconazole or itraconazole. *Clin Pharmacol Ther* 1994, 56(6 Pt 1): 601-607.
+
+**Varhe 1996a** Varhe A, Olkkola KT, Neuvonen PJ. Fluconazole, but not terbinafine, enhances the effects of triazolam by inhibiting its metabolism. *Br J Clin Pharmacol* 1996, 41(4): 319-323.
+
+**Varhe 1996b** Varhe A, Olkkola KT, Neuvonen PJ. Diltiazem enhances the effects of triazolam by inhibiting its metabolism. *Clin Pharmacol Ther* 1996, 59(4): 369-375.
+
+**Varhe 1996c** Varhe A, Olkkola KT, Neuvonen PJ. Effect of fluconazole dose on the extent of fluconazole-triazolam interaction. *Br J Clin Pharmacol* 1996, 42(4): 465-470.
+
+**Villikka 1997** Villikka K, Kivistö KT, Backman JT, Olkkola KT, Neuvonen PJ. Triazolam is ineffective in patients taking rifampin. *Clin Pharmacol Ther* 1997, 61(1): 8-14.
+
+**Villikka 1998** Villikka K, Kivistö KT, Neuvonen PJ. The effect of dexamethasone on the pharmacokinetics of triazolam. *Pharmacol Toxicol* 1998, 83(3): 135-138.
+
+**von Moltke 1996** von Moltke LL, Greenblatt DJ, Harmatz JS, Duan SX, Harrel LM, Cotreau-Bibbo MM, Pritchard GA, Wright CE, Shader RI. Triazolam biotransformation by human liver microsomes in vitro: effects of metabolic inhibitors and clinical confirmation of a predicted interaction with ketoconazole. *J Pharmacol Exp Ther* 1996, 276(2): 370-379.
+
+**Willmann 2007** Willmann S, Höhn K, Edginton A, Sevestre M, Solodenko J, Weiss W, Lippert J, Schmitt W. Development of a physiology-based whole-body population model for assessing the influence of individual variability on the pharmacokinetics of drugs. *J Pharmacokinet Pharmacodyn* 2007, 34(3): 401-431.
+
+","Markdown"
+"PBPK model","Open-Systems-Pharmacology/OSP-PBPK-Model-Library","MEDI524YTE/MEDI524YTE_evaluation_report.md",".md","15253","221","# Building and evaluation of a PBPK model for antibody MEDI-524-YTE in cynomolgus monkeys
+
+| Version | 1.0-OSP12.2 |
+| ----------------------------------------------- | ------------------------------------------------------------ |
+| based on *Model Snapshot* and *Evaluation Plan* | https://github.com/Open-Systems-Pharmacology/MEDI524YTE-Model/releases/tag/v1.0 |
+| OSP Version | 12.2 |
+| Qualification Framework Version | 3.5 |
+
+This evaluation report and the corresponding PK-Sim project file are filed at:
+
+https://github.com/Open-Systems-Pharmacology/OSP-PBPK-Model-Library/
+
+# Table of Contents
+
+ * [1 Introduction](#introduction)
+ * [2 Methods](#methods)
+ * [2.1 Modeling Strategy](#modeling-strategy)
+ * [2.2 Data](#methods-data)
+ * [2.2.1 In vitro / physico-chemical Data ](#invitro-and-physico-chemical-data)
+ * [2.2.2 PK Data ](#PK-data)
+ * [2.3 Model Parameters and Assumptions](#model-parameters-and-assumptions)
+ * [2.3.1 Absorption ](#model-parameters-and-assumptions-absorption)
+ * [2.3.2 Distribution ](#model-parameters-and-assumptions-distribution)
+ * [2.3.3 Metabolism and Elimination ](#model-parameters-and-assumptions-metabolism-and-elimination)
+ * [2.3.4 Automated Parameter Identification ](#model-parameters-and-assumptions-parameter-identification)
+ * [3 Results and Discussion](#results-and-discussion)
+ * [3.1 Final input parameters](#final-input-parameters)
+ * [3.2 Diagnostics Plots](#diagnostics-plots)
+ * [3.3 Concentration-Time Profiles](#ct-profiles)
+ * [4 Conclusion](#conclusion)
+ * [5 References](#main-references)
+
+# 1 Introduction
+
+MEDI-524-YTE is variant of the humanized monoclonal IgG1 antibody MEDI-524 against the respiratory syncytial virus (RSV). The triple YTE mutation introduced into the Fc region led to an increased affinity to FcRn and consequently to an increased plasma half-life ([Dall’Acqua2006](#5-references)).
+
+The plasma concentration–time profile after intravenous application of a 30 mg/kg dose in cynomolgus monkeys ([Dall’Acqua2006](#5-references)) were used together with pharmacokinetic (PK) data from 5 other compounds to identify unknown parameters during the development of the generic large molecule physiologically based pharmacokinetic (PBPK) model in PK-Sim ([Niederalt 2018](#5-references)).
+
+The herein presented evaluation report evaluates the performance of the PBPK model for MEDI-524-YTE in cynomolgus monkeys for the PK data used for the development of the generic large molecule model in PK-Sim.
+
+The presented MEDI-524-YTE PBPK model as well as the respective evaluation plan and evaluation report are provided open-source (https://github.com/Open-Systems-Pharmacology/MEDI524YTE-Model).
+
+# 2 Methods
+
+## 2.1 Modeling Strategy
+
+The development of the large molecule PBPK model in PK-Sim® has previously been described by Niederalt et al. ([Niederalt 2018](#5-references)). In short, the model was built as an extension of the PK-Sim® model for small molecules incorporating (i) the two-pore formalism for drug extravasation from blood plasma to interstitial space, (ii) lymph flow, (iii) endosomal clearance and (iv) protection from endosomal clearance by neonatal Fc receptor (FcRn) mediated recycling.
+
+For model development and evaluation, PK data were used from compounds with a wide range of solute radii and from different species. The PK data used for parameter estimation were from the following compounds: antibody–drug conjugate BAY 79-4620 in mice (Bayer in house data), antibody 7E3 in wild-type and FcRn knockout mice ([Garg 2007](#5-references), [Garg2009](#5-references)), domain antibody dAb2 in mice ([Sepp 2015](#5-references)), antibodies MEDI-524 and MEDI-524-YTE in monkeys ([Dall'Acqua 2006](#5-references)), and antibody CDA1 in humans ([Taylor 2008](#5-references)). The PK data used for model evaluation were from inulin in rats ([Tsuji1983](#5-references)) and tefibazumab in humans ([Reilly 2005](#5-references)).
+
+The PBPK model including the estimated physiological parameters as described by Niederalt et al. ([Niederalt 2018](#5-references)) is available in the Open Systems Pharmacology Suite from version 7.1 onwards.
+
+This evaluation report focuses on the PBPK model for the antibody antibodies MEDI-524-YTE.
+
+Details about input data (physicochemical, *in vitro* and PK) can be found in [Section 2.2](#22-data).
+
+Details about the structural model and its parameters can be found in [Section 2.3](#23-model-parameters-and-assumptions).
+
+## 2.2 Data
+
+### 2.2.1 In vitro / physico-chemical Data
+
+A literature search was performed to collect available information on physicochemical properties of MEDI-524-YTE. The obtained information from literature is summarized in the table below.
+
+| **Parameter** | **Unit** | **Value** | Source | **Description** |
+| :------------ | -------- | --------- | -------------------------------- | ------------------------------------------------------------ |
+| MW | g/mol | 150000 | [Lobo 2004](#5-references) | Molecular weight |
+| r | nm | 5.34 | [Taylor 1984](#5-references) | Hydrodynamic solute radius |
+| Kd (FcRn) | µM | 0.134 | [Dall'Acqua 2006](#5-references) | Dissociation constant for binding to cynomolgus monkey FcRn for the Fc variant MEDI-524-YTE (pH 6) |
+
+### 2.2.2 PK Data
+
+Published PK data on MEDI-524-YTE in cynomolgus monkeys were used.
+
+| Publication | Description |
+| :------------------------------- | :----------------------------------------------------------- |
+| [Dall'Acqua 2006](#5-references) | The plasma concentration–time profiles after single i.v. infusion of 30 mg/kg MEDI-524-YTE in in cynomolgus monkeys were used. |
+
+## 2.3 Model Parameters and Assumptions
+
+### 2.3.1 Absorption
+
+There is no absorption process since MEDI-524-YTE was administered intravenously.
+
+### 2.3.2 Distribution
+
+The standard lymph and fluid recirculation flow rates and the standard vascular properties of the different tissues (hydraulic conductivity, pore radii, fraction of flow via large pores) from PK-Sim were used. MEDI-524-YTE, among other compounds, has been used to identify these lymph and fluid recirculation flow rates used in PK-Sim ([Niederalt 2018](#5-references)).
+
+### 2.3.3 Metabolism and Elimination
+
+The FcRn mediated clearance present in the standard PK-Sim model was used as only clearance process. The standard physiological parameters related to FcRn mediated clearance were used (rate constants for endosomal uptake and recycling, association rate constant for FcRn binding and concentration of FcRn in the endosomal space). MEDI-524-YTE, among other compounds, has been used to identify these parameters using literature values for the drug affinities to FcRn in the endosomal space ([Niederalt 2018](#5-references)).
+
+### 2.3.4 Automated Parameter Identification
+
+No drug specific parameters were fitted. MEDI-524-YTE, among other compounds, has been used to develop the model for proteins and large molecules in PK-Sim ([Niederalt 2018](#5-references)).
+
+# 3 Results and Discussion
+
+The PBPK model for MEDI-524-YTE was evaluated with PK data in cynomolgus monkeys.
+
+These PK data have been used together with PK data from 5 other compounds to simultaneously identify parameters during the development of the generic model for proteins and large molecules in PK-Sim ([Niederalt 2018](#5-references)).
+
+The next sections show:
+
+1. the final model parameters for the building blocks: [Section 3.1](#final-input-parameters).
+2. the overall goodness of fit: [Section 3.2](#diagnostics-plots).
+3. simulated vs. observed concentration-time profiles for the clinical studies used for model building and for model verification: [Section 3.3](#ct-profiles).
+
+## 3.1 Final input parameters
+
+The compound parameter values of the final PBPK model are illustrated below.
+
+### Compound: MEDI524YTE
+
+#### Parameters
+
+Name | Value | Value Origin | Alternative | Default
+------------------------------------------ | ------------ | --------------------------------------------- | ----------- | -------
+Solubility at reference pH | 9999 mg/l | Other-/Dummy value not used in the simulation | Measurement | True
+Reference pH | 7 | Other-/Dummy value not used in the simulation | Measurement | True
+Lipophilicity | -5 Log Units | Other-/Dummy value not used in the simulation | Measurement | True
+Fraction unbound (plasma, reference value) | 1 | Other-Assumption | Measurement | True
+Is small molecule | No | | |
+Molecular weight | 150000 g/mol | Publication-Lobo2004 | |
+Plasma protein binding partner | Unknown | | |
+Radius (solute) | 0.00534 µm | Publication-Taylor1984 | |
+Kd (FcRn) in endosomal space | 0.134 µmol/l | Publication-Dall'Acqua2006 | |
+
+#### Calculation methods
+
+Name | Value
+----------------------- | ---------------
+Partition coefficients | PK-Sim Standard
+Cellular permeabilities | PK-Sim Standard
+
+#### Processes
+
+## 3.2 Diagnostics Plots
+
+Below you find the goodness-of-fit visual diagnostic plots for the PBPK model performance of all data used presented in [Section 2.2.2](#PK-data).
+
+The first plot shows observed versus simulated plasma concentration, the second weighted residuals versus time.
+
+
+
+**Table 3-1: GMFE for Goodness of fit plot for concentration in plasma**
+
+|Group |GMFE |
+|:--------------------------|:----|
+|Intravenous administration |1.10 |
+
+
+
+
+
+
+
+
+**Figure 3-1: Goodness of fit plot for concentration in plasma**
+
+
+
+
+
+
+
+
+**Figure 3-2: Goodness of fit plot for concentration in plasma**
+
+
+
+
+## 3.3 Concentration-Time Profiles
+
+Simulated versus observed concentration-time profiles of all data listed in [Section 2.2.2](#PK-data) are presented below.
+
+
+
+
+
+**Figure 3-3: Plasma concentration (linear scale)**
+
+
+
+
+
+
+
+
+**Figure 3-4: Plasma concentration (log scale)**
+
+
+
+
+# 4 Conclusion
+
+The herein presented PBPK model adequately describes the pharmacokinetics in monkeys of MEDI-524-YTE, a variant of MEDI-524 with increased plasma half life. The PK data had been used during the development of the generic large molecule PBPK model in PK-Sim ([Niederalt 2018](#5-references)) together with PK data from 5 other compounds (7E3, BAY 79-4620, CDA1, dAb2 & MEDI-524).
+
+# 5 References
+
+**Dall'Acqua 2006** Dall’Acqua WF, Kiener PA, Wu H. Properties of human IgG1s engineered for enhanced binding to the neonatal Fc receptor (FcRn). J Biol Chem. 2006 Aug; 281(33):23514-23524. doi: 10.1074/jbc.M604292200.
+
+**Garg 2007** Garg A, Balthasar JP. Physiologically-based pharmacokinetic (PBPK) model to predict IgG tissue kinetics in wild-type and FcRn-knockout mice. J Pharmacokinet Pharmacodyn. 2007 Jul; 34(5):687-709. doi: 10.1007/s10928-007-9065-1.
+
+**Garg 2009** Garg A, Balthasar J. Investigation of the influence of FcRn on the distribution of IgG to the brain. AAPS J. 2009 July; 11(3):553-557. doi: 10.1208/s12248-009-9129-9.
+
+**Lobo 2004** Lobo ED, Hansen R J, Balthasar JP. Antibody pharmacokinetics and pharmacodynamics. J Pharm Sci. 2004 Nov;93(11):2645-2668. doi: 10.1002/jps.20178.
+
+**Niederalt 2018** Niederalt C, Kuepfer L, Solodenko J, Eissing T, Siegmund HU, Block M, Willmann S, Lippert J. A generic whole body physiologically based pharmacokinetic model for therapeutic proteins in PK-Sim. J Pharmacokinet Pharmacodyn. 2018 Apr;45(2):235-257. doi: 10.1007/s10928-017-9559-4.
+
+**Reilly 2005** Reilley S, Wenzel E, Reynolds L, Bennett B, Patti JM, Hetherington S. Open-label, dose escalation study of the safety and pharmacokinetic profile of tefibazumab in healthy volunteers. Antimicrob Agents Chemother. 2005 Mar;49(3):959–962. doi: 10.1128/AAC.49.3.959-962.2005.
+
+**Sepp 2015** Sepp A, Berges A, Sanderson A, Meno-Tetang G. Development of a physiologically based pharmacokinetic model for a domain antibody in mice using the two-pore theory. J Pharmacokinet Pharmacodyn. 2015 Jan;42(2):97-109. doi: 10.1007/s10928-014-9402-0.
+
+**Taylor 1984** Taylor AE, Granger DN. Exchange of macromolecules across the microcirculation. Handbook of Physiology - Cardiovascular System. Microcirculation (Eds. Renkin EM and Michel CC. Bethesda, MD, American Physiological Society). 1984; Vol. 4(Pt 2):467–520.
+
+**Taylor 2008** Taylor CP, Tummala S, Molrine D, Davidson L, Farrell RJ, Lembo A, Hibberd PL, Lowy I, Kelly CP. Open-label, dose escalation phase I study in healthy volunteers to evaluate the safety and pharmacokinetics of a human monoclonal antibody to Clostridium difficile toxin A. Vaccine. 2008 Jun;26(27-28):3404–3409. doi: 10.1016/j.vaccine.2008.04.042.
+
+**Tsuji 1983** Tsuji A, Yoshikawa T, Nishide K, Minami H, Kimura M, Nakashima E, Terasaki T, Miyamoto E, Nightingale CH, Yamana T. Physiologically based pharmacokinetic model for beta-lactam antibiotics I: tissue distribution and elimination in rats. J Pharm Sci. 1983 Nov;72(11):1239-1252. doi: 10.1002/jps.2600721103.
+
+","Markdown"
+"PBPK model","Open-Systems-Pharmacology/OSP-PBPK-Model-Library","Alprazolam/Alprazolam_evaluation_report.md",".md","39370","567","# Building and evaluation of a PBPK model for alprazolam in healthy adults
+
+| Version | 2.0-OSP12.2 |
+| ----------------------------------------------- | ------------------------------------------------------------ |
+| based on *Model Snapshot* and *Evaluation Plan* | https://github.com/Open-Systems-Pharmacology/Alprazolam-Model/releases/tag/v2.0 |
+| OSP Version | 12.2 |
+| Qualification Framework Version | 3.5 |
+
+This evaluation report and the corresponding PK-Sim project file are filed at:
+
+[https://github.com/Open-Systems-Pharmacology/OSP-PBPK-Model-Library](https://github.com/Open-Systems-Pharmacology/OSP-PBPK-Model-Library)
+
+# Table of Contents
+
+ * [1 Introduction](#introduction)
+ * [2 Methods](#methods)
+ * [2.1 Modeling Strategy](#modeling-strategy)
+ * [2.2 Data](#data)
+ * [2.3 Model Parameters and Assumptions](#model-parameters-and-assumptions)
+ * [3 Results and Discussion](#3)
+ * [3.1 Final input parameters](#final-input-parameters)
+ * [3.2 Diagnostics Plots](#diagnostics-plots)
+ * [3.3 Concentration-Time Profiles](#ct-profiles)
+ * [4 Conclusion](#4)
+ * [5 References](#5)
+
+# 1 Introduction
+
+The presented model building and evaluation report evaluates the performance of a PBPK model for alprazolam in healthy adults.
+
+Alprazolam, sold under the trade names Xanax and Solanax, among others, belongs to the group of benzodiazepines and is commonly used in short term management of anxiety disorders. It is generally administered orally as immediate release or extended release tablet, but other forms are also available, e.g. solution or sublingual tablet.
+
+Following oral administration, alprazolam is rapidly absorbed with an absolute bioavailability ranging from 80% to 100% ([Greenblatt 1993](#5-references)). Absorption is independent of the dose and the relative bioavailability of solid and liquid dosage forms has been observed to be similar ([Dawson 1984](#5-references)). Alprazolam is widely distributed throughout the body and its free fraction in plasma, averaging around 30%, is not influenced by total alprazolam concentrations within the tested range of 0.01 to 10 mg/L ([Moschitto 1983](#5-references)). Alprazolam is extensively metabolized to various metabolites ([von Moltke 1993](#5-references)). The two major metabolites, α-hydroxy-alprazolam and 4-hydroxy-alprazolam, are formed through oxidation catalyzed by CYP3A ([Eberts 1980](#5-references), [von Moltke 1993](#5-references)). Within 72 h of a 2 mg oral dose of 14C-alprazolam, 20% of the dose have been observed to be excreted unchanged in urine ([Eberts 1980](#5-references)). Alprazolam displays dose linear pharmacokinetics and does not accumulate during multiple dose treatment ([Dawson 1984](#5-references), [Greenblatt 1993](#5-references)). Because of the predominant role of CYP3A4 in alprazolam elimination, alprazolam is often used as victim compound in drug-drug interaction (DDI) studies.
+
+The presented alprazolam PBPK model was developed for intravenous (IV) administration and oral (PO) administration of the immediate release tablet (Xanax) or extended-release formulation (Solanax) given in fasted state in healthy, non-obese adults; administration in fed state was not addressed here.
+
+# 2 Methods
+
+## 2.1 Modeling Strategy
+
+The general workflow for building an adult PBPK model has been described by Kuepfer et al. ([Kuepfer 2016](#5-references)). Relevant information on the anthropometry (height, weight) was gathered from the respective clinical study, if reported. Information on physiological parameters (e.g. blood flows, organ volumes, hematocrit) in adults was gathered from the literature and has been incorporated in PK-Sim®) as described previously ([Willmann 2007](#5-references)). The applied activity and variability of plasma proteins and active processes that are integrated into PK-Sim® are described in the publicly available 'PK-Sim® Ontogeny Database Version 7.3' ([PK-Sim Ontogeny Database Version 7.3](#5-references)).
+
+The PBPK model was developed based on clinical data of healthy, non-obese, adult subjects obtained from the literature, covering different single doses of alprazolam administered via the intravenous (IV) or oral (PO) route in the fasted state. Several oral dosage forms were included in the model building process, such as Xanax® and Solanax® tablets. Comparison of the reported alprazolam plasma concentration-time profiles following administration of Xanax® and Solanax® tablets indicated that the latter oral dosage form yields a larger tmax than the Xanax® immediate release formulation. Therefore, different dissolution kinetics were developed for these two oral dosage forms. The reported PK profiles following administration of Solanax® tablets were measured in Japanese subjects ([Yasui 1996](#5-references), [Yasui 1998](#5-references), [Yasui 2000](#5-references)). To account for ethnicity-related differences in anatomical and physiological model parameters, the European Standard Individual used per default in the simulations was scaled to a Japanese individual and the reference concentration of CYP3A4 in this individual was optimized to better match the clinical data. Finally, mass balance information on urinary excretion of unchanged 14C-alprazolam after PO administration reported by Eberts et al. ([Eberts 1980](#5-references)) was also accounted for during the model building process.
+
+Unknown parameters were simultaneously optimized using all available PK data, in particular:
+
+- 2 plasma concentration-time profiles following single IV administration of 0.25 mg
+- 2 plasma concentration-time profiles following single IV administration of 0.5 mg
+- 3 plasma concentration-time profiles following single IV administration of 1 mg
+- 1 plasma concentration-time profile following single IV administration of 1 mg followed by 1.576 mg over 8 h
+- 2 plasma concentration-time profiles following single IV administration of 2 mg
+- 3 plasma concentration-time profiles following single IV administration of 4 mg
+- 2 plasma concentration-time profiles following single PO administration of 0.5 mg
+- 3 plasma concentration-time profiles following single PO administration of Solanax® tablets containing 0.8 mg alprazolam to Japanese subjects
+- 12 plasma concentration-time profiles following single PO administration of 1 mg
+- 1 plasma concentration-time profile following single PO administration of 2 mg
+- 1 dose fraction excreted unchanged in urine following single PO administration of 2 mg
+
+Structural model selection was mainly guided by visual inspection of the resulting description of data and biological plausibility. The following parameters were identified using the Parameter Identification module provided in PK-Sim® and MoBi® ([Open Systems Pharmacology Documentation](#5-references)):
+
+- `Dissolution time (50% dissolved)`
+- `Dissolution shape`
+- `Specific intestinal permeability`
+- `Mucosa permeability (interstitial<->intracellular)`
+- `Lipophilicity`
+- `Metabolizing Enzyme - CYP3A4 - kcat`
+- `Reference concentration CYP3A4` (only for Japanese subjects)
+- `GFR fraction`
+
+Details about input data (physicochemical, *in vitro* and clinical) can be found in [Section 2.2](#22-data).
+
+Details about the structural model and its parameters can be found in [Section 2.3](#23-model-parameters-and-assumptions).
+
+## 2.2 Data
+
+### 2.2.1 In vitro / physicochemical data
+
+A literature search was carried out to collect available information on physicochemical properties of alprazolam. The obtained information from the literature is summarized in the table below and is used for model building.
+
+| **Parameter** | **Unit** | **Literature** | **Description** |
+| :------------------------ | -------- | ------------------------------------------------------------ | ------------------------------------------------------------ |
+| Molecular weight | g/mol | 308.765 ([drugbank.ca](#5-references)) | Molecular weight |
+| pKa (basic) | | 2.40 ([Cho 1983](#5-references), [Raymond 1986](#5-references)); 2.48 ± 0.01 ([Manchester 2018](#5-references)) | Acid dissociation constant |
+| logP | | 2.19 ([Machatha 2004](#5-references)) | Partition coefficient between octanol and water |
+| logD | | 1.26 ([Greenblatt 1983](#5-references)) | Partition coefficient between octanol and water at physiological pH |
+| fu | | 0.20 ([Eberts 1980](#5-references)); 0.233 ± 0.028a ([Schmith 1991](#5-references)); 0.270 ± 0.017a ([Scavone 1988](#5-references)); 0.284 ± 0.017a ([Scavone 1988](#5-references)); 0.290 ± 0.025a ([Juhl 1984](#5-references)); 0.298 [0.259 - 0.316]b ([Abernethy 1983](#5-references)); 0.311 ± 0.026a ([Ochs 1986](#5-references)); 0.316c ([Moschitto 1983](#5-references)) | Fraction unbound in human plasma of healthy adults |
+| Water solubility (pH 1.2) | mg/L | 12 ([drugbank.ca](#5-references)) | Estimated solubility in water at pH 1.2 |
+| Water solubility (pH 7.0) | mg/L | 40 ([drugbank.ca](#5-references)) | Estimated solubility in water at pH 7.0 |
+| Water solubility | mg/L | 73 ([Loftsson 2006](#5-references)) | Experimentally measured solubility in water at 22°C - 24°C |
+
+a mean ± SD
+
+b mean [range]
+
+c mean
+
+### 2.2.2 Clinical data
+
+A literature search was carried out to collect alprazolam PK data in healthy adults.
+
+The following publications were found and used for model building and evaluation:
+
+| Publication | Study description |
+| :------------------------------------- | :----------------------------------------------------------- |
+| [Adams 1984](#5-references) | IV single dose administration of 0.25 mg and 4 mg |
+| [Bertz 1997](#5-references) | IV single dose administration of 2 mg (young subjects group) |
+| [Eberts 1980](#5-references) | PO single dose administration of 2 mg 14C-alprazolam (no plasma concentration-time profile was reported, but the dose fraction excreted unchanged in urine was quantified) |
+| [Eller 1990](#5-references) | PO single dose administration of 1 mg (Treatment C: IR tablet in fasted state) |
+| [Fleishaker 1989](#5-references) | IV single dose administration of 1 mg (Treatment A) |
+| [Fleishaker 1994](#5-references) | PO multiple dose administration of 1 mg four times daily at irregular time intervals for 4 days (Control phase) |
+| [Friedman 1991](#5-references) | PO single dose administration of 1 mg |
+| [Greenblatt 1988](#5-references) | PO single dose administration of 1 mg |
+| [Greenblatt 1992](#5-references) | PO single dose administration of 1 mg (Control phase) |
+| [Greenblatt 1998](#5-references) | PO single dose administration of 1 mg (Trial A) |
+| [Greenblatt 2000](#5-references) | PO single dose administration of 1 mg (Control group) |
+| [Juhl 1984](#5-references) | PO single dose administration of 1 mg (Healthy control group) |
+| [Kaplan 1998](#5-references) | PO single dose administration of 1 mg (young subjects group) |
+| [Kirkwood 1991](#5-references) | PO single dose administration of 1 mg |
+| [Kroboth 1988](#5-references) | IV single dose administration of 0.5 mg, 1 mg followed by 72 µg over 8 h, and 2 mg |
+| [Lin 1988](#5-references) | IV single dose administration of 0.5 mg and PO single dose administration of 0.5 mg |
+| [Schmider 1999](#5-references) | PO single dose administration of 1 mg (Control phase) |
+| [Schmith 1991](#5-references) | PO single dose administration of 0.5 mg and 2 mg (normal subjects group) |
+| [Smith 1984](#5-references) | IV single dose administration of 1 mg and PO single dose administration of 1 mg |
+| [Venkatakrishnan 2005](#5-references) | IV single dose administration of 1 mg |
+| [Wennerholm 2005](#5-references) | PO single dose administration of 1 mg |
+| [Yasui 1996](#5-references) | PO single dose administration of 0.8 mg (Control phase) |
+| [Yasui 1998](#5-references) | PO single dose administration of 0.8 mg (Control phase) |
+| [Yasui 2000](#5-references) | PO single dose administration of 0.8 mg (Control phase) |
+
+## 2.3 Model Parameters and Assumptions
+
+### 2.3.1 Dissolution and absorption
+
+Dissolution of the immediate release tablet of alprazolam was described by a Weibull function with the two parameters `Dissolution shape` and `Dissolution time (50% dissolved)` being fitted to observed PK data. As described in [Section 2.1](#21-modeling-strategy), different dissolution kinetics were developed for Xanax® and Solanax® formulations to allow a slower dissolution of the latter yielding a larger tmax. Although alprazolam is sparingly soluble in water, no solubility limitation was observed in the model using a solubility value of 40 mg/L (pH 7.0). `Specific intestinal permeability (transcellular)` was also optimized to better match the observed PK data.
+
+### 2.3.2 Distribution
+
+In the model, the `fraction unbound (plasma, reference value)` was set to 0.233 which is the average value measured in young male subjects ([Schmith 1991](#5-references)). Slightly higher values around 0.30 have been reported for mid-aged subjects ([Juhl 1984](#5-references), [Ochs 1986](#5-references)) which have not been applied in the current model. `Lipophilicity` was optimized within the range of reported values for logP or logD, namely 1.26 ([Greenblatt 1983](#5-references)) - 2.19 ([Machatha 2004](#5-references)), to better match the observed PK data. The observed PK data were found to be best described using the model for estimating intracellular-to-plasma partition coefficients according to the method by `Rodgers and Rowland` ([Rodgers 2005](#5-references), [Rodgers 2006](#5-references)). Cellular permeabilities were automatically calculated using the method `PK-Sim Standard` ([Open Systems Pharmacology Documentation](#5-references)).
+
+### 2.3.3 Elimination
+
+Alprazolam is extensively metabolized via CYP3A to give two major metabolites, α-hydroxy-alprazolam and 4-hydroxy-alprazolam. In the model, these two biotransformation pathways were described by Michaelis-Menten kinetics. The `Km` values for each pathway were fixed to reported literature values, namely 269 µmol/L for the α-OH pathway and 704 µmol/L for the 4-OH pathway ([Hirota 2001](#5-references)), and the `kcat` values were optimized to better match the observed PK data while keeping the ratio between both values constant (by selecting the option `Use as Factor`). The gene expression profile of CYP3A4 was loaded from the internal PK-Sim® database using the expression data quantified by RT-PCR ([Open Systems Pharmacology Documentation](#5-references)). As described in [Section 2.1](#21-modeling-strategy), the European Standard Individual used per default in the simulations was scaled to a Japanese individual with the `Reference concentration CYP3A4` being fitted to observed data reported by Yasui et al. ([Yasui 1996](#5-references), [Yasui 1998](#5-references), [Yasui 2000](#5-references)) to account for ethnicity-related differences in anatomical and physiological model parameters.
+
+Following oral administration of 14C-alprazolam, 20% of the dose have been recovered unchanged in urine ([Eberts 1980](#5-references)). This information was accounted for in the model by implementing a glomerular filtration process and optimizing the `GFR fraction` to match the observed dose fraction excreted unchanged in urine.
+
+# 3 Results and Discussion
+
+The PBPK model for alprazolam was developed and verified with clinical pharmacokinetic data.
+
+The next sections show:
+
+1. the final model parameters for the building blocks: [Section 3.1](#31-final-input-parameters).
+2. the overall goodness of fit: [Section 3.2](#32-diagnostics-plots).
+3. simulated vs. observed concentration-time profiles for the clinical studies used for model building: [Section 3.3](#33-concentration-time-profiles).
+
+## 3.1 Final input parameters
+
+The compound parameter values of the final PBPK model are illustrated below.
+
+### Compound: Alprazolam
+
+#### Parameters
+
+Name | Value | Value Origin | Alternative | Default
+------------------------------------------------ | ---------------------- | ----------------------------------------------------------------------------------------------------------------------- | ----------- | -------
+Solubility at reference pH | 40 mg/l | | Measurement | True
+Reference pH | 7 | | Measurement | True
+Lipophilicity | 2.0799268917 Log Units | Parameter Identification-Parameter Identification-Value updated from 'Parameter Identification 3.4' on 2020-03-25 13:19 | Optimized | True
+Fraction unbound (plasma, reference value) | 0.233 | Publication-In Vivo-PMID: 1880224 | Measurement | True
+Specific intestinal permeability (transcellular) | 7.6146060669 cm/min | Parameter Identification-Parameter Identification-Value updated from 'Parameter Identification 3.4' on 2020-03-25 13:19 | Optimized | True
+Cl | 1 | | |
+Is small molecule | Yes | | |
+Molecular weight | 308.765 g/mol | | |
+Plasma protein binding partner | Unknown | | |
+
+#### Calculation methods
+
+Name | Value
+----------------------- | -------------------
+Partition coefficients | Rodgers and Rowland
+Cellular permeabilities | PK-Sim Standard
+
+#### Processes
+
+##### Metabolizing Enzyme: CYP3A4-alpha-OH pathway
+
+Molecule: CYP3A4
+
+###### Parameters
+
+Name | Value | Value Origin
+---------------------------------- | ------------------------------ | -----------------------------------------------------------------------------------------------------------------------
+In vitro Vmax for liver microsomes | 0.131 nmol/min/mg mic. protein | Publication-In Vitro-PMID: 11745908
+Km | 269 µmol/l | Publication-In Vitro-PMID: 11745908
+kcat | 0.8066945978 1/min | Parameter Identification-Parameter Identification-Value updated from 'Parameter Identification 3.4' on 2020-03-25 13:19
+
+##### Systemic Process: Glomerular Filtration-GFR
+
+Species: Human
+
+###### Parameters
+
+Name | Value | Value Origin
+------------ | ------------:| -----------------------------------------------------------------------------------------------------------------------
+GFR fraction | 0.5461456402 | Parameter Identification-Parameter Identification-Value updated from 'Parameter Identification 3.4' on 2020-03-25 13:19
+
+##### Metabolizing Enzyme: CYP3A4-4-OH pathway
+
+Molecule: CYP3A4
+
+###### Parameters
+
+Name | Value | Value Origin
+---------------------------------- | ----------------------------- | -----------------------------------------------------------------------------------------------------------------------
+In vitro Vmax for liver microsomes | 2.23 nmol/min/mg mic. protein | Publication-In Vitro-PMID: 11745908
+Km | 704 µmol/l | Publication-In Vitro-PMID: 11745908
+kcat | 13.7322820855 1/min | Parameter Identification-Parameter Identification-Value updated from 'Parameter Identification 3.4' on 2020-03-25 13:19
+
+### Formulation: Xanax_IR
+
+Type: Weibull
+
+#### Parameters
+
+Name | Value | Value Origin
+-------------------------------- | ----------------- | -----------------------------------------------------------------------------------------------------------------------
+Dissolution time (50% dissolved) | 12.1060809908 min | Parameter Identification-Parameter Identification-Value updated from 'Parameter Identification 3.4' on 2020-03-25 13:19
+Lag time | 0 min |
+Dissolution shape | 0.92 | Parameter Identification-Parameter Identification-Value updated from 'Parameter Identification 3.4' on 2020-03-25 13:19
+Use as suspension | Yes |
+
+### Formulation: Solanax
+
+Type: Weibull
+
+#### Parameters
+
+Name | Value | Value Origin
+-------------------------------- | ----------------- | -----------------------------------------------------------------------------------------------------------------------
+Dissolution time (50% dissolved) | 35.8519725483 min | Parameter Identification-Parameter Identification-Value updated from 'Parameter Identification 3.4' on 2020-03-25 13:19
+Lag time | 0 min |
+Dissolution shape | 0.92 | Parameter Identification-Parameter Identification-Value updated from 'Parameter Identification 3.4' on 2020-03-25 13:19
+Use as suspension | Yes |
+
+## 3.2 Diagnostics Plots
+
+Below you find the goodness-of-fit visual diagnostic plots for the PBPK model performance of all data used presented in [Section 2.2.2](#222-clinical-data).
+
+The first plot shows observed versus simulated plasma concentration, the second weighted residuals versus time.
+
+
+
+**Table 3-1: GMFE for Goodness of fit plot for concentration in plasma**
+
+|Group |GMFE |
+|:-----|:----|
+|IV |1.18 |
+|PO |1.18 |
+|All |1.18 |
+
+
+
+
+
+
+
+
+**Figure 3-1: Goodness of fit plot for concentration in plasma**
+
+
+
+
+
+
+
+
+**Figure 3-2: Goodness of fit plot for concentration in plasma**
+
+
+
+
+## 3.3 Concentration-Time Profiles
+
+Simulated versus observed concentration-time profiles of all data listed in [Section 2.2.2](#222-clinical-data) are presented below.
+
+
+
+
+
+**Figure 3-3: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-4: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-5: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-6: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-7: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-8: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-9: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-10: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-11: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-12: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-13: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-14: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-15: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-16: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-17: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-18: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-19: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-20: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-21: Time Profile Analysis 2**
+
+
+
+
+
+
+
+
+**Figure 3-22: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-23: Time Profile Analysis 1**
+
+
+
+
+# 4 Conclusion
+
+The final alprazolam PBPK model applies metabolism by CYP3A4, modelled as two separate pathways catalyzed by the same enzyme yielding α-hydroxy-alprazolam and 4-hydroxy-alprazolam as metabolites, and glomerular filtration. Overall, the model adequately describes the pharmacokinetics of alprazolam in healthy, non-obese adults receiving different single doses of alprazolam via the IV route or oral route as immediate release tablet in the fasted state.
+
+# 5 References
+
+**Abernethy 1983** Abernethy DR, Greenblatt DJ, Divoll M, Moschitto LJ, Harmatz JS, Shader RI. Interaction of cimetidine with the triazolobenzodiazepines alprazolam and triazolam. *Psychopharmacology (Berl)* 1983, 80(3): 275-278.
+
+**Adams 1984** Adams WJ, Bombardt PA, Brewer JE. Normal-phase liquid chromatographic determination of alprazolam in human serum. *Anal Chem* 1984, 56(9): 1590-1594.
+
+**Bertz 1997** Bertz RJ, Kroboth PD, Kroboth FJ, Reynolds IJ, Salek F, Wright CE, Smith RB. Alprazolam in young and elderly men: sensitivity and tolerance to psychomotor, sedative and memory effects. *J Pharmacol Exp Ther* 1997, 281(3): 1317-1329.
+
+**Cho 1983** Cho MJ, Scahill TA, Hester JB Jr. Kinetics and equilibrium of the reversible alprazolam ring-opening reaction. *J Pharm Sci* 1983, 72(4): 356-362.
+
+**Dawson 1984** Dawson GW, Jue SG, Brogden RN. Alprazolam: a review of its pharmacodynamic properties and efficacy in the treatment of anxiety and depression. *Drugs* 1984, 27(2): 132-147.
+
+**drugbank.ca**. (https://www.drugbank.ca/drugs/DB00404), accessed on 11-19-2019.
+
+**Eberts 1980** Eberts FS, Philopoulos Y, Reineke LM, Vliek RW. Disposition of 14-C-alprazolam, a new anxiolytic-antidepressant, in man. *Pharmacologist* 1980, 22(3): 279.
+
+**Eller 1990** Eller MG, Della‐Coletta AA. Absence of effect of food on alprazolam absorption from sustained release tablets. *Biopharm Drug Dispos* 1990, 11(1): 31-37.
+
+**Fleishaker 1989** Fleishaker JC, Phillips JP, Eller MG, Smith RB. Pharmacokinetics and pharmacodynamics of alprazolam following single and multiple oral doses of a sustained‐release formulation. *J Clin Pharmacol* 1989, 29(6): 543-549.
+
+**Fleishaker 1994** Fleishaker JC, Hulst LK. A pharmacokinetic and pharmacodynamic evaluation of the combined administration of alprazolam and fluvoxamine. *Eur J Clin Pharmacol.* 1994, 46(1): 35-39.
+
+**Friedman 1991** Friedman H, Redmond DE, Greenblatt DJ. Comparative pharmacokinetics of alprazolam and lorazepam in humans and in African Green Monkeys. *Psychopharmacology (Berl)* 1991, 104(1): 103-105.
+
+**Greenblatt 1983** Greenblatt DJ, Arendt RM, Abernethy DR, Giles HG, Sellers EM, Shader RI. In vitro quantitation of benzodiazepine lipophilicity: relation to in vivo distribution. *Br J Anaesth* 1983, 55(10): 985-989.
+
+**Greenblatt 1988** Greenblatt DJ, Harmatz JS, Dorsey C, Shader RI. Comparative single‐dose kinetics and dynamics of lorazepam, alprazolam, prazepam, and placebo. *Clin Pharmacol Ther* 1988, 44(3): 326-334.
+
+**Greenblatt 1992** Greenblatt DJ, Preskorn SH, Cotreau MM, Horst WD, Harmatz JS. Fluoxetine impairs clearance of alprazolam but not of clonazepam. *Clin Pharmacol Ther* 1992, 52(5): 479-486.
+
+**Greenblatt 1993** Greenblatt DJ, Wright CE. Clinical pharmacokinetics of alprazolam. Therapeutic implications. *Clin Pharmacokinet* 1993, 24(6): 453-471.
+
+**Greenblatt 1998** Greenblatt DJ, Wright CE, von Moltke LL, Harmatz JS, Ehrenberg BL, Harrel LM, Corbett K, Counihan M, Tobias S, Shader RI. Ketoconazole inhibition of triazolam and alprazolam clearance: differential kinetic and dynamic consequences. *Clin Pharmacol Ther* 1998, 64(3): 237-247.
+
+**Greenblatt 2000** Greenblatt DJ, von Moltke LL, Harmatz JS, Durol ALB, Daily JP, Graf JA, Mertzanis P, Hoffman JL, Shader RI. Alprazolam‐ritonavir interaction: implications for product labeling. *Clin Pharmacol Ther* 2000, 67(4): 335-341.
+
+**Hirota 2001** Hirota N, Ito K, Iwatsubo T, Green CE, Tyson CA, Shimada N, Suzuki H, Sugiyama Y. In vitro/in vivo scaling of alprazolam metabolism by CYP3A4 and CYP3A5 in humans. *Biopharm Drug Dispos* 2001, 22(2): 53-71.
+
+**Juhl 1984** Juhl RP, Van Thiel DH, Dittert LW, Smith RB. Alprazolam pharmacokinetics in alcoholic liver disease. *J Clin Pharmacol* 1984, 24(2-3): 113-119.
+
+**Kaplan 1998** Kaplan GB, Greenblatt DJ, Ehrenberg BL, Goddard JE, Harmatz JS, Shader RI. Single‐dose pharmacokinetics and pharmacodynamics of alprazolam in elderly and young subjects. *J Clin Pharmacol* 1998, 38(1): 14-21.
+
+**Kirkwood 1991** Kirkwood C, Moore A, Hayes P, DeVane CL, Pelonero A. Influence of menstrual cycle and gender on alprazolam pharmacokinetics. *Clin Pharmacol Ther* 1991, 50(4): 404-409.
+
+**Kroboth 1988** Kroboth PD, Smith RB, Erb RJ. (1988). Tolerance to alprazolam after intravenous bolus and continuous infusion: psychomotor and EEG effects. Clinical Pharmacology & Therapeutics, 43(3), 270-277.
+
+**Kuepfer 2016** Kuepfer L, Niederalt C, Wendl T, Schlender JF, Willmann S, Lippert J, Block M, Eissing T, Teutonico D. Applied concepts in PBPK modeling: how to build a PBPK/PD model. *CPT Pharmacometrics Syst Pharmacol* 2016, 5(10): 516-531.
+
+**Lin 1988** Lin KM, Lau JK, Smith R, Phillips P, Antal E, Poland RE. Comparison of alprazolam plasma levels in normal Asian and Caucasian male volunteers. *Psychopharmacology (Berl)* 1988, 96(3): 365-369.
+
+**Loftsson 2006** Loftsson T, Hreinsdôttir D. Determination of aqueous solubility by heating and equilibration: a technical note. *AAPS PharmSciTech* 2006, 7(1): E29-E32.
+
+**Machatha 2004** Machatha SG, Yalkowsky SH. Estimation of the ethanol/water solubility profile from the octanol/water partition coefficient. *Int J Pharm* 2004, 286(1-2): 111-115.
+
+**Manchester 2018** Manchester KR, Maskell PD, Waters L. Experimental versus theoretical log D7.4, pKa and plasma protein binding values for benzodiazepines appearing as new psychoactive substances. *Drug Test Anal* 2018, 10(8): 1258-1269.
+
+**Moschitto 1983** Moschitto LJ, Greenblatt DJ. Concentration-independent plasma protein binding of benzodiazepines. *J Pharm Pharmacol* 1983, 35(3): 179-180.
+
+**Ochs 1986** Ochs HR, Greenblatt DJ, Labedzki L, Smith RB. Alprazolam kinetics in patients with renal insufficiency. *J Clin Psychopharmacol* 1986, 6(5): 292-294.
+
+**Open Systems Pharmacology Documentation**. (https://docs.open-systems-pharmacology.org/), accessed on 07-30-2019.
+
+**PK-Sim Ontogeny Database Version 7.3**. (https://github.com/Open-Systems-Pharmacology/OSPSuite.Documentation/blob/38cf71b384cfc25cfa0ce4d2f3addfd32757e13b/PK-Sim%20Ontogeny%20Database%20Version%207.3.pdf), accessed on 07-30-2019.
+
+**Raymond 1986** Raymond GG, Born JL. An updated pKa listing of medicinal compounds. *Drug Intell Clin Pharm* 1986, 20(9): 683-686.
+
+**Scavone 1988** Scavone JM, Greenblatt DJ, Locniskar A, Shader RI. Alprazolam pharmacokinetics in women on low-dose oral contraceptives. *J Clin Pharmacol* 1988, 28(5): 454-457.
+
+**Schmider 1999** Schmider J, Brockmöller J, Arold G, Bauer S, Roots I. Simultaneous assessment of CYP3A4 and CYP1A2 activity in vivo with alprazolam and caffeine. *Pharmacogenetics* 1999, 9(6): 725-734.
+
+**Schmith 1991** Schmith VD, Piraino B, Smith RB, Kroboth PD. Alprazolam in end-stage renal disease: I. Pharmacokinetics. *J Clin Pharmacol* 1991, 31(6): 571-579.
+
+**Smith 1984** Smith RB, Kroboth PD, Vanderlugt JT, Phillips JP, Juhl RP. Pharmacokinetics and pharmacodynamics of alprazolam after oral and IV administration. *Psychopharmacology (Berl)* 1984, 84(4): 452-456.
+
+**Venkatakrishnan 2005** Venkatakrishnan K, Culm KE, Ehrenberg BL, Harmatz JS, Corbett KE, Fleishaker JC, Greenblatt DJ. Kinetics and dynamics of intravenous adinazolam, n‐desmethyl adinazolam, and alprazolam in healthy volunteers. *J Clin Pharmacol* 2005, 45(5): 529-537.
+
+**von Moltke 1993** von Moltke LL, Greenblatt DJ, Harmatz JS, Shader RI. Alprazolam metabolism in vitro: studies of human, monkey, mouse, and rat liver microsomes. *Pharmacology* 1993, 47(4): 268-276.
+
+**Wennerholm 2005** Wennerholm A, Allqvist A, Svensson JO, Gustafsson LL, Mirghani RA, Bertilsson L. Alprazolam as a probe for CYP3A using a single blood sample: pharmacokinetics of parent drug, and of α-and 4-hydroxy metabolites in healthy subjects. *Eur J Clin Pharmacol* 2005, 61(2): 113-118.
+
+**Willmann 2007** Willmann S, Höhn K, Edginton A, Sevestre M, Solodenko J, Weiss W, Lippert J, Schmitt W. Development of a physiology-based whole-body population model for assessing the influence of individual variability on the pharmacokinetics of drugs. *J Pharmacokinet Pharmacodyn* 2007, 34(3): 401-431.
+
+**Yasui 1996** Yasui N, Otani K, Kaneko S, Ohkubo T, Osanai T, Sugawara K, Chiba K, Ishizaki T. A kinetic and dynamic study of oral alprazolam with and without erythromycin in humans: in vivo evidence for the involvement of CYP3A4 in alprazolam metabolism. *Clin Pharmacol Ther.* 1996, 59(5): 514-519.
+
+**Yasui 1998** Yasui N, Kondo T, Otani K, Furukori H, Kaneko S, Ohkubo T, Nagasaki T, Sugawara K. Effect of itraconazole on the single oral dose pharmacokinetics and pharmacodynamics of alprazolam. *Psychopharmacology* 1998, 139(3): 269-273.
+
+**Yasui 2000** Yasui N, Kondo T, Furukori H, Kaneko S, Ohkubo T, Uno T, Osanai T, Sugawara K, Otani K. Effects of repeated ingestion of grapefruit juice on the single and multiple oral-dose pharmacokinetics and pharmacodynamics of alprazolam. *Psychopharmacology* 2000, 150(2): 185-190.
+
+","Markdown"
+"PBPK model","Open-Systems-Pharmacology/OSP-PBPK-Model-Library","Rifampicin/Rifampicin_evaluation_report.md",".md","68661","849","# Building and evaluation of a PBPK model for Rifampicin in healthy adults
+
+| Version | 2.0-OSP12.2 |
+| ----------------------------------------------- | ------------------------------------------------------------ |
+| based on *Model Snapshot* and *Evaluation Plan* | https://github.com/Open-Systems-Pharmacology/Rifampicin-Model/releases/tag/v2.0 |
+| OSP Version | 12.2 |
+| Qualification Framework Version | 3.5 |
+
+This evaluation report and the corresponding PK-Sim project file are filed at:
+
+https://github.com/Open-Systems-Pharmacology/OSP-PBPK-Model-Library/
+
+# Table of Contents
+
+ * [1 Introduction](#introduction)
+ * [2 Methods](#methods)
+ * [2.1 Modeling Strategy](#modeling-strategy)
+ * [2.2 Data](#data)
+ * [2.3 Model Parameters and Assumptions](#model-parameters-and-assumptions)
+ * [3 Results and Discussion](#results-and-discussion)
+ * [3.1 Final input parameters](#final-input-parameters)
+ * [3.2 Diagnostics Plots](#diagnostics-plots)
+ * [3.3 Concentration-Time Profiles](#ct-profiles)
+ * [4 Conclusion](#conclusion)
+ * [5 References](#main-references)
+
+# 1 Introduction
+
+Rifampicin is an antibiotic used for the treatment of mycobacterium infections, including tuberculosis and leprosy. For the investigation of drug-drug interactions (DDIs), rifampicin is an established potent inducer of multiple drug metabolizing enzymes (CYP3A4, CYP2B6, CYP2C8, CYP2C9, CYP2C19) and transporters (P-gp, MRP2, MRP3, MRP4, OATP1A2). In addition to its inducing capabilities, rifampicin also competitively inhibits enzymes and transporters like CYP3A4, P-gp, OATP1B1 and OATP1B3.
+
+The herein presented model represents the rifampicin model originally published by Hanke *et al.* ([Hanke 2018](#5-references)), and extended in later publications ([Britz 2019](#5-references), [Türk 2019](#5-references), [Hanke 2021](#5-references)). The model was originally established using various clinical studies, covering a dosing range of 300 to 600 mg after intravenous and oral administration of rifampicin. The original model focused specifically on the integration of effects on **CYP3A4** and **P-gp** by rifampicin. Britz *et al.* ([Britz 2019](#5-references)) integrated rifampicin-mediated induction of **CYP1A2** (and CYP2E1), Türk *et al.* ([Türk 2019](#5-references)) extended the model with regard to effects on **CYP2C8** and **OATP1B1**. Later, [Hanke 2021](#5-references) updated **P-gp**, **OATP1B1** and **OATP1B3** interaction and added **CYP2C9**, **BCRP** and **OATP2B1** interaction.
+
+It is known that for both CYP3A4 and P-gp, rifampicin shows inductive and inhibitory effects. While induction by rifampicin involves gene expression and therefore takes several days to fully develop, competitive inhibition has an instantaneous effect and is strongest at the time of highest exposure to the inhibitor. As a consequence, the effects of rifampicin caused via competitive inhibition are most prominent 1-2 h after its oral administration and of relatively short duration. These opposing effects of rifampicin can be reasonably considered in PBPK models.
+
+Integrating and testing processes that were described as vital to the pharmacokinetics of rifampicin itself resulted in a final model that applies transport by OATP1B1, metabolism by arylacetamide deacetylase (AADAC), transport by P-gp and glomerular filtration. Furthermore, auto-induction of OATP1B1, AADAC and P-gp expression has been incorporated.
+
+# 2 Methods
+
+## 2.1 Modeling Strategy
+
+The general concept of building a PBPK model has previously been described by Kuepfer et al. ([Kuepfer 2016](#5-references)). Relevant information on anthropometric (height, weight) and physiological parameters (e.g. blood flows, organ volumes, binding protein concentrations, hematocrit, cardiac output) in adults was gathered from the literature and has been previously published ([Willmann 2007](#5-references)). The information was incorporated into PK-Sim® and was used as default values for the simulations in adults.
+
+The applied activity and variability of plasma proteins and active processes that are integrated into PK-Sim® are described in the publicly available PK-Sim® Ontogeny Database Version 7.3 ([PK-Sim Ontogeny Database Version 7.3](#5-references)) or otherwise referenced for the specific process.
+
+The model was built combining bottom-up and top-down techniques. An extensive literature search yielded (1) physicochemical parameter values (2) information on active ADME and DDI-related (i.e. induction and inhibition) processes and (3) clinical studies of intravenous and oral administration in single and multiple dosing regimens, covering a broad dosing range with observed concentrations.
+
+A mean PBPK model was developed using a typical European individual. Enterohepatic recycling for transport processes into the bile was enabled in a continuous fashion (continuous flow from the liver to the lumen of duodenum). One study was performed in female patients after cholecystectomy ([Acocella 1972a](#5-references)). The bile of these patients was collected via a T tube. In the simulations of these patients, enterohepatic recycling was switched off and a virtual gallbladder collected the excreted rifampicin over time. Relevant ADME processes reported to influence the PK of rifampicin were implemented into the model and tested. For parameters that could not be (reliably) informed from literature, parameter identification was performed using a representative set of available clinical studies (see below). Model evaluation was based on the ability of the model to describe observed plasma concentration-time profiles and fraction excreted of unchanged drug to urine and bile.
+
+Details about input data (physicochemical, *in vitro* and clinical) can be found in [Section 2.2](#22-data).
+
+Details about the structural model and its parameters can be found in [Section 2.3](#23-model-parameters-and-assumptions).
+
+## 2.2 Data
+
+### 2.2.1 In vitro and physicochemical data
+
+A literature search was performed to collect available information on physicochemical properties of rifampicin. The obtained information from literature is summarized in the table below, and is used for model building.
+
+| **Parameter** | **Unit** | **Value** | Source | **Description** |
+| :-------------------------------------- | ---------------------------------- | ---------------- | --------------------------------- | ------------------------------------------------------------ |
+| MW | g/mol | 822.940 | [DrugBank DB01045](#5-references) | Molecular weight |
+| pKa,base | | 7.9 | [Maggi 1966](#5-references) | Basic dissociation constant |
+| pKa,acid | | 1.7 | [Maggi 1966](#5-references) | Acid dissociation constant |
+| Solubility (pH) | mg/L | 1100
(6.5) | [Baneyx 2014](#5-references) | Solubility |
+| | | 1400
(6.8) | [Panchagnula 2006](#5-references) | Solubility |
+| | | 990
(4) | [Agrawal 2005](#5-references) | Solubility |
+| | | 1650
(6) | [Agrawal 2005](#5-references) | Solubility |
+| | | 2540
(6.8) | [Agrawal 2005](#5-references) | Solubility |
+| | | 3350
(7.4) | [Agrawal 2005](#5-references) | Solubility |
+| | | 2800
(7.5) | [Boman 1974](#5-references) | Aqueous solubility |
+| logP | | 1.3 | [Baneyx 2014](#5-references) | Partition coefficient between octanol and water @ pH 7.4 |
+| | | 2.7 | [DrugBank DB01045](#5-references) | Partition coefficient between octanol and water |
+| fu | % | 11.1 | [Boman 1974](#5-references) | Fraction unbound in plasma |
+| | % | 16.0 | [Baneyx 2014](#5-references) | Fraction unbound in plasma in tuberculosis patients |
+| | % | 17 | [Templeton 2011](#5-references) | Fraction unbound in plasma |
+| | % | 17.5 | [Shou 2008](#5-references) | Fraction unbound in plasma |
+| B/P ratio | | 0.9 | [Loos 1985](#5-references) | Blood to plasma concentration ratio |
+| Vmax, Km OATP1B1 | pmol/min/mg,
µmol/L | 9.3
1.5 | [Tirona 2003](#5-references) | OATP1B1 uptake in transfected HeLa cells |
+| Vmax, Km
P-gp | nmol/h/cm2,
µmol/L | 4.3
55 | [Collett 2004](#5-references) | P-gp net secretion across Caco-2 monolayers |
+| Vmax, Km AADAC | pmol/min/mg,
µmol/L | 162.6
195.1 | [Nakajima 2011](#5-references) | Kinetic parameters of the deacetylase activity in HLM |
+| Emax, EC50 CYP3A4 | *dimensionless*
µmol/L | 9
0.34 | [Templeton 2011](#5-references) | CYP3A4 induction parameters in primary human hepatocytes,
EC50 corrected for fraction unbound in human hepatocytes of 0.419 as reported by [Shou 2008](#5-references) |
+| Ki CYP3A4 | µmol/L | 18.5 | [Kajosaari 2005](#5-references) | CYP3A4 inhibition constant |
+| Emax P-gp | *dimensionless* | 2.5 | [Greiner 1999](#5-references) | P-gp induction parameter based on an increased intestinal P-gp content in duodenal biopsies of 3.5 after rifampicin treatment |
+| Ki P-gp | µmol/L | 9.1 (169.0) | [Hanke 2021](#5-references) ([Reitman 2011](#5-references)) | P-gp inhibition constant |
+| Ki BCRP | µmol/L | 14 | [Hanke 2021](#5-references), [Prueksaritanont 2014](#5-references) | BCRP inhibition constant |
+| Ki OATP1B1 | µmol/L | 0.29 (0.477) | [Hanke 2021](#5-references) ([Hirano 2006](#5-references)) | OATP1B1 inhibition constant (based on OATP1B1-mediated pitavastatin uptake) |
+| Ki OATP1B3 | µmol/L | 0.5 (0.9) | [Hanke 2021](#5-references) ([Annaert 2010](#5-references)) | OATP1B3 inhibition constant |
+| Ki OATP2B1 | µmol/L | 78.2 | [Hanke 2021](#5-references), [Zhang 2019](#5-references) | OATP2B1 inhibition constant |
+| Emax CYP2C8 | *dimensionless* | 3.2 | [Buckley 2014](#5-references) | CYP2C8 Emax in primary human hepatocytes (based on activity) |
+| Ki CYP2C8 | µmol/L | 30.2 | [Kajosaari 2005](#5-references) | CYP2C8 inhibition constant |
+| Ki CYP2C9 | µmol/L | 150 | [Hanke 2021](#5-references), [Yoshida 2012](#5-references) | CYP2C9 inhibition constant |
+| Emax CYP1A2 | *dimensionless* | 0.65 | [Chen 2010](#5-references) | CYP1A2 Emax in cultured human hepatocytes (based on activity) |
+| Emax CYP2E1 | *dimensionless* | 0.8 | [Rae 2001](#5-references) | CYP2E1 fold induction of 1.8 calculated as the normalized ratio of expression in rifampin-treated versus vehicle control-treated cells |
+
+*AADAC* arylacetamide deacetylase
+
+### 2.2.2 Clinical data
+
+A literature search was performed to collect available clinical data (plasma concentrations, fraction excreted into urine, fraction excreted into bile) on rifampicin in adults. The rifampicin model was built and verified using various clinical studies, covering a dosing range of 300 to 600 mg, administered intravenously or orally.
+
+The following dosing scenarios were simulated and compared to respective data:
+
+| Route | Dose
[mg] | Dosing | PK Data | Used for [Optimization](#235-automated-parameter-identification) | Reference |
+| ----- | -------------- | ------------------------- | ------------------------------------- | ------------------------------------------------------------ | -------------------------------------------------- |
+| iv | 300 | SD, 30 min infusion | Plasma | x | [Sanofi-Aventis U.S. LLC. 2013](#5-references) |
+| | | SD, 3 h infusion | Plasma, excretion into urine | x | [Nitti 1977](#5-references) |
+| | 450 | SD, 3 h infusion | Plasma, excretion into urine | x | [Nitti 1977](#5-references) |
+| | 600 | SD, 30 min infusion | Plasma | x | [Sanofi-Aventis U.S. LLC. 2013](#5-references) |
+| | | SD, 3 h infusion | Plasma, excretion into urine | x | [Nitti 1977](#5-references) |
+| | | SD, 3 h infusion | Plasma, excretion into urine | x | [Acocella 1977](#5-references) |
+| | | OD (7 days), 3 h infusion | Plasma | x | [Acocella 1977](#5-references) |
+| po | 300 | SD | Plasma | x | [Chouchane 1995](#5-references) |
+| | | SD | Plasma | | [Furesz 1970](#5-references) |
+| | 450 | SD | Plasma | x | [Blume 1989](#5-references) |
+| | | | Plasma | | [Furesz 1970](#5-references) |
+| | | MD | Plasma, excretion into urine and bile | x | [Acocella 1972a](#5-references) |
+| | 600 | SD | Plasma | x | [Peloquin 1997](#5-references) |
+| | | | Plasma | | [Blume 1989](#5-references) |
+| | | | Plasma | | [Acocella 1972b](#5-references) |
+| | | | Plasma | | [Furesz 1970](#5-references) |
+| | | | Plasma, excretion into urine | x | [Eon Labs Manufacturing, Inc. 1997](#5-references) |
+| | | OD (7 days) | Plasma | x | [Baneyx 2014](#5-references) |
+
+## 2.3 Model Parameters and Assumptions
+
+### 2.3.1 Absorption
+
+Herein, the model parameter `Specific intestinal permeability` was optimized to best match clinical data (see [Section 2.3.5](#235-automated-parameter-identification)). The results of the optimization can be found in [Section 2.3.5](#235-automated-parameter-identification).
+
+Measured aqueous solubility ([Boman 1974](#5-references), see [Section 2.2.1](#221-in-vitro-and-physicochemical-data)) was set as default solubility.
+
+As observed data do not show substantial differences between different formulations for oral rifampicin administration, all oral administrations were modelled as an oral solution.
+
+### 2.3.2 Distribution
+
+Recent measurements of fraction unbound in plasma yielded values of approximately 17% ([Templeton 2011](#5-references), [Shou 2008](#5-references), see [Section 2.2.1](#221-in-vitro-and-physicochemical-data)). This value was implemented in this model.
+
+`Lipophilicity` was optimized within the range of measured values to find a best match of simulated to observed rifampicin PK profile data.
+
+After testing the available organ-plasma partition coefficient and cell permeability calculation methods built in PK-Sim®, observed clinical data was best described by choosing the partition coefficient calculation by `Rodgers and Rowland` and cellular permeability calculation by `PK-Sim Standard ` for rifampicin. The PK-Sim® calculated `Blood/plasma concentration ratio` is well in line with the observed value of 0.9 ([Loos 1985](#5-references)).
+
+### 2.3.3 Metabolism and Elimination
+
+Integrating and testing active processes that were considered vital to the PK of rifampicin after literature review resulted in a final model that applies transport by OATP1B1 ([Tirona 2003](#5-references)), metabolism by arylacetamide deacetylase (AADAC) ([Nakajima 2011](#5-references)), transport by P-gp ([Collett 2004](#5-references)) and glomerular filtration. No study clearly demonstrated that rifampicin is substrate of CYP3A4; hence, in this PBPK model rifampicin only acts as a perpetrator on CYP3A4 without being metabolized by it.
+
+The implemented expression profile of AADAC, P-gp and OATP1B1 were based on high-sensitive real-time RT-PCR ([Nishimura 2003](#5-references)) of the PK-Sim® expression database. The relative expression in the mucosa of the gut wall was modified based on an optimized value as reported by Hanke *et al.* ([Hanke 2018](#5-references)). Herein, this value was increased by a factor of 3.57 based on digoxin PK data in combination with PBPK modeling.
+
+It was assumed that the mRNA concentration is proportional to the respective protein concentration. Thus, the expression of a protein in a specific organ relates to the expression in the organ with the highest expression which is termed reference concentration of the protein ([Meyer 2012](#5-references)). OATP1B1 was configured as influx transporter and P-gp as efflux transporter. Reference concentrations of the implemented active processes (enzymes and transporters) are summarized below:
+
+| Protein | Reference concentration
[µmol/L] | Reference Organ |
+| ------- | ------------------------------------- | ---------------------- |
+| AADAC | 1.0 (assumed) | Liver |
+| P-gp | 1.41 ([Hanke 2018](#5-references)) | Mucosa Small Intestine |
+| OATP1B1 | 1.0 (assumed) | Liver |
+
+The kinetic parameters describing the rifampicin metabolism by AADAC and transport by P-gp and OATP1B1 were imputed in the model as follows: while Michaelis-Menten constants (Km values) of AADAC-catalyzed metabolism and the two transport processes were taken from reported in vitro experiments, enzymatic and transport turnover values (kcat) were optimized based on *in vivo* PK data (see [Section 2.3.5](#235-automated-parameter-identification)).
+
+Multiple dose studies that measured PK profiles of rifampicin at different days of a 600 mg po once daily regimen indicate that rifampicin exposure decreases over time due to auto-induction processes ([Baneyx 2014](#5-references), [Smythe 2012](#5-references)). *In-vitro* studies in human hepatocytes suggest that rifampicin induces P-gp ([Collett 2004](#5-references), [Dixit 2007](#5-references), [Williamson 2013](#5-references)) and OATP1B1 ([Dixit 2007](#5-references), [Williamson 2013](#5-references)). It has further been shown in DDI studies with prototypical substrates of these transporters (pravastatin and digoxin, respectively) that the induction of these transporters can also be observed *in vivo* ([Kyrklund 2000](#5-references), [Greiner 1999](#5-references)). As in the case of CYP3A4 induction, both induction processes are mediated via pregnane X receptor (PXR) ([Geick 2001](#5-references)). Furthermore, it has been demonstrated that B-esterases are inducible by rifampicin via PXR ([Smythe 2012](#5-references), [Staudinger 2010](#5-references)) and that AADAC, the enzyme catalyzing the main metabolic pathway of rifampicin, is regulated by PXR ([Zhang 2012](#5-references)). Therefore, (auto-)induction of P-gp, OATP1B1 and AADAC expression was assumed and implemented in the rifampicin model. Modelling induction of an endogenously expressed protein requires three parameters, in particular **EC50** (concentration at which induction is half maximum), **Emax** (maximum induction effect on endogenous synthesis rate) and the endogenous **protein turnover (half-life)**. Little is known about these values *in vivo* for AADAC, P-gp and OATP1B1 induction.
+
+#### (Auto-) Induction Processes: AADAC, P-gp and OATP1B1
+
+##### EC50
+
+As all induction processes are mediated by PXR, the same unbound EC50 of 0.34 µmol/L (originally measured in primary human hepatocytes for CYP3A4 induction after correcting for the fraction unbound ([Baneyx 2014](#5-references), [Shou 2008](#5-references), [Templeton 2011](#5-references))) was applied for all induction processes. This assumption is supported by the fact that Moore *et al.* [Moore 2000](#5-references) found a general EC50 value for PXR-mediated rifampicin induction of 0.71 µmol/L (resulting in an unbound EC50 of 0.30 µmol/L after correcting for the fraction unbound reported by [Shou 2008](#5-references)).
+
+##### Emax
+
+Emax values for AADAC and OATP1B1 are unknown and fitted based on observed clinical PK data of rifampicin (see [Section 2.3.5](#235-automated-parameter-identification)).
+
+A study by Greiner et al. ([Greiner 1999](#5-references)) found 3.5-fold elevated P-gp levels in human duodenal biopsies after multiple doses of rifampicin. This value was assumed to represent an *in vivo* maximum effect corresponding to an Emax value of 2.5. This Emax was included in the model for P-gp induction (see also [Section 2.2.1](#221-in-vitro-and-physicochemical-data)).
+
+##### Protein turnover (half-lives)
+
+Endogenous half-lives of these proteins are not known. Therefore, the same values applicable for CYP3A4 turnover (as implemented in PK-Sim ([PK-Sim Ontogeny Database Version 7.3](#5-references)) were assumed, i.e. 36 hours in the liver ([Obach 2007](#5-references)) and 23 hours in the intestine ([Greenblatt 2003](#5-references)).
+
+### 2.3.4 DDI Parameters
+
+The following sub-section describe the model input for DDI-related parameters, i.e. induction and inhibition of certain enzymes and transporters, for which rifampicin may act as a perpetrator. Verification of these model parameters and linked processes is not evaluated in this report. Applications are assessed in specific use cases and reported elsewhere.
+
+#### CYP3A4 induction and inhibition
+Induction of CYP3A4 was incorporated using the weighted mean **EC50** of 0.8 µmol/L and **Emax ** estimate of 9 based on CYP3A4 activity induction in primary human hepatocytes ([Templeton 2011](#5-references), see also [Section 2.2.1](#221-in-vitro-and-physicochemical-data)). Similar values for EC50 (0.77, 0.80 µmol/L) and Emax (7, 9, 10) have been reported by other groups ([Kolars 1992](#5-references), [Mills 2004](#5-references), [Sahi 2000](#5-references)). The *in vitro* value of EC50 of 0.8 µmol/L was corrected by the unbound fraction of rifampicin in hepatocytes of 0.419 to obtain an **unbound EC50** value of 0.34 µmol/L ([Baneyx 2014](#5-references), [Shou 2008](#5-references), [Templeton 2011](#5-references)) which was used in the PBPK model.
+
+Competitive inhibition of CYP3A4 by rifampicin was included using a dissociation (inhibition) constant (**Ki**) of 18.5 µmol/L determined in human liver microsomes via inhibition of midazolam 1-hydroxylation ([Kajosaari 2005](#5-references)). No correction of this *in vitro* value was applied to account for potential binding in the assay, as only 0.1 mg/mL human liver microsomal protein was used and a negligible unbound fraction of 0.90 – 0.98 was predicted ([Austin 2002](#5-references)).
+
+Time to reach newly induced CYP3A4 levels and time for de-induction depends on the half-lives of the perpetrator drug but also of the endogenous natural turnover of the induced protein. CYP3A4 turnover featured zero-order synthesis rate and first-order degradation rate. A distinct degradation rate constant (kdeg) was considered for the intestinal mucosa which rather reflects enterocytic turnover than protein turnover, while in all other CYP3A4 expressing organs CYP3A4 turnover was assumed to follow that of the liver. **CYP3A4 half-life** (= ln(2)/kdeg) of 23 and 36 h in intestine and liver, respectively, were incorporated ([Obach 2007](#5-references), [Greenblatt 2003](#5-references), [PK-Sim Ontogeny Database Version 7.3](#5-references)).
+
+#### CYP2C8 induction and inhibition
+
+For PXR-mediated induction, the same unbound EC50 of 0.34 µmol/L (originally measured in primary human hepatocytes for CYP3A4 induction after correcting for the fraction unbound ([Baneyx 2014](#5-references), [Shou 2008](#5-references), [Templeton 2011](#5-references))) was applied (see above).
+
+An Emax value reported by Buckley *et al.* ([Buckley 2014](#5-references)) served as model input (see [Section 2.2.1](#221-in-vitro-and-physicochemical-data)).
+
+CYP2C8 half-life of 23 h in the liver ([Renwick 2000](#5-references), [PK-Sim Ontogeny Database Version 7.3](#5-references)) and of 23 h in the intestine (assuming that the turnover here rather reflects enterocytic turnover than protein turnover) ([Greenblatt 2003](#5-references), [PK-Sim Ontogeny Database Version 7.3](#5-references)) were incorporated.
+
+An *in vitro* determined Ki value for rifampicin ([Kajosaari 2005](#5-references)) served directly as model input.
+
+#### CYP2C9 inhibition
+
+Competitive inhibition of CYP2C9 by rifampicin was included using a dissociation (inhibition) constant (**Ki**) of 150 µmol/L ([Yoshida 2012](#5-references), [Hanke 2021](#5-references)).
+
+#### CYP1A2 induction
+
+For PXR-mediated induction, the same unbound EC50 of 0.34 µmol/L (originally measured in primary human hepatocytes for CYP3A4 induction after correcting for the fraction unbound ([Baneyx 2014](#5-references), [Shou 2008](#5-references), [Templeton 2011](#5-references))) was applied (see above).
+
+An Emax value reported by Chen *et al.* ([Chen 2010](#5-references)) served as model input (see [Section 2.2.1](#221-in-vitro-and-physicochemical-data)).
+
+CYP1A2 half-life of 39 h in the liver ([Obach 2007](#5-references), [PK-Sim Ontogeny Database Version 7.3](#5-references)) and of 23 h in the intestine (assuming that the turnover here rather reflects enterocytic turnover than protein turnover) ([Greenblatt 2003](#5-references), [PK-Sim Ontogeny Database Version 7.3](#5-references)) were incorporated.
+
+#### CYP2E1 induction
+
+For PXR-mediated induction, the same unbound EC50 of 0.34 µmol/L (originally measured in primary human hepatocytes for CYP3A4 induction after correcting for the fraction unbound ([Baneyx 2014](#5-references), [Shou 2008](#5-references), [Templeton 2011](#5-references))) was applied (see above).
+
+An Emax value reported by Rae *et al.* ([Rae 2001](#5-references)) served as model input (see [Section 2.2.1](#221-in-vitro-and-physicochemical-data)).
+
+CYP2E1 half-life of 50 h in the liver ([Emery 1999](#5-references), [PK-Sim Ontogeny Database Version 7.3](#5-references)) and of 23 h in the intestine (assuming that the turnover here rather reflects enterocytic turnover than protein turnover) ([Greenblatt 2003](#5-references), [PK-Sim Ontogeny Database Version 7.3](#5-references)) were incorporated.
+
+#### P-gp induction and inhibition
+P-gp induction is described above.
+
+An *in vitro* determined Ki value for rifampicin ([Reitman 2011](#5-references)) was updated by [Hanke 2021](#5-references) and served as model input (see [Section 2.2.1](#221-in-vitro-and-physicochemical-data)).
+
+#### BCRP inhibition
+
+Competitive inhibition of CYP2C9 by rifampicin was included using a dissociation (inhibition) constant (**Ki**) of 14 µmol/L ([Prueksaritanont 2014](#5-references), [Hanke 2021](#5-references)).
+
+#### OATP1B1 induction and inhibition
+
+OATP1B1 induction is described above.
+
+An *in vitro* determined Ki value for rifampicin ([Hirano 2006](#5-references)) was updated by [Hanke 2021](#5-references) and served as model input (see [Section 2.2.1](#221-in-vitro-and-physicochemical-data)).
+
+#### OATP1B3 induction and inhibition
+
+The same parameters as for OATP1B1 induction were assumed.
+
+An *in vitro* determined Ki value for rifampicin ([Annaert 2010](#5-references)) was updated by [Hanke 2021](#5-references) and served as model input (see [Section 2.2.1](#221-in-vitro-and-physicochemical-data)).
+
+#### OATP2B1 inhibition
+
+Competitive inhibition of CYP2C9 by rifampicin was included using a dissociation (inhibition) constant (**Ki**) of 78.2 µmol/L ([Zhang 2019](#5-references), [Hanke 2021](#5-references)).
+
+#### Summary DDI Parameters
+
+| Protein | Ki
[µmol/L] | Emax
| EC50,u
[µmol/L] | Half-life
liver [h] | Half-life
intestine [h] |
+| ------- | --------------------------- | ---------------------------------------------------- | ------------------------------- | ------------------------ | ---------------------------- |
+| CYP1A2 | - | 0.65 | 0.34 | 39 | 23 (assumed) |
+| CYP2C8 | 30.2 | 3.2 | 0.34 | 23 | 23 (assumed) |
+| CYP2C9 | 150 | - | - | - | - |
+| CYP2E1 | - | 0.8 | 0.34 | 50 | 23 (assumed) |
+| CYP3A4 | 18.5 | 9 | 0.34 | 36 | 23 |
+| AADAC | - | [optimized](#235-automated-parameter-identification) | 0.34 | 36 (assumed) | 23 (assumed) |
+| P-gp | 9.1 | [optimized](#235-automated-parameter-identification) | 0.34 | 36 (assumed) | 23 (assumed) |
+| BCRP | 14 | - | - | - | - |
+| OATP1B1 | 0.477 | [optimized](#235-automated-parameter-identification) | 0.34 | 36 (assumed) | 23 (assumed) |
+| OATP1B3 | 0.9 | assumed to be equal to OATP1B1 | 0.34 | 36 (assumed) | 23 (assumed) |
+| OATP2B1 | 78.2 | - | - | - | - |
+
+### 2.3.5 Automated Parameter Identification
+
+This is the result of the final parameter identification:
+
+| Model Parameter | Optimized Value | Unit |
+| ----------------------------------------------------------- | ------------------------------------------------------------ | --------- |
+| `Lipophilicity` | 2.5 | Log Units |
+| `Specific intestinal permeability` | 1.24E-05 | cm/min |
+| `Fraction unbound (plasma, reference value)` | 17 FIXED (see [Section 2.2.1](#221-in-vitro-and-physicochemical-data)) | % |
+| `Solubility at reference pH` | 2800 FIXED (see [Section 2.2.1](#221-in-vitro-and-physicochemical-data)) | mg/L |
+| `kcat` AADAC (with a reference concentration of 1 µmol/L) | 9.865 | 1/min |
+| `kcat` P-gp (with a reference concentration of 1.41 µmol/L) | 0.6088 | 1/min |
+| `kcat` OATP1B1 (with a reference concentration of 1 µmol/L) | 7.796* | 1/min |
+| `Emax` AADAC | 0.985 | |
+| `Emax` P-gp | 2.5 FIXED (see [Section 2.2.1](#221-in-vitro-and-physicochemical-data)) | |
+| `Emax` OATP1B1 | 0.383 | |
+
+* The value in the model was updated to 5.210 with the release of PK-Sim 10 to account for the updated calculation method of interstitial concentrations (please refer to the respective [release notes of version 10](https://github.com/Open-Systems-Pharmacology/Suite/releases/tag/v10.0)).
+
+# 3 Results and Discussion
+
+The rifampicin model was built and verified using various clinical studies. Overall, the model shows good performance to describe plasma concentration-time profiles over a dose range of 300 to 600 mg after intravenous and oral administration.
+
+The next sections show:
+
+1. the final model input parameters for the building blocks: [Section 3.1](#31-final-input-parameters).
+2. the overall goodness of fit: [Section 3.2](#32-diagnostics-plots).
+3. simulated vs. observed concentration-time profiles for the clinical studies used for model building and for model verification: [Section 3.3](#33-concentration-time-profiles).
+
+## 3.1 Final input parameters
+
+The parameter values of the final PBPK model are illustrated below.
+
+### Compound: Rifampicin
+
+#### Parameters
+
+Name | Value | Value Origin | Alternative | Default
+------------------------------------------------ | --------------- | ---------------------------------------------------------- | ------------------ | -------
+Solubility at reference pH | 2800 mg/l | Publication-Boman 1974 | Aqueous solubility | True
+Reference pH | 7.5 | Publication-Boman 1974 | Aqueous solubility | True
+Lipophilicity | 2.5 Log Units | Publication-Parameter Identification-Hanke et al. 2018 | Optimized | True
+Fraction unbound (plasma, reference value) | 17 % | Publication-In Vitro-Templeton 2011 (equilibrium dialysis) | Templeton 2011 | True
+Specific intestinal permeability (transcellular) | 1.24E-05 cm/min | Publication-Parameter Identification-Hanke et al. 2018 | Optimized | True
+Is small molecule | Yes | | |
+Molecular weight | 822.94 g/mol | | |
+Plasma protein binding partner | Albumin | | |
+
+#### Calculation methods
+
+Name | Value
+----------------------- | -------------------
+Partition coefficients | Rodgers and Rowland
+Cellular permeabilities | PK-Sim Standard
+
+#### Processes
+
+##### Metabolizing Enzyme: AADAC-Nakajima 2011
+
+Molecule: AADAC
+
+###### Parameters
+
+Name | Value | Value Origin
+-------------------- | -------------- | ------------------------------------------------------
+Enzyme concentration | 1 µmol/l |
+Vmax | 6.5 µmol/l/min |
+Km | 195.1 µmol/l |
+kcat | 9.865 1/min | Publication-Parameter Identification-Hanke et al. 2018
+
+##### Transport Protein: P-gp-Collett 2004
+
+Molecule: P-gp
+
+###### Parameters
+
+Name | Value | Value Origin
+------------------------- | --------------- | ------------------------------------------------------
+Transporter concentration | 60 nmol/l |
+Vmax | 2.87 µmol/l/min |
+Km | 55 µmol/l |
+kcat | 0.6088 1/min | Publication-Parameter Identification-Hanke et al. 2018
+
+##### Transport Protein: OATP1B1-Tirona 2003
+
+Molecule: OATP1B1
+
+###### Parameters
+
+Name | Value | Value Origin
+------------------------- | ---------------- | ------------------------------------------------------
+Transporter concentration | 109.6 µmol/l |
+Vmax | 0.372 µmol/l/min |
+Km | 1.5 µmol/l |
+kcat | 5.21004653 1/min | Publication-Parameter Identification-Hanke et al. 2018
+
+##### Systemic Process: Glomerular Filtration-GFR
+
+Species: Human
+
+###### Parameters
+
+Name | Value | Value Origin
+------------ | -----:| ----------------------------------------
+GFR fraction | 1 | Publication-Assumption-Hanke et al. 2018
+
+##### Inhibition: CYP2C8-Kajosaari 2005
+
+Molecule: CYP2C8
+
+###### Parameters
+
+Name | Value | Value Origin
+---- | ----------- | ---------------------------------
+Ki | 30.2 µmol/l | Publication-Kajosaari et al. 2005
+
+##### Inhibition: CYP2C9-Hanke 2021
+
+Molecule: CYP2C9
+
+###### Parameters
+
+Name | Value | Value Origin
+---- | ---------- | ------------------------
+Ki | 150 µmol/l | Publication-Yoshida 2012
+
+##### Inhibition: CYP3A4-Kajosaari 2005
+
+Molecule: CYP3A4
+
+###### Parameters
+
+Name | Value | Value Origin
+---- | ----------- | ---------------------------------
+Ki | 18.5 µmol/l | Publication-Kajosaari et al. 2005
+
+##### Inhibition: BCRP-Hanke 2021
+
+Molecule: BCRP
+
+###### Parameters
+
+Name | Value | Value Origin
+---- | --------- | --------------------------------
+Ki | 14 µmol/l | Publication-Prueksaritanont 2014
+
+##### Inhibition: OATP1B1-Hanke 2021
+
+Molecule: OATP1B1
+
+###### Parameters
+
+Name | Value | Value Origin
+---- | ----------- | ----------------------------
+Ki | 0.29 µmol/l | Publication-In Vitro-Bi 2019
+
+##### Inhibition: OATP1B3-Hanke 2021
+
+Molecule: OATP1B3
+
+###### Parameters
+
+Name | Value | Value Origin
+---- | ---------- | ----------------------------
+Ki | 0.5 µmol/l | Publication-In Vitro-Bi 2019
+
+##### Inhibition: OATP2B1-Hanke 2021
+
+Molecule: OATP2B1
+
+###### Parameters
+
+Name | Value | Value Origin
+---- | ----------- | -------------------------------
+Ki | 78.2 µmol/l | Publication-In Vitro-Zhang 2019
+
+##### Inhibition: P-gp-Hanke 2021
+
+Molecule: P-gp
+
+###### Parameters
+
+Name | Value | Value Origin
+---- | ---------- | ------------------
+Ki | 9.1 µmol/l | Other-In Vitro-NBI
+
+##### Induction: CYP1A2-Chen 2010
+
+Molecule: CYP1A2
+
+###### Parameters
+
+Name | Value | Value Origin
+---- | ----------- | ------------:
+EC50 | 0.34 µmol/l |
+Emax | 0.65 |
+
+##### Induction: CYP2C8-Buckley 2014
+
+Molecule: CYP2C8
+
+###### Parameters
+
+Name | Value | Value Origin
+---- | ----------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
+EC50 | 0.34 µmol/l | Publication-Templeton IE, Houston JB, Galetin A. Predictive utility of in vitro rifampin induction data generated in fresh and cryopreserved human hepatocytes, Fa2N-4, and HepaRG cells. Drug Metab Dispos. 2011;39:1921–9; Shou M, Hayashi M, Pan Y, Xu Y, Morrissey K, Xu L, et al. Modeling, prediction, and in vitro in vivo correlation of CYP3A4 induction. Drug Metab Dispos. 2008;36:2355–70.
+Emax | 3.2 | Publication-Buckley DB, Wiegand CM, Prentiss PL, Fahmi OA. Time-course of cytochrome P450 (CYP450) induction in cultured human hepatocytes: Evaluation of activity and mRNA expression profiles for six inducible CYP450 enzymes. ISSX. 2013
+
+##### Induction: CYP2E1-Rae 2001
+
+Molecule: CYP2E1
+
+###### Parameters
+
+Name | Value | Value Origin
+---- | ----------- | ------------:
+EC50 | 0.34 µmol/l |
+Emax | 0.8 |
+
+##### Induction: CYP3A4-Templeton 2011
+
+Molecule: CYP3A4
+
+###### Parameters
+
+Name | Value | Value Origin
+---- | ----------- | --------------------------------------------------
+EC50 | 0.34 µmol/l | Publication-Templeton 2011 (weighted mean for FHH)
+Emax | 9 | Publication-Templeton 2011 (weighted mean for FHH)
+
+##### Induction: AADAC-Assumed
+
+Molecule: AADAC
+
+###### Parameters
+
+Name | Value | Value Origin
+---- | ----------- | ------------------------------------------------------
+EC50 | 0.34 µmol/l | Publication-Assumption-Hanke et al. 2018
+Emax | 0.985 | Publication-Parameter Identification-Hanke et al. 2018
+
+##### Induction: OATP1B1-Dixit 2007
+
+Molecule: OATP1B1
+
+###### Parameters
+
+Name | Value | Value Origin
+---- | ----------- | ------------------------------------------------------
+EC50 | 0.34 µmol/l | Publication-Assumption-Hanke et al. 2018
+Emax | 0.383 | Publication-Parameter Identification-Hanke et al. 2018
+
+##### Induction: P-gp-Greiner 1999
+
+Molecule: P-gp
+
+###### Parameters
+
+Name | Value | Value Origin
+---- | ----------- | ------------------------------------------
+EC50 | 0.34 µmol/l | Publication-Assumption-Hanke et al. 2018
+Emax | 2.5 | Publication-Assumption-Greiner et al. 1999
+
+## 3.2 Diagnostics Plots
+
+Below you find the goodness-of-fit visual diagnostic plots for the PBPK model performance of all data used presented in [Section 2.2.2](#222-clinical-data).
+
+The first plot shows observed versus simulated plasma concentration and the second weighted residuals versus time for itraconazole, hydroxy-itraconazole, keto-itraconazole and N-desalkyl-itraconazole.
+
+
+
+**Table 3-1: GMFE for Rifampicin concentration in plasma/serum**
+
+|Group |GMFE |
+|:-------------|:----|
+|Rifampicin iv |1.47 |
+|Rifampicin po |1.32 |
+|All |1.37 |
+
+
+
+
+
+
+
+
+**Figure 3-1: Rifampicin concentration in plasma/serum**
+
+
+
+
+
+
+
+
+**Figure 3-2: Rifampicin concentration in plasma/serum**
+
+
+
+
+## 3.3 Concentration-Time Profiles
+
+Simulated versus observed concentration-time profiles of all data listed in [Section 2.2.2](#222-clinical-data) are presented below.
+
+
+
+
+
+**Figure 3-3: Rifampicin iv 300 mg (0.5 h)**
+
+
+
+
+
+
+
+
+**Figure 3-4: Rifampicin iv 300 mg (3 h)**
+
+
+
+
+
+
+
+
+**Figure 3-5: Rifampicin iv 300 mg (3 h) - Urine**
+
+
+
+
+
+
+
+
+**Figure 3-6: Rifampicin iv 450 mg (3 h)**
+
+
+
+
+
+
+
+
+**Figure 3-7: Rifampicin iv 450 mg (3 h) - Urine**
+
+
+
+
+
+
+
+
+**Figure 3-8: Rifampicin iv 600 mg (0.5 h)**
+
+
+
+
+
+
+
+
+**Figure 3-9: Rifampicin iv 600 mg (3 h)**
+
+
+
+
+
+
+
+
+**Figure 3-10: Rifampicin iv 600 mg (3 h) - Urine**
+
+
+
+
+
+
+
+
+**Figure 3-11: Rifampicin iv 600 mg (3 h) MD OD (7 days)**
+
+
+
+
+
+
+
+
+**Figure 3-12: Rifampicin po 300 mg**
+
+
+
+
+
+
+
+
+**Figure 3-13: Rifampicin po 450 mg**
+
+
+
+
+
+
+
+
+**Figure 3-14: Rifampicin po 450 mg MD OD (7 days)**
+
+
+
+
+
+
+
+
+**Figure 3-15: Rifampicin po 450 mg MD OD (7 days) - Urine**
+
+
+
+
+
+
+
+
+**Figure 3-16: Rifampicin po 450 mg MD OD (7 days) - Bile**
+
+
+
+
+
+
+
+
+**Figure 3-17: Rifampicin po 600 mg**
+
+
+
+
+
+
+
+
+**Figure 3-18: Rifampicin po 600 mg**
+
+
+
+
+
+
+
+
+**Figure 3-19: Rifampicin po 600 mg MD OD (7 days)**
+
+
+
+
+# 4 Conclusion
+
+The herein presented PBPK model adequately describes the pharmacokinetics of rifampicin in adults. Little is known on the exact mass balance and the full metabolic profile of rifampicin. In this PBPK model, the implemented processes are those that were considered most vital to describe the pharmacokinetics of rifampicin and that could be informed either via *in vitro* data or via parameter optimization based on clinical PK data.
+
+The herein presented quantification of induction processes of OATP1B1 and AADAC are purely based on parameter optimization to describe auto-induction phenomena of rifampicin. The herein presented induction process of P-gp is based on *in vivo* observed P-gp induction measured in duodenal biopsies ([Greiner 1999](#5-references)). The derived Emax value was assumed to be applicable for P-gp induction in all tissues expressing P-gp. This needs to be considered when coupling the herein presented rifampicin model to PBPK models of potential victim drugs that are also subject to P-gp-mediated transport.
+
+Endogenous protein half-lives of OATP1B1, AADAC, and P-gp are not known. Thus, values reported for CYP3A4 were assumed in this PBPK model. These values were needed to implement induction of the three proteins. However, sensitivity of these parameters on simulated rifampicin plasma concentration is very low.
+
+The model features in particular induction of CYP3A4 based on aggregated *in vitro* CYP3A4 activity data in primary human hepatocytes ([Templeton 2011](#5-references)). The model also accounts for competitive inhibition of CYP3A4.
+
+# 5 References
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+**sanofi-aventis U.S. LLC. 2013** Access Data FDA: https://www.accessdata.fda.gov/drugsatfda_docs/label/2013/050420s075,050627s014lbl.pdf
+
+**Shou 2008** Shou M, Hayashi M, Pan Y, Xu Y, Morrissey K, Xu L, Skiles GL. Modeling, prediction, and in vitro in vivo correlation of CYP3A4 induction. Drug Metab Dispos. 2008 Nov;36(11):2355-70.
+
+**Smythe 2012** Smythe W, Khandelwal A, Merle C, Rustomjee R, Gninafon M, Bocar Lo M, Sow OB, Olliaro PL, Lienhardt C, Horton J, Smith P, McIlleron H, Simonsson US. A semimechanistic pharmacokinetic-enzyme turnover model for rifampin autoinduction in adult tuberculosis patients. Antimicrob Agents Chemother. 2012 Apr;56(4):2091-8.
+
+**Staudinger 2010** Staudinger JL, Xu C, Cui YJ, Klaassen CD. Nuclear receptor-mediated regulation of carboxylesterase expression and activity. Expert Opin Drug Metab Toxicol. 2010 Mar;6(3):261-71.
+
+**Stone 2004** Stone JA, Migoya EM, Hickey L, Winchell GA, Deutsch PJ, Ghosh K, Freeman A, Bi S, Desai R, Dilzer SC, Lasseter KC, Kraft WK, Greenberg H, Waldman SA. Potential for interactions between caspofungin and nelfinavir or rifampin. Antimicrob Agents Chemother. 2004 Nov;48(11):4306-14.
+
+**Templeton 2011** Templeton IE, Houston JB, Galetin A. Predictive utility of in vitro rifampin induction data generated in fresh and cryopreserved human hepatocytes, Fa2N-4, and HepaRG cells. Drug Metab Dispos. 2011 Oct;39(10):1921-9.
+
+**Tirona 2003** Tirona RG, Leake BF, Wolkoff AW, Kim RB. Human organic anion transporting polypeptide-C (SLC21A6) is a major determinant of rifampin-mediated pregnane X receptor activation. J Pharmacol Exp Ther. 2003 Jan;304(1):223-8.
+
+**Türk 2019** Türk D, Hanke N, Wolf S, Frechen S, Eissing T, Wendl T, Schwab M, Lehr T. Physiologically Based Pharmacokinetic Models for Prediction of Complex CYP2C8 and OATP1B1 (SLCO1B1) Drug-Drug-Gene Interactions: A Modeling Network of Gemfibrozil, Repaglinide, Pioglitazone, Rifampicin, Clarithromycin and Itraconazole. Clin Pharmacokinet. 2019 Dec;58(12):1595-1607.
+
+**Westphal 2000** Westphal K, Weinbrenner A, Zschiesche M, Franke G, Knoke M, Oertel R, Fritz P, von Richter O, Warzok R, Hachenberg T, Kauffmann HM, Schrenk D, Terhaag B, Kroemer HK, Siegmund W. Induction of P-glycoprotein by rifampin increases intestinal secretion of talinolol in human beings: a new type of drug/drug interaction. Clin Pharmacol Ther. 2000 Oct;68(4):345-55.
+
+**Williamson 2013** Williamson B, Dooley KE, Zhang Y, Back DJ, Owen A. Induction of influx and efflux transporters and cytochrome P450 3A4 in primary human hepatocytes by rifampin, rifabutin, and rifapentine. Antimicrob Agents Chemother. 2013 Dec;57(12):6366-9.
+
+**Zhang 2012** Zhang Y, Cheng X, Aleksunes L, Klaassen CD. Transcription factor-mediated regulation of carboxylesterase enzymes in livers of mice. Drug Metab Dispos. 2012 Jun;40(6):1191-7.
+
+","Markdown"
+"PBPK model","Open-Systems-Pharmacology/OSP-PBPK-Model-Library","Ethinylestradiol/Ethinylestradiol_evaluation_report.md",".md","35814","543","# Building and evaluation of a PBPK model for Ethinylestradiol in adults
+
+| Version | 2.0-OSP12.2 |
+| ----------------------------------------------- | ------------------------------------------------------------ |
+| based on *Model Snapshot* and *Evaluation Plan* | https://github.com/Open-Systems-Pharmacology/Ethinylestradiol-Model/releases/tag/v2.0 |
+| OSP Version | 12.2 |
+| Qualification Framework Version | 3.5 |
+
+This evaluation report and the corresponding PK-Sim project file are filed at:
+
+https://github.com/Open-Systems-Pharmacology/OSP-PBPK-Model-Library/
+
+# Table of Contents
+
+ * [1 Introduction](#introduction)
+ * [2 Methods](#methods)
+ * [2.1 Modeling Strategy](#modeling-strategy)
+ * [2.2 Data](#data)
+ * [2.3 Model Parameters and Assumptions](#model-parameters-and-assumptions)
+ * [3 Results and Discussion](#results-and-discussion)
+ * [3.1 Final input parameters](#final-input-parameters)
+ * [3.2 Diagnostics Plots](#diagnostics-plots)
+ * [3.3 Concentration-Time Profiles](#concentration-time-profiles)
+ * [3.3.1 Model Building](#model-building)
+ * [3.3.2 Model Verification](#model-validation)
+ * [4 Conclusion](#conclusion)
+ * [5 References](#references)
+ * [6 Glossary](#glossary)
+
+# 1 Introduction
+
+The presented PBPK model of ethinylestradiol (EE) has been developed to be used in a PBPK Drug-Drug-Interactions (DDI) network with ethinylestradiol as perpetrator of CYP1A2.
+
+Ethinylestradiol is an estrogen medication which is used widely as a birth control pills in combination with progestins. The following ADME properties characterize ethinylestradiol ([SmPC Namuscla](#5-references), [FDA. QUARTETTE](#5-references)):
+
+**Absorption**: ethinylestradiol is rapidly and completely absorbed from the gut but it undergoes some first pass metabolism in the gut wall (mediated by a.o. CYP3A4 ([Wiesinger 2015](#5-references), [Wang 2004](#5-references))). After oral administration, an initial peak occurs in plasma at 2 to 3 hours, with a secondary peak at about 12 hours after dosing; the second peak is interpreted as evidence for extensive enterohepatic circulation of ethinylestradiol.
+
+**Distribution**: ethinylestradiol is rapidly distributed throughout most body tissues with the largest concentration found in adipose tissue. It distributes into breast milk, with low concentrations. More than 80% of ethinylestradiol in serum is conjugated as sulphate and almost all the conjugated form is bound to albumin.
+
+**Metabolism**: ethinylestradiol is metabolized in the liver. Hydroxylation appears to be the main metabolic pathway. 60% of a dose is excreted in the urine and 40% in the faeces.
+
+**Excretion**: About 30% is excreted in the urine and bile as the glucuronide or sulphate conjugate. The rate of metabolism of ethinylestradiol is affected by several factors, including enzyme-inducing agents, antibiotics, and cigarette smoking. The elimination half-life of ethinylestradiol ranges from 5 to 16 hours.
+
+After i.v. administration, ethinylestradiol displays approximately linear dose relationship in the dose range 30-100 µg. A wide variability is present in the terminal part of the dose-normalized concentrations.
+
+After p.o. single dose, ethinylestradiol shows linear dose relationship in the dose range 30-3000 µg. Secondary peaks can be observed in individual data, compatible with enterohepatic re-circulation. However, mean data do not display such feature as a result of such peak being averaged out. Therefore, enterohepatic re-circulation was not taken into account in the model.
+
+# 2 Methods
+
+## 2.1 Modeling Strategy
+
+The general workflow for building an adult PBPK model has been described by Kuepfer et al. ([Kuepfer 2016](#5-references)). Relevant information on the anthropometry (height, weight) was gathered from the respective clinical study, if reported. Information on physiological parameters (e.g. blood flows, organ volumes, hematocrit) in adults was gathered from the literature and has been incorporated in PK-Sim® as described previously ([Willmann 2007](#5-references)). The applied activity and variability of plasma proteins and active processes that are integrated into PK-Sim® are described in the publicly available 'PK-Sim® Ontogeny Database Version 7.3' ([PK-Sim Ontogeny Database Version 7.3](#5-references)).
+
+The following steps were undertaken in model development:
+
+1. Define lipophilicity and distribution model on data after i.v. administration with linear total hepatic clearance fitted to data and renal clearance set to literature value ([Ezuruike 2018](#5-references)).
+
+2. Predict p.o. data after single dose and at steady state
+
+3. Detail metabolic contribution of different CYPs and UGTs to total hepatic clearance.
+
+Details about input data (physicochemical, *in vitro* and clinical) can be found in [Section 2.2](#22-data).
+
+Details about the structural model and its parameters can be found in [Section 2.3](#23-model-parameters-and-assumptions).
+
+A standard female subject was created based on the European (ICRP,2002) PK-Sim database (age = 30 y, weight = 60 kg, height = 163 cm, BMI = 22,58 kg/m2) and used for simulations, until stated otherwise. Expression of the enzymes CYP3A4, CYP2C9, CYP1A2, CYP2C8, and UGT1A1 from RT PCR database were added.
+
+## 2.2 Data
+
+### 2.2.1 In vitro and physico-chemical data
+
+A literature search was performed to collect available information on physico-chemical properties of ethinylestradiol, see [Table 1](#table-1).
+
+| **Parameter** | **Unit** | **Value** | Source | **Description** |
+| :------------------------------ | ----------------- | ---------------- | --------------------------------- | ---------------------------------------------- |
+| MW+ | g/mol | 296.4 | [DrugBank DB00977](#5-references) | Molecular weight |
+| pKa,acid+ | | 10.33 | [DrugBank DB00977](#5-references) | Acidic dissociation constant |
+| Solubility (pH)+ | mg/mL | 6.77e-3
(7) | [DrugBank DB00977](#5-references) | Aqueous Solubility |
+| logD | | 3.63 - 3.9 | [DrugBank DB00977](#5-references) | Distribution coefficient |
+| fu+ | % | 3 | [DrugBank DB00977](#5-references) | Fraction unbound in plasma |
+| CYP1A2 CL+ | µl/min/pmol | 0.51 | [Ezuruike 2018](#5-references) | Clearance by CYP1A2 |
+| CYP2C8 CL+ | µl/min/pmol | 0.13 | [Ezuruike 2018](#5-references) | Clearance by CYP2C8 |
+| CYP2C9 CL+ | µl/min/pmol | 0.51 | [Ezuruike 2018](#5-references) | Clearance by CYP2C9 |
+| CYP3A4 CL+ | µl/min/pmol | 0.5 | [Ezuruike 2018](#5-references) | Clearance by CYP3A4 |
+| Km UGT1A1+ | µmol/l | 19.22 | [Ezuruike 2018](#5-references) | UGT1A1 saturation constant |
+| Vmax UGT1A1+ | pmol/min/mg prot. | 408.5 | [Ezuruike 2018](#5-references) | Maximal metabolization rate by UGT1A1 |
+| Renal Elimination+ | l/h | 2.079 | [Stanczyk 2013](#5-references) | Renal clearance |
+| Clint HLM+ | µL/min/mg prot. | 118.83 | [Ezuruike 2018](#5-references) | Intrinsic clearance in Human Liver Microsomes |
+| Ki CYP1A2 | µmol/l | 10.6 | [Karjalainen 2008](#5-references) | CYP1A2 inhibition constant |
+
+**Table 1:** Physico-chemical and *in-vitro* metabolization properties of ethinylestradiol extracted from literature. *+: Value used in final model*
+
+### 2.2.2 Clinical data
+
+A literature search was performed to collect available clinical data on ethinylestradiol, see [Table 2](#table-2).
