Synthesis, Mass Spectrometric Characterization, and Analysis of the PPARδ Agonist GW1516 and Its Major Human Metabolites: Targets in Sports Drug Testing
Bibliographic record
Abstract
The elucidation of metabolic pathways and the detection of emerging therapeutics potentially enhancing athletic performance are of paramount importance to doping control authorities to protect the integrity of elite sports. A new drug candidate belonging to the family of the peroxisome proliferator-activated receptor-delta agonists termed GW1516 (also referred to as GW501516) has been prohibited by the World Anti-Doping Agency in 2009 due to its potential to artificially increase endurance. Consequently, sports drug testing laboratories need to establish detection methods enabling the identification of the intact substance and/or its metabolite(s) that unambiguously prove the presence or absence of the target substances in doping control specimens. Simulating human metabolic reactions using liver microsomal preparations, minute amounts of possible urinary metabolites were obtained that were characterized by mass spectrometry-based methods. Subsequently, the most abundant metabolic products were chemically synthesized and as well characterized by mass spectrometry and nuclear magnetic resonance spectroscopy. Finally, GW1516 and two oxidized metabolites were implemented in a routine doping control analytical assay based on liquid chromatography-(tandem) mass spectrometry (LC-MS/MS), which was tested for its -fitness-for-purpose using spiked urine samples.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".