Identification of Quinazoline-Based EGFR Inhibitors Against Triple-Negative Breast Cancer Using an Integrated In Silico Approach

Background: Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer characterized by the absence of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2), limiting the effectiveness of targeted therapies. Epidermal growth factor receptor (EGFR) is frequently overexpressed in TNBC, making it an attractive therapeutic target. Quinazoline derivatives have shown potential as EGFR tyrosine kinase inhibitors.

Methods: Twenty-six quinazoline derivatives were evaluated against EGFR (PDB ID: 3UG2) using an integrated in silico workflow. Hierarchical molecular docking (HTVS, SP, and XP) was performed using the Glide module in Schrödinger Maestro. The top-ranked compounds were further analyzed through ligand–protein interaction studies and Prime MM-GBSA binding free energy calculations. Pharmacokinetic and toxicity profiles were predicted using SwissADME and ProTox-II, respectively.

Results: Gefitinib exhibited the highest binding affinity with an XP GlideScore of −7.780 kcal/mol. Among the investigated derivatives, S26 showed the best docking score (−7.304 kcal/mol), followed by S16 (−6.842 kcal/mol) and S24 (−6.757 kcal/mol). All selected compounds maintained the conserved hydrogen bond with Met793 within the ATP-binding pocket. S24 exhibited the most favorable MM-GBSA binding free energy (ΔG_bind = −76.85 kcal/mol), whereas SwissADME predicted favorable drug-likeness and pharmacokinetic properties. Toxicity prediction identified S16 as the compound with the most favorable safety profile.

Conclusion: The integrated computational approach identified S26, S24, and S16 as promising quinazoline-based EGFR inhibitors. S24 emerged as the most balanced lead compound, while S26 exhibited the strongest docking affinity and S16 demonstrated the best predicted safety profile. These findings support experimental validation.