+
+| **Source** | Route | **Dose [mg]/** **Schedule \*** | **Pop.** | **Sex** | **N** | **Form.** |
+| -------------------- | ------------------------------- | ------------ | ------- | --------------------------------- | --------------------------------- | --------------------------------- |
+| [Back 1981](#5-references)+ | i.v. | 0.03 | HV | F | 5 | solution |
+| [Back 1981](#5-references)+ | p.o. | 0.03 | HV | F | 5 | tablet |
+| [Back 1979](#5-references)+ | i.v. | 0.05 | HV | F | 6 | solution |
+| [Back 1979](#5-references)+ | p.o. | 0.05 | HV | F | 6 | NA |
+| [Back 1987](#5-references) | p.o. | 0.05 q.d. | HV | F | 5 | tablet |
+| [Orme 1991](#5-references)+ | i.v. | 0.03 | HV | F | 10 | solution |
+| [Orme 1991](#5-references)+ | p.o. | 0.03 | HV | F | 10 | tablet |
+| [Kuhnz 1996](#5-references) | i.v. | 0.06 | HV | F | 19 | solution |
+| [Goebelsmann 1986](#5-references)+ | p.o. | 0.03 | HV | F | 24 | solution and tablet |
+| [Stanczyk 1983](#5-references)+ | p.o. | 0.12 | HV | F | 24 | solution and tablet |
+| [Zhang 2017](#5-references)+ | p.o. | 0.03 | HV | F | 12 | tablet |
+| [Martin 2016](#5-references) | p.o. | 0.03 q.d. | HV | F | 27 | tablet |
+| [Stockis 2014](#5-references) | p.o. | 0.03 q.d. | HV | F | 24 | tablet |
+| [Sidhu 2006](#5-references) | p.o. | 0.03 q.d. | HV | F | 16 | tablet |
+| [Kothare 2012](#5-references)+ | p.o. | 0.03/0.03 q.d. | HV | F | 20 | tablet |
+| [Timmer 2000](#5-references)+ | p.o. | 0.03 | HV | F | - | tablet |
+
+**Table 2:** Literature sources of clinical concentration data of ethinylestradiol used for model development and validation. *\*: single dose unless otherwise specified;+: Data used for final parameter identification*
+
+## 2.3 Model Parameters and Assumptions
+
+### 2.3.1 Absorption
+
+Intestinal permeability was fitted to po data. Formulation of ethinylestradiol tablet was modeled with Weibull-function and parameters `Dissolution time (50% dissolved)` and `Lag time` fitted to po data.
+
+### 2.3.2 Distribution
+
+Physico-chemical parameters were set to the reported values (see [Section 2.2.1](#221-in-vitro-and-physico-chemical-data)). It was assumed that the major binding partner in plasma is albumin. The value of lipophilicity was estimated by fitting the model to iv and po data.
+
+After testing the available organ-plasma partition coefficient and cell permeability calculation methods available in PK-Sim, observed clinical data were best described by choosing the partition coefficient calculation by `Berezhkovskiy` and cellular permeability calculation by `PK-Sim Standard`.
+
+### 2.3.3 Metabolism and Elimination
+
+Following metabolization processes have been implemented based on [Ezuruike 2018](#5-references):
+
+- Linear CYP1A2 CL
+- Linear CYP2C8 CL
+- Linear CYP2C9 CL
+- Linear CYP3A4 CL
+- Saturable UGT1A1
+- Unspecific liver metabolization
+
+Renal plasma clearance is modeled with `Plasma clearance` set to 2.079 l/h reported in literature ([Stanczyk 2013](#5-references)). The value was normalized to body weight by dividing by 70 kg.
+
+### 2.3.4 Enzyme Inhibition
+
+Simulations of co-administration of ethinylestradiol with tizanidine (see [CYP1A2 DDI Qualification report](https://github.com/Open-Systems-Pharmacology/OSP-Qualification-Reports/releases)) indicate that the reported competitive inhibition of CYP1A2 by ethinylestradiol ([Karjalainen 2008](#5-references)) is not sufficient to describe the increased concentrations of tizanidine after multiple days administration. Therefore, it was decided to fit a time-dependent inhibition (TDI) function to the CYP1A2 enzyme system. The parameters `Kinact` and `K_kinact_half` were estimated by fitting the model to concentration-time profiles of tizanidine ([Granfors 2005](#5-references)).
+
+### 2.3.5 Automated Parameter Identification
+
+Following parameter values were estimated for the model:
+
+- `Lipophilicity`
+- `Specific intestinal permeability`
+- `Dissolution time (50% dissolved)` (Weibull formulation)
+- `Lag time` (Weibull formulation)
+- `Kinact` (CYP1A2 TDI)
+- `K_kinact_half` (CYP1A2 TDI)
+
+# 3 Results and Discussion
+
+The next sections show:
+
+1. Final model input parameters for the building blocks: [Section 3.1](#31-final-input-parameters).
+2. Overall goodness of fit: [Section 3.2](#32-diagnostics-plots).
+3. Simulated vs. observed concentration-time profiles for the clinical studies used for model building and for model verification: [Section 3.3](#33-concentration-time-profiles).
+
+## 3.1 Final input parameters
+
+The parameter values of the final PBPK model are illustrated below.
+
+### Compound: Ethinylestradiol
+
+#### Parameters
+
+Name | Value | Value Origin | Alternative | Default
+------------------------------------------------ | ---------------------- | ------------------------- | ----------- | -------
+Solubility at reference pH | 0.00677 mg/ml | Database-DrugBank DB00977 | S_aq | True
+Reference pH | 7 | Database-DrugBank DB00977 | S_aq | True
+Lipophilicity | 3.4805414593 Log Units | Parameter Identification | LogP | True
+Fraction unbound (plasma, reference value) | 0.03 | Database-DrugBank DB00977 | fu_plasma | True
+Specific intestinal permeability (transcellular) | 0.000168 cm/min | Parameter Identification | Fit | True
+Is small molecule | Yes | | |
+Molecular weight | 296.4 g/mol | Database-DrugBank DB00977 | |
+Plasma protein binding partner | Albumin | | |
+
+#### Calculation methods
+
+Name | Value
+----------------------- | ---------------
+Partition coefficients | Berezhkovskiy
+Cellular permeabilities | PK-Sim Standard
+
+#### Processes
+
+##### Metabolizing Enzyme: CYP1A2-Ezuruike_2018
+
+Molecule: CYP1A2
+
+###### Parameters
+
+Name | Value | Value Origin
+------------------------------ | ---------------------------- | -------------------------
+In vitro CL/recombinant enzyme | 0.51 µl/min/pmol rec. enzyme | Publication-Ezuruike 2018
+
+##### Metabolizing Enzyme: CYP2C8-Ezuruike_2018
+
+Molecule: CYP2C8
+
+###### Parameters
+
+Name | Value | Value Origin
+------------------------------ | ---------------------------- | -------------------------
+In vitro CL/recombinant enzyme | 0.13 µl/min/pmol rec. enzyme | Publication-Ezuruike 2018
+
+##### Metabolizing Enzyme: CYP2C9-Ezuruike_2018
+
+Molecule: CYP2C9
+
+###### Parameters
+
+Name | Value | Value Origin
+------------------------------ | ---------------------------- | -------------------------
+In vitro CL/recombinant enzyme | 0.51 µl/min/pmol rec. enzyme | Publication-Ezuruike 2018
+
+##### Metabolizing Enzyme: CYP3A4-Ezuruike_2018
+
+Molecule: CYP3A4
+
+###### Parameters
+
+Name | Value | Value Origin
+------------------------------ | --------------------------- | -------------------------
+In vitro CL/recombinant enzyme | 0.5 µl/min/pmol rec. enzyme | Publication-Ezuruike 2018
+
+##### Metabolizing Enzyme: UGT1A1-Ezuruike_2018
+
+Molecule: UGT1A1
+
+###### Parameters
+
+Name | Value | Value Origin
+------------------------------------------- | ------------------------------ | -------------------------
+In vitro Vmax for liver microsomes | 408.5 pmol/min/mg mic. protein | Publication-Ezuruike 2018
+Content of CYP proteins in liver microsomes | 33.6 pmol/mg mic. protein | Publication-Ezuruike 2018
+Km | 19.22 µmol/l | Publication-Ezuruike 2018
+
+##### Systemic Process: Renal Clearances-Stanczyk_2013
+
+Species: Human
+
+###### Parameters
+
+Name | Value | Value Origin
+----------------------------- | ------------- | ------------------------------------
+Fraction unbound (experiment) | 0.03 |
+Plasma clearance | 0.0285 l/h/kg | Publication-Stanczyk_2013; 2.079/73=
+
+##### Systemic Process: Total Hepatic Clearance-Ezuruike_2018
+
+Species: Human
+
+###### Parameters
+
+Name | Value | Value Origin
+----------------------------- | ---------------------- | --------------------------------------------------------------------------------------------------------------
+Fraction unbound (experiment) | 0.03 |
+Lipophilicity (experiment) | 3.4805414593 Log Units |
+Plasma clearance | 0 ml/min/kg |
+Specific clearance | 1.1002777778 1/min | Publication-Ezuruike 2018 - Calculated from 118.83 µl/min/mg mic. protein divided by 108 pmol/mg/ mic. protein
+
+##### Inhibition: CYP1A2-Fit
+
+Molecule: CYP1A2
+
+###### Parameters
+
+Name | Value | Value Origin
+------------- | ------------------- | ------------------------
+kinact | 200 1/min | Parameter Identification
+K_kinact_half | 0.4833013314 µmol/l | Parameter Identification
+
+### Formulation: Ethinylestradiol tablet
+
+Type: Weibull
+
+#### Parameters
+
+Name | Value | Value Origin
+-------------------------------- | ----------------- | ------------------------
+Dissolution time (50% dissolved) | 36.5087007601 min | Parameter Identification
+Lag time | 6.7747764588 min | Parameter Identification
+Dissolution shape | 0.92 |
+Use as suspension | Yes |
+
+## 3.2 Diagnostics Plots
+
+The following section displays the goodness-of-fit visual diagnostic plots for the PBPK model performance of all data listed in [Section 2.2.2](#222-clinical-data).
+
+The first plot shows observed versus simulated plasma concentration, the second weighted residuals versus time.
+
+
+
+**Table 3-1: GMFE for Ethinylestradiol concentration in plasma**
+
+|Group |GMFE |
+|:--------------------------------------|:----|
+|iv administration (model building) |1.45 |
+|iv administration (model validation) |1.27 |
+|Oral administration (model building) |1.46 |
+|Oral administration (model validation) |1.26 |
+|All |1.41 |
+
+
+
+
+
+
+
+
+**Figure 3-1: Ethinylestradiol concentration in plasma**
+
+
+
+
+
+
+
+
+**Figure 3-2: Ethinylestradiol concentration in plasma**
+
+
+
+
+## 3.3 Concentration-Time Profiles
+
+Simulated versus observed concentration-time profiles of all data listed in [Section 2.2.2](#222-clinical-data) are presented below.
+
+### 3.3.1 Model Building
+
+
+
+
+
+**Figure 3-3: Ethinylestradiol 0.03 mg iv**
+
+
+
+
+
+
+
+
+**Figure 3-4: Ethinylestradiol 0.05 mg iv**
+
+
+
+
+
+
+
+
+**Figure 3-5: Ethinylestradiol 0.03 mg po**
+
+
+
+
+
+
+
+
+**Figure 3-6: Ethinylestradiol 0.03 mg po 28d**
+
+
+
+
+
+
+
+
+**Figure 3-7: Ethinylestradiol 0.05 mg po**
+
+
+
+
+
+
+
+
+**Figure 3-8: Ethinylestradiol 0.12 mg po**
+
+
+
+
+### 3.3.2 Model Verification
+
+
+
+
+
+**Figure 3-9: Ethinylestradiol 0.06 mg iv**
+
+
+
+
+
+
+
+
+**Figure 3-10: Ethinylestradiol 0.03 mg po 28d pred**
+
+
+
+
+
+
+
+
+**Figure 3-11: Ethinylestradiol 0.05 mg po 28d**
+
+
+
+
+# 4 Conclusion
+
+The developed PBPK model of ethinylestradiol is able to predict the time-profiles following single and multiple dosing of ethinylestradiol accurately.
+
+The implemented TDI mechanism for ethinylestradiol was not evident in literature ([Zanaflex prescribing information](#5-references), [Karjalainen 2008](#5-references)). The substantial and prolonged inhibition may result from CYP1A2 inhibition by EE-metabolites having a different half-life from the parent. [Chang 2009](#5-references) for example found that the EE-2hydroxy and EE-2methoxy IC50s toward rCYP1A1 and rCYP1A2 are comparable to that of the parent. However, not having the possibility to model EE-metabolites contribution, a time-dependent inhibition function on CYP1A2 was used instead to account for this effect.
+
+# 5 References
+
+**Back 1979** Back DJ, Breckenridge AM, Crawford FE, et al. An investigation of the pharmacokinetics of ethynylestradiol in women using radioimmunoassay. *Contraception*. 1979;20(3):263-273.
+
+**Back 1981** Back DJ, Bates M, Breckenridge AM, et al. The pharmacokinetics of levonorgestrel and ethynylestradiol in women - studies with Ovran and Ovranette. *Contraception*. 1981;23(3):229-239.
+
+**Back 1987** Back DJ, Grimmer SF, Rogers S, Stevenson PJ, Orme ML. Comparative pharmacokinetics of levonorgestrel and ethinyloestradiol following intravenous, oral and vaginal administration. *Contraception*. 1987;36(4):471-479.
+
+**Balogh 1995** Balogh A, Boerner A, Kuhnz W, Klinger G, Vollanth R, Henschel L. Influence of ethinylestradiol-containing combination oral contraceptives with gestodene or levonorgestrel on caffeine elimination. *Eur J Clin Pharmacol*. 1995;48(2):161-166.
+
+**Chang 2009** Chang, S. Y., Chen, C., Yang, Z., Rodrigues, A. D. (2009). Further assessment of 17α-ethinyl estradiol as an inhibitor of different human cytochrome P450 forms in vitro. *Drug Metabolism and Disposition*, 37(8), 1667-1675.
+
+**DrugBank DB00977** (https://www.drugbank.ca/drugs/DB00977)
+
+**Ezuruike 2018** Ezuruike U, Humphries H, Dickins M, Neuhoff S, Gardner I, Rowland Yeo K. Risk–Benefit Assessment of Ethinylestradiol Using a Physiologically Based Pharmacokinetic Modeling Approach. *Clin Pharmacol Ther*. 2018;104(6):1229-1239
+
+**FDA. QUARTETTE** FDA. QUARTETTE (levonorgestrel/ethinyl estradiol and ethinyl estradiol) tablets, for oral use. Website: https://www.accessdata.fda.gov/drugsatfda_docs/label/2013/204061s000lbl.pdf
+
+**Goebelsmann 1986** Goebelsmann U, Hoffman D, Chiang S, Woutersz T. The relative bioavailability of levonorgestrel and ethinyl estradiol administered as a low-dose combination oral contraceptive. *Contraception*. 1986;34(4):341-351.
+
+**Granfors 2005** Granfors MT, Backman JT, Laitila J, Neuvonen PJ. Oral contraceptives containing ethinyl estradiol and gestodene markedly increase plasma concentrations and effects of tizanidine by inhibiting cytochrome P450 1A2. *Clin Pharmacol Ther*. 2005;78(4):400-411.
+
+**Karjalainen 2008** Karjalainen, M. (Thesis, 2008). Inhibition of CYP1A2-mediated drug metabolism in vitro and in humans: With special emphasis on rofecoxib and other NSAIDs. Website: https://helda.helsinki.fi/bitstream/handle/10138/23039/inhibiti.pdf?sequence=1&origin=publication_detail
+
+**Kothare 2012** Kothare PA, Seger ME, Northrup J, Mace K, Mitchell MI, Linnebjerg H. Effect of exenatide on the pharmacokinetics of a combination oral contraceptive in healthy women: an open-label, randomised, crossover trial. *BMC Clin Pharmacol*. 2012;12:8.
+
+**Kuepfer 2016** Kuepfer L, Niederalt C, Wendl T, Schlender JF, Willmann S, Lippert J, Block M, Eissing T, Teutonico D. Applied Concepts in PBPK Modeling: How to Build a PBPK/PD Model.CPT Pharmacometrics Syst Pharmacol. 2016 Oct;5(10):516-531.
+
+**Kuhnz 1996** Kuhnz W, Humpel M, Biere H, Gross D. Influence of repeated oral doses of ethinyloestradiol on the metabolic disposition of [13C2]-ethinyloestradiol in young women. *Eur J Clin Pharmacol*. 1996;50(3):231-235.
+
+**Martin 2016** Martin P, Gillen M, Ritter J, et al. Effects of Fostamatinib on the Pharmacokinetics of Oral Contraceptive, Warfarin, and the Statins Rosuvastatin and Simvastatin: Results From Phase I Clinical Studies. *Drugs R D*. 2016;16(1):93-107.
+
+**Orme 1991** Orme M, Back DJ, Ward S, Green S. The pharmacokinetics of ethynylestradiol in the presence and absence of gestodene and desogestrel. *Contraception*. 1991;43(4):305-316.
+
+**PK-Sim Ontogeny Database Version 7.3** (https://github.com/Open-Systems-Pharmacology/OSPSuite.Documentation/blob/38cf71b384cfc25cfa0ce4d2f3addfd32757e13b/PK-Sim%20Ontogeny%20Database%20Version%207.3.pdf)
+
+**Sidhu 2006** Sidhu J, Job S, Singh S, Philipson R. The pharmacokinetic and pharmacodynamic consequences of the co-administration of lamotrigine and a combined oral contraceptive in healthy female subjects. *Br J Clin Pharmacol*. 2006;61(2):191-199.
+
+**SmPC Namuscla** SmPC Namuscla 167 mg hard capsules, 2019, website https://www.medicines.org.uk/emc/product/9838/smpc
+
+**Stanczyk 1983** Stanczyk FZ, Mroszczak EJ, Ling T, et al. Plasma levels and pharmacokinetics of norethindrone and ethinylestradiol administered in solution and as tablets to women. *Contraception*. 1983;28(3):241-251.
+
+**Stanczyk 2013** Stanczyk FZ, Archer DF, Bhavnani BR. Ethinyl estradiol and 17beta-estradiol in combined oral contraceptives: pharmacokinetics, pharmacodynamics and risk assessment. *Contraception*. 2013;87(6):706-727.
+
+**Stockis 2014** Stockis A, Watanabe S, Fauchoux N. Interaction between brivaracetam (100 mg/day) and a combination oral contraceptive: A randomized, double-blind, placebo-controlled study. *Epilepsia*. 2014;55(3):27-31.
+
+**Study c13608215-02** Study c13608215. A study to investigate the pharmacokinetic drug-drug interaction following oral administration of ethinylestradiol/levonorgestrel (Microgynon®) and BI 409306 in healthy Korean premenopausal female subjects (an open-label, two-period, fixed-sequence study).” Boehringer Ingelheim Pharma, 06-Aug-2018.
+
+**Timmer 2000** Timmer C, Mulders T. Pharmacokinetics of etonogestrel and ethinylestradiol released from a combined vaginal ring. *Clin Pharmacokinet*. 2000;39(3):233-242.
+
+**Wang 2004** Wang, B., Sanchez, R.I., Franklin, R.B., Evans, D.C.,Huskey, S.E. The involvement of CYP3A4 and CYP2C9 in the metabolism of 17 alpha-ethinylestradiol. *Drug Metab. Dispos*. 32, 1209–1212 (2004).
+
+**Wiesinger 2015** Wiesinger, H. et al. Pharmacokinetic interaction between the CYP3A4 inhibitor ketoconazole and the hormone drospirenone in combination with ethinylestradiol or estradiol. *Br. J. Clin. Pharmacol*. 80, 1399–1410 (2015).
+
+**Willmann 2007** Willmann S, Höhn K, Edginton A, Sevestre M, Solodenko J, Weiss W, Lippert J, Schmitt W. Development of a physiology-based whole-body population model for assessing the influence of individual variability on the pharmacokinetics of drugs. *J Pharmacokinet Pharmacodyn* 2007, 34(3): 401-431.
+
+**Zanaflex prescribing information** Zanaflex prescribing information. Website: https://www.accessdata.fda.gov/drugsatfda_docs/label/2006/020397s021,021447s002lbl.pdf , 2006, Acorda Therapeutics Inc
+
+**Zhang 2017** Zhang C, Li H, Xiong X, et al. An open-label, two-period comparative study on pharmacokinetics and safety of a combined ethinylestradiol/gestodene transdermal contraceptive patch. *Drug Des Devel Ther*. 2017;11:725-731.
+
+# 6 Glossary
+
+| ADME | Absorption, Distribution, Metabolism, Excretion |
+| ------- | ------------------------------------------------------------ |
+| AUC | Area under the plasma concentration versus time curve |
+| AUCinf | AUC until infinity |
+| AUClast | AUC until last measurable sample |
+| AUCR | Area under the plasma concentration versus time curve Ratio |
+| b.i.d. | Twice daily (bis in diem) |
+| CL | Clearance |
+| Clint | Intrinsic liver clearance |
+| Cmax | Maximum concentration |
+| CmaxR | Maximum concentration Ratio |
+| CYP | Cytochrome P450 oxidase |
+| CYP1A2 | Cytochrome P450 1A2 oxidase |
+| CYP2C19 | Cytochrome P450 2C19 oxidase |
+| CYP3A4 | Cytochrome P450 3A4 oxidase |
+| DDI | Drug-drug interaction |
+| e.c. | Enteric coated |
+| EE | Ethinylestradiol |
+| EM | Extensive metabolizers |
+| fm | Fraction metabolized |
+| FMO | Flavin-containing monooxygenase |
+| fu | Fraction unbound |
+| FDA | Food and Drug administration |
+| GFR | Glomerular filtration rate |
+| HLM | Human liver microsomes |
+| hm | homozygous |
+| ht | heterozygous |
+| IM | Intermediate metabolizers |
+| i.v. | Intravenous |
+| IVIVE | In Vitro to In Vivo Extrapolation |
+| Ka | Absorption rate constant |
+| kcat | Catalyst rate constant |
+| Ki | Inhibitor constant |
+| Kinact | Rate of enzyme inactivation |
+| Km | Michaelis Menten constant |
+| m.d. | Multiple dose |
+| OSP | Open Systems Pharmacology |
+| PBPK | Physiologically-based pharmacokinetics |
+| PK | Pharmacokinetics |
+| PI | Parameter identification |
+| PM | Poor metabolizers |
+| RT-PCR | Reverse transcription polymerase chain reaction |
+| p.o. | Per os |
+| q.d. | Once daily (quaque diem) |
+| SD | Single Dose |
+| SE | Standard error |
+| s.d.SPC | Single dose Summary of Product Characteristics |
+| SD | Standard deviation |
+| TDI | Time dependent inhibition |
+| t.i.d | Three times a day (ter in die) |
+| UGT | Uridine 5'-diphospho-glucuronosyltransferase |
+| UM | Ultra-rapid metabolizers |
+
+","Markdown"
+"PBPK model","Open-Systems-Pharmacology/OSP-PBPK-Model-Library","Amikacin/Amikacin_evaluation_report.md",".md","14219","257","# Building and evaluation of a PBPK model for amikacin in adults
+
+| Version | 2.0-OSP12.2 |
+| ----------------------------------------------- | ------------------------------------------------------------ |
+| based on *Model Snapshot* and *Evaluation Plan* | https://github.com/Open-Systems-Pharmacology/Amikacin-Model/releases/tag/v2.0 |
+| OSP Version | 12.2 |
+| Qualification Framework Version | 3.5 |
+
+This evaluation report and the corresponding PK-Sim project file are filed at:
+
+https://github.com/Open-Systems-Pharmacology/OSP-PBPK-Model-Library/
+
+# Table of Contents
+
+ * [1 Introduction](#introduction)
+ * [2 Methods](#methods)
+ * [2.1 Modeling Strategy](#modeling-strategy)
+ * [2.2 Data](#data)
+ * [2.3 Model Parameters and Assumptions](#model-parameters-and-assumptions)
+ * [3 Results and Discussion](#results-and-discussion)
+ * [3.1 Final input parameters](#final-input-parameters)
+ * [3.2 Diagnostics Plots](#diagnostics-plots)
+ * [3.3 Concentration-Time Profiles](#concentration-time-profiles)
+ * [4 Conclusion](#conclusion)
+ * [5 References](#references)
+
+# 1 Introduction
+
+The presented model building and evaluation report evaluates the performance of a PBPK model for amikacin in adults.
+
+Amikacin is a semi-synthetic aminoglycoside antibiotic used for a number of bacterial infections. Amikacin is administered in several forms, including intravenous or intramuscular injection. The PBPK model for amikacin was previously developed in PK-sim for adults ([Wendl 2011](#5-references)) and preterm neonates ([Claassen 2015](#5-references)). As the latter model was built more recently, this PBPK model was used to evaluate the predictive performance of glomerular filtration rate (GFR) mediated clearance in adults without further changes. In this chapter we show that amikacin adequately described the pharmacokinetics of amikacin in adults, based on the PBPK model build and reported in preterm neonates.
+
+The amikacin model is a whole-body PBPK model, allowing for dynamic translation between individuals with GFR based renal elimination. The amikacin report demonstrates the level of confidence in the amikacin PBPK model build with the OSP suite with regard to reliable predictions of amikacin PK in adults during model-informed drug development.
+
+# 2 Methods
+
+## 2.1 Modeling Strategy
+
+The general concept of building a PBPK model has previously been described by Kuepfer et al. ([Kuepfer 2016](#5-references)). Relevant information on anthropometric (height, weight) and physiological parameters (e.g. blood flows, organ volumes, binding protein concentrations, hematocrit, cardiac output) in adults was gathered from the literature and has been previously published ([Schlender 2016](#5-references)). The information was incorporated into PK-Sim® and was used as default values for the simulations in adults.
+
+The applied activity and variability of plasma proteins and active processes that are integrated into PK-Sim® are described in the publicly available PK-Sim® Ontogeny Database Version 7.3 ([PK-Sim Ontogeny Database Version 7.3](#5-references)) or otherwise referenced for the specific process.
+
+The amikacin model was evaluated in adults using data of the following publication:
+
+- Walker JM, Wise R, Mitchard M. The pharmacokinetics of amikacin and gentamicin in volunteers: a comparison of individual differences. J Antimicrob Chemother. 1979 Jan;5(1):95-9.
+ (https://academic.oup.com/jac/article/5/1/95/747852)
+
+As the PBPK model of amikacin has been previously developed, this model was rebuilt, without any further parameter identification.
+
+Simulations using this PBPK model were compared to the reported data to evaluate model appropriateness and to assess model evaluation, by means of diagnostics plots and predicted versus observed concentration-time profiles, of which the results support an adequate prediction of the PK in adults.
+
+During model building, uncertainties in data quality, as well as study differences may cause not being able to adequately describe the PK of all reported clinical studies.
+
+## 2.2 Data
+
+### 2.2.1 In vitro / physicochemical data
+
+A literature search was performed to collect available information on physicochemical properties of amikacin. The obtained information from literature is summarized in the table below, and is used for model building.
+
+| **Parameter** | **Unit** | **Value (reference)** | **Description** |
+| :-------------- | -------- | ------------------------------------------------ | -------------------------------------- |
+| MW | g/mol | 588.6 ([Claassen 2015](#5-references)) | Molecular weight |
+| pKa | | 9.7, 8.92, 8.13 ([Claassen 2015](#5-references)) | Acid dissociation constants |
+| Solubility (pH) | mg/L | 50 (7) ([Drugbank.ca](#5-references)) | Solubility |
+| logMA | | -0.48 ([Claassen 2015](#5-references)) | The logarithm of the membrane affinity |
+| fu | | 1 ([Claassen 2015](#5-references)) | Fraction unbound |
+| GFR fraction | | 1 ([Claassen 2015](#5-references)) | Glomerular Filtration Rate fraction |
+
+### 2.2.2 Clinical data
+
+A literature search was performed to collect available clinical data on amikacin in adults.
+
+The following publication was found in adults for the evaluation of the reported amikacin PBPK model:
+
+| Publication | Study description |
+| :-------------------------------- | :----------------------------------------------------------- |
+| [Walker 1979](#5-references) | The pharmacokinetics of amikacin and gentamicin in healthy volunteers |
+
+## 2.3 Model Parameters and Assumptions
+
+### 2.3.1 Absorption
+
+As amikacin was administered only intravenously, absorption did not play a role in the PK of amikacin.
+
+### 2.3.2 Distribution
+
+Amikacin is a renally cleared antibiotic with relatively low distribution volume (approximately 0.25 –0.50 L/kg in adults) ([Lanao 1982](#5-references)).
+
+As the FDA ([FDA Label](#5-references)) and the US national library of medicine ([DailyMed](#5-references)) report a serum protein binding ≤ 10% and range from 0 to 11% respectively, Claassen et al. ([Claassen 2015](#5-references)) have applied a fraction unbound of 1 in the reported amikacin PBPK model.
+
+The PK-Sim standard organ-plasma partition coefficient and cell permeability calculation methods that are built in PK-Sim were applied to evaluate the observed clinical data. Specific organ permeability normalized to surface area was automatically calculated by PK-Sim.
+
+### 2.3.3 Metabolism and Elimination
+
+Amikacin is eliminated by the kidneys without metabolism. In adults with normal renal function, 94-98% of a single IM or IV dose of amikacin is excreted unchanged by glomerular filtration in the kidney within 24 hours. ([DailyMed](#5-references))
+
+# 3 Results and Discussion
+
+The previously developed PBPK model for amikacin was evaluated with available with clinical pharmacokinetic data in healthy adults after intravenous amikacin administration.
+
+The fit resulted in an adequate description of the available data.
+
+## 3.1 Final input parameters
+
+The compound parameter values of the final amikacin PBPK model are illustrated below.
+
+### Compound: Amikacin
+
+#### Parameters
+
+Name | Value | Value Origin | Alternative | Default
+------------------------------------------ | --------------- | ------------------------------------- | ----------- | -------
+Solubility at reference pH | 50 mg/l | Publication-In Vitro-Drugbank.de | Measurement | True
+Reference pH | 7 | Publication-In Vitro-Drugbank.de | Measurement | True
+Lipophilicity | -0.48 Log Units | Publication-Other-Claassen et al 2015 | Measurement | True
+Fraction unbound (plasma, reference value) | 1 | Publication-Other-Claassen et al 2015 | Measurement | True
+Is small molecule | Yes | | |
+Molecular weight | 588.6 g/mol | Publication-Claassen et al 2015 | |
+Plasma protein binding partner | Albumin | | |
+
+#### Calculation methods
+
+Name | Value
+----------------------- | ---------------
+Partition coefficients | PK-Sim Standard
+Cellular permeabilities | PK-Sim Standard
+
+#### Processes
+
+##### Systemic Process: Glomerular Filtration-Claassen et al 2015
+
+Species: Human
+
+###### Parameters
+
+Name | Value | Value Origin
+------------ | -----:| -------------------------------
+GFR fraction | 1 | Publication-Claassen et al 2015
+
+## 3.2 Diagnostics Plots
+
+Below you find the goodness-of-fit visual diagnostic plots for amikacin PBPK model performance (Individually simulated versus observed plasma concentration) and residuals versus time of all data used for model building.
+
+
+
+**Table 3-1: GMFE for Goodness of fit plot for concentration in plasma.**
+
+|Group |GMFE |
+|:-----------|:----|
+|Amikacin iv |1.21 |
+
+
+
+
+
+
+
+
+**Figure 3-1: Goodness of fit plot for concentration in plasma.**
+
+
+
+
+
+
+
+
+**Figure 3-2: Goodness of fit plot for concentration in plasma.**
+
+
+
+
+## 3.3 Concentration-Time Profiles
+
+Simulated versus observed plasma concentration-time profiles of all data listed in [Section 2.2.2](#222-clinical-data) are presented below.
+
+
+
+
+
+**Figure 3-3: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-4: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-5: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-6: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-7: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-8: Time Profile Analysis 1**
+
+
+
+
+# 4 Conclusion
+
+The amikacin PBPK model applies glomerular filtration and adequately describes the pharmacokinetics of amikacin in adults receiving intravenous administration of amikacin.
+
+This model could be applied for the translation to special populations such as pediatrics with regard to renal elimination via glomerular filtration.
+
+# 5 References
+
+**Claassen 2015** Claassen K, Thelen K, Coboeken K, Gaub T, Lippert J, Allegaert K, Willmann S1. Development of a Physiologically-Based Pharmacokinetic Model for Preterm Neonates: Evaluation with In Vivo Data. Curr Pharm Des. 2015;21(39):5688-98.
+
+**DailyMed** (https://dailymed.nlm.nih.gov/dailymed/fda/fdaDrugXsl.cfm?setid=6ec3129b-c53b-4bdb-913d-a2d0060fa140&type=display)
+
+**Drugbank.ca** (https://go.drugbank.com/drugs/DB00479 )
+
+**FDA Label** (https://s3-us-west-2.amazonaws.com/drugbank/fda_labels/DB00479.pdf?1540245531)
+
+**Kuepfer 2016** Kuepfer L, Niederalt C, Wendl T, Schlender JF, Willmann S, Lippert J, Block M, Eissing T, Teutonico D. Applied Concepts in PBPK Modeling: How to Build a PBPK/PD Model. CPT Pharmacometrics Syst Pharmacol. 2016 Oct;5(10):516-531. doi: 10.1002/psp4.12134. Epub 2016 Oct 19.
+
+**Lanao 1982** Lanao JM, Dominguez-Gil A, Dominguez-Gil AA, et al. Modification in the pharmacokinetics of amikacin during development. Eur J Clin Pharmacol 1982; 23(2): 155-60.
+
+**PK-Sim Ontogeny Database Version 7.3** (https://github.com/Open-Systems-Pharmacology/OSPSuite.Documentation/blob/38cf71b384cfc25cfa0ce4d2f3addfd32757e13b/PK-Sim%20Ontogeny%20Database%20Version%207.3.pdf)
+
+**Walker 1979** Walker JM, Wise R, Mitchard M. The pharmacokinetics of amikacin and gentamicin in volunteers: a comparison of individual differences. J Antimicrob Chemother. 1979 Jan;5(1):95-9.
+
+**Wendl 2011** Wendl T, Niederalt C, Becker C, et al. Modeling of renal failure, dialysis, inhalation and mechanical ventilation: Development of a whole-body physiologically-based pharmacokinetic (PBPK) model for ICU patients with and without renal failure receiving inhalatively administered Amikacin via a tracheal tube. Presented at: *The Annual Meeting of the Population Approach Group in Europe* Athens. 2011; Abstr. 2194.
+
+","Markdown"
+"PBPK model","Open-Systems-Pharmacology/OSP-PBPK-Model-Library","Midazolam/Midazolam_evaluation_report.md",".md","65638","917","# Building and evaluation of a PBPK model for Midazolam in healthy adults
+
+| Version | 2.0-OSP12.2 |
+| ----------------------------------------------- | ------------------------------------------------------------ |
+| based on *Model Snapshot* and *Evaluation Plan* | https://github.com/Open-Systems-Pharmacology/Midazolam-Model/releases/tag/v2.0 |
+| OSP Version | 12.2 |
+| Qualification Framework Version | 3.5 |
+
+This evaluation report and the corresponding PK-Sim project file are filed at:
+
+https://github.com/Open-Systems-Pharmacology/OSP-PBPK-Model-Library/
+
+# Table of Contents
+
+ * [1 Introduction](#introduction)
+ * [2 Methods](#methods)
+ * [2.1 Modeling Strategy](#modeling-strategy)
+ * [2.2 Data](#data)
+ * [2.3 Model Parameters and Assumptions](#model-parameters-and-assumptions)
+ * [3 Results and Discussion](#results-and-discussion)
+ * [3.1 Final input parameters](#final-input-parameters)
+ * [3.2 Diagnostics Plots](#diagnostics-plots)
+ * [3.3 Concentration-Time Profiles](#ct-profiles)
+ * [3.3.1 Model Building](#model-building)
+ * [3.3.2 Model Verification](#model-verification)
+ * [4 Conclusion](#conclusion)
+ * [5 References](#main-references)
+
+# 1 Introduction
+
+Midazolam is a widely-used sedative, approved as premedication before surgical interventions. It is almost exclusively metabolized by CYP3A4 making it a sensitive probe and victim drug for the investigation of CYP3A4 activity *in vivo*. Midazolam shows substantial first pass metabolism resulting in a bioavailability of under 50%. Less than 1% of a midazolam dose is excreted unchanged in urine.
+
+The herein presented model represents an update of the midazolam model published by Hanke et al. ([Hanke 2018](#5-references)). The model has been developed using in particular published pharmacokinetic clinical data by Hohmann et al. ([Hohmann 2015](#5-references)), Hyland et al. 2009 ([Hyland 2009](#5-references)) and Thummel et al. 1996 ([Thummel 1996](#5-references)). It has then been evaluated by comparing observed data to simulations of a large number of clinical studies covering a dose range of 0.05 mg/kg to 20 mg after intravenous and oral administrations. Furthermore, it has been evaluated within a CYP3A4 DDI modeling network as a victim drug.
+
+Model features include:
+
+- metabolism by CYP3A4
+- (direct) metabolism by UGT1A4
+- excretion into urine via glomerular filtration
+- a decrease in the permeability between the intracellular and interstitial space (model parameters `P (intracellular->interstitial)` and `P (interstitial->intracellular)`) in intestinal mucosa to optimize quantitatively the extent of gut wall metabolism
+- and binding to a hypothetical binding partner in the brain to optimize a late redistribution phase in midazolam plasma concentrations.
+
+# 2 Methods
+
+## 2.1 Modeling Strategy
+
+The general concept of building a PBPK model has previously been described by Kuepfer et al. ([Kuepfer 2016](#5-references)). Relevant information on anthropometric (height, weight) and physiological parameters (e.g. blood flows, organ volumes, binding protein concentrations, hematocrit, cardiac output) in adults was gathered from the literature and has been previously published ([Willmann 2007](#5-references)). The information was incorporated into PK-Sim® and was used as default values for the simulations in adults.
+
+The applied activity and variability of plasma proteins and active processes that are integrated into PK-Sim® are described in the publicly available PK-Sim® Ontogeny Database Version 7.3 ([PK-Sim Ontogeny Database Version 7.3](#5-references)) or otherwise referenced for the specific process.
+
+First, a mean model was built using clinical data from single dose studies with intravenous and oral administration of midazolam by Hohmann et al. ([Hohmann 2015](#5-references)) (reporting plasma concentrations), Hyland et al. 2009 ([Hyland 2009](#5-references)) (reporting the dose fraction metabolized via UGT1A4), and Thummel et al. 1996 ([Thummel 1996](#5-references)) (reporting the dose fraction excreted into urine of unchanged drug). The mean PBPK model was developed using a typical European individual. The relative tissue-specific expressions of enzymes predominantly being involved in the metabolism of midazolam (CYP3A4 and UGT1A4) were considered ([Meyer 2012](#5-references)).
+
+A specific set of parameters (see below) was optimized using the Parameter Identification module provided in PK-Sim®. Structural model selection was mainly guided by visual inspection of the resulting description of data and biological plausibility.
+
+Once the appropriate structural model was identified, additional parameters for tablet formulations were identified.
+
+The model was then verified by simulating further clinical studies reporting pharmacokinetic concentration-time profiles of midazolam.
+
+Details about input data (physicochemical, *in vitro* and clinical) can be found in [Section 2.2](#22-data).
+
+Details about the structural model and its parameters can be found in [Section 2.3](#23-model-parameters-and-assumptions).
+
+## 2.2 Data
+
+### 2.2.1 In vitro and physicochemical data
+
+A literature search was performed to collect available information on physicochemical properties of midazolam. The obtained information from literature is summarized in the table below, and is used for model building.
+
+| **Parameter** | **Unit** | **Value** | Source | **Description** |
+| :------------------------------------ | -------------------------- | ---------------- | --------------------------------- | ------------------------------------------------------------ |
+| MW | g/mol | 325.78 | [DrugBank DB00683](#5-references) | Molecular weight |
+| pKa1 | | 10.95 | [Wang 2019](#5-references) | acid dissociation constant of conjugate acid; compound type: ampholyte |
+| pKa2 | | 6.2 | [Wang 2019](#5-references) | acid dissociation constant of conjugate acid; compound type: ampholyte |
+| Solubility (pH) | mg/mL | 0.13
(5) | [Heikkinen 2012](#5-references) | Aqueous Solubility |
+| | | 0.049
(6.5) | [Heikkinen 2012](#5-references) | FaSSIF (fasted state simulated intestinal fluid) solubility |
+| | | 0.09
(5) | [Heikkinen 2012](#5-references) | FeSSIF (fed state simulated intestinal fluid) solubility |
+| logP | | 3.53 | [Wang 2019](#5-references) | Partition coefficient between octanol and water |
+| | | 3.0 | [Dagenais 2009](#5-references) | Partition coefficient between octanol and water |
+| | | 3.37 | [Bolger 2006](#5-references) | Partition coefficient between octanol and water |
+| | | 3.1 | [Rodgers 2006](#5-references) | Partition coefficient between octanol and water |
+| fu | % | 3.1 | [Gertz 2010](#5-references) | Fraction unbound in plasma |
+| | % | 3.2 | [Wang 2019](#5-references) | Fraction unbound in plasma |
+| | % | 2.2 | [Lown 1995](#5-references) | Fraction unbound in plasma |
+| | % | 3.1 | [Björkman 2001](#5-references) | Fraction unbound in plasma in men |
+| | % | 3.1 | [Björkman 2001](#5-references) | Fraction unbound in plasma in women |
+| Vmax, Km CYP3A4 | pmol/min/pmol,
µmol/L | 1.96
2.69 | [Galentin 2004](#5-references) | CYP3A4 supersomes Michaelis-Menten kinetics (alpha-hydroxylation) |
+| Vmax, Km CYP3A4 | pmol/min/mg,
µmol/L | 850
4 | [Bolger 2006](#5-references) | CYP3A liver microsomes Michaelis-Menten kinetics |
+| Vmax, Km CYP3A4 | nmol/min/mg,
µmol/L | 4.41
3.8 | [Ito 2003](#5-references) | CYP3A liver microsomes Michaelis-Menten kinetics (alpha-hydroxylation) |
+| Vmax, Km CYP3A4 | nmol/min/mg,
µmol/L | 0.18
3.9 | [Patki 2003](#5-references) | CYP3A liver microsomes Michaelis-Menten kinetics (alpha-hydroxylation) |
+| Vmax, Km CYP3A4 | pmol/min/pmol,
µmol/L | 5.23
2.16 | [Wang 2019](#5-references) | CYP3A4 supersomes Michaelis-Menten kinetics (alpha-hydroxylation) |
+| Vmax, Km UGT1A4 | pmol/min/mg,
µmol/L | 276
37.8 | [Klieber 2008](#5-references) | UGT1A4 liver microsomes Michaelis-Menten kinetics |
+| KD GABRG2 | nmol/L | 1.8 | [Buhr 1997](#5-references) | Binding affinity to GABRG2 (Gamma-Aminobutyric Acid Type A Receptor Subunit Gamma2) |
+
+### 2.2.2 Clinical data
+
+A literature search was performed to collect available clinical data on midazolam in adults.
+
+The following publications were found in adults for model building:
+
+| Publication | Arm / Treatment / Information used for model building |
+| :---------------------------- | :----------------------------------------------------------- |
+| [Hohmann 2015](#5-references) | Plasma PK profiles in healthy subjects after single dose administrations of midazolam solutions:
- intravenous 0.001 mg
- intravenous 1 mg
- oral 0.003 mg
- oral 3 mg |
+| [Hyland 2009](#5-references) | Quantification of direct UGT1A4-formed midazolam-*N*-glucuronide (in urine) after administration of a 3 mg oral and 1 mg intravenous dose of midazolam. See table below for summary of data. |
+| [Thummel 1996](#5-references) | Quantification of unchanged midazolam in urine after administration of a 2 mg oral and 1 mg intravenous dose of midazolam. See table below for summary of data. |
+| [Ahonen 1995](#5-references) | Plasma PK profiles in healthy subjects with single dose administrations of a midazolam 7.5 mg tablet (in the absence of itraconazole) |
+| [Olkkola 1994](#5-references) | Plasma PK profiles in healthy subjects with single dose administrations of a midazolam 7.5 mg tablet (in the absence of itraconazole) |
+| [Olkkola 1996](#5-references) | Plasma PK profiles in healthy subjects with single dose administrations of a midazolam 7.5 mg tablet (in the absence of itraconazole) |
+| [Saari 2006](#5-references) | Plasma PK profiles in healthy subjects with single dose administrations of a midazolam 7.5 mg tablet (in the absence of voriconazole) |
+| [Link 2008](#5-references) | Plasma PK profiles in healthy subjects with single dose administrations of a midazolam 7.5 mg tablet (in the absence of rifampicin) |
+
+The following table shows the data from the excretion studies ([Thummel 1996](#5-references), [Hyland 2009](#5-references)) used for model building:
+
+| Observer | Value |
+| ------------------------------------------------------------ | ----- |
+| Fraction excreted to urine of unchanged midazolam after iv administration (female) | 0.27% |
+| Fraction excreted to urine of unchanged midazolam after iv administration (male) | 0.28% |
+| Fraction excreted to urine of unchanged midazolam after po administration (female) | 0.31% |
+| Fraction excreted to urine of unchanged midazolam after po administration (male) | 0.47% |
+| Fraction metabolized UGT1A4 (to midazolam-*N*-glucuronide) after iv administration | 2.16% |
+| Fraction metabolized UGT1A4 (to midazolam-*N*-glucuronide) after po administration | 1.29% |
+
+The following dosing scenarios were simulated and compared to respective data for model verification:
+
+| Scenario | Data reference |
+| ------------------------------------------------------------ | ------------------------------------ |
+| iv 0.05 mg/kg (2 min) | [Olkkola 1993](#5-references) |
+| iv 0.05 mg/kg (30 min) | [Gorski 1998](#5-references) |
+| | [Gorski 2003](#5-references) |
+| | [Quinney 2008](#5-references) |
+| iv 0.05 mg/kg (bolus) | [Szalat 2007](#5-references) |
+| iv 0.075 mg/kg (1 min) | [Allonen 1981](#5-references) |
+| | [Swart 2002](#5-references) |
+| iv 0.15 mg/kg (bolus) | [Heizmann 1983](#5-references) |
+| iv 1 mg (bolus) | [Kharasch 1997](#5-references) |
+| | [Kharasch 2004](#5-references) |
+| | [Kharasch 2011](#5-references) |
+| | [Phimmasone 2001](#5-references) |
+| | [Shin 2013](#5-references) |
+| | [Shin 2016](#5-references) |
+| iv 1 mg (2 min)
Corean CYP3A5\*3/\*3 only, CYP3A4 reference concentration adjusted | [Yu 2004](#5-references) |
+| iv 2 mg (bolus) | [Darwish 2008](#5-references) |
+| iv 5 mg (30 sec) | [Schwagmeier 1998](#5-references) |
+| iv 5 mg (bolus) | [Smith 1981](#5-references) |
+| po 0.01 mg (solution) | [Prueksaritanont 2017](#5-references) |
+| po 0.075 mg (solution) | [Eap 2004](#5-references) |
+| po 0.075 mg/kg (syrup) | [Chung 2006](#5-references) |
+| po 1 mg (solution) | [van Dyk 2018](#5-references) |
+| | [Wiesinger 2020](#5-references) |
+| | [Chattopadhyay 2018](#5-references) |
+| po 10 mg (solution) | [Lam 2003](#5-references) |
+| | [Smith 1981](#5-references) |
+| po 10 mg (tablet) | [Heizmann 1983](#5-references) |
+| | [Smith 1981](#5-references) |
+| po 15 mg (tablet) | [Allonen 1981](#5-references) |
+| | [Backman 1994](#5-references) |
+| | [Backman 1996](#5-references) |
+| | [Backman 1998](#5-references) |
+| | [Bornemann 1986](#5-references) |
+| | [Olkkola 1993](#5-references) |
+| | [Yeates 1996](#5-references) |
+| | [Zimmermann 1996](#5-references) |
+| po 15 mg (tablet) - with 1h after high-fat breakfast | [Bornemann 1986](#5-references) |
+| po 2 mg (solution) | [Templeton 2010](#5-references) |
+| | [Lutz 2018](#5-references) |
+| po 20 mg (tablet) | [Heizmann 1983](#5-references) |
+| po 3 mg (solution) | [Katzenmaier 2010](#5-references) |
+| | [Kharasch 2004](#5-references) |
+| | [Kharasch 2011](#5-references) |
+| | [Markert 2013](#5-references) |
+| po 3.5 mg (solution) | [Quinney 2008](#5-references) |
+| po 4 mg (solution) | [Gorski 1998](#5-references) |
+| | [Gorski 2003](#5-references) |
+| po 40 mg (tablet) | [Heizmann 1983](#5-references) |
+| po 5 mg (solution) | [Darwish 2008](#5-references) |
+| | [Okudaira 2007](#5-references) |
+| | [Tham 2006](#5-references) |
+| po 6 mg (solution) | [Greenblat 2003](#5-references) |
+| po 7.5 mg (solution) | [Eap 2004](#5-references) |
+| po 8 mg (solution) | [Gurley 2006](#5-references) |
+| | [Gurley 2008a](#5-references) |
+| Mikus 2017
(4 mg po solution, followed by 2 mg iv administration 6 hours later) | [Mikus 2017](#5-references) |
+
+## 2.3 Model Parameters and Assumptions
+
+### 2.3.1 Absorption
+
+The model parameter `Specific intestinal permeability` was optimized to best match clinical data (see [Section 2.3.4](#234-automated-parameter-identification)). The default solubility was assumed to be the measured value in FaSSIF (fasted state simulated intestinal fluid, see [Section 2.2.1](#221-in-vitro-and-physicochemical-data))
+
+The dissolution of tablets were implemented via an empirical Weibull dissolution tablet. However, dissolution does not seem to be relevant in terms of *rate-limiting* kinetics; see results of optimization in [Section 2.3.4](#234-automated-parameter-identification).
+
+### 2.3.2 Distribution
+
+Midazolam is moderately to highly protein bound (approx. 97 %) in plasma (see [Section 2.2.1](#221-in-vitro-and-physicochemical-data)). A value of 3.1% was used in this PBPK model for `Fraction unbound (plasma, reference value)`. It was assumed that the major binding partner is albumin.
+
+An important parameter influencing the resulting volume of distribution is lipophilicity. The reported experimental logP values are in the range of 3 (see [Section 2.2.1](#221-in-vitro-and-physicochemical-data)) which served as a starting value. Finally, the model parameters `Lipophilicity` was optimized to match best clinical data (see also [Section 2.3.4](#234-automated-parameter-identification)).
+
+After testing the available organ-plasma partition coefficient and cell permeability calculation methods built in PK-Sim, observed clinical data was best described by choosing the partition coefficient calculation by `Rodgers and Rowland` and cellular permeability calculation by `PK-Sim Standard`.
+
+Initial model building showed that the late disposition (approx. 24 hours after administration) could not be well described. This effect was assumed to be (re-)distribution-related. Finally, binding to a hypothetical binding partner in the brain was assumed (motivated by the target of midazolam: GABA receptor). After implementation of *in vitro* binding affinity to GABRG2 (Gamma-Aminobutyric Acid Type A Receptor Subunit Gamma 2) (see [Section 2.2.1](#221-in-vitro-and-physicochemical-data)), the `Reference concentration` of GABRG2 was optimized to match best clinical data (see also [Section 2.3.4](#234-automated-parameter-identification)). Note that the respective `koff` value was assumed to be 1 min-1.
+
+### 2.3.3 Metabolism and Elimination
+
+Two metabolic pathways were implement into the model via Michaelis-Menten kinetics
+
+* CYP3A4
+* UGT1A4
+
+The CYP3A4 expression profiles is based on high-sensitive real-time RT-PCR ([Nishimura 2013](#5-references)). UGT1A4 was assumed to be exclusively expressed in the liver. Absolute tissue-specific expressions were obtained by considering the respective absolute concentration in the liver. The PK-Sim database provides a default value for CYP3A4 (compare [Rodrigues 1999](#5-references) and assume 40 mg protein per gram liver). A reference concentration of 2.32 µmol/L in the liver for UGT1A4 was derived from a quantification reported by Achour *et al.* ([Achour 2014](#5-references)) with 58.0 pmol/mg in Human Liver Microsomes (assuming 40 mg protein per gram liver).
+
+Additionally, a renal clearance (assumed to be mainly driven by glomerular filtration) was implemented.
+
+The first model simulations showed that gut wall metabolism was underrepresented in the PBPK model. In order to increase gut wall metabolism, the “mucosa permeability on basolateral side” (jointly the model parameters in the mucosa: ``P (interstitial->intracellular)`` and ``P (intracellular->interstitial)``) was estimated. A decrease in this permeability may lead to higher gut wall concentrations and, in turn, to a higher gut wall elimination. This parameter was preferred over other parameters such as relative CYP3A4 expression or fraction unbound (fu) in the gut wall as it is technically not limited to a maximum value of 100%.
+
+### 2.3.4 Automated Parameter Identification
+
+This is the result of the final parameter identification for the base model:
+
+| Model Parameter | Optimized Value | Unit |
+| ------------------------------------------------------------ | ------------------------------------------------------------ | --------- |
+| `Lipophilicity` | 2.897 | Log Units |
+| `Specific intestinal permeability` | 1.555E-4 | cm/min |
+| Basolateral mucosa permeability
(``P (interstitial->intracellular)``, ``P (intracellular->interstitial)``) | 1.924E-3 | cm/min |
+| `Km` (CYP3A4) | 4 FIXED (see [Section 2.2.1](#221-in-vitro-and-physicochemical-data)) | µmol/L |
+| `kcat` (CYP3A4) | 8.761 | 1/min |
+| `Km` (UGT1A4) | 37.8 FIXED (see [Section 2.2.1](#221-in-vitro-and-physicochemical-data)) | µmol/L |
+| `kcat` (UGT1A4) | 3.591 | 1/min |
+| `GFR fraction` | 0.6401 | |
+| `Reference concentration` (GABRG2) | 1.088* | µmol/L |
+
+* The value in the model was updated to 1.041 with the release of PK-Sim 10 to account for the updated calculation method of interstitial concentrations (please refer to the respective [release notes of version 10](https://github.com/Open-Systems-Pharmacology/Suite/releases/tag/v10.0)).
+
+This is the result of the final parameter identification for the dissolution parameters of a midazolam tablet:
+
+| Model Parameter | Optimized Value | Unit |
+| ---------------------------------- | --------------- | ---- |
+| `Dissolution time (50% dissolved)` | 0.0107 | min |
+| `Dissolution shape` | 4.3803 | |
+
+# 3 Results and Discussion
+
+The PBPK model for midazolam was developed and verified with clinical pharmacokinetic data.
+
+The model was built and evaluated covering data from studies including in particular
+
+* intravenous (bolus and infusions) and oral administrations (solution and tablets).
+* a dose range of 0.001 to 40 mg.
+
+The model quantifies metabolism via CYP3A4 and UGT1A4.
+
+The next sections show:
+
+1. the final model input parameters for the building blocks: [Section 3.1](#31-final-input-parameters).
+2. the overall goodness of fit: [Section 3.2](#32-diagnostics-plots).
+3. simulated vs. observed concentration-time profiles for the clinical studies used for model building and for model verification: [Section 3.3](#33-concentration-time-profiles).
+
+## 3.1 Final input parameters
+
+The compound parameter values of the final PBPK model are illustrated below.
+
+### Compound: Midazolam
+
+#### Parameters
+
+Name | Value | Value Origin | Alternative | Default
+------------------------------------------------ | ----------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------ | ------------------ | -------
+Solubility at reference pH | 0.13 mg/ml | Publication-In Vitro-Heikkinen 2012 | Aqueous solubility | False
+Reference pH | 5 | Publication-In Vitro-Heikkinen 2012 | Aqueous solubility | False
+Solubility at reference pH | 0.049 mg/ml | Publication-In Vitro-Heikkinen 2012 | FaSSIF | True
+Reference pH | 6.5 | Publication-In Vitro-Heikkinen 2012 | FaSSIF | True
+Solubility at reference pH | 0.09 mg/ml | Publication-In Vitro-Heikkinen 2012 | FeSSIF | False
+Reference pH | 5 | Publication-In Vitro-Heikkinen 2012 | FeSSIF | False
+Lipophilicity | 2.8972038771 Log Units | Parameter Identification-Parameter Identification-Value updated from 'PI Hohmann iv+po, Hyland feUr MDZG, Thummel feUr unchanged - Pint' on 2019-04-09 16:10 | Optimized | True
+Fraction unbound (plasma, reference value) | 0.031 | Parameter Identification-Parameter Identification-Value updated from 'PI Hohmann iv+po, Hyland feUr MDZG, Thummel feUr unchanged - Pint' on 2019-04-09 16:10 | Gertz et al. 2010 | True
+Specific intestinal permeability (transcellular) | 0.00015549970673 cm/min | Parameter Identification-Parameter Identification-Value updated from 'PI Hohmann iv+po, Hyland feUr MDZG, Thummel feUr unchanged - Pint' on 2019-04-09 16:10 | Optimized | True
+Cl | 1 | | |
+F | 1 | | |
+Is small molecule | Yes | | |
+Molecular weight | 325.78 g/mol | | |
+Plasma protein binding partner | Albumin | | |
+
+#### Calculation methods
+
+Name | Value
+----------------------- | -------------------
+Partition coefficients | Rodgers and Rowland
+Cellular permeabilities | PK-Sim Standard
+
+#### Processes
+
+##### Specific Binding: GABRG2-Buhr 1997
+
+Molecule: GABRG2
+
+###### Parameters
+
+Name | Value | Value Origin
+---- | ---------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------
+koff | 1 1/min | Parameter Identification-Parameter Identification-Value updated from 'PI Hohmann iv+po, Hyland feUr MDZG, Thummel feUr unchanged - Pint' on 2019-04-09 16:10
+Kd | 1.8 nmol/l |
+
+##### Systemic Process: Glomerular Filtration-Optimized
+
+Species: Human
+
+###### Parameters
+
+Name | Value | Value Origin
+------------ | ------------:| ------------------------------------------------------------------------------------------------------------------------------------------------------------
+GFR fraction | 0.6401025724 | Parameter Identification-Parameter Identification-Value updated from 'PI Hohmann iv+po, Hyland feUr MDZG, Thummel feUr unchanged - Pint' on 2019-04-09 16:10
+
+##### Metabolizing Enzyme: CYP3A4-Optimized
+
+Molecule: CYP3A4
+
+###### Parameters
+
+Name | Value | Value Origin
+---------------------------------- | ---------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------
+In vitro Vmax for liver microsomes | 850 pmol/min/mg mic. protein |
+Km | 4 µmol/l | Other-In Vitro-aggregated from literature
+kcat | 8.7607941215 1/min | Parameter Identification-Parameter Identification-Value updated from 'PI Hohmann iv+po, Hyland feUr MDZG, Thummel feUr unchanged - Pint' on 2019-04-09 16:10
+
+##### Metabolizing Enzyme: UGT1A4-Optimized
+
+Molecule: UGT1A4
+
+###### Parameters
+
+Name | Value | Value Origin
+------------------------------------------- | ---------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------
+In vitro Vmax for liver microsomes | 276 pmol/min/mg mic. protein | Publication-Klieber 2008
+Content of CYP proteins in liver microsomes | 58 pmol/mg mic. protein | Publication-Achour 2014
+Km | 37.8 µmol/l | Publication-Klieber 2008
+kcat | 3.5911771641 1/min | Parameter Identification-Parameter Identification-Value updated from 'PI Hohmann iv+po, Hyland feUr MDZG, Thummel feUr unchanged - Pint' on 2019-04-09 16:10
+
+### Formulation: Tablet (Dormicum)
+
+Type: Weibull
+
+#### Parameters
+
+Name | Value | Value Origin
+-------------------------------- | ---------------- | -----------------------------------------------------------------------------------------------------------
+Dissolution time (50% dissolved) | 0.0107481462 min | Parameter Identification-Parameter Identification-Value updated from 'PI Tablet 7.5 mg' on 2019-04-09 16:30
+Lag time | 0 min |
+Dissolution shape | 4.3802943225 | Parameter Identification-Parameter Identification-Value updated from 'PI Tablet 7.5 mg' on 2019-04-09 16:30
+Use as suspension | Yes |
+
+## 3.2 Diagnostics Plots
+
+Below you find the goodness-of-fit visual diagnostic plots for the PBPK model performance of all data used presented in [Section 2.2.2](#222-clinical-data).
+
+The first plot shows observed versus simulated plasma concentration, the second weighted residuals versus time.
+
+
+
+**Table 3-1: GMFE for Midazolam concentration in plasma/blood**
+
+|Group |GMFE |
+|:--------------------------------------------------|:----|
+|Intravenous administration (model building) |1.22 |
+|Intravenous administration (model verification) |1.41 |
+|Mixed applications |1.27 |
+|Oral administration, solution (model building) |1.23 |
+|Oral administration, solution (model verification) |1.40 |
+|Oral administration, tablet (model building) |1.27 |
+|Oral administration, tablet (model verification) |1.68 |
+|All |1.45 |
+
+
+
+
+
+
+
+
+**Figure 3-1: Midazolam concentration in plasma/blood**
+
+
+
+
+
+
+
+
+**Figure 3-2: Midazolam concentration in plasma/blood**
+
+
+
+
+## 3.3 Concentration-Time Profiles
+
+Simulated versus observed concentration-time profiles of all data listed in [Section 2.2.2](#222-clinical-data) are presented below.
+
+### 3.3.1 Model Building
+
+
+
+
+
+**Figure 3-3: iv 0.001 mg (5 min) - Plasma**
+
+
+
+
+
+
+
+
+**Figure 3-4: iv 1 mg (5 min) - Plasma**
+
+
+
+
+
+
+
+
+**Figure 3-5: iv 1 mg (5 min) - Urine**
+
+
+
+
+
+
+
+
+**Figure 3-6: iv 1 mg (5 min) - fm UGT1A4**
+
+
+
+
+
+
+
+
+**Figure 3-7: po 0.003 mg (solution) - Plasma**
+
+
+
+
+
+
+
+
+**Figure 3-8: po 3 mg (solution) - Plasma**
+
+
+
+
+
+
+
+
+**Figure 3-9: po 3 mg (solution) - Urine**
+
+
+
+
+
+
+
+
+**Figure 3-10: po 3 mg (solution) - fm UGT1A4**
+
+
+
+
+
+
+
+
+**Figure 3-11: po 7.5 mg (tablet) - Plasma**
+
+
+
+
+### 3.3.2 Model Verification
+
+
+
+
+
+**Figure 3-12: iv 0.05 mg/kg (2 min) - Plasma**
+
+
+
+
+
+
+
+
+**Figure 3-13: iv 0.05 mg/kg (30 min) - Whole blood**
+
+
+
+
+
+
+
+
+**Figure 3-14: iv 0.05 mg/kg (bolus) - Plasma**
+
+
+
+
+
+
+
+
+**Figure 3-15: iv 0.075 mg/kg (1 min) - Plasma**
+
+
+
+
+
+
+
+
+**Figure 3-16: iv 0.15 mg/kg (bolus) - Plasma**
+
+
+
+
+
+
+
+
+**Figure 3-17: iv 1 mg (2 min) [Korean] - Plasma**
+
+
+
+
+
+
+
+
+**Figure 3-18: iv 1 mg (bolus) - Plasma**
+
+
+
+
+
+
+
+
+**Figure 3-19: iv 1 mg (bolus) - Urine**
+
+
+
+
+
+
+
+
+**Figure 3-20: iv 1 mg (bolus) - fm UGT1A4**
+
+
+
+
+
+
+
+
+**Figure 3-21: iv 2 mg (2 min) - Plasma**
+
+
+
+
+
+
+
+
+**Figure 3-22: iv 2 mg (bolus) - Plasma**
+
+
+
+
+
+
+
+
+**Figure 3-23: iv 5 mg (30 sec) - Plasma**
+
+
+
+
+
+
+
+
+**Figure 3-24: iv 5 mg (bolus) - Plasma**
+
+
+
+
+
+
+
+
+**Figure 3-25: Mikus 2017 (4 mg po followed by 2 mg iv)**
+
+
+
+
+
+
+
+
+**Figure 3-26: po 0.01 mg (solution) - Plasma**
+
+
+
+
+
+
+
+
+**Figure 3-27: po 0.075 mg (solution) - Plasma**
+
+
+
+
+
+
+
+
+**Figure 3-28: po 0.075 mg/kg (syrup) - Plasma**
+
+
+
+
+
+
+
+
+**Figure 3-29: po 1 mg (solution) - Plasma**
+
+
+
+
+
+
+
+
+**Figure 3-30: po 10 mg (solution) - Plasma**
+
+
+
+
+
+
+
+
+**Figure 3-31: po 10 mg (tablet) - Plasma**
+
+
+
+
+
+
+
+
+**Figure 3-32: po 15 mg (tablet) - Plasma**
+
+
+
+
+
+
+
+
+**Figure 3-33: po 15 mg (tablet) - with 1h after high-fat breakfast - Plasma**
+
+
+
+
+
+
+
+
+**Figure 3-34: po 2 mg (solution) - Plasma**
+
+
+
+
+
+
+
+
+**Figure 3-35: po 20 mg (tablet) - Plasma**
+
+
+
+
+
+
+
+
+**Figure 3-36: po 3.5 mg (solution) - Whole blood**
+
+
+
+
+
+
+
+
+**Figure 3-37: po 4 mg (solution) - Whole blood**
+
+
+
+
+
+
+
+
+**Figure 3-38: po 40 mg (tablet) - Plasma**
+
+
+
+
+
+
+
+
+**Figure 3-39: po 5 mg (solution) - Plasma**
+
+
+
+
+
+
+
+
+**Figure 3-40: po 6 mg (solution) - Plasma**
+
+
+
+
+
+
+
+
+**Figure 3-41: po 7.5 mg (solution) - Plasma**
+
+
+
+
+
+
+
+
+**Figure 3-42: po 8 mg (solution) - Plasma**
+
+
+
+
+# 4 Conclusion
+
+The herein presented PBPK model adequately describes the pharmacokinetics of midazolam in adults.
+
+In particular, it applies quantitative metabolism by CYP3A4. Thus, the model is fit for purpose to be applied for the investigation of drug-drug interactions with regard to its CYP3A4 metabolism.
+
+# 5 References
+
+**Achour 2014** Achour B, Russell MR, Barber J, Rostami-Hodjegan A. Simultaneous quantification of the abundance of several cytochrome P450 and uridine 5'-diphospho-glucuronosyltransferase enzymes in human liver microsomes using multiplexed targeted proteomics. Drug Metab Dispos. 2014 Apr;42(4):500-10.
+
+**Ahonen 1995** Ahonen J, Olkkola KT, Neuvonen PJ. Effect of itraconazole and terbinafine on the pharmacokinetics and pharmacodynamics of midazolam in healthy volunteers. Br J Clin Pharmacol. 1995 Sep;40(3):270-2.
+
+**Allonen 1981** Allonen H, Ziegler G, Klotz U. Midazolam kinetics. Clin Pharmacol Ther. 1981 Nov;30(5):653-61.
+
+**Backman 1994** Backman JT, Olkkola KT, Aranko K, Himberg JJ, Neuvonen PJ. Dose of midazolam should be reduced during diltiazem and verapamil treatments. Br J Clin Pharmacol. 1994 Mar;37(3):221-5.
+
+**Backman 1996** Backman JT, Olkkola KT, Neuvonen PJ. Rifampin drastically reduces plasma concentrations and effects of oral midazolam. Clin Pharmacol Ther. 1996 Jan;59(1):7-13.
+
+**Backman 1998** Backman JT, Kivistö KT, Olkkola KT, Neuvonen PJ. The area under the plasma concentration-time curve for oral midazolam is 400-fold larger during treatment with itraconazole than with rifampicin. Eur J Clin Pharmacol. 1998 Mar;54(1):53-8.
+
+**Björkman 2001** Björkman S, Wada DR, Berling BM, Benoni G. Prediction of the disposition of midazolam in surgical patients by a physiologically based pharmacokinetic model. J Pharm Sci. 2001 Sep;90(9):1226-41.
+
+**Bolger 2006** Bolger M Physiologically-based Pharmacokinetics (PBPK) Linked to Pharmacodynamics: In silico and in vitro Parameterization Presentation @ Globalization of Pharmaceutics Education Network (GPEN) , Kansas, 2006 (https://kuscholarworks.ku.edu/handle/1808/1168)
+
+**Bornemann 1986** Bornemann LD, Crews T, Chen SS, Twardak S, Patel IH. Influence of food on midazolam absorption. J Clin Pharmacol. 1986 Jan;26(1):55-9.
+
+**Buhr 1997** Buhr A, Baur R, Sigel E. Subtle changes in residue 77 of the gamma subunit of alpha1beta2gamma2 GABAA receptors drastically alter the affinity for ligands of the benzodiazepine binding site. J Biol Chem. 1997 May 2;272(18):11799-804.
+
+**Chattopadhyay 2018** Chattopadhyay N, Kanacher K, Casjens M, Frechen S, Ligges S, Zimmermann T, Rottmann A, Ploeger B, Höchel J, Schultze-Mosgau M-H. CYP3A4-mediated effects of rifampicin on the pharmacokinetics of vilaprisan and its UGT1A1-mediated effects on bilirubin glucuronidation in humans. Br J Clin Pharmacol
+. 2018 Dec;84(12):2857-2866.
+
+**Chung 2006** Chung E, Nafziger AN, Kazierad DJ, Bertino JS Jr. Comparison of midazolam and simvastatin as cytochrome P450 3A probes. Clin Pharmacol Ther. 2006 Apr;79(4):350-61.
+
+**Dagenais 2009** Dagenais C, Avdeef A, Tsinman O, Dudley A, Beliveau R. P-glycoprotein deficient mouse in situ blood-brain barrier permeability and its prediction using an in combo PAMPA model. Eur J Pharm Sci. 2009 Sep 10;38(2):121-37.
+
+**Darwish 2008** Darwish M, Kirby M, Robertson P Jr, Hellriegel ET. Interaction profile of armodafinil with medications metabolized by cytochrome P450 enzymes 1A2, 3A4 and 2C19 in healthy subjects. Clin Pharmacokinet. 2008;47(1):61-74.
+
+**DrugBank DB00683** (https://www.drugbank.ca/drugs/DB00683)
+
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+
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+
+","Markdown"
+"PBPK model","Open-Systems-Pharmacology/OSP-PBPK-Model-Library","Mexiletine/Mexiletine_evaluation_report.md",".md","32245","511","# Building and evaluation of a PBPK model for Mexiletine in adults
+
+| Version | 2.0-OSP12.2 |
+| ----------------------------------------------- | ------------------------------------------------------------ |
+| based on *Model Snapshot* and *Evaluation Plan* | https://github.com/Open-Systems-Pharmacology/Mexiletine-Model/releases/tag/v2.0 |
+| OSP Version | 12.2 |
+| Qualification Framework Version | 3.5 |
+
+This evaluation report and the corresponding PK-Sim project file are filed at:
+
+https://github.com/Open-Systems-Pharmacology/OSP-PBPK-Model-Library/
+
+# Table of Contents
+
+ * [1 Introduction](#introduction)
+ * [2 Methods](#methods)
+ * [2.1 Modeling Strategy](#modeling-strategy)
+ * [2.2 Data](#data)
+ * [2.3 Model Parameters and Assumptions](#model-parameters-and-assumptions)
+ * [3 Results and Discussion](#results-and-discussion)
+ * [3.1 Final input parameters](#final-input-parameters)
+ * [3.2 Diagnostics Plots](#diagnostics-plots)
+ * [3.3 Concentration-Time Profiles](#ct-profiles)
+ * [3.3.1 Model Building](#model-building)
+ * [3.3.2 Model Verification](#model-verification)
+ * [4 Conclusion](#conclusion)
+ * [5 References](#main-references)
+ * [6 Glossary](#glossary)
+
+# 1 Introduction
+
+The presented PBPK model of mexiletine has been developed to be used in a PBPK Drug-Drug-Interactions (DDI) network with mexiletine as a substrate and inhibitor of CYP1A2.
+
+Mexiletine is a non-selective voltage-gated sodium channel blocker which belongs to the Class IB anti-arrhythmic group of medicines. It is used to treat arrhythmias within the heart, or seriously irregular heartbeats. The following ADME properties characterize mexiletine pharmacokinetics ([Mexiletine Drugs.com](#5-references), [SmPC Namuscla](#5-references)):
+
+**Absorption**: Mexiletine is well absorbed (~90%) from the gastrointestinal tract. Its first-pass metabolism is low. Peak blood levels are reached in two to three hours.
+
+**Distribution**: It is 50% to 60% bound to plasma protein, with a volume of distribution of 5 to 7 l/kg.
+
+**Metabolism**: Mexiletine is mainly metabolized in the liver, the primary pathway being CYP2D6 metabolism, although it is also a substrate for CYP1A2. With involvement of CYP2D6, there can be either poor or extensive metabolizer phenotypes. The metabolic degradation proceeds via various pathways including aromatic and aliphatic hydroxylation, dealkylation, deamination and N-oxidation. Several of the resulting metabolites are submitted to further conjugation with glucuronic acid (phase II metabolism); among these are the major metabolites p-Hydroxymexiletine, hydroxy-methylMexiletine, and N-hydroxy-Mexiletine.
+
+**Elimination**: In normal subjects, the plasma elimination half-life of mexiletine is approximately 10 to 12 hours. Approximately 10% is excreted unchanged by the kidney.
+
+After i.v. administration, mexiletine shows linear pharmacokinetics in the dose range 167-200 mg (free base) and healthy volunteers and patients show similar profiles. p.o. data appear dose linear in the range of 83-500 mg as free base. The summary of product characteristics (SPC) for mexiletine ([Mexiletine, Drugs.com](#5-references)) reports that absorption rate of mexiletine is reduced in clinical situations such as acute myocardial infarction in which gastric emptying time is increased. For this reason, clinical data from patients after p.o. administration have not been considered during model development.
+
+# 2 Methods
+
+## 2.1 Modeling Strategy
+
+The general workflow for building an adult PBPK model has been described by Kuepfer et al. ([Kuepfer 2016](#5-references)). Relevant information on the anthropometry (height, weight) was gathered from the respective clinical study, if reported. Information on physiological parameters (e.g. blood flows, organ volumes, hematocrit) in adults was gathered from the literature and has been incorporated in PK-Sim® as described previously ([Willmann 2007](#5-references)). The applied activity and variability of plasma proteins and active processes that are integrated into PK-Sim® are described in the publicly available 'PK-Sim® Ontogeny Database Version 7.3' ([PK-Sim Ontogeny Database Version 7.3](#5-references)).
+
+A stepwise approach was used to fit the model to data.
+
+1. Define distribution model, cellular permeability, renal and metabolic clearance on data after single i.v. administration. For this purpose, literature values from [Mexiletine, Drugs.com](#5-references) were derived for renal CL and CYP2D6 combined with CYP1A2 metabolic clearance, or total hepatic CL, fitted against the data.
+
+2. Define intestinal permeability and fraction absorbed by fitting the model against data after p.o. single dose administration ([Pringle 1986](#5-references)). Investigate multiple oral doses predictions in CYP2D6 extensive and poor metabolizers ([Labbé 2000](#5-references)).
+
+The predefined “Standard European Male for DDI” individual (age = 30 y, weight = 73 kg, height = 176 cm, BMI = 23.57 kg/m2) with added CYP2D6 expression obtained from PK-Sim RT PCR database was used if not stated otherwise. For simulations of Japanese subjects ([Kusumoto 1998](#5-references)), a typical Japanese individual (age = 30 y, weight = 61.87 kg, height = 168.99 cm, BMI = 21.67 kg/m2) was created in PK-Sim from predefined database “Japanese (2015)” by adding CYP1A2 and CYP2D6 expression from PK-Sim RT PCR database.
+
+For simulations of CYP2D6 PM, the CYP2D6 pathway has been switched off.
+
+Population simulation of single 83 mg p.o. administration was conducted to visually compare the predicted concentration-time profiles to the observed concentrations reported in the literature, in terms of mean and variability. A population of 1000 male individuals was generated based on “Standard European Male for DDI”. Age range was limited to 20-40 years.
+
+Details about input data (physicochemical, *in vitro* and clinical) can be found in [Section 2.2](#22-data).
+
+Details about the structural model and its parameters can be found in [Section 2.3](#23-model-parameters-and-assumptions).
+
+## 2.2 Data
+
+### 2.2.1 In vitro and physico-chemical data
+
+A literature search was performed to collect available information on physico-chemical properties of mexiletine, see [Table 1](#table-1).
+
+| **Parameter** | **Unit** | **Value** | Source | **Description** |
+| :------------------------------ | -------- | --------------------- | --------------------------------- | ------------------------------------------------------------ |
+| MW+ | g/mol | 179.26 | [DrugBank DB00379](#5-references) | Molecular weight |
+| pKa,base+ | | 9.2 | [DrugBank DB00379](#5-references) | Basic dissociation constant |
+| Solubility (pH)+ | mg/mL | 0.54
(7) | [DrugBank DB00379](#5-references) | Aqueous Solubility |
+| logD | | 2.15 - 2.46 | [DrugBank DB00379](#5-references) | Distribution coefficient |
+| fu+ | % | 50 | [DrugBank DB00379](#5-references) | Fraction unbound in plasma |
+| CYP1A2 CL | l/h | 0.5 - 11 | [Labbé 2000](#5-references) | Partial metabolic clearance of mexiletine to N-Hydroxymexiletine |
+| CYP2D6 CL | l/h | 12 - 13 | [Labbé 2000](#5-references) | Difference in non-renal CL between CYP2D6 extensive and poor metabolizers |
+| Unspecific liver CL | l/h | 12 - 24 | [Labbé 2000](#5-references) | Non-renal CL – CYP2D6 CL – CYP1A2 CL |
+| Renal elimination+ | l/h | 1.8+ - 2.1 | [Labbé 2000](#5-references) | Renal clearance |
+| Ki CYP1A2+ | µmol/l | 0.28 | [Wei 1991](#5-references) | CYP1A2 inhibition constant |
+
+**Table 1:** Physico-chemical and *in-vitro* metabolization properties of mexiletine extracted from literature. *+: Value used in final model*
+
+### 2.2.2 Clinical data
+
+A literature search was performed to collect available clinical data on mexiletine, see [Table 2](#table-2).
+
+| **Source** | Route | **Dose [mg]/** **Schedule \*** | **Pop.** | **Sex** | **N** | **Form.** | **Comment** |
+| -------------------- | ------------------------------- | ------------ | ------- | --------------------------------- | --------------------------------- | --------------------------------- | -------------------- |
+| [Campbell 1978](#5-references)+ | i.v. | 200 | HV | m | 5 | solution | |
+| [Campbell 1978](#5-references)+ | p.o. | 200 | HV | m | 5 | - | |
+| [Begg 1982](#5-references)+ | p.o. | 333.24 | HV | m/f | 6 | tablet | 6 IDs |
+| [Labbé 2000](#5-references) | p.o. | 83.31 b.i.d. | HV | m/f | 1 | - | EM/PM |
+| [Campbell 1978](#5-references)+ | i.v. | 200 | patients | - | 10 | solution | |
+| [Pringle 1986](#5-references)+ | p.o. | 83.31 - 166.62 - 249.9 - 333.24 - 499.9 | HV | m | 12 | capsule | |
+| [Kusumoto 1998](#5-references)+ | p.o. | 166.62 | HV | m | 9 | capsule | |
+| [Pentikäinen 1984](#5-references)+ | i.v. | 166.62 | acute myocardial infarction | - | 18 | solution | acute myocardial infarction |
+| [Joeres 1987](#5-references)+ | p.o. | 200 | HV | - | 1 | - | |
+
+**Table 2:** Literature sources of clinical concentration data of mexiletine used for model development and validation. *\*: single dose unless otherwise specified; EM: extensive metabolizers; PM: poor metabolizers; +: Data used for final parameter identification*
+
+## 2.3 Model Parameters and Assumptions
+
+### 2.3.1 Absorption
+
+Gastrointestinal permeability was fitted to concentration data after single dose oral administration.
+
+The default dissolution Weibull profile (`Dissolution time (50% dissolved)` = 60min, `Dissolution shape` = 0.92) was used for description of formulation.
+
+### 2.3.2 Distribution
+
+Physico-chemical parameters were set to the reported values (see [Section 2.2.1](#221-in-vitro-and-physico-chemical-data)). It was assumed that the major binding partner in plasma is albumin.
+
+Because mexiletine is a strong base, permeation across the barriers between interstitial space and intracellular space (cellular permeability) had to be adjusted manually, as only uncharged molecules can pass through membranes, which is not accounted for by the permeability calculated by PK-Sim. The parameters `Specific organ permeability` and `Lipophilicity` defining the distribution in tissues were fitted to i.v. data.
+
+After testing the available organ-plasma partition coefficient and cell permeability calculation methods available in PK-Sim, observed clinical data were best described by choosing the partition coefficient calculation by `Rodgers and Rowland` and cellular permeability calculation by `PK-Sim Standard`.
+
+### 2.3.3 Metabolism and Elimination
+
+Following metabolization and elimination processes are implemented:
+
+- Linear CYP1A2, with specific clearance set to 28.6% of estimated total Liver Plasma Clearance (according to [Labbé 2000](#5-references))
+- Linear CYP2D6, with specific clearance set to 37.1% of estimated total Liver Plasma Clearance (according to [Labbé 2000](#5-references))
+- Liver plasma clearance, with specific clearance set to 34.3% of estimated total Liver Plasma Clearance (according to [Labbé 2000](#5-references))
+- Kidney plasma clearance with plasma clearance value set to reported value (see [Section 2.2.1](#221-in-vitro-and-physico-chemical-data))
+
+The model has been developed with kidney and liver plasma clearances only, without separating between the different enzymes. The parameter `Specific clearance` of the total hepatic clearance was estimated by fitting the model to observed data (see [Section 2.2.2](#222-clinical-data)). The parameters `Specific clearance` of the linear CYP1A2 and CYP2D6 metabolization processes have been calculated from from the total hepatic clearance by multiplying the identified total hepatic clearance by the reported percentage contribution of the respective enzyme and dividing by the `Reference concentration` of the respective enzyme as given by the PK-Sim database (1.8 µmol/l for CYP1A2 and 0.4 µmol/l for CYP2D6). This is necessary as the `Specific clearance` is multiplied by the concentration of the enzymes in the respective organ, with reference concentration of the dummy enzyme used in the total hepatic clearance being 1 µmol/l per default. With the applied parameter values, CYP1A2 in the liver is responsible for 25% of total mexiletine metabolization, while CYP2D6 in the liver is responsible for 33% of total metabolization. Additional metabolization by CYP2D6 takes place in intestinal mucosa, though to a minor extent.
+
+### 2.3.4 Automated Parameter Identification
+
+Following parameter values were estimated for the model:
+
+- `Specific clearance` (Total hepatic clearance)
+- `Specific organ permeability`
+- `Lipophilicity`
+- `Plasma clearance` of total Liver Plasma Clearance (divided between three processes in final model)
+- `Intestinal permeability (transcellular)`
+
+# 3 Results and Discussion
+
+The next sections show:
+
+1. Final model input parameters for the building blocks: [Section 3.1](#31-final-input-parameters).
+2. Overall goodness of fit: [Section 3.2](#32-diagnostics-plots).
+3. Simulated vs. observed concentration-time profiles for the clinical studies used for model building and for model verification: [Section 3.3](#33-concentration-time-profiles).
+
+## 3.1 Final input parameters
+
+The parameter values of the final PBPK model are illustrated below.
+
+### Compound: Mexiletine
+
+#### Parameters
+
+Name | Value | Value Origin | Alternative | Default
+------------------------------------------------ | ----------------------- | ------------------------- | ----------- | -------
+Solubility at reference pH | 0.54 mg/ml | Database-DrugBank DB00379 | S_aq | True
+Reference pH | 7 | Database-DrugBank DB00379 | S_aq | True
+Lipophilicity | 2.3770265519 Log Units | Parameter Identification | LogP | True
+Fraction unbound (plasma, reference value) | 0.5 | Database-DrugBank DB00379 | fu_plasma | True
+Permeability | 0.001637391584 cm/min | Parameter Identification | Fit | True
+Specific intestinal permeability (transcellular) | 0.00047373454608 cm/min | Parameter Identification | Fit | True
+Is small molecule | Yes | | |
+Molecular weight | 179.26 g/mol | Database-DrugBank DB00379 | |
+Plasma protein binding partner | Albumin | | |
+
+#### Calculation methods
+
+Name | Value
+----------------------- | -------------------
+Partition coefficients | Rodgers and Rowland
+Cellular permeabilities | PK-Sim Standard
+
+#### Processes
+
+##### Metabolizing Enzyme: CYP1A2-Linear fit
+
+Species: Human
+
+Molecule: CYP1A2
+
+###### Parameters
+
+Name | Value | Value Origin
+------------------- | ---------------- | --------------------------------------------------------------------------------------------------------
+Intrinsic clearance | 0 l/min |
+Specific clearance | 0.07944444 1/min | Other-Assumption-28.6% of 0.50 tot hep spec CL - divided by 1.8 µmol/l reference concentration of CYP1A2
+
+##### Metabolizing Enzyme: CYP2D6-Linear fit
+
+Species: Human
+
+Molecule: CYP2D6
+
+###### Parameters
+
+Name | Value | Value Origin
+------------------- | ------------- | --------------------------------------------------------------------------------------------------------
+Intrinsic clearance | 0 l/min |
+Specific clearance | 0.46375 1/min | Other-Assumption-37.1% of 0.50 tot hep spec CL - divided by 0.4 µmol/l reference concentration of CYP2D6
+
+##### Systemic Process: Total Hepatic Clearance-Linear fit
+
+Species: Human
+
+###### Parameters
+
+Name | Value | Value Origin
+----------------------------- | ---------------------- | ----------------------------------------------
+Fraction unbound (experiment) | 0.5 |
+Lipophilicity (experiment) | 2.3770265519 Log Units |
+Plasma clearance | 0 ml/min/kg |
+Specific clearance | 0.1715 1/min | Other-Assumption-34.3% of 0.50 tot hep spec CL
+
+##### Systemic Process: Renal Clearances-CLR - Rmex - Labbe2000
+
+Species: Human
+
+###### Parameters
+
+Name | Value | Value Origin
+----------------------------- | ------------------ | ------------
+Fraction unbound (experiment) | 0.5 |
+Plasma clearance | 0 ml/min/kg |
+Specific clearance | 0.1434490274 1/min | Unknown
+
+##### Inhibition: CYP1A2-Wei 1991
+
+Molecule: CYP1A2
+
+###### Parameters
+
+Name | Value | Value Origin
+---- | ----------- | --------------------
+Ki | 0.28 µmol/l | Publication-Wei 1991
+
+### Formulation: Mexiletine tablet
+
+Type: Weibull
+
+#### Parameters
+
+Name | Value | Value Origin
+-------------------------------- | ------ | ------------:
+Dissolution time (50% dissolved) | 60 min |
+Lag time | 0 min |
+Dissolution shape | 0.92 |
+Use as suspension | Yes |
+
+## 3.2 Diagnostics Plots
+
+The following section displays the goodness-of-fit visual diagnostic plots for the PBPK model performance of all data listed in [Section 2.2.2](#222-clinical-data).
+
+The first plot shows observed versus simulated plasma concentration, the second weighted residuals versus time.
+
+
+
+**Table 3-1: GMFE for Mexiletine concentration in plasma**
+
+|Group |GMFE |
+|:-------------------------------------------|:----|
+|Intravenous administration (model building) |1.19 |
+|Oral administration (model building) |1.32 |
+|Oral administration (model validation) |1.24 |
+|All |1.28 |
+
+
+
+
+
+
+
+
+**Figure 3-1: Mexiletine concentration in plasma**
+
+
+
+
+
+
+
+
+**Figure 3-2: Mexiletine concentration in plasma**
+
+
+
+
+## 3.3 Concentration-Time Profiles
+
+Simulated versus observed concentration-time profiles of all data listed in [Section 2.2.2](#222-clinical-data) are presented below.
+
+### 3.3.1 Model Building
+
+
+
+
+
+**Figure 3-3: Mexiletine 200 mg iv**
+
+
+
+
+
+
+
+
+**Figure 3-4: Mexiletine 200 mg iv 30 min**
+
+
+
+
+
+
+
+
+**Figure 3-5: Mexiletine HCL 100 mg po**
+
+
+
+
+
+
+
+
+**Figure 3-6: Mexiletine 200 mg po**
+
+
+
+
+
+
+
+
+**Figure 3-7: Mexiletine HCL 200 mg po**
+
+
+
+
+
+
+
+
+**Figure 3-8: Mexiletine HCL 200 mg po asian**
+
+
+
+
+
+
+
+
+**Figure 3-9: Mexiletine HCL 300 mg po**
+
+
+
+
+
+
+
+
+**Figure 3-10: Mexiletine HCL 400 mg po**
+
+
+
+
+
+
+
+
+**Figure 3-11: Mexiletine 400 mg po**
+
+
+
+
+
+
+
+
+**Figure 3-12: Mexiletine HCL 600 mg po**
+
+
+
+
+### 3.3.2 Model Verification
+
+
+
+
+
+**Figure 3-13: Mexiletine 83.31 mg po bid EM**
+
+
+
+
+
+
+
+
+**Figure 3-14: Mexiletine 83.31 mg po bid PM**
+
+
+
+
+
+
+
+
+**Figure 3-15: Time Profile Analysis**
+
+
+
+
+# 4 Conclusion
+
+The PBPK model developed for mexiletine was able to accurately predict the time-profiles following i.v. and p.o. dosing of mexiletine in EM and PM phenotypes. Observed variability was generally larger than the predicted in population simulations. Depending on study population, smoking status or variation in CYP-phenotypes may lead to additional variability which might be not included in the PK-Sim ontogeny factor.
+
+The predicted fraction excreted in urine was similar to the fraction reported in the label (9% vs 10%). The predicted bioavailability was 85%, compared to the observed as per label of 90%.
+
+# 5 References
+
+**Begg 1982** Begg, E., Chinwah, P., Webb, C., Day, R. & Wade, D. Enhanced metabolism of mexiletine after phenytoin administration. *British Journal of Clinical Pharmacology* **14**, 219–223 (1982).
+
+**Campbell 1978** Campbell NP, Kelly JG, Adgey AA, Shanks RG. Mexiletine in normal volunteers. *Br J Clin Pharmacol*. 1978;6(4):372-373.
+
+**DrugBank DB00379** (https://www.drugbank.ca/drugs/DB00379)
+
+**Joeres 1987** Joeres, R., Klinker, H., Heusler, H., Epping, J., Richter, E. (1987). Influence of mexiletine on caffeine elimination*. Pharmacology & therapeutics*, 33(1), 163-169.
+
+**Kuepfer 2016** Kuepfer L, Niederalt C, Wendl T, Schlender JF, Willmann S, Lippert J, Block M, Eissing T, Teutonico D. Applied Concepts in PBPK Modeling: How to Build a PBPK/PD Model.CPT Pharmacometrics Syst Pharmacol. 2016 Oct;5(10):516-531.
+
+**Kusumoto 1998** Kusumoto M, Ueno K, Tanaka K, et al. Lack of pharmacokinetic interaction between mexiletine and omeprazole. *Ann Pharmacother*. 1998;32(2):182-184.
+
+**Labbé 2000** Labbé L, O’Hara G, Lefebvre M, et al. Pharmacokinetic and pharmacodynamic interaction be mexiletine and propafenone in human beings. *Clin Pharmacol Ther*. 2000;68(1):44-57.
+
+**Mexiletine, Drugs.com** Mexiletine, Drugs.com, Website https://www.drugs.com/pro/mexiletine.html
+
+**Pentikäinen 1984** Pentikainen P, Halinen M, Helin M. Pharmacokinetics of intravenous Mexiletine in patients with acute myocardial infarction. *J Cardiovasc Pharmacol*. 1984;6:1-6.
+
+**PK-Sim Ontogeny Database Version 7.3** (https://github.com/Open-Systems-Pharmacology/OSPSuite.Documentation/blob/38cf71b384cfc25cfa0ce4d2f3addfd32757e13b/PK-Sim%20Ontogeny%20Database%20Version%207.3.pdf)
+
+**Pringle 1986** Pringle T, Fox J, McNeill J, et al. Dose independent pharmacokinetics of mexiletine in healthy volunteers. *Br J Clin Pharmacol*. 1986;21(3):319-321.
+
+**SmPC Namuscla** SmPC Namuscla 167 mg hard capsules, 2019, website https://www.medicines.org.uk/emc/product/9838/smpc
+
+**Begg 1982** Webb C, Day R, Chinwah P, Wade D, Begg E. Enhanced metabolism of mexiletine after phenytoin administration. *Br J Clin Pharmacol*. 2012;14(2):219-223.
+
+**Wei 1991** Wei X, Dai R, Zhai S, Thummel KE, Friedman FK, Vestal RE. Inhibition of human liver cytochrome P-450 1A2 by the class IB antiarrhythmics mexiletine, lidocaine, and tocainide. *J Pharmacol Exp Ther*. 1999;289(2):853-858.
+
+**Willmann 2007** Willmann S, Höhn K, Edginton A, Sevestre M, Solodenko J, Weiss W, Lippert J, Schmitt W. Development of a physiology-based whole-body population model for assessing the influence of individual variability on the pharmacokinetics of drugs. *J Pharmacokinet Pharmacodyn* 2007, 34(3): 401-431.
+
+# 6 Glossary
+
+| ADME | Absorption, Distribution, Metabolism, Excretion |
+| ------- | ------------------------------------------------------------ |
+| AUC | Area under the plasma concentration versus time curve |
+| AUCinf | AUC until infinity |
+| AUClast | AUC until last measurable sample |
+| AUCR | Area under the plasma concentration versus time curve Ratio |
+| b.i.d. | Twice daily (bis in diem) |
+| CL | Clearance |
+| Clint | Intrinsic liver clearance |
+| Cmax | Maximum concentration |
+| CmaxR | Maximum concentration Ratio |
+| CYP | Cytochrome P450 oxidase |
+| CYP1A2 | Cytochrome P450 1A2 oxidase |
+| CYP2C19 | Cytochrome P450 2C19 oxidase |
+| CYP3A4 | Cytochrome P450 3A4 oxidase |
+| DDI | Drug-drug interaction |
+| e.c. | Enteric coated |
+| EE | Ethinylestradiol |
+| EM | Extensive metabolizers |
+| fm | Fraction metabolized |
+| FMO | Flavin-containing monooxygenase |
+| fu | Fraction unbound |
+| FDA | Food and Drug administration |
+| GFR | Glomerular filtration rate |
+| HLM | Human liver microsomes |
+| hm | homozygous |
+| ht | heterozygous |
+| IM | Intermediate metabolizers |
+| i.v. | Intravenous |
+| IVIVE | In Vitro to In Vivo Extrapolation |
+| Ka | Absorption rate constant |
+| kcat | Catalyst rate constant |
+| Ki | Inhibitor constant |
+| Kinact | Rate of enzyme inactivation |
+| Km | Michaelis Menten constant |
+| m.d. | Multiple dose |
+| OSP | Open Systems Pharmacology |
+| PBPK | Physiologically-based pharmacokinetics |
+| PK | Pharmacokinetics |
+| PI | Parameter identification |
+| PM | Poor metabolizers |
+| RT-PCR | Reverse transcription polymerase chain reaction |
+| p.o. | Per os |
+| q.d. | Once daily (quaque diem) |
+| SD | Single Dose |
+| SE | Standard error |
+| s.d.SPC | Single dose Summary of Product Characteristics |
+| SD | Standard deviation |
+| TDI | Time dependent inhibition |
+| t.i.d | Three times a day (ter in die) |
+| UGT | Uridine 5'-diphospho-glucuronosyltransferase |
+| UM | Ultra-rapid metabolizers |
+
+","Markdown"
+"PBPK model","Open-Systems-Pharmacology/OSP-PBPK-Model-Library","Moclobemide/Moclobemide_evaluation_report.md",".md","34404","564","# Building and evaluation of a PBPK model for Moclobemide in adults
+
+| Version | 2.0-OSP12.2 |
+| ----------------------------------------------- | ------------------------------------------------------------ |
+| based on *Model Snapshot* and *Evaluation Plan* | https://github.com/Open-Systems-Pharmacology/Moclobemide-Model/releases/tag/v2.0 |
+| OSP Version | 12.2 |
+| Qualification Framework Version | 3.5 |
+
+This evaluation report and the corresponding PK-Sim project file are filed at:
+
+https://github.com/Open-Systems-Pharmacology/OSP-PBPK-Model-Library/
+
+# Table of Contents
+
+ * [1 Introduction](#introduction)
+ * [2 Methods](#methods)
+ * [2.1 Modeling Strategy](#modeling-strategy)
+ * [2.2 Data](#data)
+ * [2.3 Model Parameters and Assumptions](#model-parameters-and-assumptions)
+ * [3 Results and Discussion](#results-and-discussion)
+ * [3.1 Final input parameters](#final-input-parameters)
+ * [3.2 Diagnostics Plots](#diagnostics-plots)
+ * [3.3 Concentration-Time Profiles](#ct-profiles)
+ * [3.3.1 Model Building](#model-building)
+ * [3.3.2 Model Verification](#model-verification)
+ * [4 Conclusion](#conclusion)
+ * [5 References](#main-references)
+ * [6 Glossary](#glossary)
+
+# 1 Introduction
+
+The presented PBPK model of moclobemide has been developed to be used in a PBPK Drug-Drug-Interactions (DDI) network with moclobemide as a substrate and a moderate inhibitor of CYP2C19.
+
+Moclobemide is a reversible inhibitor of monoamine oxidase A (RIMA), a drug primarily used to treat depression and social anxiety ([Mayersohn 1995](#5-references)). Moclobemide pharmacokinetics is characterized by non-linearity in dose and time. Cmax concentrations decrease with dose for doses above 100 mg. Furthermore, a saturation in clearance at higher doses (200 mg and up) could be seen, as indicated by a longer terminal phase. In addition, multiple doses administration resulted in higher moclobemide concentrations compared to single dose. This could be indicative of the previously reported auto-inhibitory effect ([Nair 1993](#5-references)).
+
+**Absorption**: Moclobemide is highly soluble, and consequently fast and completely absorbed. Absolute bioavailability has been reported to be dependent on the dose, likely due to saturable (first-pass) metabolism ([Mayersohn 1995](#5-references)).
+
+**Distribution**: Moclobemide is moderately bound to plasma proteins and due to its lipophilicity distributes widely in the body (Vss ~ 1.2 L/kg) ([MHRA Label Moclobemide](#5-references)).
+
+**Metabolism**: About 99% of a dose is metabolized mainly via CYP2C19 (C-oxidation, producing metabolite RO12-8095) and FMO3 (N-oxidation, producing metabolite RO12-5637) ([Gram 1995](#5-references), [Hoskins 2001](#5-references), [Mayersohn 1995](#5-references)).
+
+**Excretion**: Less than 1% of a dose is excreted unchanged via the kidneys.
+
+# 2 Methods
+
+## 2.1 Modeling Strategy
+
+The general workflow for building an adult PBPK model has been described by Kuepfer et al. ([Kuepfer 2016](#5-references)). Relevant information on the anthropometry (height, weight) was gathered from the respective clinical study, if reported. Information on physiological parameters (e.g. blood flows, organ volumes, hematocrit) in adults was gathered from the literature and has been incorporated in PK-Sim® as described previously ([Willmann 2007](#5-references)). The applied activity and variability of plasma proteins and active processes that are integrated into PK-Sim® are described in the publicly available 'PK-Sim® Ontogeny Database Version 7.3' ([PK-Sim Ontogeny Database Version 7.3](#5-references)).
+
+In general, the following step-wise workflow was followed:
+
+1. Fit total hepatic CL as a placeholder using single dose i.v. (50 mg ([Raaflaub 1984](#5-references)) and 150 mg ([Schoerlin 1987](#5-references))) data with renal clearance fixed to 0.034 ml/min/kg as derived from [Schoerlin 1987](#5-references) to select the appropriate distribution (i.e. partition coefficient) model.
+2. Estimate the contribution of non-CYP2C19 mediated metabolism using data (p.o.) from CYP2C19 poor metabolizers (PM) ([Yu 2001](#5-references), [Gram 1995](#5-references)). This pathway is assumed to be mainly attributed to FMO (flavin-containing monooxygenase) – however, also other unspecific CL would be captured here. An Asian individual was used for simulations of data reported by [Yu 2001](#5-references).
+3. Use single dose data (i.v. and p.o.) to estimate Vmax and Km of CYP2C19.
+4. Predict concentrations after multiple oral dosing and compare to literature. Steady state levels were not predicted very well, and the model was refined by including time-dependent auto-inhibition to account for a change in CL over time.
+5. Predict single and multiple doses profiles (both i.v. and p.o.) with the updated model and compare to published profiles. Qualify model by comparing predicted CL/F and Cmax to the corresponding parameters in a review across multiple studies. Population prediction to verify the variability components of the model.
+
+A typical European male subject (age = 30 years, weight = 73 kg, height = 176 cm, BMI = 23.57 kg/m2) was created in PK-Sim using the predefined database “European (ICRP, 2002)”, by adding CYP2C19 ( PK-Sim RT PCR database) and FMO (other) expression and used in simulations, until stated otherwise. For simulations of Asian subjects, a typical Asian individual (Age = 30 y, weight = 60.03 kg, height = 169.96 cm, BMI = 20.78 kg/m2) was created from the predefined database “Asian (Tanaka, 1996)” by adding CYP2C19 ( PK-Sim RT PCR database) and FMO (other) expression.
+
+For simulations of the [Ignjatovic 2009](#5-references) data set, a typical European female subject (Age = 30 years,
+weight = 64 kg, height = 163 cm, BMI = 24.09 kg/m2) was created from the predefined
+database “European (ICRP, 2002)” by adding CYP2C19 ( PK-Sim RT PCR database) and FMO (other) expression.
+
+Initially, attempts were made to also unravel the contribution of the FMO3-specific clearance pathway and the unspecific pathway using the in vitro FMO-CL of moclobemide in a microsomal assay reported by [Hoskins 2001](#5-references). However, this route was abandoned as predictions were not in line with observations, potentially requiring the need for an in vivo - in vitro scaling factor. For the purpose of DDI predictions, the details of the CYP2C19 pathway only were considered relevant.
+
+Population simulations were carried out to evaluate if the variability incorporated in the model matches the literature reports. A population of 2000 Asian subjects with age and weight in the same range as reported by [Yu 2001](#5-references) (age: 20-36 years, weight: 40-120 kg, 13% female) was generated, and the concentration time profile following a single dose of 300 mg p.o. was simulated for each virtual subject and summarized as mean +/- SD. The simulation was also performed for CYP2C19 poor metabolizers.
+
+Details about input data (physicochemical, *in vitro* and clinical) can be found in [Section 2.2](#22-data).
+
+Details about the structural model and its parameters can be found in [Section 2.3](#23-model-parameters-and-assumptions).
+
+## 2.2 Data
+
+### 2.2.1 In vitro and physico-chemical data
+
+A literature search was performed to collect available information on physico-chemical properties of moclobemide, see [Table 1](#table-1).
+
+| **Parameter** | **Unit** | **Value** | Source | **Description** |
+| :------------------------------ | ----------------- | ------------ | --------------------------------------- | ------------------------------------------------------------ |
+| MW+ | g/mol | 268.74 | [DrugBank DB01171](#5-references) | Molecular weight |
+| pKa,base+ | | 6.2 | [IPCS INCHEM](#5-references) | Acidic dissociation constant |
+| Solubility (pH)+ | mg/mL | 3
(6.8) | [Moclobemide, INCHEM](#5-references) | Aqueous Solubility |
+| logD | | 1.79 | [Pons 1990](#5-references) | Distribution coefficient |
+| fu+ | % | 50 | [MHRA Label Moclobemide](#5-references) | Fraction unbound in plasma |
+| Km_FMO (microsomes) | mmol/L | 0.77 | [Hoskins 2001](#5-references) | |
+| Vmax_FMO (microsomes) | nmol/min/mg prot. | 1.39 | [Hoskins 2001](#5-references) | |
+| Renal Elimination | ml/min/kg | 0.03 | [Schoerlin 1987](#5-references) | Schoerlin reports 2.6 ml/min/76kg |
+| Ki_CYP2C19 (free) | µmol/L | 203.8 | [Kramer-Nielsen 1996](#5-references) | The total ki value reported by Kramer was 210 umol/L and corrected with an fu_mic of 0.97 |
+
+**Table 1:** Physico-chemical and *in-vitro* metabolization properties of moclobemide extracted from literature. *+: Value used in final model*
+
+### 2.2.2 Clinical data
+
+A literature search was performed to collect available clinical data on moclobemide in adults, see [Table 2](#table-2).
