Machine learning-aided discovery of T790M-mutant EGFR inhibitor CDDO-Me effectively suppresses non-small cell lung cancer growth.

backgroundEpidermal growth factor receptor (EGFR) T790M mutation often occurs during long durational erlotinib treatment of non-small cell lung cancer (NSCLC) patients, leading to drug resistance and disease progression. Identification of new selective EGFR-T790M inhibitors has proven challenging through traditional screening platforms. With great advances in computer algorithms, machine learning improved the screening rates of molecules at full chemical spaces, and these molecules will present higher biological activity and targeting efficiency.

methodsAn integrated machine learning approach, integrated by Bayesian inference, was employed to screen a commercial dataset of 70,413 molecules, identifying candidates that selectively and efficiently bind with EGFR harboring T790M mutation. In vitro cellular assays and molecular dynamic simulations was used for validation. EGFR knockout cell line was generated for cross-validation. In vivo xenograft moues model was constructed to investigate the antitumor efficacy of CDDO-Me.

resultsOur virtual screening and subsequent in vitro testing successfully identified CDDO-Me, an oleanolic acid derivative with anti-inflammatory activity, as a potent inhibitor of NSCLC cancer cells harboring the EGFR-T790M mutation. Cellular thermal shift assay and molecular dynamic simulation validated the selective binding of CDDO-Me to T790M-mutant EGFR. Further experimental results revealed that CDDO-Me induced cellular apoptosis and caused cell cycle arrest through inhibiting the PI3K-Akt-mTOR axis by directly targeting EGFR protein, cross-validated by sgEGFR silencing in H1975 cells. Additionally, CDDO-Me could dose-depended suppress the tumor growth in a H1975 xenograft mouse model.

conclusionCDDO-Me induced apoptosis and caused cell cycle arrest by inhibiting the PI3K-Akt-mTOR pathway, directly targeting the EGFR protein. In vivo studies in a H1975 xenograft mouse model demonstrated dose-dependent suppression of tumor growth. Our work highlights the application of machine learning-aided drug screening and provides a promising lead compound to conquer the drug resistance of NSCLC.