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Drug testing and analysis2025; 17(12); 2411-2420; doi: 10.1002/dta.3951

Identification of Candidate Biomarkers Detected in the Urine of Racehorses After Anabolic Agent Administration: Use of Orthogonal Methods for Structural Elucidation.

Abstract: Biomarker identification by mass spectrometry represents a key step in the workflow of nontargeted metabolomic studies. Given the complexity of the data, this step, which must be carried out by a trained specialist, is time-consuming, and the biomarkers discovered are not always identified. While this stage is not an obstacle to the development of new screening and classification tools, it is nonetheless crucial to a better understanding of the results obtained. For this reason, the aim of this study was to perform structural elucidation of candidate biomarkers, which had previously been displayed to screen for the administration of anabolic agents in the urine of racehorses and whose robustness had been evaluated. The present study involved a combination of various analytical strategies, including enzymatic hydrolysis, high-resolution mass spectrometry and ion mobility (LC-HRMS, LC-IMS-HRMS), and in vitro experiments. Two candidate biomarkers were identified as phase II metabolites of tebuconazole, belonging to the equine exposome. This identification opens the way to further investigations into the relationship between the presence of this compound and its disruption in horse urine following anabolic agent administration. Overall, the use of orthogonal approaches provided better complementary information on the structure of the compound and ultimately enabled us to identify biomarkers with the highest possible level of confidence.
Publication Date: 2025-09-18 PubMed ID: 40968575DOI: 10.1002/dta.3951Google Scholar: Lookup
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  • Journal Article

Summary

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Overview

  • This study focused on identifying specific biomarkers in racehorse urine after anabolic agent administration using advanced mass spectrometry techniques.
  • The researchers aimed to structurally elucidate these biomarkers to deepen understanding and improve screening methods for doping control in horses.

Background

  • Biomarker identification is crucial in non-targeted metabolomic studies, especially in doping control for racehorses.
  • The process usually involves mass spectrometry and requires expert interpretation due to complex data sets.
  • Typically, not all discovered biomarkers get confidently identified, which can limit understanding despite being useful for screening.
  • Structural elucidation provides detailed knowledge about the chemical nature of biomarkers, supporting better validation and interpretation of screening results.

Objective

  • To perform detailed structural elucidation of candidate biomarkers previously recognized as screening indicators for anabolic agents in horse urine.
  • To use a combination of complementary analytical techniques (orthogonal methods) to improve confidence in biomarker identification.

Methods

  • Sample Collection: Urine samples were collected from racehorses exposed to anabolic agents.
  • Analytical Strategies:
    • Enzymatic Hydrolysis: Used to break down metabolites and understand conjugated compounds.
    • High-Resolution Mass Spectrometry (LC-HRMS): Provided precise mass data for metabolite identification.
    • Ion Mobility Spectrometry (LC-IMS-HRMS): Offered an additional dimension of separation to describe molecular structure and isomer differentiation.
    • In Vitro Experiments: Performed to replicate and confirm metabolic pathways outside the organism.
  • Data Analysis: Integration of these orthogonal approaches enabled comprehensive structural elucidation of biomarkers.

Findings

  • Two candidate biomarkers were successfully identified as phase II metabolites of tebuconazole, a fungicide, part of the equine exposome.
  • These metabolites likely indicate environmental exposure rather than direct anabolic substance administration.
  • The disruption or changes in levels of these biomarkers in urine post-anabolic agent administration suggest interplay between doping agents and exposure metabolites.
  • The structural identification supports the robustness of using these metabolites as indirect markers in doping control.

Significance

  • Combining orthogonal analytical techniques enabled a higher confidence level in biomarker identification than using single methods alone.
  • This structural elucidation aids the interpretation of metabolomic data, facilitating better understanding of doping effects and environmental factors in racehorses.
  • The study opens avenues for further research into the role of environmental chemicals like tebuconazole in doping tests and horse metabolism.
  • It contributes to improving screening strategies and supports regulatory agencies in maintaining fair competition through reliable biomarker detection.

