Doping control analysis of trimetazidine and characterization of major metabolites using mass spectrometric approaches
Bibliographic record
Abstract
Since January 2014, the anti-anginal drug trimetazidine [1-(2,3,4-trimethoxybenzyl)-piperazine] has been classified as prohibited substance by the World Anti-Doping Agency (WADA), necessitating specific and robust detection methods in sports drug testing laboratories. In the present study, the implementation of the intact therapeutic agent into two different initial testing procedures based on gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-tandem mass spectrometry (LC-MS/MS) is reported, along with the characterization of urinary metabolites by electrospray ionization-high resolution/high accuracy (tandem) mass spectrometry. For GC-MS analyses, urine samples were subjected to liquid-liquid extraction sample preparation, while LC-MS/MS analyses were conducted by established 'dilute-and-inject' approaches. Both screening methods were validated for trimetazidine concerning specificity, limits of detection (0.5-50 ng/mL), intra-day and inter-day imprecision (<20%), and recovery (41%) in case of the GC-MS-based method. In addition, major metabolites such as the desmethylated trimetazidine and the corresponding sulfoconjugate, oxo-trimetazidine, and trimetazidine-N-oxide as identified in doping control samples were used to complement the LC-MS/MS-based assay, although intact trimetazidine was found at highest abundance of the relevant trimetazidine-related analytes in all tested sports drug testing samples. Retrospective data mining regarding doping control analyses conducted between 1999 and 2013 at the Cologne Doping Control Laboratory concerning trimetazidine revealed a considerable prevalence of the drug particularly in endurance and strength sports accounting for up to 39 findings per year.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".