Identification of novel EGFR inhibitors for glioblastoma through pharmacophore-guided virtual screening and molecular dynamics

Abstract

Glioblastoma (GB) is an aggressive and lethal brain tumor characterized by high mortality and poor prognosis. Amplification and mutation of the epidermal growth factor receptor (EGFR) gene are key drivers of GB progression, highlighting EGFR as a promising therapeutic target. This study aimed to identify novel small-molecule EGFR inhibitors through a pharmacophore-guided computational screening approach. A pharmacophore model was generated using the co-crystal ligand (PDB: HYZ) as a template. This model was employed to screen the Specs database comprising approximately 280,000 compounds. Ligand-based virtual screening yielded 323 hits that matched the pharmacophore query. These compounds were subjected to molecular docking against the EGFR active site using the Glide standard precision protocol, applying a binding affinity threshold of -9 kcal/mol. Eleven compounds with favorable docking scores were further evaluated through ADMET (adsorption, distribution, metabolism, excretion, toxicity) profiling, and eight top candidates were subjected to molecular dynamics simulations to assess binding stability. The pharmacophore-based screening and docking analysis identified eleven promising EGFR-binding compounds, of which eight demonstrated optimal ADMET characteristics and stable interactions within the active site during molecular dynamics simulations. The selected molecules exhibited strong binding affinities and favorable conformational stability, suggesting their potential efficacy as EGFR inhibitors. This study identified and characterized novel small molecules with high potential for EGFR inhibition in glioblastoma. These findings provide a foundation for further experimental validation and may contribute to the development of targeted therapies for GB.