TY - JOUR T1 - Quantitative Prediction of Mechanism-Based Inhibition Caused by Mibefradil in Rats JF - Drug Metabolism and Disposition JO - Drug Metab Dispos DO - 10.1124/dmd.110.037903 SP - dmd.110.037903 AU - Nobuo Sekiguchi AU - Motohiro Kato AU - Maiko Takada AU - Hiroo Watanabe AU - Shotaro Takata AU - Tetsuya Mitsui AU - Yoshinori Aso AU - Masaki Ishigai Y1 - 2011/01/01 UR - http://dmd.aspetjournals.org/content/early/2011/04/07/dmd.110.037903.abstract N2 - It was previously demonstrated that mibefradil, which shows mechanism-based inhibition (MBI) in humans, also caused drug-drug interaction (DDI) with midazolam (MDZ) in rats. In this study, we aimed to quantitatively predict the DDI observed in rats using a physiologically-based pharmacokinetic (PBPK) model from in vitro inactivation parameters. For more precise predictions, contribution ratios of cytochrome P450 (CYP) isozymes involved in MDZ metabolism and inactivation parameters of mibefradil against each isozyme were incorporated in the predictive model. The evaluation of metabolic rate using recombinant CYPs suggested that CYP3A2 and CYP2C11 contributed to 89% and 11% of MDZ metabolism, respectively. Inactivation studies of mibefradil against the two isozymes showed that the maximum inactivation rate constants (kinact) were considerable in both isozymes (0.231-0.565 min-1), whereas the inhibitor concentration producing half the kinact (KI,app) of CYP3A2 (0.263-0.410 μM) was a good deal lower than CYP2C11 (6.82-11.4 μM). As a result of predicting the DDI using the PBPK model, predicted increases in area under the concentration-time curves (AUCs) of MDZ with co-administration of mibefradil (284% and 510% at 6 mg/kg and 12 mg/kg of mibefradil, respectively) closely corresponded to the observed values (226% and 545%, respectively). From those results, it was thought that the construction of a predictive model for DDI using the PBPK model in detail would enable us to quantitatively predict in vivo DDI from in vitro data. This approach to predict DDI based on the contributing isozymes would be important for predicting clinical DDIs of drugs metabolized by multiple enzymes. ER -