Treffer: A versatile toolkit for drug metabolism studies with GNPS2: from drug development to clinical monitoring.
Title:
A versatile toolkit for drug metabolism studies with GNPS2: from drug development to clinical monitoring.
Authors:
Yu JS; Pharmacomicrobiomics Research Center and College of Pharmacy, Hanyang University, Ansan, Republic of Korea.; College of Pharmacy, Jeju National University, Jeju, Republic of Korea., Kwak YB; Department of Pharmaceutical Engineering, Inje University, Gimhae, Republic of Korea., Kee KH; Pharmacomicrobiomics Research Center and College of Pharmacy, Hanyang University, Ansan, Republic of Korea., Wang M; Department of Computer Science and Engineering, University of California Riverside, Riverside, CA, USA., Kim DH; Department of Pharmacology, Inje University College of Medicine, Busan, Republic of Korea., Dorrestein PC; Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA., Kang KB; College of Pharmacy and Research Institute of Pharmaceutical Sciences, Sookmyung Women's University, Seoul, Republic of Korea. kbkang@sookmyung.ac.kr., Yoo HH; Pharmacomicrobiomics Research Center and College of Pharmacy, Hanyang University, Ansan, Republic of Korea. yoohh@hanyang.ac.kr.
Source:
Nature protocols [Nat Protoc] 2025 Sep 08. Date of Electronic Publication: 2025 Sep 08.
Publication Model:
Ahead of Print
Publication Type:
Journal Article; Review
Language:
English
Journal Info:
Publisher: Nature Pub. Group Country of Publication: England NLM ID: 101284307 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1750-2799 (Electronic) Linking ISSN: 17502799 NLM ISO Abbreviation: Nat Protoc Subsets: MEDLINE
Imprint Name(s):
Original Publication: London, UK : Nature Pub. Group, 2006-
References:
Trefts, E., Gannon, M. & Wasserman, D. H. The liver. Curr. Biol. 27, R1147–R1151 (2017). (PMID: 29112863589711810.1016/j.cub.2017.09.019)
Fan, Y. & Pedersen, O. Gut microbiota in human metabolic health and disease. Nat. Rev. Microbiol. 19, 55–71 (2021). (PMID: 3288794610.1038/s41579-020-0433-9)
Lai, Y. R. et al. Recent advances in the translation of drug metabolism and pharmacokinetics science for drug discovery and development. Acta Pharm. Sin. B 12, 2751–2777 (2022). (PMID: 35755285921405910.1016/j.apsb.2022.03.009)
Ufer, M., Juif, P. E., Boof, M. L., Muehlan, C. & Dingemanse, J. Metabolite profiling in early clinical drug development: current status and future prospects. Expert Opin. Drug Metab. Toxicol. 13, 803–806 (2017). (PMID: 2868943210.1080/17425255.2017.1351944)
He, C. Y. & Wan, H. Drug metabolism and metabolite safety assessment in drug discovery and development. Expert Opin. Drug Metab. Toxicol. 14, 1071–1085 (2018). (PMID: 3021528010.1080/17425255.2018.1519546)
Gajula, S. N. R., Nadimpalli, N. & Sonti, R. Drug metabolic stability in early drug discovery to develop potential lead compounds. Drug Metab. Rev. 53, 459–477 (2021). (PMID: 3440688910.1080/03602532.2021.1970178)
Wishart, D. S. Emerging applications of metabolomics in drug discovery and precision medicine. Nat. Rev. Drug Discov. 15, 473–484 (2016). (PMID: 2696520210.1038/nrd.2016.32)
Li, F., Gonzalez, F. J. & Ma, X. C. LC-MS-based metabolomics in profiling of drug metabolism and bioactivation. Acta Pharm. Sin. B 2, 118–125 (2012). (PMID: 10.1016/j.apsb.2012.02.010)
Hsieh, Y. HPLC-MS/MS in drug metabolism and pharmacokinetic screening. Expert Opin. Drug Metab. Toxicol. 4, 93–101 (2008). (PMID: 1837086110.1517/17425255.4.1.93)
Wei, W. L. et al. Simultaneous determination of resibufogenin and its eight metabolites in rat plasma by LC-MS/MS for metabolic profiles and pharmacokinetic study. Phytomedicine 60, 159271 (2019). (PMID: 10.1016/j.phymed.2019.152971)
Schadt, S. et al. A decade in the MIST: learnings from investigations of drug metabolites in drug development under the “Metabolites in Safety Testing” regulatory guidance. Drug Metab. Dispos. 46, 865–878 (2018). (PMID: 2948714210.1124/dmd.117.079848)
Chen, C. J., Lee, D. Y., Yu, J. X., Lin, Y. N. & Lin, T. M. Recent advances in LC-MS-based metabolomics for clinical biomarker discovery. Mass Spectrom. Rev. 42, 2349–2378 (2023). (PMID: 3564514410.1002/mas.21785)
Cai, Y. P., Zhou, Z. W. & Zhu, Z. J. Advanced analytical and informatic strategies for metabolite annotation in untargeted metabolomics. Trends Anal. Chem. 158, 116903 (2023). (PMID: 10.1016/j.trac.2022.116903)
Jarmusch, S. A., van der Hooft, J. J. J., Dorrestein, P. C. & Jarmusch, A. K. Advancements in capturing and mining mass spectrometry data are transforming natural products research. Nat. Prod. Rep. 38, 2066–2082 (2021). (PMID: 34612288866778110.1039/D1NP00040C)
Wang, M. X. et al. Sharing and community curation of mass spectrometry data with Global Natural Products Social Molecular Networking. Nat. Biotechnol. 34, 828–837 (2016). (PMID: 27504778532167410.1038/nbt.3597)
Quinn, R. A. et al. Molecular networking as a drug discovery, drug metabolism, and precision medicine strategy. Trends Pharmacol. Sci. 38, 143–154 (2017). (PMID: 2784288710.1016/j.tips.2016.10.011)
Yu, J. S. et al. Tandem mass spectrometry molecular networking as a powerful and efficient tool for drug metabolism studies. Anal. Chem. 94, 1456–1464 (2022). (PMID: 3498528410.1021/acs.analchem.1c04925)
