Design and Optimization of Novel EGFR Inhibitors for NSCLC: A Computational Approach to Overcome Resistance Mechanisms

Background: Non-small cell lung cancer (NSCLC) is the most prevalent form of lung cancer, often associated with poor prognosis and resistance to treatment. The Epidermal Growth Factor Receptor (EGFR) remains a crucial target in therapy. Methods: An advanced computational workflow was used to identify and optimize EGFR inhibitors, integrating active site prediction (CB-Dock2), ligand generation (Lead3), virtual screening (AutoDock Vina), ADMET analysis (ADMETlab 2.0), and QSAR modeling. The QSAR model was validated to ensure predictive reliability. Results: Compound g18_mol18 demonstrated a binding affinity of -9.9 kcal/mol, significantly stronger than the standard compound (-7.381 kcal/mol) (p = 0.039). Interaction analysis showed that g18_mol18 formed multiple hydrogen bonds and hydrophobic contacts with key residues. Despite its strong binding affinity, ADMET analysis highlighted challenges such as poor intestinal absorption (HIA: 0.005) and potential hepatotoxicity. However, its low hERG inhibition (0.302 vs. 0.923) indicates a lower risk of cardiotoxicity, suggesting a favorable cardiac safety profile. Conclusion: The study identifies g18_mol18 as a potent EGFR inhibitor with significantly higher binding affinity and more extensive interactions than current treatments. Although it presents pharmacokinetic challenges, these findings underline its potential as a more effective and safer alternative for NSCLC treatment, warranting further experimental validation and optimization for clinical applications. Such developments could lead to durable therapeutic responses, addressing key resistance issues seen with current EGFR inhibitors.