Binding Thermodynamics of Fourth-Generation EGFR Inhibitors Revealed by Absolute Binding Free Energy Calculations.
Huaxin ZhouHaohao FuXueguang ShaoWensheng CaiPublished in: Journal of chemical information and modeling (2023)
The overexpression or mutation of the kinase domain of the epidermal growth factor receptor (EGFR) is strongly associated with non-small-cell lung cancer (NSCLC). EGFR tyrosine kinase inhibitors (TKIs) have proven to be effective in treating NSCLC patients. However, EGFR mutations can result in drug resistance. To elucidate the mechanisms underlying this resistance and inform future drug development, we examined the binding affinities of BLU-945, a recently reported fourth-generation TKI, to wild-type EGFR (EGFR WT ) and its double-mutant (L858R/T790M; EGFR DM ) and triple-mutant (L858R/T790M/C797S; EGFR TM ) forms. We compared the binding affinities of BLU-945, BLU-945 analogues, CH7233163 (another fourth-generation TKI), and erlotinib (a first-generation TKI) using absolute binding free energy calculations. Our findings reveal that BLU-945 and CH7233163 exhibit binding affinities to both EGFR DM and EGFR TM stronger than those of erlotinib, corroborating experimental data. We identified K745 and T854 as the key residues in the binding of fourth-generation EGFR TKIs. Electrostatic forces were the predominant driving force for the binding of fourth-generation TKIs to EGFR mutants. Furthermore, we discovered that the incorporation of piperidinol and sulfone groups in BLU-945 substantially enhanced its binding capacity to EGFR mutants. Our study offers valuable theoretical insights for optimizing fourth-generation EGFR TKIs.
Keyphrases
- epidermal growth factor receptor
- tyrosine kinase
- small cell lung cancer
- advanced non small cell lung cancer
- wild type
- dna binding
- molecular dynamics
- brain metastases
- ejection fraction
- density functional theory
- cell proliferation
- machine learning
- electronic health record
- artificial intelligence
- adipose tissue
- molecular docking
- molecular dynamics simulations
- glycemic control