Seo, J. I., Yu, J. S., Lee, E. K., Park, K. B. & Yoo, H. H. Molecular networking-guided strategy for the pharmacokinetic study of herbal medicines: Cudrania tricuspidata leaf extracts. Biomed. Pharmacother. 149, 112895 (2022). (PMID: 3536437910.1016/j.biopha.2022.112895)
Petras, D. et al. GNPS Dashboard: collaborative exploration of mass spectrometry data in the web browser. Nat. Methods 19, 134–136 (2022). (PMID: 34862502883145010.1038/s41592-021-01339-5)
Shah, A. K. P. et al. Statistical analysis of feature-based molecular networking results from non-targeted metabolomics data. Nat. Protoc. 20, 92–162 (2025). (PMID: 10.1038/s41596-024-01046-3)
Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 37, 852–857 (2019). (PMID: 31341288701518010.1038/s41587-019-0209-9)
Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504 (2003). (PMID: 1459765840376910.1101/gr.1239303)
Borelli, T. C. et al. Improving annotation propagation on molecular networks through random walks: introducing ChemWalker. Bioinformatics 39, btad078 (2023). (PMID: 36864626999105310.1093/bioinformatics/btad078)
Duhrkop, K. et al. SIRIUS 4: a rapid tool for turning tandem mass spectra into metabolite structure information. Nat. Methods 16, 299–302 (2019). (PMID: 3088641310.1038/s41592-019-0344-8)
Wang, M. X. et al. Mass spectrometry searches using MASST. Nat. Biotechnol. 38, 23–26 (2020). (PMID: 31894142723653310.1038/s41587-019-0375-9)
Wu, F. X. et al. Computational approaches in preclinical studies on drug discovery and development. Front. Chem. 8, 726 (2020). (PMID: 33062633751789410.3389/fchem.2020.00726)
Aron, A. T. et al. Reproducible molecular networking of untargeted mass spectrometry data using GNPS. Nat. Protoc. 15, 1954–1991 (2020). (PMID: 3240505110.1038/s41596-020-0317-5)
Marques, C. F., Pinheiro, P. F. & Justino, G. C. Protocol to study in vitro drug metabolism and identify montelukast metabolites from purified enzymes and primary cell cultures by mass spectrometry. STAR Protoc. 4, 102086 (2023). (PMID: 36853690992948410.1016/j.xpro.2023.102086)
Knights, K. M., Stresser, D. M., Miners, J. O. & Crespi, C. L. In vitro drug metabolism using liver microsomes. Curr. Protoc. Pharmacol. 74, 7.8.1–7.8.24 (2016). (PMID: 2763611110.1002/cpph.9)
Nothias, L. F. et al. Feature-based molecular networking in the GNPS analysis environment. Nat. Methods 17, 905–908 (2020). (PMID: 32839597788568710.1038/s41592-020-0933-6)
Gentry, E. C. et al. Reverse metabolomics for the discovery of chemical structures from humans. Nature 626, 419–426 (2024). (PMID: 3805222910.1038/s41586-023-06906-8)
Nishimuta, H., Watanabe, T. & Bando, K. Quantitative prediction of human hepatic clearance for P450 and non-P450 substrates from in vivo monkey pharmacokinetics study and in vitro metabolic stability tests using hepatocytes. AAPS J. 21, 20 (2019). (PMID: 3067390610.1208/s12248-019-0294-1)
Nair, A., Morsy, M. A. & Jacob, S. Dose translation between laboratory animals and human in preclinical and clinical phases of drug development. Drug. Dev. Res. 79, 373–382 (2018). (PMID: 3034349610.1002/ddr.21461)
Dalgaard, L. Comparison of minipig, dog, monkey and human drug metabolism and disposition. J. Pharmacol. Toxicol. Methods 74, 80–92 (2015). (PMID: 2554533710.1016/j.vascn.2014.12.005)
Di, L. Reaction phenotyping to assess victim drug-drug interaction risks. Expert Opin. Drug Discov. 12, 1105–1115 (2017). (PMID: 2882026910.1080/17460441.2017.1367280)
Beger, R. D., Schmidt, M. A. & Kaddurah-Daouk, R. Current concepts in pharmacometabolomics, biomarker discovery, and precision medicine. Metabolites 10, 129 (2020). (PMID: 32230776724108310.3390/metabo10040129)
Mongia, M. et al. Fast mass spectrometry search and clustering of untargeted metabolomics data. Nat. Biotechnol. 42, 1672–1677 (2024). (PMID: 3816899010.1038/s41587-023-01985-4)
Gu, W. Y. et al. Metabolites software-assisted flavonoid hunting in plants using ultra-high performance liquid chromatography-quadrupole-time of flight mass spectrometry. Molecules 20, 3955–3971 (2015). (PMID: 25738538627273110.3390/molecules20033955)
Cooper, B. & Yang, R. H. An assessment of AcquireX and Compound Discoverer software 3.3 for non-targeted metabolomics. Sci. Rep. 14, 4841 (2024). (PMID: 384188551090239410.1038/s41598-024-55356-3)
He, F. et al. Detection and identification of imperatorin metabolites in rat, dog, monkey, and human liver microsomes by ultra-high-performance liquid chromatography combined with high-resolution mass spectrometry and Compound Discoverer software. Biomed. Chromatogr. 37, e5702 (2023). (PMID: 3745536610.1002/bmc.5702)
Krotulski, A. J., Mohr, A. L. A., Papsun, D. M. & Logan, B. K. Metabolism of novel opioid agonists U-47700 and U-49900 using human liver microsomes with confirmation in authentic urine specimens from drug users. Drug Test. Anal. 10, 127–136 (2018). (PMID: 2860858610.1002/dta.2228)
Abushawish, K. Y. I. et al. Multi-omics analysis revealed a significant alteration of critical metabolic pathways due to sorafenib-resistance in Hep3B cell lines. Int. J. Mol. Sci. 23, 11975 (2022). (PMID: 36233276956981010.3390/ijms231911975)
