Identification and characterization of novel long‐term metabolites of oxymesterone and mesterolone in human urine by application of selected reaction monitoring GC‐CI‐MS/MS
Bibliographic record
Abstract
The search for metabolites with longer detection times remains an important task in, for example, toxicology and doping control. The impact of these long-term metabolites is highlighted by the high number of positive cases after reanalysis of samples that were stored for several years, e.g. samples of previous Olympic Games. A substantial number of previously alleged negative samples have now been declared positive due to the detection of various long-term steroid metabolites the existence of which was unknown during the Olympic Games of 2008 and 2012. In this work, the metabolism of oxymesterone and mesterolone, two anabolic androgenic steroids (AAS), was investigated by application of a selected reaction monitoring gas chromatography-chemical ionization-triple quadrupole mass spectrometry (GC-CI-MS/MS) protocol for metabolite detection and identification. Correlations between AAS structure and GC-CI-MS/MS fragmentation behaviour enabled the search for previously unknown but expected AAS metabolites by selection of theoretical transitions for expected metabolites. Use of different hydrolysis protocols allowed for evaluation of the detection window of both phase I and phase II metabolites. For oxymesterone, a new metabolite, 18-nor-17β-hydroxymethyl-17α-methyl-4-hydroxy-androst-4,13-diene-3-one, was identified. It was detectable up to 46 days by using GC-CI-MS/MS, whereas with a traditional screening (detection of metabolite 17-epioxymesterone with electron ionization GC-MS/MS) oxymesterone administration was only detectable for 3.5 days. A new metabolite was also found for mesterolone. It was identified as 1α-methyl-5α-androstan-3,6,16-triol-17-one and its sulfate form after hydrolysis with Helix pomatia resulted in a prolonged detection time (up to 15 days) for mesterolone abuse. Copyright © 2017 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".