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

Aug 5, 2026

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Epidermal growth factor receptor (EGFR) T790M mutation commonly emerges during long-duration erlotinib therapy in non-small cell lung cancer (NSCLC), driving resistance and disease progression. Traditional screening has struggled to identify selective EGFR-T790M inhibitors. A Bayesian-inference integrated machine learning workflow screened 70,413 compounds and selected candidates with preferential EGFR-T790M binding. In vitro assays, molecular dynamics, cellular thermal shift, and xenograft validation confirmed CDDO-Me as a selective suppressor of NSCLC growth via apoptosis, cell-cycle arrest, and PI3K-Akt-mTOR inhibition.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages25

Reading Time1 h 3 min

AccessFree to download

Background

EGFR signaling and erlotinib resistance

Methods

Bayesian inference-based machine learning screening

In vitro assays, molecular dynamics, and cross-validation

Xenograft mouse model

Results

Selective EGFR-T790M binding and cellular effects

Apoptosis and cell-cycle arrest

Dose-dependent tumor suppression in vivo

Conclusion

PI3K-Akt-mTOR inhibition and therapeutic implications

Zhou et al. Cell Communication and Signaling (2024) 22:585 [https://doi.org/10.1186/s12964-024-01954-7](https://doi.org/10.1186/s12964-024-01954-7) Cell Communication and Signaling RESEARCH Open Access Machine learning-aided discovery ofT790M- mutant EGFR inhibitor CDDO-Me effectively suppresses non-small cell lung cancer growth Rui Zhou 1†, Ziqian Liu 1†, Tongtong Wu4†, Xianwei Pan 1, Tongtong Li 1, Kaiting Miao4, Yuru Li 1, Xiaohui Hu 1, Haigang Wu4†, Andrew M. Hemmings 1,7, Beier Jiang3*, Zhenzhen Zhang3* and Ning Liu 1,2,5,6*† Abstract 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 oleanol ic 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-MetoT790M-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. | †Rui Zhou, Ziqian Liu and Tongtong Wu contributed equally to this work. | | --- | | †Haigang Wu and Ning Liu are joint senior author. | *Correspondence: Beier Jiang [674358923@qq.com](674358923@qq.com)[ ](674358923@qq.com)Zhenzhen Zhang [zz_jane@163.com](zz_jane@163.com)[ ](zz_jane@163.com)Ning Liu[nliu@shou.edu.cn](nliu@shou.edu.cn) Full list of author information is available at the end of the article © The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit [http://creati](http://creati)[vecommons.org/l](vecommons.org/l)icenses/by-nc-nd/4.0/. Zhou et al. Cell Communication and Signaling (2024) 22:585 Page 2 of 25 Conclusion CDDO-Me induced apoptosis and caused cell cycle arrest by inhibiting the PI3K-Akt-mTOR pathway, directly target

FAQs

Why is EGFR T790M important in NSCLC treatment resistance?

EGFR T790M often develops after prolonged erlotinib use, reducing drug binding affinity and leading to resistance and tumor progression in a substantial fraction of NSCLC patients.

How was the candidate CDDO-Me discovered in this study?

An integrated machine learning approach using Bayesian inference screened a commercial dataset of 70,413 molecules to identify candidates predicted to selectively bind EGFR with the T790M mutation.

What biological mechanisms did CDDO-Me show against NSCLC cells?

CDDO-Me induced apoptosis and caused cell cycle arrest by inhibiting the PI3K-Akt-mTOR axis through direct targeting of EGFR, supported by EGFR silencing in H1975 cells and computational validation.

Aug 5, 2026

Upload by Liam