+
+| **Source** | **Route** | **Dose [mg]/** **Schedule \*** | **Pop.** | Age [yrs] (mean or range) | Weight [kg] (mean or range) | **Sex** | **N** | **Form.** | **Comment** |
+| -------------------- | --------- | ------------------------------- | ------------ | ------- | ----- | --------- | --------------------------------- | --------------------------------- | --------------------------------- |
+| [Gram 1995](#5-references)+ | p.o. | 300 s.d. / b.i.d. | HV | 26 | - | m/f | 8 | tablet | EM + PM |
+| [Yu 2001](#5-references)+ | p.o. | 300 s.d. | HV-Asian | - | 60.3 | m | 8 | tablet | EM, PM and EM+OMP40 |
+| [Wiesel 1985](#5-references)+ | p.o. | 50, 100, 200 s.d. | HV or patient etc | 26.3 | 75.8 | m | 6 | tablet | |
+| [Ignjatovic 2009](#5-references) | p.o. | 150 t.i.d. | Pat | - | - | m/f | 6 | tablet | |
+| [Dingemanse 1998](#5-references) | p.o. | 300 s.d. | HV | - | - | f/m | 12 | tablet | |
+| [Schoerlin 1987](#5-references)+ | p.o. & i.v. infusion | 150 t.i.d. /s.d. | HV | 27 | 76 | m | 12 | tablet/ solution | |
+| [Guentert 1990](#5-references)+ | p.o. | 150 t.i.d. | HV | 19-29 | 59-86 | m/f | 14 | tablet | |
+| [Raaflaub 1984](#5-references)+ | p.o. & i.v. infusion | 50 s.d. | HV | 42 | 4 | m | 6 | tablet/ solution | |
+
+**Table 2:** Literature sources of clinical concentration data of moclobemide used for model development and validation. *-: respective information was not provided in the literature source; \*:single dose unless otherwise specified; EM: extensive metabolizers; PM: poor metabolizers; +: Data used for final parameter identification*
+
+## 2.3 Model Parameters and Assumptions
+
+### 2.3.1 Absorption
+
+Particle dissolution for the formulation has been selected.
+
+### 2.3.2 Distribution
+
+Physico-chemical parameters were set to the reported values (see [Section 2.2.1](#221-in-vitro-and-physico-chemical-data)). It was assumed that the major binding partner in plasma is albumin.
+
+After testing the available organ-plasma partition coefficient and cell permeability calculation methods available in PK-Sim, observed clinical data were best described by choosing the partition coefficient calculation by `Rodgers and Rowland` and cellular permeability calculation by `PK-Sim Standard`.
+
+### 2.3.3 Metabolism and Elimination
+
+Two metabolic pathways were implement in the model:
+
+* Saturable CYP2C19 mediated metabolization.
+* Linear FMO mediated to account for lumped non-CYP metabolization.
+
+Data after repeated dosing indicates some sort of time-dependent elimination. The addition of an inhibitory metabolite may be an explanation. However, it would be very challenging to incorporate the kinetics of such a metabolite in the existing model, taking into account that no data on IC50 or Ki are available for such a metabolite. Therefore, it was decided to account for the time-dependency by simply including a time-dependent autoinhibition on CYP2C19 enzyme system.
+
+Given the available data, the parameters Kinact and Kinact_half defining the time-dependent autoinhibition could not be estimated together (not separately identifiable). Assuming Kinact is enzyme but not substance specific, it was decided to fix Kinact to the value reported by [Wu 2014](#5-references) for omeprazole and only estimate Kinact_half.
+
+### 2.3.4 Automated Parameter Identification
+
+Following parameter values were estimated for the base model:
+
+- Km_2C19
+
+- Vmax_2C19
+
+- Intrinsic Clearance FMO (i.e. non CYP2C19 metabolism)
+
+- Kinacthalf CYP2C19 for time-dependent autoinhibition
+
+# 3 Results and Discussion
+
+The next sections show:
+
+1. Final model input parameters for the building blocks: [Section 3.1](#31-final-input-parameters).
+2. Overall goodness of fit: [Section 3.2](#32-diagnostics-plots).
+3. Simulated vs. observed concentration-time profiles for the clinical studies used for model building and for model verification: [Section 3.3](#33-concentration-time-profiles).
+
+## 3.1 Final input parameters
+
+The parameter values of the final PBPK model are illustrated below.
+
+### Compound: Moclobemide
+
+#### Parameters
+
+Name | Value | Value Origin | Alternative | Default
+------------------------------------------ | -------------- | ---------------------------- | ----------- | -------
+Solubility at reference pH | 3 mg/ml | Internet-Moclobemide, INCHEM | Measurement | True
+Reference pH | 6.8 | Internet-Moclobemide, INCHEM | Measurement | True
+Lipophilicity | 1.79 Log Units | Publication-Pons 1990 | logD | True
+Fraction unbound (plasma, reference value) | 0.5 | Other-MHRA Label Moclobemide | Measurement | True
+Cl | 1 | Database-DrugBank DB01171 | |
+Is small molecule | Yes | | |
+Molecular weight | 268.74 g/mol | Database-DrugBank DB01171 | |
+Plasma protein binding partner | Albumin | | |
+
+#### Calculation methods
+
+Name | Value
+----------------------- | -------------------
+Partition coefficients | Rodgers and Rowland
+Cellular permeabilities | PK-Sim Standard
+
+#### Processes
+
+##### Metabolizing Enzyme: CYP2C19-Intrinsic-CL_MM_fit
+
+Species: Human
+
+Molecule: CYP2C19
+
+###### Parameters
+
+Name | Value | Value Origin
+------------------- | ----------------------- | ------------------------
+Vmax (liver tissue) | 2.03 µmol/min/kg tissue | Parameter Identification
+Km | 1.11 µmol/l | Parameter Identification
+
+##### Metabolizing Enzyme: FMO_other-Intrinsic-CL-fit
+
+Species: Human
+
+Molecule: FMO_other
+
+###### Parameters
+
+Name | Value | Value Origin
+------------------- | ---------- | ------------------------
+Intrinsic clearance | 0.24 l/min | Parameter Identification
+
+##### Systemic Process: Renal Clearances-Schoerlin 1987
+
+Species: Human
+
+###### Parameters
+
+Name | Value | Value Origin
+----------------------------- | --------------- | --------------------------
+Fraction unbound (experiment) | 0.5 |
+Plasma clearance | 0.034 ml/min/kg | Publication-Schoerlin 1987
+
+##### Inhibition: CYP2C19-Kramer-unbound
+
+Molecule: CYP2C19
+
+###### Parameters
+
+Name | Value | Value Origin
+---- | ------------- | -------------------------------
+Ki | 203.82 µmol/l | Publication-Kramer-Nielsen 1996
+
+##### Inhibition: CYP2C19-TimeDep_AutoInh-fit
+
+Molecule: CYP2C19
+
+###### Parameters
+
+Name | Value | Value Origin
+------------- | ------------ | ------------------------
+kinact | 5 1/h | Publication-Wu2014
+K_kinact_half | 94.85 µmol/l | Parameter Identification
+
+### Formulation: Moclobemide tablet
+
+Type: Particle Dissolution
+
+#### Parameters
+
+Name | Value | Value Origin
+---------------------------------- | ------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
+Thickness (unstirred water layer) | 20 µm | Publication-Willmann S, Thelen K, Becker C, et al. Mechanism-based prediction of particle size-dependent dissolution and absorption: cilostazol pharmacokinetics in dogs. Eur J Pharm Biopharm. 2010 Sep;76(1):83-94 https://doi.org/10.1016/j.ejpb.2010.06.003
+Type of particle size distribution | Monodisperse |
+Particle radius (mean) | 10 µm |
+
+## 3.2 Diagnostics Plots
+
+The following section displays the goodness-of-fit visual diagnostic plots for the PBPK model performance of all data listed in [Section 2.2.2](#222-clinical-data).
+
+The first plot shows observed versus simulated plasma concentration, the second weighted residuals versus time.
+
+
+
+**Table 3-1: GMFE for Moclobemide concentration in plasma**
+
+|Group |GMFE |
+|:--------------------------------------|:----|
+|iv administration (model building) |1.29 |
+|Oral administration (model building) |1.56 |
+|Oral administration (model validation) |1.31 |
+|All |1.46 |
+
+
+
+
+
+
+
+
+**Figure 3-1: Moclobemide concentration in plasma**
+
+
+
+
+
+
+
+
+**Figure 3-2: Moclobemide concentration in plasma**
+
+
+
+
+## 3.3 Concentration-Time Profiles
+
+Simulated versus observed concentration-time profiles of all data listed in [Section 2.2.2](#222-clinical-data) are presented below.
+
+### 3.3.1 Model Building
+
+
+
+
+
+**Figure 3-3: 50 mg moclobemide iv**
+
+
+
+
+
+
+
+
+**Figure 3-4: 150 mg moclobemide iv 20min**
+
+
+
+
+
+
+
+
+**Figure 3-5: 50 mg moclobemide po**
+
+
+
+
+
+
+
+
+**Figure 3-6: 100 mg moclobemide po**
+
+
+
+
+
+
+
+
+**Figure 3-7: 150 mg moclobemide po**
+
+
+
+
+
+
+
+
+**Figure 3-8: 150 mg moclobemide po 14d**
+
+
+
+
+
+
+
+
+**Figure 3-9: 200 mg moclobemide po**
+
+
+
+
+
+
+
+
+**Figure 3-10: 300 mg moclobemide po**
+
+
+
+
+
+
+
+
+**Figure 3-11: 300 mg moclobemide po PM**
+
+
+
+
+
+
+
+
+**Figure 3-12: 300 mg moclobemide po asian**
+
+
+
+
+
+
+
+
+**Figure 3-13: 300 mg moclobemide po asian PM**
+
+
+
+
+
+
+
+
+**Figure 3-14: 300 mg moclobemide po 7d**
+
+
+
+
+
+
+
+
+**Figure 3-15: 300 mg moclobemide po PM 7d**
+
+
+
+
+### 3.3.2 Model Verification
+
+
+
+
+
+**Figure 3-16: 150 mg moclobemide po female day14**
+
+
+
+
+
+
+
+
+**Figure 3-17: 150 mg moclobemide po female day28**
+
+
+
+
+
+
+
+
+**Figure 3-18: 300 mg moclobemide po**
+
+
+
+
+
+
+
+
+**Figure 3-19: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-20: Time Profile Analysis**
+
+
+
+
+# 4 Conclusion
+
+The developed PBPK model of moclobemide describes the observed concentration-time courses very well.
+
+As most of the non-CYP2C19 metabolism is via FMO3 which is mainly expressed in the liver, non-CYP2C19 CL is modeled in the liver only.
+
+Given not enough data to evaluate inhibition of emerging moclobemide metabolites, auto-inhibition of moclobemide appears appropriate approximation.
+
+# 5 References
+
+**Dingemanse 1998** Dingemanse J, Wallnöfer A, Gieschke R, Guentert T, Amrein R. Pharmacokinetic and pharmacodynamic interactions between fluoxetine and moclobemide in the investigation of development of the “serotonin syndrome”. Clin Pharmacol Ther. 1998;63(4):403-413.
+
+**DrugBank DB01171** (https://www.drugbank.ca/drugs/DB01171)
+
+**Gram 1995** Gram LF, Guentert TW, Grange S, Vistisen K, Brøsen K. Moclobemide, a substrate of CYP2C19 and an inhibitor of CYP2C19, CYP2D6, and CYP1A2: A panel study. *Clinical Pharmacology & Therapeutics*. 1995 Jun;57(6):670–7.
+
+**Guentert 1990** Guentert TW, Tucker G, Korn A, Pfefen JP, Haefelfinger P, Schoerlin MP. Pharmacokinetics of moclobemide after single and multiple oral dosing with 150 milligrams 3 times daily for 15 days. Acta Psychiatr Scand. 1990;82(S360):91-93.
+
+**Hoskins 2001** Hoskins J, Shenfield G, Murray M, Gross A. Characterization of moclobemide N -oxidation in human liver microsomes. Xenobiotica. 2001 Jan;31(7):387–97.
+
+**IPCS INCHEM** Website: https://inchem.org/documents/pims/pharm/pim151.htm#SectionTitle:3.3%20%20Physical%20properties
+
+**Ignjatovic 2009** Ignjatovic AR, Miljkovic B, Todorovic D, Timotijevic I, Pokrajac M. Moclobemide monotherapy vs. combined therapy with valproic acid or carbamazepine in depressive patients: A pharmacokinetic interaction study. Br J Clin Pharmacol. 2009;67(2):199-208.
+
+**Kuepfer 2016** Kuepfer L, Niederalt C, Wendl T, Schlender JF, Willmann S, Lippert J, Block M, Eissing T, Teutonico D. Applied Concepts in PBPK Modeling: How to Build a PBPK/PD Model.CPT Pharmacometrics Syst Pharmacol. 2016 Oct;5(10):516-531.
+
+**Kramer-Nielsen 1996** Kramer-Nielsen KK, Flinois JP, Beaune P, Brøsen K. The biotransformation of clomipramine in vitro, identification of the cytochrome P450s responsible for the separate metabolic pathways. *J Pharmacol Exp Ther*. 1996 Jun;277(3):1659–64.
+
+**Mayersohn 1995** Mayersohn M, Guentert TW. Clinical Pharmacokinetics of the Monoamine Oxidase-A Inhibitor Moclobemide*, Clinical Pharmacokinetics*. 1995 Nov;29(5):292–332.
+
+**MHRA Label Moclobemide** MHRA label of Moclobemide film-coated tablets. Website: http://www.mhra.gov.uk/home/groups/par/documents/websiteresources/con097060.pdf
+
+**Moclobemide, INCHEM**, Website https://inchem.org/documents/pims/pharm/pim151.htm#PartTitle:3.%20%20PHYSICO-CHEMICAL%20PROPERTIES
+
+**Nair 1993** Nair NPV, Ahmed SK, Ng Ying Kin NMK. Biochemistry and pharmacology of reversible inhibitors of MAO-A agents: Focus on moclobemide. *J Psychiatry Neurosci*. 1993;18(5):214-225.
+
+**PK-Sim Ontogeny Database Version 7.3** (https://github.com/Open-Systems-Pharmacology/OSPSuite.Documentation/blob/38cf71b384cfc25cfa0ce4d2f3addfd32757e13b/PK-Sim%20Ontogeny%20Database%20Version%207.3.pdf)
+
+**Pons 1990** Pons G, Schoerlin MP, Tam Y. Moclobemide excretion in human breast milk. *Br J Clin Pharmacol*. 1990;29(1):27–31.
+
+**Raaflaub 1984** Raaflaub J, Haefelfinger P, Trautmann KH. Single-dose Pharmacokinetics of the MAO-Inhibitor Moclobemide in Man. *Drug Res*. 1984; 34:80–2.
+
+**Schoerlin 1987** Schoerlin M-P, Mayersohn M, Korn A, Eggers H. Disposition kinetics of moclobemide, a monoamine oxidase-A enzyme inhibitor: Single and multiple dosing in normal subjects. *Clinical Pharmacology and Therapeutics*. 1987 Oct;42(4):395–404.
+
+**Wiesel 1985** Wiesel FA, Raaflaub J, Kettler R. Pharmacokinetics of oral moclobemide in healthy human subjects and effects on MAO-activity in platelets and excretion of urine monoamine metabolites. Eur J Clin Pharmacol. 1985;28(1 Supplement):89-95.
+
+**Willmann 2007** Willmann S, Höhn K, Edginton A, Sevestre M, Solodenko J, Weiss W, Lippert J, Schmitt W. Development of a physiology-based whole-body population model for assessing the influence of individual variability on the pharmacokinetics of drugs. *J Pharmacokinet Pharmacodyn* 2007, 34(3): 401-431.
+
+**Wu 2014** Wu F, Gaohua L, Zhao P, Jamei M, Huang S-M, Bashaw ED, et al. Predicting Nonlinear Pharmacokinetics of Omeprazole Enantiomers and Racemic Drug Using Physiologically Based Pharmacokinetic Modeling and Simulation: Application to Predict Drug/Genetic Interactions. *Pharmaceutical Research*. 2014 Aug;31(8):1919–29.
+
+**Yu 2001** Yu K, Yim DS, Cho J-Y, Park SS. Effect of omeprazole on the pharmacokinetics of moclobemide according to the genetic polymorphism of CYP2C19. Clinical Pharmacology & Therapeutics. 2001 Apr;69(4):266–73.
+
+# 6 Glossary
+
+| ADME | Absorption, Distribution, Metabolism, Excretion |
+| ------- | ------------------------------------------------------------ |
+| AUC | Area under the plasma concentration versus time curve |
+| AUCinf | AUC until infinity |
+| AUClast | AUC until last measurable sample |
+| AUCR | Area under the plasma concentration versus time curve Ratio |
+| b.i.d. | Twice daily (bis in diem) |
+| CL | Clearance |
+| Clint | Intrinsic liver clearance |
+| Cmax | Maximum concentration |
+| CmaxR | Maximum concentration Ratio |
+| CYP | Cytochrome P450 oxidase |
+| CYP1A2 | Cytochrome P450 1A2 oxidase |
+| CYP2C19 | Cytochrome P450 2C19 oxidase |
+| CYP3A4 | Cytochrome P450 3A4 oxidase |
+| DDI | Drug-drug interaction |
+| e.c. | Enteric coated |
+| EE | Ethinylestradiol |
+| EM | Extensive metabolizers |
+| fm | Fraction metabolized |
+| FMO | Flavin-containing monooxygenase |
+| fu | Fraction unbound |
+| FDA | Food and Drug administration |
+| GFR | Glomerular filtration rate |
+| HLM | Human liver microsomes |
+| hm | homozygous |
+| ht | heterozygous |
+| IM | Intermediate metabolizers |
+| i.v. | Intravenous |
+| IVIVE | In Vitro to In Vivo Extrapolation |
+| Ka | Absorption rate constant |
+| kcat | Catalyst rate constant |
+| Ki | Inhibitor constant |
+| Kinact | Rate of enzyme inactivation |
+| Km | Michaelis Menten constant |
+| m.d. | Multiple dose |
+| OSP | Open Systems Pharmacology |
+| PBPK | Physiologically-based pharmacokinetics |
+| PK | Pharmacokinetics |
+| PI | Parameter identification |
+| PM | Poor metabolizers |
+| RT-PCR | Reverse transcription polymerase chain reaction |
+| p.o. | Per os |
+| q.d. | Once daily (quaque diem) |
+| SD | Single Dose |
+| SE | Standard error |
+| s.d.SPC | Single dose Summary of Product Characteristics |
+| SD | Standard deviation |
+| TDI | Time dependent inhibition |
+| t.i.d | Three times a day (ter in die) |
+| UGT | Uridine 5'-diphospho-glucuronosyltransferase |
+| UM | Ultra-rapid metabolizers |
+
+","Markdown"
+"PBPK model","Open-Systems-Pharmacology/OSP-PBPK-Model-Library","BAY794620/BAY794620_evaluation_report.md",".md","24916","498","# Building and evaluation of a PBPK model for BAY 79-4620 in mice
+
+| Version | 1.0-OSP12.2 |
+| ----------------------------------------------- | ------------------------------------------------------------ |
+| based on *Model Snapshot* and *Evaluation Plan* | https://github.com/Open-Systems-Pharmacology/BAY794620-Model/releases/tag/v1.0 |
+| OSP Version | 12.2 |
+| Qualification Framework Version | 3.5 |
+
+This evaluation report and the corresponding PK-Sim project file are filed at:
+
+https://github.com/Open-Systems-Pharmacology/OSP-PBPK-Model-Library/
+
+# Table of Contents
+
+ * [1 Introduction](#introduction)
+ * [2 Methods](#methods)
+ * [2.1 Modeling Strategy](#modeling-strategy)
+ * [2.2 Data](#methods-data)
+ * [2.2.1 In vitro / physico-chemical Data ](#invitro-and-physico-chemical-data)
+ * [2.2.2 PK Data ](#PK-data)
+ * [2.3 Model Parameters and Assumptions](#model-parameters-and-assumptions)
+ * [3 Results and Discussion](#results-and-discussion)
+ * [3.1 Final input parameters](#final-input-parameters)
+ * [3.2 Diagnostics Plots](#diagnostics-plots)
+ * [3.3 Concentration-Time Profiles](#ct-profiles)
+ * [4 Conclusion](#conclusion)
+ * [5 References](#main-references)
+
+# 1 Introduction
+
+BAY 79-4620 is an antibody–drug conjugate consisting of a human IgG1 mAb directed against human carbonic anhydrase IX (CA IX) conjugated to the toxophore monomethylauristatin E (MMAE) via a valine-citrulline based linker ([Petrul 2012](#5-references)).
+
+For BAY 79-4620, tissue concentration-time profiles for a large number of different mice tissues were measured and used together with pharmacokinetic (PK) data from 5 other compounds to identify unknown parameters during the development of the generic large molecule physiologically based pharmacokinetic (PBPK) model in PK-Sim ([Niederalt 2018](#5-references)).
+
+The herein presented evaluation report evaluates the performance of a PBPK model for BAY 79-4620 in xenograft mice for the PK data used for the development of the generic large molecule model in PK-Sim. A standard PK-Sim model was used without an additional tumor organ and without target mediated drug disposition effects from CA IX binding in the tumor - in contrast to the model which has been used in Ref. ([Niederalt 2018](#5-references)).
+
+The presented BAY 79-4620 PBPK model as well as the respective evaluation plan and evaluation report are provided open-source (https://github.com/Open-Systems-Pharmacology/BAY794620-Model).
+
+# 2 Methods
+
+## 2.1 Modeling Strategy
+
+The development of the large molecule PBPK model in PK-Sim® has previously been described by Niederalt et al. ([Niederalt 2018](#5-references)). In short, the model was built as an extension of the PK-Sim® model for small molecules incorporating (i) the two-pore formalism for drug extravasation from blood plasma to interstitial space, (ii) lymph flow, (iii) endosomal clearance and (iv) protection from endosomal clearance by neonatal Fc receptor (FcRn) mediated recycling.
+
+For model development and evaluation, PK data were used from compounds with a wide range of solute radii and from different species. The PK data used for parameter estimation were from the following compounds: antibody–drug conjugate BAY 79-4620 in mice (Bayer in house data), antibody 7E3 in wild-type and FcRn knockout mice ([Garg 2007](#5-references), [Garg2009](#5-references)), domain antibody dAb2 in mice ([Sepp 2015](#5-references)), antibodies MEDI-524 and MEDI-524-YTE in monkeys ([Dall'Acqua 2006](#5-references)), and antibody CDA1 in humans ([Taylor 2008](#5-references)). The PK data used for model evaluation were from inulin in rats ([Tsuji1983](#5-references)) and tefibazumab in humans ([Reilly 2005](#5-references)).
+
+The PBPK model including the estimated physiological parameters as described by Niederalt et al. ([Niederalt 2018](#5-references)) is available in the Open Systems Pharmacology Suite from version 7.1 onwards.
+
+This evaluation report focuses on the PBPK model for the antibody–drug conjugate BAY 79-4620.
+
+Details about input data (physicochemical, *in vitro* and PK) can be found in [Section 2.2](#22-data).
+
+Details about the structural model and its parameters can be found in [Section 2.3](#23-model-parameters-and-assumptions).
+
+## 2.2 Data
+
+### 2.2.1 In vitro / physico-chemical Data
+
+A literature search was performed to collect available information on physicochemical properties of BAY 79-4620. The obtained information from literature is summarized in the table below.
+
+| **Parameter** | **Unit** | **Value** | Source | **Description** |
+| :------------ | -------- | --------- | ---------------------------- | ------------------------------------------------------------ |
+| MW | g/mol | 152000 | Bayer in-house data | Molecular weight |
+| r | nm | 5.34 | [Taylor 1984](#5-references) | Hydrodynamic solute radius |
+| Kd (FcRn) | µM | 0.082 | [Zhou 2003](#5-references) | Dissociation constant for binding of a humane IgG1 antibody to murine FcRn at pH 6 |
+
+### 2.2.2 PK Data
+
+The biodistribution data from mice for BAY 79-4620 were Bayer AG in house data taken from two studies:
+
+| Data | Description |
+| :-------------------------- | :----------------------------------------------------------- |
+| Whole-body autoradiography | Female nude mice (NMRI nu/nu), bearing HT-29 human colon carcinoma xenografts, were dosed intravenously with 1.25 mg/kg body weight of 125I-labeled BAY 79-4620. The distribution of total radioactivity in organs and tissues was determined by quantitative whole-body autoradiography after sacrificing the mice (two per time) at various time points after administration. |
+| Wet-tissue dissection study | Female nude mice (NMRI nu/nu), bearing HT-29 human colon carcinoma xenografts, were dosed intravenously with 2 µCi (approx. 500 ng) of 125I-labeled BAY 79-4620. The distribution of total radioactivity in organs and tissues was determined after sacrificing the mice (three per time) and dissection of the organs at various time points after administration by determination of radioactivity using a gamma-counter. The concentrations were reported as percentage of dose / g tissue. These values were converted to concentrations in ng/ml assuming a density of 1 g/ml for all tissues except for bone for which a density of 1.5 g/ml was assumed (as in Ref. [Baxter 1994](#5-references)). |
+
+## 2.3 Model Parameters and Assumptions
+
+### 2.3.1 Absorption
+
+There is no absorption process since BAY 79-4620 was administered intravenously.
+
+### 2.3.2 Distribution
+
+The standard lymph and fluid recirculation flow rates and the standard vascular properties of the different tissues (hydraulic conductivity, pore radii, fraction of flow via large pores) from PK-Sim were used. BAY 79-4620, among other compounds, has been used to identify these lymph and fluid recirculation flow rates used in PK-Sim ([Niederalt 2018](#5-references)).
+
+A standard PK-Sim model was used without an additional tumor organ - in contrast to the model which has been used in Ref. ([Niederalt 2018](#5-references)). It was assumed that BAY 79-4620 is not cross-reactive to murine CA IX, i.e. there is no drug-target binding due to the neglect of tumor tissue in the present PK-Sim standard model (again in contrast to the model used in Ref. ([Niederalt 2018](#5-references))).
+
+### 2.3.3 Metabolism and Elimination
+
+The FcRn mediated clearance present in the standard PK-Sim model was used as only clearance process (in contrast to the model used in Ref. ([Niederalt 2018](#5-references)), where there is an additional target mediated clearance process in tumor tissue). The affinity to FcRn in the endosomal space was fitted to the PK data. The same value as fitted in Ref. ([Niederalt 2018](#5-references)) was used since the contribution from target mediated clearance was small.
+
+### 2.3.4 Tissue Concentrations
+
+For the comparison with experimental data the parameters `Fraction of blood for sampling` used in the Observer for the tissue concentrations were set for all organs to 0.18 for comparison with tissue dissection data and to 0.42 for comparison with autoradiography data according to the fit results (across compounds) in Ref. ([Niederalt 2018](#5-references)). (The parameter `Fraction of blood for sampling` specifies residual blood in tissue as ratio of blood volume contributing to the measured tissue concentration to the total in vivo capillary blood volume.)
+
+In the present evaluation report, the experimental intestine concentrations from the tissue dissection study were compared to simulated organ concentrations for small and large intestine separately in the goodness of fit plots as well as in the concentration-time profile plot.
+
+
+
+### 2.3.5 Automated Parameter Identification
+
+The table shows the parameter values that were specified in the model based on the parameter identification reported in Ref. ([Niederalt 2018](#5-references)), and which were not included in the PK-Sim database since version 7.1.
+
+| Model Parameter | Optimized Value | Unit |
+| ------------------------------------------------------------ | --------------- | ------ |
+| `Kd(FcRn) in endosomal space` | 12.7 | µmol/L |
+| `Fraction of blood for sampling` (all organs) - for comparison with tissue dissection data | 0.18 | |
+| `Fraction of blood for sampling` (all organs) - for comparison with autoradiography data | 0.42 | |
+
+# 3 Results and Discussion
+
+The PBPK model for BAY 79-4620 was evaluated with blood and tissue PK data from mice.
+
+These PK data have been used together with PK data from 5 other compounds to simultaneously identify parameters during the development of the generic model for proteins and large molecules in PK-Sim ([Niederalt 2018](#5-references)).
+
+The fitted dissociation constant for binding to FcRn in the endosomal space is rather high compared to usual dissociation constants. This might reflect a lowered affinity to FcRn due to the conjugation of the toxophore or alternatively is a surrogate for a clearance process not represented in the model.
+
+The next sections show:
+
+1. the final model parameters for the building blocks: [Section 3.1](#31-final-input-parameters).
+2. the overall goodness of fit: [Section 3.2](#32-diagnostics-plots).
+3. simulated vs. observed concentration-time profiles for the clinical studies used for model building and for model verification: [Section 3.3](#33-concentration-time-profiles).
+
+## 3.1 Final input parameters
+
+The compound parameter values of the final PBPK model are illustrated below.
+
+### Compound: BAY794620
+
+#### Parameters
+
+Name | Value | Value Origin | Alternative | Default
+------------------------------------------ | ------------ | --------------------------------------------- | ----------- | -------
+Solubility at reference pH | 99999 mg/l | Other-/Dummy value not used in the simulation | Measurement | True
+Reference pH | 7 | Other-/Dummy value not used in the simulation | Measurement | True
+Lipophilicity | -5 Log Units | Other-/Dummy value not used in the simulation | Measurement | True
+Fraction unbound (plasma, reference value) | 1 | Other-Assumption | Measurement | True
+Is small molecule | No | | |
+Molecular weight | 152000 g/mol | | |
+Plasma protein binding partner | Unknown | | |
+Radius (solute) | 0.00534 µm | Publication-Taylor1984 | |
+Kd (FcRn) in endosomal space | 12.7 µmol/l | Parameter Identification | |
+
+#### Calculation methods
+
+Name | Value
+----------------------- | ---------------
+Partition coefficients | PK-Sim Standard
+Cellular permeabilities | PK-Sim Standard
+
+#### Processes
+
+## 3.2 Diagnostics Plots
+
+Below you find the goodness-of-fit visual diagnostic plots for the PBPK model performance of all data used presented in [Section 2.2.2](#PK-data).
+
+The first plot shows observed versus simulated plasma concentration, the second weighted residuals versus time.
+
+
+
+**Table 3-1: GMFE for Goodness of fit plot for concentration in blood and tissues**
+
+|Group |GMFE |
+|:-------------------------------|:----|
+|BAY 79-4620 - autoradiography |1.48 |
+|BAY 79-4620 - tissue dissection |1.62 |
+|All |1.53 |
+
+
+
+
+
+
+
+
+**Figure 3-1: Goodness of fit plot for concentration in blood and tissues**
+
+
+
+
+
+
+
+
+**Figure 3-2: Goodness of fit plot for concentration in blood and tissues**
+
+
+
+
+## 3.3 Concentration-Time Profiles
+
+Simulated versus observed concentration-time profiles of all data listed in [Section 2.2.2](#PK-data) are presented below.
+
+
+
+
+
+**Figure 3-3: Blood - lin scale**
+
+
+
+
+
+
+
+
+**Figure 3-4: Blood - log scale**
+
+
+
+
+
+
+
+
+**Figure 3-5: Lung**
+
+
+
+
+
+
+
+
+**Figure 3-6: Kidney**
+
+
+
+
+
+
+
+
+**Figure 3-7: Skin**
+
+
+
+
+
+
+
+
+**Figure 3-8: Muscle**
+
+
+
+
+
+
+
+
+**Figure 3-9: Spleen**
+
+
+
+
+
+
+
+
+**Figure 3-10: Liver**
+
+
+
+
+
+
+
+
+**Figure 3-11: Heart**
+
+
+
+
+
+
+
+
+**Figure 3-12: Fat**
+
+
+
+
+
+
+
+
+**Figure 3-13: Brain**
+
+
+
+
+
+
+
+
+**Figure 3-14: Stomach**
+
+
+
+
+
+
+
+
+**Figure 3-15: Pancreas**
+
+
+
+
+
+
+
+
+**Figure 3-16: Small Intestine**
+
+
+
+
+
+
+
+
+**Figure 3-17: Ovaries**
+
+
+
+
+
+
+
+
+**Figure 3-18: Blood - lin scale**
+
+
+
+
+
+
+
+
+**Figure 3-19: Blood - log scale**
+
+
+
+
+
+
+
+
+**Figure 3-20: Lung**
+
+
+
+
+
+
+
+
+**Figure 3-21: Kidney**
+
+
+
+
+
+
+
+
+**Figure 3-22: Bone**
+
+
+
+
+
+
+
+
+**Figure 3-23: Muscle**
+
+
+
+
+
+
+
+
+**Figure 3-24: Spleen**
+
+
+
+
+
+
+
+
+**Figure 3-25: Liver**
+
+
+
+
+
+
+
+
+**Figure 3-26: Heart**
+
+
+
+
+
+
+
+
+**Figure 3-27: Fat**
+
+
+
+
+
+
+
+
+**Figure 3-28: Brain**
+
+
+
+
+
+
+
+
+**Figure 3-29: Stomach**
+
+
+
+
+
+
+
+
+**Figure 3-30: Pancreas**
+
+
+
+
+
+
+
+
+**Figure 3-31: Intestine**
+
+
+
+
+
+
+
+
+**Figure 3-32: Ovaries**
+
+
+
+
+# 4 Conclusion
+
+The herein presented PBPK model overall adequately describes the pharmacokinetics of BAY 79-4620 in mice. The tissue concentrations from the low dose tissue dissection study (dose approximately 0.025 mg/kg) are similarly well described as the tissue concentrations from the autoradiography study (dose 1.25 mg/kg), with the exception of the late concentrations at 168 h after administration from the tissue dissection study which are underestimated by the model. The largest deviations between measured and simulated concentration-time profiles are observed for spleen concentrations which are overestimated by the model and brain concentrations which are underestimated.
+
+The PK data had been used during the development of the generic large molecule PBPK model in PK-Sim ([Niederalt 2018](#5-references)) together with PK data from 5 other compounds (7E3, CDA1, dAb2, MEDI-524 & MEDI-524-YTE).
+
+# 5 References
+
+**Baxter 1994** Baxter LT, Zhu H, Mackensen DG, Jain RK. Physiologically based pharmacokinetic model for specific and nonspecific monoclonal antibodies and fragments in normal tissues and human tumor xenografts in nude mice. Cancer research. 1994 Mar; 54(6):1517-1528.
+
+**Dall'Acqua 2006** Dall’Acqua WF, Kiener PA, Wu H. Properties of human IgG1s engineered for enhanced binding to the neonatal Fc receptor (FcRn). J Biol Chem. 2006 Aug; 281(33):23514-23524. doi: 10.1074/jbc.M604292200.
+
+**Garg 2007** Garg A, Balthasar JP. Physiologically-based pharmacokinetic (PBPK) model to predict IgG tissue kinetics in wild-type and FcRn-knockout mice. J Pharmacokinet Pharmacodyn. 2007 Jul; 34(5):687-709. doi: 10.1007/s10928-007-9065-1.
+
+**Garg 2009** Garg A, Balthasar J. Investigation of the influence of FcRn on the distribution of IgG to the brain. AAPS J. 2009 July; 11(3):553-557. doi: 10.1208/s12248-009-9129-9.
+
+**Lobo 2004** Lobo ED, Hansen R J, Balthasar JP. Antibody pharmacokinetics and pharmacodynamics. J Pharm Sci. 2004 Nov;93(11):2645-2668. doi: 10.1002/jps.20178.
+
+**Niederalt 2018** Niederalt C, Kuepfer L, Solodenko J, Eissing T, Siegmund HU, Block M, Willmann S, Lippert J. A generic whole body physiologically based pharmacokinetic model for therapeutic proteins in PK-Sim. J Pharmacokinet Pharmacodyn. 2018 Apr;45(2):235-257. doi: 10.1007/s10928-017-9559-4.
+
+**Petrul 2012** Petrul HM, Schatz CA, Kopitz CC, Adnane L, McCabe TJ, Trail P, Ha S, Chang YS, Voznesensky A, Ranges G, Tamburini PP. Therapeutic mechanism and efficacy of the antibody–drug conjugate BAY 79-4620 targeting human carbonic anhydrase 9. Molecular Cancer Therapeutics. 2012 Feb;11(2):340-349. doi: 10.1158/1535-7163.MCT-11-0523.
+
+**Reilly 2005** Reilley S, Wenzel E, Reynolds L, Bennett B, Patti JM, Hetherington S. Open-label, dose escalation study of the safety and pharmacokinetic profile of tefibazumab in healthy volunteers. Antimicrob Agents Chemother. 2005 Mar;49(3):959–962. doi: 10.1128/AAC.49.3.959-962.2005.
+
+**Sepp 2015** Sepp A, Berges A, Sanderson A, Meno-Tetang G. Development of a physiologically based pharmacokinetic model for a domain antibody in mice using the two-pore theory. J Pharmacokinet Pharmacodyn. 2015 Jan;42(2):97-109. doi: 10.1007/s10928-014-9402-0.
+
+**Taylor 1984** Taylor AE, Granger DN. Exchange of macromolecules across the microcirculation. Handbook of Physiology - Cardiovascular System. Microcirculation (Eds. Renkin EM and Michel CC. Bethesda, MD, American Physiological Society). 1984; Vol. 4(Pt 2):467–520.
+
+**Taylor 2008** Taylor CP, Tummala S, Molrine D, Davidson L, Farrell RJ, Lembo A, Hibberd PL, Lowy I, Kelly CP. Open-label, dose escalation phase I study in healthy volunteers to evaluate the safety and pharmacokinetics of a human monoclonal antibody to Clostridium difficile toxin A. Vaccine. 2008 Jun;26(27-28):3404–3409. doi: 10.1016/j.vaccine.2008.04.042.
+
+**Tsuji 1983** Tsuji A, Yoshikawa T, Nishide K, Minami H, Kimura M, Nakashima E, Terasaki T, Miyamoto E, Nightingale CH, Yamana T. Physiologically based pharmacokinetic model for beta-lactam antibiotics I: tissue distribution and elimination in rats. J Pharm Sci. 1983 Nov;72(11):1239-1252. doi: 10.1002/jps.2600721103.
+
+**Zhou 2003** Zhou J, Johnson JE, Ghetie V, Ober RJ, Ward ES. Generation of mutated variants of the human form of the MHC class I-related receptor, FcRn, with increased affinity for mouse immunoglobulin G. J Mol Biol. 2003 Sep;332(4):901-913. doi: 10.1016/s0022-2836(03)00952-5.
+
+","Markdown"
+"PBPK model","Open-Systems-Pharmacology/OSP-PBPK-Model-Library","Atazanavir/Atazanavir_evaluation_report.md",".md","35639","665","# Building and evaluation of a PBPK model for atazanavir in healthy adults
+
+| Version | 2.0-OSP12.2 |
+| ----------------------------------------------- | ------------------------------------------------------------ |
+| based on *Model Snapshot* and *Evaluation Plan* | https://github.com/Open-Systems-Pharmacology/Atazanavir-Model/releases/tag/v2.0 |
+| OSP Version | 12.2 |
+| Qualification Framework Version | 3.5 |
+
+This evaluation report and the corresponding PK-Sim project file are filed at:
+
+[https://github.com/Open-Systems-Pharmacology/OSP-PBPK-Model-Library](https://github.com/Open-Systems-Pharmacology/OSP-PBPK-Model-Library)
+
+# Table of Contents
+
+ * [1 Introduction](#introduction)
+ * [2 Methods](#methods)
+ * [2.1 Modeling Strategy](#modeling-strategy)
+ * [2.2 Data](#data)
+ * [2.3 Model Parameters and Assumptions](#model-parameters-and-assumptions)
+ * [3 Results and Discussion](#results-and-discussion)
+ * [3.1 Final input parameters](#final-input-parameters)
+ * [3.2 Diagnostics Plots](#diagnostics-plots)
+ * [3.3 Concentration-Time Profiles](#ct-profiles)
+ * [4 Conclusion](#conclusion)
+ * [5 References](#references)
+
+# 1 Introduction
+
+The presented model building and evaluation report evaluates the performance of a PBPK model for atazanavir in healthy adults.
+
+Atazanavir, sold under the trade name Reyataz among others, is an azapeptide protease inhibitor and used as antiretroviral medication to treat and prevent HIV/AIDS. It is taken orally once a day at a dose of 300 mg, if co-administered with ritonavir 100 mg orally once a day, and 400 mg, if administered without ritonavir.
+
+After oral administration, atazanavir is rapidly absorbed. A positive food effect has been observed, atazanavir is recommended to be taken with food. Protein binding is relatively high (86%) and independent of the concentration of serum proteins ([US Food and Drug Administration 2002](#5-references)). Atazanavir undergoes extensive metabolism by CYP3A isoenzymes with a dose fraction excreted unchanged in urine of approximately 7% ([US Food and Drug Administration 2002](#5-references), [Le Tiec 2005](#5-references)). Previous in vitro studies suggest that atazanavir is a mechanism-based inhibitor of CYP3A ([US Food and Drug Administration 2002](#5-references), [Perloff 2005](#5-references)) as well as a competitive inhibitor of CYP1A2, CYP2C9 and UGT1A1 ([US Food and Drug Administration 2002](#5-references), [Zhang 2005](#5-references)).
+
+# 2 Methods
+
+## 2.1 Modeling Strategy
+
+The general workflow for building an adult PBPK model has been described by Kuepfer et al. ([Kuepfer 2016](#5-references)). Relevant information on the anthropometry (height, weight) was gathered from the respective clinical study, if reported. Information on physiological parameters (e.g. blood flows, organ volumes, hematocrit) in adults was gathered from the literature and has been incorporated in PK-Sim® as described previously ([Willmann 2007](#5-references)). The applied activity and variability of plasma proteins and active processes that are integrated into PK-Sim® are described in the publicly available ‘PK-Sim® Ontogeny Database Version 7.3' ([PK-Sim Ontogeny Database Version 7.3](#5-references)).
+
+The PBPK model was developed based on clinical data of healthy adult subjects obtained from the literature, covering available dosing ranges for oral administration. Plasma concentration-time profiles following multiple-dose application and mass balance information on the urinary excretion of unchanged atazanavir were included in model development.
+
+First, a base mean model was built using plasma concentration-time profiles and the dose fraction excreted unchanged in urine following single dose administration of 400 mg po. The mean PK model was developed using a typical White American individual. Structural model selection was mainly guided by visual inspection of the resulting description of data and biological plausibility. The following parameters were identified using the Parameter Identification module provided in PK-Sim® and MoBi® ([Open Systems Pharmacology Documentation](#5-references)):
+
+- `Dissolution shape`
+- `Dissolution time (50% dissolved)`
+- `Specific intestinal permeability (transcellular)`
+- `GFR fraction`
+- `CLspec/[Enzyme]`
+
+Structural model selection was mainly guided by visual inspection of the resulting description of PK data and biological plausibility. On the basis of in vitro findings, atazanavir has been suggested to be a mechanism-based inhibitor of CYP3A ([Perloff 2005](#5-references)); however, no kinetic parameters have been reported for this interaction. Hence, to avoid non-identifiability issues, mechanism-based inhibition of CYP3A was not considered during parameter identification of the mean base model for single dose administration. All models implemented in PK-Sim for estimating the intracellular-to-plasma partition coefficient and those for estimating the permeability between interstitial and intracellular space were tested in this step. Once an appropriate structural model was identified, a second parameter identification was conducted fixing all previously optimized parameter values (except the `GFR fraction`) and including additional PK data following multiple dose administration of 200 mg, 300 mg, 400 mg, and 800 mg po. Optimized parameters were:
+
+- `GFR fraction`
+- `k_inact`
+- `k_kinact_half`
+
+Of note, since neither *in vitro* data on the kinetics of the mechanism-based inhibition of CYP3A nor *in vivo* pharmacokinetic data on drug-drug-interactions (DDI) with a CYP3A index substrate and atazanavir as CYP3A-perpetrator were available, the model should **not** be used to predict CYP3A DDI studies unless it has been verified for this purpose.
+
+Details about input data (physicochemical, *in vitro* and clinical) can be found in [Section 2.2](#22-data).
+
+Details about the structural model and its parameters can be found in [Section 2.3](#23-model-parameters-and-assumptions).
+
+## 2.2 Data
+
+### 2.2.1 In vitro / physicochemical data
+
+A literature search was carried out to collect available information on physicochemical properties of atazanavir. The obtained information from the literature is summarized in the table below and is used for model building.
+
+| **Parameter** | **Unit** | **Value** | **Description** |
+| :--------------------- | -------- | ------------------------------------------------------------ | ----------------------------------------------------- |
+| Molecular weight | g/mol | 704.9 ([drugbank.ca](#5-references)) | Molecular weight |
+| pKa (basic) | | 4.7 ([Berlin 2015](#5-references)) | Acid dissociation constant |
+| logP | | 2.12 ([Hyland 2008](#5-references)) | Partition coefficient between octanol and water |
+| fu | | 0.14 ([US Food and Drug Administration 2002](#5-references)) | Fraction unbound in human plasma |
+| Solubililty in FaSSIF | µg/mL | 2.74 ([Berlin 2015](#5-references)) | Solubility in Fasted State Simulated Intestinal Fluid |
+| Solubililty in FeSSIF | µg/mL | 4.13 ([Berlin 2015](#5-references)) | Solubility in Fed State Simulated Intestinal Fluid |
+
+With regard to UGT1A1 inhibition, atazanavir inhibited 17β-Estradiol glucuronidation in recombinant UGT1A1 by a mixed-type mechanism (in-house data, [Jungmann 2019](#5-references)):
+
+| **Parameter** | **Unit** | **Value** | Source | **Description** |
+| :--------------- | -------- | --------- | ------------------------------ | ------------------------------------ |
+| Ki | µmol/L | 0.22 | [Jungmann 2019](#5-references) | Inhibition constant |
+| Alpha | | 4.5 | [Jungmann 2019](#5-references) | Alpha value in mixed-type inhibition |
+| fumic | | 0.863 | [Fricke 2020](#5-references) | determined *in vitro* at 0.22 µmol/L |
+
+### 2.2.2 Clinical data
+
+A literature search was carried out to collect available PK data on atazanavir in healthy adults.
+
+The following publications were found and used for model building and evaluation:
+
+| Publication | Study description |
+| :---------------------------------------------------- | :----------------------------------------------------------- |
+| [Acosta 2007](#5-references) | 300 mg atazanavir BID, Period 1 |
+| [Agarwala 2003](#5-references) | 400 mg atazanavir QD, Day 6 |
+| [Agarwala 2005a](#5-references) | 400 mg atazanavir QD, 400 mg AM |
+| [Agarwala 2005b](#5-references) | 400 mg atazanavir QD, 400 mg (Treatment A) |
+| [Martin 2008](#5-references) | 400 mg atazanavir QD, monotherapy |
+| [Zhu 2010](#5-references) | 300 mg atazanavir QD |
+| [Zhu 2011](#5-references) | 400 mg atazanavir QD, 400 mg QPM and QAM |
+| [US Food and Drug Administration 2002](#5-references) | Study AI424-004 (p. 94): 400 mg atazanavir single dose (treatment A);
Study AI424-014 (p. 77): 400 mg atazanavir single dose (young females & males);
Study AI424-015 (p. 81): 400 mg atazanavir single dose (normal subjects);
Study AI424-028 (p. 128): 200, 400, and 800 mg atazanavir QD (A-D Day6);
Study AI424-029 (p. 47): 400 mg [14C]atazanavir single dose;
Study AI424-040 (p. 64): 200, 400, and 800 mg atazanavir QD;
Study AI424-056 (p. 134): 300 mg atazanavir QD (without ritonavir, Day 10);
Study AI424-076 (p. 178): 400 and 800 mg atazanavir QD |
+
+## 2.3 Model Parameters and Assumptions
+
+### 2.3.1 Dissolution and absorption
+
+No PK data were available following intravenous administration of atazanavir allowing informing distribution and systemic clearance independently of dissolution and absorption. Consequently, only PK data following oral administration of atazanavir as capsule were used for model building. It was assumed that solubility is not a critical parameter for dissolution of atazanavir capsules in the GI tract; in the models, solubility was therefore fixed to a very high value (50 mg/mL) to prevent solubility being a limiting factor of dissolution. Although the equilibrium solubility of atazanavir in the biorelevant media FaSSIF and FeSSIF has been observed to be rather low (2.74 µg/mL and 4.13 µg/mL in FaSSIF and FeSSIF, respectively), dissolution of atazanavir capsules in these media yields concentrations that are considerably higher than this threshold during the complete measurement period of at least 3 h ([Berlin 2015](#5-references)). In the model, dissolution was described by a Weibull function and the two Weibull parameters, `dissolution shape` and `dissolution time (50% dissolved)`, were fitted together with the `specific intestinal permeability (transcellular)` to observed PK data as described in [Section 2.1](#21-modeling-strategy).
+
+Since no PK data following IV administration was available, a moderate correlation was observed between the fitted `dissolution time (50% dissolved)` and `GFR fraction`. Although the final model parameterization was found to slightly overestimate Cmax in fasted state, this was considered inconsequential since atazanavir must be taken with food and all further model applications encompassed fed state PK.
+
+### 2.3.2 Distribution
+
+With a fraction unbound in human plasma of approximately 0.14, atazanavir is extensively protein-bound. Equilibrium dialysis of spiked human serum or human blood samples *in vitro* showed that atazanavir is bound to serum proteins (86.5%), albumin (86.2%), α1-acid glycoprotein (88.7%), and red blood cells (29.5%). The extent of protein binding and blood cell distribution has been reported to be concentration-independent over a 100-fold range ([US Food and Drug Administration 2002](#5-references)). The observed PK data were found to be best described using the model for estimating intracellular-to-plasma partition coefficients by Rodgers et al. ([Rodgers 2005](#5-references), [Rodgers 2006](#5-references)) and the cellular permeability automatically calculated by PK-Sim® ([Open Systems Pharmacology Documentation](#5-references)).
+
+### 2.3.3 Elimination
+
+Atazanavir is extensively metabolized via CYP3A isoenzymes ([Le Tiec 2005](#5-references)). Metabolism was modeled as linear process mediated by CYP3A4 (`in vitro clearance - first order`). The gene expression profile of CYP3A4 was loaded from the internal PK-Sim® database using the expression data quantified by RT-PCR ([Open Systems Pharmacology Documentation](#5-references)).
+
+Following oral administration of 400 mg [14C]atazanavir to healthy males, approximately 7% of the radioactive dose were recovered as unchanged drug in the urine ([US Food and Drug Administration 2002](#5-references)). Renal excretion of the unchanged drug was modeled as glomerular filtration process. The `GFR fraction` was then, together with the specific clearance via CYP3A4 normalized to the enzyme concentration (`CLspec/[Enzyme]`), fitted to observed PK data as described in [Section 2.1](#21-modeling-strategy).
+
+### 2.3.2 Autoinhibition
+
+Findings from in vitro studies indicate that atazanavir irreversibly inhibits CYP3A ([US Food and Drug Administration 2002](#5-references), [Perloff 2005](#5-references)). Since no kinetic values were reported for this mechanism-based inhibition, relevant parameters in the model (`k_kinact_half` and `k_inact`) were fitted as described in [Section 2.1](#21-modeling-strategy). An attempt to fix `k_kinact_half` to a very high value (100 µmol/L) to ensure linear inhibition kinetics while fitting `k_inact` and the `GFR faction` resulted in a slightly worse description of the observed PK in the terminal phase. Hence, both `k_kinact_half` and `k_inact were` fitted together with the `GFR fraction`. This resulted in a strong correlation between the former two parameters, but also in a reduction of the total error. Furthermore, the introduction of irreversible CYP3A4 inhibition led to a slightly worse description of clearance of the single dose PK data. Furthermore, the model was found to describe the lowest and highest dose (200 and 800 mg) less accurately. Importantly, though, the PK after multiple dose administration of 300 mg and 400 mg - the only two approved doses - could be adequately captured.
+
+Of note, since neither *in vitro* data on the mechanism-based inhibition of CYP3A nor *in vivo* pharmacokinetic data on drug-drug-interactions (DDI) with a CYP3A index substrate and atazanavir as CYP3A-perpetrator were available, the model should **not** be used to predict CYP3A DDI studies unless it has been verified for this purpose.
+
+# 3 Results and Discussion
+
+The PBPK model for dapagliflozin was developed and verified with clinical pharmacokinetic data.
+
+The next sections show:
+
+1. the final model parameters for the building blocks: [Section 3.1](#31-final-input-parameters).
+2. the overall goodness of fit: [Section 3.2](#32-diagnostics-plots).
+3. simulated vs. observed concentration-time profiles for the clinical studies used for model building and for model verification: [Section 3.3](#33-concentration-time-profiles).
+
+## 3.1 Final input parameters
+
+The compound parameter values of the final PBPK model are illustrated below.
+
+### Compound: Atazanavir
+
+#### Parameters
+
+Name | Value | Value Origin | Alternative | Default
+------------------------------------------------ | ----------------------- | --------------------------------------- | ----------- | -------
+Solubility at reference pH | 50000 mg/l | Assumption | Assumption | True
+Reference pH | 7 | Assumption | Assumption | True
+Lipophilicity | 2.12 Log Units | Publication-Hyland 2008, PMID: 18647303 | Measurement | True
+Fraction unbound (plasma, reference value) | 0.14 | Publication-Rajoli 2015, PMID: 25523214 | Measurement | True
+Specific intestinal permeability (transcellular) | 9.8649602504E-06 cm/min | Parameter Identification | Optimized | True
+Is small molecule | Yes | | |
+Molecular weight | 704.8555 g/mol | Internet-drugbank.ca | |
+Plasma protein binding partner | Unknown | | |
+
+#### Calculation methods
+
+Name | Value
+----------------------- | -------------------
+Partition coefficients | Rodgers and Rowland
+Cellular permeabilities | PK-Sim Standard
+
+#### Processes
+
+##### Metabolizing Enzyme: CYP3A4-Optimized
+
+Molecule: CYP3A4
+
+###### Parameters
+
+Name | Value | Value Origin
+-------------------- | ----------------------- | ------------------------
+Enzyme concentration | 1 µmol/l |
+Specific clearance | 0 1/min |
+CLspec/[Enzyme] | 1.0383524966 l/µmol/min | Parameter Identification
+
+##### Systemic Process: Glomerular Filtration-Clinical Pharmacology Review
+
+Species: Human
+
+###### Parameters
+
+Name | Value | Value Origin
+------------ | -----------:| ------------------------
+GFR fraction | 2.014495446 | Parameter Identification
+
+##### Inhibition: CYP3A4-Perloff2005
+
+Molecule: CYP3A4
+
+###### Parameters
+
+Name | Value | Value Origin
+------------- | --------------------- | ------------------------
+kinact | 0.0033009852632 1/min | Parameter Identification
+K_kinact_half | 0.1292581489 µmol/l | Parameter Identification
+
+##### Inhibition: UGT1A1-PH-41095
+
+Molecule: UGT1A1
+
+###### Parameters
+
+Name | Value | Value Origin
+---- | -------------- | ------------------------------------------------------
+Ki_c | 0.18986 µmol/l | In Vitro-Calculated from reported Ki and fu,mic
+Ki_u | 0.85437 µmol/l | In Vitro-Calculated from reported Ki, fu,mic and alpha
+
+### Formulation: Reyataz capsule
+
+Type: Weibull
+
+#### Parameters
+
+Name | Value | Value Origin
+-------------------------------- | ----------------- | ------------------------
+Dissolution time (50% dissolved) | 78.8787658271 min | Parameter Identification
+Lag time | 0 min |
+Dissolution shape | 1.5566465018 | Parameter Identification
+Use as suspension | Yes | Other
+
+## 3.2 Diagnostics Plots
+
+Below you find the goodness-of-fit visual diagnostic plots for the PBPK model performance of all data used presented in [Section 2.2.2](#222-clinical-data).
+
+The first plot shows observed versus simulated plasma concentration, the second weighted residuals versus time.
+
+
+
+**Table 3-1: GMFE for Goodness of fit plot for concentration in plasma**
+
+|Group |GMFE |
+|:--------------------------------|:----|
+|Atazanavir plasma concentrations |1.50 |
+
+
+
+
+
+
+
+
+**Figure 3-1: Goodness of fit plot for concentration in plasma**
+
+
+
+
+
+
+
+
+**Figure 3-2: Goodness of fit plot for concentration in plasma**
+
+
+
+
+## 3.3 Concentration-Time Profiles
+
+Simulated versus observed concentration-time profiles of all data listed in [Section 2.2.2](#222-clinical-data) are presented below.
+
+
+
+
+
+**Figure 3-3: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-4: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-5: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-6: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-7: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-8: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-9: Time Profile Analysis 2**
+
+
+
+
+
+
+
+
+**Figure 3-10: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-11: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-12: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-13: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-14: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-15: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-16: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-17: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-18: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-19: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-20: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-21: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-22: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-23: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-24: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-25: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-26: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-27: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-28: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-29: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-30: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-31: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-32: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-33: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-34: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-35: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-36: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-37: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-38: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-39: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-40: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-41: Time Profile Analysis 1**
+
+
+
+
+# 4 Conclusion
+
+The final atazanavir PBPK model applies metabolism by CYP3A4, glomerular filtration and mechanism-based inhibition of CYP3A4. While the latter process has not been evaluated using another victim compound, it should only be regarded preliminary and further work is needed before this model can be applied to predict CYP3A4 DDIs. Overall, the model adequately describes the oral pharmacokinetics of atazanavir in healthy adults receiving approved atazanavir doses of 300 mg and 400 mg. It is therefore deemed fit for purpose to be applied for the investigation of DDIs involving UGT1A1 inhibition.
+
+# 5 References
+
+**Acosta 2007** Acosta EP, Kendall MA, Gerber JG, Alston-Smith B, Koletar SL, Zolopa AR, et al. Effect of concomitantly administered rifampin on the pharmacokinetics and safety of atazanavir administered twice daily. *Antimicrob Agents Chemother* 2007, 51(9): 3104-3110.
+
+**Agarwala 2003** Agarwala S, Grasela D, Child M, Geraldes M, Geiger M, O’Mara E. Characterization of the steady-state pharmacokinetic (PK) profile of atazanavir (ATV) beyond the 24-hour
+dosing interval. Poster presented at *2nd International AIDS Society Conference on HIV Pathogenesis and Treatment*, Paris, 2003.
+
+**Agarwala 2005a** Agarwala S, Eley T, Child M, Wang Y, Hughes E, Grasela D. Pharmacokinetic effects of coadministration of atazanavir and tenofovir at steady state. Poster presented at *3rd International AIDS Society Conference on HIV Pathogenesis and Treatment*, Rio de Janeiro, 2005a.
+
+**Agarwala 2005b** Agarwala S, Gray K, Eley T, Wang Y, Hughes E, Grasela D. Pharmacokinetic interaction between atazanavir and omeprazole in healthy subjects. Poster presented at *3rd International AIDS Society Conference on HIV Pathogenesis and Treatment*, Rio de Janeiro, 2005b.
+
+**Berlin 2015** Berlin M, Ruff A, Kesisoglou F, Xu W, Wang MH, Dressman JB. Advances and challenges in PBPK modeling–analysis of factors contributing to the oral absorption of atazanavir, a poorly soluble weak base. *Eur J Pharm Biopharm* 2015, 93: 267-280.
+
+**drugbank** (https://www.drugbank.ca/drugs/DB01072), accessed on 07-30-2019.
+
+**Fricke 2020** Fricke R. Vericiguat: Investigations on Binding of Atazanavir to Recombinant UGT1A1 and of Mefenamic Acid to Recombinant UGT1A9. 2020. Report-No. PH-41346.
+
+**Hyland 2008** Hyland R, Dickins M, Collins C, Jones H, Jones B. Maraviroc: in vitro assessment of
+drug–drug interaction potential. *Br J Clin Pharmacol* 2008, 66(4): 498-507.
+
+**Jungmann 2019** Jungmann N. Vericiguat: Determination of Ki Values of Atazanavir on 3-Glucuronidation of 17β-Estradiol via UGT1A1 and of Mefenamic Acid on Glucuronidation of Propofol via UGT1A9. Bayer AG Nonclinical study report. 2019 Aug. Report-No. PH-41095.
+
+**Kuepfer 2016** Kuepfer L, Niederalt C, Wendl T, Schlender JF, Willmann S, Lippert J, Block M, Eissing T, Teutonico D. Applied concepts in PBPK modeling: how to build a PBPK/PD model. *CPT Pharmacometrics Syst Pharmacol* 2016, 5(10): 516-531.
+
+**Le Tiec 2005** Le Tiec C, Barrail A, Goujard C, Taburet AM. Clinical pharmacokinetics and summary of
+efficacy and tolerability of atazanavir. *Clin Pharmacokinet.* 2005, 44(10): 1035-1050.
+
+**Martin 2008** Martin DE, Galbraith H, Schettler J, Ellis C, Doto J. Pharmacokinetic properties and tolerability of bevirimat and atazanavir in healthy volunteers: an open-label, parallel-group study. *Clin Ther.* 2008, 30(10): 1794-1805.
+
+**Open Systems Pharmacology Documentation**. (https://docs.open-systems-pharmacology.org/), accessed on 07-30-2019.
+
+**Perloff 2005** Perloff ES, Duan SX, Skolnik PR, Greenblatt DJ, von Moltke LL. Atazanavir: effects on P-glycoprotein transport and CYP3A metabolism in vitro. *Drug Metab Dispos* 2005, 33(6): 764-770.
+
+**PK-Sim Ontogeny Database Version 7.3**. (https://github.com/Open-Systems-Pharmacology/OSPSuite.Documentation/blob/38cf71b384cfc25cfa0ce4d2f3addfd32757e13b/PK-Sim%20Ontogeny%20Database%20Version%207.3.pdf), accessed on 07-30-2019.
+
+**Rodgers 2005** Rodgers T, Leahy D, Rowland M. Physiologically Based Pharmacokinetic Modeling 1: Predicting the Tissue Distribution of Moderate-to-Strong Bases. *J Pharm Sci* 2005, 94: 1259-1275.
+
+**Rodgers 2006** Rodgers T, Rowland M. Physiologically Based Pharmacokinetic Modeling 2: Predicting the Tissue Distribution of Acids, Very Weak Bases, Neutrals and Zwitterions. *J Pharm Sci* 2006, 95: 1238-1257.
+
+**US Food and Drug Administration**. Reyataz (atazanavir) capsules: Clinical Pharmacology and Biopharmaceutics Review, Application number: 21-567, 2002. Available at: https://www.accessdata.fda.gov/drugsatfda_docs/nda/2003/021567_reyataz_toc.cfm, accessed on 07-30-2019.
+
+**Willmann 2007** Willmann S, Höhn K, Edginton A, Sevestre M, Solodenko J, Weiss W, Lippert J, Schmitt W. Development of a physiology-based whole-body population model for assessing the influence of individual variability on the pharmacokinetics of drugs. *J Pharmacokinet Pharmacodyn* 2007, 34(3): 401-431.
+
+**Zhang 2005** Zhang D, Chando TJ, Everett DW, Patten CJ, Dehal SS, Humphreys WG. In vitro inhibition of UDP glucuronosyltransferases by atazanavir and other HIV protease inhibitors and the relationship of this property to in vivo bilirubin glucuronidation. *Drug Metab Dispos* 2005, 33(11): 1729-1739.
+
+**Zhu 2010** Zhu L, Butterton J, Persson A, Stonier M, Comisar W, Panebianco D, et al. Pharmacokinetics and safety of twice-daily atazanavir 300 mg and raltegravir 400 mg in healthy individuals. *Antivir Ther* 2010, 15(8): 1107-1114.
+
+**Zhu 2011** Zhu L, Persson A, Mahnke L, Eley T, Li T, Xu X, et al. Effect of low‐dose omeprazole (20 mg Daily) on the pharmacokinetics of multiple‐dose atazanavir with ritonavir in healthy subjects. *J Clin Pharmacol* 2011, 51(3): 368-377.
+
+","Markdown"
+"PBPK model","Open-Systems-Pharmacology/OSP-PBPK-Model-Library","Digoxin/Digoxin_evaluation_report.md",".md","43605","778","# Building and evaluation of a PBPK model for digoxin in adults
+
+| Version | 2.0-OSP12.2 |
+| ----------------------------------------------- | ------------------------------------------------------------ |
+| based on *Model Snapshot* and *Evaluation Plan* | https://github.com/Open-Systems-Pharmacology/Digoxin-Model/releases/tag/v2.0 |
+| OSP Version | 12.2 |
+| Qualification Framework Version | 3.5 |
+
+This evaluation report and the corresponding PK-Sim project file are filed at:
+
+https://github.com/Open-Systems-Pharmacology/OSP-PBPK-Model-Library/
+
+# Table of Contents
+
+ * [1 Introduction](#introduction)
+ * [2 Methods](#methods)
+ * [2.1 Modeling strategy](#modeling-strategy)
+ * [2.2 Data](#data)
+ * [2.3 Model parameters and assumptions](#model-parameters-and-assumptions)
+ * [3 Results and Discussion](#results-and-discussion)
+ * [3.1 Digoxin final input parameters](#final-input-parameters)
+ * [3.2 Digoxin Diagnostics Plots](#diagnostics-plots)
+ * [3.3 Digoxin Concentration-Time profiles](#ct-profiles-model-building)
+ * [4 Conclusion](#conclusion)
+ * [5 References](#references)
+
+# 1 Introduction
+
+The presented model building and evaluation report evaluates the performance of a PBPK model for digoxin in adults.
+
+Digoxin is a cardiac glycoside used to treat atrial fibrillation, atrial flutter and heart failure.
+Digoxin is transported by P-glycoprotein 1 (P-gp), also known as multidrug resistance protein 1 (MDR1) or ATP-binding cassette sub-family B member 1 (ABCB1) or cluster of differentiation 243 (CD243) poly-glycoprotein. P-gp and mainly excreted unchanged via the kidneys with a small fraction eliminated via biliary excretion and only a very low degree of hepatic metabolism ([Greiner 1999, Ochs 1978 ](#References)).
+Many other substrates of P-gp are metabolized by CYP3A4, setting digoxin apart as an
+exception and thereby turning it into a model victim drug of P-gp-mediated DDIs.
+
+Digoxin is reported to have a large volume of distribution due to extensive tissue binding and to be mainly excreted unchanged to urine (50 - 70%) while the remainder of a dose is eliminated by hepatic metabolism and biliary excretion ([Ochs 1978, Bauer 2008](#References))]. The final digoxin model applies target-binding, transport by P-gp in various organs including gut, liver and kidney, an unspecific hepatic metabolic clearance and glomerular filtration, and adequately described the pharmacokinetics of digoxin in adults.
+
+The digoxin model is a whole-body PBPK model, allowing for dynamic translation between individuals. The digoxin report demonstrates the level of confidence in the digoxin PBPK model with the OSP suite with regard to reliable predictions of digoxin PK in adults during model-informed drug development.
+
+# 2 Methods
+
+The PBPK model for digoxin in this report is based on the developed and published digoxin PBPK model by Hanke *et al.* 2018. ([Hanke 2018](#References))
+
+## 2.1 Modeling strategy
+
+The general concept of building a PBPK model has previously been described by Kuepfer et al. ([Kuepfer 2016](#References)). Relevant information on anthropometric (height, weight) and physiological parameters (e.g. blood flows, organ volumes, binding protein concentrations, hematocrit, cardiac output) in adults was gathered from the literature and has been previously published ([Schlender 2016](#References)). The information was incorporated into PK-Sim® and was used as default values for the simulations in adults.
+
+The applied activity and variability of plasma proteins and active processes that are integrated into PK-Sim® are described in the publicly available PK-Sim® Ontogeny Database Version 7.3 ([PK-Sim Ontogeny Database Version 7.3](#References)) or otherwise referenced for the specific process.
+
+First, a base mean model was built using data from the single dose escalation study to find an appropriate structure describing the PK of digoxin. The mean PK model was developed using a typical European individual. Unknown parameters were identified using the Parameter Identification module provided in PK-Sim®. Structural model selection was mainly guided by visual inspection of the resulting description of data and biological plausibility.
+
+Once the appropriate structural model was identified, additional parameters for different formulations were identified, if available.
+
+A final PBPK model was established and simulations were compared to the reported data to evaluate model appropriateness and to assess model, by means of diagnostics plots and predicted versus observed concentration-time profiles, of which the results support an adequate prediction of the PK in adults.
+
+During model building, uncertainties in data quality, as well as study differences may cause not being able to adequately describe the PK of all reported clinical studies.
+
+## 2.2 Data
+
+### 2.2.1 In vitro / physico-chemical data
+
+A literature search was performed to collect available information on physicochemical properties of digoxin. The obtained information from the literature is summarized in the table below, and is used for model building.
+
+| **Parameter** | **Unit** | **Digoxin literature** | **Description** |
+| :-------------- | ----------- | ----------------------------------- | ------------------------------------------------ |
+| MW | g/mol | 780.93 ([Drugbank](#References)) | Molecular weight |
+| pKa | | none | Acid dissociation constant |
+| Solubility (pH) | mg/L | 64.8 (7) ([Drugbank](#References)) | Solubility |
+| logP | | 1.22, 1.62, 1.67 ([Alsenz 2007, Hinderling 1984, Atkinson 1988](#References)) | Partition coefficient between octanol and water |
+| fu | | 70.0, 71.0, 77.7 ([Hinderling 1984, Obach 2008, Neuhoff 2013](#References)) | Fraction unbound |
+| ATP1A2 KD | µmol/L | 0.0256 ([Katz 2010](#References)) | Dissociation constant |
+| ATP1A2 koff | 1/min | n.a. | Dissociation rate constant |
+| P-gp KM | µmol/L | 73.0, 177.0 ([Collett 2004, Troutman 2003](#References)) | Michaelis-Menten constant |
+| P-gp kcat | 1/min | n.a. | P-gp catalytic rate constant |
+| CLhep | mL/min | n.a. | Hepatic plasma clearance |
+| GFR fraction | 1/min | 1 | Fraction of filtered drug reaching the urine |
+| Formulation | | solution | Formulation used in predictions |
+| Cell permeabilities | | PK-Sim | Permeation across cell membranes |
+| Partition coefficients | | Rodgers & Rowland | Organ-plasma partition coefficients |
+| Specific intest. perm. | dm/min | n.a. | Normalized to surface area |
+| Specific organ perm. | dm/min | n.a. | Normalized to surface area |
+
+* ATP1A2: ATPase Na+/K+ transporting subunit alpha 2, CL: clearance, GFR: glomerular filtration rate, intest.: intestinal, n.a.: not available, perm.: permeability, P-gp: Pglycoprotein, PK-Sim: PK-Sim Standard calculation method, R + R: Rodgers and Rowland calculation method
+
+
+
+### 2.2.2 Clinical data
+
+A literature search was performed to collect available clinical data on digoxin in adults.
+
+The following publications were found in adults for model building and evaluation:
+
+| Publication | Study description |
+| ----------------------------------- | ------------------------------------------------------------ |
+| [Becquemont 2001](#References) | Becquemont, L. et al. Effect of grapefruit juice on digoxin pharmacokinetics in humans. Clin. Pharmacol. Ther. 70, 311–6 (2001). |
+| [Ding 2004](#References) | Ding, R. et al. Substantial pharmacokinetic interaction between digoxin and ritonavir in healthy volunteers. Clin. Pharmacol. Ther. 76, 73–84 (2004). |
+| [Eckermann 2012](#References) | Eckermann, G., Lahu, G., Nassr, N. & Bethke, T.D. Absence of pharmacokinetic interaction between roflumilast and digoxin in healthy adults. J. Clin. Pharmacol. 52, 251–7 (2012). |
+| [Friedrich 2011](#References) | Friedrich, C. et al. Evaluation of the pharmacokinetic interaction after multiple oral doses of linagliptin and digoxin in healthy volunteers. Eur. J. Drug Metab. Pharmacokinet. 36, 17–24 (2011). |
+| [Greiner 1999](#References) | Greiner, B. et al. The role of intestinal P-glycoprotein in the interaction of digoxin and rifampin. J. Clin. Invest. 104, 147–53 (1999). |
+| [Gurley 2008b](#References) | Gurley, B.J., Swain, A., Williams, D.K., Barone, G. & Battu, S.K. Gauging the clinical significance of P-glycoprotein-mediated herb-drug interactions: comparative effects of St. John’s wort, Echinacea, clarithromycin, and rifampin on digoxin pharmacokinetics. Mol. Nutr. food Res. 52, 772–9 (2008). |
+| [Hayward 1978](#References) | Hayward, R.P., Greenwood, H. & Hamer, J. Comparison of digoxin and medigoxin in normal subjects. Br. J. Clin. Pharmacol. 6, 81–6 (1978). |
+| [Jalava 1997 ](#References) | Jalava, K.M., Partanen, J. & Neuvonen, P.J. Itraconazole decreases renal clearance of digoxin. Ther. Drug Monit. 19, 609–13 (1997). |
+| [Johne 1999](#References) | Johne, A. et al. Pharmacokinetic interaction of digoxin with an herbal extract from St John’s wort (Hypericum perforatum). Clin. Pharmacol. Ther. 66, 338–45 (1999). |
+| [Kirby 2012](#References) | Kirby B.J., Collier A.C., Kharasch E.D., Whittington D., Thummel K.E., Unadkat J.D. Complex drug interactions of the HIV protease inhibitors 3: effect of simultaneous or staggered dosing of digoxin and ritonavir, nelfinavir, rifampin, or bupropion. Drug Metab Dispos. 2012 Mar;40(3):610-6. |
+| [Kirch 1986](#References) | Kirch, W., Hutt, H.J., Dylewicz, P., Gräf, K.J. & Ohnhaus, E.E. Dose-dependence of the nifedipine-digoxin interaction? Clin. Pharmacol. Ther. 39, 35–9 (1986). |
+| [Koup 1975](#References) | Koup, J.R., Greenblatt, D.J., Jusko, W.J., Smith, T.W. & Koch-Weser, J. Pharmacokinetics of digoxin in normal subjects after intravenous bolus and infusion doses. J. Pharmacokinet. Biopharm. 3, 181–92 (1975). |
+| [Kramer 1979](#References) | Kramer, W.G. et al. Pharmacokinetics of digoxin: relationship between response intensity and predicted compartmental drug levels in man. J. Pharmacokinet. Biopharm. 7, 47–61 (1979). |
+| [Lalonde 1985](#References) | Lalonde, R.L., Deshpande, R., Hamilton, P.P., McLean, W.M. & Greenway, D.C. Acceleration of digoxin clearance by activated charcoal. Clin. Pharmacol. Ther. 37, 367–71 (1985). |
+| [Larsen 2007](#References) | Larsen, U.L. et al. Human intestinal P-glycoprotein activity estimated by the model substrate digoxin. Scand. J. Clin. Lab. Invest. 67, 123–34 (2007). |
+| [Martin 1997](#References) | Martin, D.E. et al. Lack of effect of eprosartan on the single dose pharmacokinetics of orally administered digoxin in healthy male volunteers. Br. J. Clin. Pharmacol. 43, 661–4 (1997). |
+| [Ochs 1975](#References) | Ochs, H., Bodem, G., Schäfer, P.K., Kodrat, G., Dengler, H.J. Absorption of digoxin from the distal parts of the intestine in man. Eur J Clin Pharmacol. 1975 Dec 19;9(2-3):95-7. |
+| [Ochs 1978](#References) | Ochs, H., Greenblatt, D.J., Bodem, G. & Harmatz, J.S. Dose-independent pharmacokinetics of digoxin in humans. Am. Heart J. 96, 507–11 (1978). |
+| [Oosterhuis 1991](#References) | Oosterhuis, B., Jonkman, J.H., Andersson, T., Zuiderwijk, P.B. & Jedema, J.N. Minor effect of multiple dose omeprazole on the pharmacokinetics of digoxin after a single oral dose. Br. J. Clin. Pharmacol. 32, 569–72 (1991). |
+| [Qiu 2010](#References) | Qiu, R. et al. Lack of a pharmacokinetic interaction between dimebon (latrepirdine) and digoxin in healthy subjects. Am. Soc. Clin. Pharmacol. Ther. Meet. Atlanta, GA, USA (2010). |
+| [Ragueneau 1999](#References) | Ragueneau, I. et al. Pharmacokinetic and pharmacodynamic drug interactions between digoxin and macrogol 4000, a laxative polymer, in healthy volunteers. Br. J. Clin. Pharmacol. 48, 453–6 (1999). |
+| [Rengelshausen 2003](#References) | Rengelshausen, J. et al. Contribution of increased oral bioavailability and reduced nonglomerular renal clearance of digoxin to the digoxin-clarithromycin interaction. Br. J. Clin. Pharmacol. 56, 32–8 (2003). |
+| [Rodin 1988](#References) | Rodin, S.M., Johnson, B.F., Wilson, J., Ritchie, P. & Johnson, J. Comparative effects of verapamil and isradipine on steady-state digoxin kinetics. Clin. Pharmacol. Ther. 43, 668–72 (1988). |
+| [Steiness 1982](#References) | Steiness, E., Waldorff, S. & Hansen, P.B. Renal digoxin clearance: dependence on plasma digoxin and diuresis. Eur. J. Clin. Pharmacol. 23, 151–4 (1982). |
+| [Tayrouz 2003](#References) | Tayrouz, Y. et al. Pharmacokinetic and pharmaceutic interaction between digoxin and Cremophor RH40. Clin. Pharmacol. Ther. 73, 397–405 (2003). |
+| [Tsutsumi 2002](#References) | Tsutsumi, K. et al. The effect of erythromycin and clarithromycin on the pharmacokinetics of intravenous digoxin in healthy volunteers. J. Clin. Pharmacol. 42, 1159–64 (2002). |
+| [Vaidyanathan 2008](#References) | Vaidyanathan, S. et al. Pharmacokinetics of the oral direct renin inhibitor aliskiren in combination with digoxin, atorvastatin, and ketoconazole in healthy subjects: the role of P-glycoprotein in the disposition of aliskiren. J. Clin. Pharmacol. 48, 1323–38 (2008). |
+| [Verstuyft 2003](#References) | Verstuyft, C. et al. Dipyridamole enhances digoxin bioavailability via P-glycoprotein inhibition. Clin. Pharmacol. Ther. 73, 51–60 (2003). |
+| [Wagner 1981](#References) | Wagner, J.G., Popat, K.D., Das, S.K., Sakmar, E. & Movahhed, H. Evidence of nonlinearity in digoxin pharmacokinetics. J. Pharmacokinet. Biopharm. 9, 147–66 (1981). |
+| [Westphal 2000](#References) | Westphal, K. et al. Oral bioavailability of digoxin is enhanced by talinolol: evidence for involvement of intestinal P-glycoprotein. Clin. Pharmacol. Ther. 68, 6–12 (2000). |
+
+## 2.3 Model parameters and assumptions
+
+### 2.3.1 Absorption
+
+For oral administration of digoxin, the following parameters, amongst others, play a role with regards to the absorption kinetics of a compound, which can be estimated with PBPK: solubility, lipophilicity and intestinal permeability. To accurately predict the digoxin plasma concentrations following intravenous and oral administration, the relative expression of P-gp in the intestinal mucosa was increased (3.57-fold) compared to the PK-Sim database RT-PCR expression profile (see table below). This factor has been identified in an optimization that included digoxin plasma concentrations and fraction excreted to urine data following intravenous and oral administration plus digoxin excreted to duodenum measurements after intravenous administration [Caldwell 1976](#References). The optimized P-gp expression profile shows highest expression in small intestinal mucosa (1.41 µmol/L), followed by kidney (1.00 µmol/L), large intestinal mucosa (0.56 µmol/L), liver (0.27 µmol/L) and tissues of lower expression. Implementation of transport by OATP (4C1) did not improve the model performance and was not used in the final model.
+
+| **Protein** | **Mean reference concentration [µmol protein/L in the tissue of highest expression]** | **Geometric standard deviation of reference concentration** | **Relative expression in the different organs (PK-Sim expression database profile)** | Half-life liver [h] | Half-life intestine [h] |
+| :-------------- | ----------- | ----------------------------------- | ------------------------------------------------ | --------------- | --------------- |
+| P-gp (efflux) | 1.41 optimized | 1.60 ([Prasad 2014](#References)) | RT-PCR, with the relative expression in intestinal mucosa increased by a factor of 3.57 (optimized)([Nishimura 2005](#References)) | 36 | 23 |
+
+### 2.3.2 Distribution
+
+Digoxin is reported to have a large volume of distribution due to extensive tissue binding and to be mainly excreted unchanged to urine (50 - 70%) while the remainder of a dose is eliminated by hepatic metabolism and biliary excretion ([Ochs 1978, Bauer 2008](#References)). Implementation of target-binding to the ATPase Na+/K+ transporting subunit alpha 2 (ATP1A2) was crucial, to mechanistically describe the large volume of distribution and the long plasma half-life of digoxin.
+
+It has reported that the fraction unbound of digoxin ranges from 70 to 77.9% ([Hinderling 1984, Obach 2008, Neuhoff 2013](#References)).
+
+After testing the available organ-plasma partition coefficient and cell permeability calculation methods built in PK-Sim, observed clinical data was best described by choosing the partition coefficient calculation method by Rodgers and Rowland, and PK-Sim standard cell permeability calculation method. Specific organ permeability normalized to surface area was automatically calculated by PK-Sim.
+
+### 2.3.3 Metabolism and Elimination
+
+The final digoxin model applies target-binding to the ATP1A2, transport by P-gp in various organs including gut, liver and kidney, an unspecific hepatic metabolic clearance and glomerular filtration.
+
+# 3 Results and Discussion
+
+The PBPK model for digoxin was developed with clinical pharmacokinetic data covering intravenous as well as oral administration with a dose range of 0.125 to 1.5 mg including single dose and multiple dose clinical data, for different types of tablet formulations.
+
+During the model-fitting, the following parameters were estimated (all other parameters were fixed to reported values):
+
+* Lipophilicity
+* ATP1A2 Dissociation constant (Kd)
+* P-gp catalytic rate constant (Kcat)
+* Hepatic Clearance (CLhep)
+* Specific intestinal permeability (transcellular)
+* Specific organ permeability
+
+The fit resulted in an adequate description of the clinical data. Additional implementation of transport by OATP (4C1) did not improve the model performance and was not used in the final model.
+
+## 3.1 Digoxin final input parameters
+
+The compound parameter values of the final digoxin PBPK model are illustrated below.
+
+### Compound: Digoxin
+
+#### Parameters
+
+Name | Value | Value Origin | Alternative | Default
+------------------------------------------------ | -------------------- | ------------------------------------------------- | ------------------ | -------
+Solubility at reference pH | 64.8 mg/l | Internet-In Vitro-Drugbank | Aqueous Solubility | True
+Reference pH | 7 | Internet-In Vitro-Drugbank | Aqueous Solubility | True
+Lipophilicity | 1.40017663 Log Units | Parameter Identification-Parameter Identification | fitted | True
+Fraction unbound (plasma, reference value) | 0.71 | Publication-In Vitro-Neuhoff 2013 | Neuhoff (2013) | True
+Permeability | 1.0115E-05 dm/min | | fitted | True
+Specific intestinal permeability (transcellular) | 2.7627E-07 dm/min | | fitted | True
+Is small molecule | Yes | | |
+Molecular weight | 780.93 g/mol | Internet-In Vitro-Drugbank | |
+Plasma protein binding partner | Albumin | | |
+
+#### Calculation methods
+
+Name | Value
+----------------------- | -------------------
+Partition coefficients | Rodgers and Rowland
+Cellular permeabilities | PK-Sim Standard
+
+#### Processes
+
+##### Specific Binding: ATP1A2-Katz (2010)
+
+Molecule: ATP1A2
+
+###### Parameters
+
+Name | Value | Value Origin
+---- | ---------------- | ------------------------------
+koff | 0.00098888 1/min | Publication-In Vitro-Katz 2010
+Kd | 25.6 nmol/l | Parameter Identification
+
+##### Systemic Process: Total Hepatic Clearance-Fitted
+
+Species: Human
+
+###### Parameters
+
+Name | Value | Value Origin
+----------------------------- | -------------------- | ------------------------
+Fraction unbound (experiment) | 0.71 |
+Lipophilicity (experiment) | 1.40017663 Log Units |
+Plasma clearance | 0 ml/min/kg |
+Specific clearance | 0.03758077 1/min | Parameter Identification
+
+##### Transport Protein: P-gp-Stephens (2001)
+
+Molecule: P-gp
+
+###### Parameters
+
+Name | Value | Value Origin
+------------------------- | --------------- | ------------------------
+Transporter concentration | 1 µmol/l |
+Vmax | 8.67 µmol/l/min |
+Km | 177 µmol/l |
+kcat | 71.163 1/min | Parameter Identification
+
+##### Systemic Process: Glomerular Filtration-Steiness (1982)
+
+Species: Human
+
+###### Parameters
+
+Name | Value | Value Origin
+------------ | -----:| ---------------------------
+GFR fraction | 1 | Publication-Steiness (1982)
+
+## 3.2 Digoxin Diagnostics Plots
+
+Below you find the goodness-of-fit visual diagnostic plots for digoxin PBPK model performance (observed versus individually simulated plasma concentration and weighted residuals versus time, including the geometric mean fold error (GMFE)) of all data used for model building.
+
+
+
+**Table 3-1: GMFE for Goodness of fit plot for concentration in plasma.**
+
+|Group |GMFE |
+|:----------------------|:----|
+|Digoxin colonic admin. |2.13 |
+|Digoxin iv |1.45 |
+|Digoxin po SD |1.30 |
+|Digoxin po, MD |1.64 |
+|All |1.46 |
+
+
+
+
+
+
+
+
+**Figure 3-1: Goodness of fit plot for concentration in plasma.**
+
+
+
+
+
+
+
+
+**Figure 3-2: Goodness of fit plot for concentration in plasma.**
+
+
+
+
+## 3.3 Digoxin Concentration-Time profiles
+
+Simulated versus observed plasma concentration-time profiles of all data are listed below.
+
+
+
+
+
+**Figure 3-3: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-4: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-5: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-6: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-7: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-8: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-9: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-10: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-11: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-12: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-13: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-14: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-15: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-16: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-17: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-18: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-19: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-20: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-21: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-22: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-23: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-24: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-25: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-26: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-27: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-28: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-29: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-30: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-31: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-32: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-33: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-34: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-35: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-36: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-37: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-38: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-39: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-40: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-41: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-42: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-43: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-44: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-45: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-46: Time Profile Analysis 1**
+
+
+
+
+# 4 Conclusion
+
+The final digoxin PBPK model applies elimination mainly via GFR and P-gp and adequately describes the pharmacokinetics of digoxin in adults receiving intravenous, and oral SD and MD of digoxin ranging from 0.125 to 1.5 mg, for different types of tablet formulations that were described using a single formulation.
+
+This model could be applied for the investigation of drug-drug interactions (DDI), and translation to special populations such as pediatrics with regard to P-gp based elimination.
+
+# 5 References
+
+**Alsenz 2007** Alsenz, J., Meister, E. & Haenel, E. Development of a partially automated solubility screening (PASS) assay for early drug development. J. Pharm. Sci. 96, 1748–62 (2007).
+
+**Atkinson 1988** Atkinson, H.C. & Begg, E.J. Relationship between human milk lipid-ultrafiltrate and octanolwater partition coefficients. J. Pharm. Sci. 77, 796–8 (1988).
+
+**Bauer 2008** Bauer, L.A. Applied Clinical Pharmacokinetics. (2008).
+
+**Becquemont 2001** Becquemont, L. et al. Effect of grapefruit juice on digoxin pharmacokinetics in humans. Clin. Pharmacol. Ther. 70, 311–6 (2001).
+
+**Caldwell 1976** Caldwell, J.H. & Cline, C.T. Biliary excretion of digoxin in man. Clin. Pharmacol. Ther. 19, 410–5 (1976).
+
+**Collett 2004** Collett, A., Tanianis-Hughes, J., Hallifax, D. & Warhurst, G. Predicting P-glycoprotein effects on oral absorption: correlation of transport in Caco-2 with drug pharmacokinetics in wildtype and mdr1a(-/-) mice in vivo. Pharm. Res. 21, 819–26 (2004).
+
+**Ding 2004** Ding, R. et al. Substantial pharmacokinetic interaction between digoxin and ritonavir in healthy volunteers. Clin. Pharmacol. Ther. 76, 73–84 (2004).
+
+**Drugbank.ca** (https://go.drugbank.com/drugs/DB00390)
+
+**Eckermann 2012** Eckermann, G., Lahu, G., Nassr, N. & Bethke, T.D. Absence of pharmacokinetic interaction between roflumilast and digoxin in healthy adults. J. Clin. Pharmacol. 52, 251–7 (2012).
+
+**Friedrich 2011** Friedrich, C. et al. Evaluation of the pharmacokinetic interaction after multiple oral doses of linagliptin and digoxin in healthy volunteers. Eur. J. Drug Metab. Pharmacokinet. 36, 17–24 (2011).
+
+**Greiner 1999** Greiner, B. et al. The role of intestinal P-glycoprotein in the interaction of digoxin and rifampin. J. Clin. Invest. 104, 147–53 (1999).
+
+**Gurley 2008b** Gurley, B.J., Swain, A., Williams, D.K., Barone, G. & Battu, S.K. Gauging the clinical significance of P-glycoprotein-mediated herb-drug interactions: comparative effects of St. John’s wort, Echinacea, clarithromycin, and rifampin on digoxin pharmacokinetics. Mol. Nutr. food Res. 52, 772–9 (2008).
+
+**Hanke 2018** Hanke N, Frechen S, Moj D, Britz H, Eissing T, Wendl T, Lehr T. PBPK Models for CYP3A4 and P-gp DDI Prediction: A Modeling Network of Rifampicin, Itraconazole, Clarithromycin, Midazolam, Alfentanil, and Digoxin. CPT Pharmacometrics Syst Pharmacol.. 2018 Oct;7(10):647-659.
+
+**Hayward 1978** Hayward, R.P., Greenwood, H. & Hamer, J. Comparison of digoxin and medigoxin in normal subjects. Br. J. Clin. Pharmacol. 6, 81–6 (1978).
+
+**Hinderling 1984** Hinderling, P.H. Kinetics of partitioning and binding of digoxin and its analogues in the subcompartments of blood. J. Pharm. Sci. 73, 1042–53 (1984).
+
+**Jalava 1997** Jalava, K.M., Partanen, J. & Neuvonen, P.J. Itraconazole decreases renal clearance of digoxin. Ther. Drug Monit. 19, 609–13 (1997).
+
+**Johne 1999** Johne, A. et al. Pharmacokinetic interaction of digoxin with an herbal extract from St John’s wort (Hypericum perforatum). Clin. Pharmacol. Ther. 66, 338–45 (1999).
+
+**Katz 2010** Katz, A. et al. Selectivity of digitalis glycosides for isoforms of human Na,K-ATPase. J. Biol. Chem. 285, 19582–92 (2010).
+
+**Kirch 1986** Kirch, W., Hutt, H.J., Dylewicz, P., Gräf, K.J. & Ohnhaus, E.E. Dose-dependence of the nifedipine-digoxin interaction? Clin. Pharmacol. Ther. 39, 35–9 (1986).
+
+**Koup 1975** Koup, J.R., Greenblatt, D.J., Jusko, W.J., Smith, T.W. & Koch-Weser, J. Pharmacokinetics of digoxin in normal subjects after intravenous bolus and infusion doses. J. Pharmacokinet. Biopharm. 3, 181–92 (1975).
+
+**Kramer 1979** Kramer, W.G. et al. Pharmacokinetics of digoxin: relationship between response intensity and predicted compartmental drug levels in man. J. Pharmacokinet. Biopharm. 7, 47–61 (1979).
+
+**Kuepfer 2016** Kuepfer L, Niederalt C, Wendl T, Schlender JF, Willmann S, Lippert J, Block M, Eissing T, Teutonico D. Applied Concepts in PBPK Modeling: How to Build a PBPK/PD Model.CPT Pharmacometrics Syst Pharmacol. 2016 Oct;5(10):516-531. doi: 10.1002/psp4.12134. Epub 2016 Oct 19.
+
+**Lalonde 1985** Lalonde, R.L., Deshpande, R., Hamilton, P.P., McLean, W.M. & Greenway, D.C. Acceleration of digoxin clearance by activated charcoal. Clin. Pharmacol. Ther. 37, 367–71 (1985).
+
+**Larsen 2007** Larsen, U.L. et al. Human intestinal P-glycoprotein activity estimated by the model substrate digoxin. Scand. J. Clin. Lab. Invest. 67, 123–34 (2007).
+
+**Martin 1997** Martin, D.E. et al. Lack of effect of eprosartan on the single dose pharmacokinetics of orally administered digoxin in healthy male volunteers. Br. J. Clin. Pharmacol. 43, 661–4 (1997).
+
+**Neuhoff 2013** Neuhoff, S. et al. Application of permeability-limited physiologically-based pharmacokinetic models: part I-digoxin pharmacokinetics incorporating P-glycoprotein-mediated efflux. J. Pharm. Sci. 102, 3145–60 (2013).
+
+**Nishimura 2005** Nishimura, M. & Naito, S. Tissue-specific mRNA expression profiles of human ATP-binding cassette and solute carrier transporter superfamilies. Drug Metab. Pharmacokinet. 20, 452–77 (2005).
+
+**Obach 2008** Obach, R.S., Lombardo, F. & Waters, N.J. Trend analysis of a database of intravenous pharmacokinetic parameters in humans for 670 drug compounds. Drug Metab. Dispos. 36, 1385–405 (2008).
+
+**Ochs 1975** Ochs, H., Bodem, G., Schäfer, P.K., Kodrat, G., Dengler, H.J. Absorption of digoxin from the distal parts of the intestine in man. Eur J Clin Pharmacol. 1975 Dec 19;9(2-3):95-7.
+
+**Ochs 1978** Ochs, H.R., Greenblatt, D.J., Bodem, G. & Harmatz, J.S. Dose-independent pharmacokinetics of digoxin in humans. Am. Heart J. 96, 507–11 (1978).
+
+**Oosterhuis 1991** Oosterhuis, B., Jonkman, J.H., Andersson, T., Zuiderwijk, P.B. & Jedema, J.N. Minor effect of multiple dose omeprazole on the pharmacokinetics of digoxin after a single oral dose. Br. J. Clin. Pharmacol. 32, 569–72 (1991).
+
+**PK-Sim Ontogeny Database Version 7.3** (https://github.com/Open-Systems-Pharmacology/OSPSuite.Documentation/blob/38cf71b384cfc25cfa0ce4d2f3addfd32757e13b/PK-Sim%20Ontogeny%20Database%20Version%207.3.pdf)
+
+**Prasad 2014** Prasad, B. et al. Interindividual variability in hepatic organic anion-transporting polypeptides and P-glycoprotein (ABCB1) protein expression: quantification by liquid chromatography tandem mass spectroscopy and influence of genotype, age, and sex. Drug Metab. Dispos. 42, 78–88 (2014).
+
+**Qiu 2010** Qiu, R. et al. Lack of a pharmacokinetic interaction between dimebon (latrepirdine) and digoxin in healthy subjects. Am. Soc. Clin. Pharmacol. Ther. Meet. Atlanta, GA, USA (2010).
+
+**Ragueneau 1999** Ragueneau, I. et al. Pharmacokinetic and pharmacodynamic drug interactions between digoxin and macrogol 4000, a laxative polymer, in healthy volunteers. Br. J. Clin. Pharmacol. 48, 453–6 (1999).
+
+**Rengelshausen 2003** Rengelshausen, J. et al. Contribution of increased oral bioavailability and reduced nonglomerular renal clearance of digoxin to the digoxin-clarithromycin interaction. Br. J. Clin. Pharmacol. 56, 32–8 (2003).
+
+**Rodin 1988** Rodin, S.M., Johnson, B.F., Wilson, J., Ritchie, P. & Johnson, J. Comparative effects of verapamil and isradipine on steady-state digoxin kinetics. Clin. Pharmacol. Ther. 43, 668–72 (1988).
+
+**Schlender 2016** Schlender JF, Meyer M, Thelen K, Krauss M, Willmann S, Eissing T, Jaehde U. Development of a Whole-Body Physiologically Based Pharmacokinetic Approach to Assess the Pharmacokinetics of Drugs in Elderly Individuals. Clin Pharmacokinet. 2016 Dec;55(12):1573-1589.
+
+**Steiness 1982** Steiness, E., Waldorff, S. & Hansen, P.B. Renal digoxin clearance: dependence on plasma digoxin and diuresis. Eur. J. Clin. Pharmacol. 23, 151–4 (1982).
+
+**Stephens 2001** Stephens R.H., O'Neill C.A., Warhurst A., Carlson G.L., Rowland M-, Warhurst G. Kinetic profiling of P-glycoprotein-mediated drug efflux in rat and human intestinal epithelia. J. Pharmacol Exp Ther. 2001 Feb;296(2):584-91.
+
+**Tayrouz 2003** Tayrouz, Y. et al. Pharmacokinetic and pharmaceutic interaction between digoxin and Cremophor RH40. Clin. Pharmacol. Ther. 73, 397–405 (2003).
+
+**Troutman 2003** Troutman, M.D. & Thakker, D.R. Efflux ratio cannot assess P-glycoprotein-mediated attenuation of absorptive transport: asymmetric effect of P-glycoprotein on absorptive and secretory transport across Caco-2 cell monolayers. Pharm. Res. 20, 1200–9 (2003).
+
+**Tsutsumi 2002** Tsutsumi, K. et al. The effect of erythromycin and clarithromycin on the pharmacokinetics of intravenous digoxin in healthy volunteers. J. Clin. Pharmacol. 42, 1159–64 (2002).
+
+**Vaidyanathan 2008** Vaidyanathan, S. et al. Pharmacokinetics of the oral direct renin inhibitor aliskiren in combination with digoxin, atorvastatin, and ketoconazole in healthy subjects: the role of P-glycoprotein in the disposition of aliskiren. J. Clin. Pharmacol. 48, 1323–38 (2008).
+
+**Verstuyft 2003** Verstuyft, C. et al. Dipyridamole enhances digoxin bioavailability via P-glycoprotein inhibition. Clin. Pharmacol. Ther. 73, 51–60 (2003).
+
+**Wagner 1981** Wagner, J.G., Popat, K.D., Das, S.K., Sakmar, E. & Movahhed, H. Evidence of nonlinearity in digoxin pharmacokinetics. J. Pharmacokinet. Biopharm. 9, 147–66 (1981).
+
+**Westphal 2000** Westphal, K. et al. Oral bioavailability of digoxin is enhanced by talinolol: evidence for involvement of intestinal P-glycoprotein. Clin. Pharmacol. Ther. 68, 6–12 (2000).
+
+","Markdown"
+"PBPK model","Open-Systems-Pharmacology/OSP-PBPK-Model-Library","CDA1/CDA1_evaluation_report.md",".md","15725","260","# Building and evaluation of a PBPK model for antibody CDA1 in healthy adults
+
+| Version | 1.0-OSP12.2 |
+| ----------------------------------------------- | ------------------------------------------------------------ |
+| based on *Model Snapshot* and *Evaluation Plan* | https://github.com/Open-Systems-Pharmacology/CDA1-Model/releases/tag/v1.0 |
+| OSP Version | 12.2 |
+| Qualification Framework Version | 3.5 |
+
+This evaluation report and the corresponding PK-Sim project file are filed at:
+
+https://github.com/Open-Systems-Pharmacology/OSP-PBPK-Model-Library/
+
+# Table of Contents
+
+ * [1 Introduction](#introduction)
+ * [2 Methods](#methods)
+ * [2.1 Modeling Strategy](#modeling-strategy)
+ * [2.2 Data](#methods-data)
+ * [2.2.1 In vitro / physico-chemical Data ](#invitro-and-physico-chemical-data)
+ * [2.2.2 PK Data ](#PK-data)
+ * [2.3 Model Parameters and Assumptions](#model-parameters-and-assumptions)
+ * [3 Results and Discussion](#results-and-discussion)
+ * [3.1 Final input parameters](#final-input-parameters)
+ * [3.2 Diagnostics Plots](#diagnostics-plots)
+ * [3.3 Concentration-Time Profiles](#ct-profiles)
+ * [4 Conclusion](#conclusion)
+ * [5 References](#main-references)
+
+# 1 Introduction
+
+CDA1 is a human monoclonal antibody (IgG1) against the toxin A of *Clostridium difficile*.
+
+CDA1 shows a pharmacokinetic behavior which is typical for an antibody without endogenous target. The plasma concentration–time profiles after i.v. infusion of 5, 10 and 20 mg/kg CDA1 in healthy adults ([Taylor 2008](#5-references)) were used together with pharmacokinetic (PK) data from 5 other compounds to identify unknown parameters during the development of the generic large molecule physiologically based pharmacokinetic (PBPK) model in PK-Sim ([Niederalt 2018](#5-references)).
+
+The herein presented evaluation report evaluates the performance of the PBPK model for CDA1 in healthy adults for the PK data used for the development of the generic large molecule model in PK-Sim.
+
+The presented CDA1 PBPK model as well as the respective evaluation plan and evaluation report are provided open-source (https://github.com/Open-Systems-Pharmacology/CDA1-Model).
+
+# 2 Methods
+
+## 2.1 Modeling Strategy
+
+The development of the large molecule PBPK model in PK-Sim® has previously been described by Niederalt et al. ([Niederalt 2018](#5-references)). In short, the model was built as an extension of the PK-Sim® model for small molecules incorporating (i) the two-pore formalism for drug extravasation from blood plasma to interstitial space, (ii) lymph flow, (iii) endosomal clearance and (iv) protection from endosomal clearance by neonatal Fc receptor (FcRn) mediated recycling.
+
+For model development and evaluation, PK data were used from compounds with a wide range of solute radii and from different species. The PK data used for parameter estimation were from the following compounds: antibody–drug conjugate BAY 79-4620 in mice (Bayer in house data), antibody 7E3 in wild-type and FcRn knockout mice ([Garg 2007](#5-references), [Garg2009](#5-references)), domain antibody dAb2 in mice ([Sepp 2015](#5-references)), antibodies MEDI-524 and MEDI-524-YTE in monkeys ([Dall'Acqua 2006](#5-references)), and antibody CDA1 in humans ([Taylor 2008](#5-references)). The PK data used for model evaluation were from inulin in rats ([Tsuji1983](#5-references)) and tefibazumab in humans ([Reilly 2005](#5-references)).
+
+The PBPK model including the estimated physiological parameters as described by Niederalt et al. ([Niederalt 2018](#5-references)) is available in the Open Systems Pharmacology Suite from version 7.1 onwards.
+
+This evaluation report focuses on the PBPK model for the antibody CDA1.
+
+Details about input data (physicochemical, *in vitro* and PK) can be found in [Section 2.2](#22-data).
+
+Details about the structural model and its parameters can be found in [Section 2.3](#23-model-parameters-and-assumptions).
+
+## 2.2 Data
+
+### 2.2.1 In vitro / physico-chemical Data
+
+A literature search was performed to collect available information on physicochemical properties of CDA1. The obtained information from literature is summarized in the table below.
