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

Haoqi Nina Zhao^#^{1

2}, Kine Eide Kvitne^#^{2

3}, Corinna Brungs^#^{4

5}, Siddharth Mohan², Vincent Charron-Lamoureux^{1

2}, Wout Bittremieux^{1

2

6}, Runbang Tang², Robin Schmid^{1

2

4}, Santosh Lamichhane^{2

7}, Shipei Xing^{1

2}, Yasin El Abiead^{1

2}, Mohammadsobhan S Andalibi^{8

9

10}, Helena Mannochio-Russo^{1

2}, Madison Ambre¹¹, Nicole E Avalon¹², MacKenzie Bryant¹¹, Lindsey A Burnett¹³, Andrés Mauricio Caraballo-Rodríguez^{1

2}, Martin Casas Maya¹¹, Loryn Chin¹⁴, Lluís Corominas¹⁵, Ronald J Ellis^{8

9}, Donald Franklin⁹, Sagan Girod¹⁶, Paulo Wender P Gomes^{1

2

17}, Lauren Hansen¹¹, Robert K Heaton⁹, Jennifer E Iudicello⁹, Alan K Jarmusch^{1

2

18}, Lora Khatib⁸, Scott Letendre^{10

19}, Sarolt Magyari^{2

20}, Daniel McDonald¹¹, Ipsita Mohanty^{1

2}, Andrés Cumsille^{2

21}, David J Moore^{9

10}, Prajit Rajkumar², Dylan H Ross^{22

23}, Harshada Sapre², Mohammad Reza Zare Shahneh²⁴, Ruben Gil-Solsona²⁵, Sydney P Thomas^{1

2}, Caitlin Tribelhorn¹¹, Helena M Tubb¹¹, Corinn Walker¹¹, Crystal X Wang^{9

10}, Jasmine Zemlin^{1

2

26}, Simone Zuffa^{1

2}, David S Wishart^{16

27}, Pablo Gago-Ferrero²⁵, Rima Kaddurah-Daouk^{28

29

30}, Mingxun Wang²⁴, Manuela Raffatellu^{11

26

31}, Karsten Zengler^{11

14

26

32}, Tomáš Pluskal⁴, Libin Xu²², Rob Knight^{11

26

33

34

35}, Shirley M Tsunoda², Pieter C Dorrestein^{36

37

38}

Affiliations

¹ Collaborative Mass Spectrometry Innovation Center, University of California San Diego, La Jolla, CA, USA.
² Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA.
³ Department of Pharmacy, University of Oslo, Oslo, Norway.
⁴ Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Prague, Czechia.
⁵ Department of Pharmaceutical Sciences, University of Vienna, Vienna, Austria.
⁶ Department of Computer Science, University of Antwerp, Antwerp, Belgium.
⁷ Institute of Biomedicine and Turku Bioscience Centre, University of Turku and Åbo Akademi University, Tykistönkatu 6A, 20520, Turku, Finland.
⁸ Department of Neurosciences, University of California San Diego, La Jolla, CA, USA.
⁹ Department of Psychiatry, University of California San Diego, La Jolla, CA, USA.
¹⁰ HIV Neurobehavioral Research Program, University of California San Diego, La Jolla, CA, USA.
¹¹ Department of Pediatrics, University of California San Diego, La Jolla, CA, USA.
¹² Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA, USA.
¹³ Department of Obstetrics, Gynecology and Reproductive Sciences, University of California San Diego, La Jolla, CA, USA.
¹⁴ Department of Bioengineering, University of California San Diego, La Jolla, California, USA.
¹⁵ Catalan Institute for Water Research (ICRA-CERCA), Girona, Spain.
¹⁶ Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada.
¹⁷ Faculty of Chemistry, Federal University of Pará, Belém, PA, Brazil.
¹⁸ Immunity, Inflammation, and Disease Laboratory, Division of Intramural Research, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, Bethesda, NC, USA.
¹⁹ Department of Medicine, University of California San Diego, La Jolla, CA, USA.
²⁰ Institute of Microbiology, Eidgenössische Technische Hochschule (ETH) Zürich, Zürich, Switzerland.
²¹ Department of Plant Pathology, University of Wisconsin-Madison, Madison, WI, USA.
²² Department of Medicinal Chemistry, University of Washington, Seattle, WA, USA.
²³ Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA.
²⁴ Department of Computer Science and Engineering, University of California Riverside, Riverside, CA, USA.
²⁵ Department of Environmental Chemistry, Institute of Environmental Assessment and Water Research (IDAEA), Spanish Council of Scientific Research (CSIC), Barcelona, Spain.
²⁶ Center for Microbiome Innovation, University of California San Diego, La Jolla, CA, USA.
²⁷ Department of Computing Science, University of Alberta, Edmonton, AB, Canada.
²⁸ Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA.
²⁹ Duke Institute of Brain Sciences, Duke University, Durham, NC, USA.
³⁰ Department of Medicine, Duke University, Durham, NC, USA.
³¹ Chiba University, UC San Diego Center for Mucosal Immunology, Allergy and Vaccines (CU-UCSD cMAV), La Jolla, CA, USA.
³² Program in Materials Science and Engineering, University of California San Diego, La Jolla, CA, USA.
³³ Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA, USA.
³⁴ Shu Chien-Gene Lay Department of Bioengineering, University of California San Diego, La Jolla, CA, USA.
³⁵ Halıcıoğlu Data Science Institute, University of California San Diego, La Jolla, CA, USA.
³⁶ Collaborative Mass Spectrometry Innovation Center, University of California San Diego, La Jolla, CA, USA. pdorrestein@health.ucsd.edu.
³⁷ Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA. pdorrestein@health.ucsd.edu.
³⁸ Center for Microbiome Innovation, University of California San Diego, La Jolla, CA, USA. pdorrestein@health.ucsd.edu.

