Performance assessment of an equine metabolomics model for screening a range of anabolic agents

IntroductionDespite their ban, Anabolic Androgenic Steroids (AAS) are considered as the most important threat for equine doping purposes. In the context of controlling such practices in horse racing, metabolomics has emerged as a promising alternative strategy to study the effect of a substance on metabolism and to discover new relevant biomarkers of effect. Based on the monitoring of 4 metabolomics derived candidate biomarkers in urine, a prediction model to screen for testosterone esters abuse was previously developed. The present work focuses on assessing the robustness of the associated method and define its scope of application.Materials and methodsSeveral hundred urine samples were selected from 14 different horses of ethically approved administration studies involving various doping agents' (AAS, SARMS, beta-agonists, SAID, NSAID) (328 urine samples). In addition, 553 urine samples from untreated horses of doping control population were included in the study. Samples were characterized with the previously described LC-HRMS/MS method, with the objective of assessing both its biological and analytical robustness.ResultsThe study concluded that the measurement of the 4 biomarkers involved in the model was fit for purpose. Further, the classification model confirmed its effectiveness in screening for testosterone esters use; and it demonstrated its ability to screen for the misuse of other anabolic agents, allowing the development of a global screening tool dedicated to this class of substances. Finally, the results were compared to a direct screening method targeting anabolic agents demonstrating complementary performances of traditional and omics approaches in the screening of anabolic agents in horses.

Automated summary

Despite their ban, Anabolic Androgenic Steroids (AAS) are still considered the biggest threat in equine doping. Metabolomics has emerged as a promising strategy to study the effect of substances on metabolism and discover biomarkers. This study assessed the robustness of a urine-based prediction model for detecting testosterone esters abuse and determined its application scope.