+
+| **Parameter** | **Unit** | **Value** | Source | **Description** |
+| :------------ | -------- | --------- | ---------------------------- | ------------------------------------------------------------ |
+| MW | g/mol | 150000 | [Lobo 2004](#5-references) | Molecular weight |
+| r | nm | 5.34 | [Taylor 1984](#5-references) | Hydrodynamic solute radius |
+| Kd (FcRn) | µM | 0.63 | [Zhou 2003](#5-references) | Dissociation constant for binding of a human IgG1 antibody to human FcRn at pH 6 |
+
+### 2.2.2 PK Data
+
+Published clinical PK data on CDA1 in healthy adults were used.
+
+| Publication | Description |
+| :-------------------------- | :----------------------------------------------------------- |
+| [Taylor2008](#5-references) | The plasma concentration–time profiles after single i.v. infusion of 5, 10 and 20 mg/kg CDA1 in healthy adults were used. The data for the dosages 0.3 and 1 mg/kg were not used since the PK data could not be read with sufficient accuracy from the published figure. |
+
+## 2.3 Model Parameters and Assumptions
+
+### 2.3.1 Absorption
+
+There is no absorption process since CDA1 was administered intravenously.
+
+### 2.3.2 Distribution
+
+The standard lymph and fluid recirculation flow rates and the standard vascular properties of the different tissues (hydraulic conductivity, pore radii, fraction of flow via large pores) from PK-Sim were used. CDA1, among other compounds, has been used to identify these lymph and fluid recirculation flow rates used in PK-Sim ([Niederalt 2018](#5-references)).
+
+### 2.3.3 Metabolism and Elimination
+
+The FcRn mediated clearance present in the standard PK-Sim model was used as only clearance process. The standard physiological parameters related to FcRn mediated clearance were used (rate constants for endosomal uptake and recycling, association rate constant for FcRn binding and concentration of FcRn in the endosomal space). CDA1, among other compounds, has been used to identify these parameters using literature values for the drug affinities to FcRn in the endosomal space ([Niederalt 2018](#5-references)).
+
+### 2.3.4 Automated Parameter Identification
+
+No drug specific parameters were fitted. CDA1, among other compounds, has been used to develop the model for proteins and large molecules in PK-Sim ([Niederalt 2018](#5-references)).
+
+# 3 Results and Discussion
+
+The PBPK model for CDA1 was evaluated with clinical PK data in healthy adults.
+
+These PK data have been used together with PK data from 5 other compounds to simultaneously identify parameters during the development of the generic model for proteins and large molecules in PK-Sim ([Niederalt 2018](#5-references)).
+
+The next sections show:
+
+1. the final model parameters for the building blocks: [Section 3.1](#31-final-input-parameters).
+2. the overall goodness of fit: [Section 3.2](#32-diagnostics-plots).
+3. simulated vs. observed concentration-time profiles for the clinical studies used for model building and for model verification: [Section 3.3](#33-concentration-time-profiles).
+
+## 3.1 Final input parameters
+
+The compound parameter values of the final PBPK model are illustrated below.
+
+### Compound: CDA1
+
+#### Parameters
+
+Name | Value | Value Origin | Alternative | Default
+------------------------------------------ | ------------ | --------------------------------------------- | ----------- | -------
+Solubility at reference pH | 999 mg/l | Other-/Dummy value not used in the simulation | Measurement | True
+Reference pH | 7 | Other-/Dummy value not used in the simulation | Measurement | True
+Lipophilicity | -5 Log Units | Other-/Dummy value not used in the simulation | Measurement | True
+Fraction unbound (plasma, reference value) | 1 | Other-Assumption | Measurement | True
+Is small molecule | No | | |
+Molecular weight | 150000 g/mol | Publication-Lobo2004 | |
+Plasma protein binding partner | Unknown | | |
+Radius (solute) | 5.34 nm | Publication-Taylor1984 | |
+Kd (FcRn) in endosomal space | 0.63 µmol/l | Publication-Zhou2003 | |
+
+#### Calculation methods
+
+Name | Value
+----------------------- | ---------------
+Partition coefficients | PK-Sim Standard
+Cellular permeabilities | PK-Sim Standard
+
+#### Processes
+
+## 3.2 Diagnostics Plots
+
+Below you find the goodness-of-fit visual diagnostic plots for the PBPK model performance of all data used presented in [Section 2.2.2](#PK-data).
+
+The first plot shows observed versus simulated plasma concentration, the second weighted residuals versus time.
+
+
+
+**Table 3-1: GMFE for Goodness of fit plot for concentration in plasma**
+
+|Group |GMFE |
+|:-----------|:----|
+|10 mg/kg IV |1.06 |
+|20 mg/kg IV |1.14 |
+|5 mg/kg IV |1.02 |
+|All |1.08 |
+
+
+
+
+
+
+
+
+**Figure 3-1: Goodness of fit plot for concentration in plasma**
+
+
+
+
+
+
+
+
+**Figure 3-2: Goodness of fit plot for concentration in plasma**
+
+
+
+
+## 3.3 Concentration-Time Profiles
+
+Simulated versus observed concentration-time profiles of all data listed in [Section 2.2.2](#PK-data) are presented below.
+
+
+
+
+
+**Figure 3-3: Plasma concentration - Dose 5 mg/kg (log scale)**
+
+
+
+
+
+
+
+
+**Figure 3-4: Plasma concentration - Dose 5 mg/kg (linear scale)**
+
+
+
+
+
+
+
+
+**Figure 3-5: Plasma concentration - Dose 10 mg/kg (log scale)**
+
+
+
+
+
+
+
+
+**Figure 3-6: Plasma concentration - Dose 10 mg/kg (linear scale)**
+
+
+
+
+
+
+
+
+**Figure 3-7: Plasma concentration - Dose 20 mg/kg (log scale)**
+
+
+
+
+
+
+
+
+**Figure 3-8: Plasma concentration - Dose 20 mg/kg (linear scale)**
+
+
+
+
+# 4 Conclusion
+
+The herein presented PBPK model overall adequately describes the pharmacokinetics of CDA1 in healthy humans. The initial plasma concentrations are slightly underestimated especially for the higher dose.
+
+ The PK data had been used during the development of the generic large molecule PBPK model in PK-Sim ([Niederalt 2018](#5-references)) together with PK data from 5 other compounds (7E3, BAY 79-4620, dAb2, MEDI-524 & MEDI-524-YTE).
+
+# 5 References
+
+**Dall'Acqua 2006** Dall’Acqua WF, Kiener PA, Wu H. Properties of human IgG1s engineered for enhanced binding to the neonatal Fc receptor (FcRn). J Biol Chem. 2006 Aug; 281(33):23514-23524. doi: 10.1074/jbc.M604292200.
+
+**Garg 2007** Garg A, Balthasar JP. Physiologically-based pharmacokinetic (PBPK) model to predict IgG tissue kinetics in wild-type and FcRn-knockout mice. J Pharmacokinet Pharmacodyn. 2007 Jul; 34(5):687-709. doi: 10.1007/s10928-007-9065-1.
+
+**Garg 2009** Garg A, Balthasar J. Investigation of the influence of FcRn on the distribution of IgG to the brain. AAPS J. 2009 July; 11(3):553-557. doi: 10.1208/s12248-009-9129-9.
+
+**Lobo 2004** Lobo ED, Hansen R J, Balthasar JP. Antibody pharmacokinetics and pharmacodynamics. J Pharm Sci. 2004 Nov;93(11):2645-2668. doi: 10.1002/jps.20178.
+
+**Niederalt 2018** Niederalt C, Kuepfer L, Solodenko J, Eissing T, Siegmund HU, Block M, Willmann S, Lippert J. A generic whole body physiologically based pharmacokinetic model for therapeutic proteins in PK-Sim. J Pharmacokinet Pharmacodyn. 2018 Apr;45(2):235-257. doi: 10.1007/s10928-017-9559-4.
+
+**Reilly 2005** Reilley S, Wenzel E, Reynolds L, Bennett B, Patti JM, Hetherington S. Open-label, dose escalation study of the safety and pharmacokinetic profile of tefibazumab in healthy volunteers. Antimicrob Agents Chemother. 2005 Mar;49(3):959–962. doi: 10.1128/AAC.49.3.959-962.2005.
+
+**Sepp 2015** Sepp A, Berges A, Sanderson A, Meno-Tetang G. Development of a physiologically based pharmacokinetic model for a domain antibody in mice using the two-pore theory. J Pharmacokinet Pharmacodyn. 2015 Jan;42(2):97-109. doi: 10.1007/s10928-014-9402-0.
+
+**Taylor 1984** Taylor AE, Granger DN. Exchange of macromolecules across the microcirculation. Handbook of Physiology - Cardiovascular System. Microcirculation (Eds. Renkin EM and Michel CC. Bethesda, MD, American Physiological Society). 1984; Vol. 4(Pt 2):467–520.
+
+**Taylor 2008** Taylor CP, Tummala S, Molrine D, Davidson L, Farrell RJ, Lembo A, Hibberd PL, Lowy I, Kelly CP. Open-label, dose escalation phase I study in healthy volunteers to evaluate the safety and pharmacokinetics of a human monoclonal antibody to Clostridium difficile toxin A. Vaccine. 2008 Jun;26(27-28):3404–3409. doi: 10.1016/j.vaccine.2008.04.042.
+
+**Tsuji 1983** Tsuji A, Yoshikawa T, Nishide K, Minami H, Kimura M, Nakashima E, Terasaki T, Miyamoto E, Nightingale CH, Yamana T. Physiologically based pharmacokinetic model for beta-lactam antibiotics I: tissue distribution and elimination in rats. J Pharm Sci. 1983 Nov;72(11):1239-1252. doi: 10.1002/jps.2600721103.
+
+**Zhou 2003** Zhou J, Johnson JE, Ghetie V, Ober RJ, Ward ES. Generation of mutated variants of the human form of the MHC class I-related receptor, FcRn, with increased affinity for mouse immunoglobulin G. J Mol Biol. 2003 Sep;332(4):901-913. doi: 10.1016/s0022-2836(03)00952-5.
+
+","Markdown"
+"PBPK model","Open-Systems-Pharmacology/OSP-PBPK-Model-Library","Itraconazole/Itraconazole_evaluation_report.md",".md","62906","929","# Building and evaluation of a PBPK model for Itraconazole in healthy adults
+
+| Version | 2.0-OSP12.2 |
+| ----------------------------------------------- | ------------------------------------------------------------ |
+| based on *Model Snapshot* and *Evaluation Plan* | https://github.com/Open-Systems-Pharmacology/Itraconazole-Model/releases/tag/v2.0 |
+| OSP Version | 12.2 |
+| Qualification Framework Version | 3.5 |
+
+This evaluation report and the corresponding PK-Sim project file are filed at:
+
+https://github.com/Open-Systems-Pharmacology/OSP-PBPK-Model-Library/
+
+# Table of Contents
+
+ * [1 Introduction](#introduction)
+ * [2 Methods](#methods)
+ * [2.1 Modeling Strategy](#modeling-strategy)
+ * [2.2 Data](#data)
+ * [2.3 Model Parameters and Assumptions](#model-parameters-and-assumptions)
+ * [3 Results and Discussion](#results-and-discussion)
+ * [3.1 Final input parameters](#final-input-parameters)
+ * [3.2 Diagnostics Plots](#diagnostics-plots)
+ * [3.3 Concentration-Time Profiles](#concentration-time-profiles)
+ * [4 Conclusion](#conclusion)
+ * [5 References](#references)
+
+# 1 Introduction
+
+Itraconazole is a triazole agent prescribed for the treatment of fungal infections. It is predominantly metabolized by CYP3A4, resulting in the sequential formation of several metabolites, starting with the major metabolite hydroxy-itraconazole, followed by keto-itraconazole and N-desalkyl-itraconazole. All three metabolites are further metabolized by CYP3A4 and parent and metabolites are reported to competitively inhibit CYP3A4 ([Isoherranen 2004](#5-references)). Therefore, the metabolites inhibit their own formation and itraconazole inhibits further conversion of its metabolites by CYP3A4. Itraconazole has been proposed as one of the most appropriate CYP3A4 inhibitors for clinical DDI studies, to replace the currently no longer recommended CYP3A4 inhibitor drug ketoconazole.
+
+The herein presented model represents an update of the itraconazole model publisdhed by Hanke *et al.* ([Hanke 2018](#5-references)). The model was originally established using various clinical studies, covering a dosing range from 100 to 200 mg in different formulations (solution and capsules), administered under fasted conditions or together with food. Although the plasma concentrations of keto-itraconazole and N-desalkyl-itraconazole are lower than those of itraconazole and hydroxy-itraconazole, N-desalkyl-itraconazole is reported to be a very potent inhibitor *in vitro*, and integration of the two further metabolites into the model with their inhibitory effects enabled the description the strong non-linearity and plasma accumulation of itraconazole. The model applies sequential metabolism of itraconazole to hydroxy-itraconazole to keto-itraconazole to N-desalkyl-itraconazole by CYP3A4, including competitive inhibition of CYP3A4 by the parent drug and all three metabolites, and glomerular filtration. Competitive inhibition of P-gp was included for itracaonazole.
+
+In comparison to the published version by Hanke *et al.* 2018 ([Hanke 2018](#5-references)), the dissolution and solubility has been optimized and updated for the administration of itraconazole capsules in fasted state (by integrating additional data ([Jalava 1997](#5-references)) into the optimization routine).
+
+# 2 Methods
+
+## 2.1 Modeling Strategy
+
+The general concept of building a PBPK model has previously been described by Kuepfer et al. ([Kuepfer 2016](#5-references)). Relevant information on anthropometric (height, weight) and physiological parameters (e.g. blood flows, organ volumes, binding protein concentrations, hematocrit, cardiac output) in adults was gathered from the literature and has been previously published ([Willmann 2007](#5-references)). The information was incorporated into PK-Sim® and was used as default values for the simulations in adults.
+
+The applied activity and variability of plasma proteins and active processes that are integrated into PK-Sim® are described in the publicly available PK-Sim® Ontogeny Database Version 7.3 ([PK-Sim Ontogeny Database Version 7.3](#5-references)) or otherwise referenced for the specific process.
+
+First, a mean model including sequential metabolism of itraconazole to hydroxy-itraconazole to keto-itraconazole to N-desalkyl-itraconazole by CYP3A4 was built using clinical data from single dose and multiple dose studies with intravenous and oral administration (solution, fasted state) of itraconazole. Hereby, competitive inhibition of CYP3A4 was considered for all four compounds. The mean PBPK model was developed using a typical European individual. The relative tissue-specific expressions of enzymes predominantly being involved in the metabolism of midazolam (CYP3A4) were considered.
+
+A specific set of parameters (see below) was optimized using the Parameter Identification module provided in PK-Sim®. Structural model selection was mainly guided by visual inspection of the resulting description of data and biological plausibility.
+
+Once the appropriate structural model was identified, additional parameters for different administration states (*solution fed*, *capsule fasted* and *capsule fed* versus the reference state *solution fasted*) were empirically optimized. Clinical data suggest that the bioavailability of itraconazole is enhanced when an oral solution is given in the fasted state compared to fed state ([Van de Welde 1996](#5-references), [Barone 1998a](#5-references)). In contrast, a meal significantly enhances the amount of itraconazole absorbed after administrations of capsules (in comparison to fasted state administrations of capsules) ([Barone 1993a](#5-references)). To reflect these observations, relevant parameters, in particular solubility and those describing dissolution kinetics (of capsules), were assumed to be variable between these four scenarios and were independently identified using the Parameter Identification module provided in PK-Sim®.
+
+Details about compound properties (physicochemical, *in vitro* and clinical) can be found in [Section 2.2](#22-data).
+
+Details about the structural model and its parameters can be found in [Section 2.3](#23-model-parameters-and-assumptions).
+
+## 2.2 Data
+
+### 2.2.1 In vitro and physicochemical data
+
+A literature search was performed to collect available information on physicochemical properties of itraconazole and metabolites. The obtained information from literature is summarized in the table below and was used for model building. Note that not all parameters were used in the final model. A list of final model parameters is provided below in later sections.
+
+#### Itraconazole
+
+| **Parameter** | **Unit** | **Value** | Source | **Description** |
+| :------------------------------------ | -------------------------- | -------------- | --------------------------------- | ------------------------------------------------------------ |
+| MW | g/mol | 705.633 | [DrugBank DB01167](#5-references) | Molecular weight |
+| pKa,base | | 3.7 | [Heykants 1989](#5-references) | acid dissociation constant of conjugate acid; compound type: base |
+| Solubility (pH) | mg/L | 8.0
(6.5) | [Taupitz 2013](#5-references) | Solubility in FaSSIF (fasted state simulated intestinal fluid) |
+| logP | | 5.66 | [Heykants 1989](#5-references) | Partition coefficient between octanol and water |
+| fu | % | 0.2 | [Heykants 1989](#5-references) | Fraction unbound in plasma |
+| | % | 0.2 | [Riccardi 2015](#5-references) | Fraction unbound in plasma |
+| | % | 1.58 | [Ishigam 2001](#5-references) | Fraction unbound in plasma |
+| | % | 3.6 | [Templeton 2008](#5-references) | Fraction unbound in plasma |
+| Vmax, Km CYP3A4 | pmol/min/nmol,
nmol/L | 270
3.9 | [Isoherranen 2004](#5-references) | CYP3A4 supersomes Michaelis-Menten kinetics |
+| Ki CYP3A4 | nmol/L | 1.3 | [Isoherranen 2004](#5-references) | CYP3A4 inhibition constant |
+| Ki P-gp | nmol/L | 8.0 | [Shityakov 2014](#5-references) | P-gp inhibition constant |
+
+#### Hydroxy-itraconazole
+
+| **Parameter** | **Unit** | **Value** | Source | **Description** |
+| :------------------------------------ | -------------------------- | ----------- | ------------------------------------ | ------------------------------------------------------------ |
+| MW | g/mol | 721.633 | [DrugBank DBMET00374](#5-references) | Molecular weight |
+| logP | | 4.5 | [PubChem CID 108222](#5-references) | Partition coefficient between octanol and water, computed by XLogP3 3.0 |
+| fu | % | 0.5 | [Templeton 2008](#5-references) | Fraction unbound in plasma |
+| | % | 1.7 | [Riccardi 2015](#5-references) | Fraction unbound in plasma |
+| | % | 2.12 | [Chen 2016](#5-references) | Fraction unbound in plasma |
+| Vmax, Km CYP3A4 | pmol/min/nmol,
nmol/L | 543
27 | [Isoherranen 2004](#5-references) | CYP3A4 supersomes Michaelis-Menten kinetics |
+| Ki CYP3A4 | nmol/L | 14.4 | [Isoherranen 2004](#5-references) | CYP3A4 inhibition constant |
+
+#### Keto-itraconazole
+
+| **Parameter** | **Unit** | **Value** | Source | **Description** |
+| :------------------------------------ | -------------------------- | ------------- | ------------------------------------- | ------------------------------------------------------------ |
+| MW | g/mol | 719.617 | [PubChem CID 53865186](#5-references) | Molecular weight |
+| logP | | 4.5 | [PubChem CID 53865186](#5-references) | Partition coefficient between octanol and water, computed by XLogP3 3.0 |
+| fu | % | 1.0 | [Riccardi 2015](#5-references) | Fraction unbound in plasma |
+| | % | 5.3 | [Templeton 2008](#5-references) | Fraction unbound in plasma |
+| Vmax, Km CYP3A4 | pmol/min/nmol,
nmol/L | 86.9
1.4 | [Isoherranen 2004](#5-references) | CYP3A4 supersomes Michaelis-Menten kinetics |
+| IC50 CYP3A4† | nmol/L | 7.0 | [Isoherranen 2004](#5-references) | CYP3A4 inhibition constant for half maximal inhibitory concentration at constant substrate concentration |
+
+† The IC50 values was converted to a Ki value via Cheng-Prusoff equation ([Chen 1973](#5-references)) with a substrate (*midazolam*) concentration of 1 µmol/L and an assumed substrate (*midazolam*) Km value of 2.73 µmol/L: **5.12 nmol/L**
+
+#### N-Desalkyl-itraconazole
+
+| **Parameter** | **Unit** | **Value** | Source | **Description** |
+| :--------------------------------- | -------- | --------- | ------------------------------------- | ------------------------------------------------------------ |
+| MW | g/mol | 649.527 | [PubChem CID 53789808](#5-references) | Molecular weight |
+| logP | | 4.2 | [PubChem CID 53789808](#5-references) | Partition coefficient between octanol and water, computed by XLogP3 3.0 |
+| fu | % | 1.1 | [Riccardi 2015](#5-references) | Fraction unbound in plasma |
+| | % | 1.2 | [Templeton 2008](#5-references) | Fraction unbound in plasma |
+| IC50 CYP3A4† | nmol/L | 0.44 | [Isoherranen 2004](#5-references) | CYP3A4 inhibition constant for half maximal inhibitory concentration at constant substrate concentration |
+
+† The IC50 values was converted to a Ki value via Cheng-Prusoff equation ([Chen 1973](#5-references)) with a substrate (*midazolam*) concentration of 1 µmol/L and an assumed substrate (*midazolam*) Km value of 2.73 µmol/L: **0.32 nmol/L**
+
+### 2.2.2 Clinical data
+
+A literature search was performed to collect available clinical data on itraconazole and its metabolites in adults. The itraconazole model was built and verified using various clinical studies, covering a dosing range of 100 to 200 mg with different formulations (solution *vs.* capsule), administered under fasting conditions or together with food.
+
+The following dosing senarios were simulated and compared to respective data:
+
+| Route | Formulation | Food state | Dose
[mg] | Dosing | PK Data | Used for† | Reference |
+| ----- | ----------- | ---------- | -------------- | ------ | ------------------------------------------------------------ | -------------------- | ---------------------------------- |
+| iv | - | - | 100 | SD | Itraconazole | mv | [Heykants 1989](#5-references) |
+| | | | 200 | OD | Itraconazole
Hydroxy-Itr. | mbb | [Mouton 2006](#5-references) |
+| po | solution | fasted | 100 | SD | Itraconazole | mbb | [Van de Velde 1996](#5-references) |
+| | | | | | Itraconazole
Hydroxy-Itr. | mbb | [Van Peer 1989](#5-references) |
+| | | | | OD | Itraconazole
Hydroxy-Itr.
Keto-Itr.
N-Desalkyl-Itr. | mbb | [Templeton 2008](#5-references) |
+| | | | 200 | OD | Itraconazole
Hydroxy-Itr. | mbb | [Barone 1998a](#5-references) |
+| | | fed | 100 | SD | Itraconazole
Hydroxy-Itr. | mbe | [Van de Velde 1996](#5-references) |
+| | | | | | Itraconazole | mbe | [Heykants 1989](#5-references) |
+| | | | 200 | SD | Itraconazole
Hydroxy-Itr. | mbe | [Barone 1998b](#5-references) |
+| | | | | OD | Itraconazole
Hydroxy-Itr. | mbe | [Barone 1998a](#5-references) |
+| | capsule | fasted | 100 | SD | Itraconazole | mbe | [Van Peer 1989](#5-references) |
+| | | | | BID | Itraconazole | mv | [Kivistö 1997](#5-references) |
+| | | | 200 | SD | Itraconazole
Hydroxy-Itr. | mbe | [Barone 1993](#5-references) |
+| | | | | | Itraconazole | mv | [Neuvonen 1996](#5-references) |
+| | | | | OD | Itraconazole | mbe | [Jalava 1997](#5-references) |
+| | | | | | Itraconazole | mbe | [Olkkola 1994](#5-references) |
+| | | | | | Itraconazole | mv | [Varhe 1994](#5-references) |
+| | | fed | 100 | SD | Itraconazole | mbe | [Van Peer 1989](#5-references) |
+| | | | | OD | Itraconazole | mbe | [Van Peer 1989](#5-references) |
+| | | | | | Itraconazole | mv | [Hardin 1988](#5-references) |
+| | | | 200 | SD | Itraconazole
Hydroxy-Itr. | mbe | [Barone 1993](#5-references) |
+| | | | | | Itraconazole
Hydroxy-Itr. | mbe | [Barone 1998b](#5-references) |
+| | | | | | Itraconazole | mv | [Neuvonen 1996](#5-references) |
+| | | | 200 | OD | Itraconazole | mv | [Hardin 1988](#5-references) |
+| | | | | BID | Itraconazole | mbe | [Barone 1993](#5-references) |
+| | | | | | Itraconazole
Hydroxy-Itr. | mv | [Hardin 1988](#5-references) |
+
+† *mbb* model building basic: used to inform the basic model parameters (see [Section 2.3.5](#235-automated-parameter-identification)); *mbe* model building extended: used to inform solubility and, if applicable, formulation-depenendent parameters only (see [Section 2.3.5](#235-automated-parameter-identification)); *mv* model verification only
+
+## 2.3 Model Parameters and Assumptions
+
+### 2.3.1 Absorption
+
+Clinical data suggest that the bioavailability of itraconazole is enhanced when an oral solution is given in the fasted state compared to fed state ([Van de Welde 1996](#5-references), [Barone 1998a](#5-references)). In contrast, a meal significantly enhances the amount of itraconazole absorbed after administrations of capsules (in comparison to fasted state administrations of capsules) ([Barone 1993a](#5-references)). Thus, four different scenarios can be identified: *solution fasted*, *solution fed*, *capsule fasted* and *capsule fed*. The *solution fasted* scenario was considered to be the reference scenario.
+
+Herein, the model parameter `Specific intestinal permeability` was optimized to best match clinical data (see [Section 2.3.5](#235-automated-parameter-identification)). The default solubility was assumed to be the measured value in FaSSIF (fasted state simulated intestinal fluid; see [Section 2.2.1](#221-in-vitro-and-physicochemical-data)).
+
+In a next step, the solubility was optimized for the *solution fed* scenario (in comparison to *solution fasted*).
+
+Then, for the scenarios *capsule fasted* and *capsule fed*, solubility and the dissolution rate of the capsules (implemented via an empirical Weibull dissolution) were optimized.
+
+The results of the optimization can be found in [Section 2.3.5](#235-automated-parameter-identification).
+
+### 2.3.2 Distribution
+
+Various values for the fraction unbound of itraconazole have been reported in literature, ranging from 0.2 to 3.6% (see [Section 2.2.1](#221-in-vitro-and-physicochemical-data)). For this model, the final value was optimized within this range to best match observed clinical data (see [Section 2.3.5](#235-automated-parameter-identification)). For the metabolites, the measured values reported by Riccardi *et al.* ([Riccardi 2015](#5-references)) were incorporated into the model. It was assumed that the major binding partner is albumin.
+
+No pKa values were reported for the three metabolites. Here, it was assumed that the metabolites are similar to the parent drug and the reported basic pKa value of 3.7 was applied (see also [Section 2.2.1](#221-in-vitro-and-physicochemical-data))
+
+An important parameter influencing the resulting volume of distribution is lipophilicty. The reported experimental or predicted logP values served as starting values for the four compounds. Finally, the model parameters `Lipophilicity` were optimized to match best clinical data (see also [Section 2.3.5](#235-automated-parameter-identification)).
+
+After testing the available organ-plasma partition coefficient and cell permeability calculation methods built in PK-Sim®, observed clinical data was best described by choosing the partition coefficient calculation by `Rodgers and Rowland` and cellular permeability calculation by `PK-Sim Standard ` for itraconazole and its metabolites.
+
+### 2.3.3 Metabolism and Elimination
+
+Metabolic pathways via CYP3A4 were implemented in the model via Michaelis-Menten kinetics for all four compounds. If available, *in vitro* determined unbound Km values ([Isoherranen 2004](#5-references)) served as starting values. Respective kcat values were optimized to best match clinical data (see also [Section 2.3.5](#235-automated-parameter-identification)).
+
+The CYP3A4 expression profile is based on high-sensitivity real-time RT-PCR ([Nishimura 2013](#5-references)). Absolute tissue-specific concentrations were obtained by considering the respective absolute concentration in the liver. The PK-Sim® Ontogeny Database Version 7.3 provides a default value for CYP3A4 reference concentration in the liver (compare [Rodrigues 1999](#5-references) and assume 40 mg protein per gram liver).
+
+Additionally, for all four compounds a renal clearance (assumed to be driven by glomerular filtration) was implemented.
+
+### 2.3.4 DDI Parameters
+
+The following subsections describe the model input for DDI-related parameters, i.e. inhibition of certain enzymes and transporters, for which itraconazole may act in a perpetrator role. Verification of these model parameters and linked processes in combination with sensitive CYP3A4 / P-gp substrates is not evaluated in this report. Applications are assessed in specific use cases and reported elsewhere. Note, however, that the competitive CYP3A4 inhibition of the four compounds results in inhibition of metabolite formation (of hydroxy-itraconazole, keto-itraconazole, N-desalkyl-itraconazole) and the metabolism of N-desalkyl-itraconazole. This effectively contributes to the PK non-linearity of itraconazole and its metabolites, especially after multiple doses.
+
+#### CYP3A4 inhibition
+*In vitro* determined unbound Ki values for itraconazole and hydroxy-itraconazole ([Isoherranen 2004](#5-references)) served directly as model input (see [Section 2.2.1](#221-in-vitro-and-physicochemical-data)).
+
+*In vitro* determined unbound IC50 values for keto-itraconazole and N-desalkyl-itraconazol ([Isoherranen 2004](#5-references)) were converted to Ki values via the Cheng-Prusoff equation ([Chen 1973](#5-references)) with a substrate (*midazolam*) concentration of 1 µmol/L and an assumed substrate (*midazolam*) Km value of 2.73 µmol/L (see [Section 2.2.1](#221-in-vitro-and-physicochemical-data)).
+
+#### P-gp inhibition
+An *in vitro* determined Ki values for itraconazole ([Shityakov 2014](#5-references)) served directly as model input (see [Section 2.2.1](#221-in-vitro-and-physicochemical-data)).
+
+### 2.3.5 Automated Parameter Identification
+
+This is the result of the final parameter identification for the basic model:
+
+| Compound | Model Parameter | Optimized Value | Unit |
+| --------------- | -------------------------------------------- | ------------------------------------------------------------ | --------- |
+| Itraconazole | `Lipophilicity` | 4.62 | Log Units |
+| | `Specific intestinal permeability` | 5.33E-05 | dm/min |
+| | `Fraction unbound (plasma, reference value)` | 0.6 | % |
+| | `Km` (CYP3A4) | 2.07 | nmol/L |
+| | `kcat` (CYP3A4) | 0.040 | 1/min |
+| Hydroxy-Itr. | `Lipophilicity` | 3.72 | Log Units |
+| | `Fraction unbound (plasma, reference value)` | 1.7 FIXED (see [Section 2.2.1](#221-in-vitro-and-physicochemical-data)) | % |
+| | `Km` (CYP3A4) | 4.17 | nmol/L |
+| | `kcat` (CYP3A4) | 0.020 | 1/min |
+| Keto-Itr. | `Lipophilicity` | 4.21 | Log Units |
+| | `Fraction unbound (plasma, reference value)` | 1.0 FIXED (see [Section 2.2.1](#221-in-vitro-and-physicochemical-data)) | % |
+| | `Km` (CYP3A4) | 2.22 | nmol/L |
+| | `kcat` (CYP3A4) | 0.393 | 1/min |
+| N-Desalkyl-Itr. | `Lipophilicity` | 5.18 | Log Units |
+| | `Fraction unbound (plasma, reference value)` | 1.1 FIXED (see [Section 2.2.1](#221-in-vitro-and-physicochemical-data)) | % |
+| | `Km` (CYP3A4) | 0.63 | nmol/L |
+| | `kcat` (CYP3A4) | 0.061 | 1/min |
+
+This is the result of the final parameter identification for the solubility and, in case of capsule administrations, the dissolution parameters of an empirical Weibull function according to the different administration scenarios of itraconazole:
+
+| Scenario | Model Parameter | Optimized Value | Unit |
+| --------------- | ---------------------------------- | ------------------------------------------------------------ | ---- |
+| Solution fasted | `Solubility at reference pH` | 8.0 FIXED (see [Section 2.2.1](#221-in-vitro-and-physicochemical-data)) | mg/L |
+| Solution fed | `Solubility at reference pH` | 1.58 | mg/L |
+| Capsule fasted | `Solubility at reference pH` | 0.97 | mg/L |
+| | `Dissolution time (50% dissolved)` | 406 | min |
+| | `Dissolution shape` | 1.43 | |
+| Capsule fed | `Solubility at reference pH` | 0.70 | mg/L |
+| | `Dissolution time (50% dissolved)` | 139 | min |
+| | `Dissolution shape` | 0.82 | |
+
+# 3 Results and Discussion
+
+The PBPK model for itraconazole was developed and verified with clinical pharmacokinetic data.
+
+The model was built and evaluated covering data from various clinical studies, covering a dosing range of 100 to 200 mg in different formulations (solution *vs.* capsule), administered under fasting conditions or together with food.
+
+The model quantifies metabolism via CYP3A4 and inhibition of CYP3A4.
+
+The next sections show:
+
+1. the final model input parameters for the building blocks: [Section 3.1](#31-final-input-parameters).
+2. the overall goodness of fit: [Section 3.2](#32-diagnostics-plots).
+3. simulated vs. observed concentration-time profiles for the clinical studies used for model building and for model verification: [Section 3.3](#33-concentration-time-profiles).
+
+## 3.1 Final input parameters
+
+The compound parameter values of the final PBPK model are illustrated below.
+
+### Compound: Itraconazole
+
+#### Parameters
+
+Name | Value | Value Origin | Alternative | Default
+------------------------------------------------ | ----------------------- | --------------------------------------------------------------------------------------------------------- | ------------------------------------- | -------
+Solubility at reference pH | 8 mg/l | Publication-Taupitz et al. 2013 | Solution fasted (Taupitz et al. 2013) | True
+Reference pH | 6.5 | Publication-Taupitz et al. 2013 | Solution fasted (Taupitz et al. 2013) | True
+Solubility at reference pH | 1.58 mg/l | | Solution fed | False
+Reference pH | 6.5 | | Solution fed | False
+Solubility at reference pH | 0.9728307177 mg/l | Parameter Identification-Parameter Identification-Value updated from 'Capsule fasted' on 2019-05-15 12:25 | Capsule fasted | False
+Reference pH | 6.5 | | Capsule fasted | False
+Solubility at reference pH | 0.7 mg/l | | Capsule fed | False
+Reference pH | 6.5 | | Capsule fed | False
+Lipophilicity | 4.624 Log Units | Parameter Identification-Fit | Fit | True
+Fraction unbound (plasma, reference value) | 0.6016197247 % | | Templeton, 2008 | True
+Specific intestinal permeability (transcellular) | 5.3261558344E-05 dm/min | Parameter Identification-Fit | Fit | True
+Cl | 2 | | |
+Is small molecule | Yes | | |
+Molecular weight | 705.633 g/mol | | |
+Plasma protein binding partner | Albumin | | |
+
+#### Calculation methods
+
+Name | Value
+----------------------- | -------------------
+Partition coefficients | Rodgers and Rowland
+Cellular permeabilities | PK-Sim Standard
+
+#### Processes
+
+##### Metabolizing Enzyme: CYP3A4-Isoherranen 2004
+
+Molecule: CYP3A4
+
+Metabolite: Hydroxy-Itraconazole
+
+###### Parameters
+
+Name | Value | Value Origin
+-------------------------------- | ------------------------------ | ----------------------------
+In vitro Vmax/recombinant enzyme | 0.27 pmol/min/pmol rec. enzyme | Publication-Isoherranen 2004
+Km | 2.0688492598 nmol/l | Publication-Isoherranen 2004
+kcat | 0.0402937875 1/min | Unknown
+
+##### Systemic Process: Glomerular Filtration-GFR
+
+Species: Human
+
+###### Parameters
+
+Name | Value | Value Origin
+------------ | -----:| ----------------------------
+GFR fraction | 1 | Publication-Isoherranen 2004
+
+##### Inhibition: CYP3A4-Isoherranen, 2004
+
+Molecule: CYP3A4
+
+###### Parameters
+
+Name | Value | Value Origin
+---- | ---------- | ------------------------------------------
+Ki | 1.3 nmol/l | Parameter Identification-Isoherranen, 2004
+
+##### Inhibition: ABCB1-Shityakov 2014
+
+Molecule: ABCB1
+
+###### Parameters
+
+Name | Value | Value Origin
+---- | ------------ | --------------------------
+Ki | 0.008 µmol/l | Publication-Shityakov 2014
+
+### Compound: Hydroxy-Itraconazole
+
+#### Parameters
+
+Name | Value | Value Origin | Alternative | Default
+------------------------------------------ | --------------- | --------------------------- | ------------------ | -------
+Solubility at reference pH | 1 mg/l | | No value available | True
+Reference pH | 7 | | No value available | True
+Lipophilicity | 3.718 Log Units | | Fit | True
+Fraction unbound (plasma, reference value) | 1.7 % | Publication-Templeton, 2008 | Templeton, 2008 | True
+Cl | 2 | | |
+Is small molecule | Yes | | |
+Molecular weight | 721.633 g/mol | | |
+Plasma protein binding partner | Albumin | | |
+
+#### Calculation methods
+
+Name | Value
+----------------------- | -------------------
+Partition coefficients | Rodgers and Rowland
+Cellular permeabilities | PK-Sim Standard
+
+#### Processes
+
+##### Metabolizing Enzyme: CYP3A4-Isoherranen 2004
+
+Molecule: CYP3A4
+
+Metabolite: Keto-Itraconazole
+
+###### Parameters
+
+Name | Value | Value Origin
+-------------------------------- | ------------------------------- | ----------------------------
+In vitro Vmax/recombinant enzyme | 0.543 nmol/min/pmol rec. enzyme | Publication-Isoherranen 2004
+Km | 4.1716224833 nmol/l | Publication-Isoherranen 2004
+kcat | 0.0203370845 1/min | Unknown
+
+##### Systemic Process: Glomerular Filtration-GFR
+
+Species: Human
+
+###### Parameters
+
+Name | Value | Value Origin
+------------ | -----:| -----------------------------
+GFR fraction | 1 | Publication-Isoherranen, 2004
+
+##### Inhibition: CYP3A4-Isoherranen, 2004
+
+Molecule: CYP3A4
+
+###### Parameters
+
+Name | Value | Value Origin
+---- | ----------- | -----------------------------
+Ki | 14.4 nmol/l | Publication-Isoherranen, 2004
+
+##### Inhibition: OATP1B1-Tuerk 2019
+
+Molecule: OATP1B1
+
+###### Parameters
+
+Name | Value | Value Origin
+---- | ------------------- | -------------------------------------------------
+Ki | 0.0177818488 µmol/l | Parameter Identification-Parameter Identification
+
+##### Inhibition: OATP1B3-Tuerk 2019
+
+Molecule: OATP1B3
+
+###### Parameters
+
+Name | Value | Value Origin
+---- | ------------------- | -------------------------------------------------
+Ki | 0.0111606334 µmol/l | Parameter Identification-Parameter Identification
+
+### Compound: Keto-Itraconazole
+
+#### Parameters
+
+Name | Value | Value Origin | Alternative | Default
+------------------------------------------ | ---------------------- | --------------------------- | ------------------ | -------
+Solubility at reference pH | 1 mg/l | | No value available | True
+Reference pH | 7 | | No value available | True
+Lipophilicity | 4.2109086248 Log Units | | Fit | True
+Fraction unbound (plasma, reference value) | 1 % | Publication-Templeton, 2008 | Templeton, 2008 | True
+Cl | 2 | | |
+Is small molecule | Yes | | |
+Molecular weight | 719.617 g/mol | | |
+Plasma protein binding partner | Albumin | | |
+
+#### Calculation methods
+
+Name | Value
+----------------------- | -------------------
+Partition coefficients | Rodgers and Rowland
+Cellular permeabilities | PK-Sim Standard
+
+#### Processes
+
+##### Metabolizing Enzyme: CYP3A4-Isoherranen 2004
+
+Molecule: CYP3A4
+
+Metabolite: N-desalkyl-Itraconazole
+
+###### Parameters
+
+Name | Value | Value Origin
+-------------------------------- | -------------------------------- | ----------------------------
+In vitro Vmax/recombinant enzyme | 0.0869 pmol/min/pmol rec. enzyme | Publication-Isoherranen 2004
+Km | 2.2214874285 nmol/l | Publication-Isoherranen 2004
+kcat | 0.3933927416 1/min | Unknown
+
+##### Systemic Process: Glomerular Filtration-GFR
+
+Species: Human
+
+###### Parameters
+
+Name | Value | Value Origin
+------------ | -----:| ----------------------------
+GFR fraction | 1 | Publication-Isoherranen 2004
+
+##### Inhibition: CYP3A4-Isoherranen, 2004
+
+Molecule: CYP3A4
+
+###### Parameters
+
+Name | Value | Value Origin
+---- | ----------- | ----------------------------
+Ki | 5.12 nmol/l | Publication-Isoherranen 2004
+
+### Compound: N-desalkyl-Itraconazole
+
+#### Parameters
+
+Name | Value | Value Origin | Alternative | Default
+------------------------------------------ | ---------------------- | --------------------------- | ------------------ | -------
+Solubility at reference pH | 1 mg/l | | No value available | True
+Reference pH | 7 | | No value available | True
+Lipophilicity | 5.1837535822 Log Units | | Fit | True
+Fraction unbound (plasma, reference value) | 1.1 % | Publication-Templeton, 2008 | Templeton, 2008 | True
+Cl | 2 | | |
+Is small molecule | Yes | | |
+Molecular weight | 649.527 g/mol | | |
+Plasma protein binding partner | Albumin | | |
+
+#### Calculation methods
+
+Name | Value
+----------------------- | -------------------
+Partition coefficients | Rodgers and Rowland
+Cellular permeabilities | PK-Sim Standard
+
+#### Processes
+
+##### Metabolizing Enzyme: CYP3A4-Isoherranen 2004
+
+Molecule: CYP3A4
+
+###### Parameters
+
+Name | Value | Value Origin
+-------------------------------- | --------------------------- | ----------------------------
+In vitro Vmax/recombinant enzyme | 0 nmol/min/pmol rec. enzyme |
+Km | 0.6284266369 nmol/l | Publication-Isoherranen 2004
+kcat | 0.0605873508 1/min | Unknown
+
+##### Systemic Process: Glomerular Filtration-GFR
+
+Species: Human
+
+###### Parameters
+
+Name | Value | Value Origin
+------------ | -----:| ----------------------------
+GFR fraction | 1 | Publication-Isoherranen 2004
+
+##### Inhibition: CYP3A4-Isoherranen, 2004
+
+Molecule: CYP3A4
+
+###### Parameters
+
+Name | Value | Value Origin
+---- | ----------- | -----------------------------
+Ki | 0.32 nmol/l | Publication-Isoherranen, 2004
+
+### Formulation: Capsule fasted
+
+Type: Weibull
+
+#### Parameters
+
+Name | Value | Value Origin
+-------------------------------- | ------------------ | ---------------------------------------------------------------------------------------------------------
+Dissolution time (50% dissolved) | 406.3001802552 min | Parameter Identification-Parameter Identification-Value updated from 'Capsule fasted' on 2019-05-15 12:25
+Lag time | 0 min |
+Dissolution shape | 1.4297720052 | Parameter Identification-Parameter Identification-Value updated from 'Capsule fasted' on 2019-05-15 12:25
+Use as suspension | Yes |
+
+### Formulation: Capsule fed
+
+Type: Weibull
+
+#### Parameters
+
+Name | Value | Value Origin
+-------------------------------- | ---------- | ------------:
+Dissolution time (50% dissolved) | 138.95 min |
+Lag time | 0 min |
+Dissolution shape | 0.82 |
+Use as suspension | Yes |
+
+## 3.2 Diagnostics Plots
+
+Below you find the goodness-of-fit visual diagnostic plots for the PBPK model performance of all data used presented in [Section 2.2.2](#222-clinical-data).
+
+The plot show observed versus simulated plasma concentration and second weighted residuals versus time for itraconazole, hydroxy-itraconazole, keto-itraconazole and N-desalkyl-itraconazole.
+
+
+
+**Table 3-1: GMFE for Itraconazole concentration in plasma**
+
+|Group |GMFE |
+|:-------------------------------|:----|
+|Itraconazole iv |1.29 |
+|Itraconazole po capsule fasted |1.74 |
+|Itraconazole po capsule fed |1.57 |
+|Itraconazole po solution fasted |1.54 |
+|Itraconazole po solution fed |1.46 |
+|All |1.54 |
+
+
+
+
+
+
+
+
+**Figure 3-1: Itraconazole concentration in plasma**
+
+
+
+
+
+
+
+
+**Figure 3-2: Itraconazole concentration in plasma**
+
+
+
+
+
+
+**Table 3-2: GMFE for Hydroxy-Itraconazole concentration in plasma**
+
+|Group |GMFE |
+|:-------------------------------|:----|
+|Itraconazole iv |1.21 |
+|Itraconazole po capsule fasted |1.68 |
+|Itraconazole po capsule fed |1.95 |
+|Itraconazole po solution fasted |1.38 |
+|Itraconazole po solution fed |1.48 |
+|All |1.50 |
+
+
+
+
+
+
+
+
+**Figure 3-3: Hydroxy-Itraconazole concentration in plasma**
+
+
+
+
+
+
+
+
+**Figure 3-4: Hydroxy-Itraconazole concentration in plasma**
+
+
+
+
+
+
+**Table 3-3: GMFE for Keto-Itraconazole concentration in plasma**
+
+|Group |GMFE |
+|:-------------------------------|:----|
+|Itraconazole po solution fasted |1.66 |
+
+
+
+
+
+
+
+
+**Figure 3-5: Keto-Itraconazole concentration in plasma**
+
+
+
+
+
+
+
+
+**Figure 3-6: Keto-Itraconazole concentration in plasma**
+
+
+
+
+
+
+**Table 3-4: GMFE for N-desalkyl-Itraconazole concentration in plasma**
+
+|Group |GMFE |
+|:-------------------------------|:----|
+|Itraconazole po solution fasted |1.53 |
+
+
+
+
+
+
+
+
+**Figure 3-7: N-desalkyl-Itraconazole concentration in plasma**
+
+
+
+
+
+
+
+
+**Figure 3-8: N-desalkyl-Itraconazole concentration in plasma**
+
+
+
+
+## 3.3 Concentration-Time Profiles
+
+Simulated versus observed concentration-time profiles of all data listed in [Section 2.2.2](#222-clinical-data) are presented below.
+
+
+
+
+
+**Figure 3-9: 100 mg IV SD - Plasma**
+
+
+
+
+
+
+
+
+**Figure 3-10: 200 mg IV MD - Plasma**
+
+
+
+
+
+
+
+
+**Figure 3-11: po 100 mg SD caps fast - Plasma**
+
+
+
+
+
+
+
+
+**Figure 3-12: po 200 mg MD OD caps fast - Plasma**
+
+
+
+
+
+
+
+
+**Figure 3-13: po 100 mg MD BID caps fast - Plasma**
+
+
+
+
+
+
+
+
+**Figure 3-14: po 200 mg SD caps fast - Plasma**
+
+
+
+
+
+
+
+
+**Figure 3-15: po 100 mg MD OD caps fed 1 - Plasma**
+
+
+
+
+
+
+
+
+**Figure 3-16: po 100 mg MD OD caps fed 2 - Plasma**
+
+
+
+
+
+
+
+
+**Figure 3-17: po 100 mg SD caps fed - Plasma**
+
+
+
+
+
+
+
+
+**Figure 3-18: po 200 mg MD BID caps fed 1 - Plasma**
+
+
+
+
+
+
+
+
+**Figure 3-19: po 200 mg MD BID caps fed 2 - Plasma**
+
+
+
+
+
+
+
+
+**Figure 3-20: po 200 mg MD OD caps fed - Plasma**
+
+
+
+
+
+
+
+
+**Figure 3-21: po 200 mg SD caps fed - Plasma**
+
+
+
+
+
+
+
+
+**Figure 3-22: po 200 mg SD caps fed 2 - Plasma**
+
+
+
+
+
+
+
+
+**Figure 3-23: po 100 mg MD OD sol fast - Plasma**
+
+
+
+
+
+
+
+
+**Figure 3-24: po 100 mg SD sol fast - Plasma**
+
+
+
+
+
+
+
+
+**Figure 3-25: po 200 mg MD OD sol fast - Plasma**
+
+
+
+
+
+
+
+
+**Figure 3-26: po 100 mg SD sol fed - Plasma**
+
+
+
+
+
+
+
+
+**Figure 3-27: po 200 mg MD OD sol fed - Plasma**
+
+
+
+
+
+
+
+
+**Figure 3-28: po 200 mg SD sol fed - Plasma**
+
+
+
+
+# 4 Conclusion
+
+The herein presented PBPK model adequately describes the pharmacokinetics of itraconazole in adults.
+
+In particular, the model includes the sequential metabolites hydroxy-itraconazole, keto-itraconazola and N-desalkyl-itraconazole - all of them formed by CYP3A4. The model applies competitive inhibition of CYP3A4 by itraconzaole and the three metabolites. Thus, the model is fit for purpose to be applied for the investigation of drug-drug interactions with regard to strong CYP3A4 inhibition.
+
+# 5 References
+
+**Barone 1993** Barone JA, Koh JG, Bierman RH, Colaizzi JL, Swanson KA, Gaffar MC, Moskovitz BL, Mechlinski W, Van de Velde V. Food interaction and steady-state pharmacokinetics of itraconazole capsules in healthy male volunteers. Antimicrob Agents Chemother. 1993 Apr;37(4):778-84.
+
+**Barone 1998a** Barone JA, Moskovitz BL, Guarnieri J, Hassell AE, Colaizzi JL, Bierman RH, Jessen L. Food interaction and steady-state pharmacokinetics of itraconazole oral solution in healthy volunteers. Pharmacotherapy. 1998 Mar-Apr;18(2):295-301.
+
+**Barone 1998b** Barone JA, Moskovitz BL, Guarnieri J, Hassell AE, Colaizzi JL, Bierman RH, Jessen L. Enhanced bioavailability of itraconazole in hydroxypropyl-beta-cyclodextrin solution versus capsules in healthy volunteers. Antimicrob Agents Chemother. 1998 Jul;42(7):1862-5.
+
+**Chen 2016** Chen Y, Ma F,, Lu T, Budha N, Jin JY, Kenny JR, Wong H,, Hop CE, Mao J. Development of a Physiologically Based Pharmacokinetic Model for Itraconazole Pharmacokinetics and Drug-Drug Interaction Prediction. Clin Pharmacokinet. 2016 Jun;55(6):735-49.
+
+**Cheng 1973** Cheng Y, Prusoff WH. Relationship between the inhibition constant (K1) and the concentration of inhibitor which causes 50 per cent inhibition (I50) of an enzymatic reaction. Biochem Pharmacol. 1973 Dec 1;22(23):3099-108.
+
+**DrugBank DB01167** (https://www.drugbank.ca/drugs/DB01167)
+
+**DrugBank DBMET00374** (https://www.drugbank.ca/metabolites/DBMET00374)
+
+**Hanke 2018** Hanke N, Frechen S, Moj D, Britz H, Eissing T, Wendl T, Lehr T. PBPK Models for CYP3A4 and P-gp DDI Prediction: A Modeling Network of Rifampicin, Itraconazole, Clarithromycin, Midazolam, Alfentanil, and Digoxin. CPT Pharmacometrics Syst Pharmacol. 2018 Oct;7(10):647-659.
+
+**Hardin 1988** Hardin TC, Graybill JR, Fetchick R, Woestenborghs R, Rinaldi MG, Kuhn JG. Pharmacokinetics of itraconazole following oral administration to normal volunteers. Antimicrob Agents Chemother. 1988 Sep;32(9):1310-3.
+
+**Heykants 1989** Heykants J, Van Peer A, Van de Velde V, Van Rooy P, Meuldermans W, Lavrijsen K, Woestenborghs R, Van Cutsem J, Cauwenbergh G. The clinical pharmacokinetics of itraconazole: an overview. Mycoses. 1989;32 Suppl 1:67-87.
+
+**Ishigam 2001** Ishigam M, Uchiyama M, Kondo T, Iwabuchi H, Inoue S, Takasaki W, Ikeda T, Komai T, Ito K, Sugiyama Y. Inhibition of in vitro metabolism of simvastatin by itraconazole in humans and prediction of in vivo drug-drug interactions. Pharm Res. 2001 May;18(5):622-31.
+
+**Isoherranen 2004** Isoherranen N, Kunze KL, Allen KE, Nelson WL, Thummel KE. Role of itraconazole metabolites in CYP3A4 inhibition. Drug Metab Dispos. 2004 Oct;32(10):1121-31.
+
+**Jalava 1997** Jalava KM, Partanen J, Neuvonen PJ. Itraconazole decreases renal clearance of digoxin. Ther Drug Monit. 1997 Dec;19(6):609-13.
+
+**Kivistö 1997** Kivistö KT, Lamberg TS, Kantola T, Neuvonen PJ. Plasma buspirone concentrations are greatly increased by erythromycin and itraconazole. Clin Pharmacol Ther. 1997 Sep;62(3):348-54.
+
+**Kuepfer 2016** Kuepfer L, Niederalt C, Wendl T, Schlender JF, Willmann S, Lippert J, Block M, Eissing T, Teutonico D. Applied Concepts in PBPK Modeling: How to Build a PBPK/PD Model.CPT Pharmacometrics Syst Pharmacol. 2016 Oct;5(10):516-531.
+
+**Mouton 2006** Mouton JW, van Peer A, de Beule K, Van Vliet A, Donnelly JP, Soons PA. Pharmacokinetics of itraconazole and hydroxyitraconazole in healthy subjects after single and multiple doses of a novel formulation. Antimicrob Agents Chemother. 2006 Dec;50(12):4096-102.
+
+**Neuvonen 1996** Neuvonen PJ, Varhe A, Olkkola KT. The effect of ingestion time interval on the interaction between itraconazole and triazolam. Clin Pharmacol Ther. 1996 Sep;60(3):326-31.
+
+**Nishimura 2013** Nishimura M, Yaguti H, Yoshitsugu H, Naito S, Satoh T. Tissue distribution of mRNA expression of human cytochrome P450 isoforms assessed by high-sensitivity real-time reverse transcription PCR. Yakugaku Zasshi. 2003 May;123(5):369-75.
+
+**Olkkola 1994** Olkkola KT, Backman JT, Neuvonen PJ. Midazolam should be avoided in patients receiving the systemic antimycotics ketoconazole or itraconazole. Clin Pharmacol Ther. 1994 May;55(5):481-5.
+
+**PK-Sim Ontogeny Database Version 7.3** (https://github.com/Open-Systems-Pharmacology/OSPSuite.Documentation/blob/38cf71b384cfc25cfa0ce4d2f3addfd32757e13b/PK-Sim%20Ontogeny%20Database%20Version%207.3.pdf)
+
+**PubChem CID 108222** (https://pubchem.ncbi.nlm.nih.gov/compound/Hydroxy-Itraconazole)
+
+**PubChem CID 53865186** (https://pubchem.ncbi.nlm.nih.gov/compound/Keto-Itraconazole)
+
+**PubChem CID 53789808** (https://pubchem.ncbi.nlm.nih.gov/compound/169437521#section=Computed-Properties)
+
+**Riccardi 2015** Riccardi K, Cawley S, Yates PD, Chang C, Funk C, Niosi M, Lin J, Di L. Plasma Protein Binding of Challenging Compounds. J Pharm Sci. 2015 Aug;104(8):2627-36.
+
+**Rodrigues 1999** Rodrigues AD. Integrated cytochrome P450 reaction phenotyping: attempting to bridge the gap between cDNA-expressed cytochromes P450 and native human liver microsomes. Biochem Pharmacol. 1999 Mar 1;57(5):465-80.
+
+**Shityakov 2014** Shityakov S, Förster C. In silico structure-based screening of versatile P-glycoprotein inhibitors using polynomial empirical scoring functions. Adv Appl Bioinform Chem. 2014 Mar 24;7:1-9.
+
+**Taupitz 2013** Taupitz T, Dressman JB, Buchanan CM, Klein S. Cyclodextrin-water soluble polymer ternary complexes enhance the solubility and dissolution behaviour of poorly soluble drugs. Case example: itraconazole. Eur J Pharm Biopharm. 2013 Apr;83(3):378-87.
+
+**Templeton 2008** Templeton IE, Thummel KE, Kharasch ED, Kunze KL, Hoffer C, Nelson WL, Isoherranen N. Contribution of itraconazole metabolites to inhibition of CYP3A4 in vivo. Clin Pharmacol Ther. 2008 Jan;83(1):77-85.
+
+**Van de Velde 1996** Van de Velde VJ, Van Peer AP, Heykants JJ, Woestenborghs RJ, Van Rooy P, De Beule KL, Cauwenbergh GF. Effect of food on the pharmacokinetics of a new hydroxypropyl-beta-cyclodextrin formulation of itraconazole. Pharmacotherapy. 1996 May-Jun;16(3):424-8.
+
+**Van Peer 1989** Van Peer A, Woestenborghs R, Heykants J, Gasparini R, Gauwenbergh G. The effects of food and dose on the oral systemic availability of itraconazole in healthy subjects. Eur J Clin Pharmacol. 1989;36(4):423-6.
+
+**Varhe 1994** Varhe A, Olkkola KT, Neuvonen PJ. Oral triazolam is potentially hazardous to patients receiving systemic antimycotics ketoconazole or itraconazole. Clin Pharmacol Ther. 1994 Dec;56(6 Pt 1):601-7.
+
+**Willmann 2007** Willmann S, Höhn K, Edginton A, Sevestre M, Solodenko J, Weiss W, Lippert J, Schmitt W. Development of a physiology-based whole-body population model for assessing the influence of individual variability on the pharmacokinetics of drugs. J Pharmacokinet Pharmacodyn. 2007, 34(3):401-31.
+
+","Markdown"
+"PBPK model","Open-Systems-Pharmacology/OSP-PBPK-Model-Library","Efavirenz/Efavirenz_evaluation_report.md",".md","37734","607","# Building and evaluation of a PBPK model for efavirenz in healthy adults
+
+| Version | 2.0-OSP12.2 |
+| ----------- | --------------------- |
+| Based on Model Snapshot and Evaluation Plan | https://github.com/Open-Systems-Pharmacology/Efavirenz-Model/releases/tag/v2.0 |
+| OSP Version | 12.2 |
+| Qualification Framework Version | 3.5 |
+
+This evaluation report and the corresponding PK-Sim project file are stored at:
+
+https://github.com/Open-Systems-Pharmacology/OSP-PBPK-Model-Library/
+
+# Table of Contents
+
+ * [1 Introduction](#introduction)
+ * [2 Methods](#methods)
+ * [2.1 Modeling Strategy](#modeling-strategy)
+ * [2.2 Data](#data)
+ * [2.3 Model Parameters and Assumptions](#model-parameters-and-assumptions)
+ * [3 Results and Discussion](#results-and-discussion)
+ * [3.1 Final input parameters](#final-input-parameters)
+ * [3.2 Diagnostics Plots](#diagnostics-plots)
+ * [3.3 Concentration-Time Profiles](#ct-profiles)
+ * [3.3.1 Model Building](#model-building)
+ * [3.3.2 Fitted interaction with Midazolam](#fitted-interaction-with-midazolam)
+ * [4 Conclusion](#conclusion)
+ * [5 References](#main-references)
+
+# 1 Introduction
+
+Efavirenz is a non-nucleoside reverse transcriptase inhibitor (NNRTI) and is an antiretroviral drug to treat HIV.
+
+Its major metabolizing enzyme is CYP2B6, but CYP3A4, CYP3A5, CYP1A2 and CYP2A6 also play a role ([Ward 2003](#5-references), [Ogburn 2010](#5-references)). CYP2B6 polymorphism is a major determinant of clinical efavirenz disposition and dose adjustment. Efavirenz activates the pregnane X receptor (PXR) and induces its target gene expression. As a consequence, some cytochrome P450 genes are upregulated, and, e.g. higher CYP3A4 ([Shou 2008](#5-references)) and CYP2B6 ([Ke 2016](#5-references)) activity levels can be measured.
+
+It has a long half-life ranging from 52 to 76 hours following single oral doses and 40 to 55 hours following long term administration as a result of auto-induction of efavirenz metabolism. The long plasma half-life allows for once daily administration with long term administration of a single 600 mg daily dose ([Smith 2001](#5-references)).
+
+The presented efavirenz model was established using clinical PK data of 7 publications covering a dose range from 200 to 600 mg after single and multiple oral administration.
+
+The herein presented model building and evaluation report evaluates the performance of the PBPK model for efavirenz in (healthy) adults.
+
+The established efavirenz PBPK model is verified for the use as a perpetrator drug in drug-drug interaction simulations.
+
+# 2 Methods
+
+## 2.1 Modeling Strategy
+
+The general concept of building a PBPK model has previously been described by e.g. Kuepfer et al. ([Kuepfer 2016](#5-references)). The relevant anthropometric (height, weight) and physiological information (e.g. blood flows, organ volumes, binding protein concentrations, hematocrit, cardiac output) in adults was gathered from the literature and has been previously published ([Willmann 2007](#5-references)). This information was incorporated into PK-Sim® and was used as default values for the simulations in adults.
+
+Variability of plasma proteins and CYP enzymes are integrated into PK-Sim® and described in the publicly available PK-Sim® Ontogeny Database Version 7.3 ([PK-Sim Ontogeny Database Version 7.3](#5-references)) or otherwise referenced for the specific process.
+
+First, a base mean model was built using clinical data including single and multiple dose studies with oral applications of efavirenz (Sustiva) to find an appropriate structure to describe the pharmacokinetics in plasma. The mean PBPK model was developed using a typical European individual adjusted to the demography of the respective study population. The relative tissue-specific expressions of enzymes predominantly being involved in the metabolism of efavirenz were derived from RT-PCR data from [Nishimura 2003](#5-references) and are implemented in the model as described previously ([Meyer 2012](#5-references)).
+
+Unknown parameters (see below) were identified using the Parameter Identification module provided in PK-Sim®. Structural model selection was mainly guided by visual inspection of the resulting description of data and biological plausibility.
+
+CYP3A4 plays only a minor role in efavirenz metabolism, and, therefore, auto-induction of CYP3A4 plays a minor role for efavirenz pharmacokinetics. Hence, to parameterize CYP3A4 induction, midazolam was used as victim substance to identify the respective model parameter `Emax` and `EC50` for induction. The respective parameter identification (please refer to [Section 2.3.4](#234-automated-parameter-identification)) was performed using the midazolam model [version 1.0](https://github.com/Open-Systems-Pharmacology/Midazolam-Model/releases/tag/v1.0).
+
+Details about input data (physicochemical, *in vitro* and clinical) can be found in [Section 2.2](#22-data).
+
+Details about the structural model and its parameters can be found in [Section 2.3](#23-model-parameters-and-assumptions).
+
+## 2.2 Data
+
+### 2.2.1 In vitro / physico-chemical Data
+
+A literature search was performed to collect available information on physiochemical properties of efavirenz. The obtained information from literature is summarized in the table below.
+
+| **Parameter** | **Unit** | **Value** | Source | **Description** |
+| :-------------- | -------- | --------------- | ------------------------------------------------------------ | ----------------------------------------------- |
+| MW | g/mol | 315.675 | https://www.drugbank.ca/ | Molecular weight |
+| pKa | 10.1 | (base) | [Rabel 1996](#5-references) | Acid dissociation constant |
+| Solubility (pH) | mg/L | 11.5 (6.4) | [Cristofoletti 2013](#5-references) | Water solubility |
+| logP | | 2.07, 4.6 | [Almond 2005](#5-references), https://www.drugbank.ca/ | Partition coefficient between octanol and water |
+| logD | | 5.1 | [Janneh 2009](#5-references) | Partition coefficient between octanol and buffer solution |
+| fu | | 0.006 [0.004 - 0.015] | [Almond 2005](#5-references) | Fraction unbound in plasma |
+| Emax (CYP3A4) | | 7.27, 3.15 (average 5.21) | [Shou 2008](#5-references) | Maximum induction effect |
+| EC50 (CYP3A4) | µmol/l | 12.5, 2.18 (average 7.34) | [Shou 2008](#5-references) | Concentration at half maximum induction |
+| Emax (CYP2B6) | | 5.1 | [Ke 2016](#5-references) | Maximum induction effect |
+| EC50 (CYP2B6) | µmol/l | 5.1 | [Ke 2016](#5-references) | Concentration at half maximum induction |
+
+### 2.2.2 Clinical Data
+
+A literature search was performed to collect available clinical data on efavirenz in healthy adults.
+
+#### 2.2.2.1 Model Building
+
+The following studies were used for model building:
+
+| Publication | Arm / Treatment / Information used for model building |
+| :--------------------------- | :----------------------------------------------------------- |
+| [Mouly 2002](#5-references) | Healthy subjects receiving a single oral dose of 200 and 400 mg |
+| [Ogburn 2013](#5-references) | Healthy subjects receiving a single oral dose of 600 mg |
+| [Xu 2013](#5-references) | Healthy subjects with different CYP2B6 genotypes receiving a single oral dose of 600 mg |
+| [Dooley 2012](#5-references) | Healthy subjects with different CYP2B6 genotypes receiving multiple doses of 600 mg |
+| [Garg 2013](#5-references) | Healthy subjects receiving multiple doses of 600 mg |
+| [Huang 2012](#5-references) | Healthy subjects receiving multiple doses of 600 mg |
+
+#### 2.2.2.2 Midazolam interaction studies used to parameterize CYP3A4 interaction
+
+The following studies were used for parameterization of CYP3A4 interaction:
+
+| Publication | Arm / Treatment / Information used for model building |
+| :-------------------------------- | :----------------------------------------------------------- |
+| [Mikus 2017](#5-references) | Healthy subjects receiving a single oral dose of 400 mg Efavirenz at t=0h, 4 mg midazolam at t=12h and a single intravenous dose of 2 mg midazolam at t=18h. |
+| [Katzenmaier 2010](#5-references) | Healthy subjects receiving multiple oral doses of 400 mg efavirenz QD. On day 14, subjects receive a single oral midazolam dose of 3 mg. |
+
+## 2.3 Model Parameters and Assumptions
+
+### 2.3.1 Absorption
+
+Absorption observed in clinical studies can be fully explained by passive absorption.
+
+### 2.3.2 Distribution
+
+After testing the available organ-plasma partition coefficient and cell permeability calculation methods built in PK-Sim, observed clinical data was best described by choosing the partition coefficient calculation by `Schmitt` and cellular permeability calculation by `PK-Sim Standard`.
+
+### 2.3.3 Metabolism, Elimination and Induction
+
+Efavirenz is metabolized by CYP2B6, CYP3A4, CYP3A5, CYP1A2 and CYP2A6.
+
+Induction of CYP3A4 ([Shou 2008](#5-references)) and CYP2B6 ([Ke 2016](#5-references)) was taken into account.
+
+### 2.3.4 Automated Parameter Identification
+
+The parameter identification tool in PK-Sim has been used to estimate selected model parameters by adjusting to PK data of the clinical studies that were used in the model building process. For some of the parameters, factors were optimized to maintain their ratio, e.g. a factor for the kcat clearances values for CYP2B6, CYP3A4, CYP3A5, CYP1A2 and CYP2A6 was optimized to keep the ratio constant.
+
+The is result of the final parameter identification is shown in the table below:
+
+| Model Parameter | Optimized Value | Unit |
+| -------------------------- | --------------- | ---- |
+| Lipophilicity | 3.437 | |
+| Specific intestinal permeability | 2.972E-5 | cm/min |
+| Solubility at reference pH | 39.922 | mg/l |
+| fraction unbound | 5.955E-3 | |
+| kcat CYP2B6 | 1.601 (factor: 0.31833 of literature reference) | 1/min |
+| kcat CYP3A4 | 0.051 (factor: 0.31833 of literature reference) | 1/min |
+| kcat CYP3A5 | 0.191 (factor: 0.31833 of literature reference) | 1/min |
+| kcat CYP1A2 | 0.191 (factor: 0.31833 of literature reference) | 1/min |
+| kcat CYP2A6 | 0.318 (factor: 0.31833 of literature reference) | 1/min |
+| EC50 CYP3A4 | 0.071 (factor: 0.009711of literature reference) | µmol/l |
+| EC50 CYP2B6 | 0.012 (factor: 0.009711of literature reference) | µmol/l |
+| Dissolution time (50% dissolved) | 60 | min |
+| Dissolution shape | 0.272 | |
+
+# 3 Results and Discussion
+
+The PBPK model for efavirenz was developed and evaluated using publicly available clinical pharmacokinetic data from studies listed in [Section 2.2.2](#222-clinical-data).
+
+The next sections show:
+
+1. the final model parameters for the building blocks: [Section 3.1](#31-final-input-parameters).
+2. the overall goodness of fit: [Section 3.2](#32-diagnostics-plots).
+3. simulated vs. observed concentration-time profiles for the clinical studies used for model building and for model verification: [Section 3.3](#33-concentration-time-profiles).
+
+## 3.1 Final input parameters
+
+The compound parameter values of the final PBPK model are illustrated below.
+
+### Compound: Efavirenz
+
+#### Parameters
+
+Name | Value | Value Origin | Alternative | Default
+------------------------------------------------ | ----------------------- | ---------------------------------------------------------------------------------------------------------------------------- | ----------- | -------
+Solubility at reference pH | 39.9217804729 mg/l | Parameter Identification-Parameter Identification-Value updated from 'Parameter Identification 7 (Mida)' on 2019-10-11 09:02 | Measurement | True
+Reference pH | 0 | | Measurement | True
+Lipophilicity | 3.4369753585 Log Units | Parameter Identification-Parameter Identification-Value updated from 'Parameter Identification 7 (Mida)' on 2019-10-11 09:02 | Optimized | True
+Fraction unbound (plasma, reference value) | 0.0059553692487 | Parameter Identification-Parameter Identification-Value updated from 'Parameter Identification 7 (Mida)' on 2019-10-11 09:02 | Measurement | True
+Specific intestinal permeability (transcellular) | 2.9720579005E-05 cm/min | Parameter Identification-Parameter Identification-Value updated from 'Parameter Identification 7 (Mida)' on 2019-10-11 09:02 | Optimized | True
+Cl | 1 | | |
+F | 3 | | |
+Is small molecule | Yes | | |
+Molecular weight | 315.675 g/mol | | |
+Plasma protein binding partner | Albumin | | |
+
+#### Calculation methods
+
+Name | Value
+----------------------- | ---------------
+Partition coefficients | Schmitt
+Cellular permeabilities | PK-Sim Standard
+
+#### Processes
+
+##### Metabolizing Enzyme: CYP2B6-Ward2003
+
+Molecule: CYP2B6
+
+Metabolite: 8-OH efavirenz
+
+###### Parameters
+
+Name | Value | Value Origin
+-------------------------------- | ----------------------------- | ----------------------------------------------------------------------------------------------------------------------------
+In vitro Vmax/recombinant enzyme | 3.5 pmol/min/pmol rec. enzyme |
+Km | 6.4 µmol/l |
+kcat | 1.601451904 1/min | Parameter Identification-Parameter Identification-Value updated from 'Parameter Identification 7 (Mida)' on 2019-10-11 09:02
+
+##### Metabolizing Enzyme: CYP1A2-Ward2003
+
+Molecule: CYP1A2
+
+Metabolite: 8-OH efavirenz
+
+###### Parameters
+
+Name | Value | Value Origin
+-------------------------------- | ----------------------------- | ----------------------------------------------------------------------------------------------------------------------------
+In vitro Vmax/recombinant enzyme | 0.6 pmol/min/pmol rec. enzyme |
+Km | 8.3 µmol/l |
+kcat | 0.1910198104 1/min | Parameter Identification-Parameter Identification-Value updated from 'Parameter Identification 7 (Mida)' on 2019-10-11 09:02
+
+##### Metabolizing Enzyme: CYP3A4-Ward2003
+
+Molecule: CYP3A4
+
+Metabolite: 8-OH efavirenz
+
+###### Parameters
+
+Name | Value | Value Origin
+-------------------------------- | ------------------------------ | ----------------------------------------------------------------------------------------------------------------------------
+In vitro Vmax/recombinant enzyme | 0.16 pmol/min/pmol rec. enzyme |
+Km | 23.5 µmol/l |
+kcat | 0.0509386161 1/min | Parameter Identification-Parameter Identification-Value updated from 'Parameter Identification 7 (Mida)' on 2019-10-11 09:02
+
+##### Metabolizing Enzyme: CYP3A5-Ward2003
+
+Molecule: CYP3A5
+
+Metabolite: 8-OH efavirenz
+
+###### Parameters
+
+Name | Value | Value Origin
+-------------------------------- | ----------------------------- | ----------------------------------------------------------------------------------------------------------------------------
+In vitro Vmax/recombinant enzyme | 0.6 pmol/min/pmol rec. enzyme |
+Km | 19.1 µmol/l |
+kcat | 0.1910198104 1/min | Parameter Identification-Parameter Identification-Value updated from 'Parameter Identification 7 (Mida)' on 2019-10-11 09:02
+
+##### Metabolizing Enzyme: CYP2A6-Ogburn2010
+
+Molecule: CYP2A6
+
+Metabolite: 8-OH efavirenz
+
+###### Parameters
+
+Name | Value | Value Origin
+-------------------------------- | --------------------------- | ----------------------------------------------------------------------------------------------------------------------------
+In vitro Vmax/recombinant enzyme | 1 pmol/min/pmol rec. enzyme |
+Km | 7.7 µmol/l |
+kcat | 0.3183663507 1/min | Parameter Identification-Parameter Identification-Value updated from 'Parameter Identification 7 (Mida)' on 2019-10-11 09:02
+
+##### Metabolizing Enzyme: CYP2B6-CYP2B6*1/*6
+
+Molecule: CYP2B6
+
+Metabolite: 8-OH efavirenz
+
+###### Parameters
+
+Name | Value | Value Origin
+-------------------------------- | ---------------------------------- | ------------:
+In vitro Vmax/recombinant enzyme | 2.268966 pmol/min/pmol rec. enzyme |
+Km | 6.4 µmol/l |
+
+##### Metabolizing Enzyme: CYP2B6-CYP2B6*6/*6
+
+Molecule: CYP2B6
+
+Metabolite: 8-OH efavirenz
+
+###### Parameters
+
+Name | Value | Value Origin
+-------------------------------- | ---------------------------------- | ------------:
+In vitro Vmax/recombinant enzyme | 1.448276 pmol/min/pmol rec. enzyme |
+Km | 6.4 µmol/l |
+
+##### Induction: CYP3A4-Shou2008
+
+Molecule: CYP3A4
+
+###### Parameters
+
+Name | Value | Value Origin
+---- | ------------------ | ----------------------------------------------------------------------------------------------------------------------------
+EC50 | 0.071279975 µmol/l | Parameter Identification-Parameter Identification-Value updated from 'Parameter Identification 7 (Mida)' on 2019-10-11 09:02
+Emax | 5.21 | Parameter Identification-Parameter Identification-Value updated from 'Parameter Identification 7 (Mida)' on 2019-10-11 09:02
+
+##### Induction: CYP2B6-Ke2016
+
+Molecule: CYP2B6
+
+###### Parameters
+
+Name | Value | Value Origin
+---- | ------------------- | ----------------------------------------------------------------------------------------------------------------------------
+EC50 | 0.0116534019 µmol/l | Parameter Identification-Parameter Identification-Value updated from 'Parameter Identification 7 (Mida)' on 2019-10-11 09:02
+Emax | 5.2 | Parameter Identification-Parameter Identification-Value updated from 'Parameter Identification 7 (Mida)' on 2019-10-11 09:02
+
+##### Systemic Process: Glomerular Filtration-GFR
+
+Species: Human
+
+###### Parameters
+
+Name | Value | Value Origin
+------------ | -----:| ------------:
+GFR fraction | 1 |
+
+### Formulation: Sustiva
+
+Type: Weibull
+
+#### Parameters
+
+Name | Value | Value Origin
+-------------------------------- | ------------ | ----------------------------------------------------------------------------------------------------------------------------
+Dissolution time (50% dissolved) | 60 min | Parameter Identification-Parameter Identification-Value updated from 'Parameter Identification 7 (Mida)' on 2019-10-11 09:02
+Lag time | 0 min |
+Dissolution shape | 0.2720936819 | Parameter Identification-Parameter Identification-Value updated from 'Parameter Identification 7 (Mida)' on 2019-10-11 09:02
+Use as suspension | Yes |
+
+## 3.2 Diagnostics Plots
+
+Below you find the goodness-of-fit visual diagnostic plots for the PBPK model performance of all data used presented in [Section 2.2.2](#222-clinical-data).
+
+The first plot shows simulated versus observed plasma concentration, the second weighted residuals versus time.
+
+
+
+**Table 3-1: GMFE for Goodness of fit plot for concentration in plasma**
+
+|Group |GMFE |
+|:----------------------------|:----|
+|First dose administration |1.47 |
+|Multiple dose administration |1.40 |
+|All |1.45 |
+
+
+
+
+
+
+
+
+**Figure 3-1: Goodness of fit plot for concentration in plasma**
+
+
+
+
+
+
+
+
+**Figure 3-2: Goodness of fit plot for concentration in plasma**
+
+
+
+
+## 3.3 Concentration-Time Profiles
+
+Simulated versus observed concentration-time profiles of all data listed in [Section 2.2.2](#222-clinical-data) are presented below.
+
+### 3.3.1 Model Building
+
+
+
+
+
+**Figure 3-3: Ogburn 2010, 600mg SD**
+
+
+
+
+
+
+
+
+**Figure 3-4: Xu 2013, 600mg SD EM**
+
+
+
+
+
+
+
+
+**Figure 3-5: Xu 2013, 600mg SD IM**
+
+
+
+
+
+
+
+
+**Figure 3-6: Xu 2013, 600mg SD PM**
+
+
+
+
+
+
+
+
+**Figure 3-7: Mouly 2002, 200mg MD**
+
+
+
+
+
+
+
+
+**Figure 3-8: Mouly 2002, 400mg MD**
+
+
+
+
+
+
+
+
+**Figure 3-9: Liu 2008, 400 mg PO OD**
+
+
+
+
+
+
+
+
+**Figure 3-10: Dooley 2012, 600mg MD EM**
+
+
+
+
+
+
+
+
+**Figure 3-11: Dooley 2012, 600mg MD EM**
+
+
+
+
+
+
+
+
+**Figure 3-12: Dooley 2012, 600mg MD IM**
+
+
+
+
+
+
+
+
+**Figure 3-13: Dooley 2012, 600mg MD PM**
+
+
+
+
+
+
+
+
+**Figure 3-14: Huang 2012, 600mg MD**
+
+
+
+
+
+
+
+
+**Figure 3-15: Garg 2013, 600mg MD**
+
+
+
+
+
+
+
+
+**Figure 3-16: Malvestutto 2014, 600mg PO OD**
+
+
+
+
+
+
+
+
+**Figure 3-17: Damle 2008, 600 mg PO OD**
+
+
+
+
+
+
+
+
+**Figure 3-18: Soon 2010, 600mg MD over 28 days**
+
+
+
+
+### 3.3.2 Fitted interaction with Midazolam
+
+
+
+
+
+**Figure 3-19: Mikus 2017, Midazolam alone**
+
+
+
+
+
+
+
+
+**Figure 3-20: Mikus 2017, Midazolam + Efavirenz 400mg SD**
+
+
+
+
+
+
+
+
+**Figure 3-21: Katzenmaier 2010, Midazolam alone**
+
+
+
+
+
+
+
+
+**Figure 3-22: Katzenmaier 2010, Midazolam + Efavirenz 400mg MD**
+
+
+
+
+# 4 Conclusion
+
+The herein presented PBPK model adequately describes the pharmacokinetics of efavirenz after single and multiple oral administration of various doses to healthy adults.
+
+Apart from drug-drug interaction parameters, all optimized parameters are in a close range to the measured or calculated values. EC50 values for CYP3A4 and CYP2B6 were reduced approximately 100-fold in order to reach a relevant induction.
+
+In conclusion, the presented efavirenz PBPK model is well-suited to be applied in drug-drug-interaction scenarios.
+
+# 5 References
+
+**Almond 2005** Almond LM, Hoggard PG, Edirisinghe D, Khoo SH, Back DJ. Intracellular and plasma pharmacokinetics of efavirenz in HIV-infected individuals. J Antimicrob Chemother. 2005 Oct;56(4):738-44. Epub 2005 Sep 1. PubMed PMID: 16141277.
+
+**Cristofoletti 2013** Cristofoletti R, Nair A, Abrahamsson B, Groot DW, Kopp S, Langguth P, Polli JE, Shah VP, Dressman JB. Biowaiver monographs for immediate release solid oral dosage forms: efavirenz. J Pharm Sci. 2013 Feb;102(2):318-29. doi: 10.1002/jps.23380. Epub 2012 Nov 22. Review. PubMed PMID: 23175470.
+
+**Dooley 2012** Dooley KE, Park JG, Swindells S, Allen R, Haas DW, Cramer Y, Aweeka F, Wiggins I, Gupta A, Lizak P, Qasba S, van Heeswijk R, Flexner C; ACTG 5267 Study Team. Safety, tolerability, and pharmacokinetic interactions of the antituberculous agent TMC207 (bedaquiline) with efavirenz in healthy volunteers: AIDS Clinical Trials Group Study A5267. J Acquir Immune Defic Syndr. 2012 Apr 15;59(5):455-62. doi: 10.1097/QAI.0b013e3182410503. PubMed PMID: 22126739; PubMed Central PMCID: PMC3302922.
+
+**DrugBank DB00625** (https://www.drugbank.ca/drugs/DB00625), accessed 05-15-2020.
+
+**Garg 2013** Garg V, Chandorkar G, Yang Y, Adda N, McNair L, Alves K, Smith F, van Heeswijk RP. The effect of CYP3A inhibitors and inducers on the pharmacokinetics of telaprevir in healthy volunteers. Br J Clin Pharmacol. 2013 Feb;75(2):431-9. doi: 10.1111/j.1365-2125.2012.04345.x. PubMed PMID: 22642697; PubMed Central PMCID: PMC3579258.
+
+**Hanke 2018** Hanke N, Frechen S, Moj D, Britz H, Eissing T, Wendl T, Lehr T. PBPK Models for CYP3A4 and P-gp DDI Prediction: A Modeling Network of Rifampicin, Itraconazole, Clarithromycin, Midazolam, Alfentanil, and Digoxin. CPT Pharmacometrics Syst Pharmacol. 2018 Oct;7(10):647-659. doi: 10.1002/psp4.12343. Epub 2018 Sep 7.
+
+**Huang 2012** Huang L, Parikh S, Rosenthal PJ, Lizak P, Marzan F, Dorsey G, Havlir D, Aweeka FT. Concomitant efavirenz reduces pharmacokinetic exposure to the antimalarial drug artemether-lumefantrine in healthy volunteers. J Acquir Immune Defic Syndr. 2012 Nov 1;61(3):310-6. doi: 10.1097/QAI.0b013e31826ebb5c. PubMed PMID: 22918158; PubMed Central PMCID: PMC3511816.
+
+**Janneh 2009** Janneh O, Chandler B, Hartkoorn R, Kwan WS, Jenkinson C, Evans S, Back DJ, Owen A, Khoo SH. Intracellular accumulation of efavirenz and nevirapine is independent of P-glycoprotein activity in cultured CD4 T cells and primary human lymphocytes. J Antimicrob Chemother. 2009 Nov;64(5):1002-7. doi: 10.1093/jac/dkp335. Epub 2009 Sep 11. PubMed PMID: 19748977.
+
+**Katzenmaier 2010** Katzenmaier S, Markert C, Mikus G. Proposal of a new limited sampling strategy to predict CYP3A activity using a partial AUC of midazolam. Eur J Clin Pharmacol. 2010 Nov;66(11):1137-41. doi: 10.1007/s00228-010-0878-2. Epub 2010 Aug 3. PubMed PMID: 20680253.
+
+**Ke 2016** Ke A, Barter Z, Rowland-Yeo K, Almond L. Towards a Best Practice Approach in PBPK Modeling: Case Example of Developing a Unified Efavirenz Model Accounting for Induction of CYPs 3A4 and 2B6. CPT Pharmacometrics Syst Pharmacol. 2016 Jul;5(7):367-76. doi: 10.1002/psp4.12088. Epub 2016 Jul 20. PubMed PMID: 27435752; PubMed Central PMCID: PMC4961080.
+
+**Kharasch 2012** Kharasch ED, Whittington D, Ensign D, Hoffer C, Bedynek PS, Campbell S, Stubbert K, Crafford A, London A, Kim T. Mechanism of efavirenz influence on methadone pharmacokinetics and pharmacodynamics. Clin Pharmacol Ther. 2012 Apr;91(4):673-84. doi: 10.1038/clpt.2011.276. Epub 2012 Mar 7.
+
+**Kuepfer 2016** Kuepfer L, Niederalt C, Wendl T, Schlender JF, Willmann S, Lippert J, Block M, Eissing T, Teutonico D. Applied Concepts in PBPK Modeling: How to Build a PBPK/PD Model. CPT Pharmacometrics Syst Pharmacol. 2016 Oct;5(10):516-531. doi: 10.1002/psp4.12134. Epub 2016 Oct 19.
+
+**Meyer 2012** Meyer M, Schneckener S, Ludewig B, Kuepfer L, Lippert J. Using expression data for quantification of active processes in physiologically based pharmacokinetic modeling. Drug Metab Dispos. 2012 May;40(5):892-901.
+
+**Mikus 2017** Mikus G, Heinrich T, Bödigheimer J, Röder C, Matthee AK, Weiss J, Burhenne J, Haefeli WE. Semisimultaneous Midazolam Administration to Evaluate the Time Course of CYP3A Activation by a Single Oral Dose of Efavirenz. J Clin Pharmacol. 2017 Jul;57(7):899-905. doi: 10.1002/jcph.879. Epub 2017 Feb 14. PubMed PMID: 28194792.
+
+**Mouly 2002** Mouly S, Lown KS, Kornhauser D, Joseph JL, Fiske WD, Benedek IH, Watkins PB. Hepatic but not intestinal CYP3A4 displays dose-dependent induction by efavirenz in humans. Clin Pharmacol Ther. 2002 Jul;72(1):1-9. PubMed PMID: 12151999.
+
+**Nishimura 2003** Nishimura, M., Yaguti, H., Yoshitsugu, H., Naito, S. & Satoh, T. Tissue distribution of mRNA expression of human cytochrome P450 isoforms assessed by high-sensitivity real-time reverse transcription PCR. J. Pharm. Soc. Japan 123, 369–75 (2003).
+
+**Ogburn 2010** Ogburn ET, Jones DR, Masters AR, Xu C, Guo Y, Desta Z. Efavirenz primary and secondary metabolism in vitro and in vivo: identification of novel metabolic pathways and cytochrome P450 2A6 as the principal catalyst of efavirenz 7-hydroxylation. Drug Metab Dispos. 2010 Jul;38(7):1218-29. doi: 10.1124/dmd.109.031393. Epub 2010 Mar 24. PubMed PMID: 20335270; PubMed Central PMCID: PMC2908985.
+
+**PK-Sim Ontogeny Database Version 7.3** (https://github.com/Open-Systems-Pharmacology/OSPSuite.Documentation/blob/38cf71b384cfc25cfa0ce4d2f3addfd32757e13b/PK-Sim%20Ontogeny%20Database%20Version%207.3.pdf)
+
+**Rabel 1996** Rabel SR, Maurin MB, Rowe SM, Hussain M. Determination of the pKa and pH-solubility behavior of an ionizable cyclic carbamate, (S)-6-chloro-4-(cyclopropylethynyl)-1,4-dihydro-4-
+(trifluoromethyl)-2H-3,1-benzoxazin-2-one (DMP 266). Pharm Dev Technol. 1996 Apr;1(1):91-5. PubMed PMID: 9552335.
+
+**Shou 2008** Shou M, Hayashi M, Pan Y, Xu Y, Morrissey K, Xu L, Skiles GL. Modeling, prediction, and in vitro in vivo correlation of CYP3A4 induction. Drug Metab Dispos. 2008 Nov;36(11):2355-70. doi: 0.1124/dmd.108.020602. Epub 2008 Jul 31. PubMed PMID: 18669588.
+
+**Ward 2003** Ward BA, Gorski JC, Jones DR, Hall SD, Flockhart DA, Desta Z. The cytochrome P450 2B6 (CYP2B6) is the main catalyst of efavirenz primary and secondary metabolism: implication for HIV/AIDS therapy and utility of efavirenz as a substrate marker of CYP2B6 catalytic activity. J Pharmacol Exp Ther. 2003 Jul;306(1):287-300. Epub 2003 Apr 3. PubMed PMID: 12676886.
+
+**Willmann 2007** Willmann S, Höhn K, Edginton A, Sevestre M, Solodenko J, Weiss W, Lippert J, Schmitt W. Development of a physiology-based whole-body population model for assessing the influence of individual variability on the pharmacokinetics of drugs. J Pharmacokinet Pharmacodyn. 2007, 34(3): 401-431.
+
+**Xu 2013** Xu C, Quinney SK, Guo Y, Hall SD, Li L, Desta Z. CYP2B6 pharmacogenetics-based in vitro-in vivo extrapolation of efavirenz clearance by physiologically based pharmacokinetic modeling. Drug Metab Dispos. 2013 Dec;41(12):2004-11. doi: 10.1124/dmd.113.051755. Epub 2013 Jul 11. PubMed PMID: 23846872; PubMed Central PMCID: PMC3834132.
+
+","Markdown"
+"PBPK model","Open-Systems-Pharmacology/OSP-PBPK-Model-Library","Dapagliflozin/Dapagliflozin_evaluation_report.md",".md","45023","830","# Building and Evaluation of a PBPK Model for Dapagliflozin in Adults
+
+| Version | 2.0-OSP12.2 |
+| ----------------------------------------------- | ------------------------------------------------------------ |
+| based on *Model Snapshot* and *Evaluation Plan* | https://github.com/Open-Systems-Pharmacology/Dapagliflozin-Model/releases/tag/v2.0 |
+| OSP Version | 12.2 |
+| Qualification Framework Version | 3.5 |
+
+This evaluation report and the corresponding PK-Sim project file are filed at:
+
+https://github.com/Open-Systems-Pharmacology/OSP-PBPK-Model-Library/
+
+# Table of Contents
+
+ * [1 Introduction](#introduction)
+ * [2 Methods](#methods)
+ * [2.1 Modeling Strategy](#modeling-strategy)
+ * [2.2 Data](#data)
+ * [2.3 Model Parameters and Assumptions](#model-parameters-and-assumptions)
+ * [3 Results and Discussion](#results-and-discussion)
+ * [3.1 Final input parameters](#final-input-parameters)
+ * [3.2 Diagnostics Plots](#diagnostics-plots)
+ * [3.3 Concentration-Time Profiles](#ct-profiles)
+ * [3.3.1 Model Building](#model-building)
+ * [3.3.2 Model Verification](#model-verification)
+ * [3.3.3 Overview](#ct-profiles-overview)
+ * [4 Conclusion](#conclusion)
+ * [5 References](#main-references)
+
+# 1 Introduction
+
+Dapagliflozin is an active, highly selective sodium-glucose transport protein 2 (SGLT2) inhibitor that improves glycemic control in patients with type 2 diabetes mellitus by reducing renal glucose reabsorption leading to urinary glucose excretion (glucuresis). It is administered orally.
+
+Dapagliflozin is predominantly metabolized by uridine diphosphate-glucuronosyltransferase 1A9 (UGT1A9) in the liver and kidneys to the major metabolite dapagliflozin 3-O-glucuronide and can be considered a sensitive substrate for characterization of UGT1A9 activity. In a clinical drug interaction study, co-administration of mefenamic acid with dapagliflozin resulted in a dapagliflozin AUC ratio of 1.51 and Cmax ratio of 1.13 ([Kasichayanula 2013a](#5-references)).
+
+Using published clinical data, the objective is to establish a whole-body PBPK model for dapagliflozin with a quantitative representation of its UGT1A9 metabolism.
+
+The herein presented model building and evaluation report evaluates the performance of the PBPK model for dapagliflozin in (healthy) adults.
+
+# 2 Methods
+
+## 2.1 Modeling Strategy
+
+The general concept of building a PBPK model has previously been described by Kuepfer *et al.* ([Kuepfer 2016](#5-references)). Relevant information on anthropometric (height, weight) and physiological parameters (e.g. blood flows, organ volumes, binding protein concentrations, hematocrit, cardiac output) in adults was gathered from the literature and has been previously published ([PK-Sim Ontogeny Database Version 7.3](#5-references)). The information was incorporated into PK-Sim® and was used as default values for the simulations in adults.
+
+The applied activity and variability of plasma proteins and active processes that are integrated into PK-Sim® are described in the publicly available PK-Sim® Ontogeny Database Version 7.3 ([Schlender 2016](#5-references)) or otherwise referenced for the specific process.
+
+First, a base mean model was built using clinical Phase I data including selected single dose studies with intravenous and oral applications (capsule) of dapagliflozin to find an appropriate structure to describe the pharmacokinetics in plasma. The mean PBPK model was developed using a typical European individual. The relative tissue-specific expressions of enzymes predominantly being involved in the metabolism of dapagliflozin (UGT1A9 and UGT2B7) were considered based on high-sensitive real-time RT-PCR ([Nishimura 2013](#5-references)). Absolute tissue-specific expressions were obtained by considering the respective absolute concentration in the liver as reported by Ohtsuki *et al.* ([Ohtsuki 2012](#5-references)).
+
+Unknown parameters (see below) were identified using the Parameter Identification module provided in PK-Sim®. Structural model selection was mainly guided by visual inspection of the resulting description of data and biological plausibility.
+
+Once the appropriate structural model was identified, additional parameters for tablet formulations were identified.
+
+The model was then verified by simulating:
+
+- multiple dose studies
+- a food effect study
+
+Details about input data (physicochemical, *in vitro* and clinical) can be found in [Section 2.2](#22-data).
+
+Details about the structural model and its parameters can be found in [Section 2.3](#23-model-parameters-and-assumptions).
+
+## 2.2 Data
+
+### 2.2.1 In vitro / physicochemical Data
+
+A literature search was performed to collect available information on physicochemical properties of dapagliflozin. The obtained information from literature is summarized in the table below.
+
+| **Parameter** | **Unit** | **Value** | Source | **Description** |
+| :-------------- | -------- | --------- | ------------------------------------------------ | ----------------------------------------------- |
+| MW | g/mol | 408.873 | [DrugBank DB06292](#5-references) | Molecular weight |
+| pKa | | 12.57 | [DrugBank DB06292](#5-references) | Acid dissociation constant |
+| Solubility (pH) | mg/mL | 0.173 (7) | [DrugBank DB06292](#5-references) | Aqueous Solubility |
+| logP | | 2.7 | [DrugBank DB06292](#5-references) (experimental) | Partition coefficient between octanol and water |
+| fu | % | 9 | [Obermeier 2009](#5-references) | Fraction unbound in plasma |
+| B/P ratio | | 0.88 | [Obermeier 2009](#5-references) | Blood to plasma ratio |
+
+### 2.2.2 Clinical Data
+
+A literature search was performed to collect available clinical data on dapagliflozin in healthy adults.
+
+#### 2.2.2.1 Model Building
+
+The following studies were used for model building (training data):
+
+| Publication | Arm / Treatment / Information used for model building |
+| :----------------------------------------------------------- | :----------------------------------------------------------- |
+| [Boulton 2013](#5-references) | 14C-dapagliflozin intravenous and
Dapagliflozin oral administration |
+| [DeFronzo 2013](#5-references) | Healthy subjects with a single oral dose of 10 mg |
+| [Imamura 2013](#5-references) | Control phase with a single oral dose of 10 mg |
+| [Kasichayanula 2008](#5-references) | Mass balance information |
+| [Kasichayanula 2011a](#5-references) | Fasted, single oral dose of 10 mg |
+| [Kasichayanula 2011b](#5-references) | Control phases of study 1, 2 and 3
(single oral doses of 20 mg or 50 mg) |
+| [Kasichayanula 2011c](#5-references) | Healthy subjects with a single oral dose of 10 mg |
+| [Kasichayanula 2012](#5-references) | Control phase with a single oral dose of 20 mg |
+| [Kasichayanula 2013a](#5-references) | Control phases of study 1 and 2
(single oral doses of 10 mg) |
+| [Kasichayanula 2013b](#5-references) | Healthy subjects with normal kidney function
with a single oral dose of 50 mg |
+| [Komoroski 2009](#5-references) and
[FDA Clinical Pharmacology Review for NDA 202293](#5-references) | SAD (single ascending dose) 2.5 to 500 mg (fasted)
MAD (multiple ascending dose) 2.5 to 100 mg (**day 1 data only**) |
+| [Vakkalagadda 2016](#5-references) | Dapagliflozin only (single oral dose 10 mg) |
+
+Kasichayanula *et al.* ([Kasichayanula 2008](#5-references)) investigated the mass balance of dapagliflozin in healthy subjects after a single oral dose of 50 mg. The following table gives an overview of the results:
+
+| Output | reported | normalized** |
+| -------------------------- | -------- | ------------ |
+| Total recovery after 312 h | 96.15% | |
+| Urine | 75.16% | |
+| - unchanged | 1.20% | 1.23% |
+| - as metabolites | 72.00% | 73.93% |
+| Feces | 20.99% | |
+| - unchanged | 15.40% | 18.90% |
+| - as metabolites | 1.70% | 2.09% |
+
+** to sum up to total excretion of urine and feces, respectively.
+
+The metabolic pattern was determined as shown in the following table.
+
+| Output | reported | normalized** | add fraction excretion to feces of unchanged dapagliflozin to glucuronides*** |
+| ----------------------------------- | -------- | ------------ | ------------------------------------------------------------ |
+| Dapagliflozin-3-O-glucuronide | 60.70% | 61.44% | 78.80% |
+| Dapagliflozin-2-O-glucuronide | 5.40% | 5.47% | 7.01% |
+| Dapagliflozin oxidative metabolites | 9.00% | 9.11% | 9.11% |
+| **SUM** | | **76.01%** | **94.92%** |
+
+** to sum up to the values of metabolic quantifications from the table above (73.93% + 2.09%)
+
+*** The fraction excretion to feces of unchanged dapagliflozin of 18.90% (see above) was added and distributed proportionally to dapagliflozin-3-O-glucuronide and dapagliflozin-2-O-glucuronide under the assumption that the measured fraction of unchanged dapagliflozin resulted from originally glucuronidated metabolites that underwent biliary excretion and subsequent degradation to dapagliflozin by bacterial glucuronidases in feces.
+
+The following table shows the final mass balance data used for model building under the assumption of that unchanged dapagliflozin molecules in feces were originally glucuronides. Please refer to [Section 2.3](#23-model-parameters-and-assumptions) for rationale.
+
+| Observer | Value |
+| ------------------------------------------------------------ | ---------- |
+| Fraction excreted to urine of unchanged dapagliflozin | 1.23% |
+| Fraction metabolized UGT1A9 (to dapagliflozin-3-O-glucuronide) | 78.80% |
+| Fraction metabolized UGT2B7 (to dapagliflozin-2-O-glucuronide) | 7.01% |
+| Fraction metabolized to oxidative metabolites | 9.11% |
+| **SUM** | **96.15%** |
+
+#### 2.2.2.2 Model Verification
+
+The following studies were used for model verification:
+
+| Publication | Arm / Treatment / Information used for model verification |
+| :----------------------------------------------------------- | :----------------------------------------------------------- |
+| [Chang 2015](#5-references) | Study 1 Treatment A (single oral dose of 5 mg as IC (individual component) tablet) and
Study 2 Treatment A (single oral dose of 10 mg as IC tablet) |
+| [Komoroski 2009](#5-references) and
[FDA Clinical Pharmacology Review for NDA 202293](#5-references) | MAD (multiple ascending dose) 2.5 to 100 mg (day 7 and 14) |
+| [Komoroski 2009](#5-references) | Single oral dose 250 mg (fed) |
+
+## 2.3 Model Parameters and Assumptions
+
+### 2.3.1 Absorption
+
+Studies including oral applications of dapagliflozin used for model building applied either a capsule or immediate release tablets. They all demonstrated rapid and extensive absorption. The availability of dense data during absorption, data covering a broad range of doses (from 2.5 up to 500 mg), and intravenous pharmacokinetic data ([Boulton 2013](#5-references)) allowed the identification of the *in vivo* intestinal permeability and an effective *in vivo* solubility in this PBPK model (see also [Section 2.3.4](#234-automated-parameter-identification)).
+
+During model building, two different ""data scenarios"" regarding mass balance information were tested:
+
+**Scenario 1**: The measured fraction excreted to feces as unchanged drug of approx. 19% resulted from incomplete absorption (assuming fa ~ 0.81).
+
+**Scenario 2**: The measured fraction excretion to feces of unchanged dapagliflozin resulted from originally glucuronidated metabolites that underwent biliary excretion and subsequent degradation to dapagliflozin by bacterial glucuronidases in feces (assuming fa ~ 1). The cleavage of hepatobiliary secreted glucuronides to the aglycone (e.g. parent drug) by beta-glucuronidases in the colon was reported previously ([Blaut 2013](#5-references), [Molly 1993](#5-references), [Possemiers 2004](#5-references), [Sakamoto 2002](#5-references)).
+
+Scenario 1 did not allow to find a good description of the pharmacokinetic data. Thus, scenario 2 was used during further model building. Note that this increased the fraction metabolized via UGT1A9 and UGT2B7.
+
+The dissolution of the tablets from Chang *et al.* ([Chang 2015](#5-references)) - referenced as individual component (IC) tablets - were implemented via an empirical Weibull dissolution tablet. The respective parameters were identified via manual sensitivity analysis.
+
+### 2.3.2 Distribution
+
+Dapagliflozin is moderately protein bound (91 %) in plasma ([Kasichayanula 2014](#5-references)). This value was used in this PBPK model. It was assumed that the major binding partner is albumin.
+
+An important parameter influencing the resulting volume of distribution is lipophilicty. The reported experimental logP value of 2.7 ([DrugBank DB06292](#5-references)) served as a starting value. Finally, the model parameters `Lipophilicity` and `logP (veg.oil/water)` were optimized to match best clinical data (see also [Section 2.3.4](#234-automated-parameter-identification)).
+
+After testing the available organ-plasma partition coefficient and cell permeability calculation methods built in PK-Sim, observed clinical data was best described by choosing the partition coefficient calculation by `Rodgers and Rowland` and cellular permeability calculation by `PK-Sim Standard`. The specific organ permeability was also optimized to match best clinical data (see also [Section 2.3.4](#234-automated-parameter-identification)).
+
+The reported blood to plasma ratio of 0.88 ([Obermeier 2009](#5-references)) was fixed in the model.
+
+### 2.3.3 Metabolism and Elimination
+
+As previously described in [Section 2.2.2](#222-clinical-data), mass balance data ([Kasichayanula 2008](#5-references), [Obermeier 2009](#5-references), [Kasichayanula 2014](#5-references)) indicated that UGT1A9 is predominatly responsible for the metabolism of dapagliflozin to dapagliflozin-3-O-glucuronide. A minor fraction is metabolized via UGT2B7 to dapagliflozin-2-O-glucuronide and via oxidative cytochrome-P450 enzymes.