^# Contributed equally.

PMID: 41366203
PMCID: PMC12689629
DOI: 10.1038/s41467-025-65993-5

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

Haoqi Nina Zhao et al. Nat Commun. 2025.

. 2025 Dec 9;16(1):10600.

doi: 10.1038/s41467-025-65993-5.

Authors

Affiliations

¹ Collaborative Mass Spectrometry Innovation Center, University of California San Diego, La Jolla, CA, USA.
² Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA.
³ Department of Pharmacy, University of Oslo, Oslo, Norway.
⁴ Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Prague, Czechia.
⁵ Department of Pharmaceutical Sciences, University of Vienna, Vienna, Austria.
⁶ Department of Computer Science, University of Antwerp, Antwerp, Belgium.
⁷ Institute of Biomedicine and Turku Bioscience Centre, University of Turku and Åbo Akademi University, Tykistönkatu 6A, 20520, Turku, Finland.
⁸ Department of Neurosciences, University of California San Diego, La Jolla, CA, USA.
⁹ Department of Psychiatry, University of California San Diego, La Jolla, CA, USA.
¹⁰ HIV Neurobehavioral Research Program, University of California San Diego, La Jolla, CA, USA.
¹¹ Department of Pediatrics, University of California San Diego, La Jolla, CA, USA.
¹² Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA, USA.
¹³ Department of Obstetrics, Gynecology and Reproductive Sciences, University of California San Diego, La Jolla, CA, USA.
¹⁴ Department of Bioengineering, University of California San Diego, La Jolla, California, USA.
¹⁵ Catalan Institute for Water Research (ICRA-CERCA), Girona, Spain.
¹⁶ Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada.
¹⁷ Faculty of Chemistry, Federal University of Pará, Belém, PA, Brazil.
¹⁸ Immunity, Inflammation, and Disease Laboratory, Division of Intramural Research, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, Bethesda, NC, USA.
¹⁹ Department of Medicine, University of California San Diego, La Jolla, CA, USA.
²⁰ Institute of Microbiology, Eidgenössische Technische Hochschule (ETH) Zürich, Zürich, Switzerland.
²¹ Department of Plant Pathology, University of Wisconsin-Madison, Madison, WI, USA.
²² Department of Medicinal Chemistry, University of Washington, Seattle, WA, USA.
²³ Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA.
²⁴ Department of Computer Science and Engineering, University of California Riverside, Riverside, CA, USA.
²⁵ Department of Environmental Chemistry, Institute of Environmental Assessment and Water Research (IDAEA), Spanish Council of Scientific Research (CSIC), Barcelona, Spain.
²⁶ Center for Microbiome Innovation, University of California San Diego, La Jolla, CA, USA.
²⁷ Department of Computing Science, University of Alberta, Edmonton, AB, Canada.
²⁸ Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA.
²⁹ Duke Institute of Brain Sciences, Duke University, Durham, NC, USA.
³⁰ Department of Medicine, Duke University, Durham, NC, USA.
³¹ Chiba University, UC San Diego Center for Mucosal Immunology, Allergy and Vaccines (CU-UCSD cMAV), La Jolla, CA, USA.
³² Program in Materials Science and Engineering, University of California San Diego, La Jolla, CA, USA.
³³ Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA, USA.
³⁴ Shu Chien-Gene Lay Department of Bioengineering, University of California San Diego, La Jolla, CA, USA.
³⁵ Halıcıoğlu Data Science Institute, University of California San Diego, La Jolla, CA, USA.
³⁶ Collaborative Mass Spectrometry Innovation Center, University of California San Diego, La Jolla, CA, USA. pdorrestein@health.ucsd.edu.
³⁷ Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA. pdorrestein@health.ucsd.edu.
³⁸ Center for Microbiome Innovation, University of California San Diego, La Jolla, CA, USA. pdorrestein@health.ucsd.edu.

