RESUMO
Quantitative assessment of drug-drug interactions (DDIs) via organic anion transporting polypeptide (OATP) 1B1 is one of the key issues in drug development. Although OATP1B1 inhibition exhibits unique characteristics, including preincubation dependence for some inhibitors, a limited approach has been attempted based on the static model that considers such preincubation dependence in the prediction of DDIs via OATP1B1. The present study aimed to establish the prediction of DDIs via OATP1B1 using preincubation-dependent inhibitors based on the static model and incorporating both inactivation and recovery of OATP1B1 activity. Cyclosporine A was selected as a preincubation-dependent inhibitor, as well as five substrates that include probes and pharmaceuticals. The inhibition ratio (R value) calculated on the basis of a conventional static model, considering inhibition of OATP1B1 and contribution ratio of OATP1B1 to the overall hepatic uptake, was much lower than the reported AUC ratio, even when IC50 values were estimated after preincubation conditions. Conversely, the R value that was estimated by considering inactivation and recovery parameters was closer to the AUC ratio. The R value that was calculated assuming the complete contribution of OATP1B1 was much higher than the AUC ratio, avoiding false-negative prediction. The R value estimated by considering inactivation and recovery for another combination of a preincubation-dependent inhibitor, asunaprevir, and substrate drug, rosuvastatin, was also closer to the AUC ratio. Thus, R values calculated based on such OATP1B1 kinetics would be potential alternative indexes for the quantitative prediction of OATP1B1-mediated DDIs using preincubation-dependent inhibitors, although this prediction is affected by estimation of the contribution ratio of substrates. SIGNIFICANCE STATEMENT: Static model-based quantitative prediction of organic anion transporting polypeptide 1B1-mediated drug-drug interactions induced by preincubation-dependent inhibitors was newly proposed to avoid false-negative prediction.