+
+In summary, three metabolic first order routes were implement into the model:
+
+* UGT1A9-specific clearance
+* UGT2B7-specific clearance
+* an unspecific hepatic oxidative clearance (""Hepatic-CYP"")
+ (The hypothetical lumped Hepatic-CYP enzyme was assumed to be expressed only in the liver with a reference concentration of 1 µmol/L.)
+
+Additionally, a renal clearance (assumed to be mainly driven by glomerular filtration) was implemented.
+
+This clearance and excretion pathways were quantified during parameter optimization to best match clinical data (see also [Section 2.2.2](#222-clinical-data), [Section 2.3.1](#231-absorption), and [Section 2.3.4](#234-automated-parameter-identification)).
+
+### 2.3.4 Automated Parameter Identification
+
+This is the result of the final parameter identification.
+
+| Model Parameter | Optimized Value | Unit |
+| ---------------------------------- | --------------- | ---------- |
+| `Lipophilicity` | 2.672 | Log Units |
+| `logP (veg.oil/water)` | 2.083 | Log Units |
+| `Permeability` | 3.75E-04 | cm/min |
+| `Specific intestinal permeability` | 3.97E-05 | cm/min |
+| `Solubility at reference pH` | 0.221 | mg/ml |
+| `CLspec/[Enzyme]` (UGT1A9) | 0.399 | l/µmol/min |
+| `CLspec/[Enzyme]` (UGT2B7) | 6.60E-03 | l/µmol/min |
+| `CLspec/[Enzyme]` (Hepatic-CYP) | 0.143 | l/µmol/min |
+| `GFR fraction` | 0.79 | |
+| `Blood/Plasma concentration ratio` | 0.88 FIXED | |
+
+# 3 Results and Discussion
+
+The PBPK model for dapagliflozin was developed and verified with clinical pharmacokinetic data.
+
+The model was evaluated covering data from studies including in particular
+
+* intravenous and oral administrations.
+* single and multiple doses.
+* a dose range of 2.5 to 500 mg.
+* fasted and fed state administrations.
+
+The model quantifies metabolism via UGT1A9 and UGT2B7.
+
+The next sections show:
+
+1. the final model parameters for the building blocks: [Section 3.1](#31-final-input-parameters).
+2. the overall goodness of fit: [Section 3.2](#32-diagnostics-plots).
+3. simulated vs. observed concentration-time profiles for the clinical studies used for model building and for model verification: [Section 3.3](#33-concentration-time-profiles).
+
+## 3.1 Final input parameters
+
+The compound parameter values of the final PBPK model are illustrated below.
+
+### Compound: Dapagliflozin
+
+#### Parameters
+
+Name | Value | Value Origin | Alternative | Default
+------------------------------------------------ | ----------------------- | ---------------------------------------------------------------------------------------------------------- | ---------------- | -------
+Solubility at reference pH | 0.2210041453 mg/ml | Parameter Identification-Parameter Identification-Value updated from 'PI full (perm)' on 2019-08-23 15:34 | Water solubility | True
+Reference pH | 7 | Database-DrugBank DB06292 | Water solubility | True
+Lipophilicity | 2.6719093089 Log Units | Parameter Identification-Parameter Identification-Value updated from 'PI full (perm)' on 2019-08-23 15:34 | Optimized | True
+Fraction unbound (plasma, reference value) | 0.09 | Publication-Kasichayanula et al. 2014 | Human | True
+Permeability | 0.00037527645658 cm/min | Parameter Identification-Parameter Identification-Value updated from 'PI full (perm)' on 2019-08-23 15:34 | Optimized | True
+Specific intestinal permeability (transcellular) | 3.9684694792E-05 cm/min | Parameter Identification-Parameter Identification-Value updated from 'PI full (perm)' on 2019-08-23 15:34 | Optimized | True
+Cl | 1 | | |
+Is small molecule | Yes | | |
+Molecular weight | 408.873 g/mol | | |
+Plasma protein binding partner | Albumin | | |
+
+#### Calculation methods
+
+Name | Value
+----------------------- | -------------------
+Partition coefficients | Rodgers and Rowland
+Cellular permeabilities | PK-Sim Standard
+
+#### Processes
+
+##### Metabolizing Enzyme: UGT1A9-Optimized
+
+Molecule: UGT1A9
+
+Metabolite: Dapagliflozin-3-O-glucuronide
+
+###### Parameters
+
+Name | Value | Value Origin
+-------------------- | ---------------------- | ----------------------------------------------------------------------------------------------------------
+Enzyme concentration | 1 µmol/l |
+Specific clearance | 0 1/min |
+CLspec/[Enzyme] | 0.399443557 l/µmol/min | Parameter Identification-Parameter Identification-Value updated from 'PI full (perm)' on 2019-08-23 15:34
+
+##### Metabolizing Enzyme: UGT2B7-Optimized
+
+Molecule: UGT2B7
+
+Metabolite: Dapagliflozin-2-O-glucuronide
+
+###### Parameters
+
+Name | Value | Value Origin
+-------------------- | -------------------------- | ----------------------------------------------------------------------------------------------------------
+Enzyme concentration | 1 µmol/l |
+Specific clearance | 0 1/min |
+CLspec/[Enzyme] | 0.0066043366201 l/µmol/min | Parameter Identification-Parameter Identification-Value updated from 'PI full (perm)' on 2019-08-23 15:34
+
+##### Systemic Process: Glomerular Filtration-assumed
+
+Species: Human
+
+###### Parameters
+
+Name | Value | Value Origin
+------------ | ------------:| ----------------------------------------------------------------------------------------------------------
+GFR fraction | 0.7899801465 | Parameter Identification-Parameter Identification-Value updated from 'PI full (perm)' on 2019-08-23 15:34
+
+##### Metabolizing Enzyme: Hepatic-CYP-Optimized
+
+Molecule: Hepatic-CYP
+
+###### Parameters
+
+Name | Value | Value Origin
+-------------------- | ----------------------- | ----------------------------------------------------------------------------------------------------------
+Enzyme concentration | 1 µmol/l |
+Specific clearance | 0 1/min |
+CLspec/[Enzyme] | 0.1432967727 l/µmol/min | Parameter Identification-Parameter Identification-Value updated from 'PI full (perm)' on 2019-08-23 15:34
+
+### Formulation: IC tablet (Chang 2015)
+
+Type: Weibull
+
+#### Parameters
+
+Name | Value | Value Origin
+-------------------------------- | ------ | ------------:
+Dissolution time (50% dissolved) | 30 min |
+Lag time | 0 min |
+Dissolution shape | 0.6 |
+Use as suspension | Yes |
+
+### Formulation: Dissolved
+
+Type: Dissolved
+
+## 3.2 Diagnostics Plots
+
+Below you find the goodness-of-fit visual diagnostic plots for the PBPK model performance of all data used presented in [Section 2.2.2](#222-clinical-data).
+
+The first plot shows simulated versus observed plasma concentrations, the second weighted residuals versus time.
+
+
+
+**Table 3-1: GMFE for Goodness of fit plot for concentration in plasma**
+
+|Group |GMFE |
+|:-----------------------------|:----|
+|IV (model building) |1.38 |
+|PO MD |1.22 |
+|PO SD fasted (model building) |1.20 |
+|PO SD fed |1.43 |
+|PO SD IC tablets (Chang 2015) |1.27 |
+|All |1.22 |
+
+
+
+
+
+
+
+
+**Figure 3-1: Goodness of fit plot for concentration in plasma**
+
+
+
+
+
+
+
+
+**Figure 3-2: Goodness of fit plot for concentration in plasma**
+
+
+
+
+## 3.3 Concentration-Time Profiles
+
+Simulated versus observed concentration-time profiles of all data listed in [Section 2.2.2](#222-clinical-data) are presented below.
+
+### 3.3.1 Model Building
+
+
+
+
+
+**Figure 3-3: IV 0.08 mg (perm)**
+
+
+
+
+
+
+
+
+**Figure 3-4: PO SD 2.5 mg (perm)**
+
+
+
+
+
+
+
+
+**Figure 3-5: PO SD 2.5 mg (perm)**
+
+
+
+
+
+
+
+
+**Figure 3-6: PO SD 2.5 mg (perm) (urinary excretion)**
+
+
+
+
+
+
+
+
+**Figure 3-7: PO SD 5 mg (perm)**
+
+
+
+
+
+
+
+
+**Figure 3-8: PO SD 5 mg (perm)**
+
+
+
+
+
+
+
+
+**Figure 3-9: PO SD 5 mg (perm) (urinary excretion)**
+
+
+
+
+
+
+
+
+**Figure 3-10: PO SD 10 mg (perm)**
+
+
+
+
+
+
+
+
+**Figure 3-11: PO SD 10 mg (perm)**
+
+
+
+
+
+
+
+
+**Figure 3-12: PO SD 10 mg (perm) (urinary excretion)**
+
+
+
+
+
+
+
+
+**Figure 3-13: PO SD 20 mg (perm)**
+
+
+
+
+
+
+
+
+**Figure 3-14: PO SD 20 mg (perm)**
+
+
+
+
+
+
+
+
+**Figure 3-15: PO SD 2.5 mg (perm) (urinary excretion)**
+
+
+
+
+
+
+
+
+**Figure 3-16: PO SD 50 mg (perm)**
+
+
+
+
+
+
+
+
+**Figure 3-17: PO SD 50 mg (perm)**
+
+
+
+
+
+
+
+
+**Figure 3-18: PO SD 50 mg (perm) (fraction excreted/metabolized)**
+
+
+
+
+
+
+
+
+**Figure 3-19: PO SD 100 mg (perm)**
+
+
+
+
+
+
+
+
+**Figure 3-20: PO SD 100 mg (perm)**
+
+
+
+
+
+
+
+
+**Figure 3-21: PO SD 100 mg (perm) (urinary excretion)**
+
+
+
+
+
+
+
+
+**Figure 3-22: PO SD 250 mg (perm)**
+
+
+
+
+
+
+
+
+**Figure 3-23: PO SD 250 mg (perm)**
+
+
+
+
+
+
+
+
+**Figure 3-24: PO SD 250 mg (perm) (urinary excretion)**
+
+
+
+
+
+
+
+
+**Figure 3-25: PO SD 500 mg (perm)**
+
+
+
+
+
+
+
+
+**Figure 3-26: PO SD 500 mg (perm)**
+
+
+
+
+
+
+
+
+**Figure 3-27: PO SD 500 mg (perm) (urinary excretion)**
+
+
+
+
+### 3.3.2 Model Verification
+
+
+
+
+
+**Figure 3-28: PO SD 5 mg IR tablet (perm)**
+
+
+
+
+
+
+
+
+**Figure 3-29: PO SD 5 mg IR tablet (perm)**
+
+
+
+
+
+
+
+
+**Figure 3-30: PO SD 10 mg IR tablet (perm)**
+
+
+
+
+
+
+
+
+**Figure 3-31: PO SD 10 mg IR tablet (perm)**
+
+
+
+
+
+
+
+
+**Figure 3-32: PO SD 250 mg fed (perm) (log)**
+
+
+
+
+
+
+
+
+**Figure 3-33: PO SD 250 mg fed (perm)**
+
+
+
+
+
+
+
+
+**Figure 3-34: PO MD 2.5 mg (perm)**
+
+
+
+
+
+
+
+
+**Figure 3-35: PO MD 2.5 mg (perm)**
+
+
+
+
+
+
+
+
+**Figure 3-36: PO MD 10 mg (perm)**
+
+
+
+
+
+
+
+
+**Figure 3-37: PO MD 10 mg (perm)**
+
+
+
+
+
+
+
+
+**Figure 3-38: PO MD 20 mg (perm)**
+
+
+
+
+
+
+
+
+**Figure 3-39: PO MD 20 mg (perm)**
+
+
+
+
+
+
+
+
+**Figure 3-40: PO MD 20 mg (perm)**
+
+
+
+
+
+
+
+
+**Figure 3-41: PO MD 50 mg (perm)**
+
+
+
+
+
+
+
+
+**Figure 3-42: PO MD 100 mg (perm)**
+
+
+
+
+
+
+
+
+**Figure 3-43: PO MD 100 mg (perm)**
+
+
+
+
+### 3.3.3 Overview
+
+Overview of the multiple ascending dose study stratified by dose and day ([Komoroski 2009](#5-references), [FDA Clinical Pharmacology Review for NDA 202293](#5-references)).
+
+
+
+
+
+**Figure 3-44: Multiple Dose Escalation Study (day 1)**
+
+
+
+
+
+
+
+
+**Figure 3-45: Multiple Dose Escalation Study (day 7)**
+
+
+
+
+
+
+
+
+**Figure 3-46: Multiple Dose Escalation Study (day 14)**
+
+
+
+
+# 4 Conclusion
+
+The herein presented PBPK model adequately describes the pharmacokinetics of dapagliflozin in adults.
+
+In particular, it applies quantitative metabolism by UGT1A9 and UGT2B7. Thus, the model is fit for purpose to be applied for the investigation of drug-drug interactions with regard to its UGT metabolism.
+
+# 5 References
+
+**Blaut 2013** Blaut, M., Ecology and physiology of the intestinal tract. Curr Top Microbiol Immunol, 2013. 358: p. 247-72.
+
+**Boulton 2013** Boulton DW, Kasichayanula S, Keung CF, Arnold ME, Christopher LJ, Xu XS, Lacreta F. Simultaneous oral therapeutic and intravenous 14C-microdoses to determine the absolute oral bioavailability of saxagliptin and dapagliflozin. Br J Clin Pharmacol. 2013 Mar;75(3):763-8. doi: 10.1111/j.1365-2125.2012.04391.x.
+
+**Chang 2015** Chang M, Liu X, Cui D, Liang D, LaCreta F, Griffen SC, Lubin S, Quamina-Edghill D, Boulton DW. Bioequivalence, Food Effect, and Steady-State Assessment of Dapagliflozin/Metformin Extended-release Fixed-dose Combination Tablets Relative to Single-component Dapagliflozin and Metformin Extended-release Tablets in Healthy Subjects. Clin Ther. 2015 Jul 1;37(7):1517-28. doi: 10.1016/j.clinthera.2015.05.004.
+
+**DeFronzo 2013** DeFronzo RA, Hompesch M, Kasichayanula S, Liu X, Hong Y, Pfister M, Morrow LA, Leslie BR, Boulton DW, Ching A, LaCreta FP, Griffen SC. Characterization of renal glucose reabsorption in response to dapagliflozin in healthy subjects and subjects with type 2 diabetes. Diabetes Care. 2013 Oct;36(10):3169-76. doi: 10.2337/dc13-0387.
+
+**DrugBank DB06292** (https://www.drugbank.ca/drugs/DB06292)
+
+**FDA Clinical Pharmacology Review for NDA 202293** (https://www.accessdata.fda.gov/drugsatfda_docs/nda/2014/202293Orig1s000ClinPharmR.pdf)
+
+**Imamura 2013** Imamura A, Kusunoki M, Ueda S, Hayashi N, Imai Y. Impact of voglibose on the pharmacokinetics of dapagliflozin in Japanese patients with type 2 diabetes. Diabetes Ther. 2013 Jun;4(1):41-9. doi: 10.1007/s13300-012-0016-5.
+
+**Kasichayanula 2008** Kasichayanula S, Yao M, Vachharajani M, et al. Disposition and Mass Balance of [14C]-dapagliflozin after single oral dose in healthy male volunteers. AAPS J. 2008;10(S2).
+
+**Kasichayanula 2011a** Kasichayanula S, Liu X, Zhang W, Pfister M, Reele SB, Aubry AF, LaCreta FP, Boulton DW. Effect of a high-fat meal on the pharmacokinetics of dapagliflozin, a selective SGLT2 inhibitor, in healthy subjects. Diabetes Obes Metab. 2011 Aug;13(8):770-3. doi: 10.1111/j.1463-1326.2011.01397.x.
+
+**Kasichayanula 2011b** Kasichayanula S, Liu X, Shyu WC, Zhang W, Pfister M, Griffen SC, Li T, LaCreta FP, Boulton DW. Lack of pharmacokinetic interaction between dapagliflozin, a novel sodium-glucose transporter 2 inhibitor, and metformin, pioglitazone, glimepiride or sitagliptin in healthy subjects. Diabetes Obes Metab. 2011 Jan;13(1):47-54. doi: 10.1111/j.1463-1326.2010.01314.x.
+
+**Kasichayanula 2011c** Kasichayanula S, Liu X, Zhang W, Pfister M, LaCreta FP, Boulton DW. Influence of hepatic impairment on the pharmacokinetics and safety profile of dapagliflozin: an open-label, parallel-group, single-dose study. Clin Ther. 2011 Nov;33(11):1798-808. doi: 10.1016/j.clinthera.2011.09.011.
+
+**Kasichayanula 2012** Kasichayanula S, Chang M, Liu X, Shyu WC, Griffen SC, LaCreta FP, Boulton DW. Lack of pharmacokinetic interactions between dapagliflozin and simvastatin, valsartan, warfarin, or digoxin. Adv Ther. 2012 Feb;29(2):163-77. doi: 10.1007/s12325-011-0098-x.
+
+**Kasichayanula 2013a** Kasichayanula S, Liu X, Griffen SC, Lacreta FP, Boulton DW. Effects of rifampin and mefenamic acid on the pharmacokinetics and pharmacodynamics of dapagliflozin. Diabetes Obes Metab. 2013 Mar;15(3):280-3. doi: 10.1111/dom.12024.
+
+**Kasichayanula 2013b** Kasichayanula S, Liu X, Pe Benito M, Yao M, Pfister M, LaCreta FP, Humphreys WG, Boulton DW. The influence of kidney function on dapagliflozin exposure, metabolism and pharmacodynamics in healthy subjects and in patients with type 2 diabetes mellitus. Br J Clin Pharmacol. 2013 Sep;76(3):432-44. doi: 10.1111/bcp.12056.
+
+**Kasichayanula 2014** Kasichayanula S, Liu X, Lacreta F, Griffen SC, Boulton DW. Clinical pharmacokinetics and pharmacodynamics of dapagliflozin, a selective inhibitor of sodium-glucose co-transporter type 2. Clin Pharmacokinet. 2014 Jan;53(1):17-27. doi: 10.1007/s40262-013-0104-3.
+
+**Komoroski 2009** Komoroski B, Vachharajani N, Boulton D, Kornhauser D, Geraldes M, Li L, Pfister M. Dapagliflozin, a novel SGLT2 inhibitor, induces dose-dependent glucosuria in healthy subjects. Clin Pharmacol Ther. 2009 May;85(5):520-6. doi: 10.1038/clpt.2008.251.
+
+**Kuepfer 2016** Kuepfer L, Niederalt C, Wendl T, Schlender JF, Willmann S, Lippert J, Block M, Eissing T, Teutonico D. Applied Concepts in PBPK Modeling: How to Build a PBPK/PD Model.CPT Pharmacometrics Syst Pharmacol. 2016 Oct;5(10):516-531. doi: 10.1002/psp4.12134. Epub 2016 Oct 19.
+
+**Molly 1993** Molly, K., M. Vande Woestyne, and W. Verstraete, Development of a 5-step multi-chamber reactor as a simulation of the human intestinal microbial ecosystem. Appl Microbiol Biotechnol, 1993. 39(2): p. 254-8.
+
+**Nishimura 2013** Nishimura M, Yaguti H, Yoshitsugu H, Naito S, Satoh T. Tissue distribution of mRNA expression of human cytochrome P450 isoforms assessed by high-sensitivity real-time reverse transcription PCR. Yakugaku Zasshi. 2003 May;123(5):369-75.
+
+**Obermeier 2009** Obermeier M, Yao M, Khanna A, Koplowitz B, Zhu M, Li W, Komoroski B, Kasichayanula S, Discenza L, Washburn W, Meng W, Ellsworth BA, Whaley JM, Humphreys WG. In vitro characterization and pharmacokinetics of dapagliflozin (BMS-512148), a potent sodium-glucose cotransporter type II inhibitor, in animals and humans. Drug Metab Dispos. 2010 Mar;38(3):405-14. doi: 10.1124/dmd.109.029165.
+
+**Ohtsuki 2012** Ohtsuki S, Schaefer O, Kawakami H, Inoue T, Liehner S, Saito A, Ishiguro N, Kishimoto W, Ludwig-Schwellinger E, Ebner T, Terasaki T. Simultaneous absolute protein quantification of transporters, cytochromes P450, and UDP-glucuronosyltransferases as a novel approach for the characterization of individual human liver: comparison with mRNA levels and activities. Drug Metab Dispos. 2012 Jan;40(1):83-92. doi: 10.1124/dmd.111.042259.
+
+**PK-Sim Ontogeny Database Version 7.3** (https://github.com/Open-Systems-Pharmacology/OSPSuite.Documentation/blob/38cf71b384cfc25cfa0ce4d2f3addfd32757e13b/PK-Sim%20Ontogeny%20Database%20Version%207.3.pdf)
+
+**Possemiers 2004** Possemiers, S., et al., PCR-DGGE-based quantification of stability of the microbial community in a simulator of the human intestinal microbial ecosystem. FEMS Microbiol Ecol, 2004. 49(3): p. 495-507.
+
+**Sakamoto 2002** Sakamoto, H., et al., Excretion of bisphenol A-glucuronide into the small intestine and deconjugation in the cecum of the rat. Biochim Biophys Acta, 2002. 1573(2): p. 171-6.
+
+**Schlender 2016** Schlender JF, Meyer M, Thelen K, Krauss M, Willmann S, Eissing T, Jaehde U. Development of a Whole-Body Physiologically Based Pharmacokinetic Approach to Assess the Pharmacokinetics of Drugs in Elderly Individuals. Clin Pharmacokinet. 2016 Dec;55(12):1573-1589.
+
+**Vakkalagadda 2016** Vakkalagadda B, Lubin S, Reynolds L, Liang D, Marion AS, LaCreta F, Boulton DW. Lack of a Pharmacokinetic Interaction Between Saxagliptin and Dapagliflozin in Healthy Subjects: A Randomized Crossover Study. Clin Ther. 2016 Aug;38(8):1890-9. doi: 10.1016/j.clinthera.2016.07.005.
+
+","Markdown"
+"PBPK model","Open-Systems-Pharmacology/OSP-PBPK-Model-Library","MEDI524/MEDI524_evaluation_report.md",".md","15045","221","# Building and evaluation of a PBPK model for antibody MEDI-524 in cynomolgus monkeys
+
+| Version | 1.0-OSP12.2 |
+| ----------------------------------------------- | ------------------------------------------------------------ |
+| based on *Model Snapshot* and *Evaluation Plan* | https://github.com/Open-Systems-Pharmacology/MEDI524-Model/releases/tag/v1.0 |
+| OSP Version | 12.2 |
+| Qualification Framework Version | 3.5 |
+
+This evaluation report and the corresponding PK-Sim project file are filed at:
+
+https://github.com/Open-Systems-Pharmacology/OSP-PBPK-Model-Library/
+
+# Table of Contents
+
+ * [1 Introduction](#introduction)
+ * [2 Methods](#methods)
+ * [2.1 Modeling Strategy](#modeling-strategy)
+ * [2.2 Data](#methods-data)
+ * [2.2.1 In vitro / physico-chemical Data ](#invitro-and-physico-chemical-data)
+ * [2.2.2 PK Data ](#PK-data)
+ * [2.3 Model Parameters and Assumptions](#model-parameters-and-assumptions)
+ * [2.3.1 Absorption ](#model-parameters-and-assumptions-absorption)
+ * [2.3.2 Distribution ](#model-parameters-and-assumptions-distribution)
+ * [2.3.3 Metabolism and Elimination ](#model-parameters-and-assumptions-metabolism-and-elimination)
+ * [2.3.4 Automated Parameter Identification ](#model-parameters-and-assumptions-parameter-identification)
+ * [3 Results and Discussion](#results-and-discussion)
+ * [3.1 Final input parameters](#final-input-parameters)
+ * [3.2 Diagnostics Plots](#diagnostics-plots)
+ * [3.3 Concentration-Time Profiles](#ct-profiles)
+ * [4 Conclusion](#conclusion)
+ * [5 References](#main-references)
+
+# 1 Introduction
+
+MEDI-524 is a humanized monoclonal antibody (IgG1) against the respiratory syncytial virus (RSV) ([Dall’Acqua2006](#5-references)).
+
+MEDI-524 shows a pharmacokinetic behavior which is typical for an antibody without endogenous target. The plasma concentration–time profile after intravenous application of a 30 mg/kg dose in cynomolgus monkeys ([Dall’Acqua2006](#5-references)) were used together with pharmacokinetic (PK) data from 5 other compounds to identify unknown parameters during the development of the generic large molecule physiologically based pharmacokinetic (PBPK) model in PK-Sim ([Niederalt 2018](#5-references)).
+
+The herein presented evaluation report evaluates the performance of the PBPK model for MEDI-524 in cynomolgus monkeys for the PK data used for the development of the generic large molecule model in PK-Sim.
+
+The presented MEDI-524 PBPK model as well as the respective evaluation plan and evaluation report are provided open-source (https://github.com/Open-Systems-Pharmacology/MEDI524-Model).
+
+# 2 Methods
+
+## 2.1 Modeling Strategy
+
+The development of the large molecule PBPK model in PK-Sim® has previously been described by Niederalt et al. ([Niederalt 2018](#5-references)). In short, the model was built as an extension of the PK-Sim® model for small molecules incorporating (i) the two-pore formalism for drug extravasation from blood plasma to interstitial space, (ii) lymph flow, (iii) endosomal clearance and (iv) protection from endosomal clearance by neonatal Fc receptor (FcRn) mediated recycling.
+
+For model development and evaluation, PK data were used from compounds with a wide range of solute radii and from different species. The PK data used for parameter estimation were from the following compounds: antibody–drug conjugate BAY 79-4620 in mice (Bayer in house data), antibody 7E3 in wild-type and FcRn knockout mice ([Garg 2007](#5-references), [Garg2009](#5-references)), domain antibody dAb2 in mice ([Sepp 2015](#5-references)), antibodies MEDI-524 and MEDI-524-YTE in monkeys ([Dall'Acqua 2006](#5-references)), and antibody CDA1 in humans ([Taylor 2008](#5-references)). The PK data used for model evaluation were from inulin in rats ([Tsuji1983](#5-references)) and tefibazumab in humans ([Reilly 2005](#5-references)).
+
+The PBPK model including the estimated physiological parameters as described by Niederalt et al. ([Niederalt 2018](#5-references)) is available in the Open Systems Pharmacology Suite from version 7.1 onwards.
+
+This evaluation report focuses on the PBPK model for the antibody antibodies MEDI-524.
+
+Details about input data (physicochemical, *in vitro* and PK) can be found in [Section 2.2](#22-data).
+
+Details about the structural model and its parameters can be found in [Section 2.3](#23-model-parameters-and-assumptions).
+
+## 2.2 Data
+
+### 2.2.1 In vitro / physico-chemical Data
+
+A literature search was performed to collect available information on physicochemical properties of MEDI-524. The obtained information from literature is summarized in the table below.
+
+| **Parameter** | **Unit** | **Value** | Source | **Description** |
+| :------------ | -------- | --------- | -------------------------------- | ------------------------------------------------------------ |
+| MW | g/mol | 150000 | [Lobo 2004](#5-references) | Molecular weight |
+| r | nm | 5.34 | [Taylor 1984](#5-references) | Hydrodynamic solute radius |
+| Kd (FcRn) | µM | 1.196 | [Dall'Acqua 2006](#5-references) | Dissociation constant for binding to cynomolgus monkey FcRn for MEDI-524 (pH 6) |
+
+### 2.2.2 PK Data
+
+Published PK data on MEDI-524 in cynomolgus monkeys were used.
+
+| Publication | Description |
+| :------------------------------- | :----------------------------------------------------------- |
+| [Dall'Acqua 2006](#5-references) | The plasma concentration–time profiles after single i.v. infusion of 30 mg/kg MEDI-524 in in cynomolgus monkeys were used. |
+
+## 2.3 Model Parameters and Assumptions
+
+### 2.3.1 Absorption
+
+There is no absorption process since MEDI-524 was administered intravenously.
+
+### 2.3.2 Distribution
+
+The standard lymph and fluid recirculation flow rates and the standard vascular properties of the different tissues (hydraulic conductivity, pore radii, fraction of flow via large pores) from PK-Sim were used. MEDI-524, among other compounds, has been used to identify these lymph and fluid recirculation flow rates used in PK-Sim ([Niederalt 2018](#5-references)).
+
+### 2.3.3 Metabolism and Elimination
+
+The FcRn mediated clearance present in the standard PK-Sim model was used as only clearance process. The standard physiological parameters related to FcRn mediated clearance were used (rate constants for endosomal uptake and recycling, association rate constant for FcRn binding and concentration of FcRn in the endosomal space). MEDI-524, among other compounds, has been used to identify these parameters using literature values for the drug affinities to FcRn in the endosomal space ([Niederalt 2018](#5-references)).
+
+### 2.3.4 Automated Parameter Identification
+
+No drug specific parameters were fitted. MEDI-524, among other compounds, has been used to develop the model for proteins and large molecules in PK-Sim ([Niederalt 2018](#5-references)).
+
+# 3 Results and Discussion
+
+The PBPK model for MEDI-524 was evaluated with PK data in cynomolgus monkeys.
+
+These PK data have been used together with PK data from 5 other compounds to simultaneously identify parameters during the development of the generic model for proteins and large molecules in PK-Sim ([Niederalt 2018](#5-references)).
+
+The next sections show:
+
+1. the final model parameters for the building blocks: [Section 3.1](#final-input-parameters).
+2. the overall goodness of fit: [Section 3.2](#diagnostics-plots).
+3. simulated vs. observed concentration-time profiles for the clinical studies used for model building and for model verification: [Section 3.3](#ct-profiles).
+
+## 3.1 Final input parameters
+
+The compound parameter values of the final PBPK model are illustrated below.
+
+### Compound: MEDI524
+
+#### Parameters
+
+Name | Value | Value Origin | Alternative | Default
+------------------------------------------ | ------------ | --------------------------------------------- | ----------- | -------
+Solubility at reference pH | 9999 mg/l | Other-/Dummy value not used in the simulation | Measurement | True
+Reference pH | 7 | Other-/Dummy value not used in the simulation | Measurement | True
+Lipophilicity | -5 Log Units | Other-/Dummy value not used in the simulation | Measurement | True
+Fraction unbound (plasma, reference value) | 1 | Other-Assumption | Measurement | True
+Is small molecule | No | | |
+Molecular weight | 150000 g/mol | Publication-Lobo2004 | |
+Plasma protein binding partner | Unknown | | |
+Radius (solute) | 0.00534 µm | Publication-Taylor1984 | |
+Kd (FcRn) in endosomal space | 1.196 µmol/l | Publication-Dall'Acqua2006 | |
+
+#### Calculation methods
+
+Name | Value
+----------------------- | ---------------
+Partition coefficients | PK-Sim Standard
+Cellular permeabilities | PK-Sim Standard
+
+#### Processes
+
+## 3.2 Diagnostics Plots
+
+Below you find the goodness-of-fit visual diagnostic plots for the PBPK model performance of all data used presented in [Section 2.2.2](#PK-data).
+
+The first plot shows observed versus simulated plasma concentration, the second weighted residuals versus time.
+
+
+
+**Table 3-1: GMFE for Goodness of fit plot for concentration in plasma**
+
+|Group |GMFE |
+|:--------------------------|:----|
+|Intravenous administration |1.14 |
+
+
+
+
+
+
+
+
+**Figure 3-1: Goodness of fit plot for concentration in plasma**
+
+
+
+
+
+
+
+
+**Figure 3-2: Goodness of fit plot for concentration in plasma**
+
+
+
+
+## 3.3 Concentration-Time Profiles
+
+Simulated versus observed concentration-time profiles of all data listed in [Section 2.2.2](#PK-data) are presented below.
+
+
+
+
+
+**Figure 3-3: Plasma concentration (linear scale)**
+
+
+
+
+
+
+
+
+**Figure 3-4: Plasma concentration (log scale)**
+
+
+
+
+# 4 Conclusion
+
+The herein presented PBPK model adequately describes the pharmacokinetics of MEDI-524 in monkeys. The PK data had been used during the development of the generic large molecule PBPK model in PK-Sim ([Niederalt 2018](#5-references)) together with PK data from 5 other compounds (7E3, BAY 79-4620, CDA1, dAb2 & MEDI-524-YTE).
+
+# 5 References
+
+**Dall'Acqua 2006** Dall’Acqua WF, Kiener PA, Wu H. Properties of human IgG1s engineered for enhanced binding to the neonatal Fc receptor (FcRn). J Biol Chem. 2006 Aug; 281(33):23514-23524. doi: 10.1074/jbc.M604292200.
+
+**Garg 2007** Garg A, Balthasar JP. Physiologically-based pharmacokinetic (PBPK) model to predict IgG tissue kinetics in wild-type and FcRn-knockout mice. J Pharmacokinet Pharmacodyn. 2007 Jul; 34(5):687-709. doi: 10.1007/s10928-007-9065-1.
+
+**Garg 2009** Garg A, Balthasar J. Investigation of the influence of FcRn on the distribution of IgG to the brain. AAPS J. 2009 July; 11(3):553-557. doi: 10.1208/s12248-009-9129-9.
+
+**Lobo 2004** Lobo ED, Hansen R J, Balthasar JP. Antibody pharmacokinetics and pharmacodynamics. J Pharm Sci. 2004 Nov;93(11):2645-2668. doi: 10.1002/jps.20178.
+
+**Niederalt 2018** Niederalt C, Kuepfer L, Solodenko J, Eissing T, Siegmund HU, Block M, Willmann S, Lippert J. A generic whole body physiologically based pharmacokinetic model for therapeutic proteins in PK-Sim. J Pharmacokinet Pharmacodyn. 2018 Apr;45(2):235-257. doi: 10.1007/s10928-017-9559-4.
+
+**Reilly 2005** Reilley S, Wenzel E, Reynolds L, Bennett B, Patti JM, Hetherington S. Open-label, dose escalation study of the safety and pharmacokinetic profile of tefibazumab in healthy volunteers. Antimicrob Agents Chemother. 2005 Mar;49(3):959–962. doi: 10.1128/AAC.49.3.959-962.2005.
+
+**Sepp 2015** Sepp A, Berges A, Sanderson A, Meno-Tetang G. Development of a physiologically based pharmacokinetic model for a domain antibody in mice using the two-pore theory. J Pharmacokinet Pharmacodyn. 2015 Jan;42(2):97-109. doi: 10.1007/s10928-014-9402-0.
+
+**Taylor 1984** Taylor AE, Granger DN. Exchange of macromolecules across the microcirculation. Handbook of Physiology - Cardiovascular System. Microcirculation (Eds. Renkin EM and Michel CC. Bethesda, MD, American Physiological Society). 1984; Vol. 4(Pt 2):467–520.
+
+**Taylor 2008** Taylor CP, Tummala S, Molrine D, Davidson L, Farrell RJ, Lembo A, Hibberd PL, Lowy I, Kelly CP. Open-label, dose escalation phase I study in healthy volunteers to evaluate the safety and pharmacokinetics of a human monoclonal antibody to Clostridium difficile toxin A. Vaccine. 2008 Jun;26(27-28):3404–3409. doi: 10.1016/j.vaccine.2008.04.042.
+
+**Tsuji 1983** Tsuji A, Yoshikawa T, Nishide K, Minami H, Kimura M, Nakashima E, Terasaki T, Miyamoto E, Nightingale CH, Yamana T. Physiologically based pharmacokinetic model for beta-lactam antibiotics I: tissue distribution and elimination in rats. J Pharm Sci. 1983 Nov;72(11):1239-1252. doi: 10.1002/jps.2600721103.
+
+","Markdown"
+"PBPK model","Open-Systems-Pharmacology/OSP-PBPK-Model-Library","Omeprazole/Omeprazole_evaluation_report.md",".md","62547","995","# Building and evaluation of a PBPK model for Omeprazole in adults
+
+| Version | 2.0-OSP12.2 |
+| ----------------------------------------------- | ------------------------------------------------------------ |
+| based on *Model Snapshot* and *Evaluation Plan* | https://github.com/Open-Systems-Pharmacology/Omeprazole-Model/releases/tag/v2.0 |
+| OSP Version | 12.2 |
+| Qualification Framework Version | 3.5 |
+
+This evaluation report and the corresponding PK-Sim project file are filed at:
+
+https://github.com/Open-Systems-Pharmacology/OSP-PBPK-Model-Library/
+
+# Table of Contents
+
+ * [1 Introduction](#introduction)
+ * [2 Methods](#methods)
+ * [2.1 Modeling Strategy](#modeling-strategy)
+ * [2.2 Data](#data)
+ * [2.3 Model Parameters and Assumptions](#model-parameters-and-assumptions)
+ * [3 Results and Discussion](#results-and-discussion)
+ * [3.1 Final input parameters](#final-input-parameters)
+ * [3.2 Diagnostics Plots](#diagnostics-plots)
+ * [3.3 Concentration-Time Profiles](#ct-profiles)
+ * [3.3.1 Model Building](#model-building)
+ * [3.3.2 Model Verification](#model-verification)
+ * [4 Conclusion](#conclusion)
+ * [5 References](#references)
+ * [6 Glossary](#glossary)
+
+# 1 Introduction
+
+The presented PBPK model of omeprazole has been developed to be used in a PBPK Drug-Drug-Interactions (DDI) network with omeprazole as a substrate of CYP2C19 and CYP3A4 and an inhibitor of CYP2C19.
+
+Omeprazole is a proton pump inhibitor (PPI) for the treatment of gastric acid related diseases. Omeprazole is administered as a racemic mixture of its two enantiomers, S-omeprazole and R-omeprazole.
+
+The following ADME characteristics for (R-/S-) omeprazole were taken from omeprazole 20 mg capsule [FDA SPC](#5-references):
+
+**Absorption**: Absorption of (R-/S-) omeprazole is rapid, with peak plasma levels occurring approximately 1-2 hours after dose. Absorption of omeprazole takes place in the small intestine and is usually completed within 3-6 hours. The systemic availability (bioavailability) from a single oral dose of omeprazole is approximately 40%. After repeated once-daily administration, the bioavailability increases to about 60%.
+
+**Distribution**: The apparent volume of distribution in healthy subjects is approximately 0.3 l/kg body weight. Omeprazole is 97% plasma protein bound.
+
+**Metabolism**: (R-/S-) Omeprazole is completely metabolized by the cytochrome P450 system (CYP). The major part of its metabolism is dependent on the polymorphically expressed CYP2C19, responsible for the formation of hydroxyomeprazole, the major metabolite in plasma. The remaining part is dependent on another specific isoform, CYP3A4, responsible for the formation of omeprazole sulphone. As a consequence of high affinity of omeprazole to CYP2C19, there is a potential for competitive inhibition and metabolic drug-drug interactions. No metabolite has been found to be pharmacologically active.
+
+**Elimination**: Almost 80% of an oral dose of omeprazole is excreted as metabolites in the urine, the remainder in the faeces, primarily originating from bile secretion.
+
+**Linearity/non-linearity:** The AUC of omeprazole increases with repeated administration following a non-linear dose-AUC relationship. This time- and dose- dependency is due to a decrease of first pass metabolism and systemic clearance probably caused by an inhibition of the CYP2C19 enzyme by omeprazole and/or its metabolites (e.g. the sulphone).
+
+**Stereoselectivity:** Because both enantiomers are substrates and inhibitors of CYP2C19, the PK profile of each enantiomer is expected to differ when the racemic mixture is administered as compared to as a single agent because of mutual inhibition between the enantiomers ([Äbelö 2000](#5-references)). The Ki for R-omeprazole is 2-5 higher compared to S-omeprazole ([Liu 2005](#5-references), [Wu 2014](#5-references)).
+
+# 2 Methods
+
+## 2.1 Modeling Strategy
+
+The general workflow for building an adult PBPK model has been described by Kuepfer et al. ([Kuepfer 2016](#5-references)). Relevant information on the anthropometry (height, weight) was gathered from the respective clinical study, if reported. Information on physiological parameters (e.g. blood flows, organ volumes, hematocrit) in adults was gathered from the literature and has been incorporated in PK-Sim® as described previously ([Willmann 2007](#5-references)). The applied activity and variability of plasma proteins and active processes that are integrated into PK-Sim® are described in the publicly available 'PK-Sim® Ontogeny Database Version 7.3' ([PK-Sim Ontogeny Database Version 7.3](#5-references)).
+
+The model includes distinct molecules for S- and R-omeprazole, the racemic omeprazole is represented by an observer that sums up the concentrations of S- and R-omeprazole. It was assumed that both isomers correspond to the half of the racemic omeprazole dose.
+
+In general, the following step-wise workflow was followed:
+
+1a. Define distribution and metabolism for S-omeprazole
+
+1b. Mechanism based inactivation of CYP2C19 by omeprazole
+
+2. Define S-omeprazole absorption based on p.o.
+
+3. Capsule formulation
+
+- 3a. Building racemic omeprazole
+
+- 3b. Adjust CYP2C19 expression in gut
+
+4. Define metabolism for R-omeprazole
+
+5. Refine CYP3A4 metabolism on CYP2C19 PM data
+
+6. Refine CYP2C19 metabolism on CYP2C19 EM data
+
+The predefined “Standard European Male for DDI” individual was used (age = 30 y, weight = 73 kg, height = 176 cm, BMI = 23.57 kg/m2) until stated otherwise. CYP2C19 expression from the PK-Sim in-built RT-PCR database was added and adjusted as described in [Section 2.3.3](#233-metabolism-and-elimination).
+
+Selection of the distribution model for S-omeprazole and estimation of the lipophilicity parameter were performed with i.v. data ([Wilder-Smith 2005](#5-references), [Hassan-Alin 2000](#5-references)).
+
+The kinetics of CYP2C19 and CYP3A4 metabolization processes of S-omeprazole and R-omeprazole were estimated using the Parameter Identification module provided in PK-Sim® with i.v. and p.o. data including administration of S-, R- or racemic omeprazole, see [Table 3](#table-3), [Table 4](#table-4), and [Table 5](#table-5) for more details. The kinetic parameters were assumed not to be identical for the isomers. For simulations of CYP2C19 poor metabolizers, the CYP2C19 pathway was switched off.
+
+For studies in Japanese subjects, a typical Japanese subject (age = 30 y, weight = 61.87 kg, height = 168.99 cm, BMI = 21.67 kg/m2) was created in PK-Sim from predefined database “Japanese (2015)” by adding CYP3A4 and CYP2C19 expression from PK-Sim RT PCR database, and adapting CYP2C19 expression in gut as described in [Section 2.3.3](#233-metabolism-and-elimination).
+
+Intestinal permeability of S-omeprazole was estimated by fitting the model to concentration-time data measured after p.o. administration of 20 or 40 mg given as solution ([Hassan-Alin 2005](#5-references)).
+
+Parameters `Dissolution time (50% dissolved)` and `Dissolution shape` describing the dissolution of capsule formulation were fitted to the data after single dose S-omeprazole 40 mg capsule ([Wilder-Smith 2005](#5-references)).
+
+Details about input data (physicochemical, *in vitro* and clinical) can be found in [Section 2.2](#22-data).
+
+Details about the structural model and its parameters can be found in [Section 2.3](#23-model-parameters-and-assumptions).
+
+Population simulations of single and multiple i.v. or p.o. administration over a wide range of dose levels were conducted to visually compare the predicted concentration-time profile to the observed concentrations reported in the literature, in terms of mean and variability. The simulated populations matched the race (European or Asian) and the age-weight ranges reported in the respective clinical studies. A total of 1000 individuals were generated for studies in males only, while 2000 were generated for mixed gender populations. The concentration time profile was simulated for each virtual subject and summarized as geometric mean and 95% CI. The simulations were performed for extensive and poor CYP2C19 metabolizers.
+
+## 2.2 Data
+
+### 2.2.1 In vitro and physico-chemical data
+
+A literature search was performed to collect available information on physico-chemical properties of S- and R-omeprazole and summarized in [Table 1](#table-1) and [Table 2](#table-2), respectively.
+
+| **Parameter** | **Unit** | **Value** | Source | **Description** |
+| :--------------------------------------------------------- | -------- | ------------- | ----------------------------------------------- | ------------------------------------------------------------ |
+| MW+ | g/mol | 345.42 | [DrugBank DB00338](#5-references) | Molecular weight. |
+| pKa,acid+ | | 9.29 | [DrugBank DB00338](#5-references) | Acidic dissociation constant |
+| pKa,base+ | | 4.77 | [DrugBank DB00338](#5-references) | Basic dissociation constant |
+| Solubility (pH)+ | mg/mL | 0.36
(7) | [DrugBank DB00338](#5-references) | Aqueous Solubility |
+| logD | | 2.23 | [Ogilvie 2011](#5-references) | Distribution coefficient |
+| fu+ | % | 3 | [Nexium prescribing information](#5-references) | Fraction unbound in plasma |
+| Kinact CYP2C19+ | l/h | 5 | [Wu 2014](#5-references) | Kinact of time dependent inhibition on CYP2C19 |
+| Ki CYP2C19+ | µM | 0.3 | [Wu 2014](#5-references) | KI of time dependent inhibition on CYP2C19 |
+| Ki CYP2C19 (competitive inhibition)+ | µM | 3.1 | [Liu 2005](#5-references) | The total ki value reported by Liu was 3.4 µmol/L and corrected with an fu_mic of 0.92 |
+| Renal Elimination+ | l/h | 0.037 | [Wu 2014](#5-references) | Assumed same as omeprazole |
+
+**Table 1:** Physico-chemical and *in-vitro* metabolization properties of S-omeprazole extracted from literature. *+: Value used in final model*
+
+| **Parameter** | **Unit** | **Value** | Source | **Description** |
+| :--------------------------------------------------------- | -------- | ------------- | --------------------------------- | ------------------------------------------------------------ |
+| MW+ | g/mol | 345.42 | [DrugBank DB00338](#5-references) | Molecular weight. |
+| pKa,acid+ | | 9.29 | [DrugBank DB00338](#5-references) | Acidic dissociation constant |
+| pKa,base+ | | 4.77 | [DrugBank DB00338](#5-references) | Basic dissociation constant |
+| Solubility (pH)+ | mg/mL | 0.36
(7) | [DrugBank DB00338](#5-references) | Aqueous Solubility |
+| logD | | 2.23 | [Ogilvie 2011](#5-references) | Distribution coefficient |
+| fu+ | % | 4 | [Ogilvie 2011](#5-references) | Fraction unbound in plasma; assumed same as omeprazole |
+| Kinact CYP2C19+ | l/h | 4 | [Wu 2014](#5-references) | Kinact of time dependent inhibition on CYP2C19 |
+| Ki CYP2C19+ | µM | 1.6 | [Wu 2014](#5-references) | KI of time dependent inhibition on CYP2C19 |
+| Ki CYP2C19 (competitive inhibition)+ | µM | 5.3 | [Liu 2005](#5-references) | The total ki value reported by Liu was 5.7 µmol/L and corrected with an fu_mic of 0.92 |
+| Renal Elimination+ | l/h | 0.037 | [Wu 2014](#5-references) | Assumed same as omeprazole |
+
+**Table 2:** Physico-chemical and *in-vitro* metabolization properties of R-omeprazole extracted from literature. *+: Value used in final model*
+
+### 2.2.2 Clinical data
+
+A literature search was performed to collect available clinical data on omeprazole in adults. Data used for model development and validation for omeprazole, S-omeprazole, and R-omeprazole are listed in [Table 3](#table-3), [Table 4](#table-4), and [Table 5](#table-5), respectively.
+
+| **Source** | **Route** | **Dose [mg]/** **Schedule \*** | **Pop.** | **Sex** | **N** | **Form.** | **Comment** |
+| -------------------- | --------- | ------------------------------- | ------------ | ------- | ----- | --------- | --------------------------------- |
+| [Andersson 1990](#5-references)+ | i.v. | 40 - 80 | HV | M | 10 | solution | |
+| [Andersson 1990](#5-references) | p.o. | 40 - 80 | HV | M | 10 | Oral solution | |
+| [Andersson 1991](#5-references) | p.o. | 10 - 20 – 40 q.d. | HV | M | 12 | e.c. granules | |
+| [Andersson 1991](#5-references)+ | i.v. | 10 - 20 - 40 | HV | M | 12 | solution | |
+| [Andersson 1998](#5-references) | p.o. | 20 q.d. | HV | M | 12 | capsule | EM |
+| [Andersson 1998](#5-references) | p.o. | 20 q.d. | HV | M | 2 | capsule | PM |
+| [Oosterhuis 1992](#5-references)+ | i.v. | 40 - 80 | HV | M | 8 | solution | |
+| [Uno 2007](#5-references)+ | i.v. | 20 | HV japanese | M - F | 6 | solution | hmEM |
+| [Uno 2007](#5-references)+ | i.v. | 20 | HV japanese | M - F | 6 | solution | PM |
+| [Uno 2007](#5-references) | p.o. | 40 | HV japanese | M - F | 6 | tablet | hmEM |
+| [Uno 2007](#5-references) | p.o. | 40 | HV japanese | M - F | 6 | tablet | PM |
+| [Regårdh 1990](#5-references)+ | i.v. | 10 | HV | M | 8 | solution | |
+| [Regårdh 1990](#5-references) | p.o. | 20 | HV | M | 8 | Oral solution | |
+| [Andersson 2000](#5-references) | p.o. | 15 q.d. | HV | - | 4 | Oral solution | EM |
+| [Andersson 2000](#5-references) | p.o. | 60 q.d. | HV | - | 5 | Oral solution | PM |
+| [Hassan-Alin 2005](#5-references) | p.o. | 20 – 40 q.d. | HV | - | - | Oral solution | |
+| [Cho 2002](#5-references) | p.o. | 20 | HV asian | - | - | capsule | EM +/- moclobemide |
+| [Cho 2002](#5-references) | p.o. | 20 | HV asian | - | - | capsule | PM +/- moclobemide |
+| [Yasui-Furukori 2004](#5-references) | p.o. | 40 | HV japanese | M - F | 6 | omepral | |
+| [Yasui-Furukori 2004](#5-references) | p.o. | 40 | HV japanese | M - F | 6 | omepral | PM +/- fluvoxamine |
+| [Wu 2016](#5-references) | p.o. | 40 q.d. | HV caucasian | M - F | 15 | gastro-resistant hard capsule | |
+
+**Table 3:** Literature sources of clinical concentration data of omeprazole used for model development and validation. *e.c.: enteric coated; -: respective information was not provided in the literature source; \*:single dose unless otherwise specified; EM: extensive metabolizers; PM: poor metabolizers; +: Data used for final parameter identification*
+
+| **Source** | **Route** | **Dose [mg]/** **Schedule \*** | **Pop.** | **Sex** | **N** | **Form.** | **Comment** |
+| ---------------------------------------------- | --------- | ------------------------------------------------------------ | -------- | ------- | ----- | ------------- | ----------- |
+| [Wilder-Smith 2005](#5-references)+ | i.v. | 40 q.d. | HV | M - F | 39 | solution | |
+| [Wilder-Smith 2005](#5-references)+ | p.o. | 40 q.d. | HV | M - F | 37 | capsule | |
+| [Hassan-Alin 2000](#5-references)+ | i.v. | 20 - 40 | HV | - | - | solution | |
+| [Andersson 2000](#5-references) | p.o. | 15 q.d. | HV | - | 4 | oral solution | EM |
+| [Andersson 2000](#5-references)+ | p.o. | 60 q.d. | HV | - | 5 | oral solution | PM |
+| [Hassan-Alin 2005](#5-references)+ | p.o. | 20 - 40 q.d. | HV | - | - | oral solution | |
+| [FDA Nexium Review](#5-references) | p.o. | 40 | HV | M | - | - | EM |
+| [FDA Nexium Review](#5-references)+ | p.o. | 40 | HV | M | - | - | PM |
+| [Rohss 2007](#5-references)+ | i.v. | 120mg(30min)+8mg/h - 120mg(2h)+8mg/h - 80mg(30min)+4mg/h - 80mg(30min)+8mg/h - 40mg(30min)+8mg/h | HV | - | 25 | solution | |
+
+**Table 4:** Literature sources of clinical concentration data of S-omeprazole used for model development and validation. *e.c.: enteric coated; -: respective information was not provided in the literature source; \*:single dose unless otherwise specified; EM: extensive metabolizers; PM: poor metabolizers; +: Data used for final parameter identification*
+
+| **Source** | **Route** | **Dose [mg]/** **Schedule \*** | **Pop.** | **Sex** | **N** | **Form.** | **Comment** |
+| --------------------------------------------- | --------- | ------------------------------- | -------- | ------- | ----- | ------------- | ----------- |
+| [Andersson 2000](#5-references)+ | p.o. | 15 q.d. | HV | - | 4 | oral solution | EM |
+| [Andersson 2000](#5-references)+ | p.o. | 60 q.d. | HV | - | 5 | oral solution | PM |
+| [Hassan-Alin 2005](#5-references)+ | p.o. | 20 - 40 q.d. | HV | - | - | oral solution | |
+
+**Table 5:** Literature sources of clinical concentration data of R-omeprazole used for model development and validation. *e.c.: enteric coated; -: respective information was not provided in the literature source; \*:single dose unless otherwise specified; EM: extensive metabolizers; PM: poor metabolizers; +: Data used for final parameter identification*
+
+## 2.3 Model Parameters and Assumptions
+
+### 2.3.1 Absorption
+
+The model parameter `Specific intestinal permeability` for S-omeprazole was optimized to best match clinical data (see [Section 2.3.4](#235-automated-parameter-identification)). The same parameter value was assumed for R-omeprazole.
+
+The dissolution of the capsule formulation was implemented via an empirical Weibull dissolution equation with parameters `Dissolution time (50% dissolved)` and `Dissolution shape` fitted to observed data. A `Lag time ` = 30 min was used to account for the gastric emptying time.
+
+### 2.3.2 Distribution
+
+Physico-chemical parameter values of S- and R-omeprazole were set to the reported values (see [Section 2.2.1](#221-in-vitro-and-physico-chemical-data)) except for lipophilicity of S-omeprazole, which was estimated with i.v. data. It was assumed that the major binding partner in plasma is albumin.
+
+After testing the available organ-plasma partition coefficient and cell permeability calculation methods available in PK-Sim, observed clinical data were best described by choosing the partition coefficient calculation by `Rodgers and Rowland` and cellular permeability calculation by `PK-Sim Standard`.
+
+The same distribution model and parameter values were assumed for R-omeprazole, as no i.v. data are available for R-omeprazole.
+
+### 2.3.3 Metabolism and Elimination
+
+Two linear metabolic pathways for S- and R-omeprazole were implement in the model:
+
+* CYP2C19
+* CYP3A4
+
+To describe multiple dose oral solution data, time-dependent autoinhibition (TDI) on CYP2C19 was added as irreversible inhibition / Mechanism-based inactivation as described by [Wu 2014](#5-references).
+
+Competitive inhibition of CYP2C19 by both isomers was implemented in addition to TDI ([Liu 2005](#5-references)).
+
+Simulation results suggested that the expression of CYP2C19 isoenzymes in the GI tract as provided by the RT-PCR PK-Sim database is significantly preventing R-omeprazole from entering the circulation. This was less apparent for S-omeprazole. To note, while the absolute mean CYP3A4 abundance in liver (1.03e7 pmol per liver) and the intestinal/liver CYP3A4 ratio in PK-Sim default individual are similar to values used in other models, the relative intestinal CYP2C19 and CYP2D6 abundances differ ([Table 6](#table-6)). The relative expression of CYP2C19 in gut was therefore reduced according to [Olivares-Morales 2016](#5-references) for the final model.
+
+| **Ratio** | **[Olivares-Morales 2016](#5-references)**1 | **[Galetin and Houston 2006](#5-references)**2 | **GastroPlus** | **RT-PCR PK-Sim** | **Comment** |
+| ---------------------------------------- | ------------------------------------------------------ | --------------------------------------------------------- | -------------- | ---------------- | ------------------------------------------------------------ |
+| CYP3A4/2C19 relative abundance liver | 9.8 | 11.1 | 8.1 | 5.68 | CYP2C19 abundance in liver 1.8-fold higher than other literature sources |
+| CYP3A4/2C19 relative abundance intestine | 43.8 | 43.0 | - | 1.28 | CYP2C19 abundance in intestine 22-fold higher than other literature sources |
+| CYP3A4/2D6 relative abundance liver | 17.1 | 19.4 | 14.2 | 10.80 | CYP2D6 abundance in liver 1.6-fold higher than other literature sources |
+| CYP3A4/2D6 relative abundance intestine | 83.8 | 86.0 | - | 8.64 | CYP2D6 abundance in intestine 6-fold higher than other literature sources |
+| CYP3A4 abundance Small intestine/liver | 0.66% | - | - | 0.44% | in range with literature |
+| CYP2C19 abundance Small intestine/liver | 0.15% | - | - | 1.95% | 23-fold higher than in literature |
+| CYP2D6 Small intestine/liver | 0.14% | - | - | 0.55% | 4-fold higher than in literature |
+
+**Table 6:** Comparison of CYP3A4, CYP2C19 and CYP2D6 relative abundance in liver and small intestine from different literature sources. 1 Based on Sjörgen 2014: CYP relative expressions (pmol/mg_mic_p) from Paine 2006 calibrated against total intestinal CYP3A4 abundance. 2 Mean hepatic and intestinal relative abundance based on Rowland and Yeo 2003 and Paine 2006
+
+Additionally, renal plasma clearance was implemented ([Wu 2014](#5-references)).
+
+### 2.3.4 Observer for racemic omeprazole
+
+Omeprazole concentrations in peripheral venous blood plasma at any specific time points were obtained by adding the simulated concentrations of two enantiomers together at the corresponding time points to generate the omeprazole PK profiles, according to the following formula:
+
+*fQ_art\*(C_pls_art_Eso+C_pls_art_R_O) +fQ_bon\*(C_pls_bon_Eso+C_pls_bon_R_O)+fQ_fat\*(C_pls_fat_Eso + C_pls_fat_R_O)+fQ_mus\*(C_pls_mus_Eso + C_pls_mus_R_O) +fQ_skn\*(C_pls_skn_Eso + C_pls_skn_R_O)*
+
+where *fQ_* are fraction of blood flow and *C_pls_* concentrations in plasma compartment respectively in the arterial (*art*), bone (*bon*), fat (*fat*), muscle (*mus*) or skin (*skn*) tissues. *Eso* and *R_O* stand for S- and R-omeprazole respectively.
+
+### 2.3.5 Automated Parameter Identification
+
+Following parameter values were estimated for the base model:
+
+| **Parameter** | **Compound** |
+| ------------------------------------------------ | ------------ |
+| Lipophilicity | S-Omeprazole |
+| Specific intestinal permeability (transcellular) | S-Omeprazole |
+| Dissolution time (Weibull) | S-Omeprazole |
+| Dissolution shape (Weibull) | S-Omeprazole |
+| Specific CL_2C19 | S-Omeprazole |
+| Specific CL_2C19 | R-Omeprazole |
+| Specific CL_3A4 | S-Omeprazole |
+| Specific CL_3A4 | R-Omeprazole |
+
+# 3 Results and Discussion
+
+The next sections show:
+
+1. Final model input parameters for the building blocks: [Section 3.1](#31-final-input-parameters).
+2. Overall goodness of fit: [Section 3.2](#32-diagnostics-plots).
+3. Simulated vs. observed concentration-time profiles for the clinical studies used for model building and for model verification: [Section 3.3](#33-concentration-time-profiles).
+
+## 3.1 Final input parameters
+
+The parameter values of the final PBPK model are illustrated below.
+
+### Compound: Esomeprazole
+
+#### Parameters
+
+Name | Value | Value Origin | Alternative | Default
+------------------------------------------------ | ---------------------- | ------------------------------------------------- | ----------- | -------
+Solubility at reference pH | 0.359 mg/ml | Database-DrugBank DB00338 | Measurement | True
+Reference pH | 7 | Database-DrugBank DB00338 | Measurement | True
+Lipophilicity | 1.6835584938 Log Units | Parameter Identification-Parameter Identification | LogP fit | True
+Fraction unbound (plasma, reference value) | 0.03 | Database-DrugBank DB00338 | Fu DrugBank | True
+Specific intestinal permeability (transcellular) | 9.79E-05 cm/min | Parameter Identification-Parameter Identification | Fit | True
+Is small molecule | Yes | | |
+Molecular weight | 345.416 g/mol | Database-DrugBank DB00338 | |
+Plasma protein binding partner | Albumin | | |
+
+#### Calculation methods
+
+Name | Value
+----------------------- | -------------------
+Partition coefficients | Rodgers and Rowland
+Cellular permeabilities | PK-Sim Standard
+
+#### Processes
+
+##### Metabolizing Enzyme: CYP2C19-2C19 Linear Fit
+
+Species: Human
+
+Molecule: CYP2C19
+
+###### Parameters
+
+Name | Value | Value Origin
+------------------- | ----------- | -------------------------------------------------
+Intrinsic clearance | 0 l/min |
+Specific clearance | 13.98 1/min | Parameter Identification-Parameter Identification
+
+##### Metabolizing Enzyme: CYP3A4-3A4 Linear Fit
+
+Species: Human
+
+Molecule: CYP3A4
+
+###### Parameters
+
+Name | Value | Value Origin
+------------------- | ------------------ | -------------------------------------------------
+Intrinsic clearance | 0 l/min |
+Specific clearance | 0.3707655759 1/min | Parameter Identification-Parameter Identification
+
+##### Systemic Process: Renal Clearances-Wu2014 - Table1 - CLr
+
+Species: Human
+
+###### Parameters
+
+Name | Value | Value Origin
+----------------------------- | --------------- | ---------------------
+Fraction unbound (experiment) | 0.05 |
+Plasma clearance | 0.000507 l/h/kg | Unknown-0.037l/h/73kg
+
+##### Inhibition: CYP2C19-Liu 2005 - Ki in vivo unbound
+
+Molecule: CYP2C19
+
+###### Parameters
+
+Name | Value | Value Origin
+---- | ---------- | --------------------
+Ki | 3.1 µmol/l | Publication-Liu 2005
+
+##### Inhibition: CYP2C19-Wu2014 - Table1 - TDI
+
+Molecule: CYP2C19
+
+###### Parameters
+
+Name | Value | Value Origin
+------------- | ---------- | -------------------
+kinact | 5 1/h | Publication-Wu 2014
+K_kinact_half | 0.3 µmol/l |
+
+### Compound: R-omeprazole
+
+#### Parameters
+
+Name | Value | Value Origin | Alternative | Default
+------------------------------------------------ | ---------------------- | ---------------------------------------------- | ----------- | -------
+Solubility at reference pH | 0.359 mg/ml | Database-DrugBank DB00338 | Measurement | True
+Reference pH | 7 | Database-DrugBank DB00338 | Measurement | True
+Lipophilicity | 1.6835584938 Log Units | Other-Assumption-Same as S-omeprazole | LogP fit | True
+Fraction unbound (plasma, reference value) | 0.04 | Publication-Ogilvie 2011 | Fu DrugBank | True
+Specific intestinal permeability (transcellular) | 9.79E-05 cm/min | Other-Assumption-Assumend same as S-omeprazole | Fit | True
+Is small molecule | Yes | | |
+Molecular weight | 345.416 g/mol | Database-DrugBank DB00338 | |
+Plasma protein binding partner | Albumin | | |
+
+#### Calculation methods
+
+Name | Value
+----------------------- | -------------------
+Partition coefficients | Rodgers and Rowland
+Cellular permeabilities | PK-Sim Standard
+
+#### Processes
+
+##### Metabolizing Enzyme: CYP2C19-2C19 Linear Fit
+
+Species: Human
+
+Molecule: CYP2C19
+
+###### Parameters
+
+Name | Value | Value Origin
+------------------- | -------- | ---------------------------------------------------------------------
+Intrinsic clearance | 0 l/min |
+Specific clearance | 50 1/min | Parameter Identification-Parameter Identification-Upper bound limited
+
+##### Metabolizing Enzyme: CYP3A4-3A4 Linear Fit
+
+Species: Human
+
+Molecule: CYP3A4
+
+###### Parameters
+
+Name | Value | Value Origin
+------------------- | ----------------- | -------------------------------------------------
+Intrinsic clearance | 0 l/min |
+Specific clearance | 0.161397262 1/min | Parameter Identification-Parameter Identification
+
+##### Systemic Process: Renal Clearances-Wu2014 - Table1 - CLr
+
+Species: Human
+
+###### Parameters
+
+Name | Value | Value Origin
+----------------------------- | ------------------ | -------------------
+Fraction unbound (experiment) | 0.03 |
+Plasma clearance | 0 ml/min/kg |
+Specific clearance | 0.0282095334 1/min | Publication-Wu 2014
+
+##### Inhibition: CYP2C19-Liu 2005 - Ki in vivo unbound
+
+Molecule: CYP2C19
+
+###### Parameters
+
+Name | Value | Value Origin
+---- | ---------- | --------------------
+Ki | 5.3 µmol/l | Publication-Liu 2005
+
+##### Inhibition: CYP2C19-Wu2014 - Table1 - TDI
+
+Molecule: CYP2C19
+
+###### Parameters
+
+Name | Value | Value Origin
+------------- | ---------- | -------------------
+kinact | 4 1/h | Publication-Wu 2014
+K_kinact_half | 1.6 µmol/l |
+
+### Formulation: Omeprazole capsule
+
+Type: Weibull
+
+#### Parameters
+
+Name | Value | Value Origin
+-------------------------------- | --------- | -------------------------------------------------
+Dissolution time (50% dissolved) | 41.65 min | Parameter Identification-Parameter Identification
+Lag time | 30 min | Other-Assumption-Gastric emptying
+Dissolution shape | 1.02 | Parameter Identification-Parameter Identification
+Use as suspension | Yes |
+
+## 3.2 Diagnostics Plots
+
+The following section displays the goodness-of-fit visual diagnostic plots for the PBPK model performance of all data listed in [Section 2.2.2](#222-clinical-data).
+
+The first plot shows observed versus simulated plasma concentration, the second weighted residuals versus time.
+
+
+
+**Table 3-1: GMFE for S-omeprazole concentration in plasma - mean data**
+
+|Group |GMFE |
+|:--------------------------------------|:----|
+|iv administration (model building) |1.39 |
+|Oral administration (model building) |1.43 |
+|Oral administration (model validation) |1.72 |
+|All |1.50 |
+
+
+
+
+
+
+
+
+**Figure 3-1: S-omeprazole concentration in plasma - mean data**
+
+
+
+
+
+
+
+
+**Figure 3-2: S-omeprazole concentration in plasma - mean data**
+
+
+
+
+
+
+**Table 3-2: GMFE for R-omeprazole concentration in plasma - mean data**
+
+|Group |GMFE |
+|:------------------------------------|:----|
+|Oral administration (model building) |2.40 |
+
+
+
+
+
+
+
+
+**Figure 3-3: R-omeprazole concentration in plasma - mean data**
+
+
+
+
+
+
+
+
+**Figure 3-4: R-omeprazole concentration in plasma - mean data**
+
+
+
+
+
+
+**Table 3-3: GMFE for Omeprazole concentration in plasma - mean data**
+
+|Group |GMFE |
+|:--------------------------------------|:----|
+|iv administration (model building) |1.70 |
+|iv administration (model validation) |1.30 |
+|Oral administration (model building) |1.30 |
+|Oral administration (model validation) |2.45 |
+|All |2.09 |
+
+
+
+
+
+
+
+
+**Figure 3-5: Omeprazole concentration in plasma - mean data**
+
+
+
+
+
+
+
+
+**Figure 3-6: Omeprazole concentration in plasma - mean data**
+
+
+
+
+## 3.3 Concentration-Time Profiles
+
+Simulated versus observed concentration-time profiles of all data listed in [Section 2.2.2](#222-clinical-data) are presented below.
+
+### 3.3.1 Model Building
+
+
+
+
+
+**Figure 3-7: 20 mg S-omeprazole iv infusion**
+
+
+
+
+
+
+
+
+**Figure 3-8: 40 mg S-omeprazole iv infusion**
+
+
+
+
+
+
+
+
+**Figure 3-9: 80 mg 30 min + 4 mg/h iv**
+
+
+
+
+
+
+
+
+**Figure 3-10: 40 mg 30 min + 8 mg/h iv S-omeprazole**
+
+
+
+
+
+
+
+
+**Figure 3-11: 80 mg 30 min + 8 mg/h iv S-omeprazole**
+
+
+
+
+
+
+
+
+**Figure 3-12: 120 mg 30 min + 8 mg/h iv S-omeprazole**
+
+
+
+
+
+
+
+
+**Figure 3-13: 120 mg 2h + 8 mg/h iv S-omeprazole**
+
+
+
+
+
+
+
+
+**Figure 3-14: 20 mg S-omeprazole oral solution**
+
+
+
+
+
+
+
+
+**Figure 3-15: 40 mg S-omeprazole oral solution**
+
+
+
+
+
+
+
+
+**Figure 3-16: 40 mg S-omeprazole oral capsule**
+
+
+
+
+
+
+
+
+**Figure 3-17: 15 mg R-omeprazole oral solution day 6**
+
+
+
+
+
+
+
+
+**Figure 3-18: 20 mg R-omeprazole oral solution**
+
+
+
+
+
+
+
+
+**Figure 3-19: 40 mg R-omeprazole oral solution**
+
+
+
+
+
+
+
+
+**Figure 3-20: 60 mg R-omeprazole oral solution EM day 6**
+
+
+
+
+
+
+
+
+**Figure 3-21: 10 mg omeprazole iv**
+
+
+
+
+
+
+
+
+**Figure 3-22: 10 mg omeprazole iv PM**
+
+
+
+
+
+
+
+
+**Figure 3-23: 20 mg omeprazole iv**
+
+
+
+
+
+
+
+
+**Figure 3-24: 20 mg omeprazole iv PM**
+
+
+
+
+
+
+
+
+**Figure 3-25: 40 mg omeprazole iv**
+
+
+
+
+
+
+
+
+**Figure 3-26: 80 mg omeprazole iv**
+
+
+
+
+
+
+
+
+**Figure 3-27: 60 mg omeprazole oral solution PM day 6**
+
+
+
+
+### 3.3.2 Model Verification
+
+
+
+
+
+**Figure 3-28: 15 mg S-omeprazole oral solution**
+
+
+
+
+
+
+
+
+**Figure 3-29: 40 mg S-omeprazole oral solution**
+
+
+
+
+
+
+
+
+**Figure 3-30: 60 mg S-omeprazole oral solution PM**
+
+
+
+
+
+
+
+
+**Figure 3-31: 15 mg omeprazole oral solution day 6**
+
+
+
+
+
+
+
+
+**Figure 3-32: 20 mg po solution - day 1**
+
+
+
+
+
+
+
+
+**Figure 3-33: 20 mg omeprazole oral solution - day 4**
+
+
+
+
+
+
+
+
+**Figure 3-34: 20 mg po capsule - day 1**
+
+
+
+
+
+
+
+
+**Figure 3-35: 20 mg po capsule - day 5**
+
+
+
+
+
+
+
+
+**Figure 3-36: 20 mg omeprazole oral capsule PM**
+
+
+
+
+
+
+
+
+**Figure 3-37: 40 mg omeprazole oral solution**
+
+
+
+
+
+
+
+
+**Figure 3-38: 40 mg omeprazole oral capsule**
+
+
+
+
+
+
+
+
+**Figure 3-39: 40 mg omeprazole oral capsule PM**
+
+
+
+
+
+
+
+
+**Figure 3-40: 40 mg omeprazole oral capsule Japanese**
+
+
+
+
+
+
+
+
+**Figure 3-41: 80 mg omeprazole oral solution**
+
+
+
+
+
+
+
+
+**Figure 3-42: 40 mg omeprazole oral capsule**
+
+
+
+
+# 4 Conclusion
+
+The developed PBPK model of omeprazole describes the PK data of S-, R-, and racemat omeprazole in CYP2C19 extensive and poor metabolizers after administrations of single as well as multiple p.o. doses very well.
+
+The assumption for the same distribution model and lipophilicity for R-/S-omeprazole is reasonable, as no i.v. data were available for R-omeprazole.
+
+CYP2C19 expression in gut was reduced according to [Olivares-Morales 2016](#5-references) to better describe R-omeprazole. As CYP2C19 CL and expression are obviously inter-dependent, caution should be used when extrapolating such findings to other CYP2C19 substrates. The impact of the reduced expression of CYP2C19 in gut was therefore investigated for its impact on omeprazole levels.
+
+Comparison of population simulation results with observed data show that the observations were generally within the simulated ranges, both after i.v. and p.o. dosing and for either CYP2C19 EM and PM.
+
+# 5 References
+
+**Andersson 1990** Andersson T, Regårdh CG. Pharmacokinetics of Omeprazole and Metabolites Following Single Intravenous and Oral Doses of 40 and 80mg. *Drug Investig*. 1990;2(4):255-263.
+
+**Andersson 1991** Andersson T, Cederberg C, Heggelund A, Lundborg P. The Pharmacokinetics of Single and Repeated Once-Daily Doses of 10, 20 and 40mg Omeprazole as Enteric-Coated Granules. *Drug Investig*. 1991;3(1):45-52.
+
+**Andersson 1998** Andersson T, Holmberg J, Röhss K, Walan A. Pharmacokinetics and effect on caffeine metabolism of the proton pump inhibitors, omeprazole, lansoprazole, and pantoprazole. *Br J Clin Pharmacol*. 1998;45(4):369-375.
+
+**Andersson 2000** Andersson T, Rohss K, Hassan-Alin M, et al. Pharmacokinetics (PK) and effect on pentagastrin stimulated peak acid output (PAO) of omeprazole (O) and its 2 optical isomers, S-omeprazole/esomeprazole (E) and R-omeprazole (R-O). *Gastroenterology*. 118(4):A1210
+
+**Äbelö 2000** Äbelö A, Andersson TB, Antonsson M, Naudot AK, Skanberg I, Weidolf L. Stereoselective metabolism of omeprazole by human cytochrome P450 enzymes. *Drug Metab Dispos*. 2000;28(8):966-972.
+
+**NCT01983566** Investigation of the effect of food and of increased gastric pH on the relative bioavailability of deleobuvir following single oral administration in healthy Caucasian and Japanese subjects (an open label, randomised, four-way crossover study)
+
+**Cho 2002** Cho JY, Yu KS, Jang IJ, Yang BH, Shin SG, Yim DS. Omeprazole hydroxylation is inhibited by a single dose of moclobemide in homozygotic em genotype for CYP2C19. *Br J Clin Pharmacol*. 2002;53(4):393-397.
+
+**DrugBank DB00176** (https://www.drugbank.ca/drugs/DB00176)
+
+**FDA Nexium Review** FDA – Clinical Pharmacology and Biopharmaceutics Review – Nexium delayed-Release Capsules – Esomeprazole sodium – Application number 21-153/21-154
+
+**FDA SPC** FDA_ClinPharmReview LuvoxCR, NDA 22-033, FDADrug_42.pdf, website: https://www.accessdata.fda.gov/drugsatfda_docs/nda/2008/022033s000_ClinPharmR.pdf
+
+**Galetin and Houston 2006** Galetin A, Houston JB. Intestinal and hepatic metabolic activity of five cytochrome P450 enzymes: impact on prediction of first-pass metabolism. *J Pharmacol Exp Ther*. 2006;318(3):1220-1229.
+
+**Hassan-Alin 2000** Hassan-Alin M, Andersson T, Bredberg E, Rohss K. Pharmacokinetics of esomeprazole after oral and intravenous administration of single and repeated doses to healthy subjects. *Eur J Clin Pharmacol*. 2000;56(9-10):665-670.
+
+**Hassan-Alin 2005** Hassan-Alin M, Andersson T, Niazi M, Röhss K. A pharmacokinetic study comparing single and repeated oral doses of 20 mg and 40 mg omeprazole and its two optical isomers, S-omeprazole (esomeprazole) and R-omeprazole, in healthy subjects. *Eur J Clin Pharmacol*. 2005;60(11):779-784
+
+**Nexium prescribing information** Website: https://www.accessdata.fda.gov/drugsatfda_docs/label/2014/022101s014021957s017021153s050lbl.pdf
+
+**Kuepfer 2016** Kuepfer L, Niederalt C, Wendl T, Schlender JF, Willmann S, Lippert J, Block M, Eissing T, Teutonico D. Applied Concepts in PBPK Modeling: How to Build a PBPK/PD Model.CPT Pharmacometrics Syst Pharmacol. 2016 Oct;5(10):516-531.
+
+**Liu 2005** Liu KH, Kim MJ, Shon JH, et al. Stereoselective inhibition of cytochrome P450 forms by lansoprazole and omeprazole in vitro. *Xenobiotica*. 2005;35(1):27-38
+
+**Ogilvie 2011** Ogilvie BW, Yerino P, Kazmi F, Buckley DB, Rostami-Hodjegan A, Paris BL, et al. The proton pump inhibitor, omeprazole, but not lansoprazole or pantoprazole, is a metabolism-dependent inhibitor of CYP2C19: implications for coadministration with clopidogrel. *Drug Metab Dispos.* 2011;39(11):2020–33.
+
+**Olivares-Morales 2016** Olivares-Morales A, Ghosh A, Aarons L, Rostami-Hodjegan A. Development of a Novel Simplified PBPK Absorption Model to Explain the Higher Relative Bioavailability of the OROS(R) Formulation of Oxybutynin. *AAPS J*. 2016;18(6):1532-1549.
+
+**Oosterhuis 1992** Oosterhuis B, Jonkman JHG, Andersson T, Zuiderwijk PBM. No influence of single intravenous doses of omeprazole on theophylline elimination kinetics. *J Clin Pharmacol*. 1992;32(5):470-475.
+
+**PK-Sim Ontogeny Database Version 7.3** (https://github.com/Open-Systems-Pharmacology/OSPSuite.Documentation/blob/38cf71b384cfc25cfa0ce4d2f3addfd32757e13b/PK-Sim%20Ontogeny%20Database%20Version%207.3.pdf)
+
+**Regårdh 1990** Regårdh CG, Andersson T, Lagerstrom PO, Lundborg P, Skanberg I. The pharmacokinetics of omeprazole in humans--a study of single intravenous and oral doses. *Ther Drug Monit*. 1990;12(2):163-172.
+
+**Röhss 2007** Röhss, K., Wilder-Smith, C., Kilhamn, J., Fjellman, M. & Lind, T. Suppression of gastric acid with intravenous esomeprazole and omeprazole: results of 3 studies in healthy subjects. *CP* **45**, 345–354 (2007).
+
+**Uno 2007** Uno T, Niioka T, Hayakari M, Yasui-Furukori N, Sugawara K, Tateishi T. Absolute bioavailability and metabolism of omeprazole in relation to CYP2C19 genotypes following single intravenous and oral administrations. *Eur J Clin Pharmacol*. 2007;63(2):143-149.
+
+**Wilder-Smith 2005** Wilder-Smith CH, Bondarov P, Lundgren M, et al. Intravenous esomeprazole (40 mg and 20 mg) inhibits gastric acid secretion as effectively as oral esomeprazole: Results of two randomized clinical studies. *Eur J Gastroenterol Hepatol*. 2005;17(2):191-197
+
+**Willmann 2007** Willmann S, Höhn K, Edginton A, Sevestre M, Solodenko J, Weiss W, Lippert J, Schmitt W. Development of a physiology-based whole-body population model for assessing the influence of individual variability on the pharmacokinetics of drugs. *J Pharmacokinet Pharmacodyn* 2007, 34(3): 401-431.
+
+**Wu 2014** Wu F, Gaohua L, Zhao P, Jamei M, Huang S-M, Bashaw ED, et al. Predicting Nonlinear Pharmacokinetics of Omeprazole Enantiomers and Racemic Drug Using Physiologically Based Pharmacokinetic Modeling and Simulation: Application to Predict Drug/Genetic Interactions. *Pharmaceutical Research*. 2014 Aug;31(8):1919–29.
+
+**Wu 2016** Wu, J., Gießmann, T., Lang, B., Elgadi, M. & Huang, F. Investigation of the effect of food and omeprazole on the relative bioavailability of a single oral dose of 240 mg faldaprevir, a selective inhibitor of HCV NS3/4 protease, in an open-label, randomized, three-way cross-over trial in healthy participants. *J Pharm Pharmacol* **68**, 459–466 (2016).
+
+**Yasui-Furukori 2004** Yasui-Furukori N, Takahata T, Nakagami T, et al. Different inhibitory effect of fluvoxamine on omeprazole metabolism between CYP2C19 genotypes. *Br J Clin Pharmacol*. 2004;57(4):487-494.
+
+# 6 Glossary
+
+| ADME | Absorption, Distribution, Metabolism, Excretion |
+| ------- | ------------------------------------------------------------ |
+| AUC | Area under the plasma concentration versus time curve |
+| AUCinf | AUC until infinity |
+| AUClast | AUC until last measurable sample |
+| AUCR | Area under the plasma concentration versus time curve Ratio |
+| b.i.d. | Twice daily (bis in diem) |
+| CL | Clearance |
+| Clint | Intrinsic liver clearance |
+| Cmax | Maximum concentration |
+| CmaxR | Maximum concentration Ratio |
+| CYP | Cytochrome P450 oxidase |
+| CYP1A2 | Cytochrome P450 1A2 oxidase |
+| CYP2C19 | Cytochrome P450 2C19 oxidase |
+| CYP3A4 | Cytochrome P450 3A4 oxidase |
+| DDI | Drug-drug interaction |
+| e.c. | Enteric coated |
+| EE | Ethinylestradiol |
+| EM | Extensive metabolizers |
+| fm | Fraction metabolized |
+| FMO | Flavin-containing monooxygenase |
+| fu | Fraction unbound |
+| FDA | Food and Drug administration |
+| GFR | Glomerular filtration rate |
+| HLM | Human liver microsomes |
+| hm | homozygous |
+| ht | heterozygous |
+| IM | Intermediate metabolizers |
+| i.v. | Intravenous |
+| IVIVE | In Vitro to In Vivo Extrapolation |
+| Ka | Absorption rate constant |
+| kcat | Catalyst rate constant |
+| Ki | Inhibitor constant |
+| Kinact | Rate of enzyme inactivation |
+| Km | Michaelis Menten constant |
+| m.d. | Multiple dose |
+| OSP | Open Systems Pharmacology |
+| PBPK | Physiologically-based pharmacokinetics |
+| PK | Pharmacokinetics |
+| PI | Parameter identification |
+| PM | Poor metabolizers |
+| RT-PCR | Reverse transcription polymerase chain reaction |
+| p.o. | Per os |
+| q.d. | Once daily (quaque diem) |
+| SD | Single Dose |
+| SE | Standard error |
+| s.d.SPC | Single dose Summary of Product Characteristics |
+| SD | Standard deviation |
+| TDI | Time dependent inhibition |
+| t.i.d | Three times a day (ter in die) |
+| UGT | Uridine 5'-diphospho-glucuronosyltransferase |
+| UM | Ultra-rapid metabolizers |
+
+","Markdown"
+"PBPK model","Open-Systems-Pharmacology/OSP-PBPK-Model-Library","Erythromycin/Erythromycin_evaluation_report.md",".md","94231","1341","# Building and evaluation of a PBPK model for erythromycin in healthy adults
+
+| Version | 2.0-OSP12.2 |
+| ----------------------------------------------- | ------------------------------------------------------------ |
+| based on *Model Snapshot* and *Evaluation Plan* | https://github.com/Open-Systems-Pharmacology/Erythromycin-Model/releases/tag/v2.0 |
+| OSP Version | 12.2 |
+| Qualification Framework Version | 3.5 |
+
+This evaluation report and the corresponding PK-Sim project file are filed at:
+
+https://github.com/Open-Systems-Pharmacology/OSP-PBPK-Model-Library/
+
+# Table of Contents
+
+ * [1 Introduction](#introduction)
+ * [2 Methods](#methods)
+ * [2.1 Modeling Strategy](#modeling-strategy)
+ * [2.2 Data](#data)
+ * [2.3 Model Parameters and Assumptions](#model-parameters-and-assumptions)
+ * [3 Results and Discussion](#results-and-discussion)
+ * [3.1 Final input parameters](#final-input-parameters)
+ * [3.2 Diagnostics Plots](#diagnostics-plots)
+ * [3.3 Concentration-Time Profiles](#ct-profiles)
+ * [3.3.1 Model Building](#model-building)
+ * [3.3.2 Model Verification](#model-verification)
+ * [4 Conclusion](#conclusion)
+ * [5 References](#main-references)
+
+# 1 Introduction
+
+The presented model building and evaluation report evaluates the performance of a PBPK model for erythromycin in healthy adults.
+
+Erythromycin a macrolide antibiotic used for the treatment of a number of bacterial infections, including respiratory tract infections, skin infections, chlamydia infections, and others. Erythromycin is available in the form of various salts and formulations, for example as:
+
+- erythromycin lactobionate for intravenous injection
+- erythromycin base in enteric-coated capsules or tablets for oral administration
+- erythromycin stearate in filmcoated tablets for oral administration
+- erythromycin ethylsuccinate in suspension or in filmcoated tablets for oral administration
+
+In its free form as base, erythromycin is easily hydrolyzed in acidic aqueous solution ([Mordi 2000](#5-references)). Therefore, orally administered erythromycin is given in the form of enteric-coated formulations or as more acid-stable salts or esters of erythromycin (e.g. erythromycin ethylsuccinate). Once in the small intestine, erythromycin is rapidly absorbed displaying a highly variable bioavailability ([Chun 1977](#5-references), [Mather 1981](#5-references)). Erythromycin diffuses in most tissues and accumulates in leukocytes and phagocytes ([Miller 1984](#5-references), [Carlier 1987](#5-references)). About 70% of erythromycin is bound to plasma proteins ([Barre 1987](#5-references)). Erythromycin has been shown to be a substrate for various transporters including P-gp and OATP1B1. The latter has been shown to critically affect erythromycin disposition ([Lancaster 2012](#5-references)). Erythromycin is extensively metabolized through N-demethylation catalyzed by CYP3A. Metabolism via CYP4F11 has also been suggested ([Kalsotra 2004](#5-references)). Biliary excretion also appears to play an important role in erythromycin clearance ([Acocella 1968](#5-references), [Chelvan 1979](#5-references)), but its contribution to total elimination remains unknown. The dose fraction excreted unchanged in urine is minimal and highly variable; reported fractions after IV administration range from 0.018 ± 0.005 to 0.171 ± 0.11 (mean ± SD) ([Pasic 1987](#5-references), [Austin 1980](#5-references)). There is abundant evidence from *in vitro* studies that erythromycin irreversibly inhibits CYP3A (e.g. [Larrey 1983](#5-references)) and the FDA lists erythromycin as moderate index inhibitor for CYP3A. Findings from *in vivo* studies investigating the dose linearity of erythromycin pharmacokinetics are not fully conclusive, but some studies observed a slight dose dependency ([Austin 1980](#5-references), [Josefsson 1982](#5-references)).
+
+# 2 Methods
+
+## 2.1 Modeling Strategy
+
+The general workflow for building an adult PBPK model has been described by Kuepfer et al. ([Kuepfer 2016](#5-references)). Relevant information on the anthropometry (height, weight) was gathered from the respective clinical study, if reported. Information on physiological parameters (e.g. blood flows, organ volumes, hematocrit) in adults was gathered from the literature and has been incorporated in PK-Sim® as described previously ([Willmann 2007](#5-references)). The applied activity and variability of plasma proteins and active processes that are integrated into PK-Sim® are described in the publicly available 'PK-Sim® Ontogeny Database Version 7.3' ([PK-Sim Ontogeny Database Version 7.3](#5-references)).
+
+The PBPK model was developed based on clinical data of healthy adult subjects obtained from the literature, covering different formulation types and erythromycin salts. Multiple doses and dosing schedules following intravenous (IV) and oral (PO) administration were included in model building. Mass balance information on urinary excretion of unchanged erythromycin after IV administration was also accounted for during the model building process.
+
+Unknown parameters were simultaneously optimized using all available PK data, in particular:
+
+- 4 data sets following single IV administration of 4 different doses of erythromycin (125 mg, 250 mg, 300 mg, 500 mg) as erythromycin lactobionate
+- 6 data sets following single and multiple PO administration of 3 different doses of erythromycin (250 mg, 500 mg, 1000 mg) as film-coated tablets containing erythromycin stearate
+- 2 data sets following single PO administration of 500 mg erythromycin as enteric-coated tablets containing erythromycin as base
+- 2 data sets following single and multiple PO administration of 2 different doses of erythromycin (250 mg, 500 mg) as enteric-coated capsules containing pellets of erythromycin as base
+
+Structural model selection was mainly guided by visual inspection of the resulting description of data and biological plausibility. The following parameters were identified using the Parameter Identification module provided in PK-Sim® and MoBi® ([Open Systems Pharmacology Documentation](#5-references)):
+
+- `Specific intestinal permeability (transcellular)`
+- `Transport Protein - OATP1B1 - kcat`
+- `Metabolizing Enzyme - CYP3A4 - kcat`
+- `Total Hepatic Clearance - Specific clearance`
+- `GFR fraction`
+- `K_kinact_half`
+- `kinact`
+- `Dissolution shape` (separately for the film-coated tablet containing erythromycin stearate, the enteric-coated tablet containing erythromycin as base, and the enteric-coated capsule containing pellets of erythromycin as base)
+- `Dissolution time (50% dissolved)` (separately for the film-coated tablet containing erythromycin stearate, the enteric-coated tablet containing erythromycin as base, and the enteric-coated capsule containing pellets of erythromycin as base)
+- `Dissolution lag time` (separately for the enteric-coated tablet containing erythromycin as base and the enteric-coated capsule containing pellets of erythromycin as base)
+- `Solubility at reference pH` (only for the enteric-coated tablet containing erythromycin as base)
+
+Details about input data (physicochemical, *in vitro* and clinical) can be found in [Section 2.2](#22-data).
+
+Details about the structural model and its parameters can be found in [Section 2.3](#23-model-parameters-and-assumptions).
+
+## 2.2 Data
+
+### 2.2.1 In vitro / physicochemical data
+
+A literature search was carried out to collect available information on physicochemical properties of erythromycin. The obtained information from the literature is summarized in the table below and is used for model building.
+
+| **Parameter** | **Unit** | **Literature** | **Description** |
+| :------------------------------------------- | -------- | ------------------------------------------------------------ | ------------------------------------------------ |
+| Molecular weight | g/mol | 733.9 ([drugbank.ca](#5-references)) | Molecular weight |
+| pKa (basic) | | 8.8 ([Lien 1974](#5-references)); 8.88 ([McFarland 1997](#5-references)) | Acid dissociation constant |
+| logP | | 2.48 ([Lien 1974](#5-references)); 2.92 ([Capobianco 1994](#5-references)); 3.06 ([McFarland 1997](#5-references)) | Partition coefficient between octanol and water |
+| fu | | 0.27 ± 0.05a ([Sun 2010](#5-references)); 0.28 ± 0.04a ([Iliopoulou 1982](#5-references)); 0.305 ± 0.028a ([Barre 1987](#5-references)); 0.326 ([Xu 2009](#5-references)) | Fraction unbound in human plasma |
+| Water solubility (erythromycin lactobionate) | mg/L | 200000 ([U.S. Patent 2,761,859](#5-references)) | Solubility of erythromycin lactobionate in water |
+| Water solubility (erythromycin stearate) | mg/L | 182 ([Jones 1969](#5-references)) | Solubility of erythromycin stearate in water |
+| Water solubility (erythromycin base) | mg/L | 2100 ([Manna 1998](#5-references)) | Solubility of free erythromycin as base in water |
+
+ a mean ± SD
+
+### 2.2.2 In vitro data on mechanism-based inhibition of CYP3A
+
+A literature search was carried out to collect quantitative information the kinetic parameters describing the mechanism-based inhibition of CYP3A by erythromycin, KI and kinact. Ample data was collected from the literature and is summarized in the table below.
+
+| Victim compound | In vitro system | Enzyme | KI [µM] | kinact [min-1] | Concentration range of erythromycin [µM] | Concentration of victim compound [µM] | Reference |
+| ----------------- | ----------------- | ------ | -------------------- | -------------------------------------- | ------------------------------------------ | --------------------------------------- | ----------------------------------- |
+| testosterone | rec cDNA CYP3A4 | CYP3A4 | 1.04 | 0.0293 | 0 - 20 | 100 | [Akiyoshi 2013](#5-references) |
+| testosterone | rec cDNA CYP3A4 | CYP3A4 | 1.21 | 0.0164 | 0 - 20 | 100 | [Akiyoshi 2013](#5-references) |
+| testosterone | rec cDNA CYP3A4 | CYP3A4 | 0.415 | 0.0159 | 0 - 20 | 100 | [Akiyoshi 2013](#5-references) |
+| testosterone | rec cDNA CYP3A4 | CYP3A4 | 2.24 | 0.0293 | 0 - 20 | 100 | [Akiyoshi 2013](#5-references) |
+| testosterone | rec cDNA CYP3A4 | CYP3A4 | 0.753 | 0.0248 | 0 - 20 | 100 | [Akiyoshi 2013](#5-references) |
+| midazolam | rec cDNA CYP3A4 | CYP3A | 9.5 | 0.16 | 50-fold range | 10 | [Atkinson 2005](#5-references) |
+| midazolam | rec cDNA CYP3A4 | CYP3A | 8.82 | 0.12 | 50-fold range | 10 | [Atkinson 2005](#5-references) |
+| midazolam | rec cDNA CYP3A4 | CYP3A | 8.3 | 0.09 | 50-fold range | 10 | [Atkinson 2005](#5-references) |
+| testosterone | rec cDNA CYP3A4 | CYP3A | 9.5 | 0.06 | 4 - 200 | 16.7 | [Atkinson 2005](#5-references) |
+| triazolam | HLM | CYP3A | 5.4 | 0.069 | | 400 | [Aueviriyavit 2010](#5-references) |
+| testosterone | rec cDNA CYP3A4 | CYP3A4 | 5.3 | 0.12 | 5 - 20 | 200 | [Chan 2000](#5-references) |
+| midazolam | cryopreserved HEP | CYP3A | 60 | 0.081 | 3 - 300 | 30 | [Chen 2011](#5-references) |
+| midazolam | cryopreserved HEP | CYP3A | 67.9 | 0.079 | 3 - 300 | 30 | [Chen 2011](#5-references) |
+| midazolam | rec cDNA CYP3A4 | CYP3A4 | 0.762 | 0.0648 | 0.1 - 30 | 4 | [Ishikawa 2017](#5-references) |
+| testosterone | rec cDNA CYP3A4 | CYP3A4 | 1.00 | 0.0604 | 0.1 - 30 | 150 | [Ishikawa 2017](#5-references) |
+| nifedipine | rec cDNA CYP3A4 | CYP3A4 | 0.794 | 0.0766 | 0.1 - 30 | 6 | [Ishikawa 2017](#5-references) |
+| triazolam | HLM | CYP3A4 | 15.9 | 0.062 | 3 - 100 | 300 | [Kanamitsu 2000](#5-references) |
+| triazolam | HLM | CYP3A4 | 17.4 | 0.055 | 3 - 100 | 300 | [Kanamitsu 2000](#5-references) |
+| triazolam | rec cDNA CYP3A4 | CYP3A4 | 19.1 | 0.173 | 3 - 100 | 300 | [Kanamitsu 2000](#5-references) |
+| triazolam | rec cDNA CYP3A4 | CYP3A4 | 18.9 | 0.097 | 3 - 100 | 300 | [Kanamitsu 2000](#5-references) |
+| testosterone | HLM | CYP3A4 | 29.4 | 0.0271 | | 250 | [Kosaka 2017](#5-references) |
+| testosterone | HLM | CYP3A4 | 30 | 0.040 | 0.3 - 300 | | [Kozakai 2013](#5-references) |
+| midazolam | HLM | CYP3A4 | 12 | 0.035 | 0.3 - 300 | | [Kozakai 2013](#5-references) |
+| midazolam | HLM | CYP3A4 | 20 | 0.033 | 0.3 - 300 | | [Kozakai 2013](#5-references) |
+| midazolam | cryopreserved HEP | CYP3A | 25.15 | 0.08 | 0.13 - 100 | 30 | [Mao 2011](#5-references) |
+| midazolam | HLM | CYP3A | 10.8 | 0.032 | | | [Mao 2016](#5-references) |
+| midazolam | cryopreserved HEP | CYP3A | 30.7 | 0.05 | 0 - 300 | 20 | [Mao 2016](#5-references) |
+| midazolam | cryopreserved HEP | CYP3A | 59.2 | 0.062 | 0 - 100 | 5 | [Mao 2016](#5-references) |
+| midazolam | cryopreserved HEP | CYP3A | 80.3 | 0.052 | 0 - 100 | 20 | [Mao 2016](#5-references) |
+| midazolam | rec cDNA CYP3A4 | CYP3A4 | 7.47 | 0.042 | 2 - 50 | 8 | [McConn 2004](#5-references) |
+| midazolam | HLM | CYP3A4 | 10.9 | 0.046 | 2 - 100 | 8 | [McConn 2004](#5-references) |
+| midazolam | primary HEP | CYP3A | 11 | 0.07 | 0.1 - 10 | 3 | [McGinnity 2006](#5-references) |
+| midazolam | HLM | CYP3A4 | 10 | 0.036 | 0 - 25 | | [Obach 2007](#5-references) |
+| testosterone | HLM | CYP3A4 | 9.8 | 0.039 | 0 - 25 | 500 | [Obach 2007](#5-references) |
+| testosterone | rec cDNA CYP3A4 | CYP3A4 | 0.92 | 0.058 | 5 - 100 | 250 | [Polasek 2006](#5-references) |
+| testosterone | HLM | CYP3A4 | 12.8 | 0.037 | 5 - 100 | 250 | [Polasek 2006](#5-references) |
+| midazolam | rec cDNA CYP3A4 | CYP3A4 | 5.1 | 0.30 | 0.5 - 50 | 100 | [Ring 2005](#5-references) |
+| testosterone | rec cDNA CYP3A4 | CYP3A4 | 0.92 | 0.058 | | | [Teng 2010](#5-references) |
+| testosterone | HLM | CYP3A4 | 4.579 | 0.0115 | | | [Teng 2010](#5-references) |
+| domperidone | HLM | CYP3A4 | 18.4 | 0.022 | 2.5 - 200 | 500 | [Ung 2009](#5-references) |
+| domperidone | rec cDNA CYP3A4 | CYP3A4 | 4.1 | 0.026 | 2.5 - 200 | 500 | [Ung 2009](#5-references) |
+| midazolam | HLM | CYP3A4 | 12.1 | 0.0215 | 0 - 100 | 25 | [Watanabe 2007](#5-references) |
+| nifedipine | HLM | CYP3A4 | 11.3 | 0.0295 | 0 - 100 | 50 | [Watanabe 2007](#5-references) |
+| testosterone | HLM | CYP3A4 | 10.9 | 0.0352 | 0 - 100 | 200 | [Watanabe 2007](#5-references) |
+| midazolam | HLM | CYP3A | 1.48 | 0.017 | 0.5 - 500 | 20 | [Xu 2009](#5-references) |
+| midazolam | cryopreserved HEP | CYP3A | 20.0 | 0.016 | 0.5 - 500 | 20 | [Xu 2009](#5-references) |
+| midazolam | cryopreserved HEP | CYP3A | 109 | 0.055 | 0.5 - 500 | 20 | [Xu 2009](#5-references) |
+| midazolam | HLM | CYP3A | 81.8 | 0.0665 | 20 - 400 | 10 | [Yamano 2001](#5-references) |
+| midazolam | HLM | CYP3A | 15.7 | 0.1 | | | [Zhang 2006](#5-references) |
+| testosterone | rec cDNA CYP3A4 | CYP3A4 | 5 | 0.34 | 1 - 50 | 200 | [Zhang 2009](#5-references) |
+| testosterone | HLM | CYP3A4 | 15.7 | 0.09 | 1 - 50 | 200 | [Zhang 2009](#5-references) |
+| midazolam | HLM | CYP3A4 | 26.5 | 0.041 | 2.5 - 50 | 10 | [Zimmerlin 2011](#5-references) |
+
+*Note:* Abbreviations: HEP: human hepatocytes; HLM: human liver microsomes; rec cDNA CYP3A4: human recombinant c-DNA CYP3A4 enzymes (e.g. supersomes, baculovirus-insect cell system, E. coli transfected cells)
+
+The data listed in the Table above can be statistically summarized as follows:
+
+| Parameter [unit] | Min | Q1 | Geometric mean | Median | Arithmetic mean | Q3 | Max |
+| ------------------------------------ | ------ | ------ | -------------- | ------ | --------------- | ------ | ----- |
+| KI [µM] | 0.420 | 4.89 | 8.71 | 10.9 | 18.4 | 19.3 | 109 |
+| kinact [min-1] | 0.0115 | 0.0314 | 0.0504 | 0.0535 | 0.0664 | 0.0772 | 0.340 |
+
+### 2.2.3 Clinical data
+
+A literature search was carried out to collect available PK data on erythromycin in healthy adults. The following data from the publications listed below were used for model building and evaluation:
+
+| Publication | Study description |
+| :-------------------------------- | :----------------------------------------------------------- |
+| [Austin 1980](#5-references) | IV administration of 125 mg, 250 mg, 500 mg, and 900 mg as erythromycin lactobionate; single dose |
+| [Barre 1987](#5-references) | IV administration of 500 mg as erythromycin lactobionate; single dose |
+| [Berend 1979](#5-references) | PO administration of 500 mg erythromycin stearate; multiple dose |
+| [Birkett 1990](#5-references) | PO administration of 250 mg as enteric-coated capsules containing pellets of erythromycin base; single and multiple dose |
+| [Brannan 1995](#5-references) | PO administration of 500 mg erythromycin stearate; multiple dose |
+| [DiSanto 1981](#5-references) | PO administration of 500 mg as unprotected tablets containing erythromycin base, as film-coated tablets containing erythromycin base, as enteric-coated tablets containing erythromycin base and as film-coated tablets containing erythromycin stearate; single and multiple dose |
+| [Henry 1980](#5-references) | PO administration of 500 mg erythromycin stearate; single dose |
+| [Huppertz 2011](#5-references) | IV administration of 1000 mg as erythromycin lactobionate; single dose |
+| [Iliopoulou 1982](#5-references) | PO administration of 500 mg as film-coated tablet containing erythromycin stearate; multiple dose |
+| [Josefsson 1982](#5-references) | PO administration of 500 mg as film-coated tablet containing erythromycin stearate and 250 mg, 500 mg, 1000 mg as enteric coated capsules containing pellets of erythromycin base; single dose |
+| [Kavi 1988](#5-references) | PO administration of 500 mg as film-coated tablet containing erythromycin stearate; single dose |
+| [Kivistö 1997](#5-references) | PO administration of 500 mg as enteric-coated capsules containing erythromycin base; multiple dose |
+| [Kroboth 1982](#5-references) | PO administration of 500 mg as enteric-coated tablets containing erythromycin base; single dose |
+| [Malmborg 1978](#5-references) | PO administration of 500 mg as film-coated tablet containing erythromycin stearate; multiple dose |
+| [Miglioli 1990](#5-references) | PO administration of 1000 mg erythromycin stearate; multiple dose |
+| [Olkkola 1993](#5-references) | Midazolam-erythromycin interaction study; PO administration of 500 mg as enteric-coated tablet containing erythromycin base; multiple dose |
+| [Parsons 1977](#5-references) | PO administration of 500 mg erythromycin stearate; single dose |
+| [Pasic 1987](#5-references) | IV administration of 300 mg as erythromycin lactobionate; single dose |
+| [Posti 1983](#5-references) | PO administration of 500 mg as film-coated tablets containing erythromycin stearate, as enteric-coated tablets containing erythromycin base, and as enteric-coated tablets containing erythromycin stearate; single dose |
+| [Schreiner 1984](#5-references) | PO administration of 500 mg as film-coated tablets containing erythromycin stearate and as enteric-coated capsule containing pellets of erythromycin base; single dose |
+| [Shanson 1984](#5-references) | PO administration of 1500 mg erythromycin stearate; single dose |
+| [Simon 1980](#5-references) | IV administration of 500 mg erythromycin lactobionate and PO administration of 500 mg erythromycin stearate; single dose |
+| [Sun 2010](#5-references) | IV administration of 125 mg and PO administration of 250 mg (salt and formulation type not specified); single dose |
+| [Yakatan 1979](#5-references) | PO administration of 250 mg as film-coated tablet containing erythromycin stearate; single dose |
+| [Yakatan 1980](#5-references) | PO administration of 250 mg as film-coated tablet containing erythromycin stearate and as enteric-coated tablet containing erythromycin base; single and multiple dose |
+
+## 2.3 Model Parameters and Assumptions
+
+### 2.3.1 Dissolution and absorption
+
+Development of an adequate absorption model for erythromycin was complicated by the large intersubject variability in the absorption kinetics of erythromycin ([Chun 1977](#5-references), [Mather 1981](#5-references)). Additionally, multiple formulation types are available entailing different dissolution and absorption kinetics ([Chun 1977](#5-references), [Yakatan 1980](#5-references), [Mather 1981](#5-references)). The herein presented model was developed for the following oral dosage forms:
+
+- film-coated tablets containing erythromycin stearate
+- enteric-coated tablets containing erythromycin as free base
+- enteric-coated capsules containing pellets of erythromycin as free base
+
+The following sections briefly address how each of these formulations was modeled in presented PBPK models.