^# Contributed equally.

PMID: 41366203
PMCID: PMC12689629
DOI: 10.1038/s41467-025-65993-5

Abstract

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.

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Conflict of interest statement

Competing interests: R.S. is a co-founder of mzio GmbH. D.M.: D.M. is a consultant for BiomeSense, Inc., has equity, and receives income. The terms of these arrangements have been reviewed and approved by the University of California, San Diego in accordance with its conflict of interest policies. R.K.-D.: R.K.-D. is an inventor on a series of patents on use of metabolomics for the diagnosis and treatment of CNS diseases and holds equity in Metabolon Inc., Chymia LLC, and PsyProtix. M.W. is a co-founder of Ometa Labs LLC. T.P. is a co-founder of mzio GmbH. R.K. is a scientific advisory board member, and consultant for BiomeSense, Inc., has equity, and receives income. He is a scientific advisory board member and has equity in GenCirq. He has equity in and acts as a consultant for Cybele. He is a co-founder of Biota, Inc., and has equity. He is a cofounder of Micronoma and has equity and is a scientific advisory board member. He is a board member of Microbiota Vault, Inc. He is a board member of N = 1 IBS advisory board and receives income. He is a Senior Visiting Fellow of HKUST Jockey Club Institute for Advanced Study. The terms of these arrangements have been reviewed and approved by the University of California, San Diego, in accordance with its conflict of interest policies. S.M.T.: S.M.T. receives research funding from Veloxis Pharmaceuticals. P.C.D.: P.C.D. is a scientific advisor and holds equity in Cybele, and bileOmix, and is a Scientific Co-founder, advisor, holds equity and/or received income from Ometa, Arome, and Enveda, with prior approval by UC-San Diego. The remaining authors declare no competing interests.

Figures

**Fig. 1. The GNPS Drug Library and connected pharmacologic metadata.**
a The GNPS Drug Library comprises four key resources: Drug MS/MS reference spectra, drug metabolite MS/MS reference spectra, propagated drug analogs derived from public metabolomics datasets, and pharmacologic metadata connected to each reference spectrum. b FastMASST analog search of drug spectra against public metabolomics studies yielded propagated drug-analogous MS/MS spectra, which were filtered by removing analogs for drugs with endogenous and food sources (source filter), removing mass offsets unexplained by common metabolic pathways (mass offset filter), removing analogs with GNPS library matches (library match filter), and removing analogs with unrealistic drug exposure indications (dataset testing). c Illustration of each filter employed in curating FastMASST analog match results. d Frequency of mass offsets in the propagated drug analog library. The mass offsets were grouped by unit mass and stacked based on the number of analog spectra. The most frequently observed mass offsets are colored while the rests are black. The numbers of in-source fragments, isotopes, and adducts in the drug analog library are estimated based on peak shape correlation and fragment matching strategies (see Fig. S1 for details). e An example of structural modification sites predicted by ModiFinder. Purple color highlights modified spectra and substructures, while the green color highlights unmodified ones. f Overview of the ontology-based drug metadata, highlighting common pharmaceutical classes and specific drugs in the neurology/psychiatry category. Width of the bars and lines reflects the number of unique drug structures in each class. g Barplot showing the top 20 most detected pharmacologic classes in fecal samples from the American Gut Project, a cohort of the general population from the United States (US), Europe, and Australia (1993 individuals). Bars are colored based on the pharmacologic classes. h Detected therapeutic drug class patterns by age and sex (1845 individuals with age and sex information; age 46 ± 18 years [range 3–93], with 53% being female). Bars and lines are colored based on sex. NSAID non-steroidal anti-inflammatory drugs, ACE angiotensin converting enzyme, SSRI selective serotonin reuptake inhibitor, PPI proton pump inhibitor, HSV herpes simplex virus, DHFR dihydrofolate reductase, SNRI serotonin and norepinephrine reuptake inhibitor. Source data are provided as a Source Data file.

