Machine learning-aided discovery of T790M-mutant EGFR inhibitor CDDO-Me effectively suppresses non-small cell lung
Rui Zhou1, Ziqian Liu1, Tongtong Wu2
1International Research Centre for Food and Health, College of Food Science and Technology, Shanghai Ocean University, Shanghai, 201306, China.
Background:
Epidermal 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.
Methods:
An 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.
Results:
Our 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.
Conclusion:
CDDO-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.
Insights
Machine learning identified CDDO-Me as a potent inhibitor for EGFR T790M-mutant non-small cell lung cancer. This compound induces apoptosis and suppresses tumor growth, offering a promising strategy against drug resistance.
Area of Science:
- Oncology
- Pharmacology
- Computational Biology
Background:
- Epidermal growth factor receptor (EGFR) T790M mutations confer resistance to erlotinib in non-small cell lung cancer (NSCLC).
- Traditional screening methods struggle to identify novel, selective EGFR-T790M inhibitors.
- Machine learning (ML) accelerates the identification of biologically active molecules with improved targeting efficiency.
Purpose of the Study:
- To leverage ML for identifying selective inhibitors of EGFR T790M mutations.
- To validate the efficacy of identified compounds against NSCLC harboring EGFR T790M.
- To explore novel therapeutic strategies for overcoming erlotinib resistance in NSCLC.
Main Methods:
- An integrated ML approach employing Bayesian inference screened 70,413 molecules.
- In vitro cellular assays, molecular dynamic simulations, and EGFR knockout cell lines validated candidate binding and activity.
- An in vivo xenograft mouse model assessed the antitumor efficacy of CDDO-Me.
Main Results:
- CDDO-Me, an oleanolic acid derivative, was identified as a potent inhibitor of NSCLC cells with EGFR T790M mutations.
- Selective binding of CDDO-Me to T790M-mutant EGFR was confirmed via cellular thermal shift assay and molecular dynamics simulations.
- CDDO-Me induced apoptosis and cell cycle arrest by inhibiting the PI3K-Akt-mTOR pathway, suppressing tumor growth in a xenograft model.
Conclusions:
- CDDO-Me effectively targets EGFR T790M, inducing apoptosis and cell cycle arrest via the PI3K-Akt-mTOR pathway.
- In vivo studies demonstrated CDDO-Me's dose-dependent tumor growth suppression in a NSCLC xenograft model.
- ML-aided drug screening offers a promising avenue for developing novel compounds to overcome NSCLC drug resistance.
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