+
+#### 2.3.1.1 Film-coated tablet containing erythromycin stearate
+
+Comparison of the reported erythromycin PK following administration of different doses of film-coated tablets containing the stearate salt indicated that the lowest dose (250 mg) yields a larger tmax than the higher doses (500, 1000 and 1500 mg). Different dissolution kinetics were therefore used for this formulation type; relatively slow dissolution kinetics was used for the 250 mg dose, whereas dissolution of the higher doses (500, 1000 and 1500 mg) was described by immediate-release kinetics. Both types of dissolution kinetics were described by the Weibull function. The parameters `Dissolution shape` and `Dissolution time (50% dissolved)` were optimized to optimally describe the clinical data. Additionally, solubility was also optimized as it was found during model building that solubility-limited absorption of the high doses captured the observed PK profiles better.
+
+#### 2.3.1.2 Enteric-coated tablet containing erythromycin as free base
+
+Relatively few PK data were available for enteric-coated tablets containing erythromycin as free base. For this formulation type, PK profiles were only available for the 250 mg and 500 mg dose. For these two doses, dissolution was described by the Weibull function. To account for the enteric coating (protecting the tablet from dissolving in the stomach), a `lag time` > 0 min was applied in the model. To optimally describe the clinical data, relevant parameters of the Weibull function were optimized, namely the `Dissolution shape`, `Dissolution time (50% dissolved)` and `lag time`. Similar to the film-coated tablet containing the stearate salt, it was found that the observed PK profiles of the enteric-coated tablet containing erythromycin as free base could be better described when optimizing solubility.
+
+#### 2.3.1.3 Enteric-coated capsule containing pellets of erythromycin as free base
+
+Comparison of the reported erythromycin PK following administration of different doses of enteric-coated capsules containing pellets of erythromycin base showed large variability in Cmax and tmax both within and between different doses (dose range: 250 mg - 1000 mg). Here, dissolution kinetics was also described by the Weibull function with relevant parameters (`Dissolution shape`, `Dissolution time (50% dissolved)` and `lag time`) being optimized to better describe clinical data. However, optimizing solubility did not result in a better description of the observed PK profiles. Consequently, solubility of erythromycin in this formulation type was fixed to a high value (500 mg/L) to avoid any solubility-related limitations in dissolution and absorption.
+
+For all three formulations, the same `Specific intestinal permeability (transcellular)` was used in the PBPK model. This parameter was also optimized to better describe the clinical PK data.
+
+### 2.3.2 Distribution
+
+With an average fraction unbound in human plasma of approximately 0.30, erythromycin is moderately protein-bound. In the developed model, the `fraction unbound (plasma, reference value)` was set to 0.305 which is the value reported by Barre et al. ([Barre 1987](#5-references)). `Lipophilicity` was fixed to a value of 2.82 which is the average of three experimentally measured values reported in the literature ([Lien 1974](#5-references), [Capobianco 1994](#5-references), [McFarland 1997](#5-references)). The observed PK data were found to be best described using the model for estimating intracellular-to-plasma partition coefficients according to the method by `Rodgers and Rowland` ([Rodgers 2005](#5-references), [Rodgers 2006](#5-references)). Cellular permeabilities were automatically calculated using the method `Charge dependent Schmitt` ([Open Systems Pharmacology Documentation](#5-references)). Active transfer of erythromycin by OATP1B1 was modeled as Michaelis-Menten kinetics using an `Influx` transporter type; the `Km` value of 13.2 µM was taken from the literature ([Lancaster 2012](#5-references)) and `kcat` was optimized to best match observed clinical data. The gene expression profile of OATP1B1 (default symbol for the gene: LST-3TM12) was loaded from the internal PK-Sim® database using the expression data quantified by RT-PCR ([Open Systems Pharmacology Documentation](#5-references)).
+
+### 2.3.3 Elimination
+
+Erythromycin is extensively metabolized via N-demethylation catalyzed by CYP3A. Kinetics of this biotransformation was described by a Michaelis-Menten process. The following kinetic parameters erythromycin N-demethylation have been measured in human liver microsomes (HLM) and reported in the literature (mean ± standard error):
+
+| Vmax [nmol/min/mg mic protein] | Km [µM] | Microsomal preparation | Reference |
+| ------------------------------ | ------- | ---------------------------- | ---------------------------- |
+| 2 ± 0.09 | 78 ± 9 | HLM from donor HL 3926 | [Wang 1997](#5-references) |
+| 0.41 ± 0.02 | 44 ± 7 | HLM from donor HL 24493 | [Wang 1997](#5-references) |
+| 0.345 ± 0.013 | 88 ± 10 | mixed HLM pool | [Riley 1997](#5-references) |
+
+In the PBPK model, `Vmax` and `Km` were fixed to the mean of the values tabulated above (70 µM and 0.918 nmol/min/mg mic protein). The gene expression profile of CYP3A4 was loaded from the internal PK-Sim® database using the expression data quantified by RT-PCR ([Open Systems Pharmacology Documentation](#5-references)).
+
+Although it has also been observed that erythromycin is metabolized via CYP4F11 *in vitro* ([Kalsotra 2004](#5-references)), this elimination pathway was not accounted for in the model because its contribution to overall elimination was assumed to be low. In humans, CYP4F11 is mainly expressed in the liver and to a much lesser extent in the kidney ([Cui 2000](#5-references)) and the CYP4F family makes up approximately 15% of all hepatic CYP enzymes ([Michaels 2014](#5-references)). The Km and Vmax values for the CYP4F11-mediated biotransformation reported by [Kalsotra 2004](#5-references) are similar to those measured for CYP3A4 ([Riley 1997](#5-references), [Wang 1997](#5-references)), suggesting that the relative mass balance of these two metabolism pathways mainly depends on the absolute amount of each enzyme in the liver. While no information on total CYP4F11 in the human liver could be found in the literature, CYP4F11 expression in the liver of cynomolgus monkeys was observed to be approximately 6-fold lower than that of CYP3A4 ([Uehara 2015](#5-references)). Hence, it was assumed that CYP4F11-mediated metabolism of erythromycin can be neglected in humans.
+
+Additional elimination pathways suggested for erythromycin are acid-catalyzed degradation (hydrolysis) the acidic milieu of the stomach ([Mordi 2000](#5-references)) and biliary excretion ([Acocella 1968](#5-references), [Chelvan 1979](#5-references)), but no quantitative information on the mass balance of these pathways could be found in the literature. Additionally, mechanism-based inhibition of CYP3A4 by erythromycin might constitute another clearance process which was neither considered in the model. However, a `total hepatic clearance` process was implemented in the model which could at least partly account for other elimination pathways not explicitly accounted for in the model. Of note, despite the name `total hepatic clearance`, this clearance pathway was implemented as dummy clearance accounting for additional elimination processes that are not covered by CYP3A-mediated clearance and unchanged renal excretion and it should hence rather be regarded as a partial than a total clearance.
+
+The reported dose fractions of erythromycin undergoing unchanged renal excretion after IV administration range from 0.018 ± 0.005 to 0.171 ± 0.11 (mean ± SD) ([Pasic 1987](#5-references), [Austin 1980](#5-references)). This information was accounted for in the model by implementing a glomerular filtration process and optimizing the `GFR fraction` to match the observed dose fractions excreted unchanged in urine.
+
+### 2.3.4 Autoinhibition via CYP3A4
+
+In the scientific literature, large ranges have been reported for KI and kinact ([Section 2.2.2](#222-in-vitro-data-on-mechanism-based-inhibition-of-cyp3a)). Since the exact values are unknown, `K_kinact_half` and `kinact` were both optimized within the observed range (see [Section 2.2.2](#222-in-vitro-data-on-mechanism-based-inhibition-of-cyp3a)) during model building to best match the observed clinical data.
+
+To better inform optimization of these two parameters, clinical data of a midazolam-erythromycin interaction study conducted by Olkkola et al. ([Olkkola 1993](#5-references)) were included in the parameter optimization during model building. Therefore, the midazolam PBPK model v0.9 available on OSP GitHub (https://github.com/Open-Systems-Pharmacology/Midazolam-Model/releases/tag/0.9) was loaded in the PK-Sim® erythromycin file and the study by Olkkola et al. ([Olkkola 1993](#5-references)) was simulated. However, instead of using the reported midazolam plasma concentrations as observed data in the parameter identification, the AUC of midazolam was used. More specifically, a midazolam target AUC after IV and oral administration was calculated by multiplying the simulated midazolam AUC (24.3 and 54.0 µmol min/L and after IV and oral administration, respectively) with the observed geometric mean AUC ratio (1.96 and 4.07 after IV and PO administration, respectively) ([Olkkola 1993](#5-references)) resulting in target AUCs of 47.4 and 220 µmol min/L after IV and oral administration of midazolam, respectively. These values were included as observed data values in the parameter identification during model building. Since the AUC is not a default output that can directly be used in the parameter identification, the PBPK model structure was modified prior to running the parameter identification as described in the following. After exporting the model to MoBi®, an artificial reaction of a dummy molecule was created. The reaction rate was defined as the simulated peripheral venous blood plasma concentration of midazolam, hence yielding the AUC at any specific time point. Thereafter, the model was imported in PK-Sim® and included in the parameter identification. After being used in the parameter identification during model building, the model was not used any further.
+
+### 2.3.5 Inhibition of P-gp
+
+The model also includes competitive inhibition of P-gp with a Ki of 22.7 µmol/L ([Eberl 2007](#5-references)).
+
+### 2.3.6 Automated Parameter Identification
+
+This is the result of the final parameter identification:
+
+| Model Parameter | Formulation type/salt form | Optimized Value | Unit |
+| ----------------------------------- | ---------------------------------------- | ------------------------ | ------ |
+| `Dissolution shape ` | Enteric coated pellets | 1.0564916105 | |
+| `Dissolution shape ` | Enteric coated tablet | 1.0838799888 | |
+| `Dissolution shape ` | Filmcoated tablet (except 250 mg dose) | 1.0960212213 | |
+| `Dissolution shape ` | Filmcoated tablet, 250 mg | 3.2811974117 | |
+| `Dissolution time (50% dissolved) ` | Enteric coated pellets | 1.7462743767 | min |
+| `Dissolution time (50% dissolved) ` | Enteric coated tablet | 79.6337524677 | min |
+| `Dissolution time (50% dissolved) ` | Filmcoated tablet (except 250 mg dose) | 1.7038947098 | min |
+| `Dissolution time (50% dissolved) ` | Filmcoated tablet, 250 mg | 83.6562552486 | min |
+| `GFR fraction` | | 1.1591081815 | |
+| `K_kinact_half` (CYP3A4) | | 7.6007360452 | µmol/L |
+| `kcat` (OATP1B1) | | 2.0201069202* | 1/min |
+| `kinact` (CYP3A4) | | 0.0296261146 | 1/min |
+| `Km` (OATP1B1) | | 0.735836485 | µmol/L |
+| `Lag time` | Enteric coated pellets | 54.3490442506 | min |
+| `Lag time` | Enteric coated tablet | 78.7967495765 | min |
+| `Solubility at ref pH` | Enteric coated tablet, erythromycin base | 8.3990771997 | mg/L |
+| `Solubility at ref pH` | Filmcoated tablet, erythromycin stearate | 28.0708790976 | mg/L |
+| `Specific clearance` | | 4.1462183378 | 1/min |
+| `Specific intestinal permeability` | | 0.00038668371665 | cm/min |
+
+* The value in the model was updated to 1.350032201 with the release of PK-Sim 10 to account for the updated calculation method of interstitial concentrations (please refer to the respective [release notes of version 10](https://github.com/Open-Systems-Pharmacology/Suite/releases/tag/v10.0)).
+
+# 3 Results and Discussion
+
+The PBPK model for erythromycin was developed and verified with clinical pharmacokinetic data.
+
+The next sections show:
+
+1. the final model parameters for the building blocks: [Section 3.1](#31-final-input-parameters).
+2. the overall goodness of fit: [Section 3.2](#32-diagnostics-plots).
+3. simulated vs. observed concentration-time profiles for the clinical studies used for model building and for model verification: [Section 3.3](#33-concentration-time-profiles).
+
+## 3.1 Final input parameters
+
+The compound parameter values of the final PBPK model are illustrated below.
+
+### Compound: Erythromycin
+
+#### Parameters
+
+Name | Value | Value Origin | Alternative | Default
+------------------------------------------------ | ----------------------- | -------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------- | -------
+Solubility at reference pH | 28.0708790976 mg/l | Parameter Identification-Parameter Identification-Value updated from '003 - BP conc ratio FIX' on 2019-11-14 14:43 | Erythromycin stearate film-coated tablet | True
+Reference pH | 7 | Parameter Identification-Parameter Identification-Value updated from '001' on 2019-10-01 13:46 | Erythromycin stearate film-coated tablet | True
+Solubility at reference pH | 500 mg/l | Parameter Identification-Parameter Identification-Value updated from '001' on 2019-10-01 13:46 | Erythromycin base enteric coated pellets | False
+Reference pH | 7 | Parameter Identification-Parameter Identification-Value updated from '001' on 2019-10-01 13:46 | Erythromycin base enteric coated pellets | False
+Solubility at reference pH | 200 mg/ml | Publication-In Vitro-Hoffhine, Jr Charles E. ""Aqueous soluble salts of erythromycin."" U.S. Patent 2,761,859, issued September 4, 1956. | Erythromycin lactobionate | False
+Reference pH | 7 | Publication-In Vitro-Hoffhine, Jr Charles E. ""Aqueous soluble salts of erythromycin."" U.S. Patent 2,761,859, issued September 4, 1956. | Erythromycin lactobionate | False
+Solubility at reference pH | 8.3990771997 mg/l | Parameter Identification-Parameter Identification-Value updated from 'Parameter Identification 1' on 2020-01-13 15:01 | Erythromycin base enteric coated tablet | False
+Reference pH | 7 | | Erythromycin base enteric coated tablet | False
+Lipophilicity | 2.82 Log Units | Parameter Identification-Parameter Identification-Value updated from 'Parameter Identification 1' on 2020-01-13 15:01 | Literature (average value) | True
+Fraction unbound (plasma, reference value) | 0.305 | Publication-In Vivo-PMID: 3606934 | Barre 1987 | True
+Specific intestinal permeability (transcellular) | 0.00038668371665 cm/min | Parameter Identification-Parameter Identification-Value updated from 'Parameter Identification 1' on 2020-01-13 15:01 | Fitted | True
+Is small molecule | Yes | | |
+Molecular weight | 733.927 g/mol | Internet-drugbank.ca | |
+Plasma protein binding partner | Unknown | | |
+
+#### Calculation methods
+
+Name | Value
+----------------------- | ------------------------
+Partition coefficients | Rodgers and Rowland
+Cellular permeabilities | Charge dependent Schmitt
+
+#### Processes
+
+##### Metabolizing Enzyme: CYP3A4-Biotransformation_fitted
+
+Molecule: CYP3A4
+
+###### Parameters
+
+Name | Value | Value Origin
+---------------------------------- | ---------------------------------- | ---------------------------------------------------------------------------------------------------
+In vitro Vmax for liver microsomes | 918.33333 pmol/min/mg mic. protein | Publication-In Vitro-PMID: 9566442
+Km | 70 µM | Publication-In Vitro-Average of reported values in the literature (PMID: 9107550 and PMID: 9566442)
+
+##### Systemic Process: Glomerular Filtration-fitted
+
+Species: Human
+
+###### Parameters
+
+Name | Value | Value Origin
+------------ | ------------:| ---------------------------------------------------------------------------------------------------------------------
+GFR fraction | 1.1591081815 | Parameter Identification-Parameter Identification-Value updated from 'Parameter Identification 1' on 2020-01-13 15:01
+
+##### Inhibition: CYP3A4-MBI
+
+Molecule: CYP3A4
+
+###### Parameters
+
+Name | Value | Value Origin
+------------- | ------------------- | ---------------------------------------------------------------------------------------------------------------------
+kinact | 0.0296261146 1/min | Parameter Identification-Parameter Identification-Value updated from 'Parameter Identification 1' on 2020-01-13 15:01
+K_kinact_half | 7.6007360452 µmol/l | Parameter Identification-Parameter Identification-Value updated from 'Parameter Identification 1' on 2020-01-13 15:01
+
+##### Transport Protein: OATP1B1-fitted
+
+Molecule: OATP1B1
+
+###### Parameters
+
+Name | Value | Value Origin
+------------------------- | ------------------ | ---------------------------------------------------------------------------------------------------------------------
+Transporter concentration | 1 µmol/l |
+Vmax | 11.66 pmol/ml/min | Publication-In Vitro-PMID: 22990751
+Km | 0.735836485 µmol/l | Parameter Identification-Parameter Identification-Value updated from 'Parameter Identification 1' on 2020-01-13 15:01
+kcat | 1.350032201 1/min | Parameter Identification-Parameter Identification-Value updated from 'Parameter Identification 1' on 2020-01-13 15:01
+
+##### Systemic Process: Total Hepatic Clearance-fitted
+
+Species: Human
+
+###### Parameters
+
+Name | Value | Value Origin
+----------------------------- | ------------------ | ---------------------------------------------------------------------------------------------------------------------
+Fraction unbound (experiment) | 0.305 |
+Lipophilicity (experiment) | 2.48 Log Units |
+Plasma clearance | 0 ml/min/kg |
+Specific clearance | 4.1462183378 1/min | Parameter Identification-Parameter Identification-Value updated from 'Parameter Identification 1' on 2020-01-13 15:01
+
+##### Inhibition: P-gp-Eberl2007
+
+Molecule: P-gp
+
+###### Parameters
+
+Name | Value | Value Origin
+---- | ----------- | -----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
+Ki | 22.7 µmol/l | Publication-Eberl S, Renner B, Neubert A, Reisig M, Bachmakov I, König J, Dörje F, Mürdter TE, Ackermann A, Dormann H, Gassmann KG, Hahn EG, Zierhut S, Brune K, Fromm MF. Role of p-glycoprotein inhibition for drug interactions: evidence from in vitro and pharmacoepidemiological studies. Clin Pharmacokinet. 2007;46(12):1039-49. doi: 10.2165/00003088-200746120-00004. PMID: 18027988.
+
+### Formulation: Erythromycin_Weibull_enteric-coated-pellets
+
+Type: Weibull
+
+#### Parameters
+
+Name | Value | Value Origin
+-------------------------------- | ----------------- | ---------------------------------------------------------------------------------------------------------------------
+Dissolution time (50% dissolved) | 1.7462743767 min | Parameter Identification-Parameter Identification-Value updated from 'Parameter Identification 1' on 2020-01-13 15:01
+Lag time | 54.3490442506 min | Parameter Identification-Parameter Identification-Value updated from 'Parameter Identification 1' on 2020-01-13 15:01
+Dissolution shape | 1.0564916105 | Parameter Identification-Parameter Identification-Value updated from 'Parameter Identification 1' on 2020-01-13 15:01
+Use as suspension | Yes |
+
+### Formulation: Erythromycin_Weibull_enteric-coated-tablet
+
+Type: Weibull
+
+#### Parameters
+
+Name | Value | Value Origin
+-------------------------------- | ----------------- | ---------------------------------------------------------------------------------------------------------------------
+Dissolution time (50% dissolved) | 79.6337524677 min | Parameter Identification-Parameter Identification-Value updated from 'Parameter Identification 1' on 2020-01-13 15:01
+Lag time | 78.7967495765 min | Parameter Identification-Parameter Identification-Value updated from 'Parameter Identification 1' on 2020-01-13 15:01
+Dissolution shape | 1.0838799888 | Parameter Identification-Parameter Identification-Value updated from 'Parameter Identification 1' on 2020-01-13 15:01
+Use as suspension | Yes |
+
+### Formulation: Erythromycin_Weibull_filmtablet
+
+Type: Weibull
+
+#### Parameters
+
+Name | Value | Value Origin
+-------------------------------- | ---------------- | ---------------------------------------------------------------------------------------------------------------------
+Dissolution time (50% dissolved) | 1.7038947098 min | Parameter Identification-Parameter Identification-Value updated from 'Parameter Identification 1' on 2020-01-13 15:01
+Lag time | 0 min | Other-Assumption
+Dissolution shape | 1.0960212213 | Parameter Identification-Parameter Identification-Value updated from 'Parameter Identification 1' on 2020-01-13 15:01
+Use as suspension | Yes |
+
+## 3.2 Diagnostics Plots
+
+Below you find the goodness-of-fit visual diagnostic plots for the PBPK model performance of all data used presented in [Section 2.2.3](#223-clinical-data).
+
+The first plot shows simulated versus observed plasma concentration, the second weighted residuals versus time.
+
+
+
+**Table 3-1: GMFE for Goodness of fit plot for concentration in plasma**
+
+|Group |GMFE |
+|:--------------------------------------------------------|:----|
+|IV |1.57 |
+|IV (model building) |1.46 |
+|PO enteric coated capsule, base pellets |1.82 |
+|PO enteric coated capsule, base pellets (model building) |1.24 |
+|PO enteric coated tablet, base (model building) |1.35 |
+|PO film-coated tablet, base |1.90 |
+|PO film-coated tablet, stearate |1.78 |
+|PO film-coated tablet, stearate (model building) |1.38 |
+|All |1.58 |
+
+
+
+
+
+
+
+
+**Figure 3-1: Goodness of fit plot for concentration in plasma**
+
+
+
+
+
+
+
+
+**Figure 3-2: Goodness of fit plot for concentration in plasma**
+
+
+
+
+## 3.3 Concentration-Time Profiles
+
+Simulated versus observed concentration-time profiles of all data listed in [Section 2.2.3](#223-clinical-data) are presented below.
+
+### 3.3.1 Model Building
+
+
+
+
+
+**Figure 3-3: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-4: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-5: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-6: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-7: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-8: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-9: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-10: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-11: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-12: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-13: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-14: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-15: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-16: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-17: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-18: Time Profile Analysis 2**
+
+
+
+
+
+
+
+
+**Figure 3-19: Time Profile Analysis 3**
+
+
+
+
+
+
+
+
+**Figure 3-20: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-21: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-22: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-23: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-24: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-25: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-26: Time Profile Analysis 2**
+
+
+
+
+
+
+
+
+**Figure 3-27: Time Profile Analysis 3**
+
+
+
+
+
+
+
+
+**Figure 3-28: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-29: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-30: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-31: Time Profile Analysis 1**
+
+
+
+
+### 3.3.2 Model Verification
+
+
+
+
+
+**Figure 3-32: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-33: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-34: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-35: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-36: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-37: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-38: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-39: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-40: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-41: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-42: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-43: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-44: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-45: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-46: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-47: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-48: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-49: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-50: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-51: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-52: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-53: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-54: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-55: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-56: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-57: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-58: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-59: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-60: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-61: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-62: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-63: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-64: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-65: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-66: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-67: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-68: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-69: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-70: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-71: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-72: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-73: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-74: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-75: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-76: Time Profile Analysis 2**
+
+
+
+
+
+
+
+
+**Figure 3-77: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-78: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-79: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-80: Time Profile Analysis 1**
+
+
+
+
+# 4 Conclusion
+
+The final erythromycin PBPK model applies metabolism by CYP3A4, glomerular filtration, and a dummy clearance technically implemented as hepatic plasma clearance accounting for additional clearance pathways, as well as mechanism-based inhibition of CYP3A4. Overall, the model adequately describes the oral pharmacokinetics of erythromycin in healthy adults receiving different single or multiple doses of several oral dosage forms and is considered verified for the use as a perpetrator drug in drug-drug interaction simulations.
+
+# 5 References
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+**Willmann 2007** Willmann S, Höhn K, Edginton A, Sevestre M, Solodenko J, Weiss W, Lippert J, Schmitt W. Development of a physiology-based whole-body population model for assessing the influence of individual variability on the pharmacokinetics of drugs. *J Pharmacokinet Pharmacodyn* 2007, 34(3): 401-431.
+
+**Xu 2009** Xu L, Chen Y, Pan Y, Skiles GL, Shou M. Prediction of human drug-drug interactions from time-dependent inactivation of CYP3A4 in primary hepatocytes using a population-based simulator. *Drug Metab Dispos* 2009, 37(12): 2330-2339.
+
+**Yakatan 1979** Yakatan GJ, Poynor WJ, Harris RG, Martin A, Leonard RG, Briggs AH, Doluisio JT. Single-dose fasting bioequivalence assessment of erythromycin stearate tablets in man. *J Pharmacokinet Biopharm* 1979, 7(4): 355-368.
+
+**Yakatan 1980** Yakatan GJ, Poynor WJ, Breeding SA, Lankford CE, Dighe SV, Martin AN, Doluisio JT. Single- and multiple-dose bioequivalence of erythromycin pharmaceutical alternatives. *J Clin Pharmacol* 1980, 20(11): 625-638.
+
+**Yamano 2001** Yamano K, Yamamoto K, Katashima M, Kotaki H, Takedomi S, Matsuo H, Ohtani H, Sawada Y, Iga T. Prediction of midazolam-CYP3A inhibitors interaction in the human liver from in vivo/in vitro absorption, distribution, and metabolism data. *Drug Metab Dispos* 2001, 29(4 Pt 1): 443-452.
+
+**Zhang 2006** Zhang X, Gorski J, Lucksiri A, Chien J, Quinney S, Jones D, Hall S. OIV‐B‐4; Physiologically-based pharmacokinetic models for the inhibition of midazolam clearance by erythromycin and diltiazem. *Clin Pharmacol Ther* 2006, 79: P34-P34.
+
+**Zhang 2009** Zhang X, Jones DR, Hall SD. Prediction of the effect of erythromycin, diltiazem, and their metabolites, alone and in combination, on CYP3A4 inhibition. *Drug Metab Dispos* 2009, 37(1):150-160.
+
+**Zimmerlin 2011** Zimmerlin A, Trunzer M, Faller B. CYP3A time-dependent inhibition risk assessment validated with 400 reference drugs. *Drug Metab Dispos* 2011, 39(6): 1039-1046.
+
+","Markdown"
+"PBPK model","Open-Systems-Pharmacology/OSP-PBPK-Model-Library","Sildenafil/Sildenafil_evaluation_report.md",".md","42782","537","# Building and evaluation of a PBPK model for Sildenafil in healthy adults
+
+| Version | 2.0-OSP12.2 |
+| ----------------------------------------------- | ------------------------------------------------------------ |
+| based on *Model Snapshot* and *Evaluation Plan* | https://github.com/Open-Systems-Pharmacology/Sildenafil-Model/releases/tag/v2.0 |
+| OSP Version | 12.2 |
+| Qualification Framework Version | 3.5 |
+
+This evaluation report and the corresponding PK-Sim project file are filed at:
+
+https://github.com/Open-Systems-Pharmacology/OSP-PBPK-Model-Library/
+
+# Table of Contents
+
+ * [1 Introduction](#introduction)
+ * [2 Methods](#methods)
+ * [2.1 Modeling Strategy](#modeling-strategy)
+ * [2.2 Data](#data)
+ * [2.3 Model Parameters and Assumptions](#model-parameters-and-assumptions)
+ * [3 Results and Discussion](#results-and-discussion)
+ * [3.1 Final input parameters](#final-input-parameters)
+ * [3.2 Diagnostics Plots](#diagnostics-plots)
+ * [3.3 Concentration-Time Profiles](#ct-profiles)
+ * [3.3.1 Model Building](#model-building)
+ * [3.3.2 Model Verification](#model-verification)
+ * [4 Conclusion](#conclusion)
+ * [5 References](#main-references)
+
+# 1 Introduction
+
+Sildenafil is a cGMP-specific phosphodiesterase 5 inhibitor, indicated for erectile dysfunction and pulmonary arterial hypertension. It is mostly metabolized by CYP3A4 making it a sensitive probe and victim drug for the investigation of CYP3A4 activity *in vivo*. Other CYPs are involved in sildenafil metabolism: CYP2C9 and CYP2C19. It is a BCS class II compound. Sildenafil shows substantial first pass metabolism resulting in a bioavailability of 40%.
+
+The model has been developed and evaluated by comparing observed data to simulations of a large number of clinical studies covering a dose range of 20 mg to 100 mg after intravenous and oral administrations. Furthermore, it has been evaluated within a CYP3A4 DDI modeling network as a victim drug.
+
+Model features include:
+
+- metabolism by CYP3A4
+- metabolism by CYP2C9
+- metabolism by CYP2C19
+- a decrease in the permeability between the intracellular and interstitial space (model parameters `P (intracellular->interstitial)` and `P (interstitial->intracellular)`) in intestinal mucosa to optimize quantitatively the extent of gut wall metabolism
+
+# 2 Methods
+
+## 2.1 Modeling Strategy
+
+The general concept of building a PBPK model has previously been described by Kuepfer et al. ([Kuepfer 2016](#5-references)). Relevant information on anthropometric (height, weight) and physiological parameters (e.g. blood flows, organ volumes, binding protein concentrations, hematocrit, cardiac output) in adults was gathered from the literature and has been previously published ([Willmann 2007](#5-references)). The information was incorporated into PK-Sim® and was used as default values for the simulations in adults.
+
+The applied activity and variability of plasma proteins and active processes that are integrated into PK-Sim® are described in the publicly available PK-Sim® Ontogeny Database Version 7.3 ([PK-Sim Ontogeny Database Version 7.3](#5-references)) or otherwise referenced for the specific process.
+
+First, a mean model was built using clinical data from single dose studies with intravenous and oral administration of sildenafil by Muirhead et al. 2002 ([Muirhead 2002a](#5-references)), Nichols et al. 2002 ([Nichols 2002](#5-references)), the FDA 2009 ([FDA 2009](#5-references)), and Walker et al. 1999 ([Walker 1999](#5-references)). The mean PBPK model was developed using a typical male European individual. The relative tissue-specific expressions of enzymes predominantly being involved in the metabolism of sildenafil (CYP3A4) were considered ([Meyer 2012](#5-references)).
+
+A specific selected set of parameters (see below) was optimized using the Parameter Identification module provided in PK-Sim®. Structural model selection was mainly guided by visual inspection of the resulting description of data and biological plausibility.
+
+Once the appropriate structural model was identified, a Weibull function was fitted using R 4.2.1 based on in vitro data ([Sawatdee 2019](#5-references)), and the resulting dissolution kinetic parameters were implemented in the model.
+
+The model was then evaluated by simulating further clinical studies reporting pharmacokinetic concentration-time profiles of sildenafil.
+
+Details about input data (physicochemical, *in vitro* and clinical) can be found in [Section 2.2](#22-data).
+
+Details about the structural model and its parameters can be found in [Section 2.3](#23-model-parameters-and-assumptions).
+
+## 2.2 Data
+
+### 2.2.1 In vitro and physicochemical data
+
+A literature search was performed to collect available information on physicochemical properties of sildenafil. The obtained information from literature is summarized in the table below, and is used for model building.
+
+| **Parameter** | **Unit** | **Value** | Source | **Description** |
+| :------------------------------------ | -------------------------- | ---------------- | --------------------------------- | ------------------------------------------------------------ |
+| MW | g/mol | 474.576 | [DrugBank DB00203](#5-references) | Molecular weight |
+| pKa1 | | 5.97 | [Salerno 2021](#5-references) | Acid dissociation constant of conjugate acid; compound type: basic |
+| pKa1 | | 6.78 | [Gobry 2000](#5-references) | Acid dissociation constant of conjugate acid; compound type: ampholyte |
+| pKa2 | | 9.12 | [Gobry 2000](#5-references) | Acid dissociation constant of conjugate acid; compound type: ampholyte |
+| Solubility (pH) | mg/mL | 0.025
(7.1) | [Takano 2016](#5-references) | Aqueous Solubility |
+| | | 3.5 | [Salerno 2021](#5-references) | Aqueous Solubility |
+| | | 3.5 | [DrugBank DB00203](#5-references) | Aqueous Solubility |
+| | | 4.1 | [Jung 2011](#5-references) | Aqueous Solubility |
+| | | 3.965
(3) | [Wang 2008](#5-references) | Aqueous Solubility |
+| | | 7.077
(4) | [Wang 2008](#5-references) | Aqueous Solubility |
+| | | 2.068
(5) | [Wang 2008](#5-references) | Aqueous Solubility |
+| | | 0.114
(6) | [Wang 2008](#5-references) | Aqueous Solubility |
+| | | 0.025
(7) | [Wang 2008](#5-references) | Aqueous Solubility |
+| | | 0.027
(8) | [Wang 2008](#5-references) | Aqueous Solubility |
+| | | 0.04
(9) | [Wang 2008](#5-references) | Aqueous Solubility |
+| | | 0.103
(10) | [Wang 2008](#5-references) | Aqueous Solubility |
+| | | 0.322
(11) | [Wang 2008](#5-references) | Aqueous Solubility |
+| logP | | 3.18 | [Gobry 2000](#5-references) | Partition coefficient between octanol and water |
+| | | 2.70 | [Takano 2016](#5-references) | Partition coefficient between octanol and water |
+| | | 2.70 | [Walker 1999](#5-references) | Partition coefficient between octanol and water |
+| | | 2.24 | [Wang 2008](#5-references) | Partition coefficient between octanol and water |
+| | | 1.59 | [Wang 2008](#5-references) | Partition coefficient between octanol and water |
+| | | 1.8 | [DrugBank DB00203](#5-references) | Partition coefficient between octanol and water |
+| | | 1.87 | [DrugBank DB00203](#5-references) | Partition coefficient between octanol and water |
+| fu | % | 4 | [Walker 1999](#5-references) | Fraction unbound in plasma (α1-acid glycoprotein) |
+| | % | 4.3 | [Muirhead 2002b](#5-references) | Fraction unbound in plasma (α1-acid glycoprotein) |
+| | % | 2.7 | [Muirhead 2002b](#5-references) | Fraction unbound in plasma (α1-acid glycoprotein) |
+| | % | 3.46 | [Muirhead 2002b](#5-references) | Fraction unbound in plasma (α1-acid glycoprotein) |
+| Vmax, Km CYP3A4 | pmol/min/pmol P450,
µmol/L | 78.6
4.34 | [Takano 2016](#5-references) | Recombinant CYP3A4 Michaelis-Menten kinetics |
+| Vmax, Km CYP3A4 | relative units,
µmol/L| 1.9
23.10 | [Warrington 2000](#5-references) | Recombinant CYP3A4 Michaelis-Menten kinetics |
+| Vmax, Km CYP2C9 | relative units,
µmol/L| 0.2
9.60 | [Warrington 2000](#5-references) | Recombinant CYP3A4 Michaelis-Menten kinetics |
+| Vmax, Km CYP2C19| relative units,
µmol/L| 0.02
23.10 | [Warrington 2000](#5-references) | Recombinant CYP3A4 Michaelis-Menten kinetics |
+
+### 2.2.2 Clinical data
+
+A literature search was performed to collect available clinical data on sildenafil in adults.
+
+The following publications were found in adults for model building:
+
+| Publication | Arm / Treatment / Information used for model building |
+| :------------------------------------- | :--------------------------------------------------------------------------------------------------------------------------------------------------- |
+| [Muirhead 2002a](#5-references) | Plasma PK profiles in healthy subjects with single dose administrations of a sildenafil 25 mg intravenous infusion |
+| [Nichols 2002](#5-references) | Plasma PK profiles in healthy subjects with single dose administrations of a sildenafil:
- 50 mg intravenous infusion
- 100mg oral tablet |
+| [FDA 2009](#5-references) | Plasma PK profiles in healthy subjects with single dose administrations of a sildenafil:
- 20 mg intravenous infusion
- 40 mg intravenous infusion
- 80 mg intravenous infusion|
+| [Walker 1999](#5-references) | Plasma PK profiles in healthy subjects with single dose administrations of a sildenafil 50 mg oral solution |
+| [Spence 2008](#5-references) | Plasma PK profiles in healthy subjects with single dose administrations of a sildenafil 20 mg tablet |
+| [Lee 2021](#5-references) | Plasma PK profiles in healthy subjects with single dose administrations of a sildenafil:
- 25 mg tablet (in the absence of itraconazole)
- 25 mg tablet (in the absence of clarithromycin)|
+| [Abdelkawy 2016](#5-references) | Plasma PK profiles in healthy subjects with single dose administrations of a sildenafil 50 mg tablet |
+| [Gillen 2017](#5-references) | Plasma PK profiles in healthy subjects with single dose administrations of a sildenafil 50 mg tablet (Panel 1) |
+| [Jetter 2002](#5-references) | Plasma PK profiles in healthy subjects with single dose administrations of a sildenafil 50 mg tablet |
+| [Murtadha 2021](#5-references) | Plasma PK profiles in healthy subjects with single dose administrations of a sildenafil 50 mg tablet (non-smoker group) |
+| [Wilner 2002](#5-references) | Plasma PK profiles in healthy subjects with single dose administrations of a sildenafil 50 mg tablet (study I) |
+
+The following dosing scenarios were simulated and compared to respective data for model verification:
+
+| Scenario | Data reference |
+| ------------------------------------------------------------ | ------------------------------------ |
+| po SD 50mg | [Al-Ghazawi 2010](#5-references) |
+| | [Hedaya 2006](#5-references) |
+| | [Wilner 2002](#5-references) |
+| | [Gillen 2017](#5-references) |
+| po SD 100mg | [Muirhead 2000](#5-references) |
+| po MD 20/80 mg | [Burgess 2008](#5-references) |
+| po MD 20 mg | [Gotzkowsky 2013](#5-references) |
+
+
+
+## 2.3 Model Parameters and Assumptions
+
+### 2.3.1 Absorption
+
+The model parameter `Specific intestinal permeability` was optimized to best match clinical data (see [Section 2.3.4](#234-automated-parameter-identification)). A formulation without limitation to absorption was assumed for the oral solution, therefore its solubility was set to 100 mg/L. A default solubility of 3.5 mg/L was taken from the model of [Salerno 2021](#5-references) and used for tablets (see [Section 2.2.1](#221-in-vitro-and-physicochemical-data))
+
+The dissolution of tablets was implemented via a Weibull dissolution tablet. The Weibull function was fitted using R 4.2.1 based on in vitro data ([Sawatdee 2019](#5-references)), and the resulting dissolution kinetic parameters were fixed in the model.
+
+### 2.3.2 Distribution
+
+Sildenafil is highly bound to α1-acid glycoprotein in plasma (see [Section 2.2.1](#221-in-vitro-and-physicochemical-data)). A value of 4% was used in this PBPK model for `Fraction unbound (plasma, reference value)`.
+
+An important parameter influencing the resulting volume of distribution is lipophilicity. The reported experimental logP values are in the range of 3 (see [Section 2.2.1](#221-in-vitro-and-physicochemical-data)) which served as a starting value. Finally, the model parameters `Lipophilicity` was optimized to match best clinical data (see also [Section 2.3.4](#234-automated-parameter-identification)).
+
+After testing the available organ-plasma partition coefficient and cell permeability calculation methods built in PK-Sim, observed clinical data was best described by choosing the partition coefficient calculation by `Rodgers and Rowland` and cellular permeability calculation by `PK-Sim Standard`.
+
+### 2.3.3 Metabolism and Elimination
+
+Three metabolic pathways were implement into the model via Michaelis-Menten kinetics
+
+* CYP3A4
+* CYP3A9
+* CYP3A19
+
+Relative kcat were calculated with the following inputs:
+
+| Input | Unit | CYP3A4 | CYP2C9 | CYP2C19 | Reference |
+| ------------------------------------------- | -------------------------------- | ----------- | ----------- | ----------- | --------------------------------- |
+| Contributions in vitro (scaled)* | µL/min/mg microsomal protein | 0.79 | 0.20 | 0.01 | [Warrington 2000](#5-references) |
+| CYP amount | pmol CYP/mg microsomal protein | 108 | 96 | 19 | [Rodrigues 1999](#5-references) |
+| Michaelis Menten constant (Km) | µmol/L | 23.1 | 9.6 | 23.1 | [Warrington 2000](#5-references) |
+*The contribution in vitro has initially no unit. It was scaled multiplying it by 1 µL/min/mg microsomal protein. This is a joint scaling factor over the three CYPs to keep their relative hepatic contributions fixed. It was later optimized as part of kcat.
+
+The scaled contributions in vitro were converted to specific clearance per enzyme dividing by the respective CYP amount per milligram microsomal protein. Then these relative specific clearances per enzyme were multiplied by the Km value to obtain kcat values which were then in a next step optimized with a joint factor in the parameter identification to best match clinical data (see [Section 2.3.4](#234-automated-parameter-identification))
+
+| Calculated parameters | Unit | CYP3A4 | CYP2C9 | CYP2C19 |
+| ------------------------------------------- | -------------------------------- | ----------- | ----------- | ----------- |
+| CLspec/Enzyme | L/µmol/min | 0.007324074 | 0.002083333 | 0.000436842 |
+| kcat | 1/min | 0.17 | 0.02 | 0.01 |
+
+The CYP3A4 expression profiles is based on high-sensitive real-time RT-PCR ([Nishimura 2003](#5-references)). Absolute tissue-specific expressions were obtained by considering the respective absolute concentration in the liver. The PK-Sim database provides a default value for CYP3A4 (compare [Rodrigues 1999](#5-references) and assume 40 mg protein per gram liver).
+
+The first model simulations showed that gut wall metabolism was underrepresented in the PBPK model. In order to increase gut wall metabolism, the “mucosa permeability on basolateral side” (jointly the model parameters in the mucosa: ``P (interstitial->intracellular)`` and ``P (intracellular->interstitial)``) was estimated. A decrease in this permeability may lead to higher gut wall concentrations and, in turn, to a higher gut wall elimination. This parameter was preferred over other parameters such as relative CYP3A4 expression or fraction unbound (fu) in the gut wall as it is technically not limited to a maximum value of 100%.
+
+### 2.3.4 Automated Parameter Identification
+
+This is the result of the final parameter identification for the base model:
+
+| Model Parameter | Optimized Value | Unit |
+| ------------------------------------------------------------ | ------------------------------------------------------------ | --------- |
+| `Lipophilicity` | 2.84 | Log Units |
+| `Fraction unbound` | 0.04 (FIXED) | |
+| `Specific intestinal permeability` | 1.21E-3 | cm/min |
+| Basolateral mucosa permeability
(``P (interstitial->intracellular)``, ``P (intracellular->interstitial)``) | 6.07E-4 | cm/min |
+| `kcat` (CYP3A4) | 27.21 | 1/min |
+| `kcat` (CYP2C9) | 3.22 | 1/min |
+| `kcat` (CYP2C19) | 1.62 | 1/min |
+| `Dissolution time` | 4.16 (FIXED) | min |
+| `Dissolution shape` | 1.37 (FIXED) | |
+
+# 3 Results and Discussion
+
+The PBPK model for sildenafil was developed and verified with clinical pharmacokinetic data.
+
+The model was built and evaluated covering data from studies including in particular
+
+* intravenous (infusions) and oral administrations (solutions and tablets).
+* a dose range of 20 to 100 mg.
+
+The model quantifies metabolism via CYP3A4, CYP2C9 and CYP2C19.
+
+The next sections show:
+
+1. the final model input parameters for the building blocks: [Section 3.1](#31-final-input-parameters).
+2. the overall goodness of fit: [Section 3.2](#32-diagnostics-plots).
+3. simulated vs. observed concentration-time profiles for the clinical studies used for model building and for model verification: [Section 3.3](#33-concentration-time-profiles).
+
+## 3.1 Final input parameters
+
+The compound parameter values of the final PBPK model are illustrated below.
+
+### Compound: Sildenafil
+
+#### Parameters
+
+Name | Value | Value Origin | Alternative | Default
+------------------------------------------------ | ---------------------- | ------------------------------------------------------------------------------------------------------------------------------ | ------------- | -------
+Solubility at reference pH | 3.5 mg/l | Publication-Salerno 2021 | Measurement | True
+Reference pH | 7 | Publication-Salerno 2021 | Measurement | True
+Solubility at reference pH | 100 mg/l | Other-Assumption | Oral solution | False
+Reference pH | 7 | Other-Assumption | Oral solution | False
+Lipophilicity | 2.8413676507 Log Units | Parameter Identification-Parameter Identification-Value updated from 'PI_All_DissoKineticFit_P calculated' on 2023-03-24 17:29 | Measurement | True
+Fraction unbound (plasma, reference value) | 0.04 | Parameter Identification-Parameter Identification-Value updated from 'PI_All_DissoKineticFit_P calculated' on 2023-03-24 17:29 | Measurement | True
+Permeability | 0.0758702742 cm/min | Parameter Identification-Parameter Identification-Value updated from 'PI_All_DissoKineticFit' on 2022-08-12 15:40 | Optimized | False
+Specific intestinal permeability (transcellular) | 0.0012071993309 cm/min | Parameter Identification-Parameter Identification-Value updated from 'PI_All_DissoKineticFit_P calculated' on 2023-03-24 17:29 | Optimized | True
+Is small molecule | Yes | | |
+Molecular weight | 474.58 g/mol | Internet-DrugBank DB00203 | |
+Plasma protein binding partner | α1-acid glycoprotein | | |
+
+#### Calculation methods
+
+Name | Value
+----------------------- | -------------------
+Partition coefficients | Rodgers and Rowland
+Cellular permeabilities | PK-Sim Standard
+
+#### Processes
+
+##### Metabolizing Enzyme: CYP2C19-Warrington
+
+Molecule: CYP2C19
+
+###### Parameters
+
+Name | Value | Value Origin
+-------------------------------- | ------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------
+In vitro Vmax/recombinant enzyme | 0.01009105 pmol/min/pmol rec. enzyme | Publication-Warrington 2000
+Km | 23.1 µmol/l | Publication-Warrington 2000
+kcat | 1.6232116026 1/min | Parameter Identification-Parameter Identification-Value updated from 'PI_All_DissoKineticFit_P calculated' on 2023-03-24 17:29
+
+##### Metabolizing Enzyme: CYP2C9-Warrington
+
+Molecule: CYP2C9
+
+###### Parameters
+
+Name | Value | Value Origin
+-------------------------------- | ------------------------------ | ------------------------------------------------------------------------------------------------------------------------------
+In vitro Vmax/recombinant enzyme | 0.02 pmol/min/pmol rec. enzyme | Publication-Warrington 2000
+Km | 9.6 µmol/l | Publication-Warrington 2000
+kcat | 3.2171312254 1/min | Parameter Identification-Parameter Identification-Value updated from 'PI_All_DissoKineticFit_P calculated' on 2023-03-24 17:29
+
+##### Metabolizing Enzyme: CYP3A4-Warrington
+
+Molecule: CYP3A4
+
+###### Parameters
+
+Name | Value | Value Origin
+-------------------------------- | ------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------
+In vitro Vmax/recombinant enzyme | 0.16918611 pmol/min/pmol rec. enzyme | Publication-Warrington 2000
+Km | 23.1 µmol/l | Publication-Warrington 2000
+kcat | 27.2146958692 1/min | Parameter Identification-Parameter Identification-Value updated from 'PI_All_DissoKineticFit_P calculated' on 2023-03-24 17:29
+
+### Formulation: Sildenafil Tablet
+
+Type: Weibull
+
+#### Parameters
+
+Name | Value | Value Origin
+-------------------------------- | ------------ | ------------------------------------------------------------------------------------------------------------------------------
+Dissolution time (50% dissolved) | 4.164698 min | Parameter Identification-Parameter Identification-Value updated from 'PI_All_DissoKineticFit_P calculated' on 2023-03-24 17:29
+Lag time | 0 min |
+Dissolution shape | 1.37405 | Parameter Identification-Parameter Identification-Value updated from 'PI_All_DissoKineticFit_P calculated' on 2023-03-24 17:29
+Use as suspension | Yes |
+
+## 3.2 Diagnostics Plots
+
+Below you find the goodness-of-fit visual diagnostic plots for the PBPK model performance of all data used presented in [Section 2.2.2](#222-clinical-data).
+
+The first plot shows observed versus simulated plasma concentration, the second weighted residuals versus time.
+
+
+
+**Table 3-1: GMFE for Sildenafil concentration in plasma**
+
+|Group |GMFE |
+|:------------------------------------------------|:----|
+|Intravenous administration (model building) |1.64 |
+|Oral administration, solution (model building) |1.37 |
+|Oral administration, tablet (model building) |1.44 |
+|Oral administration, tablet (model verification) |1.82 |
+|All |1.62 |
+
+
+
+
+
+
+
+
+**Figure 3-1: Sildenafil concentration in plasma**
+
+
+
+
+
+
+
+
+**Figure 3-2: Sildenafil concentration in plasma**
+
+
+
+
+## 3.3 Concentration-Time Profiles
+
+Simulated versus observed concentration-time profiles of all data listed in [Section 2.2.2](#222-clinical-data) are presented below.
+
+### 3.3.1 Model Building
+
+
+
+
+
+**Figure 3-3: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-4: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-5: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-6: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-7: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-8: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-9: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-10: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-11: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-12: Time Profile Analysis**
+
+
+
+
+### 3.3.2 Model Verification
+
+
+
+
+
+**Figure 3-13: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-14: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-15: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-16: Time Profile Analysis**
+
+
+
+
+# 4 Conclusion
+
+The herein presented PBPK model adequately describes the pharmacokinetics of sildenafil in adults.
+
+In particular, it applies quantitative metabolism by CYP3A4, CYP2C9 and CYP2C19. Thus, the model is fit for purpose to be applied for the investigation of drug-drug interactions with regard to its CYP3A4 metabolism.
+
+# 5 References
+
+**Abdelkawy 2016** Abdelkawy KSDonia AM, Turner RB, Elbarbry F. Effects of Lemon and Seville Orange Juices on the Pharmacokinetic Properties of Sildenafil in Healthy Subjects. Drugs R D. 2016;16(3):271-8.
+
+**Al-Ghazawi 2010** Al-Ghazawi MATutunji MS, AbuRuz SM. The effects of pummelo juice on pharmacokinetics of sildenafil in healthy adult male Jordanian volunteers. Eur J Clin Pharmacol. 2010;66(2):159-63.
+
+**Burgess 2008** Burgess GHoogkamer H, Collings L, Dingemanse J. Mutual pharmacokinetic interactions between steady-state bosentan and sildenafil. Eur J Clin Pharmacol. 2008;64(1):43-50.
+
+**DrugBank DB00203** https://go.drugbank.com/drugs/DB00203
+
+**FDA 2009** Food and Drug Administration, Clinical Pharmacology and biopharmaceutics review of Revatio. 2009; https://www.accessdata.fda.gov/drugsatfda_docs/nda/2009/022473s000_ClinPharmR.pdf
+
+**Gillen 2017** Gillen M, Yang C, Wilson D, Valdez S, Lee C, Kerr B, et al. Evaluation of Pharmacokinetic Interactions Between Lesinurad, a New Selective Urate Reabsorption Inhibitor, and CYP Enzyme Substrates Sildenafil, Amlodipine, Tolbutamide, and Repaglinide. Clin Pharmacol Drug Dev. 2017;6(4):363-76.
+
+**Gobry 2000** Gobry V, Bouchard G, Carrupt PA, Testa B, Girault HH. Physicochemical characterization of sildenafil: ionization, lipophilicity behavior, and ionic‐partition diagram studied by two‐phase titration and electrochemistry. Helvetica Chimica Acta. 2000;83(7):1465-74.
+
+**Gotzkowsky 2013** Gotzkowsky SK, Kumar P, Mottola D, Laliberte K. Lack of a pharmacokinetic interaction between treprostinil diolamine and sildenafil in healthy adult volunteers. J Cardiovasc Pharmacol. 2013;61(5):444-51.
+
+**Hedaya 2006** Hedaya MA, El-Afify DR, El-Maghraby GM. The effect of ciprofloxacin and clarithromycin on sildenafil oral bioavailability in human volunteers. Biopharm Drug Dispos. 2006;27(2):103-10.
+
+**Jetter 2002** Jetter A, Kinzig-Schippers M, Walchner-Bonjean M, Hering U, Bulitta J, Schreiner P, et al. Effects of grapefruit juice on the pharmacokinetics of sildenafil. Clin Pharmacol Ther. 2002;71(1):21-9.
+
+**Jung 2011** Jung SY, Seo YG, Kim GK, Woo JS, Yong CS, Choi HG. Comparison of the solubility and pharmacokinetics of sildenafil salts. Arch Pharm Res. 2011;34(3):451-4.
+
+**Kuepfer 2016** Kuepfer L, Niederalt C, Wendl T, Schlender JF, Willmann S, Lippert J, et al. Applied Concepts in PBPK Modeling: How to Build a PBPK/PD Model. CPT Pharmacometrics Syst Pharmacol. 2016;5(10):516-31.
+
+**Lee 2021** Lee S, Kim AH, Yoon S, Lee J, Lee Y, Ji SC, et al. The utility of CYP3A activity endogenous markers for evaluating drug-drug interaction between sildenafil and CYP3A inhibitors in healthy subjects. Drug Metab Pharmacokinet. 2021;36:100368.
+
+**Meyer 2012** Meyer M, Schneckener S, Ludewig B, Kuepfer L, Lippert J. Using expression data for quantification of active processes in physiologically based pharmacokinetic modeling. Drug Metab Dispos. 2012;40(5):892-901.
+
+**Muirhead 2000** Muirhead GJ, Wulff MB, Fielding A, Kleinermans D, Buss N. Pharmacokinetic interactions between sildenafil and saquinavir/ritonavir. Br J Clin Pharmacol. 2000;50(2):99-107.
+
+**Muirhead 2002a** Muirhead GJ, Rance DJ, Walker DK, Wastall P. Comparative human pharmacokinetics and metabolism of single-dose oral and intravenous sildenafil. Br J Clin Pharmacol. 2002;53 Suppl 1(Suppl 1):13s-20s.
+
+**Muirhead 2002b** Muirhead GJ, Wilner K, Colburn W, Haug-Pihale G, Rouviex B. The effects of age and renal and hepatic impairment on the pharmacokinetics of sildenafil. Br J Clin Pharmacol. 2002;53 Suppl 1(Suppl 1):21s-30s.
+
+**Murtadha 2021** Murtadha M, Raslan MA, Fahmy SF, Sabri NA. Changes in the Pharmacokinetics and Pharmacodynamics of Sildenafil in Cigarette and Cannabis Smokers. Pharmaceutics. 2021;13(6).
+
+**Nichols 2002** Nichols DJ, Muirhead GJ, Harness JA. Pharmacokinetics of sildenafil after single oral doses in healthy male subjects: absolute bioavailability, food effects and dose proportionality. Br J Clin Pharmacol. 2002;53 Suppl 1(Suppl 1):5s-12s.
+
+**Nishimura 2003** Nishimura M, Yaguti H, Yoshitsugu H, Naito S, Satoh T. Tissue distribution of mRNA expression of human cytochrome P450 isoforms assessed by high-sensitivity real-time reverse transcription PCR. Yakugaku Zasshi. 2003;123(5):369-75.
+
+**PK-Sim Ontogeny Database Version 7.3** https://github.com/Open-Systems-Pharmacology/OSPSuite.Documentation/blob/38cf71b384cfc25cfa0ce4d2f3addfd32757e13b/PK-Sim%20Ontogeny%20Database%20Version%207.3.pdf
+
+**Rodrigues 2003** Rodrigues E, Vilarem MJ, Ribeiro V, Maurel P, Lechner MC. Two CCAAT/enhancer binding protein sites in the cytochrome P4503A1 locus. Potential role in the glucocorticoid response. Eur J Biochem. 2003;270(3):556-64.
+
+**Salerno 2021** Salerno SN, Edginton A, Gerhart JG, Laughon MM, Ambalavanan N, Sokol GM, et al. Physiologically-Based Pharmacokinetic Modeling Characterizes the CYP3A-Mediated Drug-Drug Interaction Between Fluconazole and Sildenafil in Infants. Clin Pharmacol Ther. 2021;109(1):253-62.
+
+**Sawatdee 2019** Sawatdee S, Atipairin A, Sae Yoon A, Srichana T, Changsan N. Enhanced dissolution of sildenafil citrate as dry foam tablets. Pharm Dev Technol. 2019;24(1):1-11.
+
+**Spence 2008** Spence R, Mandagere A, Dufton C, Venitz J. Pharmacokinetics and safety of ambrisentan in combination with sildenafil in healthy volunteers. J Clin Pharmacol. 2008;48(12):1451-9.
+
+**Takano 2016** Takano J, Maeda K, Bolger MB, Sugiyama Y. The Prediction of the Relative Importance of CYP3A/P-glycoprotein to the Nonlinear Intestinal Absorption of Drugs by Advanced Compartmental Absorption and Transit Model. Drug Metab Dispos. 2016;44(11):1808-18.
+
+**Walker 1999** Walker DK, Ackland MJ, James GC, Muirhead GJ, Rance DJ, Wastall P, et al. Pharmacokinetics and metabolism of sildenafil in mouse, rat, rabbit, dog and man. Xenobiotica. 1999;29(3):297-310.
+
+**Wang 2008** Wang Y, Chow MS, Zuo Z. Mechanistic analysis of pH-dependent solubility and trans-membrane permeability of amphoteric compounds: application to sildenafil. Int J Pharm. 2008;352(1-2):217-24.
+
+**Warrington 2000** Warrington JS, Shader RI, von Moltke LL, Greenblatt DJ. In vitro biotransformation of sildenafil (Viagra): identification of human cytochromes and potential drug interactions. Drug Metab Dispos. 2000;28(4):392-7.
+
+**Willmann 2007** Willmann S, Höhn K, Edginton A, Sevestre M, Solodenko J, Weiss W, et al. Development of a physiology-based whole-body population model for assessing the influence of individual variability on the pharmacokinetics of drugs. J Pharmacokinet Pharmacodyn. 2007;34(3):401-31.
+
+**Wilner 2002** Wilner K, Laboy L, LeBel M. The effects of cimetidine and antacid on the pharmacokinetic profile of sildenafil citrate in healthy male volunteers. Br J Clin Pharmacol. 2002;53 Suppl 1(Suppl 1):31s-6s.
+
+","Markdown"
+"PBPK model","Open-Systems-Pharmacology/OSP-PBPK-Model-Library","Tefibazumab/Tefibazumab_evaluation_report.md",".md","15896","281","# Building and evaluation of a PBPK model for tefibazumab in healthy adults
+
+| Version | 1.0-OSP12.2 |
+| ----------------------------------------------- | ------------------------------------------------------------ |
+| based on *Model Snapshot* and *Evaluation Plan* | https://github.com/Open-Systems-Pharmacology/Tefibazumab-Model/releases/tag/v1.0 |
+| OSP Version | 12.2 |
+| Qualification Framework Version | 3.5 |
+
+This evaluation report and the corresponding PK-Sim project file are filed at:
+
+https://github.com/Open-Systems-Pharmacology/OSP-PBPK-Model-Library/
+
+# Table of Contents
+
+ * [1 Introduction](#introduction)
+ * [2 Methods](#methods)
+ * [2.1 Modeling Strategy](#modeling-strategy)
+ * [2.2 Data](#methods-data)
+ * [2.2.1 In vitro / physico-chemical Data ](#invitro-and-physico-chemical-data)
+ * [2.2.2 PK Data ](#PK-data)
+ * [2.3 Model Parameters and Assumptions](#model-parameters-and-assumptions)
+ * [2.3.1 Absorption ](#model-parameters-and-assumptions-absorption)
+ * [2.3.2 Distribution ](#model-parameters-and-assumptions-distribution)
+ * [2.3.3 Metabolism and Elimination ](#model-parameters-and-assumptions-metabolism-and-elimination)
+ * [2.3.4 Automated Parameter Identification ](#model-parameters-and-assumptions-parameter-identification)
+ * [3 Results and Discussion](#results-and-discussion)
+ * [3.1 Final input parameters](#final-input-parameters)
+ * [3.2 Diagnostics Plots](#diagnostics-plots)
+ * [3.3 Concentration-Time Profiles](#ct-profiles)
+ * [4 Conclusion](#conclusion)
+ * [5 References](#main-references)
+
+# 1 Introduction
+
+Tefibazumab is a humanized monoclonal antibody (IgG1) against the clumping factor A (ClfA) of *Staphylococcus aureus*. Tefibazumab shows a pharmacokinetic (PK) behavior which is typical for an antibody without endogenous target.
+
+The herein presented evaluation report evaluates the performance of the physiologically based pharmacokinetic (PBPK) model for tefibazumab in healthy adults.
+
+The presented Tefibazumab PBPK model as well as the respective evaluation plan and evaluation report are provided open-source (https://github.com/Open-Systems-Pharmacology/Tefibazumab-Model).
+
+# 2 Methods
+
+## 2.1 Modeling Strategy
+
+The development of the large molecule PBPK model in PK-Sim® has previously been described by Niederalt et al. ([Niederalt 2018](#5-references)). In short, the model was built as an extension of the PK-Sim® model for small molecules incorporating (i) the two-pore formalism for drug extravasation from blood plasma to interstitial space, (ii) lymph flow, (iii) endosomal clearance and (iv) protection from endosomal clearance by neonatal Fc receptor (FcRn) mediated recycling.
+
+For model development and evaluation, PK data were used from compounds with a wide range of solute radii and from different species. The PK data used for parameter estimation were from the following compounds: antibody–drug conjugate BAY 79-4620 in mice (Bayer in house data), antibody 7E3 in wild-type and FcRn knockout mice ([Garg 2007](#5-references), [Garg2009](#5-references)), domain antibody dAb2 in mice ([Sepp 2015](#5-references)), antibodies MEDI-524 and MEDI-524-YTE in monkeys ([Dall'Acqua 2006](#5-references)), and antibody CDA1 in humans ([Taylor 2008](#5-references)). The PK data used for model evaluation were from inulin in rats ([Tsuji1983](#5-references)) and tefibazumab in humans ([Reilly 2005](#5-references)).
+
+The PBPK model including the estimated physiological parameters as described by Niederalt et al. ([Niederalt 2018](#5-references)) is available in the Open Systems Pharmacology Suite from version 7.1 onwards.
+
+This evaluation report focuses on the PBPK model for the antibody antibodies tefibazumab.
+
+Details about input data (physicochemical, *in vitro* and PK) can be found in [Section 2.2](#22-data).
+
+Details about the structural model and its parameters can be found in [Section 2.3](#23-model-parameters-and-assumptions).
+
+## 2.2 Data
+
+### 2.2.1 In vitro / physico-chemical Data
+
+A literature search was performed to collect available information on physicochemical properties of tefibazumab. The obtained information from literature is summarized in the table below.
+
+| **Parameter** | **Unit** | **Value** | Source | **Description** |
+| :------------ | -------- | --------- | ---------------------------- | ------------------------------------------------------------ |
+| MW | g/mol | 150000 | [Lobo 2004](#5-references) | Molecular weight |
+| r | nm | 5.34 | [Taylor 1984](#5-references) | Hydrodynamic solute radius |
+| Kd (FcRn) | µM | 0.63 | [Zhou 2003](#5-references) | Dissociation constant for binding of a human IgG1 antibody to human FcRn at pH 6 |
+
+### 2.2.2 PK Data
+
+Published clinical PK data on tefibazumab in healthy adults were used.
+
+| Publication | Description |
+| :--------------------------- | :----------------------------------------------------------- |
+| [Reilly 2005](#5-references) | The plasma concentration–time profiles after single dose 15 min i.v. infusion of 2, 5, 10, or 20 mg/kg body weight in healthy adults were used. |
+
+## 2.3 Model Parameters and Assumptions
+
+### 2.3.1 Absorption
+
+There is no absorption process since tefibazumab was administered intravenously.
+
+### 2.3.2 Distribution
+
+The standard lymph and fluid recirculation flow rates and the standard vascular properties of the different tissues (hydraulic conductivity, pore radii, fraction of flow via large pores) from PK-Sim were used ([Niederalt 2018](#5-references)).
+
+### 2.3.3 Metabolism and Elimination
+
+The FcRn mediated clearance present in the standard PK-Sim model was used as only clearance process. The standard physiological parameters related to FcRn mediated clearance were used (rate constants for endosomal uptake and recycling, association rate constant for FcRn binding and concentration of FcRn in the endosomal space) ([Niederalt 2018](#5-references)).
+
+### 2.3.4 Automated Parameter Identification
+
+The Kd(FcRn) was fitted to the experimental plasma concentrations.
+
+| Model Parameter | Optimized Value | Unit |
+| --------------- | --------------- | ---- |
+| `Kd(FcRn)` | 0.85 | µM |
+
+# 3 Results and Discussion
+
+The PBPK model for tefibazumab was evaluated with clinical PK data.
+
+The next sections show:
+
+1. the final model parameters for the building blocks: [Section 3.1](#final-input-parameters).
+2. the overall goodness of fit: [Section 3.2](#diagnostics-plots).
+3. simulated vs. observed concentration-time profiles for the clinical studies used for model building and for model verification: [Section 3.3](#ct-profiles).
+
+## 3.1 Final input parameters
+
+The compound parameter values of the final PBPK model are illustrated below.
+
+### Compound: Tefibazumab
+
+#### Parameters
+
+Name | Value | Value Origin | Alternative | Default
+------------------------------------------ | ------------ | --------------------------------------------- | ----------- | -------
+Solubility at reference pH | 9999 mg/l | Other-/Dummy value not used in the simulation | Measurement | True
+Reference pH | 7 | Other-/Dummy value not used in the simulation | Measurement | True
+Lipophilicity | -5 Log Units | Other-/Dummy value not used in the simulation | Measurement | True
+Fraction unbound (plasma, reference value) | 1 | Other-Assumption | Measurement | True
+Is small molecule | No | | |
+Molecular weight | 150000 g/mol | Publication-Lobo2004 | |
+Plasma protein binding partner | Unknown | | |
+Radius (solute) | 0.00534 µm | Publication-Taylor1984 | |
+Kd (FcRn) in endosomal space | 0.85 µmol/l | Parameter Identification | |
+
+#### Calculation methods
+
+Name | Value
+----------------------- | ---------------
+Partition coefficients | PK-Sim Standard
+Cellular permeabilities | PK-Sim Standard
+
+#### Processes
+
+## 3.2 Diagnostics Plots
+
+Below you find the goodness-of-fit visual diagnostic plots for the PBPK model performance of all data used presented in [Section 2.2.2](#PK-data).
+
+The first plot shows observed versus simulated plasma concentration, the second weighted residuals versus time.
+
+
+
+**Table 3-1: GMFE for Goodness of fit plot for concentration in plasma**
+
+|Group |GMFE |
+|:-------------|:----|
+|10 mg/kg dose |1.11 |
+|2 mg/kg dose |1.43 |
+|20 mg/kg dose |1.15 |
+|5 mg/kg dose |1.15 |
+|All |1.20 |
+
+
+
+
+
+
+
+
+**Figure 3-1: Goodness of fit plot for concentration in plasma**
+
+
+
+
+
+
+
+
+**Figure 3-2: Goodness of fit plot for concentration in plasma**
+
+
+
+
+## 3.3 Concentration-Time Profiles
+
+Simulated versus observed concentration-time profiles of all data listed in [Section 2.2.2](#PK-data) are presented below.
+
+
+
+
+
+**Figure 3-3: Plasma concentration - 2 mg/kg dose (linear scale)**
+
+
+
+
+
+
+
+
+**Figure 3-4: Plasma concentration - 2 mg/kg dose (log scale)**
+
+
+
+
+
+
+
+
+**Figure 3-5: Plasma concentration - 5 mg/kg dose (linear scale)**
+
+
+
+
+
+
+
+
+**Figure 3-6: Plasma concentration - 5 mg/kg dose (log scale)**
+
+
+
+
+
+
+
+
+**Figure 3-7: Plasma concentration - 10 mg/kg dose (linear scale)**
+
+
+
+
+
+
+
+
+**Figure 3-8: Plasma concentration - 10 mg/kg dose (log scale)**
+
+
+
+
+
+
+
+
+**Figure 3-9: Plasma concentration - 20 mg/kg dose (linear scale)**
+
+
+
+
+
+
+
+
+**Figure 3-10: Plasma concentration - 20 mg/kg dose (log scale)**
+
+
+
+
+# 4 Conclusion
+
+The herein presented PBPK model adequately describes the pharmacokinetics of tefibazumab in adults after adjusting the affinity to FcRn except for the lowest dose for which the plasma concentrations are underestimated. The initial plasma concentrations are slightly underestimated also for higher doses.
+
+# 5 References
+
+**Dall'Acqua 2006** Dall’Acqua WF, Kiener PA, Wu H. Properties of human IgG1s engineered for enhanced binding to the neonatal Fc receptor (FcRn). J Biol Chem. 2006 Aug; 281(33):23514-23524. doi: 10.1074/jbc.M604292200.
+
+**Garg 2007** Garg A, Balthasar JP. Physiologically-based pharmacokinetic (PBPK) model to predict IgG tissue kinetics in wild-type and FcRn-knockout mice. J Pharmacokinet Pharmacodyn. 2007 Jul; 34(5):687-709. doi: 10.1007/s10928-007-9065-1.
+
+**Garg 2009** Garg A, Balthasar J. Investigation of the influence of FcRn on the distribution of IgG to the brain. AAPS J. 2009 July; 11(3):553-557. doi: 10.1208/s12248-009-9129-9.
+
+**Lobo 2004** Lobo ED, Hansen R J, Balthasar JP. Antibody pharmacokinetics and pharmacodynamics. J Pharm Sci. 2004 Nov;93(11):2645-2668. doi: 10.1002/jps.20178.
+
+**Niederalt 2018** Niederalt C, Kuepfer L, Solodenko J, Eissing T, Siegmund HU, Block M, Willmann S, Lippert J. A generic whole body physiologically based pharmacokinetic model for therapeutic proteins in PK-Sim. J Pharmacokinet Pharmacodyn. 2018 Apr;45(2):235-257. doi: 10.1007/s10928-017-9559-4.
+
+**Reilly 2005** Reilley S, Wenzel E, Reynolds L, Bennett B, Patti JM, Hetherington S. Open-label, dose escalation study of the safety and pharmacokinetic profile of tefibazumab in healthy volunteers. Antimicrob Agents Chemother. 2005 Mar;49(3):959–962. doi: 10.1128/AAC.49.3.959-962.2005.
+
+**Sepp 2015** Sepp A, Berges A, Sanderson A, Meno-Tetang G. Development of a physiologically based pharmacokinetic model for a domain antibody in mice using the two-pore theory. J Pharmacokinet Pharmacodyn. 2015 Jan;42(2):97-109. doi: 10.1007/s10928-014-9402-0.
+
+**Taylor 1984** Taylor AE, Granger DN. Exchange of macromolecules across the microcirculation. Handbook of Physiology - Cardiovascular System. Microcirculation (Eds. Renkin EM and Michel CC. Bethesda, MD, American Physiological Society). 1984; Vol. 4(Pt 2):467–520.
+
+**Taylor 2008** Taylor CP, Tummala S, Molrine D, Davidson L, Farrell RJ, Lembo A, Hibberd PL, Lowy I, Kelly CP. Open-label, dose escalation phase I study in healthy volunteers to evaluate the safety and pharmacokinetics of a human monoclonal antibody to Clostridium difficile toxin A. Vaccine. 2008 Jun;26(27-28):3404–3409. doi: 10.1016/j.vaccine.2008.04.042.
+
+**Tsuji 1983** Tsuji A, Yoshikawa T, Nishide K, Minami H, Kimura M, Nakashima E, Terasaki T, Miyamoto E, Nightingale CH, Yamana T. Physiologically based pharmacokinetic model for beta-lactam antibiotics I: tissue distribution and elimination in rats. J Pharm Sci. 1983 Nov;72(11):1239-1252. doi: 10.1002/jps.2600721103.
+
+**Zhou 2003** Zhou J, Johnson JE, Ghetie V, Ober RJ, Ward ES. Generation of mutated variants of the human form of the MHC class I-related receptor, FcRn, with increased affinity for mouse immunoglobulin G. J Mol Biol. 2003 Sep;332(4):901-913. doi: 10.1016/s0022-2836(03)00952-5.
+
+","Markdown"
+"PBPK model","Open-Systems-Pharmacology/OSP-PBPK-Model-Library","Mefenamic_acid/Mefenamic_acid_evaluation_report.md",".md","27691","391","# Building and Evaluation of a PBPK Model for Mefenamic Acid in Adults
+
+| Version | 2.0-OSP12.2 |
+| ----------------------------------------------- | ------------------------------------------------------------ |
+| based on *Model Snapshot* and *Evaluation Plan* | https://github.com/Open-Systems-Pharmacology/Mefenamic-acid-Model/releases/tag/v2.0 |
+| OSP Version | 12.2 |
+| Qualification Framework Version | 3.5 |
+
+This evaluation report and the corresponding PK-Sim project file are filed at:
+
+https://github.com/Open-Systems-Pharmacology/OSP-PBPK-Model-Library/
+
+# Table of Contents
+
+ * [1 Introduction](#introduction)
+ * [2 Methods](#methods)
+ * [2.1 Modeling Strategy](#modeling-strategy)
+ * [2.2 Data](#data)
+ * [2.3 Model Parameters and Assumptions](#model-parameters-and-assumptions)
+ * [3 Results and Discussion](#results-and-discussion)
+ * [3.1 Final input parameters](#final-input-parameters)
+ * [3.2 Diagnostics Plots](#diagnostics-plots)
+ * [3.3 Concentration-Time Profiles](#concentration-time-profiles)
+ * [4 Conclusion](#conclusion)
+ * [5 References](#references)
+
+# 1 Introduction
+
+Mefenamic acid is a nonsteroidal anti-inflammatory drug (NSAID). The mechanism of action of mefenamic acid, like that of other NSAIDs, is not completely understood but involves inhibition of cyclooxygenase (COX-1 and COX-2).
+
+Mefenamic acid has been described to undergo metabolism by CYP2C9; it is also glucuronidated directly ([DrugBank DB00784](#5-references)).
+
+Furthermore, mefenamic acid is known to be a potent inhibitor of uridine diphosphate-glucuronosyltransferase 1A9 (UGT1A9) and used in clinical drug-drug interaction (DDI) studies as a perpetrator to investigate the DDI potential of potential UGT1A9 substrates.
+
+The presented model building and evaluation report evaluates the performance of a PBPK model for mefenamic acid in adults.
+
+The objective is to establish a whole-body PBPK model for mefenamic acid featuring:
+
+* a description of the systemic plasma concentration of mefenamic acid after oral administration.
+* reversible UGT1A9 inhibition.
+
+The presented model building and evaluation report evaluates the performance of the PBPK model for mefenamic acid in (healthy) adults.
+
+# 2 Methods
+
+## 2.1 Modeling Strategy
+
+The general concept of building a PBPK model has previously been described by Kuepfer *et al.* ([Kuepfer 2016](#5-references)). Relevant information on anthropometric (height, weight) and physiological parameters (e.g. blood flows, organ volumes, binding protein concentrations, hematocrit, cardiac output) in adults was gathered from the literature and has been previously published ([PK-Sim Ontogeny Database Version 7.3](#5-references)). The information was incorporated into PK-Sim® and was used as default values for the simulations in adults.
+
+The applied activity and variability of plasma proteins and active processes that are integrated into PK-Sim® are described in the publicly available PK-Sim® Ontogeny Database Version 7.3 ([Schlender 2016](#5-references)) or otherwise referenced for the specific process.
+
+A base mean model was built using clinical Phase I data including data from published single dose studies after oral application of mefenamic acid and data from an in-house clinical multiple-dose study to find an appropriate structure to describe the pharmacokinetics in plasma. The mean PBPK model was developed using a typical European individual.
+
+Unknown parameters (see below) were identified using the Parameter Identification module provided in PK-Sim®. Structural model selection was mainly guided by visual inspection of the resulting description of data and biological plausibility.
+
+Finally, an *in vitro* in-house determined Ki value of mefenamic acid on glucuronidation of propofol via UGT1A9 was applied to incorporate reversible UGT1A9 inhibition.
+
+Details about input data (physicochemical, *in vitro* and clinical) can be found in [Section 2.2](#22-data).
+
+Details about the structural model and its parameters can be found in [Section 2.3](#23-model-parameters-and-assumptions).
+
+## 2.2 Data
+
+### 2.2.1 In vitro / physicochemical Data
+
+A literature search was performed to collect available information on physicochemical properties of mefenamic acid. The obtained information from literature is summarized in the table below.
+
+| **Parameter** | **Unit** | **Value** | Source | **Description** |
+| :-------------- | -------- | --------- | ------------------------------------------------------------ | ----------------------------------------------- |
+| MW | g/mol | 241.29 | [DrugBank DB00784](#5-references) | Molecular weight |
+| pKa | | 4.2 | [DrugBank DB00784](#5-references) | Acid dissociation constant |
+| Solubility (pH) | mg/L | 20 (7) | [DrugBank DB00784](#5-references) | Aqueous Solubility |
+| logP | | 5.12 | [DrugBank DB00784](#5-references)
(experimental) | Partition coefficient between octanol and water |
+| | | 5.33 | [Vitas-M Lab ID: STK666691](#5-references)
(experimental) | Partition coefficient between octanol and water |
+| fu | % | 1.9 | [Goosen 2016](#5-references) | Fraction unbound in plasma |
+
+With regard to UGT1A9 inhibition, mefenamic acid inhibited propofol glucuronidation in recombinant UGT1A9 by a mixed-type mechanism, however close to a competitive type (*BAYER in-house*: [Jungmann 2019](#5-references)):
+
+| **Parameter** | **Unit** | **Value** | Source | **Description** |
+| :--------------- | -------- | --------- | ------------------------------- | ------------------------------------ |
+| Ki | µmol/L | 0.30 | [Jungmann 2019](#5-references) | Inhibition constant |
+| Alpha | | 71 | [Jungmann 2019](#5-references) | Alpha value in mixed-type inhibition |
+| fuinc | % | 1 | [Fricke 2020](#5-references) | determined *in vitro* at 0.30 µmol/L of mefenamic acid |
+
+### 2.2.2 Clinical Data
+
+A literature search was performed to collect available clinical data on mefenamic acid in adults.
+
+The following publications were found for adults and, unless noted otherwise, used for model building and evaluation:
+
+| Publication | Study description |
+| :---------------------------------------------------- | :----------------------------------------------------------- |
+| [Hamaguchi 1987](#5-references) | Treatment 2 - fasted with 200 mL of water - with an oral single dose of 250 mg, fasted |
+| [Mahadik 2012](#5-references) | Reference (Ponstan capsule) with an oral single dose of 250 mg, fasted |
+| [Rouini 2005](#5-references) | Reference (Ponstan capsule) with an oral single dose of 250 mg, fasted |
+| [Becker 2015](#5-references)
(*BAYER in-house*) | 500 mg oral dose, fed condition,
then 250 mg oral dose every 6 h (8 doses), fed conditions
***confidential data*** |
+| [Goosen 2017](#5-references) | ***not used for model building (unclear study design)***
500 mg oral dose |
+
+## 2.3 Model Parameters and Assumptions
+
+### 2.3.1 Absorption
+
+Studies including only oral applications of mefenamic acid could be used for model building. During model building the *in vivo* intestinal permeability and an effective *in vivo* solubility in this PBPK model were optimized (see also [Section 2.3.5](#235-automated-parameter-identification)).
+
+Dissolution kinetics of the Ponstan capsule were implemented via an empirical Weibull dissolution function. It was tried to identify the respective parameters. Model building, however, showed that these parameters do not appear to be rate-limiting. Thus, the values were fixed to an instantaneous release with a `Dissolution time (50% dissolved)` of 1 minute and a `Dissolution shape` of 10.
+
+Mefenamic acid is typically administered in fed conditions. Mefenamic acid was administered in the in-house study ([Becker 2015](#5-references)) with meals or snacks. For the 5th administration at 24 h in this study (simultaneous administration with vericiguat) a standard meal in PK-Sim `Meal: High-fat breakfast (Human)` was considered. All other administration considered a snack. The parameter `Meal energy content` for this snack was optimized to best match clinical data (see also [Section 2.3.5](#235-automated-parameter-identification)).
+
+### 2.3.2 Distribution
+
+Mefenamic acid was reported as being greater than 90% bound to albumin in plasma ([Champion 1978](#5-references)). However, exact values are unknown. Goosen *et al.* ([Goosen 2017](#5-references)) reported a fraction unbound in 2% bovine serum albumin solution of 3.8%. Assuming human serum albumin (HSA) as major binding partner and a HSA concentration in plasma *in vivo* of 40 g/dL = 4%, a calculated fraction unbound in plasma of 1.9% can be obtained. This value was used in this PBPK model.
+
+An important parameter influencing the resulting volume of distribution is lipophilicty. The reported experimental logP values were in the range of 5. This value served as a starting value. Finally, the model parameter `Lipophilicity` was optimized to best match clinical data (see also [Section 2.3.5](#235-automated-parameter-identification)).
+
+After testing the available organ-plasma partition coefficient and cell permeability calculation methods built in PK-Sim, observed clinical data was best described by choosing the partition coefficient calculation by `Rodgers and Rowland` and cellular permeability calculation by `PK-Sim Standard`.
+
+### 2.3.3 Metabolism and Elimination
+
+Since this PBPK model was built for the purpose of acting as a perpetrator drug for UGT1A9-mediated drug-drug interactions, no detailed representation of the metabolism and excretion was implemented. A simple unspecific hepatic clearance was optimized to best match clinical data (see also [Section 2.3.5](#235-automated-parameter-identification)).
+
+### 2.3.4 UGT1A9 Inhibition
+
+An in-house *in vitro* study ([Jungmann 2019](#5-references)) evaluated the inhibitory constant (Ki) of mefenamic acid on the glucuronidation of the selective substrate propofol in recombinant UGT1A9. A mixed-type mechanism, however close to a competitive type, was found. After correcting for fraction unbound (but here this was 1 ([Fricke 2020](#5-references)), the obtained *in vitro* values were directly implemented:
+
+| **Model Parameter** | Value | Unit | **Description** |
+| :------------------ | ----- | ------ | ----------------------------------------- |
+| `Ki_c` | 0.3 | µmol/L | Ki * fu,inc |
+| `Ki_u` | 21.3 | µmol/L | Alpha * Ki * fu,inc |
+
+### 2.3.5 Automated Parameter Identification
+
+This is the result of the final parameter identification.
+
+| Model Parameter | Optimized Value | Unit |
+| ----------------------------------------------------- | --------------- | ---------- |
+| `Lipophilicity` | 5.030 | Log Units |
+| `Specific intestinal permeability` | 1.41E-05 | cm/min |
+| `Solubility at reference pH` | 80.95 | µg/ml |
+| `Specific clearance` (unspecific hepatic clearance) | 9.503 | l/µmol/min |
+| `Meal energy content` of snack (mefenamic acid study) | 29.27 | kcal |
+| `Dissolution time (50% dissolved)` of Ponstan capsule | 1 FIXED | min |
+| `Dissolution shape` of Ponstan capsule | 10 FIXED | |
+
+# 3 Results and Discussion
+
+The PBPK model for mefenamic acid was developed. The model was evaluated covering data from studies including
+
+* single and multiple doses
+* a dose range of 250 to 500 mg
+* fasted and fed administration.
+
+The model does not quantify specific metabolic pathways of mefenamic acid as it was developed to be used in the context of UGT inhibition. UGT1A9 inhibition was implemented as a (reversible) mixed-type inhibition. Input values were directly incorporated from an in-house *in vitro* experiment.
+
+The next sections show:
+
+1. the final model parameters for the building blocks: [Section 3.1](#31-final-input-parameters).
+2. the overall goodness of fit: [Section 3.2](#32-diagnostics-plots).
+ *Note that data from [Becker 2015](#5-references) are not shown for data confidentiality reasons.*
+3. simulated vs. observed concentration-time profiles for the clinical studies used for model building and for model verification: [Section 3.3](#33-concentration-time-profiles).
+ *Note that data from [Becker 2015](#5-references) are not shown for data confidentiality reasons.*
+
+## 3.1 Final input parameters
+
+The compound parameter values of the final mefenamic acid PBPK model are illustrated below.
+
+### Compound: Mefenamic acid
+
+#### Parameters
+
+Name | Value | Value Origin | Alternative | Default
+------------------------------------------------ | ----------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------- | -------
+Solubility at reference pH | 80.9542823654 µg/ml | Parameter Identification-Parameter Identification-Value updated from 'PI (Pint, CLspec, Lipo, Solub, meal; fu=1.9, Disso fix) FINAL' on 2019-08-06 18:52 | Optimized | True
+Reference pH | 5.5 | | Optimized | True
+Lipophilicity | 5.0302455255 Log Units | Parameter Identification-Parameter Identification-Value updated from 'PI (Pint, CLspec, Lipo, Solub, meal; fu=1.9, Disso fix) FINAL' on 2019-08-06 18:52 | Optimized | True
+Fraction unbound (plasma, reference value) | 1.9 % | Parameter Identification-Parameter Identification-Value updated from 'PI (Pint, CLspec, Lipo, Solub, meal; fu=1.9, Disso fix) FINAL' on 2019-08-06 18:52 | Goosen 2016 | True
+Specific intestinal permeability (transcellular) | 1.4111809841E-05 cm/min | Parameter Identification-Parameter Identification-Value updated from 'PI (Pint, CLspec, Lipo, Solub, meal; fu=1.9, Disso fix) FINAL' on 2019-08-06 18:52 | Optimized | True
+Is small molecule | Yes | | |
+Molecular weight | 241.29 g/mol | | |
+Plasma protein binding partner | Albumin | | |
+
+#### Calculation methods
+
+Name | Value
+----------------------- | -------------------
+Partition coefficients | Rodgers and Rowland
+Cellular permeabilities | PK-Sim Standard
+
+#### Processes
+
+##### Systemic Process: Total Hepatic Clearance-Simcyp (oral CL)
+
+Species: Human
+
+###### Parameters
+
+Name | Value | Value Origin
+----------------------------- | ------------------ | --------------------------------------------------------------------------------------------------------------------------------------------------------
+Fraction unbound (experiment) | 0.01 |
+Lipophilicity (experiment) | 3.52 Log Units |
+Plasma clearance | 0.2328761 l/h/kg |
+Specific clearance | 9.5031504329 1/min | Parameter Identification-Parameter Identification-Value updated from 'PI (Pint, CLspec, Lipo, Solub, meal; fu=1.9, Disso fix) FINAL' on 2019-08-06 18:52
+
+##### Inhibition: UGT1A9-PH-41095
+
+Molecule: UGT1A9
+
+###### Parameters
+
+Name | Value | Value Origin
+---- | ----------- | ------------:
+Ki_c | 0.3 µmol/l |
+Ki_u | 21.3 µmol/l |
+
+### Formulation: Ponstan capsule
+
+Type: Weibull
+
+#### Parameters
+
+Name | Value | Value Origin
+-------------------------------- | ----- | --------------------------------------------------------------------------------------------------------------------------------------------------------
+Dissolution time (50% dissolved) | 1 min | Parameter Identification-Parameter Identification-Value updated from 'PI (Pint, CLspec, Lipo, Solub, meal; fu=1.9, Disso fix) FINAL' on 2019-08-06 18:52
+Lag time | 0 min |
+Dissolution shape | 10 | Parameter Identification-Parameter Identification-Value updated from 'PI (Pint, CLspec, Lipo, Solub, meal; fu=1.9, Disso fix) FINAL' on 2019-08-06 18:52
+Use as suspension | Yes |
+
+## 3.2 Diagnostics Plots
+
+Below you find the goodness-of-fit visual diagnostic plots for the PBPK model performance of all data used presented in [Section 2.2.2](#222-clinical-data).
+
+The first plot shows simulated versus observed plasma concentrations, the second weighted residuals versus time.
+
+
+
+**Table 3-1: GMFE for Goodness of fit plot for concentration in plasma**
+
+|Group |GMFE |
+|:---------------------------------------------|:----|
+|PO fasted - Ponstan capsule SD administration |1.61 |
+
+
+
+
+
+
+
+
+**Figure 3-1: Goodness of fit plot for concentration in plasma**
+
+
+
+
+
+
+
+
+**Figure 3-2: Goodness of fit plot for concentration in plasma**
+
+
+
+
+## 3.3 Concentration-Time Profiles
+
+Simulated versus observed concentration-time profiles of all data listed in [Section 2.2.2](#222-clinical-data) are presented below.
+
+*Note that data from [Becker 2015](#5-references) are not shown for data confidentiality reasons. Some plots may be duplicated.*
+
+
+
+
+
+**Figure 3-3: PO MD 500 mg loading / 250 mg every 6 h**
+
+
+
+
+
+
+
+
+**Figure 3-4: PO MD 500 mg loading / 250 mg every 6 h**
+
+
+
+
+
+
+
+
+**Figure 3-5: PO MD 500 mg loading / 250 mg every 6 h (log, geomean)**
+
+
+
+
+
+
+
+
+**Figure 3-6: PO MD 500 mg loading / 250 mg every 6 h (log, individuals)**
+
+
+
+
+
+
+
+
+**Figure 3-7: PO MD 500 mg loading / 250 mg every 6 h (log, individuals)**
+
+
+
+
+
+
+
+
+**Figure 3-8: PO SD 250 mg**
+
+
+
+
+
+
+
+
+**Figure 3-9: PO SD 250 mg**
+
+
+
+
+
+
+
+
+**Figure 3-10: PO SD 500 mg**
+
+
+
+
+
+
+
+
+**Figure 3-11: PO SD 500 mg**
+
+
+
+
+# 4 Conclusion
+
+The herein presented PBPK model adequately describes the pharmacokinetics of mefenamic acid in adults.
+
+In particular, it applies inhibition of UGT1A9. The model is fit for purpose to be applied for the investigation of drug-drug interactions with regard to UGT1A9 inhibition.
+
+# 5 References
+
+**Becker 2015** Becker C, Boettcher M.-F. Study 17116: Interaction study to investigate the influence of a starting dose of 500 mg followed by multiple doses of 250 mg mefenamic acid every 6 hours on pharmacokinetics as well as safety and tolerability of a single dose of 2.5 mg vericiguat in comparison to a single dose of 2.5 mg vericiguat alone in healthy male subjects in a randomized, non-blinded, non-placebo-controlled, two-fold cross-over design. Bayer AG Clinical study report. 2015 Oct. Report-No. PH-38616.
+***confidential data***
+
+**Champion 1978** Champion GD, Graham GG: Pharmacokinetics of non-steroidal anti-inflammatory agents. Aust NZ J Med. 8 (Supp 1): 94-100, Jun 1978.
+
+**DrugBank DB00784** (https://www.drugbank.ca/drugs/DB00784)
+
+**Fricke 2020** Fricke R. Vericiguat: Investigations on Binding of Atazanavir to Recombinant UGT1A1 and of Mefenamic Acid to Recombinant UGT1A9. 2020. Report-No. PH-41346.
+
+**Goosen 2016** Goosen TC, Callegari E, Lin J, Tse S, Sahasrabudhe V. Physiologically based Pharmacokinetic Modeling of Drug-drug Interaction following Coadministration of Ertugliflozin and UGT Inhibitor Mefenamic Acid. Presented as poster at the 7th International Workshop on Regulatory Requirements and Current Scientific Aspects on the Preclinical and Clinical Investigations of Drug-Drug Interactions. 2016 May 29-31. Marbach Castle, Germany.
+
+**Goosen 2017** Goosen TC, Callegari E, Lin J, Tse S, Sahasrabudhe V. Characterization of UGT Inhibition as a
+Necessary and Important Strategy in Drug Development. Presented at the 20th Anniversary of the International Conference on Drug-Drug Interactions , Washington. 2017 June 21. Seattle, Washington, USA.
+
+**Hamaguchi 1987** Hamaguchi T, Shinkuma D, Yamanaka Y, Mizuno N. Effects of food on absorption of mefenamic acid from two commercial capsules differing in bioavailability under the fasting state. J Pharmacobiodyn. 1987 Jan;10(1):21-5.
+
+**Jungmann 2019** Jungmann N. Vericiguat: Determination of Ki Values of Atazanavir on 3-Glucuronidation of 17β-Estradiol via UGT1A1 and of Mefenamic Acid on Glucuronidation of Propofol via UGT1A9. Bayer AG Nonclinical study report. 2019 Aug. Report-No. PH-41095.
+
+**Kuepfer 2016** Kuepfer L, Niederalt C, Wendl T, Schlender JF, Willmann S, Lippert J, Block M, Eissing T, Teutonico D. Applied Concepts in PBPK Modeling: How to Build a PBPK/PD Model.CPT Pharmacometrics Syst Pharmacol. 2016 Oct;5(10):516-531. doi: 10.1002/psp4.12134. Epub 2016 Oct 19.
+
+**Mahadik 2013** Mahadik M, Dhaneshwar S, Bhavsar R. A high performance liquid chromatography-tandem mass spectrometric method for the determination of mefenamic acid in human plasma: application to pharmacokinetic study. Biomed Chromatogr. 2012 Oct;26(10):1137-42. doi: 10.1002/bmc.1755.
+
+**Nishimura 2013** Nishimura M, Yaguti H, Yoshitsugu H, Naito S, Satoh T. Tissue distribution of mRNA expression of human cytochrome P450 isoforms assessed by high-sensitivity real-time reverse transcription PCR. Yakugaku Zasshi. 2003 May;123(5):369-75.
+
+**Ohtsuki 2012** Ohtsuki S, Schaefer O, Kawakami H, Inoue T, Liehner S, Saito A, Ishiguro N, Kishimoto W, Ludwig-Schwellinger E, Ebner T, Terasaki T. Simultaneous absolute protein quantification of transporters, cytochromes P450, and UDP-glucuronosyltransferases as a novel approach for the characterization of individual human liver: comparison with mRNA levels and activities. Drug Metab Dispos. 2012 Jan;40(1):83-92. doi: 10.1124/dmd.111.042259.
+
+**PK-Sim Ontogeny Database Version 7.3** (https://github.com/Open-Systems-Pharmacology/OSPSuite.Documentation/blob/38cf71b384cfc25cfa0ce4d2f3addfd32757e13b/PK-Sim%20Ontogeny%20Database%20Version%207.3.pdf)
+
+**Rouini 2004** Rouini MR, Asadipour A, Ardakani YH, Aghdasi F. Liquid chromatography method for determination of mefenamic acid in human serum. J Chromatogr B Analyt Technol Biomed Life Sci. 2004 Feb 5;800(1-2):189-92.
+
+**Schlender 2016** Schlender JF, Meyer M, Thelen K, Krauss M, Willmann S, Eissing T, Jaehde U. Development of a Whole-Body Physiologically Based Pharmacokinetic Approach to Assess the Pharmacokinetics of Drugs in Elderly Individuals. Clin Pharmacokinet. 2016 Dec;55(12):1573-1589.
+
+**Vitas-M Lab ID: STK666691** (https://www.vitasmlab.biz/finded-stk/?stk=STK666691)
+
+","Markdown"
+"PBPK model","Open-Systems-Pharmacology/OSP-PBPK-Model-Library","Inulin/Inulin_evaluation_report.md",".md","18077","320","# Building and evaluation of a PBPK model for inulin in rats
+
+| Version | 1.0-OSP12.2 |
+| ----------------------------------------------- | ------------------------------------------------------------ |
+| based on *Model Snapshot* and *Evaluation Plan* | https://github.com/Open-Systems-Pharmacology/Inulin-Model/releases/tag/v1.0 |
+| OSP Version | 12.2 |
+| Qualification Framework Version | 3.5 |
+
+This evaluation report and the corresponding PK-Sim project file are filed at:
+
+https://github.com/Open-Systems-Pharmacology/OSP-PBPK-Model-Library/
+
+# Table of Contents
+
+ * [1 Introduction](#introduction)
+ * [2 Methods](#methods)
+ * [2.1 Modeling Strategy](#modeling-strategy)
+ * [2.2 Data](#methods-data)
+ * [2.2.1 In vitro / physico-chemical Data ](#invitro-and-physico-chemical-data)
+ * [2.2.2 PK Data ](#PK-data)
+ * [2.3 Model Parameters and Assumptions](#model-parameters-and-assumptions)
+ * [2.3.1 Absorption ](#model-parameters-and-assumptions-absorption)
+ * [2.3.2 Distribution ](#model-parameters-and-assumptions-distribution)
+ * [2.3.3 Metabolism and Elimination ](#model-parameters-and-assumptions-metabolism-and-elimination)
+ * [2.3.4 Tissue Concentrations ](#model-parameters-and-assumptions-tissue-concentrations)
+ * [2.3.5 Automated Parameter Identification ](#model-parameters-and-assumptions-parameter-identification)
+ * [3 Results and Discussion](#results-and-discussion)
+ * [3.1 Final input parameters](#final-input-parameters)
+ * [3.2 Diagnostics Plots](#diagnostics-plots)
+ * [3.3 Concentration-Time Profiles](#ct-profiles)
+ * [4 Conclusion](#conclusion)
+ * [5 References](#main-references)
+
+# 1 Introduction
+
+Inulin is a highly hydrophilic polysaccharide which does not distribute into cells and is cleared via glomerular filtration.
+
+Inulin has a considerably smaller solute radius than the proteins which had been used to develop the generic large molecule physiologically based pharmacokinetic (PBPK) model in PK-Sim ([Niederalt 2018](#5-references)).
+
+The herein presented evaluation report evaluates the performance of the PBPK model for inulin in rats using the large molecule model in PK-Sim.
+
+The presented inulin PBPK model as well as the respective evaluation plan and evaluation report are provided open-source (https://github.com/Open-Systems-Pharmacology/Inulin-Model).
+
+# 2 Methods
+
+## 2.1 Modeling Strategy
+
+The development of the large molecule PBPK model in PK-Sim® has previously been described by Niederalt et al. ([Niederalt 2018](#5-references)). In short, the model was built as an extension of the PK-Sim® model for small molecules incorporating (i) the two-pore formalism for drug extravasation from blood plasma to interstitial space, (ii) lymph flow, (iii) endosomal clearance and (iv) protection from endosomal clearance by neonatal Fc receptor (FcRn) mediated recycling.
+
+For model development and evaluation, PK data were used from compounds with a wide range of solute radii and from different species. The PK data used for parameter estimation were from the following compounds: antibody–drug conjugate BAY 79-4620 in mice (Bayer in house data), antibody 7E3 in wild-type and FcRn knockout mice ([Garg 2007](#5-references), [Garg2009](#5-references)), domain antibody dAb2 in mice ([Sepp 2015](#5-references)), antibodies MEDI-524 and MEDI-524-YTE in monkeys ([Dall'Acqua 2006](#5-references)), and antibody CDA1 in humans ([Taylor 2008](#5-references)). The PK data used for model evaluation were from inulin in rats ([Tsuji1983](#5-references)) and tefibazumab in humans ([Reilly 2005](#5-references)).
+
+The PBPK model including the estimated physiological parameters as described by Niederalt et al. ([Niederalt 2018](#5-references)) is available in the Open Systems Pharmacology Suite from version 7.1 onwards.
+
+This evaluation report focuses on the PBPK model for inulin.
+
+Details about input data (physicochemical, *in vitro* and PK) can be found in [Section 2.2](#22-data).
+
+Details about the structural model and its parameters can be found in [Section 2.3](#23-model-parameters-and-assumptions).
+
+## 2.2 Data
+
+### 2.2.1 In vitro / physico-chemical Data
+
+A literature search was performed to collect available information on physicochemical properties of Inulin. The obtained information from literature is summarized in the table below.
+
+| **Parameter** | **Unit** | **Value** | Source | **Description** |
+| :------------ | -------- | --------- | -------------------------------- | ------------------------------------------------------------ |
+| MW | g/mol | 5000-5500 | [Ohno 1978](#5-references) | Molecular weight |
+| r | nm | 1.39 | [Ghandehari 1997](#5-references) | Hydrodynamic solute radius |
+| logP | µM | < -10 | [Dubbelboer 2022](#5-references) | Lipophilicity (octanol/water partition coefficient). Inulin is highly hydrophilic. A logP = -10 is insensitively small in the PBPK model. |
+| Kd (FcRn) | µM | 999,999 | | Dissociation constant for binding to FcRn. High value representing no FcRn binding. |
+
+### 2.2.2 PK Data
+
+Published plasma and tissue PK data on inulin in rats were used.
+
+| Publication | Description |
+| :-------------------------- | :----------------------------------------------------------- |
+| [Tsuji 1983](#5-references) | Plasma and tissue concentrations after i.v. application of 20 and 200 mg/kg inulin in rats (for 200 mg/kg plasma only). |
+
+## 2.3 Model Parameters and Assumptions
+
+### 2.3.1 Absorption
+
+There is no absorption process since inulin was administered intravenously
+
+### 2.3.2 Distribution
+
+The standard vascular properties of the different tissues (hydraulic conductivity, pore radii, fraction of flow via large pores) and standard lymph and fluid recirculation flow rates from PK-Sim were used ([Niederalt 2018](#5-references)).
+
+### 2.3.3 Metabolism and Elimination
+
+Inulin is renally excreted via glomerular filtration. The standard glomerular filtration rate from the PK-Sim library was used (GFR fraction = 1).
+
+### 2.3.4 Tissue Concentrations
+
+For the comparison with experimental data the parameters `Fraction of blood for sampling` used in the Observer for the tissue concentrations were set for all organs to 0.18. This value is based on the parameter identification for different compounds reported in Ref. ([Niederalt 2018](#5-references)) for comparison with tissue dissection data. (The parameter `Fraction of blood for sampling` specifies residual blood in tissue as ratio of blood volume contributing to the measured tissue concentration to the total in vivo capillary blood volume.)
