A multi-stage computational pipeline for repurposing FDA-approved drugs: application to EGFR C797S-mutant NSCLC.

PMID 41987835 | PMCID PMC13077434 | DOI 10.3389/fchem.2026.1753911 · Frontiers in chemistry · 2026 · View on PubMed ↗

BACKGROUND/OBJECTIVES: Non-small cell lung cancer (NSCLC) is a leading cause of cancer-related death globally. Resistance to third-generation epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors, particularly due to the C797S mutation, poses a significant clinical challenge. This study utilized a comprehensive multiphase computational drug repurposing approach to discover FDA-approved medications that may be effective against the EGFR C797S mutation. METHODS: A library of 1,650 compounds from the ZINC15 database was subjected to shape-based screening, followed by hierarchical molecular docking using high-throughput, standard precision, and extra-precision methods. The top candidates were further analyzed using MM-GBSA binding free-energy calculations, covalent docking simulations, and three 300-ns molecular dynamics simulations to evaluate the binding stability and interaction persistence under dynamic conditions. RESULTS: Among the evaluated compounds, doripenem, norgestrel, oxymetholone, norethisterone, and ertapenem exhibited high docking scores and consistent interactions with crucial hinge-region residues, such as MET-793 and mutant SER-797, within the ATP-binding site. Through molecular dynamics analyses, ertapenem and oxymetholone were identified as the most stable complexes, exhibiting minimal root-mean-square deviation fluctuations and maintaining hydrogen-bond networks similar to that of the reference inhibitor osimertinib. CONCLUSION: These findings suggest that ertapenem and oxymetholone are promising structurally unique scaffolds for targeting osimertinib-resistant EGFR C797S-driven NSCLC. While experimental validation is necessary, this study provides a computational framework for swift drug repurposing and lays a rational foundation for future biochemical evaluation and structure-guided optimization of next-generation EGFR inhibitors.