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

Deadline for manuscript submissions: closed (31 December 2020) | Viewed by 50261

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Prof. Dr. Thorsten Lehr

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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
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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

Published Papers (11 papers)

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22 pages, 26184 KiB

Open AccessArticle

Physiologically Based Pharmacokinetic Modeling of Bupropion and Its Metabolites in a CYP2B6 Drug-Drug-Gene Interaction Network

by Fatima Zahra Marok, Laura Maria Fuhr, Nina Hanke, Dominik Selzer and Thorsten Lehr

Pharmaceutics 2021, 13(3), 331; https://doi.org/10.3390/pharmaceutics13030331 - 4 Mar 2021

Cited by 7 | Viewed by 5400

Abstract

The noradrenaline and dopamine reuptake inhibitor bupropion is metabolized by CYP2B6 and recommended by the FDA as the only sensitive substrate for clinical CYP2B6 drug–drug interaction (DDI) studies. The aim of this study was to build a whole-body physiologically based pharmacokinetic (PBPK) model of bupropion including its DDI-relevant metabolites, and to qualify the model using clinical drug–gene interaction (DGI) and DDI data. The model was built in PK-Sim^® applying clinical data of 67 studies. It incorporates CYP2B6-mediated hydroxylation of bupropion, metabolism via CYP2C19 and 11β-HSD, as well as binding to pharmacological targets. The impact of CYP2B6 polymorphisms is described for normal, poor, intermediate, and rapid metabolizers, with various allele combinations of the genetic variants CYP2B6*1, *4, *5 and *6. DDI model performance was evaluated by prediction of clinical studies with rifampicin (CYP2B6 and CYP2C19 inducer), fluvoxamine (CYP2C19 inhibitor) and voriconazole (CYP2B6 and CYP2C19 inhibitor). Model performance quantification showed 20/20 DGI ratios of hydroxybupropion to bupropion AUC ratios (DGI AUC_HBup/Bup ratios), 12/13 DDI AUC_HBup/Bup ratios, and 7/7 DDGI AUC_HBup/Bup ratios within 2-fold of observed values. The developed model is freely available in the Open Systems Pharmacology model repository. Full article

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

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19 pages, 4393 KiB

Open AccessArticle

Pharmacokinetics of the CYP3A4 and CYP2B6 Inducer Carbamazepine and Its Drug–Drug Interaction Potential: A Physiologically Based Pharmacokinetic Modeling Approach

by Laura Maria Fuhr, Fatima Zahra Marok, Nina Hanke, Dominik Selzer and Thorsten Lehr

Pharmaceutics 2021, 13(2), 270; https://doi.org/10.3390/pharmaceutics13020270 - 17 Feb 2021

Cited by 33 | Viewed by 5189

Abstract

The anticonvulsant carbamazepine is frequently used in the long-term therapy of epilepsy and is a known substrate and inducer of cytochrome P450 (CYP) 3A4 and CYP2B6. Carbamazepine induces the metabolism of various drugs (including its own); on the other hand, its metabolism can be affected by various CYP inhibitors and inducers. The aim of this work was to develop a physiologically based pharmacokinetic (PBPK) parent−metabolite model of carbamazepine and its metabolite carbamazepine-10,11-epoxide, including carbamazepine autoinduction, to be applied for drug–drug interaction (DDI) prediction. The model was developed in PK-Sim, using a total of 92 plasma concentration−time profiles (dosing range 50–800 mg), as well as fractions excreted unchanged in urine measurements. The carbamazepine model applies metabolism by CYP3A4 and CYP2C8 to produce carbamazepine-10,11-epoxide, metabolism by CYP2B6 and UDP-glucuronosyltransferase (UGT) 2B7 and glomerular filtration. The carbamazepine-10,11-epoxide model applies metabolism by epoxide hydroxylase 1 (EPHX1) and glomerular filtration. Good DDI performance was demonstrated by the prediction of carbamazepine DDIs with alprazolam, bupropion, erythromycin, efavirenz and simvastatin, where 14/15 DDI AUC_last ratios and 11/15 DDI C_max ratios were within the prediction success limits proposed by Guest et al. The thoroughly evaluated model will be freely available in the Open Systems Pharmacology model repository. Full article

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

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23 pages, 5288 KiB

Open AccessArticle

A Physiologically-Based Pharmacokinetic Framework for Prediction of Drug Exposure in Malnourished Children

by Erik Sjögren, Joel Tarning, Karen I. Barnes and E. Niclas Jonsson

Pharmaceutics 2021, 13(2), 204; https://doi.org/10.3390/pharmaceutics13020204 - 2 Feb 2021