Cite This Article

APA
Cloteau C, Delcourt V, Loup B, Chabot B, Pescher M, Susdorf E, Kaabia Z, Garcia P, Popot MA, Le Bizec B, Dervilly G, Bailly-Chouriberry L. (2025). Identification of Candidate Biomarkers Detected in the Urine of Racehorses After Anabolic Agent Administration: Use of Orthogonal Methods for Structural Elucidation. Drug Test Anal, 17(12), 2411-2420. https://doi.org/10.1002/dta.3951

Publication

ISSN: 1942-7611
NlmUniqueID: 101483449
Country: England
Language: English
Volume: 17
Issue: 12
Pages: 2411-2420

Researcher Affiliations

Cloteau, C
  • Oniris, INRAE, LABERCA, Nantes, France.
  • GIE LCH, Laboratoire des Courses Hippiques, Verrières-le-Buisson, France.
Delcourt, V
  • GIE LCH, Laboratoire des Courses Hippiques, Verrières-le-Buisson, France.
Loup, B
  • GIE LCH, Laboratoire des Courses Hippiques, Verrières-le-Buisson, France.
Chabot, B
  • GIE LCH, Laboratoire des Courses Hippiques, Verrières-le-Buisson, France.
Pescher, M
  • GIE LCH, Laboratoire des Courses Hippiques, Verrières-le-Buisson, France.
Susdorf, E
  • GIE LCH, Laboratoire des Courses Hippiques, Verrières-le-Buisson, France.
Kaabia, Z
  • GIE LCH, Laboratoire des Courses Hippiques, Verrières-le-Buisson, France.
Garcia, P
  • GIE LCH, Laboratoire des Courses Hippiques, Verrières-le-Buisson, France.
Popot, M A
  • GIE LCH, Laboratoire des Courses Hippiques, Verrières-le-Buisson, France.
Le Bizec, B
  • Oniris, INRAE, LABERCA, Nantes, France.
Dervilly, G
  • Oniris, INRAE, LABERCA, Nantes, France.
Bailly-Chouriberry, L
  • GIE LCH, Laboratoire des Courses Hippiques, Verrières-le-Buisson, France.

MeSH Terms

  • Horses / urine
  • Animals
  • Biomarkers / urine
  • Anabolic Agents / urine
  • Anabolic Agents / administration & dosage
  • Anabolic Agents / metabolism
  • Doping in Sports
  • Substance Abuse Detection / methods
  • Substance Abuse Detection / veterinary
  • Mass Spectrometry / methods
  • Chromatography, Liquid / methods
  • Triazoles / urine
  • Triazoles / metabolism
  • Triazoles / administration & dosage

