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

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  • Md Amin Hossain

    (Northeastern University;Boston
    Barnett Institute of Chemical and Biological Analysis;Boston
    Harvard Medical School;Boston)

  • Rutali R. Brahme

    (Northeastern University;Boston
    Barnett Institute of Chemical and Biological Analysis;Boston)

  • Brandon C. Miller

    (Northeastern University;Boston)

  • Jakal Amin

    (Northeastern University;Boston
    Barnett Institute of Chemical and Biological Analysis;Boston)

  • Marcela Barros

    (Northeastern University;Boston)

  • Jaime L. Schneider

    (Harvard Medical School;Boston)

  • Jared R. Auclair

    (Northeastern University;Boston
    Barnett Institute of Chemical and Biological Analysis;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
    Northeastern University;Boston)

  • Jeffrey N. Agar

    (Northeastern University;Boston
    Barnett Institute of Chemical and Biological Analysis;Boston
    Northeastern University;Boston)

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.

Suggested Citation

  • 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 , 2025. "Mass spectrometry methods and mathematical PK/PD model for decision tree-guided covalent drug development," Nature Communications, Nature, vol. 16(1), pages 1-14, December.
  • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-56985-6
    DOI: 10.1038/s41467-025-56985-6
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    References listed on IDEAS

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