<p>Despite extensive efforts, extracting medication exposure information from clinical records remains challenging. To complement this approach, here we show the Global Natural Product Social Molecular Networking (GNPS) Drug Library, a tandem mass spectrometry (MS/MS) based resource designed for drug screening with untargeted metabolomics. This resource integrates MS/MS references of drugs and their metabolites/analogs with standardized vocabularies on their exposure sources, pharmacologic classes, therapeutic indications, and mechanisms of action. It enables direct analysis of drug exposure and metabolism from untargeted metabolomics data, supporting flexible summarization at multiple ontology levels to align with different research goals. We demonstrate its application by stratifying participants in a human immunodeficiency virus (HIV) cohort based on detected drug exposures. We uncover drug-associated alterations in microbiota-derived <i>N</i>-acyl lipids that are not captured when stratifying by self-reported medication use. Overall, GNPS Drug Library provides a scalable resource for empirical drug screening in clinical, nutritional, environmental, and other research disciplines, facilitating insights into the ecological and health consequences of drug exposures. While not intended for immediate clinical decision-making, it supports data-driven exploration of drug exposures where traditional records are limited or unreliable.</p>

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A resource to empirically establish drug exposure records directly from untargeted metabolomics data

  • Haoqi Nina Zhao,
  • Kine Eide Kvitne,
  • Corinna Brungs,
  • Siddharth Mohan,
  • Vincent Charron-Lamoureux,
  • Wout Bittremieux,
  • Runbang Tang,
  • Robin Schmid,
  • Santosh Lamichhane,
  • Shipei Xing,
  • Yasin El Abiead,
  • Mohammadsobhan S. Andalibi,
  • Helena Mannochio-Russo,
  • Madison Ambre,
  • Nicole E. Avalon,
  • MacKenzie Bryant,
  • Lindsey A. Burnett,
  • Andrés Mauricio Caraballo-Rodríguez,
  • Martin Casas Maya,
  • Loryn Chin,
  • Lluís Corominas,
  • Ronald J. Ellis,
  • Donald Franklin,
  • Sagan Girod,
  • Paulo Wender P. Gomes,
  • Lauren Hansen,
  • Robert K. Heaton,
  • Jennifer E. Iudicello,
  • Alan K. Jarmusch,
  • Lora Khatib,
  • Scott Letendre,
  • Sarolt Magyari,
  • Daniel McDonald,
  • Ipsita Mohanty,
  • Andrés Cumsille,
  • David J. Moore,
  • Prajit Rajkumar,
  • Dylan H. Ross,
  • Harshada Sapre,
  • Mohammad Reza Zare Shahneh,
  • Ruben Gil-Solsona,
  • Sydney P. Thomas,
  • Caitlin Tribelhorn,
  • Helena M. Tubb,
  • Corinn Walker,
  • Crystal X. Wang,
  • Jasmine Zemlin,
  • Simone Zuffa,
  • David S. Wishart,
  • Pablo Gago-Ferrero,
  • Rima Kaddurah-Daouk,
  • Mingxun Wang,
  • Manuela Raffatellu,
  • Karsten Zengler,
  • Tomáš Pluskal,
  • Libin Xu,
  • Rob Knight,
  • Shirley M. Tsunoda,
  • Pieter C. Dorrestein

摘要

Despite extensive efforts, extracting medication exposure information from clinical records remains challenging. To complement this approach, here we show the Global Natural Product Social Molecular Networking (GNPS) Drug Library, a tandem mass spectrometry (MS/MS) based resource designed for drug screening with untargeted metabolomics. This resource integrates MS/MS references of drugs and their metabolites/analogs with standardized vocabularies on their exposure sources, pharmacologic classes, therapeutic indications, and mechanisms of action. It enables direct analysis of drug exposure and metabolism from untargeted metabolomics data, supporting flexible summarization at multiple ontology levels to align with different research goals. We demonstrate its application by stratifying participants in a human immunodeficiency virus (HIV) cohort based on detected drug exposures. We uncover drug-associated alterations in microbiota-derived N-acyl lipids that are not captured when stratifying by self-reported medication use. Overall, GNPS Drug Library provides a scalable resource for empirical drug screening in clinical, nutritional, environmental, and other research disciplines, facilitating insights into the ecological and health consequences of drug exposures. While not intended for immediate clinical decision-making, it supports data-driven exploration of drug exposures where traditional records are limited or unreliable.