Extensive Computational Studies for the Identification of Potential Therapeutic Candidates Against Breast Cancer

Abstract

Introduction:: Breast cancer holds the distinction of being the most frequent type of cancer among women when compared to other forms of cancer. Estrogen Receptors (ER) are intracellular transcription factors that are essential for a variety of biological functions that are regulated by estrogen in the body. With its ability to modulate gene expression, Estrogen Receptors exert significant influence over cell growth, development, reproduction, and other important biological functions. Estrogen Receptors are overexpressed in breast cancer events; dysregulation of estrogen signaling pathways caused by this overexpression results in aberrant cell growth and proliferation, which make them the hallmarks of breast cancer. Methods:: A thorough study of different molecular structures and properties was done using extensive computational analyses and simulations in order to identify compounds with the potential to inhibit ER activity. Diverse chemical libraries were subjected to docking against the target ER-α, and molecules with docking scores less than -8.00 kcal/mol were retained. Results:: Further, these virtual hits were evaluated using 3D-QSAR models for predicting activity. ADME/Tox screening was performed to retain compounds with optimal pharmacokinetic profiles. Six compounds with excellent binding potential predicted biological activity and favorable ADME/Tox profiles were chosen. Prolonged molecular dynamics simulations were conducted to assess structural stability over time. Conclusion:: The computational study on breast cancer on the target ER has yielded significant progress with the identification of six promising compounds that can be further evaluated through experimental validations.