Integrating computational 3D-QSAR and pharmacophore modelling in diverse natural phytochemicals library for targeted therapy against breast cancer estrogen receptors

Abstract

Breast cancer stands as a pervasive global health challenge, profoundly impacting the physical and emotional well-being of individuals. The relentless growth of malignant cells within breast tissues necessitates comprehensive research into the disease's origin, early detection, and treatment strategies. This study focused on constructing pharmacophores and 3D-QSAR models to dissect the crucial structure attributes essential for inhibiting Estrogen receptors. By employing ligand-based pharmacophore modeling and Atom-based-3D-QSAR techniques within the Schrödinger Phase module, we analyzed the inhibitory activity of 202 natural compounds and Tamoxifen as the control drug. To identify promising candidates, five molecules (Genistein, Coumestrol, Apigenin, Emodin, and Daidzein) were strategically chosen based on prediction from the 3D-QSAR pharmacophore model. These molecules are further assessed through model validation and virtual screening. The generated pharmacophore model (ADHR.10) reveals key features like hydrogen bond donors (HBD), hydrogen bond acceptors (HBA), and hydrophobic interactions. The resulting 3D-QSAR model predicted and stability was validated through the Xternal validation plus tool. The ADHR.10 model guided the systematic screening of natural compounds, resulting in the identification of the top five hits with promising pharmacological activity. Molecular docking scores demonstrated the compound's binding affinity with ESR1 (PDB ID: 6PSJ) and ESR2 (PDB ID: 1QKM). One standout compound, Coumestrol, displayed an exceptional binding affinity with ESR2 outperforming Tamoxifen. The top compounds exhibited both strong binding and stability, making them a promising candidate. Furthermore, Molecular Dynamics Simulation (MDS) demonstrated Coumestrol's superior stability compared to the control drug against the ESR1 and ERS2 proteins. Coumestrol exhibits lower average fluctuation in RMSD and residue-specific dynamics (RMSF) indicating stability than control. Specifically, designed compounds, rooted in natural sources, demonstrated excellent potential as breast cancer treatment candidates. These findings highlight them as promising lead candidates for the development of targeted breast cancer therapies and emphasize the potential of computational approaches in discovering new therapeutic avenues.