LC‐MS/(MS) confirmatory doping control analysis of intact phase II metabolites of methenolone and mesterolone after Girard's Reagent T derivatization
Bibliographic record
Abstract
In the present study, the application and evaluation of Girard's Reagent T (GRT) derivatization for the simultaneous detection and significantly important identification of different phase II methenolone and mesterolone metabolites by LC-MS/(MS) are presented. For the LC-MS analysis of target analytes two complementary isolation methods were developed; a derivatization and shoot method in which native urine is diluted with derivatization reagent and is injected directly to LC-MS and a liquid-liquid extraction method, using ethyl acetate at pH 4.5, for the effective isolation of both sulfate and glucuronide metabolites of the named steroids as well as of their free counterparts. For the evaluation of the proposed protocols, urine samples from methenolone and mesterolone excretion studies were analyzed against at least one sample from a different excretion study. Retention times, along with product ion ratios, were evaluated according to the WADA TD2021IDCR requirements, in order to determine maximum detection and identification time windows for each metabolite. Established identification windows obtained after LC-MS/(MS) analysis were further compared with those obtained after GC-MS/(MS) analysis of the same samples from the same excretion studies, for the most common analytes monitored by GC-MS/(MS). Full validation was performed for the developed derivatization and shoot method for the identification of methenolone metabolite, 3α-hydroxy-1-methylen-5α-androstan-17-one-3-glucuronide (mth3). Overall, the GRT derivatization presented herein offers a tool for the simultaneous sensitive detection of free, intact glucuronide and sulfate metabolites by LC-MS/(MS) that enhance significantly the detection and identification time windows of specific methenolone and mesterolone metabolites for doping control analysis.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| 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) | 0.000 | 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".