+
+| Model Parameter | Value | Unit |
+| --------------------------------------------- | ----- | ---- |
+| `Fraction of blood for sampling` (all organs) | 0.18 | |
+
+Experimentally, gut concentrations (from duodenum to the cecum) were measured ([Tsuji 1983](#5-references)). In the present evaluation report, the experimental gut concentrations were compared to simulated organ concentrations for small and large intestine separately in the goodness of fit plots as well as in the concentration-time profile plot.
+
+### 2.3.5 Automated Parameter Identification
+
+No drug specific parameters were fitted.
+
+# 3 Results and Discussion
+
+The PBPK model for inulin was evaluated with plasma and tissue PK data from rats.
+
+The next sections show:
+
+1. the final model parameters for the building blocks: [Section 3.1](#final-input-parameters).
+2. the overall goodness of fit: [Section 3.2](#diagnostics-plots).
+3. simulated vs. observed concentration-time profiles for the clinical studies used for model building and for model verification: [Section 3.3](#ct-profiles).
+
+## 3.1 Final input parameters
+
+The compound parameter values of the final PBPK model are illustrated below.
+
+### Compound: Inulin
+
+#### Parameters
+
+Name | Value | Value Origin | Alternative | Default
+------------------------------------------ | ------------- | --------------------------------------------- | ----------- | -------
+Solubility at reference pH | 9999 mg/l | Other-/Dummy value not used in the simulation | Measurement | True
+Reference pH | 7 | Other-/Dummy value not used in the simulation | Measurement | True
+Lipophilicity | -10 Log Units | Other-Highly hydrophilic | Measurement | True
+Fraction unbound (plasma, reference value) | 1 | Other-Assumption | Measurement | True
+Is small molecule | Yes | | |
+Molecular weight | 5500 g/mol | Publication-Ohno1978 | |
+Plasma protein binding partner | Unknown | | |
+Radius (solute) | 0.00139 µm | Publication-Ghandehari1997 | |
+
+#### Calculation methods
+
+Name | Value
+----------------------- | ---------------
+Partition coefficients | PK-Sim Standard
+Cellular permeabilities | PK-Sim Standard
+
+#### Processes
+
+##### Systemic Process: Glomerular Filtration-GFR
+
+Species: Human
+
+###### Parameters
+
+Name | Value | Value Origin
+------------ | -----:| ---------------------------------------------------------
+GFR fraction | 1 | Publication-Other-Tsuji1983 (DOI: 10.1002/jps.2600721103)
+
+## 3.2 Diagnostics Plots
+
+Below you find the goodness-of-fit visual diagnostic plots for the PBPK model performance of all data used presented in [Section 2.2.2](#PK-data).
+
+The first plot shows observed versus simulated plasma concentration, the second weighted residuals versus time.
+
+
+
+**Table 3-1: GMFE for Goodness of fit plot for concentration in plasma and tissues**
+
+|Group |GMFE |
+|:-------------------------------------------|:----|
+|Plasma concentrations (20 & 200 mg/kg dose) |1.38 |
+|Tissue concentrations (20 mg/kg dose) |2.01 |
+|All |1.71 |
+
+
+
+
+
+
+
+
+**Figure 3-1: Goodness of fit plot for concentration in plasma and tissues**
+
+
+
+
+
+
+
+
+**Figure 3-2: Goodness of fit plot for concentration in plasma and tissues**
+
+
+
+
+## 3.3 Concentration-Time Profiles
+
+Simulated versus observed concentration-time profiles of all data listed in [Section 2.2.2](#PK-data) are presented below.
+
+
+
+
+
+**Figure 3-3: Plasma concentration (linear scale)**
+
+
+
+
+
+
+
+
+**Figure 3-4: Plasma concentration (log scale)**
+
+
+
+
+
+
+
+
+**Figure 3-5: Plasma (linear scale)**
+
+
+
+
+
+
+
+
+**Figure 3-6: Plasma (log scale)**
+
+
+
+
+
+
+
+
+**Figure 3-7: Lung**
+
+
+
+
+
+
+
+
+**Figure 3-8: Muscle**
+
+
+
+
+
+
+
+
+**Figure 3-9: Bone**
+
+
+
+
+
+
+
+
+**Figure 3-10: Heart**
+
+
+
+
+
+
+
+
+**Figure 3-11: Skin**
+
+
+
+
+
+
+
+
+**Figure 3-12: Gut**
+
+
+
+
+# 4 Conclusion
+
+The herein presented PBPK model overall adequately describes the plasma pharmacokinetics of inulin in rats. Tissue concentrations tend to be overestimated by the model, the largest deviations being observed for lung, gut, and heart concentrations. Simulations are predictions without adjusting any compound specific parameter (i.e., using a literature value for the solute radius of inulin and the default physiological parameters in PK-Sim especially for the glomerular filtration rate, vascular properties and compartment volumes).
+
+# 5 References
+
+**Dall'Acqua 2006** Dall’Acqua WF, Kiener PA, Wu H. Properties of human IgG1s engineered for enhanced binding to the neonatal Fc receptor (FcRn). J Biol Chem. 2006 Aug; 281(33):23514-23524. doi: 10.1074/jbc.M604292200.
+
+**Dubbelboer 2022** Dubbelboer, I. R., Sjögren, E. Overview of authorized drug products for subcutaneous administration: Pharmaceutical, therapeutic, and physicochemical properties. European Journal of Pharmaceutical Sciences. 2022 Jun; 173:106181. doi.org/10.1016/j.ejps.2022.106181.
+
+**Garg 2007** Garg A, Balthasar JP. Physiologically-based pharmacokinetic (PBPK) model to predict IgG tissue kinetics in wild-type and FcRn-knockout mice. J Pharmacokinet Pharmacodyn. 2007 Jul; 34(5):687-709. doi: 10.1007/s10928-007-9065-1.
+
+**Garg 2009** Garg A, Balthasar J. Investigation of the influence of FcRn on the distribution of IgG to the brain. AAPS J. 2009 July; 11(3):553-557. doi: 10.1208/s12248-009-9129-9.
+
+**Ghandehari 1997** Ghandehari H, Smith PL, Ellens H, Yeh PY, Kopecek J. Size-dependent permeability of hydrophilic probes across rabbit colonic epithelium. J Pharmacol Exp Ther. 1997 Feb; 280(2):747-753.
+
+**Lobo 2004** Lobo ED, Hansen R J, Balthasar JP. Antibody pharmacokinetics and pharmacodynamics. J Pharm Sci. 2004 Nov;93(11):2645-2668. doi: 10.1002/jps.20178.
+
+**Niederalt 2018** Niederalt C, Kuepfer L, Solodenko J, Eissing T, Siegmund HU, Block M, Willmann S, Lippert J. A generic whole body physiologically based pharmacokinetic model for therapeutic proteins in PK-Sim. J Pharmacokinet Pharmacodyn. 2018 Apr;45(2):235-257. doi: 10.1007/s10928-017-9559-4.
+
+**Ohno 1978** Ohno K, Pettigrew KD, Rapoport SI. Lower limits of cerebrovascular permeability to nonelectrolytes in the conscious rat. American Journal of Physiology-Heart and Circulatory Physiology. 1978 Sep;235(3):H299-H307. doi: 10.1152/ajpheart.1978.235.3.H299.
+
+**Reilly 2005** Reilley S, Wenzel E, Reynolds L, Bennett B, Patti JM, Hetherington S. Open-label, dose escalation study of the safety and pharmacokinetic profile of tefibazumab in healthy volunteers. Antimicrob Agents Chemother. 2005 Mar;49(3):959–962. doi: 10.1128/AAC.49.3.959-962.2005.
+
+**Sepp 2015** Sepp A, Berges A, Sanderson A, Meno-Tetang G. Development of a physiologically based pharmacokinetic model for a domain antibody in mice using the two-pore theory. J Pharmacokinet Pharmacodyn. 2015 Jan;42(2):97-109. doi: 10.1007/s10928-014-9402-0.
+
+**Taylor 1984** Taylor AE, Granger DN. Exchange of macromolecules across the microcirculation. Handbook of Physiology - Cardiovascular System. Microcirculation (Eds. Renkin EM and Michel CC. Bethesda, MD, American Physiological Society). 1984; Vol. 4(Pt 2):467–520.
+
+**Taylor 2008** Taylor CP, Tummala S, Molrine D, Davidson L, Farrell RJ, Lembo A, Hibberd PL, Lowy I, Kelly CP. Open-label, dose escalation phase I study in healthy volunteers to evaluate the safety and pharmacokinetics of a human monoclonal antibody to Clostridium difficile toxin A. Vaccine. 2008 Jun;26(27-28):3404–3409. doi: 10.1016/j.vaccine.2008.04.042.
+
+**Tsuji 1983** Tsuji A, Yoshikawa T, Nishide K, Minami H, Kimura M, Nakashima E, Terasaki T, Miyamoto E, Nightingale CH, Yamana T. Physiologically based pharmacokinetic model for beta-lactam antibiotics I: tissue distribution and elimination in rats. J Pharm Sci. 1983 Nov;72(11):1239-1252. doi: 10.1002/jps.2600721103.
+
+","Markdown"
+"PBPK model","Open-Systems-Pharmacology/OSP-PBPK-Model-Library","Clarithromycin/Clarithromycin_evaluation_report.md",".md","34704","684","# Building and evaluation of a PBPK model for clarithromycin in healthy adults
+
+| Version | 2.0-OSP12.2 |
+| ----------------------------------------------- | ------------------------------------------------------------ |
+| based on *Model Snapshot* and *Evaluation Plan* | https://github.com/Open-Systems-Pharmacology/Clarithromycin-Model/releases/tag/v2.0 |
+| OSP Version | 12.2 |
+| Qualification Framework Version | 3.5 |
+
+This evaluation report and the corresponding PK-Sim project file are filed at:
+
+https://github.com/Open-Systems-Pharmacology/OSP-PBPK-Model-Library/
+
+# Table of Contents
+
+ * [1 Introduction](#introduction)
+ * [2 Methods](#methods)
+ * [2.1 Modeling Strategy](#modeling-strategy)
+ * [2.2 Data](#data)
+ * [2.3 Model Parameters and Assumptions](#model-parameters-and-assumptions)
+ * [3 Results and Discussion](#results-and-discussion)
+ * [3.1 Final input parameters](#final-input-parameters)
+ * [3.2 Diagnostics Plots](#diagnostics-plots)
+ * [3.3 Concentration-Time Profiles](#ct-profiles)
+ * [3.3.1 Model Building](#model-building)
+ * [3.3.2 Model Verification](#model-verification)
+ * [4 Conclusion](#conclusion)
+ * [5 References](#references)
+
+# 1 Introduction
+
+Clarithromycin is a widely prescribed macrolide antibiotic and a substrate and mechanism-based inactivator of CYP3A4. Furthermore, clarithromycin is a substrate and inhibitor of P-gp and an inhibitor of OATP1B1 and OATP1B3 ([Eberl 2007](#5-references), [Seithel 2007](#5-references)). Clarithromycin has been proposed as one of the best alternative CYP3A4 inhibitors for clinical DDI studies to avoid further use of ketoconazole.
+
+Objectives were to develop a fully mechanistic PBPK model for clarithromycin, describing its metabolism by CYP3A4 and its mechanism-based inactivation of the respective enzyme as well as its inhibition of P-gp.
+
+The presented clarithromycin model was developed by Moj et al. ([Moj 2017](#5-references)) and revised by Hanke et al. ([Hanke 2018](#5-references)).
+
+# 2 Methods
+
+## 2.1 Modeling Strategy
+
+The general workflow for building an adult PBPK model has been described by Kuepfer et al. ([Kuepfer 2016](#5-references)). Relevant information on the anthropometry (height, weight) was gathered from the respective clinical study, if reported. Information on physiological parameters (e.g. blood flows, organ volumes, hematocrit) in adults was gathered from the literature and has been incorporated in PK-Sim® as described previously ([Willmann 2007](#5-references)). The applied activity and variability of plasma proteins and active processes that are integrated into PK-Sim® are described in the publicly available 'PK-Sim® Ontogeny Database Version 7.3' ([PK-Sim Ontogeny Database Version 7.3](#5-references)).
+
+A typical European individual was used for the development of the clarithromycin model. The relative tissue-specific expression of CYP3A4 was implemented in accordance with literature information using the PK-Sim expression database RT-PCR profile. Enterohepatic recirculation was enabled as it is active under physiological conditions.
+
+Unknown parameters (see [Section 2.3.4](#234-automated-parameter-identification)) were identified using the Parameter Identification module provided in PK-Sim®.
+
+The model was then verified by simulating the PK of additional clinical studies including a dose range of 100 to 1200 mg administered as single dose or as multiple doses.
+
+Details about input data (physicochemical, *in vitro* and clinical) can be found in [Section 2.2](#22-data).
+
+Details about the structural model and its parameters can be found in [Section 2.3](#23-model-parameters-and-assumptions).
+
+## 2.2 Data
+
+### 2.2.1 In vitro / physicochemical Data
+
+A literature search was performed to collect available information on physiochemical properties of clarithromycin. The obtained information from literature is summarized in the table below.
+
+| **Parameter** | **Unit** | **Value** | Source | **Description** |
+| :----------------------- | -------- | ----------------------- | ------------------------------- | ------------------------------------------------------------ |
+| MW | g/mol | 747.95 | [drugbank.ca](#5-references) | Molecular weight |
+| pKa (base) | | 8.99 | [McFarland 1997](#5-references) | Acid dissociation constant |
+| Solubility (pH) | mg/L | 12170 (2.4) | [Salem 2003](#5-references) | Solubility |
+| logP | | 2.3 | [Lappin 2011](#5-references) | Partition coefficient between octanol and water |
+| fu | % | 28.0 | [Davey 1991](#5-references) | Fraction unbound in plasma |
+| | % | 30.0 | [Chu 1993b](#5-references) | Fraction unbound in plasma |
+| | % | 40.0 | [Noreddin 2002](#5-references) | Fraction unbound in plasma |
+| CYP3A4 Km | µmol/L | 48.7 | [Rodrigues 1997](#5-references) | Michaelis-Menten constant for CYP3A4 metabolism |
+| CLren | L/h | 6.66 - 12.8a | [Rodvold 1999](#5-references) | Renal clearance |
+| CYP3A4 KI | µmol/L | 2.25 | [Polasek 2006](#5-references) | Conc. for half-maximal inactivation measured in recombinant CYP3A4 |
+| | µmol/L | 29.5 | [Polasek 2006](#5-references) | Conc. for half-maximal inactivation measured in human liver microsomes |
+| | µmol/L | 41.4 | [Ito 2003](#5-references) | Conc. for half-maximal inactivation measured in human liver microsomes for α-hydroxylation of midazolam |
+| | µmol/L | 37.0 | [Ito 2003](#5-references) | Conc. for half-maximal inactivation measured in human liver microsomes for 4-hydroxylation of midazolam |
+| | µmol/L | 5.49 | [Mayhew 2000](#5-references) | Conc. for half-maximal inactivation measured in human liver microsomes |
+| CYP3A4 kinact | 1/min | 0.04 | [Polasek 2006](#5-references) | Maximum inactivation rate measured in recombinant CYP3A4 |
+| | 1/min | 0.05 | [Polasek 2006](#5-references) | Maximum inactivation rate measured in human liver microsomes |
+| | 1/min | 0.0423 | [Ito 2003](#5-references) | Maximum inactivation rate measured in human liver microsomes for α-hydroxylation of midazolam |
+| | 1/min | 0.0459 | [Ito 2003](#5-references) | Maximum inactivation rate measured in human liver microsomes for 4-hydroxylation of midazolam |
+| | 1/min | 0.072 | [Mayhew 2000](#5-references) | Maximum inactivation rate measured in human liver microsomes |
+| P-gp Ki | µmol/L | 4.1 | [Eberl 2007](#5-references) | Conc. for half-maximal inhibition |
+| OATP1B1 IC50 | µmol/L | 5.3 ± 1.3b | [Vermeer 2016](#5-references) | Half-maximal inhibitory concentration |
+| OATP1B3 IC50 | µmol/L | 14 ± 2b | [Vermeer 2016](#5-references) | Half-maximal inhibitory concentration |
+
+a denotes range of reported values
+
+b denotes mean ± standard error of the mean of the measurements (two assays, each performed in triplicate)
+
+### 2.2.2 Clinical Data
+
+A literature search was performed to collect available clinical data on clarithromycin in healthy adults. The clarithromycin model was developed using 17 clinical studies covering a dosing range from 100 to 1200 mg.
+
+#### 2.2.2.1 Model Building
+
+The following studies were used for model building (training data):
+
+| Publication | Arm / Treatment / Information used for model building |
+| :------------------------- | :----------------------------------------------------------- |
+| [Chu 1992b](#5-references) | Healthy subjects with intravenous infusion of 250 mg over 45 min |
+| [Chu 1993a](#5-references) | Healthy subjects with oral administration of 250 or 500 mg as single dose or twice daily for 5 days |
+
+#### 2.2.2.2 Model Verification
+
+The following studies were used for model verification (test data):
+
+| Publication | Arm / Treatment / Information used for model building |
+| :---------------------------------- | :----------------------------------------------------------- |
+| [Chu 1992a](#5-references) | Healthy Subjects with oral administration of single doses ranging from 100 to 1200 mg |
+| [Kees 1995](#5-references) | Healthy subjects with oral administration of 250 or 500 mg as single or multiple dose |
+| [Rengelshausen 2003](#5-references) | Oral administration of 250 mg twice a day for 1.5 days |
+| [Abduljalil 2009](#5-references) | Oral administration of 500 mg twice a day for 3.5 days |
+
+## 2.3 Model Parameters and Assumptions
+
+### 2.3.1 Absorption
+
+The specific intestinal permeability was optimized during parameter identification to accurately describe the absorption of clarithromycin after oral administration.
+
+### 2.3.2 Distribution
+
+Values for lipophilicity and fraction unbound in plasma were fixed to literature values (namely to 2.3 ([Lappin 2011](#5-references)) and 0.30 ([Chu 1993b](#5-references)) for lipophilicity and fraction unbound, respectively).
+
+It was not possible to adequately describe the observed plasma concentration-time profile after intravenous administration using standard input parameters (e.g. lipophilicity) and calculation methods. Simulated concentration-time profiles overestimated Cmax and underestimated the observed data for time to Cmax (Tmax). According to literature data, clarithromycin accumulates in mononuclear and polymorphonuclear leukocytes, probably via active transport ([Ishiguro 1989](#5-references)). Implementing this process in the model improved the model fit significantly. Due to limited knowledge on this active transport, this process was technically implemented in the model by adjusting the permeability of clarithromycin across the membrane of the red blood cells (`P (blood cells->plasma)` and `P (plasma->blood cells)`).
+
+After testing the available organ-plasma partition coefficient and cell permeability calculation methods built in PK-Sim, observed clinical data was best described by choosing the partition coefficient calculation by `Rodgers and Rowland` and cellular permeability calculation by `PK-Sim Standard`.
+
+### 2.3.3 Metabolism and Elimination
+
+Metabolism was described using Michaelis-Menten kinetics, while the Michaelis-Menten constant Km was taken from in-vitro experiments from literature and the turnover rate kcat was optimized during parameter identification.
+
+KI and kinact to describe the mechanism-based inhibition of CYP3A4 were optimized during parameter identification.
+
+A kidney plasma clearance was implemented to describe the renal elimination of clarithromycin. The specific renal clearance was optimized during parameter identification.
+
+### 2.3.4 Automated Parameter Identification
+
+This is the result of the final parameter identification.
+
+| Model Parameter | Optimized Value | Unit |
+| ----------------------------------------------- | --------------- | ------ |
+| `kcat` (CYP3A4) | 76.5 | 1/min |
+| `Specific clearance` in process renal clearance | 0.87 | 1/min |
+| `Specific intestinal permeability` | 1.23 E-6 | dm/min |
+| `P (plasma->blood cells)` | 3.62 E-5 | dm/min |
+| `P (blood cells->plasma)` | 1.04 E-6 | dm/min |
+| `K_kinact_half` (KI) | 6.04 | µmol/L |
+| `kinact` (kinact) | 0.04 | 1/min |
+
+# 3 Results and Discussion
+
+The PBPK model for clarithromycin was developed and verified with clinical pharmacokinetic data.
+
+The model was evaluated covering data from studies including in particular
+
+* intravenous and oral administrations
+* a dose range of 100 mg to 1200 mg
+* single and multiple doses
+
+The model quantifies metabolism via CYP3A4, including also the mechanism-based inhibition of the respective enzyme, as well as elimination via kidney. The model also includes inhibition of P-gp.
+
+The next sections show:
+
+1. the final model parameters for the building blocks: [Section 3.1](#31-final-input-parameters).
+2. the overall goodness of fit: [Section 3.2](#32-diagnostics-plots).
+3. simulated vs. observed concentration-time profiles for the clinical studies used for model building and for model verification: [Section 3.3](#33-concentration-time-profiles).
+
+## 3.1 Final input parameters
+
+The compound parameter values of the final PBPK model are illustrated below.
+
+### Compound: Clarithromycin
+
+#### Parameters
+
+Name | Value | Value Origin | Alternative | Default
+------------------------------------------------ | --------------- | ---------------------------------- | ----------- | -------
+Solubility at reference pH | 12.17 mg/ml | Publication-Salem 2003 | Measurement | True
+Reference pH | 2.4 | Publication-Salem 2003 | Measurement | True
+Lipophilicity | 2.3 Log Units | Publication-Lappin 2011 | Measurement | True
+Fraction unbound (plasma, reference value) | 0.3 | Publication-Chu 1993 | Measurement | True
+Specific intestinal permeability (transcellular) | 1.23E-06 dm/min | Parameter Identification-optimized | fit | True
+Is small molecule | Yes | | |
+Molecular weight | 747.9534 g/mol | | |
+Plasma protein binding partner | Albumin | | |
+
+#### Calculation methods
+
+Name | Value
+----------------------- | -------------------
+Partition coefficients | Rodgers and Rowland
+Cellular permeabilities | PK-Sim Standard
+
+#### Processes
+
+##### Metabolizing Enzyme: CYP3A4-fit
+
+Molecule: CYP3A4
+
+###### Parameters
+
+Name | Value | Value Origin
+---------------------------------- | -------------------------- | --------------------------
+In vitro Vmax for liver microsomes | 0 pmol/min/mg mic. protein |
+Km | 48.7 µmol/l | Publication-Rodrigues 1997
+kcat | 76.5 1/min | Parameter Identification
+
+##### Systemic Process: Renal Clearances-fitted
+
+Species: Human
+
+###### Parameters
+
+Name | Value | Value Origin
+----------------------------- | -------------- | ------------
+Body weight | 71.5 kg | Unknown
+Blood flow rate (kidney) | 1.31 l/min | Unknown
+Fraction unbound (experiment) | 0.4 |
+Plasma clearance | 1.75 ml/min/kg |
+
+##### Inhibition: P-gp-Eberl (2007)
+
+Molecule: P-gp
+
+###### Parameters
+
+Name | Value | Value Origin
+---- | ---------- | ----------------------
+Ki | 4.1 µmol/l | Publication-Eberl 2007
+
+##### Inhibition: CYP3A4-fitted
+
+Molecule: CYP3A4
+
+###### Parameters
+
+Name | Value | Value Origin
+------------- | ----------- | ------------:
+kinact | 0.04 1/min |
+K_kinact_half | 6.04 µmol/l |
+
+##### Inhibition: OATP1B1-Vermeer 2016
+
+Molecule: OATP1B1
+
+###### Parameters
+
+Name | Value | Value Origin
+---- | ---------- | ------------------------
+Ki | 5.3 µmol/l | Publication-Vermeer 2016
+
+##### Inhibition: OATP1B3-Vermeer 2016
+
+Molecule: OATP1B3
+
+###### Parameters
+
+Name | Value | Value Origin
+---- | --------- | ------------------------
+Ki | 14 µmol/l | Publication-Vermeer 2016
+
+### Formulation: Tablet Clarithromycin
+
+Type: Weibull
+
+#### Parameters
+
+Name | Value | Value Origin
+-------------------------------- | ----- | ------------:
+Dissolution time (50% dissolved) | 5 min |
+Lag time | 0 min |
+Dissolution shape | 2.9 |
+Use as suspension | No |
+
+## 3.2 Diagnostics Plots
+
+Below you find the goodness-of-fit visual diagnostic plots for the PBPK model performance of all data used presented in [Section 2.2.2](#222-clinical-data).
+
+The first plot shows simulated versus observed plasma concentrations, the second weighted residuals versus time.
+
+
+
+**Table 3-1: GMFE for Goodness of fit plot for concentration in plasma**
+
+|Group |GMFE |
+|:----------------|:----|
+|model building |1.21 |
+|model evaluation |1.62 |
+|All |1.58 |
+
+
+
+
+
+
+
+
+**Figure 3-1: Goodness of fit plot for concentration in plasma**
+
+
+
+
+
+
+
+
+**Figure 3-2: Goodness of fit plot for concentration in plasma**
+
+
+
+
+## 3.3 Concentration-Time Profiles
+
+Simulated versus observed concentration-time profiles of all data listed in [Section 2.2.2](#222-clinical-data) are presented below.
+
+### 3.3.1 Model Building
+
+
+
+
+
+**Figure 3-3: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-4: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-5: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-6: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-7: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-8: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-9: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-10: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-11: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-12: Time Profile Analysis 1**
+
+
+
+
+### 3.3.2 Model Verification
+
+
+
+
+
+**Figure 3-13: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-14: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-15: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-16: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-17: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-18: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-19: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-20: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-21: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-22: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-23: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-24: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-25: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-26: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-27: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-28: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-29: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-30: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-31: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-32: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-33: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-34: Time Profile Analysis 1**
+
+
+
+
+
+
+
+
+**Figure 3-35: Time Profile Analysis**
+
+
+
+
+
+
+
+
+**Figure 3-36: Time Profile Analysis 1**
+
+
+
+
+# 4 Conclusion
+
+The herein presented PBPK model adequately describes the pharmacokinetics of clarithromycin in adults. In particular, it applies increased transfer and accumulation in red blood cells, metabolism by CYP3A4, renal clearance as unchanged drug and mechanism-based inactivation of CYP3A4. Thus, the model is fit for purpose to be applied for the investigation of drug-drug interactions with regard to inhibition of CYP3A4 and P-gp.
+
+# 5 References
+
+**Abduljalil 2009** Abduljalil, K. et al. Modeling the autoinhibition of clarithromycin metabolism during repeated oral administration. Antimicrob. Agents Chemother. 53, 2892–901 (2009).
+
+**Chu 1992a** Chu, S.Y. et al. Pharmacokinetics of clarithromycin, a new macrolide, after single ascending oral doses. Antimicrob. Agents Chemother. 36, 2447–53 (1992).
+
+**Chu 1992b** Chu, S.Y., Deaton, R. & Cavanaugh, J. Absolute bioavailability of clarithromycin after oral administration in humans. Antimicrob. Agents Chemother. 36, 1147–50 (1992).
+
+**Chu 1993a** Chu, S. et al. Single- and multiple-dose pharmacokinetics of clarithromycin, a new macrolide antimicrobial. J. Clin. Pharmacol. 33, 719–26 (1993).
+
+**Chu 1993b** Chu, S.Y. et al. Effect of moderate or severe hepatic impairment on clarithromycin pharmacokinetics. J. Clin. Pharmacol. 33, 480–5 (1993).
+
+**Davey 1991** Davey, P.G. The pharmacokinetics of clarithromycin and its 14-OH metabolite. J. Hosp. Infect. 19 Suppl A, 29–37 (1991).
+
+**drugbank.ca**. (https://www.drugbank.ca/drugs/DB01211), accessed on 04-28-2020.
+
+**Eberl 2007** Eberl, S. et al. Role of p-glycoprotein inhibition for drug interactions: evidence from in vitro and pharmacoepidemiological studies. Clin. Pharmacokinet. 46, 1039–49 (2007).
+
+**Hanke 2018** Hanke, N. et al. PBPK Models for CYP3A4 and P-gp DDI Prediction: A Modeling Network of Rifampicin, Itraconazole, Clarithromycin,Midazolam, Alfentanil, and Digoxin. CPT Pharmacometrics Syst. Pharmacol. 7, 647-659 (2018)
+
+**Ishiguro 1989** Ishiguro M, Koga H, Kohno S, Hayashi T, Yamaguchi K, Hirota M. Penetration of macrolides into human polymorphonuclear leucocytes. J Antimicrob Chemother. 24, 719–29 (1989)
+
+**Ito 2003** Ito, K., Ogihara, K., Kanamitsu, S.-I. & Itoh, T. Prediction of the in vivo interaction between midazolam and macrolides based on in vitro studies using human liver microsomes. Drug Metab. Dispos. 31, 945–54 (2003).
+
+**Kees 1995** Kees, F., Wellenhofer, M. & Grobecker, H. Serum and cellular pharmacokinetics of clarithromycin 500 mg q.d. and 250 mg b.i.d. in volunteers. Infection 23, 168–72 (1995).
+
+**Kuepfer 2016** Kuepfer L, Niederalt C, Wendl T, Schlender JF, Willmann S, Lippert J, Block M, Eissing T, Teutonico D. Applied Concepts in PBPK Modeling: How to Build a PBPK/PD Model.CPT Pharmacometrics Syst Pharmacol. 2016 Oct;5(10):516-531. doi: 10.1002/psp4.12134. Epub 2016 Oct 19.
+
+**Lappin 2011** Lappin, G. et al. Comparative pharmacokinetics between a microdose and therapeutic dose for clarithromycin, sumatriptan, propafenone, paracetamol (acetaminophen), and phenobarbital in human volunteers. Eur. J. Pharm. Sci. 43, 141–50 (2011).
+
+**Mayhew 2000** Mayhew, B.S., Jones, D.R. & Hall, S.D. An in vitro model for predicting in vivo inhibition of cytochrome P450 3A4 by metabolic intermediate complex formation. Drug Metab. Dispos. 28, 1031–7 (2000).
+
+**McFarland 1997** McFarland, J.W. et al. Quantitative structure-activity relationships among macrolide antibacterial agents: in vitro and in vivo potency against Pasteurella multocida. J. Med. Chem. 40, 1340–6 (1997).
+
+**Moj 2017** Moj, D. et al. Clarithromycin, midazolam, and digoxin: application of PBPK modeling to gain new insights into drug-drug interactions and co-medication regimens. AAPS J. 19, 298–312 (2017).
+
+**Noreddin 2002** Noreddin, A.M. et al. Pharmacodynamic modeling of clarithromycin against macrolide-resistant [PCR-positive mef(A) or erm(B)] Streptococcus pneumoniae simulating clinically achievable serum and epithelial lining fluid free-drug concentrations. Antimicrob. Agents Chemother. 46, 4029–34 (2002).
+
+**PK-Sim Ontogeny Database Version 7.3** (https://github.com/Open-Systems-Pharmacology/OSPSuite.Documentation/blob/38cf71b384cfc25cfa0ce4d2f3addfd32757e13b/PK-Sim%20Ontogeny%20Database%20Version%207.3.pdf)
+
+**Polasek 2006** Polasek, T.M. & Miners, J.O. Quantitative prediction of macrolide drug-drug interaction potential from in vitro studies using testosterone as the human cytochrome P4503A substrate. Eur. J. Clin. Pharmacol. 62, 203–8 (2006).
+
+**Rengelshausen 2003** Rengelshausen, J. et al. Contribution of increased oral bioavailability and reduced nonglomerular renal clearance of digoxin to the digoxin-clarithromycin interaction. Br. J. Clin. Pharmacol. 56, 32–8 (2003).
+
+**Rodrigues 1997** Rodrigues, A.D., Roberts, E.M., Mulford, D.J., Yao, Y. & Ouellet, D. Oxidative metabolism of clarithromycin in the presence of human liver microsomes. Major role for the cytochrome P4503A (CYP3A) subfamily. Drug Metab. Dispos. 25, 623–30 (1997).
+
+**Rodvold 1999** Rodvold, K.A. Clinical pharmacokinetics of clarithromycin. Clin. Pharmacokinet. 37, 385–98 (1999).
+
+**Salem 2003** Salem, I.I. & Düzgünes, N. Efficacies of cyclodextrin-complexed and liposome-encapsulated clarithromycin against Mycobacterium avium complex infection in human macrophages. Int. J. Pharm. 250, 403–14 (2003).
+
+**Schlender 2016** Schlender JF, Meyer M, Thelen K, Krauss M, Willmann S, Eissing T, Jaehde U. Development of a Whole-Body Physiologically Based Pharmacokinetic Approach to Assess the Pharmacokinetics of Drugs in Elderly Individuals. Clin Pharmacokinet. 2016 Dec;55(12):1573-1589.
+
+**Seithel 2007** Seithel, A. et al. The influence of macrolide antibiotics on the uptake of organic anions and drugs mediated by OATP1B1 and OATP1B3. Drug Metab. Dispos. 35, 779–86 (2007).
+
+**Vermeer 2016** Vermeer, L. M., Isringhausen, C. D., Ogilvie, B. W., & Buckley, D. B. Evaluation of ketoconazole and its alternative clinical CYP3A4/5 inhibitors as inhibitors of drug transporters: the in vitro effects of ketoconazole, ritonavir, clarithromycin, and itraconazole on 13 clinically-relevant drug transporters. Drug Metab. Dispos. 44, 453–459 (2016).
+
+","Markdown"
+"PBPK model","metrumresearchgroup/r-pharma-pkpd-2020","content/rifampin_midazolam_ddi.md",".md","11568","376","Rifampicin PBPK Model to Predict Complex DDIs
+================
+Metrum Research Group
+
+ - [Reference](#reference)
+ - [Rifampin PBPK](#rifampin-pbpk)
+ - [Single rifampicin dose](#single-rifampicin-dose)
+ - [Multiple rifampicin doses](#multiple-rifampicin-doses)
+ - [PBPK model for rifampicin / midazolam
+ DDI](#pbpk-model-for-rifampicin-midazolam-ddi)
+ - [Dose-response for midazolam/rifampin
+ DDI](#dose-response-for-midazolamrifampin-ddi)
+
+``` r
+library(tidyverse)
+library(mrgsolve)
+library(PKPDmisc)
+theme_set(theme_bw() + theme(legend.position = ""top""))
+```
+
+# Reference
+
+This model and vignette was derived from this publication
+
+**Comprehensive PBPK Model of Rifampicin for Quantitative Prediction of
+Complex Drug-Drug Interactions: CYP3A/2C9 Induction and OATP Inhibition
+Effects** - Asaumi R, Toshimoto K, Tobe Y, Hashizume K, Nunoya KI,
+Imawaka H, Lee W, Sugiyama Y. CPT Pharmacometrics Syst Pharmacol. 2018
+Jan 25. PMID: 29368402 -
+
+# Rifampin PBPK
+
+``` r
+model_dir <- ""model""
+
+mod <- mread_cache(""rifampicin_midazolam"", model_dir, delta = 0.1)
+mod
+```
+
+ .
+ .
+ . --------- source: rifampicin_midazolam.cpp ---------
+ .
+ . project: /Users/kyleb/git...content/model
+ . shared object: rifampicin_midazolam-so-cca215f11e6a
+ .
+ . time: start: 0 end: 24 delta: 0.1
+ . add:
+ .
+ . compartments: Xgutlumen Mgutlumen central Cmuscle
+ . Cskin Cadipose Cserosa Cmucblood Cent
+ . CHE1 CHE2 CHE3 CHE4 CHE5 CHC1 CHC2 CHC3
+ . CHC4 CHC5 mcentral mCmuscle mCskin
+ . ... [41]
+ . parameters: Rdif beta gamma Km_u_uptake SFKp mSFKp
+ . Emax_UGT_RIF EC50_u_UGT_RIF
+ . kdeg_UGT_liver kdeg_UGT_ent
+ . fm_UGT_liver fm_UGT_ent Emax_CYP3A4_RIF
+ . ... [61]
+ . captures: Ccentral mCcentral Cmidazolam [3]
+ . omega: 0x0
+ . sigma: 0x0
+ .
+ . solver: atol: 1e-08 rtol: 1e-08 maxsteps: 20k
+ . ------------------------------------------------------
+
+``` r
+param(mod)
+```
+
+ .
+ . Model parameters (N=61):
+ . name value . name value . name value
+ . beta 0.2 | kdeg_UGT_liver 0.0158 | Qh_kg 1.24
+ . CLrenal_kg 0.011 | Km_u_uptake 0.146 | Qmuscle_kg 0.642
+ . EC50_u_CYP3A4_RIF 0.0526 | Kp_adipose 0.0629 | Qportal_kg 0.531
+ . EC50_u_UGT_RIF 0.0526 | Kp_muscle 0.0947 | Qserosa_kg 0.274
+ . Emax_CYP3A4_RIF 4.57 | Kp_serosa 0.2 | Qskin_kg 0.257
+ . Emax_UGT_RIF 1.34 | Kp_skin 0.326 | Qvilli_kg 0.257
+ . Fa 1 | mCLperm_gut_kg 0.151 | Rdif 0.129
+ . fB 0.0778 | mCLrenal 0 | SFKp 6.65
+ . fBCLint_all_kg 0.251 | mFa 1 | Vadipose_kg 0.143
+ . fE 0.115 | mfB 0.0545 | Vcentral_kg 0.0743
+ . Fg 0.943 | mfBCLint_kg 0.469 | Vent_kg 0.00739
+ . fH 0.0814 | mfECLint_E_kg 0.107 | VHC_kg 0.0174
+ . fm_CYP3A4_ent 1 | mka 1.29 | VHE_kg 0.0067
+ . fm_CYP3A4_liver 0.93 | mKp_adipose 34.4 | Vmucblood_kg 0.00099
+ . fm_UGT_ent 0.759 | mKp_liver 6.96 | Vmuscle_kg 0.429
+ . fm_UGT_liver 0.759 | mKp_muscle 4 | Vportal_kg 0.001
+ . gamma 0.778 | mKp_skin 20.4 | Vserosa_kg 0.00893
+ . ka 37.6 | mSFKp 0.201 | Vskin_kg 0.111
+ . kdeg_CYP3A4_ent 0.0288 | mVcentral_kg 0.571 | WT 80
+ . kdeg_CYP3A4_liver 0.0158 | PSdif_E_kg 0.161 | . .
+ . kdeg_UGT_ent 0.0288 | Qadipose_kg 0.223 | . .
+
+``` r
+init(mod)
+```
+
+ .
+ . Model initial conditions (N=41):
+ . name value . name value . name
+ . Cadipose (6) 0 | CLIV2 (25) 0 | CYP3A4_ratio_HC5 (40)
+ . Cent (9) 0 | CLIV3 (26) 0 | mCadipose (23)
+ . central (3) 0 | CLIV4 (27) 0 | mcentral (20)
+ . CHC1 (15) 0 | CLIV5 (28) 0 | mCmuscle (21)
+ . CHC2 (16) 0 | Cmucblood (8) 0 | mCskin (22)
+ . CHC3 (17) 0 | Cmuscle (4) 0 | Mgutlumen (2)
+ . CHC4 (18) 0 | Cportal (29) 0 | UGT_ratio_ent (35)
+ . CHC5 (19) 0 | Cserosa (7) 0 | UGT_ratio_HC1 (30)
+ . CHE1 (10) 0 | Cskin (5) 0 | UGT_ratio_HC2 (31)
+ . CHE2 (11) 0 | CYP3A4_ratio_ent (41) 1 | UGT_ratio_HC3 (32)
+ . CHE3 (12) 0 | CYP3A4_ratio_HC1 (36) 1 | UGT_ratio_HC4 (33)
+ . CHE4 (13) 0 | CYP3A4_ratio_HC2 (37) 1 | UGT_ratio_HC5 (34)
+ . CHE5 (14) 0 | CYP3A4_ratio_HC3 (38) 1 | Xgutlumen (1)
+ . CLIV1 (24) 0 | CYP3A4_ratio_HC4 (39) 1 | . ...
+ . value
+ . 1
+ . 0
+ . 0
+ . 0
+ . 0
+ . 0
+ . 1
+ . 1
+ . 1
+ . 1
+ . 1
+ . 1
+ . 0
+ . .
+
+## Single rifampicin dose
+
+``` r
+rif <- ev(amt = 600)
+rif
+```
+
+ . Events:
+ . time amt cmt evid
+ . 1 0 600 1 1
+
+``` r
+mod %>%
+ ev(rif) %>%
+ Req(Ccentral) %>%
+ mrgsim(end = 48) %>%
+ plot()
+```
+
+
+
+## Multiple rifampicin doses
+
+``` r
+rif <- mutate(rif, ii = 24, addl = 9)
+
+rif
+```
+
+ . Events:
+ . time amt ii addl cmt evid
+ . 1 0 600 24 9 1 1
+
+``` r
+out <-
+ mod %>%
+ ev(rif) %>%
+ mrgsim(end = 240)
+```
+
+What is going on here?
+
+``` r
+p <- plot(out, Ccentral ~ time)
+
+ggplot(as_tibble(out), aes(time/24,Ccentral)) +
+ geom_line(lwd=1) + theme_bw() +
+ scale_x_continuous(name = ""Time (day)"", breaks = seq(0,10,2))
+```
+
+
+
+Let’s investigate
+
+``` r
+aucs <-
+ out %>%
+ mutate(DAY = 1+floor(time/24)) %>%
+ group_by(DAY) %>%
+ summarise(AUC = auc_partial(time,Ccentral)) %>%
+ ungroup %>%
+ mutate(pAUC = 100*AUC/first(AUC)) %>%
+ filter(DAY < 10)
+
+ggplot(aucs, aes(factor(DAY),pAUC)) +
+ geom_col(alpha = 0.6) +
+ geom_hline(yintercept = 70, lty = 2, col = ""firebrick"") +
+ scale_y_continuous(breaks = seq(0,100,10))
+```
+
+
+
+Both `CYP3A4` and `UGT` metabolic activity increased after multiple
+rifampicin doses
+
+``` r
+simsm <-
+ out %>%
+ as_tibble() %>%
+ gather(variable, value, c(UGT_ratio_HC5, CYP3A4_ratio_HC5))
+
+simsm %>%
+ ggplot(., aes(time/24, value, col = variable)) +
+ geom_line(lwd =1 ) +
+ scale_color_brewer(palette = ""Set2"",name="""",labels=c(""CYP3A4"", ""UGT"")) +
+ scale_x_continuous(name=""Time (day)"", breaks = seq(0,10,2)) +
+ ylab(""fold induction"") + theme(legend.position = ""right"")
+```
+
+
+
+# PBPK model for rifampicin / midazolam DDI
+
+ - Recall that our PBPK model is really a combination of two models:
+ one for rifampicin and one for midazolam
+
+A single 3 mg midazolam dose
+
+``` r
+mid <- ev(amt = 3, cmt = ""Mgutlumen"")
+
+mid
+```
+
+ . Events:
+ . time amt cmt evid
+ . 1 0 3 Mgutlumen 1
+
+``` r
+mod %>% mrgsim_e(mid, outvars = ""Cmidazolam"") %>% plot()
+```
+
+
+
+Now, a single 3 mg midazolam dose after 7 days of rifampin 75 mg QD
+
+``` r
+rif <- ev(amt = 75, ii = 24, addl = 6, cmt = ""Xgutlumen"")
+
+rif_mid <- ev_seq(rif, wait = -12, mid)
+
+mid <- filter(rif_mid, cmt==""Mgutlumen"")
+
+both <- as_data_set(mid, rif_mid)
+
+both
+```
+
+ . ID time cmt evid amt ii addl
+ . 1 1 156 Mgutlumen 1 3 0 0
+ . 2 2 0 Xgutlumen 1 75 24 6
+ . 3 2 156 Mgutlumen 1 3 0 0
+
+``` r
+sims <-
+ mod %>%
+ mrgsim_d(both, Req=""Cmidazolam"", end = 166) %>%
+ filter_sims(time >= 156) %>%
+ mutate(ID = factor(ID, labels = c(""Midazolam"", ""Midazolam after Rif"")))
+
+
+ggplot(sims, aes(time-156,Cmidazolam,col = factor(ID))) +
+ geom_line(lwd = 1) +
+ scale_y_continuous(trans = ""log10"", limits = c(0.1, 10)) +
+ scale_x_continuous(name = ""Time (hr)"", breaks = seq(0,10,2)) +
+ scale_color_brewer(palette = ""Set2"", name="""")
+```
+
+
+
+Midazolam exposure is reduced after rifampicin 75 mg daily x 7d
+
+``` r
+sims %>%
+ group_by(ID) %>%
+ summarise(AUC = auc_partial(time,Cmidazolam)) %>%
+ mutate(percent_reduction = 100*(1-AUC/first(AUC)))
+```
+
+ . # A tibble: 2 x 3
+ . ID AUC percent_reduction
+ .
+ . 1 Midazolam 24.7 0
+ . 2 Midazolam after Rif 6.84 72.3
+
+## Dose-response for midazolam/rifampin DDI
+
+Make a function to wrap up the workflow for a single dose
+
+``` r
+sim_ddi <- function(rif_dose, mid_dose = 3) {
+ mid <- ev(amt = mid_dose, cmt = 2)
+ rif <- ev(amt = rif_dose, ii = 24, addl = 6)
+ rif_mid <- ev_seq(rif, wait = -12, mid)
+
+ mod %>%
+ mrgsim_e(rif_mid, Req=""Cmidazolam"", end = 166) %>%
+ filter_sims(time >= 156) %>%
+ mutate(rif = rif_dose, mid = mid_dose)
+}
+
+sim_ddi(600)
+```
+
+ . # A tibble: 102 x 5
+ . ID time Cmidazolam rif mid
+ .
+ . 1 1 156 0 600 3
+ . 2 1 156 0 600 3
+ . 3 1 156. 0.323 600 3
+ . 4 1 156. 0.597 600 3
+ . 5 1 156. 0.758 600 3
+ . 6 1 156. 0.845 600 3
+ . 7 1 156. 0.884 600 3
+ . 8 1 157. 0.893 600 3
+ . 9 1 157. 0.883 600 3
+ . 10 1 157. 0.860 600 3
+ . # … with 92 more rows
+
+``` r
+out <- map_df(seq(0,600,10), .f = sim_ddi)
+```
+
+Summarize the simulations by both rifampicin dose and midazolam dose.
+Because we simulated the zero rifampicin dose, we get the percent
+reduction in AUC by dividing by the “first” auc in the series
+
+``` r
+summ <-
+ out %>%
+ group_by(rif,mid) %>%
+ summarise(auc = auc_partial(time,Cmidazolam)) %>%
+ ungroup() %>%
+ mutate(pAUC = 100*(auc/first(auc)))
+
+summ
+```
+
+ . # A tibble: 61 x 4
+ . rif mid auc pAUC
+ .
+ . 1 0 3 24.7 100
+ . 2 10 3 13.9 56.3
+ . 3 20 3 11.2 45.4
+ . 4 30 3 9.74 39.4
+ . 5 40 3 8.76 35.5
+ . 6 50 3 8.05 32.6
+ . 7 60 3 7.49 30.3
+ . 8 70 3 7.04 28.5
+ . 9 80 3 6.66 27.0
+ . 10 90 3 6.33 25.6
+ . # … with 51 more rows
+
+``` r
+ggplot(summ, aes(rif,pAUC)) +
+ geom_line(lwd = 1) +
+ scale_y_continuous(breaks = seq(0,100,10), limits = c(0,100),
+ name = ""Midazolam AUC after Rif (% of no-Rif AUC)"") +
+ scale_x_continuous(name = ""Rifampicin dose (mg)"", breaks=seq(0,600,100)) +
+ theme_bw()
+```
+
+
+","Markdown"
+"PBPK model","metrumresearchgroup/r-pharma-pkpd-2020","content/tools_optimization_indomethacin.md",".md","9703","443","Pooled analysis of indomethacin PK data
+================
+Metrum Research Group
+
+ - [Packages](#packages)
+ - [Load indomethacin data set](#load-indomethacin-data-set)
+ - [Data assembly](#data-assembly)
+ - [Load a PK model](#load-a-pk-model)
+ - [Create an objective function
+ function](#create-an-objective-function-function)
+ - [Fit with one-compartment model](#fit-with-one-compartment-model)
+ - [Make a plot of the output](#make-a-plot-of-the-output)
+ - [Your turn](#your-turn)
+ - [Answer](#answer)
+ - [Fit the data with
+ `RcppDE::DEoptim`](#fit-the-data-with-rcppdedeoptim)
+ - [Check the estimates and the final value of the objective
+ function](#check-the-estimates-and-the-final-value-of-the-objective-function)
+ - [Some global search with NLOPTR](#some-global-search-with-nloptr)
+
+# Packages
+
+``` r
+library(tidyverse)
+theme_set(theme_bw())
+library(mrgsolve)
+```
+
+# Load indomethacin data set
+
+``` r
+data(Indometh)
+```
+
+ - Take a look at what is there
+
+
+
+``` r
+head(Indometh)
+```
+
+ . Grouped Data: conc ~ time | Subject
+ . Subject time conc
+ . 1 1 0.25 1.50
+ . 2 1 0.50 0.94
+ . 3 1 0.75 0.78
+ . 4 1 1.00 0.48
+ . 5 1 1.25 0.37
+ . 6 1 2.00 0.19
+
+``` r
+count(Indometh, Subject)
+```
+
+ . Grouped Data: conc ~ time | Subject
+ . Subject n
+ . 1 1 11
+ . 2 4 11
+ . 3 2 11
+ . 4 5 11
+ . 5 6 11
+ . 6 3 11
+
+``` r
+ggplot(Indometh, aes(time,conc,group=Subject)) +
+ geom_point() + geom_line() +
+ scale_y_continuous(trans = ""log"", breaks = 10^seq(-4,4))
+```
+
+
+
+This is individual-level data, but we are going to do naive pooled
+analysis.
+
+# Data assembly
+
+``` r
+data <- readRDS(""data/indometh.RDS"")
+
+head(data)
+```
+
+ . time conc evid cmt ID amt
+ . 1 0.00 NA 1 2 1 25
+ . 2 0.25 1.50 0 0 1 NA
+ . 3 0.50 0.94 0 0 1 NA
+ . 4 0.75 0.78 0 0 1 NA
+ . 5 1.00 0.48 0 0 1 NA
+ . 6 1.25 0.37 0 0 1 NA
+
+# Load a PK model
+
+ - We’ll try out one-compartment first
+
+
+
+``` r
+mod <- modlib(""pk1"")
+
+param(mod)
+```
+
+ .
+ . Model parameters (N=3):
+ . name value . name value
+ . CL 1 | V 20
+ . KA 1 | . .
+
+Pick some parameters to estimate:
+
+``` r
+theta <- log(c(CL = 1, V = 100))
+```
+
+``` r
+names(theta)
+```
+
+ . [1] ""CL"" ""V""
+
+# Create an objective function function
+
+ - For starters, just do OLS estimation
+ - Note that we *need* to name the parameters (`p`)
+ - Parameter updates require names in `mrgsolve`
+ - Generally, don’t expect `p` to retain any names that you might
+ pass in through the initial estimates
+ - We also pass in the `data` and the dependent variable (`dv`)
+
+
+
+``` r
+obj <- function(p, theta, data, dv =""conc"", pred = FALSE) {
+
+ names(p) <- names(theta)
+
+ p <- lapply(p,exp)
+
+ mod <- param(mod, p)
+
+ out <- mrgsim_q(mod, data, output=""df"")
+
+ if(pred) return(out)
+
+ sqr <- (out[[""CP""]] - data[[dv]])^2
+
+ sum(sqr, na.rm=TRUE)
+}
+```
+
+# Fit with one-compartment model
+
+ - First generate some initial estimates
+ - These *need* to be named in a way that is consistent with the model
+ we are using
+ - I usually run a test with the objective function function to make
+ sure the logic works out
+
+
+
+``` r
+obj(theta,theta,data)
+```
+
+ . [1] 33.69619
+
+ - Nelder-Mead optimization
+
+
+
+``` r
+fit <- optim(par = theta, fn=obj, theta = theta, data=data)
+```
+
+ - And generate some predictions based on the final estimates
+
+
+
+``` r
+pred <- obj(fit$par, theta, data, pred = TRUE)
+
+data$pred <- pred$CP
+
+head(data)
+```
+
+ . time conc evid cmt ID amt pred
+ . 1 0.00 NA 1 2 1 25 2.7771715
+ . 2 0.25 1.50 0 0 1 NA 1.9814023
+ . 3 0.50 0.94 0 0 1 NA 1.4136524
+ . 4 0.75 0.78 0 0 1 NA 1.0085852
+ . 5 1.00 0.48 0 0 1 NA 0.7195857
+ . 6 1.25 0.37 0 0 1 NA 0.5133960
+
+# Make a plot of the output
+
+ - What do you think? Good fit?
+
+
+
+``` r
+ggplot(data = data) +
+ geom_point(aes(time,conc)) +
+ scale_y_log10() +
+ geom_line(aes(time,pred),col=""firebrick"", lwd=1)
+```
+
+
+
+# Your turn
+
+ - Try fitting the same indomethacin data with a 2-compartment model
+
+
+
+``` r
+mod <- modlib(""pk2"")
+```
+
+ - Take a look at the model and generate a call to `minqa::newuoa`
+ using the OLS objective function above to fit the data
+
+ - You will also need try out a new set of initial estimates for all of
+ the volumes and clearances for 2-compartment, IV bolus model
+
+ - What do you think of the fit using the the OLS objective function?
+
+ - Can you make a simple modification to the OLS objective function
+ that might make the fit look a little better?
+
+ - Suppose we’re worried about the `newuoa` optimizer and want to try a
+ global search algorithm
+
+ - Can you construct a call to `RcppDE::DEoptim` that will also fit
+ the data?
+ - Remember that `DEoptim` doesn’t use initial estimates the same
+ way `stats::optim` or `minqa::newuoa` does; you have to specify
+ one vector of lower boundaries and one vector of upper
+ boundaries, with a lower and upper bound for each parameter
+
+# Answer
+
+ - Set the initial estimates for two compartment model
+
+
+
+``` r
+param(mod)
+```
+
+ .
+ . Model parameters (N=5):
+ . name value . name value
+ . CL 1 | V2 20
+ . KA 1 | V3 10
+ . Q 2 | . .
+
+``` r
+theta <- log(c(CL = 2, V2 = 50, Q = 10, V3 = 50))
+```
+
+``` r
+fit <- optim(par = theta, fn=obj, theta = theta, data=data)
+```
+
+ - And generate some predictions based on the final estimates
+
+
+
+``` r
+pred <- obj(fit$par, theta, data, pred = TRUE)
+
+data$pred <- pred$CP
+
+ggplot(data = data) +
+ geom_point(aes(time,conc)) +
+ scale_y_log10() +
+ geom_line(aes(time,pred),col=""firebrick"", lwd=1)
+```
+
+ . Warning: Removed 6 rows containing missing values (geom_point).
+
+
+
+ - Try weighted least squares
+
+
+
+``` r
+obj <- function(p, theta, data, wt, pred = FALSE) {
+ names(p) <- names(theta)
+ p <- lapply(p,exp)
+ out <- mod %>% param(p) %>% mrgsim_q(data, output=""df"")
+ if(pred) return(out)
+ return(sum(((out$CP - data[[""conc""]])*wt)^2, na.rm=TRUE))
+}
+```
+
+``` r
+dv <- data[[""conc""]]
+
+fit_wt <- minqa::newuoa(par = theta, fn=obj, theta = theta, data=data, wt=1/dv)
+```
+
+Final estimates and final value of objective function
+
+``` r
+exp(fit_wt$par)
+```
+
+ . [1] 8.926545 8.873479 6.319330 19.684318
+
+``` r
+obj(fit_wt$par,theta,data,dv)
+```
+
+ . [1] 8.630392
+
+ - Generate predictions for the final and initial estimates
+
+
+
+``` r
+pred <- obj(fit$par, theta, data, wt = 1/dv, pred = TRUE)
+predi <- obj(theta, theta, data, wt = 1/dv, pred = TRUE)
+predw <- obj(fit_wt$par, theta, data, wt = 1/dv, pred = TRUE)
+
+
+data$pred <- pred$CP
+data$predi <- predi$CP
+data$predw <- predw$CP
+head(data)
+```
+
+ . time conc evid cmt ID amt pred predi predw
+ . 1 0.00 NA 1 2 1 25 3.2236198 0.5000000 2.8173842
+ . 2 0.25 1.50 0 0 1 NA 2.0547326 0.4714730 1.8483732
+ . 3 0.50 0.94 0 0 1 NA 1.3586833 0.4456942 1.2371868
+ . 4 0.75 0.78 0 0 1 NA 0.9405602 0.4223898 0.8505654
+ . 5 1.00 0.48 0 0 1 NA 0.6860398 0.4013128 0.6049255
+ . 6 1.25 0.37 0 0 1 NA 0.5280509 0.3822414 0.4478397
+
+ - Plot the predictions
+
+
+
+``` r
+pred <- distinct(data, time, .keep_all = TRUE)
+
+ggplot(data = data) +
+ geom_point(aes(time,conc)) +
+ scale_y_log10() +
+ geom_line(data=pred,aes(time,pred),col=""black"", lwd=1, alpha = 0.6) +
+ geom_line(data=pred,aes(time,predi),col=""darkgreen"", lwd=1) +
+ geom_line(data = pred, aes(time,predw), col=""firebrick"", lwd = 1)
+```
+
+ . Warning: Removed 6 rows containing missing values (geom_point).
+
+
+
+## Fit the data with `RcppDE::DEoptim`
+
+``` r
+fit <- DEoptim::DEoptim(
+ obj,
+ lower = rep(-4,4),
+ upper = rep(4,4),
+ theta = theta, data = data, wt = 1/dv,
+ control = DEoptim::DEoptim.control(itermax=120,trace=20)
+)
+```
+
+## Check the estimates and the final value of the objective function
+
+``` r
+tibble(
+ DE = exp(fit$optim$bestmem),
+ Nelder = exp(fit_wt$par)
+)
+
+tibble(
+ DE = obj(fit$optim$bestmem, theta,data,1/dv),
+ Nelder = obj(fit_wt$par, theta, data, 1/dv)
+)
+```
+
+# Some global search with NLOPTR
+
+
+
+``` r
+library(nloptr)
+a0 <- obj(theta,theta=theta,data=data,wt = 1/dv, pred = FALSE)
+
+lowr <- rep(-5,length(theta))
+uppr <- rep(5, length(theta))
+
+x <- isres(
+ x0 = theta,
+ fn=obj,
+ lower = lowr,
+ upper = uppr,
+ theta=theta,
+ data=data,
+ wt = 1/dv,
+ maxeval=20000
+)
+
+y <- crs2lm(
+ x0 = theta,
+ fn=obj,
+ lower = lowr,
+ upper = uppr,
+ theta=theta,
+ data=data,
+ wt = 1/dv,
+ maxeval=5000
+)
+
+z <- newuoa(x0 = y$par, fn = obj,theta = theta, data = data, wt = 1/dv)
+
+
+tibble(a0 = theta, a = fit_wt$par, x = x$par, y = y$par, z= z$par) %>% exp
+tibble(a0 = a0,a = fit_wt$fval, x = x$value, y= y$value, z = z$value)
+
+
+direct <- directL(
+ fn = obj,
+ lower = lowr,
+ upper = uppr,
+ theta = theta,
+ data = data,
+ wt = 1/dv,
+ control = list(maxeval=2500)
+)
+
+
+tibble(a0 = theta, a = fit_wt$par, x = x$par, y = y$par, z= z$par,d = direct$par) %>% exp
+tibble(a0 = a0,a = fit_wt$fval, x = x$value, y= y$value, z = z$value, d = direct$value)
+```
+","Markdown"
+"PBPK model","metrumresearchgroup/r-pharma-pkpd-2020","content/tools_optimization_intro.md",".md","15264","573","Introduction to parameter optimization
+================
+Metrum Research Group
+
+ - [Swiss Fertility and Socioeconomic
+ Indicators](#swiss-fertility-and-socioeconomic-indicators)
+ - [Optimization the `R` way](#optimization-the-r-way)
+ - [Model](#model)
+ - [Data](#data)
+ - [Objective function](#objective-function)
+ - [Parameter search](#parameter-search)
+ - [Optimize](#optimize)
+ - [Use other optimizers](#use-other-optimizers)
+ - [`minqa::newuoa`](#minqanewuoa)
+ - [DEoptim](#deoptim)
+ - [Maximum likelihood estimation](#maximum-likelihood-estimation)
+ - [Extended least squares](#extended-least-squares)
+ - [Get standard error of estimate](#get-standard-error-of-estimate)
+ - [Plot predicted and observed
+ values](#plot-predicted-and-observed-values)
+ - [Let’s try it](#lets-try-it)
+
+``` r
+library(tidyverse)
+```
+
+ ## Warning: replacing previous import 'vctrs::data_frame' by 'tibble::data_frame'
+ ## when loading 'dplyr'
+
+``` r
+library(broom)
+library(ggplot2)
+theme_set(theme_bw())
+```
+
+# Swiss Fertility and Socioeconomic Indicators
+
+We’ll get started with the introduciton to optimization with an easy
+data set and an easy model.
+
+Data were collected from 47 French-speaking provences around 1888.
+
+``` r
+data(swiss)
+glimpse(swiss, width = 60, strict.width=""cut"")
+```
+
+ . Rows: 47
+ . Columns: 6
+ . $ Fertility 80.2, 83.1, 92.5, 85.8, 76.9, 76…
+ . $ Agriculture 17.0, 45.1, 39.7, 36.5, 43.5, 35…
+ . $ Examination 15, 6, 5, 12, 17, 9, 16, 14, 12,…
+ . $ Education 12, 9, 5, 7, 15, 7, 7, 8, 7, 13,…
+ . $ Catholic 9.96, 84.84, 93.40, 33.77, 5.16,…
+ . $ Infant.Mortality 22.2, 22.2, 20.2, 20.3, 20.6, 26…
+
+``` r
+ggplot(swiss, aes(Examination,Fertility)) + geom_point() + geom_smooth()
+```
+
+![]()
+
+We’ll work on a regression model for the standardized fertility measure
+(dependent variable) as function of the `Examination` predictor (percent
+draftees receiving highest mark on army examination).
+
+Usually we’d fit this model in R like this:
+
+``` r
+fit <- lm(Fertility ~ Examination, swiss)
+
+tidy(fit)
+```
+
+ . # A tibble: 2 x 5
+ . term estimate std.error statistic p.value
+ .
+ . 1 (Intercept) 86.8 3.26 26.7 3.35e-29
+ . 2 Examination -1.01 0.178 -5.68 9.45e- 7
+
+Using `lm` is the “right” way to model this data. We’re going to write
+come code that also get parameters for this data. But we’ll put all of
+the pieces together ourselves. So this isn’t the proper way to get these
+parameter estimates. But this simple example will help us better
+understand the mechanics of parameter optimization in R.
+
+# Optimization the `R` way
+
+We’ll need
+
+1. A **model**
+ - The model generates the data (dependent variable) based on
+ parameters and predictors
+2. Some **data**
+ - Using `swiss` for now
+3. An **objective function**
+ - To quantify how consistent a set of parameters are with the
+ observed data
+4. An **optimizer**
+ - Search the parameter space for most optimal parameter value
+
+## Model
+
+The parameters are
+
+ - `intercept`
+ - `slope`
+
+And the predictor is `ex` … in our example `Examination`
+
+This is pretty simple stuff. But we’ll wrap it up in a function to call
+like this:
+
+``` r
+linear_model <- function(intercept, slope, ex) {
+ intercept + slope * ex
+}
+```
+
+So we can get the predicted `Fertility` by passing in the intercept,
+slope, and the `Examination` value
+
+E(y|x)
+
+``` r
+linear_model(intercept = 90, slope = -2, ex = 20)
+```
+
+ . [1] 50
+
+## Data
+
+How you want to set this up is a bit dependent on your application. I’m
+going to get vectors called `ex` for the `Examination` value (predictor)
+and `fer` for `Fertility` (the dependent variable).
+
+``` r
+ex <- swiss[[""Examination""]]
+
+fer <- swiss[[""Fertility""]]
+```
+
+## Objective function
+
+We’ll write this function so that
+
+1. The first argument is `par`, the parameters we want to evaluate
+ - Par will be a vector of length 2, the intercept and the slope
+2. We will also pass in the predictor (`ex`) and the data (`fer`),
+ which we’ll need to calculate the objective function
+
+
+
+``` r
+ofv <- function(par, ex, fer) {
+
+ fer_hat <- linear_model(par[1], par[2], ex)
+
+ sum((fer-fer_hat)^2)
+}
+```
+
+This is an *O* rdinary *L* east *S* quares objective function.
+
+Working backward:
+
+1. We return the squared difference between the predicted values
+ (`fer_hat`) and the data
+2. We generate the predicted values from our linear model function, the
+ proposed parameters and the data
+3. The optimizer will propose a set of parameters for us to evaluate
+
+Let’s test the objective function
+
+``` r
+theta <- c(70, -2)
+
+ofv(theta, ex, fer)
+```
+
+ . [1] 58605.31
+
+Good or bad? Looking back at the data, the intercept doesn’t look like
+it is 70 … more like 80. Let’s try that:
+
+``` r
+theta <- c(80,-2)
+
+ofv(theta, ex, fer)
+```
+
+ . [1] 32171.31
+
+Ok the objective function is lower now. The second set of parameters we
+tried looks better than the first set.
+
+What about slope?
+
+``` r
+theta <- c(80,-1.5)
+
+ofv(theta, ex, fer)
+```
+
+ . [1] 15284.46
+
+This is even better. But we can’t keep going like this.
+
+## Parameter search
+
+Let’s do this for a big batch of parameters
+
+ - intercept from 75 to 95
+ - slope from -2 to 0
+
+
+
+``` r
+test <- expand.grid(intercept = seq(75,95,1), slope = seq(-2,0,0.1))
+
+head(test)
+```
+
+ . intercept slope
+ . 1 75 -2
+ . 2 76 -2
+ . 3 77 -2
+ . 4 78 -2
+ . 5 79 -2
+ . 6 80 -2
+
+Now calculate the value of the objective function for each paramter set
+
+``` r
+test <- mutate(
+ test,
+ value = pmap_dbl(test, .f=function(intercept,slope) {
+ ofv(c(intercept,slope), ex = ex, fer = fer)
+ })
+)
+
+arrange(test,value) %>% head
+```
+
+ . intercept slope value
+ . 1 87 -1.0 4190.31
+ . 2 86 -1.0 4202.71
+ . 3 88 -1.1 4210.30
+ . 4 85 -0.9 4219.86
+ . 5 89 -1.1 4230.90
+ . 6 84 -0.9 4265.26
+
+``` r
+ggplot(test) + geom_contour(aes(intercept,slope,z=value),bins=80)
+```
+
+![]()
+
+## Optimize
+
+We know there is a set of parameters that really gets us the smallest
+value of the objective function and are therefor the “optimal”
+parameters.
+
+We invoke an optizer in R to search the parameter space and find that
+set of parameters.
+
+Start with an optimizer that comes with R in the `stats` package.
+`optim` by default does Nelder-Mead optimization algorithm.
+
+When we call `optim`, we have to give an inital guess (`par`) and the
+function to minimize (`ofv`). We also pass in the predictor and the
+vector of observed data so we can calculate the sum of squares.
+
+``` r
+fit <- optim(c(100,1), ofv, ex = ex, fer = fer)
+```
+
+``` r
+fit$par
+```
+
+ . [1] 86.822915 -1.011682
+
+``` r
+lm(Fertility~Examination, swiss)
+```
+
+ .
+ . Call:
+ . lm(formula = Fertility ~ Examination, data = swiss)
+ .
+ . Coefficients:
+ . (Intercept) Examination
+ . 86.819 -1.011
+
+# Use other optimizers
+
+## `minqa::newuoa`
+
+``` r
+library(minqa)
+
+fit <- newuoa(theta, ofv, ex = ex, fer = fer, control = list(iprint=20))
+```
+
+ . npt = 4 , n = 2
+ . rhobeg = 0.95 , rhoend = 9.5e-07
+ . start par. = 80 -1.5 fn = 15284.46
+ . rho: 0.095 eval: 5 fn: 4835.84 par: 80.0000 -0.550000
+ . 20: 4319.4571: 83.2325 -0.872836
+ . rho: 0.0095 eval: 24 fn: 4250.71 par: 84.0677 -0.868220
+ . 40: 4188.6677: 86.0555 -0.973636
+ . rho: 0.00095 eval: 55 fn: 4184.30 par: 86.6095 -1.00569
+ . 60: 4183.8628: 86.6348 -1.00216
+ . rho: 9.5e-05 eval: 74 fn: 4183.57 par: 86.8189 -1.01133
+ . rho: 9.5e-06 eval: 78 fn: 4183.57 par: 86.8185 -1.01131
+ . 80: 4183.5671: 86.8185 -1.01132
+ . rho: 9.5e-07 eval: 81 fn: 4183.57 par: 86.8185 -1.01131
+ . At return
+ . eval: 89 fn: 4183.5671 par: 86.8185 -1.01132
+
+``` r
+fit$par
+```
+
+ . [1] 86.818529 -1.011317
+
+## DEoptim
+
+Differential evolution algorithm
+
+``` r
+library(DEoptim)
+
+lower <- c(intercept=0, slope=-100)
+upper <- c(intercept = 1000, slope=100)
+
+con <- DEoptim.control(itermax = 80, trace = 2)
+
+set.seed(112233)
+fit <- DEoptim(ofv, lower, upper, ex = ex, fer = fer, control = con)
+```
+
+ . Iteration: 2 bestvalit: 318953.193160 bestmemit: 265.976666 -8.389177
+ . Iteration: 4 bestvalit: 150708.467549 bestmemit: 213.881420 -7.849656
+ . Iteration: 6 bestvalit: 67109.936741 bestmemit: 150.706030 -2.848407
+ . Iteration: 8 bestvalit: 20714.032107 bestmemit: 90.987682 -0.195885
+ . Iteration: 10 bestvalit: 20714.032107 bestmemit: 90.987682 -0.195885
+ . Iteration: 12 bestvalit: 4444.885030 bestmemit: 90.987682 -1.133708
+ . Iteration: 14 bestvalit: 4444.885030 bestmemit: 90.987682 -1.133708
+ . Iteration: 16 bestvalit: 4444.885030 bestmemit: 90.987682 -1.133708
+ . Iteration: 18 bestvalit: 4444.885030 bestmemit: 90.987682 -1.133708
+ . Iteration: 20 bestvalit: 4214.123010 bestmemit: 85.878019 -0.926806
+ . Iteration: 22 bestvalit: 4214.123010 bestmemit: 85.878019 -0.926806
+ . Iteration: 24 bestvalit: 4214.123010 bestmemit: 85.878019 -0.926806
+ . Iteration: 26 bestvalit: 4214.123010 bestmemit: 85.878019 -0.926806
+ . Iteration: 28 bestvalit: 4214.123010 bestmemit: 85.878019 -0.926806
+ . Iteration: 30 bestvalit: 4194.444474 bestmemit: 87.819083 -1.072268
+ . Iteration: 32 bestvalit: 4183.716589 bestmemit: 86.949012 -1.017892
+ . Iteration: 34 bestvalit: 4183.716589 bestmemit: 86.949012 -1.017892
+ . Iteration: 36 bestvalit: 4183.716589 bestmemit: 86.949012 -1.017892
+ . Iteration: 38 bestvalit: 4183.716589 bestmemit: 86.949012 -1.017892
+ . Iteration: 40 bestvalit: 4183.716589 bestmemit: 86.949012 -1.017892
+ . Iteration: 42 bestvalit: 4183.687877 bestmemit: 86.935280 -1.017368
+ . Iteration: 44 bestvalit: 4183.599647 bestmemit: 86.759307 -1.008059
+ . Iteration: 46 bestvalit: 4183.594865 bestmemit: 86.768295 -1.008241
+ . Iteration: 48 bestvalit: 4183.594865 bestmemit: 86.768295 -1.008241
+ . Iteration: 50 bestvalit: 4183.567802 bestmemit: 86.823236 -1.011722
+ . Iteration: 52 bestvalit: 4183.567474 bestmemit: 86.813777 -1.011176
+ . Iteration: 54 bestvalit: 4183.567474 bestmemit: 86.813777 -1.011176
+ . Iteration: 56 bestvalit: 4183.567474 bestmemit: 86.813777 -1.011176
+ . Iteration: 58 bestvalit: 4183.567347 bestmemit: 86.817505 -1.011155
+ . Iteration: 60 bestvalit: 4183.567254 bestmemit: 86.814953 -1.011134
+ . Iteration: 62 bestvalit: 4183.567149 bestmemit: 86.819223 -1.011336
+ . Iteration: 64 bestvalit: 4183.567149 bestmemit: 86.819223 -1.011336
+ . Iteration: 66 bestvalit: 4183.567149 bestmemit: 86.819223 -1.011336
+ . Iteration: 68 bestvalit: 4183.567149 bestmemit: 86.819223 -1.011336
+ . Iteration: 70 bestvalit: 4183.567146 bestmemit: 86.817748 -1.011278
+ . Iteration: 72 bestvalit: 4183.567146 bestmemit: 86.817748 -1.011278
+ . Iteration: 74 bestvalit: 4183.567144 bestmemit: 86.818997 -1.011346
+ . Iteration: 76 bestvalit: 4183.567143 bestmemit: 86.818129 -1.011302
+ . Iteration: 78 bestvalit: 4183.567141 bestmemit: 86.818502 -1.011316
+ . Iteration: 80 bestvalit: 4183.567141 bestmemit: 86.818502 -1.011316
+
+# Maximum likelihood estimation
+
+Let’s write a new (R) function where we optimize based on a normal
+likelihood function.
+
+The arguments are the same as the OLS function. Now, rather than
+comparing predictions against data using sum of squares, we compare
+based on normal likelihood function.
+
+``` r
+ml <- function(p, ex, fer) {
+
+ fer_hat <- linear_model(p[1], p[2], ex)
+
+ like <- dnorm(fer, fer_hat, p[3], log = TRUE)
+
+ -1*sum(like)
+}
+```
+
+**Note**
+
+1. We have an extra parameter now … the standard deviation for
+ likelihood function
+2. We use `log=TRUE` to get the log likelihood; then the joint
+ likelihood of all the data is the sum of the individual likelihoods
+3. We return minus-1 times the log likelihood; we are doing *maximum*
+ likelihood but the optimizers find the *minimum* of a function
+
+Test the function now
+
+``` r
+theta <- c(intercept = 10, slope = 1, sd = 2)
+
+ml(theta, ex, fer)
+```
+
+ . [1] 13274.61
+
+And we get the same answer
+
+``` r
+fit <- newuoa(theta, ml, ex = ex, fer = fer)
+
+fit$par
+```
+
+ . [1] 86.818530 -1.011317 9.434621
+
+``` r
+fit$fval
+```
+
+ . [1] 172.1763
+
+# Extended least squares
+
+``` r
+els <- function(p, ex, fer) {
+
+ fer_hat <- linear_model(p[1], p[2], ex)
+
+ 0.5 * sum((fer - fer_hat)^2/p[3] + log(p[3]))
+}
+```
+
+``` r
+fit.els <- newuoa(theta, els, ex = ex, fer = fer)
+
+fit.els$par
+```
+
+ . [1] 86.818523 -1.011317 89.012071
+
+``` r
+fit.els$fval
+```
+
+ . [1] 128.9861
+
+# Get standard error of estimate
+
+We use `numDeriv::hessian` to get the hessian
+
+``` r
+library(numDeriv)
+
+he <- hessian(ml, fit$par, ex = ex, fer = fer)
+
+he
+```
+
+ . [,1] [,2] [,3]
+ . [1,] 5.280182e-01 8.706684e+00 -4.878745e-09
+ . [2,] 8.706684e+00 1.764592e+02 2.751098e-07
+ . [3,] -4.878745e-09 2.751098e-07 1.056036e+00
+
+To derive the standard error
+
+1. Invert the hessian matrix
+2. Get the diagonal elements
+3. Take the squre root
+
+
+
+``` r
+he %>% solve() %>% diag() %>% sqrt()
+```
+
+ . [1] 3.1875394 0.1743644 0.9731070
+
+And compare against the answer we got from `lm`
+
+``` r
+lm(Fertility ~ Examination, data = swiss) %>%
+ tidy() %>%
+ pull(std.error)
+```
+
+ . [1] 3.2576034 0.1781971
+
+You can also try `nlme::fdHess`
+
+``` r
+library(nlme)
+
+he <- fdHess(pars = fit$par, fun = ml, ex = ex, fer = fer)
+
+he$Hessian %>% solve() %>% diag() %>% sqrt()
+```
+
+ . [1] 3.1875681 0.1743662 0.9731027
+
+In my experience, it is frequently necessary to just bootstrap the data
+set. We will look at likelihood profile in a separate vignette.
+
+# Plot predicted and observed values
+
+Take the final parameter estimates
+
+``` r
+fit$par
+```
+
+ . [1] 86.818530 -1.011317 9.434621
+
+and pass them into our `linear_model` to generate predicted values.
+
+``` r
+data <- tibble(
+ ex = ex,
+ fer = fer,
+ pred = linear_model(fit$par[1], fit$par[2], ex)
+)
+```
+
+``` r
+ggplot(data = data) +
+ geom_point(aes(x = ex, y = fer)) +
+ geom_line(aes(x = ex, y = pred), lwd = 2, col=""red3"")
+```
+
+![]()
+
+# Let’s try it
+
+``` r
+data <- readRDS(""data/pdfit.RDS"")
+
+head(data)
+```
+
+ . # A tibble: 6 x 2
+ . auc response
+ .
+ . 1 66.3 109.
+ . 2 22.9 66.3
+ . 3 22.2 75.9
+ . 4 81.4 116.
+ . 5 109. 129.
+ . 6 98.6 119.
+
+``` r
+ggplot(data, aes(auc,response)) + geom_point()
+```
+
+![]()
+","Markdown"
+"PBPK model","metrumresearchgroup/r-pharma-pkpd-2020","content/tools_sensitivity_local.md",".md","4980","194","Introduction to parameter optimization
+================
+Metrum Research Group
+
+ - [Sensitivity analysis with PBPK
+ model](#sensitivity-analysis-with-pbpk-model)
+ - [Load the model](#load-the-model)
+ - [Load a data set](#load-a-data-set)
+ - [Define a function for sensitivity
+ analysis](#define-a-function-for-sensitivity-analysis)
+ - [Pick parameters for sensitivity
+ analysis](#pick-parameters-for-sensitivity-analysis)
+ - [Summarize](#summarize)
+
+``` r
+library(tidyverse)
+theme_set(theme_bw() + theme(legend.position = ""top""))
+library(mrgsolve)
+library(FME)
+options(mrgsolve.soloc = ""build"")
+```
+
+# Sensitivity analysis with PBPK model
+
+ - For a small change in a model parameter `p`, what is the change in
+ model output `y`?
+ - **Local** sensitivity analysis
+ - Use the `sensFun` function from the FME package
+
+## Load the model
+
+``` r
+mod <- mread_cache(""model/yoshikado.cpp"")
+mod <- update(mod, end = 12, delta = 0.025, atol=1E-12,rtol=1E-12)
+```
+
+## Load a data set
+
+``` r
+data <- read_csv(""data/fig4a.csv"")
+data <- mutate(data, DV = ifelse(DV < 0, NA_real_, DV))
+data <- filter(data, ID==2)
+dose <- filter(data, evid==1)
+```
+
+Statin / cyclosporine DDI
+
+``` r
+mod %>% mrgsim_d(dose,delta=0.025) %>% plot(CP~time)
+```
+
+![]()
+
+## Define a function for sensitivity analysis
+
+Do what we just did, but wrap it up in a function and pass in some
+parameters.
+
+``` r
+fun <- function(pars,data) {
+ mod %>%
+ param(pars) %>%
+ mrgsim_d(dose,obsonly=TRUE,output=""df"") %>%
+ select(-ID)
+}
+```
+
+Just like in the optimization function, we update the model object with
+whatever parameters were passed in and simulate.
+
+IMPORTANT to return a data frame of simulated data
+
+## Pick parameters for sensitivity analysis
+
+These are the parameters that we were focusing on in the regression
+model. Adding `Vadi` here as a negative control.
+
+``` r
+pars <- as.numeric(param(mod))
+pars <- pars[c(""fbCLintall"", ""ikiu"", ""fbile"", ""ka"", ""ktr"", ""Vadi"")]
+
+pars
+```
+
+ . fbCLintall ikiu fbile ka ktr Vadi
+ . 0.7371429 0.0118000 0.3300000 1.0600000 0.6790000 0.1430000
+
+Call `sensFun` from the FME package
+
+ - `func` - the sensitivity function we defined above
+ - `parms` - parameters to investigate
+ - `sensvar` - the output(s) that you want to look at
+ - `tiny` the step size for sensitivity analysis
+ - `data` this is an argument for our sensivitity function
+
+
+
+``` r
+locSens <- FME::sensFun(
+ func=fun,
+ parms=pars,
+ sensvar=""CP"",
+ tiny=1e-5,
+ data=data
+)
+```
+
+## Summarize
+
+``` r
+summary(locSens)
+```
+
+ . value scale L1 L2 Mean Min Max N
+ . fbCLintall 0.737 0.737 1.121 1.23 -1.121 -1.773 0.000 481
+ . ikiu 0.012 0.012 0.553 0.62 -0.553 -1.041 0.000 481
+ . fbile 0.330 0.330 0.583 0.78 0.583 0.000 1.397 481
+ . ka 1.060 1.060 0.226 0.31 -0.065 -0.496 0.989 481
+ . ktr 0.679 0.679 0.285 0.41 0.221 -0.372 0.847 481
+ . Vadi 0.143 0.143 0.023 0.03 0.011 -0.056 0.059 481
+
+**Summary plots**
+
+``` r
+plot(locSens, legpos=""topright"", lwd=2)
+```
+
+![]()
+
+``` r
+plot(summary(locSens))
+```
+
+![]()
+
+A nicer view
+
+``` r
+summ <-
+ as_tibble(summary(locSens)) %>%
+ mutate(parms = names(pars))
+
+ggplot(data=summ, aes(x=reorder(parms, Mean), y=Mean)) +
+ geom_col() +
+ labs(x=""Parameter"", y=""Coefficient"") +
+ coord_flip() +
+ geom_hline(yintercept = 0, lty=2)
+```
+
+![]()
+
+``` r
+#nicer view
+df_temp <- as_tibble(locSens) %>%
+ gather(Parameter, Coefficient, -x, -var) %>%
+ mutate(Parameter = factor(Parameter)) %>%
+ rename(time=x) %>%
+ group_by(Parameter) %>%
+ mutate(Coefficient = Coefficient - first(Coefficient)) %>%
+ ungroup()
+
+ggplot(data=df_temp, aes(x=time, y=Coefficient, col=Parameter)) +
+ geom_line(lwd=1) +
+ theme(legend.position=""right"") +
+ facet_wrap(~var)
+```
+
+![]()
+
+``` r
+fun2 <- function(pars,data) {
+ mod <- param(mod, pars)
+ out <- mrgsim_d(mod,data,output=""df"")
+ wres <- (out[[""CP""]] - data[[""DV""]]) * 1/data[[""DV""]]
+ ofv <- sum(wres, na.rm=TRUE)
+ tibble(time=12,ofv = ofv)
+}
+
+
+locSens <- sensFun(func=fun2, parms=pars, tiny=1e-5,data=data)
+
+summ <-
+ as_tibble(summary(locSens)) %>%
+ mutate(parms = names(pars))
+
+ggplot(data=summ, aes(x=reorder(parms, Mean), y=Mean)) +
+ geom_col() +
+ labs(x=""Parameter"", y=""Coefficient"") +
+ coord_flip() +
+ geom_hline(yintercept = 0, lty=2)
+```
+
+![]()
+","Markdown"
+"PBPK model","metrumresearchgroup/r-pharma-pkpd-2020","content/tools_optimization_pbpk_ddi.md",".md","9268","378","Estimate parameters in a PBPK model
+================
+Metrum Research Group
+
+ - [Packages and setup](#packages-and-setup)
+ - [Reference](#reference)
+ - [Data](#data)
+ - [PBPK model: pitavastatin / CsA
+ DDI](#pbpk-model-pitavastatin-csa-ddi)
+ - [Objective function](#objective-function)
+ - [Prediction function](#prediction-function)
+ - [Data grooming](#data-grooming)
+ - [Optimize](#optimize)
+ - [`nloptr::newuoa`: minimization without
+ derivatives](#nloptrnewuoa-minimization-without-derivatives)
+ - [Get some predictions to look at how the fit
+ went](#get-some-predictions-to-look-at-how-the-fit-went)
+ - [Make some plots](#make-some-plots)
+ - [A nicer plot](#a-nicer-plot)
+ - [The final objective function value and
+ estimates](#the-final-objective-function-value-and-estimates)
+
+# Packages and setup
+
+``` r
+library(tidyverse)
+library(PKPDmisc)
+library(mrgsolve)
+source(""script/functions.R"")
+source(""script/global.R"")
+```
+
+``` r
+set.seed(10101)
+```
+
+``` r
+theme_set(theme_bw() + theme(legend.position = ""top""))
+scale_colour_discrete <- function(...) scale_color_brewer(palette=""Set2"")
+```
+
+Models are located here:
+
+``` r
+model_dir <- ""model""
+```
+
+# Reference
+
+**Quantitative Analyses of Hepatic OATP-Mediated Interactions Between
+Statins and Inhibitors Using PBPK Modeling With a Parameter Optimization
+Method**
+
+ - T Yoshikado, K Yoshida, N Kotani, T Nakada, R Asaumi, K Toshimoto, K
+ Maeda, H Kusuhara and Y Sugiyama
+
+ - CLINICAL PHARMACOLOGY & THERAPEUTICS | VOLUME 100 NUMBER 5 |
+ NOVEMBER 2016
+
+ -
+
+# Data
+
+ - Example taken from figure 4a from the publication
+ - Using this as example data to fit
+
+
+
+``` r
+data.file <- ""data/fig4a.csv""
+
+data <-
+ data.file %>%
+ read_csv() %>%
+ mutate(
+ profile = NULL,
+ type=ID,
+ typef=factor(ID, labels = c(""Statin"", ""Statin+CsA"")),
+ DV = ifelse(DV==-1, NA_real_, DV)
+ )
+
+
+data
+```
+
+ . # A tibble: 23 x 8
+ . ID time DV evid amt cmt type typef
+ .
+ . 1 2 0 NA 1 2000 2 2 Statin+CsA
+ . 2 2 1 NA 1 30 1 2 Statin+CsA
+ . 3 2 1.49 73.7 0 0 0 2 Statin+CsA
+ . 4 2 1.99 102. 0 0 0 2 Statin+CsA
+ . 5 2 2.49 59.9 0 0 0 2 Statin+CsA
+ . 6 2 3.00 37.6 0 0 0 2 Statin+CsA
+ . 7 2 3.97 15.7 0 0 0 2 Statin+CsA
+ . 8 2 5.01 9.24 0 0 0 2 Statin+CsA
+ . 9 2 6.99 3.54 0 0 0 2 Statin+CsA
+ . 10 2 9.01 2.22 0 0 0 2 Statin+CsA
+ . # … with 13 more rows
+
+``` r
+data %>% filter(evid==1)
+```
+
+ . # A tibble: 3 x 8
+ . ID time DV evid amt cmt type typef
+ .
+ . 1 2 0 NA 1 2000 2 2 Statin+CsA
+ . 2 2 1 NA 1 30 1 2 Statin+CsA
+ . 3 1 1 NA 1 30 1 1 Statin
+
+ - The goal is to fit the pitavastatin data either alone or in
+ combination with cyclosporin administered 1 hour before the
+ pitavastatin
+
+
+
+``` r
+ggplot(data=data,aes(time,DV)) +
+ geom_point(aes(col = typef), size = 3) +
+ geom_line(col = ""darkgrey"", aes(group = typef)) +
+ scale_y_continuous(trans=""log"", limits=c(0.1,300), breaks=logbr())
+```
+
+![]()
+
+# PBPK model: pitavastatin / CsA DDI
+
+ - Check out the model / data with a quick simulation
+
+
+
+``` r
+mod <- mread_cache(""yoshikado"", model_dir)
+```
+
+Make some persistent updates to the model
+
+ - Simulate out to 14 hours
+ - Only interested in `CP`, the pitavastatin concentration
+
+
+
+``` r
+mod <- mod %>% update(end=14, delta=0.1) %>% Req(CP)
+```
+
+A practice simulation
+
+``` r
+dose <- filter(data, evid==1) %>% mutate(typef=NULL)
+
+sims <-
+ mod %>%
+ mrgsim_d(dose, obsaug=TRUE) %>%
+ mutate(type = typef(ID))
+
+ggplot(sims, aes(time,CP,col=type)) +
+ geom_line(lwd = 1) +
+ scale_x_continuous(breaks = seq(0,12,2)) +
+ scale_y_log10(name = ""Pitavastatin concentration"")
+```
+
+![]()
+
+``` r
+sims %>%
+ group_by(type) %>%
+ summarise(auc = auc_partial(time,CP)) %>%
+ mutate(fold_increase = auc /first(auc))
+```
+
+ . # A tibble: 2 x 3
+ . type auc fold_increase
+ .
+ . 1 Pitavastatin alone 44.1 1
+ . 2 Pitavastatin + CsA 161. 3.65
+
+# Objective function
+
+ - Least squares objective function
+ - Weighted by the observations
+
+Arguments:
+
+ - `dv` the observed data
+ - `pred` the predicted data
+
+
+
+``` r
+wss <- function(dv, pred, weight = 1/dv) {
+ sum(((dv-pred)*weight)^2,na.rm=TRUE)
+}
+```
+
+### Prediction function
+
+ - Let’s go through step by step what each line is doing for us
+
+Arguments:
+
+ - `p` the parameters proposed by the optimizer
+ - `.data` the simulation template (doses and observation records)
+ - `yobs` a vector of observed data which matches observations in
+ `.data`
+ - `pred` logical; if `TRUE`, just return predicted data
+
+
+
+``` r
+sim_ofv <- function(p, data, pred = FALSE) {
+
+ names(p) <- names(theta)
+
+ p <- lapply(p,exp)
+
+ mod <- param(mod, p)
+
+ out <- mrgsim_q(mod, data = data, output=""df"")
+
+ if(pred) return(out)
+
+ ofv <- wss(data[[""DV""]], out[[""CP""]])
+
+ return(ofv)
+
+ #return(-1*sum(dnorm(log(yobs),log(out$CP),.par$sigma,log=TRUE)))
+
+}
+```
+
+What this function does:
+
+1. Take in arguments; focus is on a new set of parameters `p` proposed
+ by the optimizer; other arguments are just fixed data that we need
+2. Get the parameters out of log scale
+3. Also, put names on the list of parameters; this is crutial
+4. Update the model object with the new parameters
+5. (optionally simulate and return)
+6. Simulate from the data set, taking only observed values
+7. Calculate and return the objective function value
+
+# Data grooming
+
+ - Pick out the observations
+ - Drop the non-numeric columns
+
+
+
+``` r
+data <- dplyr::select(data, -typef)
+```
+
+# Optimize
+
+First, set up the initial estimates
+
+``` r
+theta <- c(
+ fbCLintall = 1.2,
+ ikiu = 1.2,
+ fbile = 0.9,
+ ka = 0.1,
+ ktr = 0.1
+) %>% log()
+```
+
+## `nloptr::newuoa`: minimization without derivatives
+
+``` r
+fit <- nloptr::newuoa(x0 = theta, fn = sim_ofv, data = data)
+```
+
+``` r
+fit
+```
+
+ . $par
+ . [1] -0.20421286 -4.51432097 -1.06749446 -0.01109318 -0.37133662
+ .
+ . $value
+ . [1] 0.6860763
+ .
+ . $iter
+ . [1] 351
+ .
+ . $convergence
+ . [1] 4
+ .
+ . $message
+ . [1] ""NLOPT_XTOL_REACHED: Optimization stopped because xtol_rel or xtol_abs (above) was reached.""
+
+### Get some predictions to look at how the fit went
+
+Recall that our (transformed) parameters are
+
+``` r
+fit$par
+```
+
+ . [1] -0.20421286 -4.51432097 -1.06749446 -0.01109318 -0.37133662
+
+We can generate a prediction that matches our data like this
+
+``` r
+sim_ofv(fit$par, data = dose, pred = TRUE) %>% filter(time >= 1) %>% head
+```
+
+ . ID time CP
+ . 1 2 1.0 0.00000
+ . 2 2 1.0 0.00000
+ . 3 2 1.1 18.19345
+ . 4 2 1.2 28.55603
+ . 5 2 1.3 36.22961
+ . 6 2 1.4 42.13621
+
+We can also get the predictions under the initial conditions by passing
+in `theta` rather than `fit$par`
+
+In the next block, generate
+
+1. Predictions with the final estimates
+2. Predications with the initial estimates
+3. Observed data to overlay
+
+
+
+``` r
+df_pred <- sim_ofv(fit$par, dose, pred=TRUE) %>% mutate(type = typef(ID))
+df_init <- sim_ofv(theta, dose, pred=TRUE) %>% mutate(type = typef(ID))
+df_obs <- mutate(data, type=typef(ID))
+```
+
+### Make some plots
+
+``` r
+ggplot(df_pred, aes(time,CP)) +
+ geom_line(lwd=1) +
+ geom_point(data = df_obs, aes(time,DV),col=""firebrick"",size=2) +
+ facet_wrap(~type) + scale_y_log10()
+```
+
+![]()
+
+### A nicer plot
+
+``` r
+ggplot(data=df_pred) +
+ geom_line(data=df_init,aes(time,CP,lty=""A""), col=""black"", lwd=0.7) +
+ geom_line(aes(time,CP,lty=""B""),col=""black"",lwd=0.7) +
+ geom_point(data=df_obs,aes(time,DV,col=type),size=3) +
+ facet_wrap(~type) +
+ scale_y_continuous(trans=""log"",breaks=10^seq(-4,4),
+ limits=c(0.1,100),
+ ""Pitavastatin concentration (ng/mL)"") +
+ scale_x_continuous(name=""Time (hours)"", breaks=seq(0,14,2)) +
+ scale_linetype_manual(values= c(2,1), guide = FALSE,
+ labels=c(""Initial estimates"", ""Final estimates""), name="""") +
+ theme_bw() + theme(legend.position=""top"")
+```
+
+![]()
+
+### The final objective function value and estimates
+
+``` r
+sim_ofv(fit$par,data=data)
+```
+
+ . [1] 0.6860763
+
+``` r
+exp(fit$par)
+```
+
+ . [1] 0.81528881 0.01095104 0.34386902 0.98896812 0.68981170
+","Markdown"
+"PBPK model","metrumresearchgroup/r-pharma-pkpd-2020","content/tools_optimization_methods.md",".md","11590","459","Estimate parameters in a PBPK model
+================
+Metrum Research Group
+
+ - [Packages and setup](#packages-and-setup)
+ - [Reference](#reference)
+ - [Data](#data)
+ - [PBPK model: pitavastatin / CsA
+ DDI](#pbpk-model-pitavastatin-csa-ddi)
+ - [Objective function](#objective-function)
+ - [Prediction function](#prediction-function)
+ - [Data grooming](#data-grooming)
+ - [Optimize](#optimize)
+ - [`nloptr::newuoa`: minimization without
+ derivatives](#nloptrnewuoa-minimization-without-derivatives)
+ - [The final objective function value and
+ estimates](#the-final-objective-function-value-and-estimates)
+ - [`optim`: Nelder-Mead](#optim-nelder-mead)
+ - [`neldermead`: Alternate
+ Nelder-Mead](#neldermead-alternate-nelder-mead)
+ - [`DEoptim`: differential evolution
+ algorithm](#deoptim-differential-evolution-algorithm)
+ - [DA for the plot](#da-for-the-plot)
+ - [`GenSA`: simulated annealing](#gensa-simulated-annealing)
+ - [`hydroPSO`: particle swarm
+ optimization](#hydropso-particle-swarm-optimization)
+ - [`nloptr::CRS`: controlled random
+ search](#nloptrcrs-controlled-random-search)
+ - [Compare optimization methods](#compare-optimization-methods)
+
+# Packages and setup
+
+``` r
+library(tidyverse)
+library(PKPDmisc)
+library(mrgsolve)
+library(nloptr)
+library(DEoptim)
+library(GenSA)
+library(hydroPSO)
+source(""script/functions.R"")
+source(""script/global.R"")
+```
+
+``` r
+set.seed(10101)
+```
+
+``` r
+theme_set(theme_bw() + theme(legend.position = ""top""))
+scale_colour_discrete <- function(...) scale_color_brewer(palette=""Set2"")
+```
+
+Models are located here:
+
+``` r
+model_dir <- ""model""
+```
+
+# Reference
+
+**Quantitative Analyses of Hepatic OATP-Mediated Interactions Between
+Statins and Inhibitors Using PBPK Modeling With a Parameter Optimization
+Method**
+
+ - T Yoshikado, K Yoshida, N Kotani, T Nakada, R Asaumi, K Toshimoto, K
+ Maeda, H Kusuhara and Y Sugiyama
+
+ - CLINICAL PHARMACOLOGY & THERAPEUTICS | VOLUME 100 NUMBER 5 |
+ NOVEMBER 2016
+
+ -
+
+# Data
+
+ - Example taken from figure 4a from the publication
+ - Using this as example data to fit
+
+
+
+``` r
+data.file <- ""data/fig4a.csv""
+
+data <-
+ data.file %>%
+ read_csv() %>%
+ mutate(
+ profile = NULL,
+ type=ID,
+ typef=factor(ID, labels = c(""Statin"", ""Statin+CsA"")),
+ DV = ifelse(DV==-1, NA_real_, DV)
+ )
+```
+
+ - The goal is to fit the pitavastatin data either alone or in
+ combination with cyclosporin administered 1 hour before the
+ pitavastatin
+
+
+
+``` r
+ggplot(data=data,aes(time,DV)) +
+ geom_point(aes(col = typef), size = 3) +
+ geom_line(col = ""darkgrey"", aes(group = typef)) +
+ scale_y_continuous(trans=""log"", limits=c(0.1,300), breaks=logbr())
+```
+
+![]()
+
+# PBPK model: pitavastatin / CsA DDI
+
+ - Check out the model / data with a quick simulation
+
+
+
+``` r
+mod <- mread_cache(""yoshikado"", model_dir)
+```
+
+Make some persistent updates to the model
+
+ - Simulate out to 14 hours
+ - Only interested in `CP`, the pitavastatin concentration
+
+
+
+``` r
+mod <- mod %>% update(end=14, delta=0.1) %>% Req(CP)
+```
+
+A practice simulation
+
+``` r
+dose <- filter(data, evid==1) %>% mutate(typef=NULL)
+
+sims <-
+ mod %>%
+ mrgsim_d(dose, obsaug=TRUE) %>%
+ mutate(type = typef(ID))
+
+ggplot(sims, aes(time,CP,col=type)) +
+ geom_line(lwd = 1) +
+ scale_x_continuous(breaks = seq(0,12,2)) +
+ scale_y_log10(name = ""Pitavastatin concentration"")
+```
+
+![]()
+
+``` r
+sims %>%
+ group_by(type) %>%
+ summarise(auc = auc_partial(time,CP)) %>%
+ mutate(fold_increase = auc /first(auc))
+```
+
+ . # A tibble: 2 x 3
+ . type auc fold_increase
+ .
+ . 1 Pitavastatin alone 44.1 1
+ . 2 Pitavastatin + CsA 161. 3.65
+
+# Objective function
+
+ - Least squares objective function
+ - Weighted by the observations
+
+Arguments:
+
+ - `dv` the observed data
+ - `pred` the predicted data
+
+
+
+``` r
+wss <- function(dv, pred, weight = 1/dv) {
+ sum(((dv-pred)*weight)^2,na.rm=TRUE)
+}
+```
+
+#### Prediction function
+
+ - Let’s go through step by step what each line is doing for us
+
+Arguments:
+
+ - `p` the parameters proposed by the optimizer
+ - `data` the simulation template (doses and observation records)
+ - `pred` logical; if `TRUE`, just return predicted data
+
+
+
+``` r
+sim_ofv <- function(p, data, pred = FALSE) {
+
+ names(p) <- names(theta)
+
+ p <- lapply(p,exp)
+
+ out <- mod %>% param(p) %>% mrgsim_q(data, output=""df"")
+
+ if(pred) return(out)
+
+ ofv <- wss(data[[""DV""]], out[[""CP""]])
+
+ return(ofv)
+
+}
+```
+
+What this function does:
+
+1. Take in arguments; focus is on a new set of parameters `p` proposed
+ by the optimizer; other arguments are just fixed data that we need
+2. Get the parameters out of log scale
+3. Also, put names on the list of parameters; this is crutial
+4. Update the model object with the new parameters
+5. (optionally simulate and return)
+6. Simulate from the data set, taking only observed values
+7. Calculate and return the objective function value
+
+# Data grooming
+
+ - Drop the non-numeric columns
+
+
+
+``` r
+data <- dplyr::select(data, -typef)
+```
+
+# Optimize
+
+First, set up the initial estimates
+
+``` r
+theta <- c(
+ fbCLintall = 1,
+ ikiu = 1,
+ fbile = 0.5,
+ ka = 1,
+ ktr = 1
+) %>% log()
+```
+
+## `nloptr::newuoa`: minimization without derivatives
+
+``` r
+fit <- nloptr::newuoa(x0 = theta, fn = sim_ofv, data = data)
+fit
+```
+
+ . $par
+ . [1] -0.20428246 -4.51446804 -1.06769436 -0.01128541 -0.37159534
+ .
+ . $value
+ . [1] 0.6860764
+ .
+ . $iter
+ . [1] 386
+ .
+ . $convergence
+ . [1] 4
+ .
+ . $message
+ . [1] ""NLOPT_XTOL_REACHED: Optimization stopped because xtol_rel or xtol_abs (above) was reached.""
+
+``` r
+fit_minqa <- minqa::newuoa(theta, fn = sim_ofv, data = data)
+```
+
+#### The final objective function value and estimates
+
+``` r
+sim_ofv(fit$par,data=data)
+```
+
+ . [1] 0.6860764
+
+``` r
+exp(fit$par) %>% set_names(names(theta))
+```
+
+ . fbCLintall ikiu fbile ka ktr
+ . 0.81523207 0.01094943 0.34380028 0.98877803 0.68963325
+
+## `optim`: Nelder-Mead
+
+``` r
+fit1b <- optim(theta, sim_ofv, data=data, control = list(maxit = 1000))
+```
+
+## `neldermead`: Alternate Nelder-Mead
+
+``` r
+fit1c <- nloptr::neldermead(x0=theta, fn=sim_ofv, data = data )
+```
+
+## `DEoptim`: differential evolution algorithm
+
+
+
+“Performs evolutionary global optimization via the Differential
+Evolution algorithm.”
