Characterization of equine urinary metabolites of selective androgen receptor modulators (SARMs) S1, S4 and S22 for doping control purposes
Bibliographic record
Abstract
Selective androgen receptor modulators, SARMs, constitute a class of compounds with anabolic properties but with few androgenic side-effects. This makes them possible substances of abuse and the World Anti-Doping Agency (WADA) has banned the entire class of substances. There have been several cases of illicit use of aryl propionamide SARMs in human sports and in 2013, 13 cases were reported. These substances have been found to be extensively metabolized in humans, making detection of metabolites necessary for doping control. SARMs are also of great interest to equine doping control, but the in vivo metabolite pattern and thus possible analytical targets have not been previously studied in this species. In this study, the urinary metabolites of the SARMs S1, S4, and S22 in horses were studied after intravenous injection, using ultra high performance liquid chromatography coupled to quadrupole time-of-flight mass spectrometry (UHPLC-QToF-MS). Eight different metabolites were found for SARM S1, nine for SARM S4, and seven for SARM S22. The equine urinary metabolite profiles differed significantly from those of humans. The parent compounds were only detected for SARMs S4 and S22 and only at the first sampling time point at 3 h post administration, making them unsuitable as target compounds. For all three SARMs tested, the metabolite yielding the highest response had undergone amide hydrolysis, hydroxylation and sulfonation. The resulting phase II metabolites (4-nitro-3-trifluoro-methyl-phenylamine sulfate for SARMs S1 and S4 and 4-cyano-3-trifluoro-methyl-phenylamine sulfate for SARM S22) are proposed as analytical targets for use in equine doping control.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".