Cited by 6 | Viewed by 2163

Abstract

Malnutrition in children is a global health problem, particularly in developing countries. The effects of an insufficient supply of nutrients on body composition and physiological functions may have implications for drug disposition and ultimately affect the clinical outcome in this vulnerable population. Physiologically-based pharmacokinetic (PBPK) modeling can be used to predict the effect of malnutrition as it links physiological changes to pharmacokinetic (PK) consequences. However, the absence of detailed information on body composition and the limited availability of controlled clinical trials in malnourished children complicates the establishment and evaluation of a generic PBPK model in this population. In this manuscript we describe the creation of physiologically-based bridge to a malnourished pediatric population, by combining information on (a) the differences in body composition between healthy and malnourished adults and (b) the differences in physiology between healthy adults and children. Model performance was confirmed using clinical reference data. This study presents a physiologically-based translational framework for prediction of drug disposition in malnourished children. The model is readily applicable for dose recommendation strategies to address the urgent medicinal needs of this vulnerable population. Full article

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

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18 pages, 2486 KiB

Open AccessArticle

Physiologically Based Pharmacokinetic Modeling of Metoprolol Enantiomers and α-Hydroxymetoprolol to Describe CYP2D6 Drug-Gene Interactions

by Simeon Rüdesheim, Jan-Georg Wojtyniak, Dominik Selzer, Nina Hanke, Felix Mahfoud, Matthias Schwab and Thorsten Lehr

Pharmaceutics 2020, 12(12), 1200; https://doi.org/10.3390/pharmaceutics12121200 - 11 Dec 2020

Cited by 16 | Viewed by 4637

Abstract

The beta-blocker metoprolol (the sixth most commonly prescribed drug in the USA in 2017) is subject to considerable drug–gene interaction (DGI) effects caused by genetic variations of the CYP2D6 gene. CYP2D6 poor metabolizers (5.7% of US population) show approximately five-fold higher metoprolol exposure compared to CYP2D6 normal metabolizers. This study aimed to develop a whole-body physiologically based pharmacokinetic (PBPK) model to predict CYP2D6 DGIs with metoprolol. The metoprolol (R)- and (S)-enantiomers as well as the active metabolite α-hydroxymetoprolol were implemented as model compounds, employing data of 48 different clinical studies (dosing range 5–200 mg). To mechanistically describe the effect of CYP2D6 polymorphisms, two separate metabolic CYP2D6 pathways (α-hydroxylation and O-demethylation) were incorporated for both metoprolol enantiomers. The good model performance is demonstrated in predicted plasma concentration–time profiles compared to observed data, goodness-of-fit plots, and low geometric mean fold errors of the predicted AUC_last (1.27) and C_max values (1.23) over all studies. For DGI predictions, 18 out of 18 DGI AUC_last ratios and 18 out of 18 DGI C_max ratios were within two-fold of the observed ratios. The newly developed and carefully validated model was applied to calculate dose recommendations for CYP2D6 polymorphic patients and will be freely available in the Open Systems Pharmacology repository. Full article

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

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19 pages, 1195 KiB

Open AccessArticle

A Physiologically-Based Pharmacokinetic Model of Trimethoprim for MATE1, OCT1, OCT2, and CYP2C8 Drug–Drug–Gene Interaction Predictions

by Denise Türk, Nina Hanke and Thorsten Lehr

Pharmaceutics 2020, 12(11), 1074; https://doi.org/10.3390/pharmaceutics12111074 - 10 Nov 2020

Cited by 9 | Viewed by 4750

Abstract

Trimethoprim is a frequently-prescribed antibiotic and therefore likely to be co-administered with other medications, but it is also a potent inhibitor of multidrug and toxin extrusion protein (MATE) and a weak inhibitor of cytochrome P450 (CYP) 2C8. The aim of this work was to develop a physiologically-based pharmacokinetic (PBPK) model of trimethoprim to investigate and predict its drug–drug interactions (DDIs). The model was developed in PK-Sim^®, using a large number of clinical studies (66 plasma concentration–time profiles with 36 corresponding fractions excreted in urine) to describe the trimethoprim pharmacokinetics over the entire published dosing range (40 to 960 mg). The key features of the model include intestinal efflux via P-glycoprotein (P-gp), metabolism by CYP3A4, an unspecific hepatic clearance process, and a renal clearance consisting of glomerular filtration and tubular secretion. The DDI performance of this new model was demonstrated by prediction of DDIs and drug–drug–gene interactions (DDGIs) of trimethoprim with metformin, repaglinide, pioglitazone, and rifampicin, with all predicted DDI and DDGI AUC_last and C_max ratios within 1.5-fold of the clinically-observed values. The model will be freely available in the Open Systems Pharmacology model repository, to support DDI studies during drug development. Full article