Telu, K. H., Yan, X. J., Wallace, W. E., Stein, S. E. & Simón-Manso, Y. Analysis of human plasma metabolites across different liquid chromatography/mass spectrometry platforms: cross-platform transferable chemical signatures. Rapid Commun. Mass Spectrom. 30, 581–593 (2016). (PMID: 26842580511484710.1002/rcm.7475)
Baesu, A. & Feng, Y. L. Development of a robust non-targeted analysis approach for fast identification of endocrine disruptors and their metabolites in human urine for exposure assessment. Chemosphere 363, 142754 (2024). (PMID: 3896472010.1016/j.chemosphere.2024.142754)
Olivon, F. et al. MetGem software for the generation of molecular networks based on the t-SNE algorithm. Anal. Chem. 90, 13900–13908 (2018). (PMID: 3033596510.1021/acs.analchem.8b03099)
Schmid, R. et al. Integrative analysis of multimodal mass spectrometry data in MZmine 3. Nat. Biotechnol. 41, 447–449 (2023). (PMID: 368597161049661010.1038/s41587-023-01690-2)
Heuckeroth, S. et al. Reproducible mass spectrometry data processing and compound annotation in MZmine 3. Nat. Protoc. 19, 2597–2641 (2024). (PMID: 3876914310.1038/s41596-024-00996-y)
Huber, F., van der Burg, S., van der Hooft, J. J. & Ridder, L. MS2DeepScore: a novel deep learning similarity measure to compare tandem mass spectra. J. Cheminform. 13, 84 (2021). (PMID: 34715914855691910.1186/s13321-021-00558-4)
Huber, F. et al. Spec2Vec: improved mass spectral similarity scoring through learning of structural relationships. PLoS Comput. Biol. 17, e1008724 (2021). (PMID: 33591968790962210.1371/journal.pcbi.1008724)
Tsugawa, H. et al. MS-DIAL: data-independent MS/MS deconvolution for comprehensive metabolome analysis. Nat. Methods 12, 523–526 (2015). (PMID: 25938372444933010.1038/nmeth.3393)
Rost, H. L. et al. OpenMS: a flexible open-source software platform for mass spectrometry data analysis. Nat. Methods 13, 741–748 (2016). (PMID: 2757562410.1038/nmeth.3959)
Protsyuk, I. et al. 3D molecular cartography using LC-MS facilitated by Optimus and ‘ili software. Nat. Protoc. 13, 134–154 (2018). (PMID: 2926609910.1038/nprot.2017.122)
Smith, C. A., Want, E. J., O’Maille, G., Abagyan, R. & Siuzdak, G. XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Anal. Chem. 78, 779–787 (2006). (PMID: 1644805110.1021/ac051437y)
Batsoyol, N., Pullman, B., Wang, M. X., Bandeira, N. & Swanson, S. P-MASSIVE: a real-time search engine for a multi-terabyte mass spectrometry database. SC22: International Conference for High Performance Computing, Networking, Storage and Analysis 1–15 (IEEE, Dallas, Texas, USA, 2022).
Bushuiev, R. et al. Self-supervised learning of molecular representations from millions of tandem mass spectra using DreaMS. Nat. Biotechnol. https://doi.org/10.1038/s41587-025-02663-3 (2025).
Giera, M., Aisporna, A., Uritboonthai, W. & Siuzdak, G. The hidden impact of in-source fragmentation in metabolic and chemical mass spectrometry data interpretation. Nat. Metab. 6, 1647–1648 (2024). (PMID: 389185341182648010.1038/s42255-024-01076-x)
Bernardo-Bermejo, S. et al. Quantitative multiple fragment monitoring with enhanced in-source fragmentation/annotation mass spectrometry. Nat. Protoc. 18, 1296–1315 (2023). (PMID: 367551311036409210.1038/s41596-023-00803-0)
Chen, Y. N. et al. In-source fragmentation of nucleosides in electrospray ionization towards more sensitive and accurate nucleoside analysis. Analyst 148, 1500–1506 (2023). (PMID: 3688365610.1039/D3AN00047H)
El Abiead, Y. et al. Heterogeneous multimeric metabolite ion species observed in LC-MS based metabolomics data sets. Anal. Chim. Acta 1229, 340352 (2022). (PMID: 3615623110.1016/j.aca.2022.340352)
Kuhl, C., Tautenhahn, R., Bottcher, C., Larson, T. R. & Neumann, S. CAMERA: an integrated strategy for compound spectra extraction and annotation of liquid chromatography/mass spectrometry data sets. Anal. Chem. 84, 283–289 (2012). (PMID: 2211178510.1021/ac202450g)
Mahieu, N. G., Genenbacher, J. L. & Patti, G. J. A roadmap for the XCMS family of software solutions in metabolomics. Curr. Opin. Chem. Biol. 30, 87–93 (2016). (PMID: 2667382510.1016/j.cbpa.2015.11.009)
Schmid, R. et al. Ion identity molecular networking for mass spectrometry-based metabolomics in the GNPS environment. Nat. Commun. 12, 3832 (2021). (PMID: 34158495821973110.1038/s41467-021-23953-9)
Nash, W. J., Ngere, J. B., Najdekr, L. & Dunn, W. B. Characterization of electrospray ionization complexity in untargeted metabolomic studies. Anal. Chem. 96, 10935–10942 (2024). (PMID: 389173471123815610.1021/acs.analchem.4c00966)
Shahneh, M. R. Z. et al. ModiFinder: tandem mass spectral alignment enables structural modification site localization. J. Am. Soc. Mass Spectrom. 35, 2564–2578 (2024). (PMID: 388301431154072310.1021/jasms.4c00061)
Jeong, E. et al. Qualitative metabolomics-based characterization of a phenolic UDP-xylosyltransferase with a broad substrate spectrum from Lentinus brumalis. Proc. Natl Acad. Sci. USA 120, e2301007120 (2023). (PMID: 373993711033477310.1073/pnas.2301007120)
Wang, M. et al. Universal MS/MS visualization and retrieval with the metabolomics spectrum resolver web service. Preprint at bioRxiv https://doi.org/10.1101/2020.05.09.086066 (2020).
