Abstract：
Extrapolating in vivo hepatic clearance from in vitro uptake data is a known challenge, especially for organic anion-transporting polypeptide transporter (OATP) substrates, and the well-stirred model (WSM) commonly yields systematic underpredictions for those anionic drugs, hypothetically due to \"albumin-mediated hepatic drug uptake\". In the present study, we demonstrate that the WSM incorporating the dynamic free fraction (f D), a measure of drug protein binding affinity, performs reasonably well in predicting hepatic clearance of OATP substrates. For a selection of anionic drugs, including atorvastatin, fluvastatin, pravastatin, rosuvastatin, pitavastatin, cerivastatin, and repaglinide, this dynamic well-stirred model (dWSM) correctly predicts hepatic plasma clearance within 2-fold error for six out of seven OATP substrates examined. The geometric mean of clearance ratios between the predicted and the observed values falls in the range of 1.21-1.38. As expected, the WSM with unbound fraction (f u) systematically underpredicts hepatic clearance with greater than 2-fold error for five out of seven drugs, and the geometric mean of clearance ratios between the predicted and the observed values is in the range of 0.20-0.29. The results suggest that, despite its simplicity, the dWSM operates well for transporter-mediated uptake clearance, and that clearance under-prediction of OATP substrates may not necessarily be associated with the chemical class of the anionic drugs, nor is it a result of albumin-mediated hepatic drug uptake as currently hypothesized. Instead, the superior prediction power of the dWSM confirms the utility of the dynamic free fraction in clearance prediction and the importance of drug plasma binding kinetics in hepatic uptake clearance. SIGNIFICANCE STATEMENT: The traditional well-stirred model (WSM) consistently underpredicts organin anion-transporting polypeptide transporter (OATP)-mediated hepatic uptake clearance, hypothetically due to the albumin-mediated hepatic drug uptake. In this manuscript, we apply the dynamic WSM to extrapolate hepatic clearance of the OATP substrates, and our results show significant improvements in clearance prediction without assuming albumin-mediated hepatic drug uptake.
摘要：
从体外摄取数据推断体内肝清除是一个已知的挑战，特别是对于OATP底物，而充分搅拌模型（WSM）通常会对那些阴离子药物产生系统的低估预测，假设是由于“白蛋白介导的肝脏药物摄取”。在本研究中,我们证明了包含动态自由分数(fD)的WSM，药物蛋白结合亲和力的量度，在预测OATP底物的肝清除方面表现良好。为了选择阴离子药物，包括阿托伐他汀，氟伐他汀,普伐他汀,瑞舒伐他汀,匹伐他汀,西立伐他汀,和瑞格列奈,对于所检查的7种OATP底物中的6种,这种动态良好搅拌模型(dWSM)在2倍误差内正确预测肝血浆清除率.预测值和观测值之间的间隙比的几何平均值落在1.21-1.38的范围内。不出所料,具有未结合分数（fu）的WSM系统地低估了7种药物中5种的肝清除率，误差大于2倍，预测值和观测值之间的间隙比的几何平均值在0.20-0.29范围内。结果表明,尽管它很简单，dWSM在转运蛋白介导的摄取清除方面运作良好，并且OATP底物的清除率预测不足可能不一定与阴离子药物的化学类别有关，这也不是目前假设的白蛋白介导的肝脏药物摄取的结果。相反，dWSM的优越预测能力证实了动态游离分数在清除预测中的实用性以及药物血浆结合动力学在肝脏摄取清除中的重要性.意义陈述传统的良好搅拌模型（WSM）一致预测OATP介导的肝摄取清除，假设是由于白蛋白介导的肝脏药物摄取。在这份手稿中,我们应用动态良好搅拌模型（dWSM）外推OATP底物的肝清除，我们的结果表明，在不假设“白蛋白介导的肝脏药物摄取”的情况下，清除率预测显着改善。
