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通过表皮生长因子受体(EGFR)导向研究对靶向共价抑制剂进行分析和优化

Profiling and Optimizing Targeted Covalent Inhibitors through EGFR-Guided Studies.

作者信息

Damghani Tahereh, Chitnis Surbhi P, Abidakun Omobolanle A, Patel Kishan B, Lin Kaly S, Ouellette Emily A, Lantry Abigail M, Heppner David E

机构信息

Department of Chemistry, The State University of New York at Buffalo, Buffalo, New York 14260, United States.

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.

出版信息

J Med Chem. 2025 Aug 28;68(16):17917-17932. doi: 10.1021/acs.jmedchem.5c01661. Epub 2025 Aug 13.

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 (/). Selective inhibition of the oncogenic L858R/T790M mutant over the wild-type is achieved by tuning this balance, particularly for TCIs exhibiting the fastest /. 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.

摘要

靶向共价抑制剂(TCIs)因其延长的靶点结合时间和临床疗效,在药物研发中备受关注。尽管动力学参数为其优化提供了途径,但系统的设计策略和实用指南仍有待深入探索。在本研究中,表皮生长因子受体(EGFR)激酶被用作模型系统,以阐明指导不可逆TCIs优化的关键结构和功能决定因素。功能分析揭示了一个两阶段的优化过程,强调了平衡(而非最大化)失活效率速率(/)的重要性。通过调整这种平衡,特别是对于具有最快/的TCIs,可实现对致癌性L858R/T790M突变体相对于野生型的选择性抑制。结构研究表明,某些疏水和亲水相互作用与L858R/T790M选择性相关,为结构导向设计提供了见解。这些结果为化合物的优先排序提供了一种广泛适用的方法,并支持在TCI发现活动中整合动力学和选择性数据。

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