Mass spectrometry methods and mathematical PK/PD model for decision tree-guided covalent drug development

Md Amin Hossain, Rutali R. Brahme, Brandon C. Miller, Jakal Amin, Marcela Barros, Jaime L. Schneider, Jared R. Auclair, Carla Mattos, Qingping Wang, Nathalie Y. R. Agar, David J. Greenblatt, Roman Manetsch and Jeffrey N. Agar ()
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Md Amin Hossain: Northeastern University;Boston
Rutali R. Brahme: Northeastern University;Boston
Brandon C. Miller: Northeastern University;Boston
Jakal Amin: Northeastern University;Boston
Marcela Barros: Northeastern University;Boston
Jaime L. Schneider: Harvard Medical School;Boston
Jared R. Auclair: Northeastern University;Boston
Carla Mattos: Northeastern University;Boston
Qingping Wang: Drug Metabolism and Pharmacokinetics;Cambridge
Nathalie Y. R. Agar: Harvard Medical School;Boston
David J. Greenblatt: Tufts University School of Medicine; Boston
Roman Manetsch: Northeastern University;Boston
Jeffrey N. Agar: Northeastern University;Boston

Nature Communications, 2025, vol. 16, issue 1, 1-14

Abstract: Abstract Covalent drug discovery efforts are growing rapidly but have major unaddressed limitations. These include high false positive rates during hit-to-lead identification; the inherent uncoupling of covalent drug concentration and effect [i.e., uncoupling of pharmacokinetics (PK) and pharmacodynamics (PD)]; and a lack of bioanalytical and modeling methods for determining PK and PD parameters. We present a covalent drug discovery workflow that addresses these limitations. Our bioanalytical methods are based upon a mass spectrometry (MS) assay that can measure the percentage of drug-target protein conjugation (% target engagement) in biological matrices. Further we develop an intact protein PK/PD model (iPK/PD) that outputs PK parameters (absorption and distribution) as well as PD parameters (mechanism of action, protein metabolic half-lives, dose, regimen, effect) based on time-dependent target engagement data. Notably, the iPK/PD model is applicable to any measurement (e.g., bottom-up MS and other drug binding studies) that yields % of target engaged. A Decision Tree is presented to guide researchers through the covalent drug development process. Our bioanalytical methods and the Decision Tree are applied to two approved drugs (ibrutinib and sotorasib); the most common plasma off-target, human serum albumin; three protein targets (KRAS, BTK, SOD1), and to a promising SOD1-targeting ALS drug candidates.

Date: 2025
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DOI: 10.1038/s41467-025-56985-6

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