Screening for metabolically stable aryl‐propionamide‐derived selective androgen receptor modulators for doping control purposes
Bibliographic record
Abstract
Anabolic agents have been among the most frequently detected drugs in amateur and professional sport. A novel class of therapeutics presumably complementing anabolic steroids in the near future includes so-called selective androgen receptor modulators (SARMs) that have been under clinical investigations for several years. Although not yet commercially available, their potential for misuse in sports is high. Four aryl-propionamide-derived SARMs were synthesized in order to establish a fast and robust screening procedure using liquid chromatography/electrospray ionization tandem mass spectrometry. Synthesized compounds were characterized by high-resolution/high-accuracy mass analysis employing a linear ion trap-Orbitrap hybrid mass spectrometer while routine analyses were conducted on a triple-quadrupole mass spectrometer. Characteristic product ions obtained by collision-induced dissociation were found at m/z 289 and 261 as well as m/z 269 and 241 representing the bisubstituted aniline residues of selected model compounds. Assay validation was performed regarding lower limit of detection (1 ng/mL), recovery (85-105%), intraday precision (7.6-11.6%) and interday precision (9.9-14.4%), and precursor ion scan experiments on diagnostic product ions enabled the detection of a structurally related compound at 50 ng/mL.
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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 | 0.000 | 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) | 0.002 | 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".