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Design and pharmacophoric identification of flavonoid scaffold‐based aromatase inhibitors
Author(s) -
Banjare Laxmi,
Verma Sant Kumar,
Jain Akhlesh Kumar,
Thareja Suresh
Publication year - 2020
Publication title -
journal of heterocyclic chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.321
H-Index - 59
eISSN - 1943-5193
pISSN - 0022-152X
DOI - 10.1002/jhet.4068
Subject(s) - chemistry , aromatase , pharmacophore , stereochemistry , active site , steric effects , docking (animal) , flavonoid , aromatization , ligand (biochemistry) , enzyme , binding site , combinatorial chemistry , catalysis , biochemistry , receptor , breast cancer , cancer , medicine , nursing , antioxidant
Aromatase is a crucial enzyme for the catalysis of aromatization reaction at the last and rate‐limiting step involved in the conversion of androgenic substrates to an estrogenic substrate. A hormone‐dependent breast cancer in postmenopausal woman can be cured by inhibition of estrogen biosynthesis by the help of aromatase inhibitors (AIs). The mode of interactions of flavonones with the active site of aromatase has been studied in search of potent and selective AIs as a substitute of the natural steroidal ligand. Structure‐based computational approach namely, molecular docking simulations were performed to investigate the structural features of the docked complex of aromatase and flavonoid ligands. A nonsteroidal flavonoid pharmacophore showing electrostatic and steric features for selective binding within the main pocket of the catalytic active site of aromatase has been identified as an outcome of the study. The binding affinity of quercetin and isoflavone were predicted within aromatase. Isoflavone was used as a negative control to compare its binding affinities with the selected dataset. The predicted binding affinity of negative control isoflavone was in accordance with its in vitro AI efficacy. Isoflavone showed poor binding affinity and ranked last in terms of MolDock score (−86.309 kcal/molÅ) compared to dataset molecules. The generated pharmacophoric information will be helpful for the synthetic chemist to design and synthesize selective AIs with comparable binding affinity to the natural steroidal ligand.