+
+``` r
+lower <- rep(-6,length(theta)) %>% setNames(names(theta))
+upper <- rep(5, length(theta)) %>% setNames(names(theta))
+
+set.seed(330303)
+
+decontrol <- DEoptim.control(
+ trace = 10,
+ NP=10*length(theta),
+ CR=0.925,
+ F=0.85,
+ itermax=90,
+ storepopfrom=0
+)
+
+fit2 <- DEoptim(
+ fn=sim_ofv,
+ lower=lower,
+ upper=upper,
+ control=decontrol,
+ data=data
+)
+```
+
+ . Iteration: 10 bestvalit: 2.359438 bestmemit: 0.090828 -4.810051 -1.032300 -0.596372 -0.748306
+ . Iteration: 20 bestvalit: 2.359438 bestmemit: 0.090828 -4.810051 -1.032300 -0.596372 -0.748306
+ . Iteration: 30 bestvalit: 0.745879 bestmemit: -0.220189 -4.444982 -1.045812 -0.135432 -0.454597
+ . Iteration: 40 bestvalit: 0.733471 bestmemit: -0.201757 -4.500855 -1.075498 -0.170296 -0.414804
+ . Iteration: 50 bestvalit: 0.691808 bestmemit: -0.220169 -4.512342 -1.101670 -0.043517 -0.409125
+ . Iteration: 60 bestvalit: 0.688542 bestmemit: -0.210497 -4.516279 -1.078079 0.011272 -0.380982
+ . Iteration: 70 bestvalit: 0.686352 bestmemit: -0.203172 -4.509802 -1.066623 -0.004658 -0.362483
+ . Iteration: 80 bestvalit: 0.686121 bestmemit: -0.203278 -4.514187 -1.064788 -0.010720 -0.370142
+ . Iteration: 90 bestvalit: 0.686081 bestmemit: -0.204019 -4.514555 -1.067274 -0.011397 -0.372682
+
+#### DA for the plot
+
+``` r
+pops <- lapply(fit2$member$storepop, as.data.frame)
+hx <- bind_rows(pops)
+hx <- mutate(hx, iteration=rep(1:decontrol$itermax,each=decontrol$NP))
+hx <- mutate(hx, pop = rep(1:decontrol$NP, time=decontrol$itermax))
+hxm <- gather(hx, variable, value, 1:5) %>% mutate(value = exp(value))
+best <- as_tibble(fit2$member$bestmemit) %>%
+ mutate(iteration = 1:decontrol$itermax)
+bestm <- gather(best,variable,value,1:5) %>% mutate(value = exp(value))
+```
+
+``` r
+ggplot(data=hxm) +
+ geom_line(aes(iteration,value,group=pop),col=""darkslateblue"") +
+ geom_line(data=bestm,aes(iteration,value),col=""orange"",lwd=1) +
+ scale_y_continuous(trans=""log"", breaks=10^seq(-4,4), name=""Parameter value"") +
+ facet_wrap(~variable, ncol=2, scales=""free_y"") + theme_bw()
+```
+
+![]()
+
+## `GenSA`: simulated annealing
+
+``` r
+set.seed(11001)
+
+sacontrol <- list(maxit = 100, nb.stop.improvement = 20, verbose = TRUE)
+
+fit3 <- GenSA(
+ NULL, sim_ofv, lower=lower+1, upper=upper-1, data = data, control = sacontrol
+)
+```
+
+ . Initializing par with random data inside bounds
+ . It: 1, obj value: 3.470357641
+ . It: 25, obj value: 0.6860778737
+
+## `hydroPSO`: particle swarm optimization
+
+
+
+``` r
+set.seed(22022013)
+
+fit4 <- hydroPSO(
+ theta, fn = sim_ofv, lower = lower, upper = upper,
+ control = list(maxit = 100, REPORT = 5),
+ data = data
+)
+```
+
+## `nloptr::CRS`: controlled random search
+
+``` r
+set.seed(11000222)
+
+crs <- crs2lm(
+ x0 = theta,
+ fn=sim_ofv,
+ lower = lower,
+ upper = upper,
+ data=data,
+ maxeval=5500
+)
+```
+
+# Compare optimization methods
+
+``` r
+results <- list(theta, fit$par, fit1b$par, fit1c$par, fit2$optim$bestmem, fit3$par, fit4$par, crs$par)
+
+results <- map(results, set_names, nm = names(theta))
+
+results <- map(results, exp)
+
+tibble(
+ method = c(""initial"", ""newuoa"", ""nelder"", ""nelder2"", ""DEoptim"", ""SA"", ""PSO"",""CRS""),
+ fbCLintall = map_dbl(results, ""fbCLintall""),
+ ikiu = map_dbl(results, ""ikiu""),
+ fbile = map_dbl(results, ""fbile""),
+ ka = map_dbl(results, ""ka""),
+ ktr = map_dbl(results, ""ktr"")
+) %>% mutate_if(is.double, list(signif), digits = 4)
+```
+
+ . # A tibble: 8 x 6
+ . method fbCLintall ikiu fbile ka ktr
+ .
+ . 1 initial 1 1 0.5 1 1
+ . 2 newuoa 0.815 0.0110 0.344 0.989 0.690
+ . 3 nelder 0.815 0.0110 0.344 0.990 0.690
+ . 4 nelder2 0.815 0.0110 0.344 0.989 0.690
+ . 5 DEoptim 0.815 0.0110 0.344 0.989 0.689
+ . 6 SA 0.815 0.0110 0.344 0.989 0.689
+ . 7 PSO 0.815 0.0110 0.344 0.988 0.690
+ . 8 CRS 0.815 0.0110 0.344 0.989 0.690
+
+``` r
+value0 <- sim_ofv(theta,data)
+results <- c(value0, fit$value, fit1b$value, fit1c$value, fit2$optim$bestval, fit3$value, fit4$value, crs$value)
+
+tibble(
+ method = c(""initial"", ""newuoa"", ""nelder"", ""nelder2"", ""DEoptim"", ""SA"", ""PSO"",""CRS""),
+ value = results
+)
+```
+
+ . # A tibble: 8 x 2
+ . method value
+ .
+ . 1 initial 5.00
+ . 2 newuoa 0.686
+ . 3 nelder 0.686
+ . 4 nelder2 0.686
+ . 5 DEoptim 0.686
+ . 6 SA 0.686
+ . 7 PSO 0.686
+ . 8 CRS 0.686
+","Markdown"
+"PBPK model","metrumresearchgroup/r-pharma-pkpd-2020","content/simulate-ddi.R",".R","496","27","library(tidyverse)
+library(PKPDmisc)
+library(mrgsolve)
+
+mod <- mread(""model/yoshikado.cpp"", end = 12, delta = 0.1)
+
+ddi <- c(
+ ev(amt = 2000, cmt = 2, time = 0),
+ ev(amt = 30, cmt = 1, time = 1)
+)
+
+n <- 2000
+idata <- tibble(ikiu = rlnorm(n, log(mod$ikiu),sqrt(0.09)))
+
+out <- mrgsim_ei(mod, events = ddi, idata = idata)
+
+head(out)
+
+summ <-
+ out %>%
+ group_by(ID) %>%
+ summarise(auc = auc_partial(time,CP), .groups = ""drop"")
+
+ggplot(summ, aes(x = auc)) + geom_histogram(col = ""white"")
+
+
+","R"
+"PBPK model","metrumresearchgroup/r-pharma-pkpd-2020","content/get-started.md",".md","22294","971","Get Started with mrgsolve
+================
+Metrum Research Group
+2020-10-09
+
+ - [About `mrgsolve`](#about-mrgsolve)
+ - [Background](#background)
+ - [Orientation](#orientation)
+ - [What we will cover today](#what-we-will-cover-today)
+ - [Three (basic) simulation
+ workflows](#three-basic-simulation-workflows)
+ - [Single profile](#single-profile)
+ - [Event object](#event-object)
+ - [Plot](#plot)
+ - [Control time span of the
+ simulation](#control-time-span-of-the-simulation)
+ - [More-complicated events](#more-complicated-events)
+ - [Event sequence](#event-sequence)
+ - [Batch](#batch)
+ - [Create an idata set](#create-an-idata-set)
+ - [Simulate with event object](#simulate-with-event-object)
+ - [Population](#population)
+ - [Read a NMTRAN like data set](#read-a-nmtran-like-data-set)
+ - [Some other ways to create population
+ inputs](#some-other-ways-to-create-population-inputs)
+ - [Get work done](#get-work-done)
+ - [Work with output](#work-with-output)
+ - [Coerce output](#coerce-output)
+ - [Corerce via dplyr verbs](#corerce-via-dplyr-verbs)
+ - [Return data frame](#return-data-frame)
+ - [Carry-Out](#carry-out)
+ - [Recover](#recover)
+
+# About `mrgsolve`
+
+ - `R` package for simulation from ODE-based models
+ - Free, OpenSource, GitHub, CRAN
+ - Language
+ - Models written in `C++` inside model specification format
+ - General purpose solver: `ODEPACK` / `DLSODA` (`FORTRAN`)
+ - Automatically detect and switch between non-stiff (Adams)
+ and stiff (BDF) methods for solving the differential
+ equations
+ - Simulation workflow in `R`
+ - Hierarchical (population) simulation
+ - `ID`, \(\eta\), \(\varepsilon\)
+ - Integrated PK functionaility
+ - Bolus, infusion, `F`, `ALAG`, `SS` etc, handled under the hood
+ - 1- and 2-cmt PK models in closed-form
+ - Extensible using `R`, `C++`, `Rcpp`, `boost`, `RcppArmadillo`
+ - `R` is it’s natural habitat
+
+## Background
+
+ - Motivation: large bone/mineral homeostatsis model (CaBone)
+ - History using
+ - Berkeley Madonna
+ - WinBUGS
+ - NONMEM (attempted)
+ - 2010: write `R` front end to `deSolve`
+ - 2012: write `C++` interface to `DLSODA`
+ - Develop dosing / event capability
+ - More recently, expose functionality provided by
+ - `Rcpp` - vectors, matrices, functions, environments, random
+ numbers
+ - `boost` - numerical tools in `C++`
+ - users’ own `C++` code (functions, data structures, classes)
+ - Translator from `SBML` to `mrgsolve` using `R` bindings to `libSBML`
+
+## Orientation
+
+ -
+
+ - GitHub site:
+
+ - Issues and questions:
+
+
+ - mrgsolve website:
+
+ - User Guide:
+
+ - Blog:
+
+ - Vignettes:
+
+ - Compare against NONMEM:
+
+## What we will cover today
+
+1. Three basic workflows
+2. Loading the model into R
+3. Event objects
+4. Data sets
+
+Emphasis is on getting you running your own simulations today.
+
+# Three (basic) simulation workflows
+
+``` r
+library(tidyverse)
+library(mrgsolve)
+```
+
+ - Single profile
+ - Batch
+ - Population
+
+These aren’t entirely different, but I like to organize this way. When
+I’m planning an simulation, I first think “what type of output do I
+want?” and the answer to that question directs me on what to do next.
+
+## Single profile
+
+This is how we load a simulation model into mrgsolve.
+
+Load a two-compartment model from the internal library
+
+``` r
+mod <- modlib(""pk2"")
+```
+
+ . Loading model from cache.
+
+We now have a 2-compartment PK model with which we can simulate. It is
+important to know how this works and we will talk in depth about this.
+But for now, let’s simulate some stuff.
+
+First, we’ll just simulate from this model object (`mrgsim()`)
+
+``` r
+mrgsim(mod)
+```
+
+ . Model: pk2
+ . Dim: 25 x 6
+ . Time: 0 to 24
+ . ID: 1
+ . ID time EV CENT PERIPH CP
+ . 1: 1 0 0 0 0 0
+ . 2: 1 1 0 0 0 0
+ . 3: 1 2 0 0 0 0
+ . 4: 1 3 0 0 0 0
+ . 5: 1 4 0 0 0 0
+ . 6: 1 5 0 0 0 0
+ . 7: 1 6 0 0 0 0
+ . 8: 1 7 0 0 0 0
+
+In the output
+
+ - Essentially a data frame of simulated data
+ - First column `ID`
+ - Second column: `time`
+ - Next columns: compartments
+ - Last columns: derived quantities
+
+***Investigate the model object a bit***
+
+ - overview
+
+
+
+``` r
+mod
+```
+
+ .
+ .
+ . ----------------- source: pk2.cpp -----------------
+ .
+ . project: /Users/kyleb/git...gsolve/models
+ . shared object: pk2-so-47bb5b2e99ab
+ .
+ . time: start: 0 end: 24 delta: 1
+ . add:
+ .
+ . compartments: EV CENT PERIPH [3]
+ . parameters: CL V2 Q V3 KA [5]
+ . captures: CP [1]
+ . omega: 0x0
+ . sigma: 0x0
+ .
+ . solver: atol: 1e-08 rtol: 1e-08 maxsteps: 20k
+ . ------------------------------------------------------
+
+ - parameters
+
+
+
+``` r
+param(mod)
+```
+
+ .
+ . Model parameters (N=5):
+ . name value . name value
+ . CL 1 | V2 20
+ . KA 1 | V3 10
+ . Q 2 | . .
+
+ - compartments
+
+
+
+``` r
+init(mod)
+```
+
+ .
+ . Model initial conditions (N=3):
+ . name value . name value
+ . CENT (2) 0 | PERIPH (3) 0
+ . EV (1) 0 | . ... .
+
+ - outputs
+
+
+
+``` r
+outvars(mod)
+```
+
+ . $cmt
+ . [1] ""EV"" ""CENT"" ""PERIPH""
+ .
+ . $capture
+ . [1] ""CP""
+
+### Event object
+
+Now, we’ll create an “event object” to simulate from. This is just a
+concise statement of some intervention. Like a one-liner … easy to make.
+
+Let’s do 100 mg x1 to the first compartment, then simulate:
+
+``` r
+mod %>% ev(amt = 100) %>% mrgsim()
+```
+
+ . Model: pk2
+ . Dim: 26 x 6
+ . Time: 0 to 24
+ . ID: 1
+ . ID time EV CENT PERIPH CP
+ . 1: 1 0 0.0000 0.00 0.000 0.000
+ . 2: 1 0 100.0000 0.00 0.000 0.000
+ . 3: 1 1 36.7879 58.21 3.253 2.911
+ . 4: 1 2 13.5335 72.56 8.790 3.628
+ . 5: 1 3 4.9787 72.43 13.823 3.621
+ . 6: 1 4 1.8316 68.18 17.695 3.409
+ . 7: 1 5 0.6738 63.31 20.438 3.165
+ . 8: 1 6 0.2479 58.86 22.258 2.943
+
+We use `ev()` to create a set of intervention(s) for the simulation.
+Here, it is just a single 100 mg dose into the first compartment. The
+event object looks like this:
+
+``` r
+ev(amt = 100)
+```
+
+ . Events:
+ . time amt cmt evid
+ . 1 0 100 1 1
+
+We have the following columns
+
+1. `time` - whatever is your model time
+2. `amt` - whatever is the mass unit for your compartments
+3. `cmt` could be number or name
+4. `evid` just like nonmem - mostly using 1
+
+You can also use:
+\- `rate` - infusion
+\- `ss` - steady state (1 or 2)
+\- `ii` - interdose interval
+\- `addl` - additional doses
+\- `tinf` - infusion time (rather than `rate`)
+\- `total` - total number of doses (rather than `addl`)
+
+See `?ev`
+
+### Plot
+
+Simulate 100 mg x1 again and now we pipe it to `plot()`
+
+``` r
+mod %>% ev(amt = 100) %>% mrgsim() %>% plot()
+```
+
+
+
+### Control time span of the simulation
+
+I would like this to look a little nicer.
+
+ - 100 mg x1
+ - Run the end of the simulation out to 72 hours with delta 0.1
+ - Make the line smoother
+ - Plot the result
+
+
+
+``` r
+mod %>% ev(amt = 100) %>% mrgsim(end = 72, delta = 0.1) %>% plot()
+```
+
+
+
+We can make this change permanent
+
+ - end: 72 hours
+ - delta: 0.1 hours
+
+
+
+``` r
+mod2 <- update(mod, end = 72, delta = 0.1)
+```
+
+### More-complicated events
+
+We said that the `event` objects were simple. But we can combine them to
+make more complicated sequences.
+
+Let’s load a PK model for azithromycin (`azithro-single`):
+
+``` r
+mod <- mread(""azithro-single"", project = ""model"")
+```
+
+ . Building azithro-single ... done.
+
+**Check out the model**
+
+``` r
+mod
+```
+
+ .
+ .
+ . ------------ source: azithro-single.cpp ------------
+ .
+ . project: /Users/kyleb/git...content/model
+ . shared object: azithro-single-so-4b257dfcc58
+ .
+ . time: start: 0 end: 240 delta: 0.1
+ . add:
+ .
+ . compartments: GUT CENT PER2 PER3 [4]
+ . parameters: TVCL TVV1 TVQ2 TVV2 Q3 V3 KA WT [8]
+ . captures: CP [1]
+ . omega: 0x0
+ . sigma: 0x0
+ .
+ . solver: atol: 1e-08 rtol: 1e-08 maxsteps: 20k
+ . ------------------------------------------------------
+
+Create an event object to implement the z-pak dose:
+
+ - 500 mg po on day 1 (`load`)
+ - 250 mg po daily on days 2 through 5 (`continue`)
+
+
+
+``` r
+load <- ev(amt = 500)
+continue <- ev(amt = 250, ii = 24, addl = 3, time = 24)
+zpak <- c(load, continue)
+```
+
+Look at the zpak dosing object
+
+``` r
+zpak
+```
+
+ . Events:
+ . time amt cmt evid ii addl
+ . 1 0 500 1 1 0 0
+ . 2 24 250 1 1 24 3
+
+We can also accompilsh this just with 250 mg tablets
+
+``` r
+zpak <- c(ev(amt = 250), ev(amt = 250, ii = 24, addl = 4))
+
+zpak
+```
+
+ . Events:
+ . time amt cmt evid ii addl
+ . 1 0 250 1 1 0 0
+ . 2 0 250 1 1 24 4
+
+Now, simulate and plot from the zpak event object
+
+``` r
+mrgsim(mod, zpak) %>% plot()
+```
+
+
+
+### Event sequence
+
+ - 100 mg daily x 7 **then**
+ - 50 mg BID x7
+
+## Batch
+
+Let’s use our fixed-effects azithromycin model to look at how weight
+affects PK. We’ll use that `zpak` object that we created in the previous
+section.
+
+### Create an idata set
+
+Now, let’s make a data frame that contains the weights that we want to
+investigate (from 40 kg to 140 kg by 10 kg).
+
+``` r
+wt <- tibble(WT = seq(40, 140, 10))
+
+head(wt)
+```
+
+ . # A tibble: 6 x 1
+ . WT
+ .
+ . 1 40
+ . 2 50
+ . 3 60
+ . 4 70
+ . 5 80
+ . 6 90
+
+**IMPORTANT**: the key here is that we have `WT` as a column in the data
+set and we have `WT` as a parameter in the model (look at the
+parameters)
+
+``` r
+param(mod)
+```
+
+ .
+ . Model parameters (N=8):
+ . name value . name value
+ . KA 0.259 | TVV1 186
+ . Q3 10.6 | TVV2 2890
+ . TVCL 100 | V3 2610
+ . TVQ2 180 | WT 70
+
+When we make the names agree, mrgsolve will update the `WT` parameter as
+the simulation advances across individuals.
+
+### Simulate with event object
+
+Now we can pass this set of weights into the problem as “idata”. We will
+use just the first record from the zpak dosing object.
+
+ - Load the `azithro-single` model
+ - Create a dosing event with 500 mg x1
+ - Simulate with `idata`
+ - End the simulation at 96 hours
+
+
+
+``` r
+mod <- mread(""azithro-single"", project = ""model"")
+```
+
+ . Building azithro-single ... done.
+
+``` r
+load <- ev(amt = 500)
+
+out <- mrgsim(mod, events = load, idata = wt, end = 96)
+```
+
+Take a quick look at the output (`head`)
+
+``` r
+out
+```
+
+ . Model: azithro-single
+ . Dim: 10582 x 7
+ . Time: 0 to 96
+ . ID: 11
+ . ID time GUT CENT PER2 PER3 CP
+ . 1: 1 0.0 0.0 0.00 0.0000 0.00000 0.0
+ . 2: 1 0.0 500.0 0.00 0.0000 0.00000 0.0
+ . 3: 1 0.1 487.2 11.68 0.6712 0.06027 109.9
+ . 4: 1 0.2 474.8 21.11 2.5042 0.22543 198.6
+ . 5: 1 0.3 462.6 28.69 5.2643 0.47505 270.0
+ . 6: 1 0.4 450.8 34.74 8.7577 0.79227 326.9
+ . 7: 1 0.5 439.3 39.53 12.8247 1.16317 371.9
+ . 8: 1 0.6 428.0 43.27 17.3336 1.57628 407.1
+
+Plot the ouptut, looking only at `CP` and on log scale
+
+``` r
+plot(out, CP ~ time, logy = TRUE)
+```
+
+
+
+This idata set functionality is typically used with an event object (as
+we have here) but isn’t required.
+
+## Population
+
+The last workflow I’m calling “population”. Here, population just refers
+to the input data set.
+
+We can have a data frame that contains many individuals with all
+different types of dosing interventions. This is just like the data set
+that you use to do your NONMEM run.
+
+### Read a NMTRAN like data set
+
+Meropenem PopPK
+
+
+For example, read in `data/meropenem.csv`
+
+``` r
+data <- readr::read_csv(""data/meropenem.csv"", na = '.')
+```
+
+ . Parsed with column specification:
+ . cols(
+ . ID = col_double(),
+ . TIME = col_double(),
+ . GROUP = col_double(),
+ . DV = col_double(),
+ . MDV = col_double(),
+ . EVID = col_double(),
+ . AMT = col_double(),
+ . RATE = col_double(),
+ . AGE = col_double(),
+ . WT = col_double(),
+ . CLCR = col_double(),
+ . CMT = col_double(),
+ . DUR = col_double()
+ . )
+
+ - glimpse the data (`head`)
+ - count `EVID`, `DUR`, `AMT`
+ - number of IDs
+
+
+
+``` r
+head(data)
+```
+
+ . # A tibble: 6 x 13
+ . ID TIME GROUP DV MDV EVID AMT RATE AGE WT CLCR CMT DUR
+ .
+ . 1 1 0 1 NA 1 1 500 1000 29.7 63.8 83 1 0.5
+ . 2 1 0.5 1 31.1 NA 0 NA 0 29.7 63.8 83 0 0.5
+ . 3 1 1 1 11.2 NA 0 NA 0 29.7 63.8 83 0 0.5
+ . 4 1 2 1 6.21 NA 0 NA 0 29.7 63.8 83 0 0.5
+ . 5 1 3 1 4.00 NA 0 NA 0 29.7 63.8 83 0 0.5
+ . 6 1 4 1 2.33 NA 0 NA 0 29.7 63.8 83 0 0.5
+
+``` r
+count(data, EVID, DUR, AMT)
+```
+
+ . # A tibble: 8 x 4
+ . EVID DUR AMT n
+ .
+ . 1 0 0.5 NA 280
+ . 2 0 3 NA 273
+ . 3 1 0.5 500 14
+ . 4 1 0.5 1000 13
+ . 5 1 0.5 2000 13
+ . 6 1 3 500 13
+ . 7 1 3 1000 13
+ . 8 1 3 2000 13
+
+``` r
+length(unique(data$ID))
+```
+
+ . [1] 79
+
+Now, load the meropenem model (`meropenem_pk.cpp`, in the `model`
+directory)
+
+``` r
+mod <- mread(""meropenem_pk"", project = ""model"")
+```
+
+ . Building meropenem_pk ... done.
+
+And simulate with `mod` and `data`
+
+``` r
+out <- mrgsim(mod, data)
+```
+
+Then plot using `Y` output
+
+``` r
+plot(out, Y ~ TIME)
+```
+
+
+
+Resimulate and plot by duration
+
+``` r
+out <- mrgsim(mod, data, carry_out = ""DUR"")
+```
+
+Plot `Y` versus `TIME` by `DUR`
+
+``` r
+plot(out, Y ~ TIME | DUR)
+```
+
+
+
+Recall that we have both observations and doses in the data set (as
+usual for your NONMEM data set)
+
+Count `EVID` in `data`
+
+``` r
+count(data, EVID)
+```
+
+ . # A tibble: 2 x 2
+ . EVID n
+ .
+ . 1 0 553
+ . 2 1 79
+
+When mrgsolve finds records with `EVID=0` in the data set, it will
+assume that you have specified every time that you want a simulated
+value in the data set. In other words the design of the simulated output
+will match the design of the input data:
+
+Check `dim()` in `data` and `out`:
+
+``` r
+dim(data)
+```
+
+ . [1] 632 13
+
+``` r
+dim(out)
+```
+
+ . [1] 632 5
+
+Let’s look to see what happens when we don’t include any observation
+records in the input data:
+
+Filter into `doses`:
+
+``` r
+doses <- filter(data, EVID==1)
+```
+
+Now we still have the same number of people, with different doses and
+infusion times.
+
+Check unique `ID` and count `EVID`, `AMT`, and `DUR`
+
+``` r
+length(unique(doses$ID))
+```
+
+ . [1] 79
+
+``` r
+count(doses, EVID, AMT, DUR)
+```
+
+ . # A tibble: 6 x 4
+ . EVID AMT DUR n
+ .
+ . 1 1 500 0.5 14
+ . 2 1 500 3 13
+ . 3 1 1000 0.5 13
+ . 4 1 1000 3 13
+ . 5 1 2000 0.5 13
+ . 6 1 2000 3 13
+
+ - Simulate from this sparse data set; end = 8 hours
+ - Get `DUR` in to the output
+ - Plot `log(Y)` versus `time` by `DUR`
+
+
+
+``` r
+mod %>%
+ mrgsim(doses, carry_out = ""DUR"", end = 8) %>%
+ plot(Y~TIME|factor(DUR), logy=TRUE)
+```
+
+
+
+The principle is: when mrgsolve does NOT find any observation records,
+it will fill them in for you according to the time grid that we looked
+at previously.
+
+This can be very helpful in reducing the data assembly burden when
+running your simulations.
+
+### Some other ways to create population inputs
+
+ - `expand.ev()` makes all combinations of your inputs
+ - Sensible defaults are provided
+ - `time`, `cmt`, `evid`
+
+
+
+``` r
+data <- expand.ev(amt = c(100, 300, 1000), ii = c(12, 24))
+
+data
+```
+
+ . ID time amt ii cmt evid
+ . 1 1 0 100 12 1 1
+ . 2 2 0 300 12 1 1
+ . 3 3 0 1000 12 1 1
+ . 4 4 0 100 24 1 1
+ . 5 5 0 300 24 1 1
+ . 6 6 0 1000 24 1 1
+
+ - `as_data_set()` takes event objects and creates a data frame / set
+ - 100 mg and 300 mg doses daily x7
+ - 20 individuals each
+
+
+
+``` r
+data <- as_data_set(
+ ev(amt = 100, ii = 24, total = 7, ID = 1:20),
+ ev(amt = 300, ii = 24, total = 7, ID = 1:20)
+)
+```
+
+# Get work done
+
+## Work with output
+
+ - names
+ - summary
+ - head
+ - `$`
+
+
+
+``` r
+mod <- modlib(""pk1"")
+```
+
+ . Loading model from cache.
+
+``` r
+out <- mrgsim(mod)
+
+names(out)
+```
+
+ . [1] ""ID"" ""time"" ""EV"" ""CENT"" ""CP""
+
+``` r
+summary(out)
+```
+
+ . ID time EV CENT CP
+ . Min. :1 Min. : 0 Min. :0 Min. :0 Min. :0
+ . 1st Qu.:1 1st Qu.: 6 1st Qu.:0 1st Qu.:0 1st Qu.:0
+ . Median :1 Median :12 Median :0 Median :0 Median :0
+ . Mean :1 Mean :12 Mean :0 Mean :0 Mean :0
+ . 3rd Qu.:1 3rd Qu.:18 3rd Qu.:0 3rd Qu.:0 3rd Qu.:0
+ . Max. :1 Max. :24 Max. :0 Max. :0 Max. :0
+
+``` r
+head(out)
+```
+
+ . ID time EV CENT CP
+ . 1 1 0 0 0 0
+ . 2 1 1 0 0 0
+ . 3 1 2 0 0 0
+ . 4 1 3 0 0 0
+ . 5 1 4 0 0 0
+ . 6 1 5 0 0 0
+
+``` r
+out$time[1:5]
+```
+
+ . [1] 0 1 2 3 4
+
+## Coerce output
+
+ - data.frame
+ - tibble
+ - matrix
+
+
+
+``` r
+out <- mrgsim(mod)
+
+class(out)
+```
+
+ . [1] ""mrgsims""
+ . attr(,""package"")
+ . [1] ""mrgsolve""
+
+``` r
+as_tibble(out)
+```
+
+ . # A tibble: 25 x 5
+ . ID time EV CENT CP
+ .
+ . 1 1 0 0 0 0
+ . 2 1 1 0 0 0
+ . 3 1 2 0 0 0
+ . 4 1 3 0 0 0
+ . 5 1 4 0 0 0
+ . 6 1 5 0 0 0
+ . 7 1 6 0 0 0
+ . 8 1 7 0 0 0
+ . 9 1 8 0 0 0
+ . 10 1 9 0 0 0
+ . # … with 15 more rows
+
+## Corerce via dplyr verbs
+
+ - Simulate and pipe the output to `mutate()`
+
+
+
+``` r
+out <- mrgsim(mod) %>% mutate(name = ""kyle"")
+
+class(out)
+```
+
+ . [1] ""tbl_df"" ""tbl"" ""data.frame""
+
+``` r
+head(out)
+```
+
+ . # A tibble: 6 x 6
+ . ID time EV CENT CP name
+ .
+ . 1 1 0 0 0 0 kyle
+ . 2 1 1 0 0 0 kyle
+ . 3 1 2 0 0 0 kyle
+ . 4 1 3 0 0 0 kyle
+ . 5 1 4 0 0 0 kyle
+ . 6 1 5 0 0 0 kyle
+
+## Return data frame
+
+ - Use `output` argument
+
+
+
+``` r
+out <- mrgsim(mod, output = ""df"")
+
+class(out)
+```
+
+ . [1] ""data.frame""
+
+ - Use `mrgsim_df()`
+
+
+
+``` r
+out <- mrgsim_df(mod)
+
+class(out)
+```
+
+ . [1] ""data.frame""
+
+## Carry-Out
+
+ - 100 mg x1
+ - need `dose` in the output
+ - contrast that with getting `amt` in the output
+
+First create the event
+
+``` r
+e <- ev(amt = 100, dose = amt)
+
+e
+```
+
+ . Events:
+ . time amt cmt evid dose
+ . 1 0 100 1 1 100
+
+Then recover `dose`
+
+``` r
+mrgsim(mod, events = e, carry_out = ""dose"")
+```
+
+ . Model: pk1
+ . Dim: 26 x 6
+ . Time: 0 to 24
+ . ID: 1
+ . ID time dose EV CENT CP
+ . 1: 1 0 100 0.0000 0.00 0.000
+ . 2: 1 0 100 100.0000 0.00 0.000
+ . 3: 1 1 100 36.7879 61.41 3.070
+ . 4: 1 2 100 13.5335 81.00 4.050
+ . 5: 1 3 100 4.9787 85.36 4.268
+ . 6: 1 4 100 1.8316 84.25 4.213
+ . 7: 1 5 100 0.6738 81.27 4.063
+ . 8: 1 6 100 0.2479 77.72 3.886
+
+## Recover
+
+ - `dose`: 100 mg
+ - `trt`: 100 mg x1
+ - need `dose` and `trt` in the output
+
+First, create the event
+
+``` r
+e <- ev(amt = 100, trt = ""100 mg x1"", dose = amt)
+
+e
+```
+
+ . Events:
+ . time amt cmt evid trt dose
+ . 1 0 100 1 1 100 mg x1 100
+
+Then simulate and recover `trt` and `amt`
+
+``` r
+mrgsim(mod, events = e, recover = ""trt,amt"")
+```
+
+ . Model: pk1
+ . Dim: 26 x 7
+ . Time: 0 to 24
+ . ID: 1
+ . ID time EV CENT CP trt amt
+ . 1: 1 0 0.0000 0.00 0.000 100 mg x1 100
+ . 2: 1 0 100.0000 0.00 0.000 100 mg x1 100
+ . 3: 1 1 36.7879 61.41 3.070 100 mg x1 100
+ . 4: 1 2 13.5335 81.00 4.050 100 mg x1 100
+ . 5: 1 3 4.9787 85.36 4.268 100 mg x1 100
+ . 6: 1 4 1.8316 84.25 4.213 100 mg x1 100
+ . 7: 1 5 0.6738 81.27 4.063 100 mg x1 100
+ . 8: 1 6 0.2479 77.72 3.886 100 mg x1 100
+","Markdown"
+"PBPK model","metrumresearchgroup/r-pharma-pkpd-2020","content/model/azithro-single.cpp",".cpp","1404","44","[PROB]
+1: Zhao Q, Tensfeldt TG, Chandra R, Mould DR. Population pharmacokinetics of
+azithromycin and chloroquine in healthy adults and paediatric malaria subjects
+following oral administration of fixed-dose azithromycin and chloroquine
+combination tablets. Malar J. 2014 Jan 29;13:36. doi: 10.1186/1475-2875-13-36.
+PubMed PMID: 24472224; PubMed Central PMCID: PMC3909452.
+
+https://malariajournal.biomedcentral.com/articles/10.1186/1475-2875-13-36
+
+[SET] end=10*24, delta=0.1
+
+[PARAM] @annotated
+TVCL : 100 : Typical value of clearance (L/h)
+TVV1 : 186 : Typical central volume of distribution (L)
+TVQ2 : 180 : Typical intercomp clearance 1 (L/h)
+TVV2 : 2890 : Typical peripheral volume of distribution 1 (L)
+Q3 : 10.6 : Intercomp clearance 2 (L/h)
+V3 : 2610 : Peripheral volume of distirbution 2 (L)
+KA : 0.259 : Absorption rate constant (1/h)
+WT : 70 : Patient weight (kg)
+
+[CMT] @annotated
+GUT : Dosing compartment (mg)
+CENT : Central comaprtment (mg)
+PER2 : First peripheral compartment (mg)
+PER3 : Second peripheral compartment (mg)
+
+[MAIN]
+double CL = TVCL*pow(WT/70.0,0.75);
+double V1 = TVV1*(WT/70);
+double Q2 = TVQ2*pow(WT/70.0,0.75);
+double V2 = TVV2*(WT/70.0);
+
+[ODE]
+dxdt_GUT = -KA*GUT;
+dxdt_CENT = KA*GUT - (CL+Q2+Q3)*CENT/V1 + Q2*PER2/V2 + Q3*PER3/V3;
+dxdt_PER2 = Q2*(CENT/V1 - PER2/V2);
+dxdt_PER3 = Q3*(CENT/V1 - PER3/V3);
+
+[TABLE]
+capture CP = CENT/(V1/1000.0);
+
+
+","C++"
+"PBPK model","metrumresearchgroup/r-pharma-pkpd-2020","content/model/rifampicin_midazolam.cpp",".cpp","10963","389","[ PROB ]
+
+1: Asaumi R, Toshimoto K, Tobe Y, Hashizume K, Nunoya KI, Imawaka H, Lee W,
+Sugiyama Y. Comprehensive PBPK Model of Rifampicin for Quantitative Prediction of
+Complex Drug-Drug Interactions: CYP3A/2C9 Induction and OATP Inhibition Effects.
+CPT Pharmacometrics Syst Pharmacol. 2018 Jan 25. doi: 10.1002/psp4.12275. [Epub
+ahead of print] PubMed PMID: 29368402.
+
+https://www.ncbi.nlm.nih.gov/pubmed/29368402
+http://onlinelibrary.wiley.com/doi/10.1002/psp4.12275/abstract
+
+[ PARAM ]
+
+Rdif = 0.129
+beta = 0.2 // 0.2/0.5/0.8
+gamma = 0.778
+
+Km_u_uptake = 0.146 // 1.23 // ug/mL
+
+SFKp = 6.65 // 1
+mSFKp = 0.201 // 1
+
+Emax_UGT_RIF = 1.34 // 1.00
+EC50_u_UGT_RIF = 0.0526 // ug/mL
+
+kdeg_UGT_liver = 0.0158 // per h
+kdeg_UGT_ent = 0.0288 // per h
+
+fm_UGT_liver = 0.759 // same
+fm_UGT_ent = 0.759 // same
+
+Emax_CYP3A4_RIF = 4.5700 // 12.3
+EC50_u_CYP3A4_RIF = 0.0526 // ug/mL
+
+kdeg_CYP3A4_liver = 0.0158 // per h
+kdeg_CYP3A4_ent = 0.0288 // per hr
+
+fm_CYP3A4_liver = 0.93
+fm_CYP3A4_ent = 1.00
+
+mCLperm_gut_kg = 0.151
+
+
+[ PARAM ]
+fB = 0.0778
+mfB = 0.0545
+fH = 0.0814
+fE = 0.115
+
+Fa = 1.000
+mFa = 1.000
+Fg = 0.943
+
+[ PARAM ]
+Kp_skin = 0.326
+Kp_muscle = 0.0947
+Kp_adipose = 0.0629
+Kp_serosa = 0.200
+
+mKp_liver = 6.96
+mKp_muscle = 4.00
+mKp_skin = 20.4
+mKp_adipose = 34.4
+
+[PARAM]
+fBCLint_all_kg = 0.251 // 0.204 // L/h/kg
+mfBCLint_kg = 0.469 // 0.528 // L/h/kg
+mfECLint_E_kg = 0.107 // L/h/kg
+
+PSdif_E_kg = 0.161 // 0.143 // L/h/kg
+CLrenal_kg = 0.011 // L/h/kg
+mCLrenal = 0.000
+
+[ PARAM ]
+Qvilli_kg = 0.257 // L/h/kg
+Qh_kg = 1.240 // L/h/kg
+Qmuscle_kg = 0.642 // L/h/kg
+Qskin_kg = 0.257 // L/h/kg
+Qadipose_kg = 0.223 // L/h/kg
+Qserosa_kg = 0.274 // L/h/kg
+Qportal_kg = 0.531 // L/h/kg
+
+[ PARAM ]
+VHE_kg = 0.0067 // L/kg (Vi)
+VHC_kg = 0.0174 // L/kg (Vh)
+Vcentral_kg = 0.0743 // L/kg (VbRif)
+mVcentral_kg = 0.571 // L/kg (estimated)
+Vskin_kg = 0.111 // L/kg
+Vadipose_kg = 0.143 // L/kg
+Vmuscle_kg = 0.429 // L/kg
+Vserosa_kg = 0.00893 // L/kg
+Vent_kg = 0.00739 // L/kg
+Vmucblood_kg = 0.00099 // L/kg
+Vportal_kg = 0.001 // L/kg
+
+[PARAM]
+ka = 37.6 // 3.26 // per hr
+mka = 1.29 // 5.51 per hr
+WT = 80 // Not sure
+
+[ TABLE ]
+capture Cmidazolam = 1000*mCcentral;
+
+[CAPTURE] Ccentral mCcentral
+
+[ MAIN ]
+if(NEWIND <= 1) {
+ // -------------------------------------------
+ double fBCLint_all = fBCLint_all_kg*WT;
+ double CLint_all = fBCLint_all / fB;
+ double mfBCLint = mfBCLint_kg*WT;
+ double mCLperm_gut = mCLperm_gut_kg*WT;
+ double mfECLint_E = mfECLint_E_kg*WT;
+ double CLrenal = CLrenal_kg*WT;
+ double PSdif_E = PSdif_E_kg*WT;
+ // -------------------------------------------
+ double Qvilli = Qvilli_kg*WT;
+ double Qh = Qh_kg*WT;
+ double Qmuscle = Qmuscle_kg*WT;
+ double Qskin = Qskin_kg*WT;
+ double Qadipose = Qadipose_kg*WT;
+ double Qserosa = Qserosa_kg*WT;
+ double Qhart = Qh - Qserosa - Qvilli;
+ double Qportal = Qportal_kg*WT;
+ // -------------------------------------------
+ double VHE = VHE_kg*WT;
+ double VHC = VHC_kg*WT;
+ double Vcentral = Vcentral_kg*WT;
+ double Vskin = Vskin_kg*WT;
+ double Vadipose = Vadipose_kg*WT;
+ double Vmuscle = Vmuscle_kg*WT;
+ double Vserosa = Vserosa_kg*WT;
+ double Vent = Vent_kg*WT;
+ double Vmucblood = Vmucblood_kg*WT;
+ double mVcentral = mVcentral_kg*WT;
+ double Vportal = Vportal_kg*WT;
+ // -------------------------------------------
+ double Vmax_uptake = 1.0 / (1 + Rdif) * CLint_all / beta * Km_u_uptake;
+ double PSdif_inf = Rdif / (1 + Rdif) * CLint_all / beta;
+ double PSdif_eff = Rdif / (1 + Rdif) * CLint_all / beta / gamma;
+ double CLint = Rdif / (1 + Rdif) * CLint_all / (1 - beta) / gamma;
+ double Qgut = fE * PSdif_E * Qvilli / (Qvilli + fB * PSdif_E);
+ double mQgut = Qvilli * mCLperm_gut / (Qvilli + mCLperm_gut);
+ double CLint_E = (Qgut * (1.0 / Fg - 1.0) - (1.0- Fa) * fE * PSdif_E * 20.0) / fE;
+}
+
+[CMT]
+Xgutlumen Mgutlumen
+central Cmuscle Cskin Cadipose Cserosa Cmucblood Cent
+CHE1 CHE2 CHE3 CHE4 CHE5
+CHC1 CHC2 CHC3 CHC4 CHC5
+
+mcentral mCmuscle mCskin mCadipose
+CLIV1 CLIV2 CLIV3 CLIV4 CLIV5
+Cportal
+
+[INIT]
+UGT_ratio_HC1 = 1
+UGT_ratio_HC2 = 1
+UGT_ratio_HC3 = 1
+UGT_ratio_HC4 = 1
+UGT_ratio_HC5 = 1
+UGT_ratio_ent = 1
+
+CYP3A4_ratio_HC1 = 1
+CYP3A4_ratio_HC2 = 1
+CYP3A4_ratio_HC3 = 1
+CYP3A4_ratio_HC4 = 1
+CYP3A4_ratio_HC5 = 1
+CYP3A4_ratio_ent = 1
+
+[ ODE ]
+
+double Ccentral = central/Vcentral;
+
+dxdt_central =
+ Qh * CHE5 -
+ Qhart * Ccentral -
+ Qserosa * Ccentral -
+ Qvilli * Ccentral -
+ CLrenal * Ccentral +
+ Qmuscle * (Cmuscle / (SFKp * Kp_muscle) - Ccentral) +
+ Qskin * (Cskin / (SFKp * Kp_skin) - Ccentral) +
+ Qadipose * (Cadipose / (SFKp * Kp_adipose) - Ccentral);
+
+dxdt_Cmuscle =
+ (1.0/Vmuscle) * Qmuscle * (Ccentral - Cmuscle / (SFKp * Kp_muscle));
+
+dxdt_Cskin =
+ (1.0/Vskin) * Qskin * (Ccentral - Cskin / (SFKp * Kp_skin));
+
+dxdt_Cadipose =
+ (1.0/Vadipose) * Qadipose * (Ccentral - Cadipose / (SFKp * Kp_adipose));
+
+dxdt_Cserosa =
+ (1.0/Vserosa) * Qserosa * (Ccentral - Cserosa / (SFKp * Kp_serosa));
+
+dxdt_Cmucblood =
+ Qvilli * (Ccentral - Cmucblood) +
+ fE * PSdif_E * Cent - fB * PSdif_E * Cmucblood;
+
+dxdt_Cmucblood = dxdt_Cmucblood * (1/Vmucblood);
+
+dxdt_Xgutlumen =
+ - ka / Fa * Xgutlumen + fE * PSdif_E * 20 * Cent;
+
+dxdt_Cent =
+ ka * Xgutlumen +
+ fB * PSdif_E * Cmucblood -
+ fE * (PSdif_E * 21 +
+ CLint_E * (1 + fm_UGT_ent * (UGT_ratio_ent - 1))) * Cent;
+
+dxdt_Cent = dxdt_Cent * (1/Vent);
+
+dxdt_UGT_ratio_HC1 =
+ kdeg_UGT_liver *
+ (1 + Emax_UGT_RIF * fH * CHC1 / (fH * CHC1 + EC50_u_UGT_RIF) - UGT_ratio_HC1);
+
+dxdt_UGT_ratio_HC2 =
+ kdeg_UGT_liver *
+ (1 + Emax_UGT_RIF * fH * CHC2 / (fH * CHC2 + EC50_u_UGT_RIF) - UGT_ratio_HC2);
+
+dxdt_UGT_ratio_HC3 =
+ kdeg_UGT_liver *
+ (1 + Emax_UGT_RIF * fH * CHC3 / (fH * CHC3 + EC50_u_UGT_RIF) - UGT_ratio_HC3);
+
+dxdt_UGT_ratio_HC4 =
+ kdeg_UGT_liver *
+ (1 + Emax_UGT_RIF * fH * CHC4 / (fH * CHC4 + EC50_u_UGT_RIF) - UGT_ratio_HC4);
+
+dxdt_UGT_ratio_HC5 =
+ kdeg_UGT_liver *
+ (1 + Emax_UGT_RIF * fH * CHC5 / (fH * CHC5 + EC50_u_UGT_RIF) - UGT_ratio_HC5);
+
+dxdt_UGT_ratio_ent =
+ kdeg_UGT_ent *
+ (1 + Emax_UGT_RIF * fE * Cent / (fE * Cent + EC50_u_UGT_RIF) - UGT_ratio_ent);
+
+dxdt_CHE1 =
+ Qhart * Ccentral +
+ Qvilli * Cmucblood +
+ Qserosa * Cserosa / (SFKp * Kp_serosa) -
+ Qh * CHE1 +
+ (fH * PSdif_eff * CHC1 -
+ fB * (Vmax_uptake / (Km_u_uptake + fB * CHE1) + PSdif_inf) * CHE1) / 5.0;
+
+dxdt_CHE1 = dxdt_CHE1 * (5.0/VHE);
+
+dxdt_CHE2 =
+ Qh * (CHE1 - CHE2) +
+ (fH * PSdif_eff * CHC2 -
+ fB * (Vmax_uptake / (Km_u_uptake + fB * CHE2) + PSdif_inf) * CHE2) / 5.0;
+
+dxdt_CHE2 = dxdt_CHE2 * (5.0/VHE);
+
+dxdt_CHE3 =
+ Qh * (CHE2 - CHE3) +
+ (fH * PSdif_eff * CHC3 -
+ fB * (Vmax_uptake / (Km_u_uptake + fB * CHE3) + PSdif_inf) * CHE3) / 5.0;
+
+dxdt_CHE3 = dxdt_CHE3 * (5.0/VHE);
+
+dxdt_CHE4 =
+ Qh * (CHE3 - CHE4) +
+ (fH * PSdif_eff * CHC4 -
+ fB * (Vmax_uptake / (Km_u_uptake + fB * CHE4) + PSdif_inf) * CHE4) / 5.0;
+
+dxdt_CHE4 = dxdt_CHE4 * (5.0/VHE);
+
+dxdt_CHE5 =
+ Qh * (CHE4 - CHE5) +
+ (fH * PSdif_eff * CHC5 -
+ fB * (Vmax_uptake / (Km_u_uptake + fB * CHE5) + PSdif_inf) * CHE5) / 5.0;// (i = 2~5)
+
+dxdt_CHE5 = dxdt_CHE5 * (5.0/VHE);
+
+dxdt_CHC1 =
+ (5.0/VHC) *
+ (fB * (Vmax_uptake / (Km_u_uptake + fB * CHE1) + PSdif_inf) * CHE1 -
+ fH * PSdif_eff * CHC1 -
+ fH * CLint * (1 + fm_UGT_liver * (UGT_ratio_HC1 - 1)) * CHC1) / 5.0;
+
+dxdt_CHC2 =
+ (5.0/VHC) *
+ (fB * (Vmax_uptake / (Km_u_uptake + fB * CHE2) + PSdif_inf) * CHE2 -
+ fH * PSdif_eff * CHC2 -
+ fH * CLint * (1 + fm_UGT_liver * (UGT_ratio_HC2 - 1)) * CHC2) / 5.0;
+
+dxdt_CHC3 =
+ (5.0/VHC) *
+ (fB * (Vmax_uptake / (Km_u_uptake + fB * CHE3) + PSdif_inf) * CHE3 -
+ fH * PSdif_eff * CHC3 -
+ fH * CLint * (1 + fm_UGT_liver * (UGT_ratio_HC3 - 1)) * CHC3) / 5.0;
+
+dxdt_CHC4 =
+ (5.0/VHC) *
+ (fB * (Vmax_uptake / (Km_u_uptake + fB * CHE4) + PSdif_inf) * CHE4 -
+ fH * PSdif_eff * CHC4 -
+ fH * CLint * (1 + fm_UGT_liver * (UGT_ratio_HC4 - 1)) * CHC4) / 5.0;
+
+dxdt_CHC5 =
+ (5.0/VHC) *
+ (fB * (Vmax_uptake / (Km_u_uptake + fB * CHE5) + PSdif_inf) * CHE5 -
+ fH * PSdif_eff * CHC5 -
+ fH * CLint * (1 + fm_UGT_liver * (UGT_ratio_HC5 - 1)) * CHC5) / 5.0;
+
+double mCcentral = mcentral/mVcentral;
+dxdt_mcentral =
+ Qh * (CLIV5 / (mSFKp * mKp_liver)) -
+ (Qh-Qportal) * mCcentral +
+ Qmuscle * (mCmuscle / (mSFKp * mKp_muscle) - mCcentral) +
+ Qskin * (mCskin / (mSFKp * mKp_skin) - mCcentral) +
+ Qadipose * (mCadipose / (mSFKp * mKp_adipose) - mCcentral) -
+ Qportal * mCcentral -
+ mCLrenal * mCcentral;
+
+dxdt_CYP3A4_ratio_HC1 =
+ kdeg_CYP3A4_liver *
+ (1 + Emax_CYP3A4_RIF * fH * CHC1 / (fH * CHC1 + EC50_u_CYP3A4_RIF) - CYP3A4_ratio_HC1);
+dxdt_CYP3A4_ratio_HC2 =
+ kdeg_CYP3A4_liver *
+ (1 + Emax_CYP3A4_RIF * fH * CHC2 / (fH * CHC2 + EC50_u_CYP3A4_RIF) - CYP3A4_ratio_HC2);
+dxdt_CYP3A4_ratio_HC3 =
+ kdeg_CYP3A4_liver *
+ (1 + Emax_CYP3A4_RIF * fH * CHC3 / (fH * CHC3 + EC50_u_CYP3A4_RIF) - CYP3A4_ratio_HC3);
+dxdt_CYP3A4_ratio_HC4 =
+ kdeg_CYP3A4_liver *
+ (1 + Emax_CYP3A4_RIF * fH * CHC4 / (fH * CHC4 + EC50_u_CYP3A4_RIF) - CYP3A4_ratio_HC4);
+dxdt_CYP3A4_ratio_HC5 =
+ kdeg_CYP3A4_liver *
+ (1 + Emax_CYP3A4_RIF * fH * CHC5 / (fH * CHC5 + EC50_u_CYP3A4_RIF) - CYP3A4_ratio_HC5);
+dxdt_CYP3A4_ratio_ent =
+ kdeg_CYP3A4_ent *
+ (1 + Emax_CYP3A4_RIF * fE * Cent / (fE * Cent + EC50_u_CYP3A4_RIF) - CYP3A4_ratio_ent);
+
+dxdt_CLIV1 =
+ (Qh-Qportal) * mCcentral +
+ Qportal * Cportal -
+ Qh * CLIV1 / (mSFKp * mKp_liver) -
+ mfBCLint * (1 + fm_CYP3A4_liver * (CYP3A4_ratio_HC1 - 1)) / 5 * CLIV1 /
+ (mSFKp * mKp_liver);
+
+dxdt_CLIV1 = dxdt_CLIV1 * (5/(VHE+VHC));
+
+dxdt_CLIV2 =
+ (Qh * (CLIV1 - CLIV2) -
+ mfBCLint * (1 + fm_CYP3A4_liver * (CYP3A4_ratio_HC2 - 1)) / 5 * CLIV2) /
+ (mSFKp * mKp_liver);
+
+dxdt_CLIV2 = dxdt_CLIV2 * (5/(VHE+VHC));
+
+dxdt_CLIV3 =
+ (Qh * (CLIV2 - CLIV3) -
+ mfBCLint * (1 + fm_CYP3A4_liver * (CYP3A4_ratio_HC3 - 1)) / 5 * CLIV3) /
+ (mSFKp * mKp_liver);
+
+dxdt_CLIV3 = dxdt_CLIV3 * (5/(VHE+VHC));
+
+dxdt_CLIV4 =
+ (Qh * (CLIV3 - CLIV4) -
+ mfBCLint * (1 + fm_CYP3A4_liver * (CYP3A4_ratio_HC4 - 1)) / 5 * CLIV4) /
+ (mSFKp * mKp_liver);
+
+dxdt_CLIV4 = dxdt_CLIV4 * (5/(VHE+VHC));
+
+dxdt_CLIV5 =
+ (Qh * (CLIV4 - CLIV5) -
+ mfBCLint * (1 + fm_CYP3A4_liver * (CYP3A4_ratio_HC5 - 1)) / 5 * CLIV5) /
+ (mSFKp * mKp_liver);
+
+dxdt_CLIV5 = dxdt_CLIV5 * (5/(VHE+VHC));
+
+dxdt_Cportal =
+ Qportal * (mCcentral - Cportal) +
+ mka * mQgut / (mQgut + mfECLint_E * (1 + fm_CYP3A4_ent * (CYP3A4_ratio_ent - 1))) * Mgutlumen;
+dxdt_Cportal = dxdt_Cportal * (1/Vportal);
+
+dxdt_mCmuscle =
+ (1/Vmuscle) * Qmuscle * (mCcentral - mCmuscle / (mSFKp * mKp_muscle));
+
+dxdt_mCskin =
+ (1/Vskin) * Qskin * (mCcentral - mCskin / (mSFKp * mKp_skin));
+
+dxdt_mCadipose =
+ (1/Vadipose) * Qadipose * (mCcentral - mCadipose / (mSFKp * mKp_adipose));
+
+dxdt_Mgutlumen = -mka/mFa * Mgutlumen;
+
+","C++"
+"PBPK model","metrumresearchgroup/r-pharma-pkpd-2020","content/model/yoshikado.cpp",".cpp","4941","221","[PROB]
+# Yoshikado et al. (2016)
+
+1: Yoshikado T, Yoshida K, Kotani N, Nakada T, Asaumi R, Toshimoto K, Maeda K,
+Kusuhara H, Sugiyama Y. Quantitative Analyses of Hepatic OATP-Mediated
+Interactions Between Statins and Inhibitors Using PBPK Modeling With a Parameter
+Optimization Method. Clin Pharmacol Ther. 2016 Nov;100(5):513-523.
+doi: 10.1002/cpt.391. Epub 2016 Jul 28. PubMed PMID: 27170342.
+
+https://www.ncbi.nlm.nih.gov/pubmed/27170342
+
+- Parameters: 40
+- Compartments: 31
+
+[CMT]
+
+// Dosing
+gut igut
+
+// Statin compartments
+cent mus adi ski
+ehc1 ehc2 ehc3
+he1 he2 he3 he4 he5
+hc1 hc2 hc3 hc4 hc5
+
+// CsA compartments
+icent
+me se ae
+mc sc ac
+iliv1 iliv2 iliv3 iliv4 iliv5
+
+[PARAM] // CSA
+iKp_mus = 2.98
+iKp_adi = 17.3
+iKp_ski = 13.6
+iKp_liv = 16.7
+ifb = 0.06
+ikiu = 0.0118
+imw = 1202.61
+
+PSmus = 245/70
+PSski = 37.4/70
+PSadi = 10.2/70
+ifhCLint = 0.587/70
+
+ifafg = 0.572
+iClr = 0
+ika = 0.999
+itlag = 0.254
+
+
+Kp_ski = 0.481
+Kp_mus = 0.113
+Kp_adi = 0.086
+CLr = 0.0
+Vcent = 0.075
+fafg = 1.0
+ktr = 0.679
+ka = 1.06
+fb = 0.008
+fh = 0.035
+fbCLintall = 51.6/70
+fbile = 0.330
+gamma = 0.244
+beta = 0.8
+Rdiff = 0.0345
+tlag = 1
+
+[PARAM]
+Qh = 1.200
+Qmus = 0.642
+Qski = 0.257
+Qadi = 0.223
+
+Vliv = 0.0241
+Vmus = 0.4290
+Vski = 0.1110
+Vadi = 0.1430
+
+exFliv = 0.278
+exFmus = 0.146
+exFski = 0.321
+exFadi = 0.145
+
+
+[MAIN]
+
+if(NEWIND <=1) {
+ double CLintall = fbCLintall/fb;
+ double PSact = 1.0/(1.0+Rdiff)*CLintall/beta;
+ double PSdiffi = Rdiff/(1.0+Rdiff)*CLintall/beta;
+ double PSdiffe = Rdiff/(1.0+Rdiff)/gamma*CLintall/beta;
+ double CLint = CLintall/(1.0-beta)*Rdiff/(1.0+Rdiff)/gamma;
+
+ double Vme = Vmus*exFmus;
+ double Vae = Vadi*exFadi;
+ double Vse = Vski*exFski;
+ double Vmc = Vmus-Vme;
+ double Vac = Vadi-Vae;
+ double Vsc = Vski-Vse;
+ double dVliv = Vliv/5.0;
+ double ikitot = imw*ikiu/ifb;
+}
+
+// ALAG_gut = tlag;
+//ALAG_igut = itlag;
+
+[ODE]
+
+// Statin concentrations
+double Ccent = cent/Vcent;
+double Cmus = mus/Vmus;
+double Cski = ski/Vski;
+double Cadi = adi/Vadi;
+
+// Volume liv
+double Vhe = dVliv*exFliv;
+double Vhc = dVliv*(1-exFliv);
+
+double Chc1 = hc1/Vhc;
+double Chc2 = hc2/Vhc;
+double Chc3 = hc3/Vhc;
+double Chc4 = hc4/Vhc;
+double Chc5 = hc5/Vhc;
+
+double Che1 = he1/Vhe;
+double Che2 = he2/Vhe;
+double Che3 = he3/Vhe;
+double Che4 = he4/Vhe;
+double Che5 = he5/Vhe;
+
+// CsA concentrations
+double iCcent = icent/Vcent;
+
+double Cme = me/Vme;
+double Cse = se/Vse;
+double Cae = ae/Vae;
+
+double Cmc = mc/Vmc;
+double Csc = sc/Vsc;
+double Cac = ac/Vac;
+
+double iCliv1 = iliv1/dVliv;
+double iCliv2 = iliv2/dVliv;
+double iCliv3 = iliv3/dVliv;
+double iCliv4 = iliv4/dVliv;
+double iCliv5 = iliv5/dVliv;
+
+dxdt_igut = -ika/ifafg*igut;
+
+dxdt_icent =
+ Qh*iCliv5/iKp_liv
+ - Qh*iCcent
+ - iClr*iCcent
+ - Qmus*(iCcent-Cme)
+ - Qski*(iCcent-Cse)
+ - Qadi*(iCcent-Cae);
+
+dxdt_me = Qmus*(iCcent-Cme) - PSmus*ifb*(Cme-Cmc/iKp_mus);
+dxdt_se = Qski*(iCcent-Cse) - PSski*ifb*(Cse-Csc/iKp_ski);
+dxdt_ae = Qadi*(iCcent-Cae) - PSadi*ifb*(Cae-Cac/iKp_adi);
+
+dxdt_mc = PSmus*ifb*(Cme-Cmc/iKp_mus);
+dxdt_sc = PSski*ifb*(Cse-Csc/iKp_ski);
+dxdt_ac = PSadi*ifb*(Cae-Cac/iKp_adi);
+
+dxdt_iliv1 = Qh*(iCcent-iCliv1/iKp_liv) - (ifhCLint/5.0)*iCliv1 + ika*igut;
+dxdt_iliv2 = Qh*(iCliv1-iCliv2)/iKp_liv - (ifhCLint/5.0)*iCliv2;
+dxdt_iliv3 = Qh*(iCliv2-iCliv3)/iKp_liv - (ifhCLint/5.0)*iCliv3;
+dxdt_iliv4 = Qh*(iCliv3-iCliv4)/iKp_liv - (ifhCLint/5.0)*iCliv4;
+dxdt_iliv5 = Qh*(iCliv4-iCliv5)/iKp_liv - (ifhCLint/5.0)*iCliv5;
+
+// CsA effect on Statin
+double csai1 = 1.0+(iCliv1/iKp_liv)/ikitot;
+double csai2 = 1.0+(iCliv2/iKp_liv)/ikitot;
+double csai3 = 1.0+(iCliv3/iKp_liv)/ikitot;
+double csai4 = 1.0+(iCliv4/iKp_liv)/ikitot;
+double csai5 = 1.0+(iCliv5/iKp_liv)/ikitot;
+
+
+double hex2 = fh*(PSdiffe/5.0);
+dxdt_he1 = Qh*(Ccent-Che1)-(fb*(PSact/csai1+PSdiffi)/5.0)*Che1+hex2*Chc1 + ka*gut;
+dxdt_he2 = Qh*(Che1 -Che2)-(fb*(PSact/csai2+PSdiffi)/5.0)*Che2+hex2*Chc2;
+dxdt_he3 = Qh*(Che2 -Che3)-(fb*(PSact/csai3+PSdiffi)/5.0)*Che3+hex2*Chc3;
+dxdt_he4 = Qh*(Che3 -Che4)-(fb*(PSact/csai4+PSdiffi)/5.0)*Che4+hex2*Chc4;
+dxdt_he5 = Qh*(Che4 -Che5)-(fb*(PSact/csai5+PSdiffi)/5.0)*Che5+hex2*Chc5;
+
+
+double hcx2 = fh*((PSdiffe+CLint)/5.0);
+dxdt_hc1 = fb*((PSact/csai1+PSdiffi)/5.0)*Che1 - hcx2*Chc1;
+dxdt_hc2 = fb*((PSact/csai2+PSdiffi)/5.0)*Che2 - hcx2*Chc2;
+dxdt_hc3 = fb*((PSact/csai3+PSdiffi)/5.0)*Che3 - hcx2*Chc3;
+dxdt_hc4 = fb*((PSact/csai4+PSdiffi)/5.0)*Che4 - hcx2*Chc4;
+dxdt_hc5 = fb*((PSact/csai5+PSdiffi)/5.0)*Che5 - hcx2*Chc5;
+
+dxdt_cent =
+ Qh*Che5
+ - Qh*Ccent
+ - CLr*Ccent
+ - Qmus*(Ccent-Cmus/Kp_mus)
+ - Qski*(Ccent-Cski/Kp_ski)
+ - Qadi*(Ccent-Cadi/Kp_adi);
+
+dxdt_mus = Qmus*(Ccent-Cmus/Kp_mus);
+dxdt_ski = Qski*(Ccent-Cski/Kp_ski);
+dxdt_adi = Qadi*(Ccent-Cadi/Kp_adi);
+
+
+dxdt_gut = ktr*ehc3 - ka/fafg*gut;
+dxdt_ehc1 = fbile*fh*(CLint/5.0)*(Chc1+Chc2+Chc3+Chc4+Chc5)-ktr*ehc1;
+dxdt_ehc2 = ktr*(ehc1-ehc2);
+dxdt_ehc3 = ktr*(ehc2-ehc3);
+
+
+
+[TABLE]
+capture CP = cent/Vcent;
+capture CSA = icent/Vcent;
+capture CSAliv = iliv1/dVliv;
+
+","C++"
+"PBPK model","metrumresearchgroup/r-pharma-pkpd-2020","content/model/meropenem_pk.cpp",".cpp","1162","54","[PROB]
+Meropenem PopPK
+
+https://www.ncbi.nlm.nih.gov/pubmed/16988206
+
+[SET] delta=0.1, end=24, req=""""
+
+[PKMODEL] cmt = ""CENT, PERIPH""
+
+[PARAM] @annotated
+WT : 70 : Weight (kg)
+CLCR : 83 : Creatinine clearance (ml/min)
+AGE : 35 : Age (years)
+
+[THETA] @annotated
+ 1.50E+01 : Typical value of clearance (L/h)
+ 1.27E+01 : Typical value of volume 1 (L)
+ 1.52E+01 : Intercompartmental clearance (L/h)
+ 1.24E+01 : Typical value of volume 2 (L)
+-4.47E-01 : AGE on CL
+ 8.20E-01 : WT on V1
+ 1.88E-01 : Proportional error standard deviation
+ 4.76E-01 : Additive error standard deviation
+ 6.20E-01 : CLCR on CL
+
+[MAIN]
+
+double TVCL = THETA1;
+double TVV1 = THETA2;
+double TVQ = THETA3;
+double TVV2 = THETA4;
+double CL_AGE = THETA5;
+double V1_WT = THETA6;
+double CL_CLCR = THETA9;
+
+double LOGTWT = log((WT/70.0));
+
+double LOGTAGE = log((AGE/35.0));
+
+double LOGTCLCR = log((CLCR/83.0));
+
+double CL = exp(log(TVCL) + CL_AGE * LOGTAGE + CL_CLCR * LOGTCLCR) ;
+
+double V1 = exp(log(TVV1) + V1_WT * LOGTWT) ;
+
+double Q = exp(log(TVQ)) ;
+
+double V2 = exp(log(TVV2));
+
+[TABLE]
+capture CC = (CENT/V1);
+double IPRED = CC;
+capture Y = IPRED;
+","C++"
+"PBPK model","metrumresearchgroup/r-pharma-pkpd-2020","content/mapk/functions.R",".R","539","15","logbr <- function() {
+ x <- 10^seq(-5,5)
+ sort(c(x,3*x))
+}
+library(ggplot2)
+noline <- element_blank()
+theme_plain <- function(...) {
+ theme_bw() + theme(panel.grid.major=noline,panel.grid.minor=noline,
+ plot.margin=margin(0.5,0.5,1,0.5,unit=""cm""),...)
+}
+rotx <- function(angle=30) theme(axis.text.x = element_text(angle = angle, hjust = 1))
+roty <- function(angle=30) theme(axis.text.y = element_text(angle = angle, hjust = 1))
+typef <- function(x) factor(x, c(1,2), c(""Pitavastatin alone"", ""Pitavastatin + CsA""))
+
+","R"
+"PBPK model","metrumresearchgroup/r-pharma-pkpd-2020","content/mapk/mapk_inhibitors_in_colorectal_cancer.md",".md","9889","352","Clinical responses to ERK inhibition in BRAF{V600E}-mutant colorectal
+cancer
+================
+Metrum Research Group
+
+ - [Reference](#reference)
+ - [Introduction](#introduction)
+ - [Cast of characters](#cast-of-characters)
+ - [Translation](#translation)
+ - [Set up](#set-up)
+ - [Explore](#explore)
+ - [Simulate with ERK inhibitor
+ GDC-0944](#simulate-with-erk-inhibitor-gdc-0944)
+ - [Sensitivity analysis](#sensitivity-analysis)
+ - [Predicting clinical outcomes for combination
+ therapies](#predicting-clinical-outcomes-for-combination-therapies)
+ - [Generate dosing regimens](#generate-dosing-regimens)
+ - [Simulate all combination
+ therapies](#simulate-all-combination-therapies)
+ - [Summarize and plot](#summarize-and-plot)
+
+# Reference
+
+**Clinical responses to ERK inhibition in BRAF{V600E}-mutant colorectal
+cancer predicted using a computational model**
+
+ - Daniel C. Kirouac, Gabriele Schaefer, Jocelyn Chan, Mark Merchant,
+ Christine Orr, Shih-Min A. Huang, John Moffat, Lichuan Liu, Kapil
+ Gadkar and Saroja Ramanujan
+
+ - npj Systems Biology and Applications (2017) 3:14 ; /
+ s41540-017-0016-1
+
+## Introduction
+
+(Summarized from Introduction in the reference)
+
+ - The V600E/K mutation results in constitutively active BRAF, with
+ subsequent signalling through MEK and ERK
+
+ - BRAF and MEK inhibitors were found to be effective in V600E mutant
+ melanoma, but not so much in colorectal cancer
+
+ - Could resistance to BRAF inhibitors be mediated through EGFR
+ signalling through RAS and CRAF?
+ - What about inhibition at ERK?
+ - Could the effectiveness of different combination therapies be
+ predicted with a model characterizing this biology?
+
+## Cast of characters
+
+ - **vemurafenib**: BRAF inhibitor (selective for V600E mutant)
+ - **cobimetinib**: MEK inhibitor
+ - **cetuximab**: EGFR antibody
+ - **GDC-0994**: ERK inhibitor (the star)
+
+# Translation
+
+ - Model published as SBML
+ - Translator from previous project work using R bindings to libSBML
+ - Minor modifications to the translator code to accommodate the MAPK
+ model as published
+
+# Set up
+
+``` r
+library(mrgsolve)
+library(tidyverse)
+library(parallel)
+source(""functions.R"")
+# mclapply <- lapply
+```
+
+Read in the virtual population
+
+``` r
+vp <- readRDS(""s10vpop_pk.RDS"") %>% mutate(VPOP2 = seq(n()))
+
+dim(vp)
+```
+
+ . [1] 250 147
+
+Load the model and pick one parameter set from vpop
+
+``` r
+mod <- mread(""mapk"") %>% update(end = 56)
+
+mod <- param(mod, filter(vp,VPOP2==41))
+```
+
+# Explore
+
+## Simulate with ERK inhibitor GDC-0944
+
+``` r
+e <- expand.ev(amt = seq(100,600,100), cmt = 12, ii = 1, addl = 20)
+
+e <- ev_seq(e, wait = 7, e) %>% as_tibble() %>% arrange(ID)
+
+mod %>%
+ data_set(e) %>%
+ Req(TUMOR,ERKi) %>%
+ mrgsim(delta = 0.25) %>%
+ plot(ERKi+TUMOR ~ time)
+```
+
+
+
+## Sensitivity analysis
+
+The authors note two parameters that are particularly influential with
+respect to response rates:
+
+ - wOR: MAPK pathway dependence parameter
+ - \(\delta_{max}\): the maximum cell death rate
+
+
+
+``` r
+vp %>% select(wOR,dmax) %>%
+ map(quantile, probs = seq(0,1,0.1)) %>%
+ bind_cols() %>% mutate(pctile = seq(0,1,0.1))
+```
+
+ . # A tibble: 11 x 3
+ . wOR dmax pctile
+ .
+ . 1 0.754 0.0331 0
+ . 2 0.828 0.0369 0.1
+ . 3 0.860 0.0380 0.2
+ . 4 0.873 0.0400 0.3
+ . 5 0.890 0.0416 0.4
+ . 6 0.906 0.0426 0.5
+ . 7 0.918 0.0440 0.6
+ . 8 0.951 0.0449 0.7
+ . 9 0.977 0.0464 0.8
+ . 10 1 0.0485 0.9
+ . 11 1 0.0522 1
+
+# Predicting clinical outcomes for combination therapies
+
+ - Re-create figure 6B in the publication
+
+## Generate dosing regimens
+
+ - **No treatment**
+
+
+
+``` r
+data0 <- ev(amt=0, cmt=8)
+```
+
+ - **BRAF inhibitor** - vemurafanib (VEMU)
+ - Compartment 8
+
+
+
+``` r
+dataV <- ev(amt=960, cmt=8, ii=0.5, addl=120)
+```
+
+ - **ERK inhibitor** - GCD-994 (GDC)
+ - Compartment 12
+
+
+
+``` r
+dataG <- ev(amt = 400, cmt = 12, ii = 1, addl = 20)
+
+dataG <- seq(dataG, wait = 7, dataG)
+
+out <- mrgsim(mod, ev=dataG, end=56, delta = 0.1)
+
+plot(out, ERKi_C~time)
+```
+
+
+
+ - **MEK inhibitor** - cobimetinib (COBI)
+ - Compartment 10
+
+
+
+``` r
+dataCO <- mutate(dataG,amt=60,cmt=10)
+```
+
+ - **EGFR inihbitor** - cetuximab (CETUX)
+ - Compartment 7
+
+
+
+``` r
+dataCE <- ev(cmt=7,ii=7,addl=7,amt=450)
+```
+
+We create two functions: one to combine dosing regimens and the other to
+simulate from a dosing regimen
+
+``` r
+comb <- function(...) {
+ x <- lapply(list(...), as.data.frame)
+ bind_rows(x) %>% arrange(time)
+}
+
+sim <- function(Data,Vp,Mod) {
+ Mod %>%
+ ev(as.ev(Data)) %>%
+ mrgsim(idata=Vp,end=-1, add = 56) %>%
+ filter(time==56)
+}
+```
+
+For example
+
+``` r
+comb(dataCE,dataV)
+```
+
+ . time cmt amt evid ii addl
+ . 1 0 7 450 1 7.0 7
+ . 2 0 8 960 1 0.5 120
+
+``` r
+sim(comb(dataCE,dataV), Vp = slice(vp,seq(10)), Mod = mod)
+```
+
+ . # A tibble: 10 x 25
+ . ID time TD1 CELLS FB1 FB2 FB3 FB4 RTK1i_blood
+ .
+ . 1 1 56 0.996 0.871 6.95e-1 6.95e-1 6.95e-1 0.437 246.
+ . 2 2 56 0.401 0.963 1.44e-4 1.44e-4 1.44e-4 0.547 246.
+ . 3 3 56 0.363 1.18 1.15e-4 1.15e-4 1.15e-4 0.703 246.
+ . 4 4 56 0.186 0.726 1.52e-4 1.52e-4 1.51e-4 0.649 246.
+ . 5 5 56 0.348 0.903 2.21e-2 2.20e-2 2.20e-2 0.662 246.
+ . 6 6 56 0.999 1.06 9.39e-1 9.39e-1 9.39e-1 0.731 246.
+ . 7 7 56 0.999 1.42 9.02e-1 9.02e-1 9.02e-1 0.731 246.
+ . 8 8 56 0.636 1.02 2.39e-4 2.30e-4 2.27e-4 0.630 246.
+ . 9 9 56 0.952 1.16 1.95e-1 1.95e-1 1.95e-1 0.688 246.
+ . 10 10 56 0.964 1.02 2.52e-1 2.52e-1 2.52e-1 0.649 246.
+ . # … with 16 more variables: RAFi_gut , RAFi_blood ,
+ . # MEKi_gut , MEKi_blood , ERKi_gut , ERKi_blood ,
+ . # AKTi_gut , AKTi_blood , MEKi_V3 , RTK1i_gut ,
+ . # ERKi , ERKi_C , RAFi , MEKi , TUMOR ,
+ . # GDC
+
+## Simulate all combination therapies
+
+Generate a data frame of runs to do
+
+``` r
+sims <-
+ tribble(
+ ~label, ~object,
+ ""No Treatment"", data0,
+ ""CETUX"", dataCE,
+ ""VEMU"", dataV,
+ ""COBI"", dataCO,
+ ""GDC"", dataG,
+ ""CETUX+VEMU"", comb(dataCE, dataV),
+ ""CETUX+COBI"", comb(dataCE, dataCO),
+ ""CETUX+GDC"", comb(dataCE, dataG),
+ ""VEMU+COBI"", comb(dataV, dataG),
+ ""VEMU+GDC"", comb(dataV, dataG),
+ ""COBI+GDC"", comb(dataCO, dataG),
+ ""CETUX+VEMU+COBI"", comb(dataCE, dataV, dataCO),
+ ""CETUX+VEMU+GDC"", comb(dataCE, dataV, dataG),
+ ""CETUX+COBI+GDC"", comb(dataCE, dataCO, dataG),
+ ""VEMU+COBI+GDC"", comb(dataV, dataCO, dataG),
+ ""CETUX+VEMU+COBI+GDC"", comb(dataCE, dataV, dataCO, dataG)
+ ) %>% mutate(object = map(object,as.data.frame))
+```
+
+Run the
+simulation
+
+``` r
+sims <- mutate(sims, out = parallel::mclapply(object, sim, Vp = vp, Mod = mod))
+```
+
+## Summarize and plot
+
+Get ready to plot
+
+``` r
+sms <- select(sims, label, out) %>% unnest()
+
+sms <- mutate(
+ sms,
+ labelf = fct_inorder(label),
+ gdc = factor(grepl(""GDC"", label))
+)
+
+head(sms)
+```
+
+ . # A tibble: 6 x 28
+ . label ID time TD1 CELLS FB1 FB2 FB3 FB4 RTK1i_blood
+ .
+ . 1 No T… 1 56 0.998 0.872 0.822 0.822 0.822 0.714 0
+ . 2 No T… 2 56 0.998 1.42 0.800 0.800 0.800 0.478 0
+ . 3 No T… 3 56 0.999 1.78 0.916 0.916 0.916 0.708 0
+ . 4 No T… 4 56 0.995 1.51 0.583 0.583 0.583 0.644 0
+ . 5 No T… 5 56 0.999 1.43 0.951 0.951 0.951 0.664 0
+ . 6 No T… 6 56 0.999 1.06 0.940 0.940 0.940 0.731 0
+ . # … with 18 more variables: RAFi_gut , RAFi_blood ,
+ . # MEKi_gut , MEKi_blood , ERKi_gut , ERKi_blood ,
+ . # AKTi_gut , AKTi_blood , MEKi_V3 , RTK1i_gut ,
+ . # ERKi , ERKi_C , RAFi , MEKi , TUMOR ,
+ . # GDC , labelf , gdc
+
+``` r
+p1 <-
+ ggplot(data=sms) +
+ geom_point(aes(x=labelf, y=TUMOR),position=position_jitter(width=0.15),col=""grey"") +
+ geom_hline(yintercept=0.7,col=""black"", lty=1,lwd=0.7) +
+ scale_y_continuous(limits=c(0,2.5),name=""Tumor size"",breaks=c(0,0.5,1,1.5,2,2.5,3)) +
+ scale_x_discrete(name="""") +
+ geom_boxplot(aes(x=labelf,y=TUMOR,col=gdc),fill=""darkslateblue"",alpha=0.2) +
+ scale_color_manual(values = c(""darkslateblue"", ""firebrick""), guide = FALSE) +
+ theme_plain() + rotx(30) +
+ ggtitle(""Note: GDC-0944 +/- cobimetinib"")
+p1
+```
+
+
+
+``` r
+names(sms)
+```
+
+ . [1] ""label"" ""ID"" ""time"" ""TD1"" ""CELLS""
+ . [6] ""FB1"" ""FB2"" ""FB3"" ""FB4"" ""RTK1i_blood""
+ . [11] ""RAFi_gut"" ""RAFi_blood"" ""MEKi_gut"" ""MEKi_blood"" ""ERKi_gut""
+ . [16] ""ERKi_blood"" ""AKTi_gut"" ""AKTi_blood"" ""MEKi_V3"" ""RTK1i_gut""
+ . [21] ""ERKi"" ""ERKi_C"" ""RAFi"" ""MEKi"" ""TUMOR""
+ . [26] ""GDC"" ""labelf"" ""gdc""
+
+``` r
+readr::write_csv(sms, path=""mapk_sims.csv"")
+saveRDS(sms, file = ""mapk_sims.RDS"")
+
+count(sms, time)
+```
+
+ . # A tibble: 1 x 2
+ . time n
+ .
+ . 1 56 4000
+","Markdown"
+"PBPK model","metrumresearchgroup/r-pharma-pkpd-2020","content/mapk/mapk.cpp",".cpp","7183","219","$GLOBAL
+
+//-- #include ""global.h""
+//-- Y = (max(0,x).^k)./(tau^k + max(0,x).^k);
+
+double HillEQ(double x, double k, double tau) {
+ double a = pow(std::max(x,0.0),k);
+ return a/(pow(tau,k) + a);
+}
+
+// Created: Wed Jun 28 11:21:18 2017
+
+$PARAM
+// Created: Wed Jun 28 11:21:18 2017
+// Parameters (103)
+RASb = 0 // param 0
+RASt = 1 // param 1
+k1 = 1 // param 2
+tau1 = 1 // param 3
+k3 = 1 // param 4
+tau3 = 1 // param 5
+MEKt = 1 // param 6
+MEKb = 0 // param 7
+k2 = 1 // param 8
+tau2 = 1 // param 9
+ERKb = 0 // param 10
+ERKt = 1 // param 11
+k4 = 1 // param 12
+kFB1 = 1 // param 13
+tauFB1 = 1 // param 14
+tau4 = 1 // param 15
+S6b = 0 // param 16
+S6t = 1 // param 17
+k6 = 1 // param 18
+tau6 = 1 // param 19
+k5 = 1 // param 20
+tau5 = 1 // param 21
+Vmax = 10 // param 22
+dmax = 0 // param 23
+kg = 1 // param 24
+taug = 1 // param 25
+umax = 1 // param 26
+wOR = 0.8 // param 27
+AKTb = 1 // param 28
+AKTt = 1 // param 29
+k8 = 1 // param 30
+tau8 = 1 // param 31
+PI3Kb = 1 // param 32
+PI3Kt = 1 // param 33
+k7 = 1 // param 34
+tau7 = 1 // param 35
+ki1 = 1 // param 36
+taui1 = 1 // param 37
+ki2 = 1 // param 38
+taui2 = 1 // param 39
+kFB2 = 1 // param 40
+tauFB2 = 1 // param 41
+kFB3 = 1 // param 42
+ki4 = 1 // param 43
+tauFB3 = 1 // param 44
+taui4 = 1 // param 45
+BRAFb = 1 // param 46
+BRAFt = 1 // param 47
+CRAFb = 1 // param 48
+CRAFt = 1 // param 49
+ki3 = 1 // param 50
+taui3 = 1 // param 51
+G13 = 1 // param 52
+G23 = 1 // param 53
+RTK2b = 1 // param 54
+RTK2t = 1 // param 55
+ki5 = 1 // param 56
+taui5 = 1 // param 57
+G14 = 1 // param 58
+RTK1b = 1 // param 59
+RTK1t = 1 // param 60
+kFB4 = 1 // param 61
+tauFB4 = 1 // param 62
+G24 = 1 // param 63
+F1 = 1 // param 64
+V1 = 1 // param 65
+ka1 = 1 // param 66
+ke1 = 1 // param 67
+p1 = 1 // param 68
+G33 = 1 // param 69
+G34 = 1 // param 70
+RTK3b = 1 // param 71
+F2 = 1 // param 72
+V2 = 1 // param 73
+ka2 = 1 // param 74
+ke2 = 1 // param 75
+F3 = 1 // param 76
+V3 = 1 // param 77
+ka3 = 1 // param 78
+ke3 = 1 // param 79
+p2 = 1 // param 80
+p3 = 1 // param 81
+F4 = 1 // param 82
+V4 = 1 // param 83
+ka4 = 1 // param 84
+ke4 = 1 // param 85
+p4 = 1 // param 86
+wRAS = 1 // param 87
+RTK3t = 1 // param 88
+F5 = 1 // param 89
+V5 = 1 // param 90
+ka5 = 1 // param 91
+ke5 = 1 // param 92
+p5 = 1 // param 93
+r4 = 1 // param 94
+r2 = 1 // param 95
+r3 = 1 // param 96
+r1 = 1 // param 97
+q2 = 1 // param 98
+V3b = 1 // param 99
+r5 = 1 // param 100
+Gdusp = 1 // param 101
+Gspry = 1 // param 102
+
+$INIT
+// Created: Wed Jun 28 11:21:18 2017
+// Initial conditions (17)
+TD1 = 0 // species 6
+CELLS = 1 // species 7
+FB1 = 0 // species 8
+FB2 = 0 // species 9
+FB3 = 0 // species 10
+FB4 = 0 // species 20
+RTK1i_blood = 0 // species 22
+RAFi_gut = 0 // species 23
+RAFi_blood = 0 // species 24
+MEKi_gut = 0 // species 25
+MEKi_blood = 0 // species 26
+ERKi_gut = 0 // species 27
+ERKi_blood = 0 // species 28
+AKTi_gut = 0 // species 29
+AKTi_blood = 0 // species 30
+MEKi_V3 = 0 // species 31
+RTK1i_gut = 0 // species 32
+
+$ODE
+
+// Created: Wed Jun 28 11:21:18 2017
+// RULES (21)
+double RTK1i_C = RTK1i_blood / V1;
+double RAFi_C = RAFi_blood / V2;
+double MEKi_C = MEKi_blood / V3;
+double ERKi_C = ERKi_blood / V4;
+double AKTi_C = AKTi_blood / V5;
+double RTK1i = p1 * RTK1i_C;
+double RAFi = p2 * RAFi_C;
+double MEKi = p3 * MEKi_C;
+double ERKi = p4 * ERKi_C;
+double AKTi = p5 * AKTi_C;
+double RTK1 = (RTK1b + (RTK1t - RTK1b) * (1 - G13 * HillEQ(FB3, kFB3, tauFB3)) * (1 - G14 * HillEQ(FB4, kFB4, tauFB4))) * (1 - HillEQ(RTK1i, ki1, taui1));
+double RTK2 = RTK2b + (RTK2t - RTK2b) * (1 - G23 * HillEQ(FB3, kFB3, tauFB3)) * (1 - G24 * HillEQ(FB4, kFB4, tauFB4));
+double RTK3 = RTK3b + (RTK3t - RTK3b) * (1 - G33 * HillEQ(FB3, kFB3, tauFB3)) * (1 - G34 * HillEQ(FB4, kFB4, tauFB4));
+double RAS = (RASb + (RASt - RASb) * HillEQ(RTK1 + RTK2, k1, tau1)) * (1 - Gspry * HillEQ(FB2, kFB2, tauFB2));
+double BRAF = (BRAFb + (BRAFt - BRAFb) * HillEQ(RAS, k2, tau2)) * (1 - HillEQ(RAFi, ki2, taui2));
+double CRAF = CRAFb + (CRAFt - CRAFb) * HillEQ(RAS, k5, tau5);
+double MEK = (MEKb + (MEKt - MEKb) * HillEQ(BRAF + CRAF, k3, tau3)) * (1 - HillEQ(MEKi, ki3, taui3));
+double ERK = (ERKb + (ERKt - ERKb) * HillEQ(MEK, k4, tau4)) * (1 - Gdusp * HillEQ(FB1, kFB1, tauFB1)) * (1 - HillEQ(ERKi, ki4, taui4));
+double PI3K = PI3Kb + (PI3Kt - PI3Kb) * HillEQ(RTK3 + wRAS * RAS, k7, tau7);
+double AKT = (AKTb + (AKTt - AKTb) * HillEQ(PI3K, k8, tau8)) * (1 - HillEQ(AKTi, ki5, taui5));
+double S6 = S6b + (S6t - S6b) * HillEQ(wOR * ERK + (1 - wOR) * AKT, k6, tau6);
+
+// Created: Wed Jun 28 11:21:18 2017
+// Reactions (22)
+double fb1 = r1 * (ERK - FB1);
+double fb2 = r2 * (ERK - FB2);
+double fb3 = r3 * (ERK - FB3);
+double TD = r5 * (S6 - TD1);
+double cell = (umax * HillEQ(TD1, kg, taug) - dmax) * CELLS * (1 - CELLS / Vmax);
+double fb4 = r4 * (AKT - FB4);
+double PK1a_RAFi = ka2 * F2 * RAFi_gut;
+double PK1a_MEKi = ka3 * F3 * MEKi_gut;
+double PK1a_ERKi = ka4 * F4 * ERKi_gut;
+double PK1a_AKTi = ka5 * F5 * AKTi_gut;
+double PK2_RTK1i = ke1 * RTK1i_blood;
+double PK2_RAFi = ke2 * RAFi_blood;
+double PK2_MEKi = ke3 * MEKi_blood;
+double PK2_ERKi = ke4 * ERKi_blood;
+double PK2_AKTi = ke5 * AKTi_blood;
+double PK3_MEKi = q2 / V3 * MEKi_blood - q2 / V3b * MEKi_V3;
+double PK1a_RTK1i = ka1 * F1 * RTK1i_gut;
+double PK1b_RTK1i = ka1 * (1 - F1) * RTK1i_gut;
+double PK1b_RAFi = ka2 * (1 - F2) * RAFi_gut;
+double PK1b_MEKi = ka3 * (1 - F3) * MEKi_gut;
+double PK1b_ERKi = ka4 * (1 - F4) * ERKi_gut;
+double PK1b_AKTi = ka5 * (1 - F5) * AKTi_gut;
+
+
+// Created: Wed Jun 28 11:21:18 2017
+// ODEs (17)
+dxdt_AKTi_blood = PK1a_AKTi - PK2_AKTi;
+dxdt_AKTi_gut = - PK1a_AKTi - PK1b_AKTi;
+dxdt_CELLS = cell;
+dxdt_ERKi_blood = PK1a_ERKi - PK2_ERKi;
+dxdt_ERKi_gut = - PK1a_ERKi - PK1b_ERKi;
+dxdt_FB1 = fb1;
+dxdt_FB2 = fb2;
+dxdt_FB3 = fb3;
+dxdt_FB4 = fb4;
+dxdt_MEKi_blood = PK1a_MEKi - PK2_MEKi - PK3_MEKi;
+dxdt_MEKi_gut = - PK1a_MEKi - PK1b_MEKi;
+dxdt_MEKi_V3 = PK3_MEKi;
+dxdt_RAFi_blood = PK1a_RAFi - PK2_RAFi;
+dxdt_RAFi_gut = - PK1a_RAFi - PK1b_RAFi;
+dxdt_RTK1i_blood = PK1a_RTK1i - PK2_RTK1i;
+dxdt_RTK1i_gut = - PK1a_RTK1i - PK1b_RTK1i;
+dxdt_TD1 = TD;
+
+$TABLE
+capture TUMOR = CELLS;
+capture GDC = ERKi;
+
+$CAPTURE ERKi ERKi_C RAFi MEKi
+
+","C++"
+"PBPK model","metrumresearchgroup/r-pharma-pkpd-2020","content/script/global.R",".R","178","9","options(mrgsolve.soloc = ""build"")
+knitr::opts_chunk$set(
+ comment = '.',
+ fig.align = ""center"",
+ message = FALSE,
+ warning = FALSE
+)
+ggplot2::theme_set(ggplot2::theme_bw())
+","R"
+"PBPK model","metrumresearchgroup/r-pharma-pkpd-2020","content/script/functions.R",".R","539","15","logbr <- function() {
+ x <- 10^seq(-5,5)
+ sort(c(x,3*x))
+}
+library(ggplot2)
+noline <- element_blank()
+theme_plain <- function(...) {
+ theme_bw() + theme(panel.grid.major=noline,panel.grid.minor=noline,
+ plot.margin=margin(0.5,0.5,1,0.5,unit=""cm""),...)
+}
+rotx <- function(angle=30) theme(axis.text.x = element_text(angle = angle, hjust = 1))
+roty <- function(angle=30) theme(axis.text.y = element_text(angle = angle, hjust = 1))
+typef <- function(x) factor(x, c(1,2), c(""Pitavastatin alone"", ""Pitavastatin + CsA""))
+
+","R"
+"PBPK model","metrumresearchgroup/r-pharma-pkpd-2020","content/script/build.R",".R","1039","22","library(rmarkdown)
+render(""tools_optimization_intro.Rmd"", output_format = ""github_document"")
+render(""tools_optimization_intro.Rmd"", output_format = ""html_document"")
+
+render(""tools_optimization_pbpk_ddi.Rmd"", output_format = ""github_document"")
+render(""tools_optimization_pbpk_ddi.Rmd"", output_format = ""html_document"")
+
+render(""tools_optimization_methods.Rmd"", output_format = ""github_document"")
+render(""tools_optimization_methods.Rmd"", output_format = ""html_document"")
+
+render(""tools_optimization_indomethacin.Rmd"", output_format = ""github_document"")
+render(""tools_optimization_indomethacin.Rmd"", output_format = ""html_document"")
+
+render(""tools_sensitivity_local.Rmd"", output_format = ""github_document"")
+render(""tools_sensitivity_local.Rmd"", output_format = ""html_document"")
+
+render(""get-started.Rmd"", output_format = ""github_document"")
+render(""get-started.Rmd"", output_format = ""html_document"")
+
+render(""rifampin_midazolam_ddi.Rmd"", output_format = ""github_document"")
+render(""rifampin_midazolam_ddi.Rmd"", output_format = ""html_document"")
+","R"
+"PBPK model","metrumresearchgroup/r-pharma-pkpd-2020","renv/activate.R",".R","10064","348","local({
+
+ # the requested version of renv
+ version <- ""0.12.0""
+
+ # the project directory
+ project <- getwd()
+
+ # avoid recursion
+ if (!is.na(Sys.getenv(""RENV_R_INITIALIZING"", unset = NA)))
+ return(invisible(TRUE))
+
+ # signal that we're loading renv during R startup
+ Sys.setenv(""RENV_R_INITIALIZING"" = ""true"")
+ on.exit(Sys.unsetenv(""RENV_R_INITIALIZING""), add = TRUE)
+
+ # signal that we've consented to use renv
+ options(renv.consent = TRUE)
+
+ # load the 'utils' package eagerly -- this ensures that renv shims, which
+ # mask 'utils' packages, will come first on the search path
+ library(utils, lib.loc = .Library)
+
+ # check to see if renv has already been loaded
+ if (""renv"" %in% loadedNamespaces()) {
+
+ # if renv has already been loaded, and it's the requested version of renv,
+ # nothing to do
+ spec <- .getNamespaceInfo(.getNamespace(""renv""), ""spec"")
+ if (identical(spec[[""version""]], version))
+ return(invisible(TRUE))
+
+ # otherwise, unload and attempt to load the correct version of renv
+ unloadNamespace(""renv"")
+
+ }
+
+ # load bootstrap tools
+ bootstrap <- function(version, library) {
+
+ # read repos (respecting override if set)
+ repos <- Sys.getenv(""RENV_CONFIG_REPOS_OVERRIDE"", unset = NA)
+ if (is.na(repos))
+ repos <- getOption(""repos"")
+
+ # fix up repos
+ on.exit(options(repos = repos), add = TRUE)
+ repos[repos == ""@CRAN@""] <- ""https://cloud.r-project.org""
+ options(repos = repos)
+
+ # attempt to download renv
+ tarball <- tryCatch(renv_bootstrap_download(version), error = identity)
+ if (inherits(tarball, ""error""))
+ stop(""failed to download renv "", version)
+
+ # now attempt to install
+ status <- tryCatch(renv_bootstrap_install(version, tarball, library), error = identity)
+ if (inherits(status, ""error""))
+ stop(""failed to install renv "", version)
+
+ }
+
+ renv_bootstrap_download_impl <- function(url, destfile) {
+
+ mode <- ""wb""
+
+ # https://bugs.r-project.org/bugzilla/show_bug.cgi?id=17715
+ fixup <-
+ Sys.info()[[""sysname""]] == ""Windows"" &&
+ substring(url, 1L, 5L) == ""file:""
+
+ if (fixup)
+ mode <- ""w+b""
+
+ download.file(
+ url = url,
+ destfile = destfile,
+ mode = mode,
+ quiet = TRUE
+ )
+
+ }
+
+ renv_bootstrap_download <- function(version) {
+
+ methods <- list(
+ renv_bootstrap_download_cran_latest,
+ renv_bootstrap_download_cran_archive,
+ renv_bootstrap_download_github
+ )
+
+ for (method in methods) {
+ path <- tryCatch(method(version), error = identity)
+ if (is.character(path) && file.exists(path))
+ return(path)
+ }
+
+ stop(""failed to download renv "", version)
+
+ }
+
+ renv_bootstrap_download_cran_latest <- function(version) {
+
+ # check for renv on CRAN matching this version
+ db <- as.data.frame(available.packages(), stringsAsFactors = FALSE)
+
+ entry <- db[db$Package %in% ""renv"" & db$Version %in% version, ]
+ if (nrow(entry) == 0) {
+ fmt <- ""renv %s is not available from your declared package repositories""
+ stop(sprintf(fmt, version))
+ }
+
+ message(""* Downloading renv "", version, "" from CRAN ... "", appendLF = FALSE)
+
+ info <- tryCatch(
+ download.packages(""renv"", destdir = tempdir()),
+ condition = identity
+ )
+
+ if (inherits(info, ""condition"")) {
+ message(""FAILED"")
+ return(FALSE)
+ }
+
+ message(""OK"")
+ info[1, 2]
+
+ }
+
+ renv_bootstrap_download_cran_archive <- function(version) {
+
+ name <- sprintf(""renv_%s.tar.gz"", version)
+ repos <- getOption(""repos"")
+ urls <- file.path(repos, ""src/contrib/Archive/renv"", name)
+ destfile <- file.path(tempdir(), name)
+
+ message(""* Downloading renv "", version, "" from CRAN archive ... "", appendLF = FALSE)
+
+ for (url in urls) {
+
+ status <- tryCatch(
+ renv_bootstrap_download_impl(url, destfile),
+ condition = identity
+ )
+
+ if (identical(status, 0L)) {
+ message(""OK"")
+ return(destfile)
+ }
+
+ }
+
+ message(""FAILED"")
+ return(FALSE)
+
+ }
+
+ renv_bootstrap_download_github <- function(version) {
+
+ enabled <- Sys.getenv(""RENV_BOOTSTRAP_FROM_GITHUB"", unset = ""TRUE"")
+ if (!identical(enabled, ""TRUE""))
+ return(FALSE)
+
+ # prepare download options
+ pat <- Sys.getenv(""GITHUB_PAT"")
+ if (nzchar(Sys.which(""curl"")) && nzchar(pat)) {
+ fmt <- ""--location --fail --header \""Authorization: token %s\""""
+ extra <- sprintf(fmt, pat)
+ saved <- options(""download.file.method"", ""download.file.extra"")
+ options(download.file.method = ""curl"", download.file.extra = extra)
+ on.exit(do.call(base::options, saved), add = TRUE)
+ } else if (nzchar(Sys.which(""wget"")) && nzchar(pat)) {
+ fmt <- ""--header=\""Authorization: token %s\""""
+ extra <- sprintf(fmt, pat)
+ saved <- options(""download.file.method"", ""download.file.extra"")
+ options(download.file.method = ""wget"", download.file.extra = extra)
+ on.exit(do.call(base::options, saved), add = TRUE)
+ }
+
+ message(""* Downloading renv "", version, "" from GitHub ... "", appendLF = FALSE)
+
+ url <- file.path(""https://api.github.com/repos/rstudio/renv/tarball"", version)
+ name <- sprintf(""renv_%s.tar.gz"", version)
+ destfile <- file.path(tempdir(), name)
+
+ status <- tryCatch(
+ renv_bootstrap_download_impl(url, destfile),
+ condition = identity
+ )
+
+ if (!identical(status, 0L)) {
+ message(""FAILED"")
+ return(FALSE)
+ }
+
+ message(""Done!"")
+ return(destfile)
+
+ }
+
+ renv_bootstrap_install <- function(version, tarball, library) {
+
+ # attempt to install it into project library
+ message(""* Installing renv "", version, "" ... "", appendLF = FALSE)
+ dir.create(library, showWarnings = FALSE, recursive = TRUE)
+
+ # invoke using system2 so we can capture and report output
+ bin <- R.home(""bin"")
+ exe <- if (Sys.info()[[""sysname""]] == ""Windows"") ""R.exe"" else ""R""
+ r <- file.path(bin, exe)
+ args <- c(""--vanilla"", ""CMD"", ""INSTALL"", ""-l"", shQuote(library), shQuote(tarball))
+ output <- system2(r, args, stdout = TRUE, stderr = TRUE)
+ message(""Done!"")
+
+ # check for successful install
+ status <- attr(output, ""status"")
+ if (is.numeric(status) && !identical(status, 0L)) {
+ header <- ""Error installing renv:""
+ lines <- paste(rep.int(""="", nchar(header)), collapse = """")
+ text <- c(header, lines, output)
+ writeLines(text, con = stderr())
+ }
+
+ status
+
+ }
+
+ renv_bootstrap_prefix <- function() {
+
+ # construct version prefix
+ version <- paste(R.version$major, R.version$minor, sep = ""."")
+ prefix <- paste(""R"", numeric_version(version)[1, 1:2], sep = ""-"")
+
+ # include SVN revision for development versions of R
+ # (to avoid sharing platform-specific artefacts with released versions of R)
+ devel <-
+ identical(R.version[[""status""]], ""Under development (unstable)"") ||
+ identical(R.version[[""nickname""]], ""Unsuffered Consequences"")
+
+ if (devel)
+ prefix <- paste(prefix, R.version[[""svn rev""]], sep = ""-r"")
+
+ # build list of path components
+ components <- c(prefix, R.version$platform)
+
+ # include prefix if provided by user
+ prefix <- Sys.getenv(""RENV_PATHS_PREFIX"")
+ if (nzchar(prefix))
+ components <- c(prefix, components)
+
+ # build prefix
+ paste(components, collapse = ""/"")
+
+ }
+
+ renv_bootstrap_library_root <- function(project) {
+
+ path <- Sys.getenv(""RENV_PATHS_LIBRARY"", unset = NA)
+ if (!is.na(path))
+ return(path)
+
+ path <- Sys.getenv(""RENV_PATHS_LIBRARY_ROOT"", unset = NA)
+ if (!is.na(path))
+ return(file.path(path, basename(project)))
+
+ file.path(project, ""renv/library"")
+
+ }
+
+ renv_bootstrap_validate_version <- function(version) {
+
+ loadedversion <- utils::packageDescription(""renv"", fields = ""Version"")
+ if (version == loadedversion)
+ return(TRUE)
+
+ # assume four-component versions are from GitHub; three-component
+ # versions are from CRAN
+ components <- strsplit(loadedversion, ""[.-]"")[[1]]
+ remote <- if (length(components) == 4L)
+ paste(""rstudio/renv"", loadedversion, sep = ""@"")
+ else
+ paste(""renv"", loadedversion, sep = ""@"")
+
+ fmt <- paste(
+ ""renv %1$s was loaded from project library, but renv %2$s is recorded in lockfile."",
+ ""Use `renv::record(\""%3$s\"")` to record this version in the lockfile."",
+ ""Use `renv::restore(packages = \""renv\"")` to install renv %2$s into the project library."",
+ sep = ""\n""
+ )
+
+ msg <- sprintf(fmt, loadedversion, version, remote)
+ warning(msg, call. = FALSE)
+
+ FALSE
+
+ }
+
+ renv_bootstrap_load <- function(project, libpath, version) {
+
+ # try to load renv from the project library
+ if (!requireNamespace(""renv"", lib.loc = libpath, quietly = TRUE))
+ return(FALSE)
+
+ # warn if the version of renv loaded does not match
+ renv_bootstrap_validate_version(version)
+
+ # load the project
+ renv::load(project)
+
+ TRUE
+
+ }
+
+ # construct path to library root
+ root <- renv_bootstrap_library_root(project)
+
+ # construct library prefix for platform
+ prefix <- renv_bootstrap_prefix()
+
+ # construct full libpath
+ libpath <- file.path(root, prefix)
+
+ # attempt to load
+ if (renv_bootstrap_load(project, libpath, version))
+ return(TRUE)
+
+ # load failed; attempt to bootstrap
+ bootstrap(version, libpath)
+
+ # exit early if we're just testing bootstrap
+ if (!is.na(Sys.getenv(""RENV_BOOTSTRAP_INSTALL_ONLY"", unset = NA)))
+ return(TRUE)
+
+ # try again to load
+ if (requireNamespace(""renv"", lib.loc = libpath, quietly = TRUE)) {
+ message(""Successfully installed and loaded renv "", version, ""."")
+ return(renv::load())
+ }
+
+ # failed to download or load renv; warn the user
+ msg <- c(
+ ""Failed to find an renv installation: the project will not be loaded."",
+ ""Use `renv::activate()` to re-initialize the project.""
+ )
+
+ warning(paste(msg, collapse = ""\n""), call. = FALSE)
+
+})","R"