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Special Issue "Applications of Physiologically-Based Pharmacokinetic (PBPK) Modeling, 2nd Edition"

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Dear Colleagues,

Physiologically-based pharmacokinetic (PBPK) modeling has matured to a powerful and widely accepted approach to integrate information from different stages of drug research and development, to predict untested scenarios and to support decision making. The three major regulatory agencies (FDA, EMA, and PMDA) encourage PBPK modeling for the assessment of drug-drug interaction (DDI) potential, the development of alternative dosing regimens or even to waive clinical studies. Furthermore, PBPK models are increasingly used to evaluate the effects of patient factors on drug exposure, and they are excellent tools to develop alternative dosing regimens for patients.

This Special Issue is dedicated to new, cutting-edge examples of the application of PBPK modeling, to show its versatility and to highlight recent advances in the field. Modeling scientists are cordially invited to share their research covering the full spectrum of PBPK modeling and simulation, including (but not limited to) the pharmacokinetics of special populations (e.g. geriatrics, pediatrics, pregnancy, ICU), modeling of pathophysiology (e.g. renal impairment, hepatic impairment), drug-gene interactions, drug-drug interactions, integration of tissue concentration (e.g. by imaging techniques), PBPK covariate modeling, PBPK-based precision dosing, PBPK/PD modeling, PBPK-based Quantitative Systems Pharmacology (QSP) approaches or technical advances.

Prof. Dr. Thorsten Lehr
Guest Editor

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Keywords

physiologically based pharmacokinetic (PBPK) modeling
translational pharmacology
special populations
drug-drug interactions
drug-gene interactions
PBPK-based precision dosing
PBPK-based Quantitative Systems Pharmacology

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Research

Open AccessArticle

Predicting Hydroxychloroquine Clearance in Healthy and Diseased Populations Using a Physiologically Based Pharmacokinetic Approach

Faleh Alqahtani

Ali Mohammed Asiri

Ammara Zamir

Muhammad Fawad Rasool

Amer S. Alali

Sary Alsanea

and

Ismail A. Walbi

Pharmaceutics 2023, 15(4), 1250; https://doi.org/10.3390/pharmaceutics15041250 - 15 Apr 2023

Viewed by 890

Abstract

Hydroxychloroquine (HCQ), a congener of chloroquine, is widely used in prophylaxis and the treatment of malaria, and also as a cure for rheumatoid arthritis, systemic lupus erythematosus, and various other diseases. Physiologically based pharmacokinetic modeling (PBPK) has attracted great interest in the past few years in predicting drug pharmacokinetics (PK). This study focuses on predicting the PK of HCQ in the healthy population and extrapolating it to the diseased populations, i.e., liver cirrhosis and chronic kidney disease (CKD), utilizing a systematically built whole-body PBPK model. The time vs. concentration profiles and drug-related parameters were obtained from the literature after a laborious search and in turn were integrated into PK-Sim software for designing healthy intravenous, oral, and diseased models. The model’s evaluation was performed using observed-to-predicted ratios (Robs/Rpre) and visual predictive checks within a 2-fold error range. The healthy model was then extrapolated to liver cirrhosis and CKD populations after incorporating various disease-specific pathophysiological changes. Box–whisker plots showed an increase in AUC_0-t in liver cirrhosis, whereas a decrease in AUC_0-t was seen in the CKD population. These model predictions may assist clinicians in adjusting the administered HCQ doses in patients with different degrees of hepatic and renal impairment. Full article

(This article belongs to the Special Issue Applications of Physiologically-Based Pharmacokinetic (PBPK) Modeling, 2nd Edition)

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Open AccessArticle

A Physiologically Based Pharmacokinetic Model of Ketoconazole and Its Metabolites as Drug–Drug Interaction Perpetrators

and

Pharmaceutics 2023, 15(2), 679; https://doi.org/10.3390/pharmaceutics15020679 - 17 Feb 2023

Cited by 1 | Viewed by 1370

Abstract

The antifungal ketoconazole, which is mainly used for dermal infections and treatment of Cushing’s syndrome, is prone to drug–food interactions (DFIs) and is well known for its strong drug–drug interaction (DDI) potential. Some of ketoconazole’s potent inhibitory activity can be attributed to its metabolites that predominantly accumulate in the liver. This work aimed to develop a whole-body physiologically based pharmacokinetic (PBPK) model of ketoconazole and its metabolites for fasted and fed states and to investigate the impact of ketoconazole’s metabolites on its DDI potential. The parent–metabolites model was developed with PK-Sim^® and MoBi^® using 53 plasma concentration-time profiles. With 7 out of 7 (7/7) DFI AUC_last and DFI C_max ratios within two-fold of observed ratios, the developed model demonstrated good predictive performance under fasted and fed conditions. DDI scenarios that included either the parent alone or with its metabolites were simulated and evaluated for the victim drugs alfentanil, alprazolam, midazolam, triazolam, and digoxin. DDI scenarios that included all metabolites as reversible inhibitors of CYP3A4 and P-gp performed best: 26/27 of DDI AUC_last and 21/21 DDI C_max ratios were within two-fold of observed ratios, while DDI models that simulated only ketoconazole as the perpetrator underperformed: 12/27 DDI AUC_last and 18/21 DDI C_max ratios were within the success limits. Full article

(This article belongs to the Special Issue Applications of Physiologically-Based Pharmacokinetic (PBPK) Modeling, 2nd Edition)

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Special Issue "Applications of Physiologically-Based Pharmacokinetic (PBPK) Modeling, 2nd Edition"

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