Physiologically‐based pharmacokinetic modeling to evaluate in vitro‐to‐in vivo extrapolation for intestinal P‐glycoprotein inhibition

As one of the key components in model‐informed drug discovery and development, physiologically‐based pharmacokinetic (PBPK) modeling linked with in vitro‐to‐in vivo extrapolation (IVIVE) is widely applied to quantitatively predict drug–drug interactions (DDIs) on drug‐metabolizing enzymes and transporters. This study aimed to investigate an IVIVE for intestinal P‐glycoprotein (Pgp, ABCB1)‐mediated DDIs among three Pgp substrates, digoxin, dabigatran etexilate, and quinidine, and two Pgp inhibitors, itraconazole and verapamil, via PBPK modeling. For Pgp substrates, assuming unbound Michaelis‐Menten constant (K (m)) to be intrinsic, in vitro‐to‐in vivo scaling factors for maximal Pgp‐mediated efflux rate (J (max)) were optimized based on the clinically observed results without co‐administration of Pgp inhibitors. For Pgp inhibitors, PBPK models utilized the reported in vitro values of Pgp inhibition constants (K (i)), 1.0 μM for itraconazole and 2.0 μM for verapamil. Overall, the PBPK modeling sufficiently described Pgp‐mediated DDIs between these substrates and inhibitors with the prediction errors of less than or equal to ±25% in most cases, suggesting a reasonable IVIVE for Pgp kinetics in the clinical DDI results. The modeling results also suggest that Pgp kinetic parameters of both the substrates (K (m) and J (max)) and the inhibitors (K (i)) are sensitive to Pgp‐mediated DDIs, thus being key for successful DDI prediction. It would also be critical to incorporate appropriate unbound inhibitor concentrations at the site of action into PBPK models. The present results support a quantitative prediction of Pgp‐mediated DDIs using in vitro parameters, which will significantly increase the value of in vitro studies to design and run clinical DDI studies safely and effectively.