Sun Yongkai, Chothe Paresh P, Sager Jennifer E, Tsao Hong, Moore Amanda, Laitinen Leena, Hariparsad Niresh
Drug Metabolism and Pharmacokinetics, Vertex Pharmaceuticals Inc., Boston, Massachusetts.
Drug Metabolism and Pharmacokinetics, Vertex Pharmaceuticals Inc., Boston, Massachusetts
Drug Metab Dispos. 2017 Jun;45(6):692-705. doi: 10.1124/dmd.117.075481. Epub 2017 Mar 23.
Typically, concentration-response curves are based upon nominal inducer concentrations for in-vitro-to-in-vivo extrapolation of CYP3A4 induction. The limitation of this practice is that it assumes the hepatocyte culture model is a static system. We assessed whether correcting for: 1) changes in perpetrator concentration in the induction medium during the incubation period, 2) perpetrator binding to proteins in the induction medium, and 3) nonspecific binding of perpetrator can improve the accuracy of CYP3A4 induction predictions. Of the seven compounds used in this evaluation, significant parent loss and nonspecific binding were observed for rifampicin (29.3-38.3%), pioglitazone (64.3-78.6%), and rosiglitazone (57.1-75.5%). As a result, the free measured EC values (EC) of pioglitazone, rosiglitazone, and rifampicin were significantly lower than the nominal EC values. In general, the accuracy of the induction predictions, using multiple static models, improved when corrections were made for measured medium concentrations, medium protein binding, and nonspecific binding of the perpetrator, as evidenced by 18-29% reductions in the root mean square error. The relative induction score model performed better than the basic static and mechanistic static models, resulting in lower prediction error and no false-positive or false-negative predictions. However, even when the EC value was used, the induction prediction for bosentan, which is a substrate of organic anion transporter proteins, was overpredicted by approximately 2-fold. Accounting for the ratio of unbound intracellular concentrations to unbound medium concentrations (K) (0.5-7.5) and the predicted multiple-dose K (0.6) for bosentan resulted in induction predictions within 35% of the observed interaction.
通常,浓度-反应曲线基于体外到体内CYP3A4诱导外推的名义诱导剂浓度。这种做法的局限性在于它假设肝细胞培养模型是一个静态系统。我们评估了对以下因素进行校正是否能提高CYP3A4诱导预测的准确性:1)孵育期间诱导培养基中肇事者浓度的变化;2)肇事者与诱导培养基中蛋白质的结合;3)肇事者的非特异性结合。在本次评估中使用的七种化合物中,观察到利福平(29.3 - 38.3%)、吡格列酮(64.3 - 78.6%)和罗格列酮(57.1 - 75.5%)存在显著的母体损失和非特异性结合。因此,吡格列酮、罗格列酮和利福平的游离实测EC值(EC)显著低于名义EC值。一般来说,当对实测培养基浓度、培养基蛋白质结合和肇事者的非特异性结合进行校正时,使用多个静态模型的诱导预测准确性得到提高,均方根误差降低了18 - 29%证明了这一点。相对诱导评分模型的表现优于基本静态模型和机制静态模型,预测误差更低,且没有假阳性或假阴性预测。然而,即使使用EC值,对于有机阴离子转运蛋白底物波生坦的诱导预测也被高估了约2倍。考虑到未结合细胞内浓度与未结合培养基浓度的比值(K)(0.5 - 7.5)以及波生坦预测的多剂量K(0.6),诱导预测结果在观察到的相互作用的35%以内。