Specific characterization of non‐steroidal selective androgen peceptor modulators using supercritical fluid chromatography coupled to ion‐mobility mass spectrometry: application to the detection of enobosarm in bovine urine
Bibliographic record
Abstract
Currently under development for therapeutic purposes in human medicine, non‐steroidal selective androgen receptor modulators (non‐steroidal SARMs) are also known to impact growth associated pathways. As such, they present a potential for abuse in sports and food‐producing animals as interesting alternative anabolic substances. Forbidden since 2008 by the World Anti‐Doping Agency (WADA) these compounds are however easily available and could be (mis)used in livestock production as growth promoters. To prevent such practices, dedicated analytical strategies have to be developed for specific and sensitive detection of these compounds in biological matrices. Using an innovative analytical platform constituted of supercritical fluid chromatography coupled to ion mobility‐mass spectrometry, the present study enabled efficient separation and identification in urine of 4 of these drugs (andarine, bicalutamide, hydroxyflutamide, and enobosarm) in accordance with European Union criteria (Commission Decision 2002/657/EC). Besides providing information about compounds structure and behaviour in gas phase, such a coupling enabled reaching low limits of detection (LOD < 0.05 ng.mL −1 for andarine and limits of detection < 0.005 ng.mL −1 for the three others) in urine with good repeatability (CV < 21 %). The workflow has been applied to quantitative determination of enobosarm elimination in urine of treated bovine (200 mg, oral). Copyright © 2016 John Wiley & Sons, Ltd.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".