QSAR-based permeability model for drug-like compounds

Abstract

A QSAR model was developed for predicting intestinal drug permeability, one of the most important parameters when evaluating compounds in drug discovery projects. First, a set of relevant properties for establishing a drug-like chemical space was applied to a database of compounds with Caco-2 permeability values obtained from previous studies. Several QSAR regression models were then developed from this set of drug-like structures. The best model was selected based on the accuracy of correct classifications obtained for training and validation subsets previously defined, including 17 structures from the FDA Biopharmaceutics Classification System (BCS). Further validation of the QSAR model was performed by applying it to 21 drugs for which Caco-2 permeability values were experimentally determined by us. The good agreement between predictions and experimental values in all cases confirmed the reliability of the equation. Since the model was developed with very simple descriptors, easy to calculate, its applicability to large collections of in silico chemicals is guaranteed.