Physiologically-based pharmacokinetic model-based translation of OATP1B-mediated drug-drug interactions from coproporphyrin I to probe drugs

The accurate prediction of OATP1B-mediated drug-drug interactions (DDIs) is challenging for drug development. Here, we report a physiologically-based pharmacokinetic (PBPK) model analysis for clinical DDI data generated in heathy subjects who received oral doses of cyclosporin A (CysA; 20 and 75 mg) as an OATP1B inhibitor, and the probe drugs (pitavastatin, rosuvastatin, and valsartan). PBPK models of CysA and probe compounds were combined assuming inhibition of hepatic uptake of endogenous coproporphyrin I (CP-I) by CysA. In vivo K-i of unbound CysA for OATP1B (K-i,K-OATP1B), and the overall intrinsic hepatic clearance per body weight of CP-I (CLint,all,unit) were optimized to account for the CP-I data (K-i,K-OATP1B, 0.536 +/- 0.041 nM; CLint,all,unit, 41.9 +/- 4.3 L/h/kg). DDI simulation using K-i,K-OATP1B reproduced the dose-dependent effect of CysA (20 and 75 mg) and the dosing interval (1 and 3 h) on the time profiles of blood concentrations of pitavastatin and rosuvastatin, but DDI simulation using in vitro K-i,K-OATP1B failed. The Cluster Gauss-Newton method was used to conduct parameter optimization using 1000 initial parameter sets for the seven pharmacokinetic parameters of CP-I (beta, CLint, all, FaFg, R-dif, f(bile), f(syn), and v(syn)), and K-i,K-OATP1B and K-i,K-MRP2 of CysA. Based on the accepted 546 parameter sets, the range of CLint, all and K-i,K-OATP1B was narrowed, with coefficients of variation of 12.4% and 11.5%, respectively, indicating that these parameters were practically identifiable. These results suggest that PBPK model analysis of CP-I is a promising translational approach to predict OATP1B-mediated DDIs in drug development.

Automated summary

In this study, a physiologically-based pharmacokinetic (PBPK) model analysis was performed to interpret clinical drug-drug interaction (DDI) data, and the results suggest that PBPK model analysis of CP-I is a promising translational approach to predict OATP1B-mediated DDIs in drug development.