Abstract：
To select a drug candidate for clinical development, accurately and promptly predicting human pharmacokinetic (PK) profiles, assessing drug-drug interactions (DDIs), and anticipating potential PK variations in disease populations are crucial steps in drug discovery. The complexity of predicting human PK significantly increases when hepatic transporters are involved in drug clearance (CL) and volume of distribution (Vss). A strategic framework is developed here, utilizing pitavastatin as an example. The framework includes the construction of a monkey physiologically-based PK (PBPK) model, model calibration to obtain scaling factors (SF) of in vitro-in vivo extrapolation (IVIVE) for various clearance parameters, human model development and validation, and assessment of DDIs and PK variations in disease populations. Through incorporating in vitro human parameters and calibrated SFs from the monkey model of 3.45, 0.14, and 1.17 for CLint,active, CLint,passive, and CLint,bile, respectively, and together with the relative fraction transported by individual transporters obtained from in vitro studies and the optimized Ki values for OATP inhibition, the model reasonably captured observed pitavastatin PK profiles, DDIs and PK variations in human subjects carrying genetic polymorphisms, i.e., AUC within 20%. Lastly, when applying the functional reduction based on measured OATP1B biomarkers, the model adequately predicted PK changes in the hepatic impairment population. The present study presents a strategic framework for early-stage drug development, enabling the prediction of PK profiles and assessment of PK variations in scenarios like DDIs, genetic polymorphism, and hepatic impairment-related disease states, specifically focusing on OATP substrates.
摘要：
为临床开发选择候选药物，准确及时地预测人体药代动力学（PK）谱，评估药物-药物相互作用（DDI），预测疾病人群中潜在的PK变化是药物发现的关键步骤。当肝转运蛋白参与药物清除（CL）和分布体积（Vss）时，预测人类PK的复杂性显着增加。这里制定了一个战略框架，以匹伐他汀为例。该框架包括构建基于猴子生理的PK（PBPK）模型，模型校准，以获得各种清除参数的体外-体内外推（IVIVIVE）的比例因子（SF），人体模型的开发和验证，以及疾病人群中DDI和PK变化的评估。通过整合体外人体参数和来自Clint的3.45、0.14和1.17的猴子模型的校准SF，活跃,CLINT,被动,和CLINT,胆汁,分别,以及从体外研究中获得的单个转运蛋白转运的相对分数和OATP抑制的优化Ki值，该模型合理地捕获了观察到的匹伐他汀PK曲线，携带遗传多态性的人类受试者的DDI和PK变异，即，AUC在20%以内。最后,当应用基于测量的OATP1B生物标志物的功能减少时，该模型可以充分预测肝功能损害人群的PK变化。本研究提出了早期药物开发的战略框架，能够预测PK概况和评估DDI等场景中的PK变化，遗传多态性,和肝功能损害相关的疾病状态，特别关注OATP底物。