Grant Funding

  • 2019/1948 / Association Nationale de la Recherche et de la Technologie

References

This article includes 46 references
  1. Gonzalez‐Covarrubias V, Martínez‐Martínez E, del Bosque‐Plata L. The Potential of Metabolomics in Biomedical Applications. Metabolites 12, no. 2 (2022): 194.
    doi: 10.3390/metabo12020194google scholar: lookup
  2. Vilanova C, Porcar M. Art‐Omics: Multi‐Omics Meet Archaeology and Art Conservation. Microbial Biotechnology 13, no. 2 (2020): 435–441.
    doi: 10.1111/1751‐7915.13480google scholar: lookup
  3. Dervilly‐Pinel G, Courant F, Chéreau S. Metabolomics in Food Analysis: Application to the Control of Forbidden Substances. Drug Testing and Analysis 4 (2012): 59–69.
    doi: 10.1002/dta.1349google scholar: lookup
  4. Hernández‐Mesa M, Le Bizec B, Dervilly G. Metabolomics in Chemical Risk Analysis‐A Review. Analytica Chimica Acta 1154 (2021): 338298.
    doi: 10.1016/j.aca.2021.338298google scholar: lookup
  5. Narduzzi L, Dervilly G, Audran M, Le Bizec B, Buisson C. A Role for Metabolomics in the Antidoping Toolbox?. Drug Testing and Analysis 12, no. 6 (2020): 677–690.
    doi: 10.1002/dta.2788google scholar: lookup
  6. Keen B, Cawley A, Reedy B, Fu S. Metabolomics in Clinical and Forensic Toxicology, Sports Anti‐Doping and Veterinary Residues. Drug Testing and Analysis 14, no. 5 (2022): 794–807.
    doi: 10.1002/dta.3245google scholar: lookup
  7. Cloteau C, Dervilly G, Loup B. Performance Assessment of an Equine Metabolomics Model for Screening a Range of Anabolic Agents. Metabolomics 19, no. 4 (2023): 38.
  8. Kaabia Z, Laparre J, Cesbron N, Le Bizec B, Dervilly‐Pinel G. Comprehensive Steroid Profiling by Liquid Chromatography Coupled to High Resolution Mass Spectrometry. Journal of Steroid Biochemistry and Molecular Biology 183 (2018): 106–115.
  9. Dervilly‐Pinel G, Royer A‐L, Bozzetta E. When LC‐HRMS Metabolomics Gets ISO17025 Accredited and Ready for Official Controls ‐ Application to the Screening of Forbidden Compounds in Livestock. Food Additives & Contaminants: Part A 35, no. 10 (2018): 1948–1958.
  10. Cloteau C, Dervilly G, Kaabia Z. From a Non‐Targeted Metabolomics Approach to a Targeted Biomarkers Strategy to Highlight Testosterone Abuse in Equine. Illustration of a Methodological Transfer Between Platforms and Laboratories. Drug Testing and Analysis 14, no. 5 (2022): 864–878.
    doi: 10.1002/dta.3221google scholar: lookup
  11. Kieken F, Pinel G, Antignac J‐P. Development of a Metabonomic Approach Based on LC‐ESI‐HRMS Measurements for Profiling of Metabolic Changes Induced by Recombinant Equine Growth Hormone in Horse Urine. Analytical and Bioanalytical Chemistry 394 (2009): 2119–2128.
  12. Joré C, Loup B, Garcia P. Liquid Chromatography‐High Resolution Mass Spectrometry‐Based Metabolomic Approach for the Detection of Continuous Erythropoiesis Receptor Activator Effects in Horse Doping Control. Journal of Chromatography A 1521 (2017): 90–99.
  13. Creek DJ, Dunn WB, Fiehn O. Metabolite Identification: Are You Sure? And How Do Your Peers Gauge Your Confidence?. Metabolomics 10 (2014): 350–353.
  14. Theodoridis G, Gika H, Raftery D, Goodacre R, Plumb RS, Wilson ID. Ensuring Fact‐Based Metabolite Identification in Liquid Chromatography‐Mass Spectrometry‐Based Metabolomics. Analytical Chemistry 95, no. 8 (2023): 3909–3916.
  15. Smith CA, O'Maille G, Want EJ. METLIN: A Metabolite Mass Spectral Database. Therapeutic Drug Monitoring 27, no. 6 (2005): 747–751.
