Emerging drugs: mechanism of action, mass spectrometry and doping control analysis
Bibliographic record
Abstract
The number of compounds and doping methods in sports is in a state of constant flux. In addition to 'traditional' doping agents, such as anabolic androgenic steroids or erythropoietin, new therapeutics and emerging drugs have considerable potential for misuse in elite sport. Such compounds are commonly based on new chemical structures, and the mechanisms underlying their modes of action represent new therapeutic approaches arising from recent advances in medical research; therefore, sports drug testing procedures need to be continuously modified and complementary methods developed, preferably based on mass spectrometry, to enable comprehensive doping controls. This tutorial not only discusses emerging drugs that can be categorized as anabolic agents (selective androgen receptor modulators, SARMs), gene doping [hypoxia-inducible factor stabilizers, peroxisome-proliferator-activated receptor (PPAR)delta-agonists] and erythropoietin-mimetics (Hematide) but also compounds with potentially performance-enhancing properties that are not classified in the current list of the World Anti-Doping Agency. Compounds such as ryanodine-calstabin-complex modulators (benzothiazepines) are included, their mass spectrometric properties discussed, and current approaches in sports drug testing outlined.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".