Ridder, L. & Wagener, M. SyGMa: combining expert knowledge and empirical scoring in the prediction of metabolites. ChemMedChem 3, 821–832 (2008). (PMID: 1831174510.1002/cmdc.200700312)
Wishart, D. S. et al. BioTransformer 3.0—a web server for accurately predicting metabolic transformation products. Nucleic Acids Res. 50, W115–W123 (2022). (PMID: 35536252925279810.1093/nar/gkac313)
Djoumbou-Feunang, Y. et al. BioTransformer: a comprehensive computational tool for small molecule metabolism prediction and metabolite identification. J. Cheminform. 11, 2 (2019). (PMID: 30612223668987310.1186/s13321-018-0324-5)
Schüller, A., Schneider, G. & Byvatov, E. SMILIB: rapid assembly of combinatorial libraries in SMILES notation. QSAR Comb. Sci. 22, 719–721 (2003). (PMID: 10.1002/qsar.200310008)
Schüller, A., Hähnke, V. & Schneider, G. SmiLib v2.0: a Java-based tool for rapid combinatorial library enumeration. QSAR Comb. Sci. 26, 407–410 (2007). (PMID: 10.1002/qsar.200630101)
Hoffmann, M. A. et al. High-confidence structural annotation of metabolites absent from spectral libraries. Nat. Biotechnol. 40, 411–421 (2022). (PMID: 3465027110.1038/s41587-021-01045-9)
Ruttkies, C., Schymanski, E. L., Wolf, S., Hollender, J. & Neumann, S. MetFrag relaunched: incorporating strategies beyond in silico fragmentation. J. Cheminform. 8, 3 (2016). (PMID: 26834843473200110.1186/s13321-016-0115-9)
Mohimani, H. et al. Dereplication of microbial metabolites through database search of mass spectra. Nat. Commun. 9, 4035 (2018). (PMID: 30279420616852110.1038/s41467-018-06082-8)
Dührkop, K., Shen, H. B., Meusel, M., Rousu, J. & Böcker, S. Searching molecular structure databases with tandem mass spectra using CSI:FingerID. Proc. Natl Acad. Sci. USA 112, 12580–12585 (2015). (PMID: 26392543461163610.1073/pnas.1509788112)
Ludwig, M., Fleischauer, M., Duhrkop, K., Hoffmann, M. A. & Bocker, S. De novo molecular formula annotation and structure elucidation using SIRIUS 4. Methods Mol. Biol. 2104, 185–207 (2020). (PMID: 3195381910.1007/978-1-0716-0239-3_11)
da Silva, R. R. et al. Propagating annotations of molecular networks using in silico fragmentation. PloS Comput. Biol. 14, e1006089 (2018). (PMID: 29668671592746010.1371/journal.pcbi.1006089)
Pang, Z. et al. Using MetaboAnalyst 5.0 for LC–HRMS spectra processing, multi-omics integration and covariate adjustment of global metabolomics data. Nat. Protoc. 17, 1735–1761 (2022). (PMID: 3571552210.1038/s41596-022-00710-w)
Pang, Z. et al. MetaboAnalyst 6.0: towards a unified platform for metabolomics data processing, analysis and interpretation. Nucleic Acids Res. 52, W398–W406 (2024). (PMID: 385872011122379810.1093/nar/gkae253)
Charron-Lamoureux, V. et al. A guide to reverse metabolomics—a framework for big data discovery strategy. Nat. Protoc. https://doi.org/10.1038/s41596-024-01136-2 (2025).
