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Pharmaceutics
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Applications of Physiologically-Based Pharmacokinetic (PBPK) Modeling, 2nd Edition
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Applications of Physiologically-Based Pharmacokinetic (PBPK) Modeling, 2nd Edition

A special issue of Pharmaceutics (ISSN 1999-4923). This special issue belongs to the section "Pharmacokinetics and Pharmacodynamics".

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 9609

Special Issue Editor

Prof. Dr. Thorsten Lehr

E-Mail Website
Guest Editor
Clinical Pharmacy, Saarland University, 66123 Saarbrücken, Germany
Interests: model informed drug discovery and development (MID3); pharmacometrics; PBPK modeling; PK/PD modeling; individualized therapy; precision dosing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

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

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Pharmaceutics is an international peer-reviewed open access monthly journal published by MDPI.

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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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Published Papers (3 papers)

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17 pages, 2749 KiB  
Article
Translation of Monoclonal Antibodies Pharmacokinetics from Animal to Human Using Physiologically Based Modeling in Open Systems Pharmacology (OSP) Suite: A Retrospective Analysis of Bevacizumab
by Blaise Pasquiers, Salih Benamara, Mathieu Felices, David Ternant, Xavier Declèves and Alicja Puszkiel
Pharmaceutics 2023, 15(8), 2129; https://doi.org/10.3390/pharmaceutics15082129 - 14 Aug 2023
Cited by 2 | Viewed by 2196
Abstract
Interspecies translation of monoclonal antibodies (mAbs) pharmacokinetics (PK) in presence of target-mediated drug disposition (TMDD) is particularly challenging. Incorporation of TMDD in physiologically based PK (PBPK) modeling is recent and needs to be consolidated and generalized to provide better prediction of TMDD regarding [...] Read more.
Interspecies translation of monoclonal antibodies (mAbs) pharmacokinetics (PK) in presence of target-mediated drug disposition (TMDD) is particularly challenging. Incorporation of TMDD in physiologically based PK (PBPK) modeling is recent and needs to be consolidated and generalized to provide better prediction of TMDD regarding inter-species translation during preclinical and clinical development steps of mAbs. The objective of this study was to develop a generic PBPK translational approach for mAbs using the open-source software (PK-Sim® and Mobi®). The translation of bevacizumab based on data in non-human primates (NHP), healthy volunteers (HV), and cancer patients was used as a case example for model demonstration purpose. A PBPK model for bevacizumab concentration-time data was developed using data from literature and the Open Systems Pharmacology (OSP) Suite version 10. PK-sim® was used to build the linear part of bevacizumab PK (mainly FcRn-mediated), whereas MoBi® was used to develop the target-mediated part. The model was first developed for NHP and used for a priori PK prediction in HV. Then, the refined model obtained in HV was used for a priori prediction in cancer patients. A priori predictions were within 2-fold prediction error (predicted/observed) for both area under the concentration-time curve (AUC) and maximum concentration (Cmax) and all the predicted concentrations were within 2-fold average fold error (AFE) and average absolute fold error (AAFE). Sensitivity analysis showed that FcRn-mediated distribution and elimination processes must be accounted for at all mAb concentration levels, whereas the lower the mAb concentration, the more significant the target-mediated elimination. This project is the first step to generalize the full PBPK translational approach in Model-Informed Drug Development (MIDD) of mAbs using OSP Suite. Full article
(This article belongs to the Special Issue Applications of Physiologically-Based Pharmacokinetic (PBPK) Modeling, 2nd Edition)
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13 pages, 1797 KiB  
Article
Predicting Hydroxychloroquine Clearance in Healthy and Diseased Populations Using a Physiologically Based Pharmacokinetic Approach
by 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
Cited by 3 | Viewed by 2489
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 [...] Read more.
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 AUC0-t in liver cirrhosis, whereas a decrease in AUC0-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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22 pages, 4265 KiB  
Article
A Physiologically Based Pharmacokinetic Model of Ketoconazole and Its Metabolites as Drug–Drug Interaction Perpetrators
by Fatima Zahra Marok, Jan-Georg Wojtyniak, Laura Maria Fuhr, Dominik Selzer, Matthias Schwab, Johanna Weiss, Walter Emil Haefeli and Thorsten Lehr
Pharmaceutics 2023, 15(2), 679; https://doi.org/10.3390/pharmaceutics15020679 - 17 Feb 2023
Cited by 7 | Viewed by 4165
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 [...] Read more.
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 AUClast and DFI Cmax 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 AUClast and 21/21 DDI Cmax ratios were within two-fold of observed ratios, while DDI models that simulated only ketoconazole as the perpetrator underperformed: 12/27 DDI AUClast and 18/21 DDI Cmax 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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