Quantitative structure–activity relationship studies on nitrofuranyl anti-tubercular agents

Abstract

A series of nitrofuranylamide and related aromatic compounds displaying potent activity against Mycobacterium tuberculosis have been investigated utilizing 3-dimensional quantitative structure–activity relationship (3D-QSAR) techniques. Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) methods were used to produce 3D-QSAR models that correlated the minimum inhibitory concentration (MIC) values against M. tuberculosis with the molecular structures of the active compounds. A training set of 95 active compounds was used to develop the models, which were then evaluated by a series of internal and external cross-validation techniques. A test set of 15 compounds was used for the external validation. Different alignment and ionization rules were investigated as well as the effect of global molecular descriptors including lipophilicity (c Log P, Log D), polar surface area (PSA), and steric bulk (CMR), on model predictivity. Models with greater than 70% predictive ability, as determined by external validation, and high internal validity (cross-validated r 2 > .5) have been developed. Incorporation of lipophilicity descriptors into the models had negligible effects on model predictivity. The models developed will be used to predict the activity of proposed new structures and advance the development of next generation nitrofuranyl and related nitroaromatic anti-tuberculosis agents.