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

Abstract

BackgroundDrug–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.ObjectivesThe 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.MethodsPBPK 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.ResultsThe 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.ConclusionsWhole-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.Electronic supplementary materialThe online version of this article (10.1007/s40262-019-00777-x) contains supplementary material, which is available to authorized users.

Absence of OATP1B (Organic Anion–Transporting Polypeptide) Induction by Rifampin in Cynomolgus Monkeys: Determination Using the Endogenous OATP1B Marker Coproporphyrin and Tissue Gene Expression

“…If this inductive effect of RIF is also absent in humans, it would help us gain mechanistic insight for complex DDIs involving OATP1B and enzyme. In previous reports no clear cut induction of OATP1B with respect to activity, mRNA or protein but an increase in OATP1B activity was assumed in the RIF-treated subjects (Lutz et al, 2018, Turk et al, 2019, raising the question of linkages between CYP3A, OATP1B, P-gp, BCRP, and MRP2 expression in humans and nonhuman primate species. By measuring the plasma RIF concentration, we have clearly shown that RIF stimulates its own clearance via induction of the metabolic enzyme (and transporter).…”

“…Drug interactions may reduce drug metabolizing enzyme and transporter activities through inhibition or may increase their activities through induction. Induction of enzymes and transporters can, in clinical practice, lead to reduced absorption and enhanced clearance of the drug itself and/or of a coadministered drug, thereby resulting in decreased drug exposure and loss of therapeutic efficacy (Chu et al, 2009, Turk et al, 2019. Compared to drug metabolizing JPET-AR-2020-000139 enzymes, much less is known of the induction of drug transporters.…”

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

“…Trimethoprim DD(G)I modeling was performed with three different victim drugs (metformin, repaglinide, and pioglitazone) and one perpetrator drug (rifampicin). The parameters of the previously-developed PBPK models of metformin [ 20 ], repaglinide, pioglitazone [ 21 ], and rifampicin [ 22 ] that were applied for DDI modeling are reproduced in Tables S8, S11, S14 and S17 and DDI model processes are illustrated in Figures S14, S18, S21 and S25 of the Supplementary Materials .…”

“…To predict the published trimethoprim DDGI studies with metformin ( SLC22A2 808G>T , increased metformin transport) and pioglitazone ( CYP2C8*3 , increased pioglitazone metabolism), the trimethoprim model was applied with previously-built and evaluated DGI models of metformin and pioglitazone [ 20 , 21 ]. For the competitive inhibition of the variant OCT2 or CYP2C8 isoforms by trimethoprim, the same K i values as for the wildtype transporter or enzyme were applied.…”

“…The rifampicin–trimethoprim DDI was modeled as induction of P-gp trimethoprim transport and CYP3A4 trimethoprim metabolism by rifampicin, with simultaneous competitive inhibition of P-gp and CYP3A4. The parameter values to model these interactions were taken from literature (values and references are listed in the rifampicin drug-dependent parameter Table S17 in the Supplementary Materials ) and have been evaluated in previous DDI analyses [ 21 , 22 ]. Due to the lack of in vitro information regarding the metabolism of trimethoprim, the clinical data of the rifampicin–trimethoprim DDI were included into the training dataset, and the inducible fraction of trimethoprim metabolism was attributed to metabolism by CYP3A4.…”