**Fig. 2. Drug exposures in the HIV Neurobehavioral Research Center (HNRC) cohort with connections to microbial and endogenous metabolites.**
From the HNRC cohort, 322 fecal samples were analyzed, with 222 samples from people with HIV and 100 samples from people without HIV. a Peak area visualization of drugs detected with at least two metabolites or analogs and in >10% samples. Each column represents one sample, and each row represents one drug annotation. Drug annotations were grouped based on the parent drugs and separated by gap spaces. Drug annotations were denoted based on their annotation types (as drug, drug metabolites, or drug analogs) and the pharmacologic classes of the parent drugs. All annotated ion/adduct forms of the parent drugs were visualized, leading to multiple rows of parent annotations for some drugs. Asterisks on the drug name mark parent drug annotations confirmed with commercial standards based on retention time and MS/MS spectral matches. Raw peak areas were log-transformed. b Retention time and MS/MS spectra mirror matches for drug analogs observed in both the fecal samples and the drug microbial incubations. Purple traces represent the fecal samples, while red traces represent the drug microbial incubation. Blue traces represent mixtures of the fecal samples and the microbial incubations at 1:1 volume ratio. The atomic changes of the drug analogs were based on [M + H]⁺ ion of the parent drug. c Hierarchical clustering of the samples from people with HIV (n = 222) based on detected antiretroviral drugs (ARV). Each row represents one detected ARV, with peak areas summed for the drug, metabolite, and analog detections followed by log-transformation (visualized with the same color scale as panel a). ARVs detected in <10% of samples are not shown. Each column represents one sample, clustered into four groups by hierarchical clustering with Ward’s linkage and Euclidean distance. d Sample-to-sample peak areas of N-acyl lipids in people with HIV, separated by the clusters derived from ARV detections shown in (c) (Group 1, n = 47; Group 2, n = 64; Group 3, n = 52; Group 4, n = 59). For each compound, the peak area in each sample was standardized to the maximum value observed across all samples. A non-parametric Kruskal-Wallis test followed by pairwise Wilcoxon test and Benjamini-Hochberg correction for multiple comparisons was performed. P-values < 0.05 were noted in the figure. Boxplots showcase the median value, first (lower) and third (upper) quartiles, and whiskers indicate the error range as 1.5 times the interquartile range. Source data are provided as a Source Data file.

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Empirically establishing drug exposure records directly from untargeted metabolomics data.
Zhao HN, Kvitne KE, Brungs C, Mohan S, Charron-Lamoureux V, Bittremieux W, Tang R, Schmid R, Lamichhane S, El Abiead Y, Andalibi MS, Mannochio-Russo H, Ambre M, Avalon NE, Bryant M, Caraballo-Rodríguez AM, Maya MC, Chin L, Ellis RJ, Franklin D, Girod S, Gomes PWP, Hansen L, Heaton R, Iudicello JE, Jarmusch AK, Khatib L, Letendre S, Magyari S, McDonald D, Mohanty I, Cumsille A, Moore DJ, Rajkumar P, Ross DH, Sapre H, Shahneh MRZ, Thomas SP, Tribelhorn C, Tubb HM, Walker C, Wang CX, Xing S, Zemlin J, Zuffa S, Wishart DS, Kaddurah-Daouk R, Wang M, Raffatellu M, Zengler K, Pluskal T, Xu L, Knight R, Tsunoda SM, Dorrestein PC. Zhao HN, et al. bioRxiv [Preprint]. 2024 Oct 26:2024.10.07.617109. doi: 10.1101/2024.10.07.617109. bioRxiv. 2024. Update in: Nat Commun. 2025 Dec 9;16(1):10600. doi: 10.1038/s41467-025-65993-5. PMID: 39416075 Free PMC article. Updated. Preprint.

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

Affiliations

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

Authors

Affiliations

Abstract

Conflict of interest statement

Figures

Update of

References

MeSH terms

Grants and funding

LinkOut - more resources

Full Text Sources