Physiologically-Based Pharmacokinetic Models for Evaluating Membrane Transporter Mediated Drug-Drug Interactions: Current Capabilities, Case Studies, Future Opportunities, and Recommendations

Abstract

Physiologically-based pharmacokinetic (PBPK) modeling has been extensively used to quantitatively translate in vitro data and evaluate temporal effects from drug-drug interactions (DDIs), arising due to reversible enzyme and transporter inhibition, irreversible time-dependent inhibition, enzyme induction, and/or suppression. PBPK modeling has now gained reasonable acceptance with the regulatory authorities for the cytochrome-P450-mediated DDIs and is routinely used. However, the application of PBPK for transporter-mediated DDIs (tDDI) in drug development is relatively uncommon. Because the predictive performance of PBPK models for tDDI is not well established, here, we represent and discuss examples of PBPK analyses included in regulatory submission (the US Food and Drug Administration (FDA), the European Medicines Agency (EMA), and the Pharmaceuticals and Medical Devices Agency (PMDA)) across various tDDIs. The goal of this collaborative effort (involving scientists representing 17 pharmaceutical companies in the Consortium and from academia) is to reflect on the use of current databases and models to address tDDIs. This challenges the common perceptions on applications of PBPK for tDDIs and further delves into the requirements to improve such PBPK predictions. This review provides a reflection on the current trends in PBPK modeling for tDDIs and provides a framework to promote continuous use, verification, and improvement in industrialization of the transporter PBPK modeling.

Figure 1

Summary of examples of transporter‐mediated drug–drug interaction (DDI) physiologically‐based pharmacokinetic analyses and their impact on drug development stages including regulatory decision outcomes. BCRP, breast cancer resistance protein; EMA, European Medicines Agency; FDA, US Food and Drug Administration; MATE, multidrug and toxin extrusion; OAT, organic anion transporter; OATP, organic anion‐transporting polypeptide; P‐gp, P‐glycoprotein; PMDA, Pharmaceuticals and Medical Devices Agency.

Figure 2

Schematics (workflow diagram) of strategy recommendation for using a physiologically‐based pharmacokinetic (PBPK) model (general) to address transporter‐mediated drug–drug interactions (DDIs). (a) Recommendations to build and verify a victim drug PBPK model. (b) Recommendations to build and verify a perpetrator drug PBPK model. AUC, area under the curve; CL, clearance; C_max, peak plasma concentration; FIH, first‐in‐human; MAD, multiple‐ascending dose; PK, pharmacokinetic; SAD, single‐ascending dose.

Figure 3

(a) Matrix approach for hepatic organic anion‐transporting polypeptides (OATPs). Three key OATPs, OATP1B1, OATP1B3, and OATP2B1, are expressed within the healthy liver of adults. Because there is a significant overlap in probe selectivity for OATPs and highly potent and selective probes are currently unknown for any specific OATP, a matrix approach is proposed. For the compounds in the box to the left, physiologically‐based pharmacokinetic (PBPK) models have been published for these substrates of OATPs, but all models are fit‐for‐purpose models using a lumped clearance for OATPs. For more mechanistic understanding of hepatic OATPs, kinetics for the transporters, and it should be included into the model as done for the compounds listed as “substrates.” The dotted line indicates that the clinical drug‐drug interaction (DDI) is not available for verification. (b) Matrix approach for renal organic anion transporters (OATs). Three OATs, OAT1, OAT3, and OAT4, are expressed within the healthy kidney of adults. Because there is a significant overlap in probe selectivity for OATs and highly potent and selective probes are currently unknown for any specific OAT, a matrix approach is proposed. For the compounds in the box to the left, PBPK models have been published for these substrates of OATs, but all models are fit‐for‐purpose models using a lumped clearance for OATs. For more mechanistic understanding of renal transporter‐mediated DDIs, kinetics for the transporters, and it should be included into the model as done for the compounds listed as “substrates." The dotted line indicates that the clinical DDI is not available for verification. BCRP, breast cancer resistance protein; MATE, multidrug and toxin extrusion; P‐gp, P‐glycoprotein. [Colour figure can be viewed at http://www.wileyonlinelibrary.com]