Bowen, T. J. et al. Simultaneously discovering the fate and biochemical effects of pharmaceuticals through untargeted metabolomics. Nat. Commun. 14, 4653 (2023). (PMID: 375371841040063510.1038/s41467-023-40333-7)
Berger, T. et al. A MassQL-integrated molecular networking approach for the discovery and substructure annotation of bioactive cyclic peptides. J. Nat. Prod. 87, 692–704 (2024). (PMID: 3838576710.1021/acs.jnatprod.3c00750)
El Abiead, Y. et al. Discovery of metabolites prevails amid in-source fragmentation. Nat. Metab. 7, 435–437 (2025). (PMID: 4002193510.1038/s42255-025-01239-4)
Fan, Y. & Pedersen, O. Gut microbiota in human metabolic health and disease. Nat. Rev. Microbiol. 19, 55–71 (2021). (PMID: 3288794610.1038/s41579-020-0433-9)
Lai, Y. R. et al. Recent advances in the translation of drug metabolism and pharmacokinetics science for drug discovery and development. Acta Pharm. Sin. B 12, 2751–2777 (2022). (PMID: 35755285921405910.1016/j.apsb.2022.03.009)
Ufer, M., Juif, P. E., Boof, M. L., Muehlan, C. & Dingemanse, J. Metabolite profiling in early clinical drug development: current status and future prospects. Expert Opin. Drug Metab. Toxicol. 13, 803–806 (2017). (PMID: 2868943210.1080/17425255.2017.1351944)
He, C. Y. & Wan, H. Drug metabolism and metabolite safety assessment in drug discovery and development. Expert Opin. Drug Metab. Toxicol. 14, 1071–1085 (2018). (PMID: 3021528010.1080/17425255.2018.1519546)
Gajula, S. N. R., Nadimpalli, N. & Sonti, R. Drug metabolic stability in early drug discovery to develop potential lead compounds. Drug Metab. Rev. 53, 459–477 (2021). (PMID: 3440688910.1080/03602532.2021.1970178)
Wishart, D. S. Emerging applications of metabolomics in drug discovery and precision medicine. Nat. Rev. Drug Discov. 15, 473–484 (2016). (PMID: 2696520210.1038/nrd.2016.32)
Li, F., Gonzalez, F. J. & Ma, X. C. LC-MS-based metabolomics in profiling of drug metabolism and bioactivation. Acta Pharm. Sin. B 2, 118–125 (2012). (PMID: 10.1016/j.apsb.2012.02.010)
Hsieh, Y. HPLC-MS/MS in drug metabolism and pharmacokinetic screening. Expert Opin. Drug Metab. Toxicol. 4, 93–101 (2008). (PMID: 1837086110.1517/17425255.4.1.93)
Wei, W. L. et al. Simultaneous determination of resibufogenin and its eight metabolites in rat plasma by LC-MS/MS for metabolic profiles and pharmacokinetic study. Phytomedicine 60, 159271 (2019). (PMID: 10.1016/j.phymed.2019.152971)
Schadt, S. et al. A decade in the MIST: learnings from investigations of drug metabolites in drug development under the “Metabolites in Safety Testing” regulatory guidance. Drug Metab. Dispos. 46, 865–878 (2018). (PMID: 2948714210.1124/dmd.117.079848)
Chen, C. J., Lee, D. Y., Yu, J. X., Lin, Y. N. & Lin, T. M. Recent advances in LC-MS-based metabolomics for clinical biomarker discovery. Mass Spectrom. Rev. 42, 2349–2378 (2023). (PMID: 3564514410.1002/mas.21785)
Cai, Y. P., Zhou, Z. W. & Zhu, Z. J. Advanced analytical and informatic strategies for metabolite annotation in untargeted metabolomics. Trends Anal. Chem. 158, 116903 (2023). (PMID: 10.1016/j.trac.2022.116903)
Jarmusch, S. A., van der Hooft, J. J. J., Dorrestein, P. C. & Jarmusch, A. K. Advancements in capturing and mining mass spectrometry data are transforming natural products research. Nat. Prod. Rep. 38, 2066–2082 (2021). (PMID: 34612288866778110.1039/D1NP00040C)
Wang, M. X. et al. Sharing and community curation of mass spectrometry data with Global Natural Products Social Molecular Networking. Nat. Biotechnol. 34, 828–837 (2016). (PMID: 27504778532167410.1038/nbt.3597)
Quinn, R. A. et al. Molecular networking as a drug discovery, drug metabolism, and precision medicine strategy. Trends Pharmacol. Sci. 38, 143–154 (2017). (PMID: 2784288710.1016/j.tips.2016.10.011)
Yu, J. S. et al. Tandem mass spectrometry molecular networking as a powerful and efficient tool for drug metabolism studies. Anal. Chem. 94, 1456–1464 (2022). (PMID: 3498528410.1021/acs.analchem.1c04925)
Seo, J. I., Yu, J. S., Lee, E. K., Park, K. B. & Yoo, H. H. Molecular networking-guided strategy for the pharmacokinetic study of herbal medicines: Cudrania tricuspidata leaf extracts. Biomed. Pharmacother. 149, 112895 (2022). (PMID: 3536437910.1016/j.biopha.2022.112895)
Petras, D. et al. GNPS Dashboard: collaborative exploration of mass spectrometry data in the web browser. Nat. Methods 19, 134–136 (2022). (PMID: 34862502883145010.1038/s41592-021-01339-5)
Shah, A. K. P. et al. Statistical analysis of feature-based molecular networking results from non-targeted metabolomics data. Nat. Protoc. 20, 92–162 (2025). (PMID: 10.1038/s41596-024-01046-3)
Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 37, 852–857 (2019). (PMID: 31341288701518010.1038/s41587-019-0209-9)
Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504 (2003). (PMID: 1459765840376910.1101/gr.1239303)
Borelli, T. C. et al. Improving annotation propagation on molecular networks through random walks: introducing ChemWalker. Bioinformatics 39, btad078 (2023). (PMID: 36864626999105310.1093/bioinformatics/btad078)
Duhrkop, K. et al. SIRIUS 4: a rapid tool for turning tandem mass spectra into metabolite structure information. Nat. Methods 16, 299–302 (2019). (PMID: 3088641310.1038/s41592-019-0344-8)
Wang, M. X. et al. Mass spectrometry searches using MASST. Nat. Biotechnol. 38, 23–26 (2020). (PMID: 31894142723653310.1038/s41587-019-0375-9)
Wu, F. X. et al. Computational approaches in preclinical studies on drug discovery and development. Front. Chem. 8, 726 (2020). (PMID: 33062633751789410.3389/fchem.2020.00726)
Aron, A. T. et al. Reproducible molecular networking of untargeted mass spectrometry data using GNPS. Nat. Protoc. 15, 1954–1991 (2020). (PMID: 3240505110.1038/s41596-020-0317-5)
