Investigation of the selective androgen receptor modulators S1, S4 and S22 and their metabolites in equine plasma using high‐resolution mass spectrometry
Bibliographic record
Abstract
RATIONALE: Selective androgen receptor modulators (SARMs) are prohibited in sports due to their performance enhancing ability. It is important to investigate the metabolism to determine appropriate targets for doping control. This is the first study where the equine metabolites of SARMs S1, S4 (Andarine) and S22 (Ostarine) have been studied in plasma. METHODS: Each SARM was administered to three horses as an intravenous bolus dose and plasma samples were collected. The samples were pretreated with protein precipitation using cold acetonitrile before separation by liquid chromatography. The mass spectrometric analysis was performed using negative electrospray, quadrupole time-of-flight mass spectrometry operated in MS(E) mode and triple-quadrupole mass spectrometry operated in selected reaction monitoring mode. For the quantification of SARM S1, a deuterated analogue was used as internal standard. RESULTS: The numbers of observed metabolites were eight, nine and four for the SARMs S1, S4 and S22, respectively. The major metabolite was formed by the same metabolic reactions for all three SARMs, namely amide hydrolysis, hydroxylation and sulfonation. The values of the determined maximum plasma concentrations were in the range of 97-170 ng/mL for SARM S1, 95-115 ng/mL for SARM S4 and 92-147 ng/mL for SARM S22 and the compounds could be detected for 96 h, 12 h and 18 h, respectively. CONCLUSIONS: The maximum plasma concentration of SARMs S1, S4 and S22 was measured in the first sample (5 min) after administration and they were eliminated fast from plasma. The proposed targets to be used in equine doping control are the parent compounds for all three SARMs, but with the metabolite yielding the highest response as a complementary target. Copyright © 2016 John Wiley & Sons, Ltd.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".