2. Academic Validation
  3. Profiling and Optimizing Targeted Covalent Inhibitors through EGFR-Guided Studies

Profiling and Optimizing Targeted Covalent Inhibitors through EGFR-Guided Studies

  • J Med Chem. 2025 Aug 28;68(16):17917-17932. doi: 10.1021/acs.jmedchem.5c01661.
Tahereh Damghani 1,  Surbhi P Chitnis 1,  Omobolanle A Abidakun 1,  Kishan B Patel 1,  Kaly S Lin 1,  Emily A Ouellette 1,  Abigail M Lantry 1,  David E Heppner 1 2 3 4
Affiliations

Affiliations

  • 1 Department of Chemistry, The State University of New York at Buffalo, Buffalo, New York 14260, United States.
  • 2 Department of Structural Biology, The State University of New York at Buffalo, Jacobs School of Medicine and Biomedical Sciences, Buffalo, New York 14203, United States.
  • 3 Department of Pharmaceutical Sciences, The State University of New York at Buffalo, Buffalo, New York 14214, United States.
  • 4 Department of Pharmacology and Therapeutics, Roswell Park Comprehensive Cancer Center, Buffalo, New York 14203, United States.
PMID: 40801664 DOI: 10.1021/acs.jmedchem.5c01661
Abstract

Targeted covalent inhibitors (TCIs) are actively pursued in drug discovery due to their prolonged target engagement and clinical efficacy. Although kinetic parameters provide a path to their optimization, systematic design strategies and practical guidance remain underexplored. In this study, the EGFR kinase is deployed as a model system to elucidate structural and functional determinants critical for directing the optimization of irreversible TCIs. Functional analyses reveal a two-phase optimization process, underscoring the importance of balancing─rather than maximizing─the inactivation efficiency rate (kinact/KI). Selective inhibition of the oncogenic L858R/T790M mutant over the wild-type is achieved by tuning this balance, particularly for TCIs exhibiting the fastest kinact/KI. Structural studies indicate that certain hydrophobic and hydrophilic interactions are associated with L858R/T790M selectivity, offering insights into structure-guided design. These results offer a broadly applicable approach for prioritizing compounds and support the integration of kinetic and selectivity data in TCI discovery campaigns.

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