A Comprehensive Computational Approach for Identifying Estrogen Receptor Alpha Inhibitors in Breast Cancer Treatment: Integrating Biophysical Analysis and In Vitro Validation

Abstract

Breast cancer is a major global health issue, with estrogen receptor alpha (ERα) being a key therapeutic target. This study utilized computational drug discovery to identify potential ERα inhibitors from the DrugLib library, aiming to develop novel treatments. Through the MtiOpenScreen webserver, virtual screening with a Lipinski filter identified 1500 compounds with docking scores between −12.4 and −9.1 kcal/mol. Three promising compounds (Tradipitant, Flezelastine, Zaldaride) were selected for further analysis, including re-docking, molecular dynamics (MD) simulations, Molecular mechanics with generalized Born and surface area solvation (MM/GBSA), and free energy landscape (FEL) analysis. These compounds demonstrated strong inhibitory potential against ERα, showing stable binding in the receptor's pocket. MMGBSA calculations further confirmed favorable binding energies, whereas FEL analysis provided insights into the dynamic stability of inhibitor-ERα complexes. Isothermal titration calorimetry (ITC) confirmed their binding constants and thermodynamic profiles. Additionally, their anticancer activity was assessed using MTT assays on the ERα-specific MCF-7 cell line, showing comparable efficacy to Doxorubicin. The study's computational and experimental findings establish a promising basis for these compounds' further development as ERα inhibitors for breast cancer treatment.