Molecular docking and 3D-QSAR-based virtual screening of flavonoids as potential aromatase inhibitors against estrogen-dependent breast cancer

Abstract

Aromatase, catalyzing final step of estrogen biosynthesis, is considered a key target for the development of drug against estrogen-dependent breast cancer (EDBC). Identification and development of naturally occurring compounds, such as flavonoids, as drugs against EDBC is in demand due to their lesser toxicity when compared to those of synthetic ones. Thus, a three-dimensional quantitative structure–activity relationship, using comparative molecular field analysis (CoMFA) was done on a series of 45 flavonoids against human aromatase. A significant cross-validated correlation coefficient (q²) of 0.827 was obtained. The best predictive CoMFA model explaining the biological activity of the training and test sets with correlation coefficient values (r²) of 0.916 and 0.710, respectively, when used for virtual screening of a flavanoids database following molecular docking revealed a flavanone namely, 7-hydroxyflavanone beta-D-glucopyranoside showing highest predicted activity of 1.09 μM. In comparison to a well-established inhibitor of aromatase, namely 7-hydroxyflavanone (IC₅₀: 3.8 μM), the derivative identified in the present study, namely 7-hydroxyflavanone beta-D-glucopyranoside exhibited about 3.5 folds higher inhibitory activity against aromatase. The result of virtual screening was further validated using molecular dynamics (MD) simulation analysis. Thus, a 25 ns MD simulation analysis revealed high stability and effective binding of 7-hydroxyflavanone beta-D-glucopyranoside within the active site of aromatase. To the best of our knowledge, this is the first report of CoMFA-based QSAR model for virtual screening of flavonoids as inhibitors of aromatase.

Keywords:

• aromatase
• breast cancer
• 3D-QSAR
• flavonoids
• 7-hydroxyflavanone beta-D-glucopyranoside
• molecular dynamics simulation

Acknowledgment

Financial supports from the Department of Biotechnology, Govt. of India, New Delhi, under the BIF program, the Department of Higher Education, Govt. of U.P., under the Center of Excellence in Bioinformatics program and from the Department of Science & Technology, Govt. of India, New Delhi, under DST-INSPIRE-JRF(SS) and DST-PURSE programs are gratefully acknowledged.