Abstract

Multidrug resistance (MDR) is a phenomenon whereby cancer cells experience intrinsic or acquired resistance to a broad spectrum of structurally and functionally distinct chemotherapeutic agents. Permeability glycoprotein (P-gp) is the key protein responsible for the development of MDR in cancer cells, as it exports chemotherapeutic agents from cells. In the present study, comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA), and hologram quantitative structure activity relationship (HQSAR) techniques were used to derive predictive models for phenylsulfonylfuroxan derivatives as P-gp inhibitors. Cross-validated correlation coefficients (q²) of 0.811, 0.855, and 0.907 and non-cross-validated correlation coefficients (r²) of 0.87, 0.985, and 0.973 were obtained for CoMFA, CoMSIA, and HQSAR derived models, respectively. The predictive power of the models were assessed using an external test set of five compounds and showed reasonable external predictabilities (r² _pred) of 0.704, 0.517, and 0.713, respectively. Contour and atomic contribution maps were generated to investigate physicochemical requirements of ligands for better receptor binding affinity. 3D Contour maps suggested molecular interactions such as steric and electrostatic effects and hydrogen bond formation. However, atomic contribution maps indicated that ortho and para positions of the R¹ phenylsulfonyl ring are the most desirable regions to modulate P-gp antagonism. The 3^rd and 4^th positions of the central five-membered ring were also found to be important. Our results are in line with previous reports. Information obtained from the contour and atomic contribution maps were utilized to design more potent compounds containing different R¹ fragments. In addition, the activities of these more potent compounds were predicted using derived models.

Keywords: Anti-cancer agent, CoMFA, CoMSIA, HQSAR, P-gp, Phenylsulfonylfuroxan.