Trafficking of drug candidates relevant for sports drug testing: Detection of non‐approved therapeutics categorized as anabolic and gene doping agents in products distributed via the Internet
Bibliographic record
Abstract
Identifying the use of non-approved drugs by cheating athletes has been a great challenge for doping control laboratories. This is due to the additional complexities associated with identifying relatively unknown and uncharacterized compounds and their metabolites as opposed to known and well-studied therapeutics. In 2010, the prohibited drug candidates and gene doping substances AICAR and GW1516, together with the selective androgen receptor modulator (SARM) MK-2866 were obtained by the Cologne Doping Control Laboratory from Internet suppliers and their structure, quantity, and formulation elucidated. All three compounds proved authentic as determined by liquid chromatography-high resolution/high accuracy (tandem) mass spectrometry and comparison to reference material. While AICAR was provided as a colourless powder in 100 mg aliquots, GW1516 was obtained as an orange/yellow suspension in water/glycerol (150 mg/ml), and MK-2866 (25 mg/ml) was shipped dissolved in polyethylene glycol (PEG) 300. In all cases, the quantified amounts were considerably lower than indicated on the label. The substances were delivered via courier, with packaging identifying them as containing 'amino acids' and 'green tea extract', arguably to circumvent customs control. Although all of the substances were declared 'for research only', their potential misuse in illicit performance-enhancement cannot be excluded; moreover sports drug testing authorities should be aware of the facile availability of black market copies of these drug candidates.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".