Screening for 2‐quinolinone‐derived selective androgen receptor agonists in doping control analysis
Bibliographic record
Abstract
Selective androgen receptor modulators (SARMs) represent a class of emerging drugs with high potential for misuse in sports, and therefore members of this group are banned as anabolic agents by the World Anti-Doping Agency. Preventive approaches to restrict their use include early implementation of target analytes into doping control screening assays and evaluation of the mass spectrometric behavior of these drugs to allow their unequivocal identification as well as the characterization of structurally related compounds and metabolic products. Four model SARMs with the 6-alkylamino-2-quinolinone structure, including the advanced drug candidate LGD-2226, were synthesized. Fragmentation pathways after positive electrospray ionization and collision-induced dissociation were studied using an LTQ Orbitrap mass analyzer, and diagnostic product ions and common dissociation pathways were employed to establish a screening procedure targeting intact quinolinone-based SARMs as well as putative metabolic products such as dealkylated analogues. Therefore, features of a triple quadrupole mass analyzer such as multiple reaction monitoring and precursor ion scanning were utilized. Sample preparation based on commonly employed liquid-liquid extraction and subsequent liquid chromatographic/tandem mass spectrometric measurement allowed for detection limits of 0.01-0.2 ng/mL, and intra- and interday precisions between 3.2 and 8.5% and between 6.3 and 16.6%, respectively. Recoveries varied from 81 to 98%, and tests for ion suppression or enhancement effects were negative for all analytes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| 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) | 0.000 | 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 teacher head, 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".