DOI: 10.6084/m9.figshare.24942000.v1
title: T6496 targeting EGFR mediated by T790M or C797S mutant: machine learning, virtual screening and bioactivity evaluation study

description: Acquired resistance to EGFR is a major impediment in lung cancer treatment, highlighting the urgent need to discover novel compounds to overcome EGFR drug resistance. In this study, we utilized in silico methods and bioactivity evaluation for drug discovery to identify novel active anticancer agents targeting EGFR<sup>T790M/L858R</sup> and EGFR<sup>T790M/C797S/L858R</sup>. Firstly, we employed ROC-guided machine learning to retrieve nearly 7,765 compounds from a collection of three libraries (comprising over 220,000 compounds). Next, virtual screening, cluster analysis, and binding model analysis were employed to identify six potential compounds. Additionally, the kinase assay revealed that these six compounds demonstrated higher sensitivity to EGFR than c-Met. Among these compounds, <b>T6496</b> inhibited both EGFR<sup>T790M/L858R</sup> and EGFR<sup>T790M/C797S/L858R</sup> kinases, with an IC<sub>50</sub> of 3.30 and 8.72 μM. Furthermore, we evaluated the antitumor effects of the six selected compounds, and compound <b>T6496</b> exhibited the strongest anticancer activity against H1975 cell lines, with an IC<sub>50</sub> value of 2.7 μM. These results suggest that <b>T6496</b> may mitigate EGFR resistance caused by T790M or C797S mutations. Moreover, the AO staining assay, JC-1 staining, ROS experiment and hemolytic toxicity evaluation revealed that <b>T6496</b> could induce apoptosis in H1975 cell lines in a time-dependent and concentration-dependent manner, and is a potential compound for further structural optimization. EGFR drug resistance is a major challenge in lung cancer treatment. This study used in silico methods and bioactivity evaluation to discover novel compounds targeting resistant EGFR mutations. Through machine learning and virtual screening, we identified five potential compounds with higher sensitivity to EGFR than c-Met. T6496 showed potent inhibition of EGFR<sup>T790M-L858R</sup> and EGFR<sup>T790M/C797S/L858R</sup> kinases, with IC<sub>50</sub> values of 3.30 and 8.72 μM. It also exhibited strong anticancer activity against H1975 cell lines, inducing apoptosis in a time and concentration-dependent manner. These findings suggest T6496 as a potential candidate for further optimization. ROC guided machine learning, virtual screening and bioevaluation was applied to discover six hit compounds for overcoming EGFR resistance mediated by T790M or C797S.The promising compound T6496 could both inhibit EGFR<sup>T790M/L858R</sup> and EGFR<sup>T790M/C797S/L858R</sup>, with an IC<sub>50</sub> of 3.30 and 8.72 μM.In addition, T6496 and AO-365/43489452 show excellent anticancer activity even better than AZD9291.AO staining assay, JC-1 staining, and ROS experiment revealed that compounds T6496 could induce apoptosis in H1975 cell lines in a time-dependent and concentration-dependent manner. ROC guided machine learning, virtual screening and bioevaluation was applied to discover six hit compounds for overcoming EGFR resistance mediated by T790M or C797S. The promising compound T6496 could both inhibit EGFR<sup>T790M/L858R</sup> and EGFR<sup>T790M/C797S/L858R</sup>, with an IC<sub>50</sub> of 3.30 and 8.72 μM. In addition, T6496 and AO-365/43489452 show excellent anticancer activity even better than AZD9291. AO staining assay, JC-1 staining, and ROS experiment revealed that compounds T6496 could induce apoptosis in H1975 cell lines in a time-dependent and concentration-dependent manner.