Multitargeted comparative evaluation suggests 2-Aoeobenoxmide shows favourable in silico binding compared to Tucatinib against ERα, HER2, AKT1, EGFR, and PIK3CA in breast cancer.
Breast cancer is a leading cause of cancer-related morbidity and mortality globally, with the WHO reporting approximately 2.3 million new cases and 685,000 deaths annually. Drug resistance in breast cancer complicates treatment, with mutations in critical proteins contributing to therapy failure. Key oncogenic proteins involved in breast cancer progression-namely ERα (a ligand-activated nuclear receptor; PDB: 1A52) and the kinase domains of HER2 (PDB ID: 3PP0), AKT1 (PDB ID: 4EJN), EGFR (PDB ID: 4I23) and PIK3CA (PDB ID: 7R9V)-are pivotal in tumour progression and resistance mechanisms. Targeting these proteins using multitargeted therapeutic strategies may overcome resistance by disrupting key signalling pathways involved in cell proliferation, survival, and metastasis. Such combinatorial approaches promise to improve treatment efficacy and patient outcomes in cases of resistant breast cancer. In this study, we performed multitarget docking on prepared and validated protein structures against the ZINC natural compound library using HTVS, SP, and XP, with pose validation using MM-GBSA. We identified 2-Aoeobenoxmide (2-[1-(2-amino-2-oxo-ethoxy)-6-oxo-benzo[c]chromen-3-yl]oxyacetamide, ZINC134008) with docking and MM-GBSA scores ranging from -8.162 to -10.327 kcal/mol and from -47.18 to -57.62 kcal/mol, respectively, and compared the results with the FDA-approved drug Tucatinib, which exhibited lower binding affinity scores. We further evaluated pharmacokinetic properties using QikProp and electronic properties using DFT (Jaguar) and compared the descriptors of 2-Aoeobenoxmide with those of Tucatinib and with accepted reference ranges. We also performed the WaterMap for 5 nanoseconds (ns), computed various energies, interactions and hydration sites, and the comparison suggests that 2-Aoeobenoxmide shows more favourable hydration-site displacement and binding interactions than Tucatinib. Additionally, a 100 ns MD Simulation has resulted in far less deviation, fluctuations, and intermolecular interactions than Tucatinib, suggesting stable protein-ligand interactions, while the binding free energy and total complex energy computed across 0-1000 frames of the MD trajectories indicate that 2-Aoeobenoxmide is a promising in silico candidate. Importantly, because the entire study is computational, the findings should be interpreted as in silico hypotheses, and experimental validation through in vitro and in vivo assays is warranted before any clinical translation is considered.