  16. Wishart DS, Jewison T, Guo AC. HMDB 3.0—The Human Metabolome Database in 2013. Nucleic Acids Research 41, no. D1 (2012): D801–D807.
    doi: 10.1093/nar/gks1065google scholar: lookup
  17. Horai H, Arita M, Kanaya S. MassBank: A Public Repository for Sharing Mass Spectral Data for Life Sciences. Journal of Mass Spectrometry 45, no. 7 (2010): 703–714.
    doi: 10.1002/jms.1777google scholar: lookup
  18. Vinaixa M, Schymanski EL, Neumann S, Navarro M, Salek RM, Yanes O. Mass Spectral Databases for LC/MS‐And GC/MS‐Based Metabolomics: State of the Field and Future Prospects. TrAC Trends in Analytical Chemistry 78 (2016): 23–35.
  19. Bittremieux W, Wang M, Dorrestein PC. The Critical Role That Spectral Libraries Play in Capturing the Metabolomics Community Knowledge. Metabolomics 18, no. 12 (2022): 94.
  20. Dührkop K, Fleischauer M, Ludwig M. SIRIUS 4: A Rapid Tool for Turning Tandem Mass Spectra Into Metabolite Structure Information. Nature Methods 16, no. 4 (2019): 299–302.
    doi: 10.1038/s41592-019-0344-8google scholar: lookup
  21. Hernández-Mesa M, Ropartz D, García-Campaña AM, Rogniaux H, Dervilly-Pinel G, Le Bizec B. Ion Mobility Spectrometry in Food Analysis: Principles, Current Applications and Future Trends. Molecules 24, no. 15 (2019): 2706.
    doi: 10.3390/molecules24152706google scholar: lookup
  22. Cai Y, Zhou Z, Zhu Z-J. Advanced Analytical and Informatic Strategies for Metabolite Annotation in Untargeted Metabolomics. TrAC Trends in Analytical Chemistry 158 (2023): 116903.
  23. Scarth JP, Spencer HA, Hudson SC, Teale P, Gray BP, Hillyer LL. The Application of In Vitro Technologies to Study the Metabolism of the Androgenic/Anabolic Steroid Stanozolol in the Equine. Steroids 75, no. 1 (2010): 57–69.
  24. Delcourt V, Garcia P, Pottier I. Development of a Standardized Microflow LC Gradient to Enable Sensitive and Long‐Term Detection of Synthetic Anabolic‐Androgenic Steroids for High‐Throughput Doping Controls. Analytical Chemistry 93, no. 47 (2021): 15590–15596.
  25. Trevisiol S, Moulard Y, Delcourt V. Comprehensive Characterization of the Peroxisome Proliferator Activated Receptor‐δ Agonist GW501516 for Horse Doping Control Analysis. Drug Testing and Analysis 13, no. 6 (2021): 1191–1202.
    doi: 10.1002/dta.3013google scholar: lookup
  26. Bowen TJ, Southam AD, Hall AR. Simultaneously Discovering the Fate and Biochemical Effects of Pharmaceuticals Through Untargeted Metabolomics. Nature Communications 14, no. 1 (2023): 4653.
  27. Damont A, Legrand A, Cao C, Fenaille F, Tabet J-C. Hydrogen/Deuterium Exchange Mass Spectrometry in the World of Small Molecules. Mass Spectrometry Reviews 42, no. 4 (2023): 1300–1331.
    doi: 10.1002/mas.21765google scholar: lookup
  28. Schymanski EL, Jeon J, Gulde R. Identifying Small Molecules via High Resolution Mass Spectrometry: Communicating Confidence. Environmental Science & Technology 48 (2014): 2097–2098.
    doi: 10.1021/es5002105google scholar: lookup
  29. Schrimpe-Rutledge AC, Codreanu SG, Sherrod SD, McLean JA. Untargeted Metabolomics Strategies—Challenges and Emerging Directions. Journal of the American Society for Mass Spectrometry 27, no. 12 (2016): 1897–1905.
    doi: 10.1007/s13361-016-1469-ygoogle scholar: lookup
  30. Szyrwiel L, Sinn L, Ralser M, Demichev V. Slice-PASEF: Fragmenting All Ions for Maximum Sensitivity in Proteomics. BioRxiv (2022): 2022–2010.
    doi: 10.1101/2022.10.31.514544google scholar: lookup
