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. ) † Venkatesh Pilla Reddy and Kunal S. Taskar equally contributed to this article and are joint first authors. REVIEW(1)) CL H,int = (PS inf ,act + PS inf,pas ) * (CL int,met + CL int,sec ) PS ef f,act + PS ef f,pas + CL int,met + CL int,sec (3) CL H,int = PS inf * REVIEW

Physiologically‐Based Pharmacokinetic Modeling for the Prediction of a Drug–Drug Interaction of Combined Effects on P‐glycoprotein and Cytochrome P450 3A

“…In any PBPK analysis, qualification of the PBPK platform with its intended use is important for assessing the accuracy of the modeling and simulation results 29 . The qualification of transporter‐mediated DDI (tDDI) predictions is one of the more challenging aspects because of the difficulties in in vitro to in vivo extrapolation; lack of specific substrates, inhibitors, and inducers; and lack of transporter‐specific clinical DDI studies 30 . To estimate the net effect of inhibitors/inducers that have combined effects on enzymes and transporters, a stepwise approach that separately validates the interaction on enzymes and transporters is useful, although it is possible only when enough clinical DDI results are available.…”

A Study of the Effect of Cyclosporine on Fevipiprant Pharmacokinetics and its Absolute Bioavailability Using an Intravenous Microdose Approach

“…The disposition of transporter substrates such as fevipiprant is complex, making prospective predictions of exposure changes with inhibitors of transporter activity challenging (Poirier et al, 2009b;Poirier et al, 2009a;Jamei et al, 2014;Taskar et al, 2020). Part of the challenge is that active transport processes influence absorption, clearance and tissue distribution, making it difficult to derive clean PK input parameters for modeling from oral data only.…”

Pharmacokinetics under the COVID-19 storm!

“…Great advances have been made in the use of modeling tools to predict and optimize doses and dosing schedules for clinical trial optimization and to inform drug labels [13,[29][30][31]. The current situation surrounding COVID-19 requires fast decision in clinical trial design with limited information, consequently, resulting in a potentially higher risk or lower benefit.…”