Computational Approaches to Drug Development for Triple-Negative Breast Cancer Management

Abstract

Basal like breast cancer or triple-negative breast cancer (TNBC) is a clinically aggressive category of breast cancer in which no receptors for (ER) estrogen, (PR) progesterone, or HER2 are expressed, making its detection therapeutically challenging. This study investigated novel drug candidates from the in-silico drug discovery pipeline for key TNBC genes, such as EGFR, TP53, CD274, BRCA1, and PIK3CA. Extensive bioinformatic studies encompass BLAST searches, among other techniques, for cavity detection on the protein structure to identify legitimate active sites. Molecular docking simulations were conducted using both approved drugs, such as capecitabine, doxorubicin, and olaparib, and de novo design ligands adhering to guidelines used to predict the drug-likeness by Pfizer or more commonly known as Lipinski’s rule of five, which ensures oral bioavailability. The designed ligands exhibited strong binding affinities and stable interactions with the target proteins, as supported by comparisons using Marvin View and Swiss-PDB Viewer. This study suggests a promising ligand candidate with excellent pharmacological profile for further validation via both in-vivo and artificial analyses. These findings emphasize the prospects of computational approaches to expedite the development of targeted therapies for this particular subtype of breast cancer.