Artificial intelligence combined with affinity chromatography discover that 7-Epitaxol promotes autophagy in NSCLC cells by interacting with EGFR: Discovery of novel EGFR antagonist based on DL and CMC.

Background: Epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKI) are widely regarded as the most promising strategy to treat non-small cell lung cancer (NSCLC). While the existing development model cannot meet the requirements.

Methods: Here, we developed a drug prediction and screening platform based on state-of-the-art deep learning (DL) algorithms and cell membrane chromatography (CMC). Using this platform, we discovered several novel EGFR antagonists from massive molecules. The interactions between target compounds and EGFR were investigated using frontal analysis, Surface plasmon resonance analysis, cellular thermal shift assay, and molecule docking. Xenografts models were established to study the anti-tumor activity of most promising compound. Proteomics, immunofluorescence, and Western blots were conducted to explore its mechanism.

Results: The deep neural network had good performance, with an area under the curve of 0.950 ± 0.012. Among the top 100 molecules predicted by the model, 25 compounds had retention on SNAP-tagged (ST)-EGFR/CMC, of which 12 molecules exhibited significant anti-tumor activity. Interaction analysis revealed that Geraniin, Brazilin, and 7-Epitaxol would directly bind to EGFR and inhibit its activation. 7-Epitaxol exhibited significant anti-tumor activity in vivo and in vitro. 7-Epitaxol combined with EGFR and inhibited its phosphorylation, blocked PI3K/AKT pathways, thereby exerting its anti-tumor activity by promoting autophagy in A549 cells.

Conclusion: The results provide a novel and powerful platform for drug discovery and development in NSCLC research. By using this platform, Geraniin, Brazilin, and 7-epitaxol were identified as novel EGFR antagonists. We also innovatively demonstrated that 7-epitaxol promotes autophagy in NSCLC.