Identifying novel and potent inhibitors of EGFR protein for the drug development against the breast cancer

Abstract

The epidermal growth factor receptor (EGFR) has been shown to be extremely important in numerous signaling pathways, particularly those involved in cancer progression. Many therapeutic inhibitors, consisting of both small molecules and monoclonal antibodies, have been developed to target inflammatory, triple-negative and metastatic breast cancer. With the emergence of resistance in breast cancer treatment strategies, there is a need to develop novel drug targets that not only overcome resistance, but also exhibit low toxicity and high specificity. The work presented here focuses on the identification of new inhibitors against the EGFR protein using combined computational approaches. Using a comprehensive machine learning-based virtual screening approach complemented by other computational approaches, we identified six new molecules from the ZINC database. The gold docking score of these six novel molecules is 125.95, 125.38, 123.13, 119.71, 115.64 and 113.73, respectively, while the gold score of the control group is 120.74. In addition, we also analyzed the FEC value of these compounds and found that the values of compounds 1, 2, 3 and 4 (−61.82, −63.98, −67.98 and −63.32, respectively) were higher are than those of the control group (−61.05). Furthermore, these molecules showed highly stable RMSD plots and good interaction of hydrogen bonds. The identified inhibitors provided interesting insights for understanding the electronic, hydrophobic, steric and structural requirements for EGFR inhibitory activity. Distinguishing these novel molecules could lead to the development of new drugs useful in treating breast cancer.

Communicated by Ramaswamy H. Sarma

Keywords:

• Inhibitors
• EGFR
• drug discovery
• molecular dynamics
• pharmacophore
• machine learning

Authors’ contributions

Conceptualization, A.J.S., M.P. and M.A.; methodology, F.B., W.A., S.J., M.K. and M.S.; validation, M.S., W.A., A.A., M.K. and S.J; formal analysis, M.A., R.B., F.B., M.P., M.K. and W.A.; investigation, F.B., W.A., R.B., M.S., A.A. and S.J.; data curation, F.B., M.P., A.A., M.K. and R.B.; writing—original draft preparation, A.J.S. M.P. and M.A.; writing—review and editing, A.J.S., and S.J.; visualization, A.A., A.B., M.S., M.A. and R.B.; supervision, A.J.S.; project administration, A.J.S. All authors have read and agreed to the published version of the manuscript.

Disclosure statement

The authors declare no conflict of interest.

Additional information

Funding

This research has been funded by Scientific Research Deanship at University of Ha'il, Ha'il, Saudi Arabia through project number RG-22009.