Theoretical calculation and prediction of P-glycoprotein-interacting drugs using MolSurf parametrization and PLS statistics.

A method for the modelling and prediction of P-glycoprotein-associated ATPase activity using theoretically computed molecular descriptors and multivariate statistics has been investigated using 22 diverse drug-like compounds. The program MolSurf was used to compute theoretical molecular descriptors related to physicochemical properties such as lipophilicity, polarity, polarizability and hydrogen bonding. The multivariate partial least squares projections to latent structures (PLS) method was used to delineate the relationship between the P-glycoprotein-associated ATPase activity and the theoretically computed molecular descriptors. The PLS analysis of the entire data set, with the exclusion of tamoxifen, resulted in one significant PLS component according to cross-validation with R(2)=0.718, Q(2)=0. 695, S.D.=0.475, F=48.37, RMSE(tr)=0.452, p<0.001. Properties associated with the size of the molecular surface, polarizability and hydrogen bonding had the largest impact on the P-glycoprotein-associated ATPase activity. All these properties should be high to promote high ATPase activity.