Proposing New Potential Candidates to Inhibit EGFR via Machine Learning Algorithm

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Abstract One of the main issues in solid tumours is progressive mutation in epidermal growth factor receptors (EGFR) gene, which activates signalling pathways that create new blood vessels. In this study, it was attempted to find new a therapeutic candidate to inhibit EGFR. One of the cost-effective alternative methods to find new inhibitors has been the repositioning approach of existing drugs. The critical point of computational drug repositioning method is saving time and cost to remove the pre-clinical step and accelerate the drug discovery process. Hence, an ensemble computational-experimental approach, consisting of three different steps, a machine learning model, simulation of drug-target interaction and experimental characterization, was developed. The machine learning type used here was different tree classification method, which is one of the best randomize machine learning model to identify potential inhibitors from weak inhibitors. The machine learning step aimed to discover the approved drugs with the highest possible success rate in the experimental step. Finally, out of the nine chosen drugs, seven compounds had been confirmed to inhibit EGFR in the published articles since 2019. Hence, two identified compounds, in addition to gefitinib, as a positive control, and one neutral, were considered via molecular docking study. Finally, eight proposed drugs, including gefitinib, were investigated using MTT assay and In-Cell ELISA to characterise the drugs effect on A431 cell growth and EGFR-signaling. From our experiments, we could conclude that salicylic acid and piperazine could play an EGFR-inhibitor role like gefitinib.

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