Application of CoMFA and CoMSIA 3D-QSAR and docking studies in optimization of mercaptobenzenesulfonamides as HIV-1 integrase inhibitors

An essential step in the HIV life cycle is integration of the viral DNA into the host chromosome. This step is catalyzed by a 32-kDa viral enzyme HIV integrase (IN). HIV-1 IN is an important and validated target, and the drugs that selectively inhibit this enzyme, when used in combination with reverse transcriptase (RT) and protease (PR) inhibitors, are believed to be highly effective in suppressing the viral replication. IN catalyzes two discrete enzymatic processes referred to as 3' processing and DNA strand transfer. As a part of a study to optimize new lead molecules we previously identified from a series of 2-mercaptobenzenesulfonamides (MBSAs), we applied three-dimensional quantitative structure-activity relationship methods, comparative molecular field analysis (CoMFA), and comparative molecular similarity indices analysis (CoMSIA) to training sets of up to 66 compounds. Two different conformational templates were used: Conf-d, obtained from docking into the HIV-1 IN active site and Conf-s obtained by a systematic conformational search, using lead compounds 1 and 14, respectively. Reliable models of good predictive power were obtained after removal of compounds with high residuals. The Conf-s models tended to perform better than the Conf-d models. Cross-validated coefficients (q(2)) of up to 0.719 (strand transfer CoMSIA, Conf-s) regression coefficients (r(2)) of up to 0.932 (strand transfer CoMSIA, Conf-d) were obtained, with the number of partial least squares (PLS) components varying from 3 to 6, and the number of outliers being 4 in most of the models. Because all biological data were determined under exactly the same conditions using the same enzyme preparation, our predictive models are promising for drug optimization. Therefore, these results combined with docking studies were used to guide the rational design of new inhibitors. Further synthesis of 12 new analogues was undertaken, and these were used as a test set for validation of the quantitative structure-activity relationship (QSAR) models. For compounds with closely related structures, binding energies given by the Flexx scoring function correlated with HIV-1 IN inhibitory activity.