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

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 AUClast (1.27) and Cmax values (1.23) over all studies. For DGI predictions, 18 out of 18 DGI AUClast ratios and 18 out of 18 DGI Cmax 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.

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

Pharmaceutics ◽

10.3390/pharmaceutics13030331 ◽

2021 ◽

Vol 13 (3) ◽

pp. 331

Author(s):

Fatima Zahra Marok ◽

Laura Maria Fuhr ◽

Nina Hanke ◽

Dominik Selzer ◽

Thorsten Lehr

Keyword(s):

Open Systems ◽

Model Performance ◽

Interaction Network ◽

Gene Interaction ◽

Whole Body ◽

Gene Interaction Network ◽

Physiologically Based Pharmacokinetic ◽

Pharmacological Targets ◽

Physiologically Based ◽

The Impact

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 AUCHBup/Bup ratios), 12/13 DDI AUCHBup/Bup ratios, and 7/7 DDGI AUCHBup/Bup ratios within 2-fold of observed values. The developed model is freely available in the Open Systems Pharmacology model repository.

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Physiologically Based Pharmacokinetic Modeling of Rosuvastatin to Predict Transporter-Mediated Drug-Drug Interactions

Pharmaceutical Research ◽

10.1007/s11095-021-03109-6 ◽

2021 ◽

Author(s):

Nina Hanke ◽

José David Gómez-Mantilla ◽

Naoki Ishiguro ◽

Peter Stopfer ◽

Valerie Nock

Keyword(s):

Drug Interactions ◽

Pbpk Model ◽

Open Systems ◽

Whole Body ◽

Good Prediction ◽

Active Efflux ◽

Physiologically Based Pharmacokinetic ◽

Positron Emission ◽

Physiologically Based

Abstract Purpose To build a physiologically based pharmacokinetic (PBPK) model of the clinical OATP1B1/OATP1B3/BCRP victim drug rosuvastatin for the investigation and prediction of its transporter-mediated drug-drug interactions (DDIs). Methods The Rosuvastatin model was developed using the open-source PBPK software PK-Sim®, following a middle-out approach. 42 clinical studies (dosing range 0.002–80.0 mg), providing rosuvastatin plasma, urine and feces data, positron emission tomography (PET) measurements of tissue concentrations and 7 different rosuvastatin DDI studies with rifampicin, gemfibrozil and probenecid as the perpetrator drugs, were included to build and qualify the model. Results The carefully developed and thoroughly evaluated model adequately describes the analyzed clinical data, including blood, liver, feces and urine measurements. The processes implemented to describe the rosuvastatin pharmacokinetics and DDIs are active uptake by OATP2B1, OATP1B1/OATP1B3 and OAT3, active efflux by BCRP and Pgp, metabolism by CYP2C9 and passive glomerular filtration. The available clinical rifampicin, gemfibrozil and probenecid DDI studies were modeled using in vitro inhibition constants without adjustments. The good prediction of DDIs was demonstrated by simulated rosuvastatin plasma profiles, DDI AUClast ratios (AUClast during DDI/AUClast without co-administration) and DDI Cmax ratios (Cmax during DDI/Cmax without co-administration), with all simulated DDI ratios within 1.6-fold of the observed values. Conclusions A whole-body PBPK model of rosuvastatin was built and qualified for the prediction of rosuvastatin pharmacokinetics and transporter-mediated DDIs. The model is freely available in the Open Systems Pharmacology model repository, to support future investigations of rosuvastatin pharmacokinetics, rosuvastatin therapy and DDI studies during model-informed drug discovery and development (MID3).

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

Pharmaceutics ◽

10.3390/pharmaceutics12060556 ◽

2020 ◽

Vol 12 (6) ◽

pp. 556 ◽

Cited By ~ 1

Author(s):

Nina Hanke ◽

Denise Türk ◽

Dominik Selzer ◽

Sabrina Wiebe ◽

Éric Fernandez ◽

...

Keyword(s):

Drug Interaction ◽

Pharmacokinetic Model ◽

Competitive Inhibition ◽

Open Systems ◽

Whole Body ◽

Physiologically Based Pharmacokinetic ◽

Physiologically Based ◽

Drug Drug Interaction ◽

Enantioselective Metabolism

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 Ctrough 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.

