Drug Metabolism and Pharmacokinetics Research Laboratories, Daiichi Sankyo Co., Ltd., Tokyo, Japan (M.Y., S.I., D.S., Y.N., T.I., A.W., K.W., N.W.) and Faculty of Pharmaceutical Sciences, Setsunan University, Osaka, Japan (S.Y.)
Drug Metabolism and Pharmacokinetics Research Laboratories, Daiichi Sankyo Co., Ltd., Tokyo, Japan (M.Y., S.I., D.S., Y.N., T.I., A.W., K.W., N.W.) and Faculty of Pharmaceutical Sciences, Setsunan University, Osaka, Japan (S.Y.).
Drug Metab Dispos. 2020 Apr;48(4):288-296. doi: 10.1124/dmd.119.089599. Epub 2020 Jan 29.
A great deal of effort has been being made to improve the accuracy of the prediction of drug-drug interactions (DDIs). In this study, we addressed CYP3A-mediated weak DDIs, in which a relatively high false prediction rate was pointed out. We selected 17 orally administered drugs that have been reported to alter area under the curve (AUC) of midazolam, a typical CYP3A substrate, 0.84-1.47 times. For weak CYP3A perpetrators, the predicted AUC ratio mainly depends on intestinal DDIs rather than hepatic DDIs because the drug concentration in the enterocytes is higher. Thus, DDI prediction using simulated concentration-time profiles in each segment of the digestive tract was made by physiologically based pharmacokinetic (PBPK) modeling software GastroPlus. Although mechanistic static models tend to overestimate the risk to ensure the safety of patients, some underestimation is reported about PBPK modeling. Our in vitro studies revealed that 16 out of 17 tested drugs exhibited time-dependent inhibition (TDI) of CYP3A, and the subsequent DDI simulation that ignored these TDIs provided false-negative results. This is considered to be the cause of past underestimation. Inclusion of the DDI parameters of all the known DDI mechanisms, reversible inhibition, TDI, and induction, which have opposite effects on midazolam AUC, to PBPK model was successful in improving predictability of the DDI without increasing false-negative prediction as trade-off. This comprehensive model-based analysis suggests the importance of the intestine in assessing weak DDIs via CYP3A and the usefulness of PBPK in predicting intestinal DDIs. SIGNIFICANCE STATEMENT: Although drug-drug interaction (DDI) prediction has been extensively performed previously, the accuracy of prediction for weak interactions via CYP3A has not been thoroughly investigated. In this study, we simulate DDIs considering drug concentration-time profile in the enterocytes and discuss the importance and the predictability of intestinal DDIs about weak CYP3A perpetrators.
人们付出了大量努力来提高药物相互作用（DDI）预测的准确性。在这项研究中，我们解决了 CYP3A 介导的弱 DDI 问题，其中指出了相对较高的错误预测率。我们选择了 17 种已报道的口服药物，这些药物将咪达唑仑的 AUC（曲线下面积）改变 0.84-1.47 倍，咪达唑仑是一种典型的 CYP3A 底物。对于弱 CYP3A 实施者，预测的 AUC 比主要取决于肠内 DDI，而不是肝内 DDI，因为肠细胞中的药物浓度更高。因此，使用生理基于药代动力学（PBPK）建模软件 GastroPlus 对每个消化道段的模拟浓度-时间曲线进行 DDI 预测。尽管机制静态模型往往会高估风险以确保患者的安全，但据报道，PBPK 建模存在一些低估。我们的体外研究表明，17 种测试药物中有 16 种表现出 CYP3A 的时间依赖性抑制（TDI），而忽略这些 TDI 的后续 DDI 模拟会提供假阴性结果。这被认为是过去低估的原因。将所有已知 DDI 机制（可逆抑制、TDI 和诱导）的 DDI 参数包括在内，这些机制对咪达唑仑 AUC 具有相反的影响，成功地改善了 DDI 的预测能力，而不会增加作为权衡的假阴性预测。这种综合的基于模型的分析表明，在通过 CYP3A 评估弱 DDI 时，肠道的重要性以及 PBPK 在预测肠道 DDI 方面的有用性。意义陈述：尽管以前已经广泛进行了药物相互作用（DDI）预测，但通过 CYP3A 进行弱相互作用的预测准确性尚未得到彻底研究。在这项研究中，我们模拟了考虑肠细胞中药物浓度-时间曲线的 DDI，并讨论了弱 CYP3A 实施者的肠道 DDI 的重要性和可预测性。
