Modeling Study of Phenylsulfonylfuroxan Derivatives as P-gp Inhibitors: A Combined Approach of CoMFA, CoMSIA and HQSAR
Changdev G. Gadhe,
Pavithra K. Balasubramanian,
Seung Joo Cho.
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 3rd and 4th 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.
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