Fused in silico and bioactivity evaluation method for drug discovery: T001-10027877 was identified as an antiproliferative agent that targets EGFRT790M/C797S/L858R and EGFRT790M/L858R

Abstract

Abstract Background Facing 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 T790M/C797S/L858R mutant, a key player in resistance mechanisms. Methods Our 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. Results Kinase 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 IC50 values of 4.34 µM against EGFRT790M/C797S/L858R and 1.27 µM against EGFRT790M/L858R. Further investigations into the antiproliferative effects in H1975, A549, H460 and Ba/F3-EGFRL858/T790M/C797S cancer cells revealed that T001-10027877 was the most potent anticancer agent among the tested compounds. Additionally, the induction of H1975 cell apoptosis and cell cycle arrest by T001-10027877 were confirmed, elucidating its mechanism of action. Conclusions This 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.

Keywords

• EGFRT790M/C797S/L858R and EGFRT790M/L858R
• Machine learning
• Virtual screening
• Bioactivity evaluation
• Antiproliferative agent