Investigation of Knema-derived phytochemicals as potential inhibitors of estrogen receptor-alpha for breast cancer treatment: an In Silico approach

Natural products are widely acknowledged as a valuable source of bioactive compounds with potential anticancer properties. However, the discovery of new anticancer compounds from natural sources remains a laborious and resource-intensive process. Computational methods have emerged as a promising alternative to traditional drug discovery methods. The genus Knema, comprising evergreen trees and shrubs, has been extensively studied for its medicinal and pharmacological properties. This study aims to use computational techniques to predict the activity of natural products isolated from Knema species against a cancer cell line. Specifically, we aimed to predict the inhibitory activity of these compounds against estrogen receptor-α, a protein commonly overexpressed in breast cancer. This study presents the first comprehensive in silico screening of Knema species compounds against ER-α, identifying potential lead compounds for breast cancer treatment. Molecular docking simulations and in silico ADMET predictions were employed to identify lead compounds for further development. Our findings suggest that several compounds previously isolated from Knema species have better docking scores than gefitinib, a frequently used inhibitor in cancer treatment. Among these compounds, 7-hydroxy-3′,4′-methylenedioxyflavan (22) showed good binding affinity, drug-likeness, and ADMET properties. Furthermore, the ADMET prediction indicates that compound (22) has the ability to penetrate the blood–brain barrier, suggesting its potential for further investigation in Alzheimer’s disease research, alongside its estrogen receptor-α inhibitory activity. Overall, this research provides insights into the possible therapeutic applications of natural products from Knema species as sources of bioactive compounds for cancer research. The identification of compound (22) as a potential lead compound highlights the value of computational methods in guiding drug discovery from natural products, warranting further experimental validation.