Chien Jenny Y, Lucksiri Aroonrut, Ernest Charles S, Gorski J Christopher, Wrighton Steven A, Hall Stephen D
Department of Drug Disposition, Lilly Research Laboratories, Indianapolis, IN 46285, USA.
Drug Metab Dispos. 2006 Jul;34(7):1208-19. doi: 10.1124/dmd.105.008730. Epub 2006 Apr 12.
Conventional methods to forecast CYP3A-mediated drug-drug interactions have not employed stochastic approaches that integrate pharmacokinetic (PK) variability and relevant covariates to predict inhibition in terms of probability and uncertainty. Empirical approaches to predict the extent of inhibition may not account for nonlinear or non-steady-state conditions, such as first-pass effects or accumulation of inhibitor concentration with multiple dosing. A physiologically based PK model was developed to predict the inhibition of CYP3A by ketoconazole (KTZ), using midazolam (MDZ) as the substrate. The model integrated PK models of MDZ and KTZ, in vitro inhibition kinetics of KTZ, and the variability and uncertainty associated with these parameters. This model predicted the time- and dose-dependent inhibitory effect of KTZ on MDZ oral clearance. The predictive performance of the model was validated using the results of five published KTZ-MDZ studies. The model improves the accuracy of predicting the inhibitory effect of increasing KTZ dosing on MDZ PK by incorporating a saturable KTZ efflux from the site of enzyme inhibition in the liver. The results of simulations using the model supported the KTZ dose of 400 mg once daily as the optimal regimen to achieve maximum inhibition by KTZ. Sensitivity analyses revealed that the most influential variable on the prediction of inhibition was the fractional clearance of MDZ mediated by CYP3A. The model may be used prospectively to improve the quantitative prediction of CYP3A inhibition and aid the optimization of study designs for CYP3A-mediated drug-drug interaction studies in drug development.
预测CYP3A介导的药物相互作用的传统方法未采用整合药代动力学（PK）变异性和相关协变量以根据概率和不确定性预测抑制作用的随机方法。预测抑制程度的经验方法可能无法考虑非线性或非稳态条件，如首过效应或多次给药时抑制剂浓度的累积。构建了一个基于生理学的PK模型，以咪达唑仑（MDZ）为底物预测酮康唑（KTZ）对CYP3A的抑制作用。该模型整合了MDZ和KTZ的PK模型、KTZ的体外抑制动力学以及与这些参数相关的变异性和不确定性。该模型预测了KTZ对MDZ口服清除率的时间和剂量依赖性抑制作用。使用五项已发表的KTZ-MDZ研究结果对该模型的预测性能进行了验证。该模型通过纳入肝脏中酶抑制部位的可饱和KTZ流出，提高了预测增加KTZ剂量对MDZ PK抑制作用的准确性。使用该模型的模拟结果支持每日一次400 mg的KTZ剂量是实现KTZ最大抑制作用的最佳方案。敏感性分析表明，对抑制作用预测最有影响的变量是CYP3A介导的MDZ的分数清除率。该模型可前瞻性地用于改善CYP3A抑制作用的定量预测，并有助于优化药物开发中CYP3A介导的药物相互作用研究的研究设计。
