Riede Julia, Poller Birk, Umehara Ken-ichi, Huwyler Jörg, Camenisch Gian
Division of Drug Metabolism and Pharmacokinetics, Integrated Drug Disposition Section, Novartis Institutes for BioMedical Research, CH-4056 Basel, Switzerland; Department of Pharmaceutical Sciences, Division of Pharmaceutical Technology, University of Basel, CH-4056 Basel, Switzerland.
Division of Drug Metabolism and Pharmacokinetics, Integrated Drug Disposition Section, Novartis Institutes for BioMedical Research, CH-4056 Basel, Switzerland.
Eur J Pharm Sci. 2016 Apr 30;86:96-102. doi: 10.1016/j.ejps.2016.02.022. Epub 2016 Mar 3.
Total human clearance is a key determinant for the pharmacokinetic behavior of drug candidates. Our group recently introduced the Extended Clearance Model (ECM) as an accurate in vitro-in vivo extrapolation (IVIVE) method for the prediction of hepatic clearance. Yet, knowledge about relative elimination pathway contributions is needed in order to predict the total human clearance of drug candidates. In the present work, a training set of 18 drug compounds was used to describe the affiliations between in vitro sinusoidal uptake clearance and the fractional contributions of hepatic (metabolic and biliary) or renal clearance to overall in vivo elimination. By means of these quantitative relationships and using a validation set of 10 diverse drug molecules covering different (sub)classes of the Extended Clearance Concept Classification System (ECCCS), the relative contributions of elimination pathways were calculated and demonstrated to well correlate with human reference data. Likewise, ECM- and pathway-based predictions of total clearances from both data sets demonstrated a strong correlation with the observed clinical values with 26 out of 28 compounds within a three-fold deviation. Hence, total human clearance and relative contributions of elimination pathways were successfully predicted by the presented method using solely hepatocyte and microsome in vitro data.
人体总清除率是候选药物药代动力学行为的关键决定因素。我们团队最近引入了扩展清除模型(ECM),作为预测肝清除率的一种准确的体外-体内外推(IVIVE)方法。然而,为了预测候选药物的人体总清除率,需要了解相对消除途径的贡献。在本研究中,使用了一个包含18种药物化合物的训练集来描述体外肝血窦摄取清除率与肝(代谢和胆汁)或肾清除率对总体体内消除的分数贡献之间的关系。通过这些定量关系,并使用一个包含10种不同药物分子的验证集,这些药物分子涵盖了扩展清除概念分类系统(ECCCS)的不同(亚)类别,计算了消除途径的相对贡献,并证明其与人体参考数据具有良好的相关性。同样,基于ECM和途径对两个数据集中总清除率的预测显示,与观察到的临床值具有很强的相关性,28种化合物中有26种在三倍偏差范围内。因此,仅使用肝细胞和微粒体体外数据,通过本方法成功预测了人体总清除率和消除途径的相对贡献。