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A Comprehensive Whole-Body Physiologically Based Pharmacokinetic Drug–Drug–Gene Interaction Model of Metformin and Cimetidine in Healthy Adults and Renally Impaired Individuals

Clinical Pharmacokinetics ◽

10.1007/s40262-020-00896-w ◽

2020 ◽

Vol 59 (11) ◽

pp. 1419-1431 ◽

Cited By ~ 3

Author(s):

Nina Hanke ◽

Denise Türk ◽

Dominik Selzer ◽

Naoki Ishiguro ◽

Thomas Ebner ◽

...

Keyword(s):

Interaction Model ◽

Gene Interaction ◽

Healthy Adults ◽

Whole Body ◽

Physiologically Based Pharmacokinetic ◽

Physiologically Based ◽

Pharmacokinetic Drug

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

Pharmaceutics ◽

10.3390/pharmaceutics12111074 ◽

2020 ◽

Vol 12 (11) ◽

pp. 1074

Author(s):

Denise Türk ◽

Nina Hanke ◽

Thorsten Lehr

Keyword(s):

Open Systems ◽

Hepatic Clearance ◽

Gene Interaction ◽

Tubular Secretion ◽

Glycoprotein P ◽

Time Profiles ◽

Physiologically Based Pharmacokinetic ◽

P Glycoprotein ◽

Physiologically Based ◽

Intestinal Efflux

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 AUClast and Cmax 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.

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

Pharmaceutics ◽

10.3390/pharmaceutics13020204 ◽

2021 ◽

Vol 13 (2) ◽

pp. 204

Author(s):

Erik Sjögren ◽

Joel Tarning ◽

Karen I. Barnes ◽

E. Niclas Jonsson

Keyword(s):

Body Composition ◽

Drug Exposure ◽

Drug Disposition ◽

Pediatric Population ◽

Model Performance ◽

Vulnerable Population ◽

Pbpk Modeling ◽

Malnourished Children ◽

Physiologically Based Pharmacokinetic ◽

Physiologically Based

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.

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Physiologically Based Pharmacokinetic Models of Probenecid and Furosemide to Predict Transporter Mediated Drug-Drug Interactions

Pharmaceutical Research ◽

10.1007/s11095-020-02964-z ◽

2020 ◽

Vol 37 (12) ◽

Author(s):

Hannah Britz ◽

Nina Hanke ◽

Mitchell E. Taub ◽

Ting Wang ◽

Bhagwat Prasad ◽

...

Keyword(s):

Plasma Concentration ◽

Organic Anion ◽

Plasma Concentrations ◽

Whole Body ◽

Time Profiles ◽

Pbpk Models ◽

Physiologically Based Pharmacokinetic ◽

Concentration Time ◽

Anion Transporter ◽

Physiologically Based

Abstract Purpose To provide whole-body physiologically based pharmacokinetic (PBPK) models of the potent clinical organic anion transporter (OAT) inhibitor probenecid and the clinical OAT victim drug furosemide for their application in transporter-based drug-drug interaction (DDI) modeling. Methods PBPK models of probenecid and furosemide were developed in PK-Sim®. Drug-dependent parameters and plasma concentration-time profiles following intravenous and oral probenecid and furosemide administration were gathered from literature and used for model development. For model evaluation, plasma concentration-time profiles, areas under the plasma concentration–time curve (AUC) and peak plasma concentrations (Cmax) were predicted and compared to observed data. In addition, the models were applied to predict the outcome of clinical DDI studies. Results The developed models accurately describe the reported plasma concentrations of 27 clinical probenecid studies and of 42 studies using furosemide. Furthermore, application of these models to predict the probenecid-furosemide and probenecid-rifampicin DDIs demonstrates their good performance, with 6/7 of the predicted DDI AUC ratios and 4/5 of the predicted DDI Cmax ratios within 1.25-fold of the observed values, and all predicted DDI AUC and Cmax ratios within 2.0-fold. Conclusions Whole-body PBPK models of probenecid and furosemide were built and evaluated, providing useful tools to support the investigation of transporter mediated DDIs.

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