A rapid computational filter for predicting the rate of human renal clearance

In silico models that predict the rate of human renal clearance for a diverse set of drugs, that exhibit both active secretion and net re-absorption, have been produced using three statistical approaches. Partial Least Squares (PLS) and Random Forests (RF) have been used to produce continuous models whereas Classification And Regression Trees (CART) has only been used for a classification model. The best models generated from either PLS or RF produce significant models that can predict acids/zwitterions, bases and neutrals with approximate average fold errors of 3, 3 and 4, respectively, for an independent test set that covers oral drug-like property space. These models contain additional information on top of any influence arising from plasma protein binding on the rate of renal clearance. Classification And Regression Trees (CART) has been used to generate a classification tree leading to a simple set of Renal Clearance Rules (RCR) that can be applied to man. The rules are influenced by lipophilicity and ion class and can correctly predict 60% of an independent test set. These percentages increase to 71% and 79% for drugs with renal clearances of < 0.1 ml/min/kg and > 1 ml/min/kg, respectively. As far as the authors are aware these are the first set of models to appear in the literature that predict the rate of human renal clearance and can be used to manipulate molecular properties leading to new drugs that are less likely to fail due to renal clearance.