新規キナゾリン系EGFR阻害剤の3D-QSAR、ドッキング、ADMET、および分子動力学によるインシリコ開発

A library of thirty-one quinazoline derivatives was assessed as potential inhibitors of epidermal growth factor receptor kinase (EGFR) using 3D-QSAR methods, namely Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA). Training and test sets were generated by aligning the molecules to the lowest-energy conformer of the most active compound. The optimized models exhibited strong statistical performance, with R² values of 0.981 (CoMFA) and 0.978 (CoMSIA), and cross-validation coefficients (Q²) of 0.645 and 0.729, respectively. External validation confirmed their predictive power, yielding R² values of 0.929 and 0.909. Guided by these models, eighteen new quinazoline candidates were designed and evaluated for drug likeness and ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) properties using in silico approaches. Molecular docking and molecular dynamics simulations highlighted the binding features and stability of these derivatives, with compound Pred65 demonstrating superior affinity and stability compared to Erlotinib. Collectively, the study provides valuable insights for the optimization of quinazoline scaffolds as EGFR inhibitors, supporting the development of promising anticancer leads.