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

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 ( C max ) values, DDI studies as well as DGI studies. All 34 of the modeled DDI AUC ratios (AUC during DDI/AUC control) and DDI C max ratios ( C max during DDI/ C max 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.