Lead Generation, DMPK and Physical Chemistry, AstraZeneca R&D Mölndal, SE-431 83 Mölndal, Sweden.
Curr Drug Metab. 2010 Sep;11(7):583-94. doi: 10.2174/138920010792927334.
The in vitro metabolic stability assays are indispensable for screening the metabolic liability of new chemical entities (NCEs) in drug discovery. Intrinsic clearance (CL(int)) values from liver microsomes and/or hepatocytes are frequently used to assess metabolic stability as well as to quantitatively predict in vivo hepatic plasma clearance (CL(H)). An often used approximation is the so called well-stirred model which has gained widespread use. The applications of the well-stirred model are typically dependent on several measured parameters and hence with potential for error-propagation. Despite widespread use, it was recently suggested that the well-stirred model in some circumstances has been misused for in vitro in vivo extrapolation (IVIVE). In this work, we follow up that discussion and present a retrospective analysis of IVIVE for hepatic clearance prediction from in vitro metabolic stability data. We focus on the impact of input parameters on the well stirred model; in particular comparing "reference model" (with all experimentally determined values as input parameters) versus simplified models (with incomplete input parameters in the models). Based on a systematic comparative analysis and model comparison using datasets of diverse drug-like compounds and NCEs from rat and human, we conclude that simplified models, disregarding binding data, may be sufficiently good for IVIVE evaluation and compound ranking at early stage for cost-effective screening. Factors that can influence prediction accuracy are discussed, including in vitro intrinsic clearance (CL(int)) and in vivo CL(int) scaling factor used, non-specific binding to microsomes (fu(m)), blood to plasma ratio (C(B)/C(P)) and in particular fraction unbound in plasma (fu). In particular, the fu discrepancies between literature data and in-house values and between two different compound concentrations 1 and 10 µM are exemplified and its potential impact on prediction performance is demonstrated using a simulation example.
体外代谢稳定性测定对于筛选药物发现中新化学实体 (NCE) 的代谢不良性是不可或缺的。肝微粒体和/或肝细胞中的内在清除率 (CL(int)) 值常用于评估代谢稳定性以及定量预测体内肝血浆清除率 (CL(H))。常用的近似方法是所谓的充分混合模型，该模型已得到广泛应用。充分混合模型的应用通常依赖于几个测量参数，因此存在误差传播的可能性。尽管广泛使用，但最近有人建议，在某些情况下，充分混合模型被错误地用于体外体内外推 (IVIVE)。在这项工作中，我们跟进了这一讨论，并对基于体外代谢稳定性数据预测肝清除率的 IVIVE 进行了回顾性分析。我们重点关注输入参数对充分混合模型的影响；特别是比较“参考模型”（所有实验确定的值作为输入参数）与简化模型（模型中的输入参数不完整）。基于对来自大鼠和人类的不同类药性化合物和 NCE 的数据集进行系统的比较分析和模型比较，我们得出结论，简化模型，不考虑结合数据，对于 IVIVE 评估和化合物早期的排名可能足够好，以实现具有成本效益的筛选。讨论了可能影响预测准确性的因素，包括体外内在清除率 (CL(int)) 和体内 CL(int) 标度因子、对微粒体的非特异性结合 (fu(m))、血液与血浆比 (C(B)/C(P)) 以及特别是血浆中未结合部分 (fu)。特别是，说明了文献数据与内部值之间以及两种不同化合物浓度 1 和 10 µM 之间的 fu 差异，并使用模拟示例说明了其对预测性能的潜在影响。
