De Novo Prediction of P-Glycoprotein-Mediated Efflux Liability for Druglike Compounds

P-glycoprotein (Pgp) is capable of recognizing and transporting a wide range of chemically diverse compounds in vivo. Overcoming Pgp-mediated efflux can represent a significant challenge when penetration into the central nervous system is required or within the context of developing anticancer therapies. While numerous in silico models have been developed to predict Pgp-mediated efflux, these models rely on training sets and are best suited to make interpolations. Therefore, it is desirable to develop ab initio models that can be used to predict efflux liabilities. Herein, we present a de novo method that can be used to predict Pgp-mediated efflux potential for druglike compounds. A model, which correlates the computed solvation free energy differences obtained in water and chloroform with Pgp-mediated efflux (in logarithmic scale), was successful in predicting Pgp efflux ratios for a wide range of chemically diverse compounds with a R-2 and root-mean-square error of 0.65 and 0.29, respectively.