Multifaceted 3D-QSAR analysis for the identification of pharmacophoric features of biphenyl analogues as aromatase inhibitors

Aromatase, a cytochrome P450 enzyme, is responsible for the conversion of androgens to estrogens, which fuel the multiplication of cancerous cells. Inhibition of estrogen biosynthesis by aromatase inhibitors (AIs) is one of the highly advanced therapeutic approach available for the treatment of estrogen-positive breast cancer. Biphenyl moiety aids lipophilicity to the conjugated scaffold and enhances the accessibility of the ligand to the target. The present study is focused on the investigation of, the mode of binding of biphenyl with aromatase, prediction of ligand-target binding affinities, and pharmacophoric features essential for favorable for aromatase inhibition. A multifaceted 3D-QSAR (SOMFA, Field and Gaussian) along with molecular docking, molecular dynamic simulations and pharmacophore mapping were performed on a series of biphenyl bearing molecules (1-33) with a wide range of aromatase inhibitory activity (0.15-920 nM). Among the generated 3D-QSAR models, the Force field-based 3D-QSAR model (R-2 = 0.9151) was best as compared to SOMFA and Gaussian Field (R-2=0.7706, 0.9074, respectively). However, all the generated 3D-QSAR models were statistically fit, robust enough, and reliable to explain the variation in biological activity in relation to pharmacophoric features of dataset molecules. A four-point pharmacophoric features with three acceptor sites (A), one aromatic ring (R) features, AAAR_1, were obtained with the site and survival score values 0.890 and 4.613, respectively. The generated 3D-QSAR plots in the study insight into the structure-activity relationship of dataset molecules, which may help in the designing of potent biphenyl derivatives as newer inhibitors of aromatase. Communicated by Ramaswamy H. Sarma

Automated summary

This study investigated a series of biphenyl bearing molecules with a wide range of aromatase inhibitory activity using a multifaceted approach including 3D-QSAR analysis, molecular docking, molecular dynamic simulations, and pharmacophore mapping. The results showed that the force field-based 3D-QSAR model was the best in terms of fitness, and the generated pharmacophoric features were able to explain the variation in biological activity. The findings offer guidance for designing potent biphenyl derivatives as aromatase inhibitors.