Marques, C. F., Pinheiro, P. F. & Justino, G. C. Protocol to study in vitro drug metabolism and identify montelukast metabolites from purified enzymes and primary cell cultures by mass spectrometry. STAR Protoc. 4, 102086 (2023). (PMID: 36853690992948410.1016/j.xpro.2023.102086)
Knights, K. M., Stresser, D. M., Miners, J. O. & Crespi, C. L. In vitro drug metabolism using liver microsomes. Curr. Protoc. Pharmacol. 74, 7.8.1–7.8.24 (2016). (PMID: 2763611110.1002/cpph.9)
Nothias, L. F. et al. Feature-based molecular networking in the GNPS analysis environment. Nat. Methods 17, 905–908 (2020). (PMID: 32839597788568710.1038/s41592-020-0933-6)
Gentry, E. C. et al. Reverse metabolomics for the discovery of chemical structures from humans. Nature 626, 419–426 (2024). (PMID: 3805222910.1038/s41586-023-06906-8)
Nishimuta, H., Watanabe, T. & Bando, K. Quantitative prediction of human hepatic clearance for P450 and non-P450 substrates from in vivo monkey pharmacokinetics study and in vitro metabolic stability tests using hepatocytes. AAPS J. 21, 20 (2019). (PMID: 3067390610.1208/s12248-019-0294-1)
Nair, A., Morsy, M. A. & Jacob, S. Dose translation between laboratory animals and human in preclinical and clinical phases of drug development. Drug. Dev. Res. 79, 373–382 (2018). (PMID: 3034349610.1002/ddr.21461)
Dalgaard, L. Comparison of minipig, dog, monkey and human drug metabolism and disposition. J. Pharmacol. Toxicol. Methods 74, 80–92 (2015). (PMID: 2554533710.1016/j.vascn.2014.12.005)
Di, L. Reaction phenotyping to assess victim drug-drug interaction risks. Expert Opin. Drug Discov. 12, 1105–1115 (2017). (PMID: 2882026910.1080/17460441.2017.1367280)
Beger, R. D., Schmidt, M. A. & Kaddurah-Daouk, R. Current concepts in pharmacometabolomics, biomarker discovery, and precision medicine. Metabolites 10, 129 (2020). (PMID: 32230776724108310.3390/metabo10040129)
Mongia, M. et al. Fast mass spectrometry search and clustering of untargeted metabolomics data. Nat. Biotechnol. 42, 1672–1677 (2024). (PMID: 3816899010.1038/s41587-023-01985-4)
Gu, W. Y. et al. Metabolites software-assisted flavonoid hunting in plants using ultra-high performance liquid chromatography-quadrupole-time of flight mass spectrometry. Molecules 20, 3955–3971 (2015). (PMID: 25738538627273110.3390/molecules20033955)
Cooper, B. & Yang, R. H. An assessment of AcquireX and Compound Discoverer software 3.3 for non-targeted metabolomics. Sci. Rep. 14, 4841 (2024). (PMID: 384188551090239410.1038/s41598-024-55356-3)
He, F. et al. Detection and identification of imperatorin metabolites in rat, dog, monkey, and human liver microsomes by ultra-high-performance liquid chromatography combined with high-resolution mass spectrometry and Compound Discoverer software. Biomed. Chromatogr. 37, e5702 (2023). (PMID: 3745536610.1002/bmc.5702)
Krotulski, A. J., Mohr, A. L. A., Papsun, D. M. & Logan, B. K. Metabolism of novel opioid agonists U-47700 and U-49900 using human liver microsomes with confirmation in authentic urine specimens from drug users. Drug Test. Anal. 10, 127–136 (2018). (PMID: 2860858610.1002/dta.2228)
Abushawish, K. Y. I. et al. Multi-omics analysis revealed a significant alteration of critical metabolic pathways due to sorafenib-resistance in Hep3B cell lines. Int. J. Mol. Sci. 23, 11975 (2022). (PMID: 36233276956981010.3390/ijms231911975)
Telu, K. H., Yan, X. J., Wallace, W. E., Stein, S. E. & Simón-Manso, Y. Analysis of human plasma metabolites across different liquid chromatography/mass spectrometry platforms: cross-platform transferable chemical signatures. Rapid Commun. Mass Spectrom. 30, 581–593 (2016). (PMID: 26842580511484710.1002/rcm.7475)
Baesu, A. & Feng, Y. L. Development of a robust non-targeted analysis approach for fast identification of endocrine disruptors and their metabolites in human urine for exposure assessment. Chemosphere 363, 142754 (2024). (PMID: 3896472010.1016/j.chemosphere.2024.142754)
Olivon, F. et al. MetGem software for the generation of molecular networks based on the t-SNE algorithm. Anal. Chem. 90, 13900–13908 (2018). (PMID: 3033596510.1021/acs.analchem.8b03099)
Schmid, R. et al. Integrative analysis of multimodal mass spectrometry data in MZmine 3. Nat. Biotechnol. 41, 447–449 (2023). (PMID: 368597161049661010.1038/s41587-023-01690-2)
Heuckeroth, S. et al. Reproducible mass spectrometry data processing and compound annotation in MZmine 3. Nat. Protoc. 19, 2597–2641 (2024). (PMID: 3876914310.1038/s41596-024-00996-y)
Huber, F., van der Burg, S., van der Hooft, J. J. & Ridder, L. MS2DeepScore: a novel deep learning similarity measure to compare tandem mass spectra. J. Cheminform. 13, 84 (2021). (PMID: 34715914855691910.1186/s13321-021-00558-4)
Huber, F. et al. Spec2Vec: improved mass spectral similarity scoring through learning of structural relationships. PLoS Comput. Biol. 17, e1008724 (2021). (PMID: 33591968790962210.1371/journal.pcbi.1008724)
Tsugawa, H. et al. MS-DIAL: data-independent MS/MS deconvolution for comprehensive metabolome analysis. Nat. Methods 12, 523–526 (2015). (PMID: 25938372444933010.1038/nmeth.3393)
Rost, H. L. et al. OpenMS: a flexible open-source software platform for mass spectrometry data analysis. Nat. Methods 13, 741–748 (2016). (PMID: 2757562410.1038/nmeth.3959)
Protsyuk, I. et al. 3D molecular cartography using LC-MS facilitated by Optimus and ‘ili software. Nat. Protoc. 13, 134–154 (2018). (PMID: 2926609910.1038/nprot.2017.122)
Smith, C. A., Want, E. J., O’Maille, G., Abagyan, R. & Siuzdak, G. XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Anal. Chem. 78, 779–787 (2006). (PMID: 1644805110.1021/ac051437y)
Batsoyol, N., Pullman, B., Wang, M. X., Bandeira, N. & Swanson, S. P-MASSIVE: a real-time search engine for a multi-terabyte mass spectrometry database. SC22: International Conference for High Performance Computing, Networking, Storage and Analysis 1–15 (IEEE, Dallas, Texas, USA, 2022).
