In Silico Prediction of Volume of Distribution in Human Using Linear and Nonlinear Models on a 669 Compound Data Set

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• 作者：Giuliano Berellini ; Clayton Springer ; Nigel J. Waters ; Franco Lombardo
• 刊名：Journal of Medicinal Chemistry
• 出版年：2009
• 出版时间：July 23, 2009
• 年：2009
• 卷：52
• 期：14
• 页码：4488-4495
• 全文大小：847K
• 年卷期：v.52,no.14(July 23, 2009)
• ISSN：1520-4804

文摘

The prediction of human pharmacokinetics early in the drug discovery cycle has become of paramount importance, aiding candidate selection and benefit−risk assessment. We present herein computational models to predict human volume of distribution at steady state (VD_ss) entirely from in silico structural descriptors. Using both linear and nonlinear statistical techniques, partial least-squares (PLS), and random forest (RF) modeling, a data set of human VD_ss values for 669 drug compounds recently published (Drug Metab. Disp. 2008, 36, 1385−1405) was explored. Descriptors covering 2D and 3D molecular topology, electronics, and physical properties were calculated using MOE and Volsurf+. Model evaluation was accomplished using a leave-class-out approach on nine therapeutic or structural classes. The models were assessed using an external test set of 29 additional compounds. Our analysis generated models, both via a single method or consensus which were able to predict human VD_ss within geometric mean 2-fold error, a predictive accuracy considered good even for more resource-intensive approaches such as those requiring data generated from studies in multiple animal species.