Characterization and quantitation of a sulfoconjugated metabolite for detection of methyltestosterone misuse and direct identification by LC-MS
Bibliographic record
Abstract
Methyltestosterone (MT) is one of the most frequently misused anabolic androgenic steroids detected in doping control analysis. The metabolism of MT in humans leads to several phase І metabolites and their corresponding phase Ⅱ conjugates. Previous studies have postulated the 3α-sulfoconjugate of 17α-methyl-5β-androstane-3α,17β-diol (S2) as principal sulfate metabolite of MT, with a detection window exceeding 10 days. However, a final direct and unambiguous confirmation of the structure of this metabolite is missing until now. In this study, we established an approach to detect and identify S2, using intact analysis by liquid chromatography hyphenated with tandem mass spectrometry (LC-MS/MS) without complex sample pretreatment. An in vitro study yielded the LC-MS/MS reference retention times of all 3-sulfated 17-methylandrostane-3,17-diol diastereomers, allowing for accurate structure assignment of potentially detected metabolites. In an in vivo excretion study with a single healthy male volunteer, the presence of the metabolite S2 was confirmed after a single oral dose of 10 mg MT. The reference standard was chemically synthesized, characterized by accurate mass mass spectrometry (MS) and nuclear magnetic resonance (NMR), and quantified by quantitative NMR (qNMR). Thus, this study finally provides accurate structure information on the S2 metabolite and a direct analytical method for detection of MT misuse. The availability of the reference material is expected to facilitate further evaluation and subsequent analytical method validation in anti-doping research.
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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 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".