Structure-based virtual screening, pharmacokinetic prediction, molecular dynamics studies for the identification of novel EGFR inhibitors in breast cancer

Breast cancer is one of the most prevalent malignancy cancer types especially affecting women globally. EGFR is a proto onco gene as well as the first identified tyrosine kinase receptor. It plays a dynamic role in many biological tasks such as apoptosis, cell cycle progression, differentiation, development and transcription. Somatic mutation in the EGFR kinase domain derails the normal kinase activity and over expression leads to the progression of cancer especially breast cancer. EGFR is one of the well-known therapeutic targets for breast cancer. In this scenario, we attempt to identify novel potent inhibitors of EGFR. Initially, we performed structure-based virtual screening and identified four potential compounds effective against EGFR. Further, the compounds were subjected to ADME prediction as part of evaluation of the druggability and all the four compounds found to fall under satisfactory range with predicted pharmacokinetic properties. Eventually, the conformational stability of protein-ligand complex was analyzed at different time scale by using Gromacs software. Molecular dynamics simulation run of 20 ns is carried out and results were analyzed using root mean square deviation (RMSD), root mean square fluctuation (RMSF) to signify the stability of protein-igand complex. The stability of the protein-ligand complex is more stable throughout entire simulation. From the results obtained fromin silicostudies, we propose that these compounds are exceptionally useful for further lead optimization and drug development. Communicated by Ramaswamy H. Sarma