Doping control analysis of emerging drugs in human plasma – identification of GW501516, S‐107, JTV‐519, and S‐40503
Bibliographic record
Abstract
An important aspect of preventive doping research is the rapid implementation of tests for emerging drugs with potential for misuse into routine doping control assays. New therapeutics of different classes such as PPARdelta-agonists (e.g. GW501516), ryanodine-calstabin-complex stabilizers (e.g. S-107 and JTV-519), and selective androgen receptor modulators (SARMs, e.g. S-40503) are currently used for the treatment of particular medical conditions such as metabolic syndrome, cardiac arrhythmia, debilitating diseases and osteoporosis, respectively. Due to their being at an early stage of clinical trials and the limited availability of data on the metabolism and possible renal elimination of the active drugs, the development of protocols for doping control analyses of plasma specimens could be an option for the detection of the circulating agents. The mass spectrometric fragmentation of four emerging drug candidates (GW501516, S-107, JTV-519, and S-40503) was elucidated by positive electrospray ionization and collision-induced dissociation using a high resolution/high accuracy mass spectrometer. A screening and confirmation procedure was established based on liquid chromatography/tandem mass spectrometry requiring a volume of 100 microL of plasma. Proteins were precipitated using acetonitrile, the specimens were centrifuged and the supernatant analyzed using a triple-quadrupole mass spectrometer employing multiple reaction monitoring of diagnostic ion transitions. The method was validated with regard to specificity, limits of detection (0.4-8.3 ng/mL), recoveries (72-98%), intraday and interday precisions (12-21%), and ion suppression/enhancement effects.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".