Physiologically-based pharmacokinetic model-based translation of OATP1B-mediated drug-drug interactions from coproporphyrin I to probe drugs.
Physiologically-based pharmacokinetic model-based translation of OATP1B-mediated drug-drug interactions from coproporphyrin I to probe drugs.
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DOI：
10.1111/cts.13272
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发表时间：
2022-06
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准确预测OATP 1B介导的药物相互作用（DDI）对药物开发具有挑战性。在本文中，我们报告了一种基于生理学的药代动力学（PBPK）模型分析，用于分析在接受口服环孢素A（CysA; 20和75 mg）作为OATP 1B抑制剂和探针药物（匹伐他汀、瑞舒伐他汀和缬沙坦）的健康受试者中生成的临床DDI数据。假设CysA抑制内源性粪卟啉I（CP-I）的肝摄取，将CysA和探针化合物的PBPK模型组合。优化OATP 1B的未结合CysA体内Ki（Ki，OATP 1B）和CP-I的总体固有肝清除率（克林特，所有，单位），以解释CP-I数据（Ki，OATP 1B，0.536 ± 0.041 nM;克林特，所有，单位，41.9 ± 4.3 L/h/kg）。使用Ki，OATP 1B的DDI模拟再现了CysA（20和75 mg）和给药间隔（1和3 h）对匹伐他汀和瑞舒伐他汀血药浓度时间曲线的剂量依赖性效应，但使用体外Ki，OATP 1B的DDI模拟失败。采用聚类高斯-牛顿法，使用1000个初始参数集对CP‐I的7个药代动力学参数（β、克林特、all、FaFg、Rdif、fbile、fsyn和v syn）以及CysA的Ki、OATP 1B和Ki、MRP 2进行参数优化。基于可接受的546个参数集，克林特，all和Ki，OATP 1B的范围缩小，变异系数分别为12.4%和11.5%，表明这些参数实际上是可识别的。这些结果表明，CP-I的PBPK模型分析是预测药物开发中OATP 1B介导的DDI的一种有前景的转化方法。
The accurate prediction of OATP1B‐mediated drug–drug interactions (DDIs) is challenging for drug development. Here, we report a physiologically‐based pharmacokinetic (PBPK) model analysis for clinical DDI data generated in heathy subjects who received oral doses of cyclosporin A (CysA; 20 and 75 mg) as an OATP1B inhibitor, and the probe drugs (pitavastatin, rosuvastatin, and valsartan). PBPK models of CysA and probe compounds were combined assuming inhibition of hepatic uptake of endogenous coproporphyrin I (CP‐I) by CysA. In vivo Ki of unbound CysA for OATP1B (Ki,OATP1B), and the overall intrinsic hepatic clearance per body weight of CP‐I (CLint,all,unit) were optimized to account for the CP‐I data (Ki,OATP1B, 0.536 ± 0.041 nM; CLint,all,unit, 41.9 ± 4.3 L/h/kg). DDI simulation using Ki,OATP1B reproduced the dose‐dependent effect of CysA (20 and 75 mg) and the dosing interval (1 and 3 h) on the time profiles of blood concentrations of pitavastatin and rosuvastatin, but DDI simulation using in vitro Ki,OATP1B failed. The Cluster Gauss–Newton method was used to conduct parameter optimization using 1000 initial parameter sets for the seven pharmacokinetic parameters of CP‐I (β, CLint, all, FaFg, Rdif, fbile, fsyn, and v syn), and Ki,OATP1B and Ki,MRP2 of CysA. Based on the accepted 546 parameter sets, the range of CLint, all and Ki,OATP1B was narrowed, with coefficients of variation of 12.4% and 11.5%, respectively, indicating that these parameters were practically identifiable. These results suggest that PBPK model analysis of CP‐I is a promising translational approach to predict OATP1B‐mediated DDIs in drug development.
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