Drug Interaction PBPK Modeling: Review of the Literature Exposes the Need for Increased Verification of Model Inputs and Outputs as Part of Credibility Assessment

Abstract

In vitro data are routinely used to support both static and physiologically based pharmacokinetic (PBPK) model-based drug–drug interaction (DDI) predictions. Such efforts are possible after years of rapid progress, enabled by the greater availability of in vitro reagents, kinetic models, and ready access to PBPK software packages with increased computing power supported by drug-specific compound files. While acknowledging the progress, however, various investigators have documented the challenges and pitfalls associated with PBPK modeling and have called for improved model verification, credibility assessment, and greater confidence building. As summarized in the current narrative, a review of the DDI literature does expose the need for PBPK model parameter (input and output) verification. Representative examples of PBPK-based modeling involving induction (cytochrome P450 (CYP) 3A4 and organic anion transporting polypeptide 1B1 and 1B3), pregnancy-associated upregulation (CYP2D6), and inhibition (CYP1A2-mediated metabolism and creatinine renal clearance) are described. The narrative also includes the clinical application of biomarkers (e.g., CYP3A4 and CYP2D6) and tissue biopsy expression profiling as a means of providing additional mechanistic information and DDI data that are independent and complementary to PBPK models. With the advent of in vitro microphysiological systems, biomarkers, burgeoning plasma-based (liquid) biopsy protocols, and the possibility of machine learning-enabled literature searches integrated with modeling software, it is envisioned that such tools could be used jointly to further enhance PBPK model verification efforts within a predefined credibility assessment framework. Ultimately, the goal is to deploy PBPK modeling with greater confidence in lieu of time and resource-intensive clinical DDI studies.