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<p>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 <Emphasis Type="Underline">i</Emphasis>ntact protein <Emphasis Type="Underline">PK/PD</Emphasis> model (<i>i</i>PK/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 <i>i</i>PK/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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年2月19日

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  • Md Amin Hossain,
  • Rutali R. Brahme,
  • Brandon C. Miller,
  • Jakal Amin,
  • Marcela de Barros,
  • Jaime L. Schneider,
  • Jared R. Auclair,
  • Carla Mattos,
  • Qingping Wang,
  • Nathalie Y. R. Agar,
  • David J. Greenblatt,
  • Roman Manetsch,
  • Jeffrey N. Agar

摘要

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.