首页|Application of Empirical Scalars To Enable Early Prediction of Human Hepatic Clearance Using In Vitro-In Vivo Extrapolation in Drug Discovery: An Evaluation of 173 Drugs
Application of Empirical Scalars To Enable Early Prediction of Human Hepatic Clearance Using In Vitro-In Vivo Extrapolation in Drug Discovery: An Evaluation of 173 Drugs
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NSTL
The utilization of in vitro data to predict drug pharmacokinetics (PK) in vivo has been a consistent practice in early drug discovery for decades. However, its success is hampered by mispredictions attributed to uncharacterized biological phenomena/experimental artifacts. Predicted drug clearance (CL) from experimental data (i.e., intrinsic clearance: CL_int; fraction unbound in plasma: f_u,p) is often systematically underpredicted using the well-stirred model (WSM). The objective of this study was to evaluate using empirical scalars in the WSM to correct for CL mispredictions. Drugs (N = 28) were used to generate numerical scalars on CL_int (α) and f_u,p (β) to minimize the absolute average fold error (AAFE) for CL predictions. These scalars were validated using an additional dataset (N = 28 drugs) and applied to a non-redundant AstraZeneca (AZ) dataset available in the literature (N = 117 drugs) for a total of 173 compounds. CL predictions using the WSM were improved for most compounds using an α value of 3.66 (~64% < 2-fold) compared with no scaling (~46% < 2-fold). Similarly, using a β value of 0.55 or combination of α and β scalars (values of 1.74 and 0.66, respectively) resulted in a similar improvement in predictions (~64% < 2-fold and ~65% < 2-fold, respectively). For highly bound compounds (fu,p ≤ 0.01), AAFE was substantially reduced across all scaling methods. Using the β scalar alone or a combination of α and β appeared optimal and produced larger magnitude corrections for highly bound compounds. Some drugs are still dis-proportionally mispredicted; however, the improvements in prediction error and simplicity of applying these scalars suggest its utility for early-stage CL predictions.
Christian Leung、Jae H. Chang、Ning Liu、Zhengyin Yan、Jane R. Kenny、Fabio Broccatelli、Suzanne Brown、Robert S. Jones
展开 >
Drug Metabolism and Pharmacokinetics, Genentech, Inc., South San Francisco, California
NSTL
