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25.05.2019 | Original Research Article | Ausgabe 12/2019 Open Access

Clinical Pharmacokinetics 12/2019

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

Zeitschrift:
Clinical Pharmacokinetics > Ausgabe 12/2019
Autoren:
Denise Türk, Nina Hanke, Sarah Wolf, Sebastian Frechen, Thomas Eissing, Thomas Wendl, Matthias Schwab, Thorsten Lehr
Wichtige Hinweise

Electronic supplementary material

The online version of this article (https://​doi.​org/​10.​1007/​s40262-019-00777-x) contains supplementary material, which is available to authorized users.

Abstract

Background

Drug–drug interactions (DDIs) and drug–gene interactions (DGIs) pose a serious health risk that can be avoided by dose adaptation. These interactions are investigated in strictly controlled setups, quantifying the effect of one perpetrator drug or polymorphism at a time, but in real life patients frequently take more than two medications and are very heterogenous regarding their genetic background.

Objectives

The first objective of this study was to provide whole-body physiologically based pharmacokinetic (PBPK) models of important cytochrome P450 (CYP) 2C8 perpetrator and victim drugs, built and evaluated for DDI and DGI studies. The second objective was to apply these models to describe complex interactions with more than two interacting partners.

Methods

PBPK models of the CYP2C8 and organic-anion-transporting polypeptide (OATP) 1B1 perpetrator drug gemfibrozil (parent–metabolite model) and the CYP2C8 victim drugs repaglinide (also an OATP1B1 substrate) and pioglitazone were developed using a total of 103 clinical studies. For evaluation, these models were applied to predict 34 different DDI studies, establishing a CYP2C8 and OATP1B1 PBPK DDI modeling network.

Results

The newly developed models show a good performance, accurately describing plasma concentration–time profiles, area under the plasma concentration–time curve (AUC) and maximum plasma concentration (Cmax) values, DDI studies as well as DGI studies. All 34 of the modeled DDI AUC ratios (AUC during DDI/AUC control) and DDI Cmax ratios (Cmax during DDI/Cmax control) are within twofold of the observed values.

Conclusions

Whole-body PBPK models of gemfibrozil, repaglinide, and pioglitazone have been built and qualified for DDI and DGI prediction. PBPK modeling is applicable to investigate complex interactions between multiple drugs and genetic polymorphisms.

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Acknowledgement to Referees

Acknowledgement to Referees