2023 Volume 143 Issue 1 Pages 11-19
Quantitative prediction of the potential for drug–drug interaction (DDI) is essential to guarantee the safety and efficacy of drugs. DDI screening, modeling, and prediction is standard practice in the pharmaceutical industry. This review describes our work on (1) the establishment of a standard framework for determining physiologically based pharmacokinetic (PBPK) model structures and parameters useful for quantitatively analyzing DDIs via hepatic organic anion transporting polypeptides (OATPs). By analyzing clinically observed DDIs involving several statins as substrates, and cyclosporin A and rifampicin as inhibitors, similar in vivo inhibition constants for OATPs by each inhibitor were obtained, regardless of the substrate. (2) We took a PBPK modeling-based approach to define rate-determining processes in hepatic elimination of several OATPs and CYP3A dual substrates using our clinical DDI data with specific inhibitors for OATPs and CYP3A. Essential in vivo parameters (the passive diffusion/active transport ratio in the uptake, and the fraction of intrinsic clearance in the total drug elimination from the hepatocytes) dominating the rate-determining process in hepatic elimination were estimated quantitatively. (3) Finally, using our clinical DDI data with rifampicin, we established a PBPK model for coproporphyrin I (CP-I), which is expected to act as an endogenous substrate (biomarker) supporting the prediction of DDI involving hepatic OATPs. Our PBPK modeling-based approach with several in vitro experiments using CP-I and OATP probe substrates (statins) demonstrated the usefulness of the translation of the effect of an OATP inhibitor on CP-I pharmacokinetics into that on OATP probe substrates in drug discovery and development.