Bushuiev, R. et al. Self-supervised learning of molecular representations from millions of tandem mass spectra using DreaMS. Nat. Biotechnol. https://doi.org/10.1038/s41587-025-02663-3 (2025).
Giera, M., Aisporna, A., Uritboonthai, W. & Siuzdak, G. The hidden impact of in-source fragmentation in metabolic and chemical mass spectrometry data interpretation. Nat. Metab. 6, 1647–1648 (2024). (PMID: 389185341182648010.1038/s42255-024-01076-x)
Bernardo-Bermejo, S. et al. Quantitative multiple fragment monitoring with enhanced in-source fragmentation/annotation mass spectrometry. Nat. Protoc. 18, 1296–1315 (2023). (PMID: 367551311036409210.1038/s41596-023-00803-0)
Chen, Y. N. et al. In-source fragmentation of nucleosides in electrospray ionization towards more sensitive and accurate nucleoside analysis. Analyst 148, 1500–1506 (2023). (PMID: 3688365610.1039/D3AN00047H)
El Abiead, Y. et al. Heterogeneous multimeric metabolite ion species observed in LC-MS based metabolomics data sets. Anal. Chim. Acta 1229, 340352 (2022). (PMID: 3615623110.1016/j.aca.2022.340352)
Kuhl, C., Tautenhahn, R., Bottcher, C., Larson, T. R. & Neumann, S. CAMERA: an integrated strategy for compound spectra extraction and annotation of liquid chromatography/mass spectrometry data sets. Anal. Chem. 84, 283–289 (2012). (PMID: 2211178510.1021/ac202450g)
Mahieu, N. G., Genenbacher, J. L. & Patti, G. J. A roadmap for the XCMS family of software solutions in metabolomics. Curr. Opin. Chem. Biol. 30, 87–93 (2016). (PMID: 2667382510.1016/j.cbpa.2015.11.009)
Schmid, R. et al. Ion identity molecular networking for mass spectrometry-based metabolomics in the GNPS environment. Nat. Commun. 12, 3832 (2021). (PMID: 34158495821973110.1038/s41467-021-23953-9)
Nash, W. J., Ngere, J. B., Najdekr, L. & Dunn, W. B. Characterization of electrospray ionization complexity in untargeted metabolomic studies. Anal. Chem. 96, 10935–10942 (2024). (PMID: 389173471123815610.1021/acs.analchem.4c00966)
Shahneh, M. R. Z. et al. ModiFinder: tandem mass spectral alignment enables structural modification site localization. J. Am. Soc. Mass Spectrom. 35, 2564–2578 (2024). (PMID: 388301431154072310.1021/jasms.4c00061)
Jeong, E. et al. Qualitative metabolomics-based characterization of a phenolic UDP-xylosyltransferase with a broad substrate spectrum from Lentinus brumalis. Proc. Natl Acad. Sci. USA 120, e2301007120 (2023). (PMID: 373993711033477310.1073/pnas.2301007120)
Wang, M. et al. Universal MS/MS visualization and retrieval with the metabolomics spectrum resolver web service. Preprint at bioRxiv https://doi.org/10.1101/2020.05.09.086066 (2020).