  31. Willems S, Voytik E, Skowronek P, Strauss MT, Mann M. AlphaTims: Indexing Trapped Ion Mobility Spectrometry–TOF Data for Fast and Easy Accession and Visualization. Molecular & Cellular Proteomics 20 (2021): 100149.
  32. Stricker T, Bonner R, Lisacek F, Hopfgartner G. Adduct Annotation in Liquid Chromatography/High‐Resolution Mass Spectrometry to Enhance Compound Identification. Analytical and Bioanalytical Chemistry 413 (2021): 503–517.
  33. Bonner R, Hopfgartner G. Annotation of Complex Mass Spectra by Multilayered Analysis. Analytica Chimica Acta 1193 (2022): 339317.
    doi: 10.1016/j.aca.2021.339317google scholar: lookup
  34. Hsu F-F, Bohrer A, Turk J. Formation of Lithiated Adducts of Glycerophos‐Phocholine Lipids Facilitates Their Identification by Electrospray Ionization Tandem Mass Spectrometry. Journal of the American Society for Mass Spectrometry 9, no. 5 (1998): 516–526.
  35. Lu W, Xing X, Wang L. Improved Annotation of Untargeted Metabolomics Data Through Buffer Modifications That Shift Adduct Mass and Intensity. Analytical Chemistry 92, no. 17 (2020): 11573–11581.
  36. Kind T, Fiehn O. Metabolomic Database Annotations via Query of Elemental Compositions: Mass Accuracy Is Insufficient Even at Less Than 1 ppm. BMC Bioinformatics 7 (2006): 1–10.
    doi: 10.1186/1471-2105-7-234google scholar: lookup
  37. Pellegrin V. Molecular Formulas of Organic Compounds: The Nitrogen Rule and Degree of Unsaturation. Journal of Chemical Education 60, no. 8 (1983): 626.
  38. Holčapek M, Kolářová L, Nobilis M. High‐Performance Liquid Chromatography–Tandem Mass Spectrometry in the Identification and Determination of Phase I and Phase II Drug Metabolites. Analytical and Bioanalytical Chemistry 391 (2008): 59–78.
    doi: 10.1007/s00216-008-1962-7google scholar: lookup
  39. Satapute P, Sanakal RD, Mulla SI, Kaliwal B. Molecular Interaction of the Triazole Fungicide Propiconazole With Homology Modelled Superoxide Dismutase and Catalase. Environmental Sustainability 2 (2019): 429–439.
  40. Dixit D, Verma PK, Marwaha RK. A Review on ‘Triazoles’: Their Chemistry, Synthesis and Pharmacological Potentials. Journal of the Iranian Chemical Society 18, no. 10 (2021): 2535–2565.
  41. Hillebrands L, Lamshoeft M, Lagojda A, Stork A, Kayser O. In Vitro Metabolism of Tebuconazole, Flurtamone, Fenhexamid, Metalaxyl‐M and Spirodiclofen in Cannabis Sativa L. (Hemp) Callus Cultures. Pest Management Science 77, no. 12 (2021): 5356–5366.
    doi: 10.1002/ps.6575google scholar: lookup
  42. Wan KX, Vidavsky I, Gross ML. Comparing Similar Spectra: From Similarity Index to Spectral Contrast Angle. Journal of the American Society for Mass Spectrometry 13, no. 1 (2002): 85–88.
  43. Celma A, Sancho JV, Schymanski EL. Improving Target and Suspect Screening High‐Resolution Mass Spectrometry Workflows in Environmental Analysis by Ion Mobility Separation. Environmental Science & Technology 54, no. 23 (2020): 15120–15131.
    doi: 10.1021/acs.est.0c05713google scholar: lookup
  44. Liu L, Wang Z, Zhang Q. Ion Mobility Mass Spectrometry for the Separation and Characterization of Small Molecules. Analytical Chemistry 95, no. 1 (2023): 134–151.
  45. Hrynko I, Kaczyński P, Pietruszyńska M, Łozowicka B. The Effect of Food Thermal Processes on the Residue Concentration of Systemic and Nonsystemic Pesticides in Apples. Food Control 143 (2023): 109267.
  46. Luo M, Yin Y, Zhou Z. A Mass Spectrum‐Oriented Computational Method for Ion Mobility‐Resolved Untargeted Metabolomics. Nature Communications 14, no. 1 (2023): 1813.

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