Fused in silico and bioactivity evaluation method for drug discovery: T001-10027877 was identified as an antiproliferative agent that targets EGFR

backgroundFacing the significant challenge of overcoming drug resistance in cancer treatment, particularly resistance caused by mutations in epidermal growth factor receptor (EGFR), the aim of our study was to identify potent EGFR inhibitors effective against the

methodsOur integrated in silico approach harnessed machine learning, virtual screening, and activity evaluation techniques to screen 5105 compounds from three libraries, aiming to find candidates capable of overcoming the resistance conferred by the T790M and C797S mutations within EGFR. This methodical process narrowed the search down to six promising compounds for further examination.

resultsKinase assays identified three compounds to which the T790M/C797S/L858R mutant exhibited increased sensitivity compared to the T790M/L858R mutant, highlighting the potential efficacy of these compounds against resistance mechanisms. Among them, T001-10027877 exhibited dual inhibitory effects, with IC

conclusionsThis study highlights the efficacy of combining computational techniques with bioactivity assessments in the quest for novel antiproliferative agents targeting complex EGFR mutations. In particular, T001-10027877 has great potential for overcoming EGFR-mediated resistance and merits further in vivo exploration. Our findings contribute valuable insights into the development of next-generation anticancer therapies, demonstrating the power of an integrated drug discovery approach.