Departments of Clinical Pharmacology (R.S., K.W.K.C., T.F., L.M.) and Drug Metabolism and Pharmacokinetics (T.F., R.L., E.P.), Genentech, Inc., South San Francisco, California; and SOLVO Biotechnology, Budapest, Hungary (P.K., A.B., E.K., Z.G.)
Departments of Clinical Pharmacology (R.S., K.W.K.C., T.F., L.M.) and Drug Metabolism and Pharmacokinetics (T.F., R.L., E.P.), Genentech, Inc., South San Francisco, California; and SOLVO Biotechnology, Budapest, Hungary (P.K., A.B., E.K., Z.G.).
Drug Metab Dispos. 2020 Dec;48(12):1264-1270. doi: 10.1124/dmd.120.000149. Epub 2020 Oct 9.
Organic anion-transporting polypeptide (OATP) 1B1/3-mediated drug-drug interaction (DDI) potential is evaluated in vivo with rosuvastatin (RST) as a probe substrate in clinical studies. We calibrated our assay with RST and estradiol 17--D-glucuronide (E17G)/cholecystokinin-8 (CCK8) as in vitro probes for qualitative and quantitative prediction of OATP1B-mediated DDI potential for RST. In vitro OATP1B1/1B3 inhibition using E17G and CCK8 yielded higher area under the curve (AUC) ratio (AUCR) values numerically with the static model, but all probes performed similarly from a qualitative cutoff-based prediction, as described in regulatory guidances. However, the magnitudes of DDI were not captured satisfactorily. Considering that clearance of RST is also mediated by gut breast cancer resistance protein (BCRP), inhibition of BCRP was also incorporated in the DDI prediction if the gut inhibitor concentrations were 10 × IC for BCRP inhibition. This combined static model closely predicted the magnitude of RST DDI with root-mean-square error values of 0.767-0.812 and 1.24-1.31 with and without BCRP inhibition, respectively, for in vitro-in vivo correlation of DDI. Physiologically based pharmacokinetic (PBPK) modeling was also used to simulate DDI between RST and rifampicin, asunaprevir, and velpatasvir. Predicted AUCR for rifampicin and asunaprevir was within 1.5-fold of that observed, whereas that for velpatasvir showed a 2-fold underprediction. Overall, the combined static model incorporating both OATP1B and BCRP inhibition provides a quick and simple mathematical approach to quantitatively predict the magnitude of transporter-mediated DDI for RST for routine application. PBPK complements the static model and provides a framework for studying molecules when a dynamic model is needed. SIGNIFICANCE STATEMENT: Using 22 drugs, we show that a static model for organic anion-transporting polypeptide (OATP) 1B1/1B3 inhibition can qualitatively predict potential for drug-drug interaction (DDI) using a cutoff-based approach, as in regulatory guidances. However, consideration of both OATP1B1/3 and gut breast cancer resistance protein inhibition provided a better prediction of the magnitude of the transporter-mediated DDI of these inhibitors with rosuvastatin. Based on these results, we have proposed an empirical mechanistic-static approach for a more reliable prediction of transporter-mediated DDI liability with rosuvastatin that drug development teams can leverage.
有机阴离子转运多肽 (OATP) 1B1/3 介导的药物相互作用 (DDI) 潜力在临床研究中使用瑞舒伐他汀 (RST) 作为探针底物进行体内评估。我们使用 RST 和雌二醇 17-β-D-葡萄糖醛酸 (E17G)/胆囊收缩素-8 (CCK8) 对我们的测定进行校准，作为定性和定量预测 RST 对 OATP1B 介导的 DDI 潜力的体外探针。使用 E17G 和 CCK8 对 OATP1B1/1B3 进行体外抑制，从数值上得到更高的曲线下面积 (AUC) 比值 (AUCR) 值，但所有探针在基于监管指南的定性截止值预测方面表现相似。然而，DDI 的幅度没有得到令人满意的捕捉。考虑到 RST 的清除也受肠道乳腺癌耐药蛋白 (BCRP) 介导，因此，如果肠道抑制剂浓度为 BCRP 抑制的 10×IC，则也将 BCRP 抑制纳入 DDI 预测中。该组合静态模型通过根均方误差值为 0.767-0.812 和 1.24-1.31，分别在有和没有 BCRP 抑制的情况下，紧密预测了 RST DDI 的幅度，用于 DDI 的体内-体外相关性。基于生理的药代动力学 (PBPK) 建模也用于模拟 RST 与利福平、asunaprevir 和 velpatasvir 之间的 DDI。预测的利福平和 asunaprevir 的 AUCR 值在观察值的 1.5 倍以内，而 velpatasvir 的预测值则低了 2 倍。总体而言，纳入 OATP1B 和 BCRP 抑制的组合静态模型为定量预测瑞舒伐他汀转运体介导的 DDI 提供了一种快速简便的数学方法，可用于常规应用。PBPK 补充了静态模型，并为研究需要动态模型的分子提供了一个框架。意义声明：使用 22 种药物，我们表明，基于截止值的方法可以定性预测药物相互作用 (DDI) 的潜力，这是监管指南中的一种方法，用于有机阴离子转运多肽 (OATP) 1B1/1B3 抑制的静态模型。然而，考虑到 OATP1B1/3 和肠道乳腺癌耐药蛋白的抑制作用，可以更好地预测这些抑制剂与瑞舒伐他汀的转运体介导的 DDI 幅度。基于这些结果，我们提出了一种经验性的机制-静态方法，以更可靠地预测瑞舒伐他汀的转运体介导的 DDI 倾向，药物开发团队可以利用该方法。
