In Silico Prediction of Human Intravenous Pharmacokinetic Parameters with Improved Accuracy

Human pharmacokinetics is of great significance in the selection of drug candidates, and in silico estimation of pharmacokinetic parameters in the early stage of drug development has become the trend of drug research owing to its time- and cost-saving advantages. Herein, quantitative structure-property relationship studies were carried out to predict four human pharmacokinetic parameters including volume of distribution at steady state (VD_ss), clearance (CL), terminal half-life (t_1/2), and fraction unbound in plasma (f_u), using a data set consisting of 1352 drugs. A series of regression models were built using the most suitable features selected by Boruta algorithm and four machine learning methods including support vector machine (SVM), random forest (RF), gradient boosting machine (GBM), and XGBoost (XGB). For VD_ss, SVM showed the best performance with R²_test = 0.870 and RMSE_test = 0.208. For the other three pharmacokinetic parameters, the RF models produced the superior prediction accuracy (for CL, R²_test = 0.875 and RMSE_test = 0.103; for t_1/2, R²_test = 0.832 and RMSE_test = 0.154; for f_u, R²_test = 0.818 and RMSE_test = 0.291). Assessed by 10-fold cross validation, leave-one-out cross validation, Y-randomization test and applicability domain evaluation, these models demonstrated excellent stability and predictive ability. Compared with other published models for human pharmacokinetic parameters estimation, it was further confirmed that our models obtained better predictive ability and could be used in the selection of preclinical candidates.