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

TLDR

The prediction of human pharmacokinetics early in the drug discovery cycle has become of paramount importance, aiding candidate selection and benefit–risk assessment. The study presents computational models to predict human volume of distribution at steady state (VDss) solely from in silico structural descriptors. The authors applied linear (PLS) and nonlinear (RF) models to a 669‑compound dataset, using 2D/3D molecular descriptors from MOE and Volsurf+, and evaluated performance with a leave‑class‑out strategy across nine therapeutic classes and an external test set of 29 compounds. The resulting models achieved a geometric mean 2‑fold error in predicting human VDss, demonstrating accuracy comparable to more resource‑.

Abstract

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 (VDss) 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 VDss 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 VDss 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.