Design, ADME, In silico studies of Quinazoline-based Derivatives as New EGFR Tyrosine Kinase Inhibitors for Anticancer Therapy

Abstract

Background:

EGFR represents a critical therapeutic target in multiple cancer types, yet resistance mutations (T790M, C797S) limit the efficacy of current EGFR inhibitors. Quinazoline scaffolds, exemplified by clinically approved drugs (gefitinib, erlotinib, and osimertinib), offer a validated structural framework for EGFR inhibition. Early computational ADME profiling enables rapid identification of drug-like candidates, accelerating the discovery of novel EGFR inhibitors with enhanced potency and resistance profiles.

Methods:

Ten quinazoline-based derivatives (cp1–cp10) with diverse aromatic substituents were evaluated using integrated computational approaches: SwissADME for ADME profiling and drug-likeness assessment, Schrodinger Suite for molecular docking against EGFR (PDB: 3W33), and Desmond-based 50 ns molecular dynamics simulation for stability evaluation of the lead candidate cp6.

Results:

All derivatives exhibited excellent drug-likeness (Lipinski compliance), with molecular weights of 308.29–366.37 g/mol, optimal lipophilicity (iLOGP 1.6–2.68), high gastrointestinal absorption, and no blood-brain barrier permeability. Compound cp5 demonstrated superior docking affinity (−11.08 kcal/mol), approaching the reference inhibitor 19b (−11.94 kcal/mol). Cp6, selected as the lead based on balanced ADME and synthetic feasibility, showed stable binding interaction (docking score: −9.06 kcal/mol). Molecular dynamics confirmed robust complex stability (protein RMSD: 1.6–2.1 Å; ligand RMSD: < 2.0 Å) with critical interactions maintained for 60–85% of the trajectory.

Conclusion:

Quinazoline-based derivatives cp6 and cp5 emerge as promising EGFR inhibitor leads, combining excellent pharmacokinetic properties with strong binding affinity and computational stability. These candidates warrant experimental validation through in vitro EGFR enzymatic assays, cellular proliferation studies, and selectivity profiling against resistance-associated EGFR mutants.