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

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12 pages, 914 KiB

Open AccessArticle

1,1-Difluoroethane Detection Time in Blood after Inhalation Abuse Estimated by Monte Carlo PBPK Modeling

by Raul Huet and Gunnar Johanson

Pharmaceutics 2020, 12(10), 997; https://doi.org/10.3390/pharmaceutics12100997 - 20 Oct 2020

Cited by 7 | Viewed by 3816 | Correction

Abstract

(1) Background: Inhalant abuse and misuse are still widespread problems. 1,1-Difluoroethane abuse is reported to be potentially fatal and to cause acute and chronic adverse health effects. Lab testing for difluoroethane is seldom done, partly because the maximum detection time (MDT) is unknown. We sought to reliably estimate the MDT of difluoroethane in blood after inhalation abuse; (2) Methods: MDT were estimated for the adult male American population using a physiologically based pharmacokinetic (PBPK) model and abuse patterns detailed by two individuals. Based on sensitivity analyses, variability in huffing pattern and body mass index was introduced in the model by Monte Carlo simulation; (3) Results: With a detection limit of 0.14 mg/L, the median MDT was estimated to be 10.5 h (5th–95th percentile 7.8–12.8 h) after the 2-h abuse scenario and 9.5 h (6.5–11.8 h) after the 6-h scenario. The ranges reflect variability in body mass index (and, hence, amount of body fat) and, more so, variable inhalation patterns; (4) Conclusions: Our simulations suggest that the MDT of difluoroethane in blood after abuse ranges from 6.5 to 12.8 h. Although shorter compared to many other drugs, these MDT are sufficient to allow for testing several hours after suspected intoxication in a patient. Full article

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

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20 pages, 824 KiB

Open AccessArticle

Physiologically-Based Pharmacokinetic (PBPK) Modeling Providing Insights into Fentanyl Pharmacokinetics in Adults and Pediatric Patients

by Lukas Kovar, Andreas Weber, Michael Zemlin, Yvonne Kohl, Robert Bals, Bernd Meibohm, Dominik Selzer and Thorsten Lehr

Pharmaceutics 2020, 12(10), 908; https://doi.org/10.3390/pharmaceutics12100908 - 23 Sep 2020

Cited by 12 | Viewed by 5695

Abstract

Fentanyl is widely used for analgesia, sedation, and anesthesia both in adult and pediatric populations. Yet, only few pharmacokinetic studies of fentanyl in pediatrics exist as conducting clinical trials in this population is especially challenging. Physiologically-based pharmacokinetic (PBPK) modeling is a mechanistic approach to explore drug pharmacokinetics and allows extrapolation from adult to pediatric populations based on age-related physiological differences. The aim of this study was to develop a PBPK model of fentanyl and norfentanyl for both adult and pediatric populations. The adult PBPK model was established in PK-Sim^® using data from 16 clinical studies and was scaled to several pediatric subpopulations. ~93% of the predicted AUC_last values in adults and ~88% in pediatrics were within 2-fold of the corresponding value observed. The adult PBPK model predicted a fraction of fentanyl dose metabolized to norfentanyl of ~33% and a fraction excreted in urine of ~7%. In addition, the pediatric PBPK model was used to simulate differences in peak plasma concentrations after bolus injections and short infusions. The novel PBPK models could be helpful to further investigate fentanyl pharmacokinetics in both adult and pediatric populations. Full article

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

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22 pages, 1677 KiB

Open AccessArticle

Physiologically-Based Pharmacokinetic (PBPK) Modeling of Buprenorphine in Adults, Children and Preterm Neonates

by Lukas Kovar, Christina Schräpel, Dominik Selzer, Yvonne Kohl, Robert Bals, Matthias Schwab and Thorsten Lehr

Pharmaceutics 2020, 12(6), 578; https://doi.org/10.3390/pharmaceutics12060578 - 23 Jun 2020