Ridder, L. & Wagener, M. SyGMa: combining expert knowledge and empirical scoring in the prediction of metabolites. ChemMedChem 3, 821–832 (2008). (PMID: 1831174510.1002/cmdc.200700312)
Wishart, D. S. et al. BioTransformer 3.0—a web server for accurately predicting metabolic transformation products. Nucleic Acids Res. 50, W115–W123 (2022). (PMID: 35536252925279810.1093/nar/gkac313)
Djoumbou-Feunang, Y. et al. BioTransformer: a comprehensive computational tool for small molecule metabolism prediction and metabolite identification. J. Cheminform. 11, 2 (2019). (PMID: 30612223668987310.1186/s13321-018-0324-5)
Schüller, A., Schneider, G. & Byvatov, E. SMILIB: rapid assembly of combinatorial libraries in SMILES notation. QSAR Comb. Sci. 22, 719–721 (2003). (PMID: 10.1002/qsar.200310008)
Schüller, A., Hähnke, V. & Schneider, G. SmiLib v2.0: a Java-based tool for rapid combinatorial library enumeration. QSAR Comb. Sci. 26, 407–410 (2007). (PMID: 10.1002/qsar.200630101)
Hoffmann, M. A. et al. High-confidence structural annotation of metabolites absent from spectral libraries. Nat. Biotechnol. 40, 411–421 (2022). (PMID: 3465027110.1038/s41587-021-01045-9)
Ruttkies, C., Schymanski, E. L., Wolf, S., Hollender, J. & Neumann, S. MetFrag relaunched: incorporating strategies beyond in silico fragmentation. J. Cheminform. 8, 3 (2016). (PMID: 26834843473200110.1186/s13321-016-0115-9)
Mohimani, H. et al. Dereplication of microbial metabolites through database search of mass spectra. Nat. Commun. 9, 4035 (2018). (PMID: 30279420616852110.1038/s41467-018-06082-8)
Dührkop, K., Shen, H. B., Meusel, M., Rousu, J. & Böcker, S. Searching molecular structure databases with tandem mass spectra using CSI:FingerID. Proc. Natl Acad. Sci. USA 112, 12580–12585 (2015). (PMID: 26392543461163610.1073/pnas.1509788112)
Ludwig, M., Fleischauer, M., Duhrkop, K., Hoffmann, M. A. & Bocker, S. De novo molecular formula annotation and structure elucidation using SIRIUS 4. Methods Mol. Biol. 2104, 185–207 (2020). (PMID: 3195381910.1007/978-1-0716-0239-3_11)
da Silva, R. R. et al. Propagating annotations of molecular networks using in silico fragmentation. PloS Comput. Biol. 14, e1006089 (2018). (PMID: 29668671592746010.1371/journal.pcbi.1006089)
Pang, Z. et al. Using MetaboAnalyst 5.0 for LC–HRMS spectra processing, multi-omics integration and covariate adjustment of global metabolomics data. Nat. Protoc. 17, 1735–1761 (2022). (PMID: 3571552210.1038/s41596-022-00710-w)
Pang, Z. et al. MetaboAnalyst 6.0: towards a unified platform for metabolomics data processing, analysis and interpretation. Nucleic Acids Res. 52, W398–W406 (2024). (PMID: 385872011122379810.1093/nar/gkae253)
Charron-Lamoureux, V. et al. A guide to reverse metabolomics—a framework for big data discovery strategy. Nat. Protoc. https://doi.org/10.1038/s41596-024-01136-2 (2025).
Bowen, T. J. et al. Simultaneously discovering the fate and biochemical effects of pharmaceuticals through untargeted metabolomics. Nat. Commun. 14, 4653 (2023). (PMID: 375371841040063510.1038/s41467-023-40333-7)
Berger, T. et al. A MassQL-integrated molecular networking approach for the discovery and substructure annotation of bioactive cyclic peptides. J. Nat. Prod. 87, 692–704 (2024). (PMID: 3838576710.1021/acs.jnatprod.3c00750)
El Abiead, Y. et al. Discovery of metabolites prevails amid in-source fragmentation. Nat. Metab. 7, 435–437 (2025). (PMID: 4002193510.1038/s42255-025-01239-4)
Grant Information:
RS-2023-00217123 National Research Foundation of Korea (NRF); RS-2023-00217123 National Research Foundation of Korea (NRF); RS-2023-00217123 National Research Foundation of Korea (NRF); RS-2025-00523337, RS-2022-NR070845, RS-2022-NR068419 National Research Foundation of Korea (NRF); 5U24DK133658 U.S. Department of Health and Human Services (U.S. Department of Health & Human Services); DE-AC02-05CH11231 U.S. Department of Energy (DOE); U19AG063744 U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging); RS-2024-00436674 Korea Basic Science Institute (KBSI)
Entry Date(s):
Date Created: 20250908 Latest Revision: 20250908
Update Code:
20250909
DOI:
10.1038/s41596-025-01237-6
PMID:
40921758
Database:
MEDLINE
Weitere Informationen
Metabolism is a fundamental process that shapes the pharmacological and toxicological profiles of drugs, making metabolite identification and analysis critical in drug development and biological research. Global Natural Products Social Networking (GNPS) is a community-driven infrastructure for mass spectrometry data analysis, storage and knowledge dissemination. GNPS2 is an improved version of the platform offering higher processing speeds, improved data analysis tools and a more intuitive user interface. Molecular networking based on tandem mass spectrometry spectral alignments, combined with other tools in the GNPS2 analysis environment, enables the discovery of candidate drug metabolites without prior knowledge, even from complex biological matrices. This protocol represents an extension of a previously established protocol for fundamental molecular networking in GNPS, with a specific focus on metabolism studies. This article uses the example of the drug sildenafil to identify candidate metabolites obtained from liquid chromatography-quadrupole time-of-flight mass spectrometry analysis of liver microsomal fractions and mice plasma to guide the reader through a step-by-step process consisting of five GNPS2-based analytical workflows. It demonstrates how the tools in GNPS2 can be used not only to identify candidate drug metabolites from in vitro studies but also to evaluate the translational relevance of these in vitro findings to humans by using reverse metabolomics. We provide a step-by-step analytical approach based on published studies to showcase how GNPS2 can be effectively applied in drug metabolism studies.
(© 2025. Springer Nature Limited.)
Competing interests: M.W. is a co-founder of Ometa Labs LLC. P.C.D. is an advisor to and holds equity in Cybele, BileOmix and Sirenas and is a scientific co-founder of and advisor to and holds equity in Ometa, Enveda and Arome with prior approval by the University of California San Diego.