Cited by 30 | Viewed by 8027

Abstract

Buprenorphine plays a crucial role in the therapeutic management of pain in adults, adolescents and pediatric subpopulations. However, only few pharmacokinetic studies of buprenorphine in children, particularly neonates, are available as conducting clinical trials in this population is especially challenging. Physiologically-based pharmacokinetic (PBPK) modeling allows the prediction of drug exposure in pediatrics based on age-related physiological differences. The aim of this study was to predict the pharmacokinetics of buprenorphine in pediatrics with PBPK modeling. Moreover, the drug-drug interaction (DDI) potential of buprenorphine with CYP3A4 and P-glycoprotein perpetrator drugs should be elucidated. A PBPK model of buprenorphine and norbuprenorphine in adults has been developed and scaled to children and preterm neonates, accounting for age-related changes. One-hundred-percent of the predicted AUC_last values in adults (geometric mean fold error (GMFE): 1.22), 90% of individual AUC_last predictions in children (GMFE: 1.54) and 75% in preterm neonates (GMFE: 1.57) met the 2-fold acceptance criterion. Moreover, the adult model was used to simulate DDI scenarios with clarithromycin, itraconazole and rifampicin. We demonstrate the applicability of scaling adult PBPK models to pediatrics for the prediction of individual plasma profiles. The novel PBPK models could be helpful to further investigate buprenorphine pharmacokinetics in various populations, particularly pediatric subgroups. Full article

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

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19 pages, 1433 KiB

Open AccessArticle

A Mechanistic, Enantioselective, Physiologically Based Pharmacokinetic Model of Verapamil and Norverapamil, Built and Evaluated for Drug–Drug Interaction Studies

by Nina Hanke, Denise Türk, Dominik Selzer, Sabrina Wiebe, Éric Fernandez, Peter Stopfer, Valerie Nock and Thorsten Lehr

Pharmaceutics 2020, 12(6), 556; https://doi.org/10.3390/pharmaceutics12060556 - 16 Jun 2020

Cited by 12 | Viewed by 5598

Abstract

The calcium channel blocker and antiarrhythmic agent verapamil is recommended by the FDA for drug–drug interaction (DDI) studies as a moderate clinical CYP3A4 index inhibitor and as a clinical Pgp inhibitor. The purpose of the presented work was to develop a mechanistic whole-body physiologically based pharmacokinetic (PBPK) model to investigate and predict DDIs with verapamil. The model was established in PK-Sim^®, using 45 clinical studies (dosing range 0.1–250 mg), including literature as well as unpublished Boehringer Ingelheim data. The verapamil R- and S-enantiomers and their main metabolites R- and S-norverapamil are represented in the model. The processes implemented to describe the pharmacokinetics of verapamil and norverapamil include enantioselective plasma protein binding, enantioselective metabolism by CYP3A4, non-stereospecific Pgp transport, and passive glomerular filtration. To describe the auto-inhibitory and DDI potential, mechanism-based inactivation of CYP3A4 and non-competitive inhibition of Pgp by the verapamil and norverapamil enantiomers were incorporated based on in vitro literature. The resulting DDI performance was demonstrated by prediction of DDIs with midazolam, digoxin, rifampicin, and cimetidine, with 21/22 predicted DDI AUC ratios or C_trough ratios within 1.5-fold of the observed values. The thoroughly built and qualified model will be freely available in the Open Systems Pharmacology model repository to support model-informed drug discovery and development. Full article

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

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2 pages, 159 KiB

Open AccessCorrection

Correction: Huet, R.; Johanson, G. 1,1-Difluoroethane Detection Time in Blood after Inhalation Abuse Estimated by Monte Carlo PBPK Modeling. Pharmaceutics 2020, 12, 997

by Raul Huet and Gunnar Johanson

Pharmaceutics 2021, 13(7), 1037; https://doi.org/10.3390/pharmaceutics13071037 - 7 Jul 2021

Viewed by 1386

Abstract

The authors wish to make the following corrections to this paper [...] Full article

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

1 pages, 145 KiB

Open AccessErratum

Erratum: Huet, R.; et al. 1,1-Difluoroethane Detection Time in Blood after Inhalation Abuse Estimated by Monte Carlo PBPK Modeling. Pharmaceutics, 2020, 12, 997

by Raul Huet and Gunnar Johanson

Pharmaceutics 2021, 13(1), 50; https://doi.org/10.3390/pharmaceutics13010050 - 31 Dec 2020

Viewed by 1109

Abstract

The authors wish to make the following corrections to this paper [...] Full article

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

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