Characterization of <i>in vitro</i> generated metabolites of the selective androgen receptor modulators S‐22 and S‐23 and <i>in vivo</i> comparison to post‐administration canine urine specimens
Bibliographic record
Abstract
Selective androgen receptor modulators (SARMs) have great therapeutic potential in various diseases including cancer cachexia, sarcopenia, and osteoporosis, and the number of drug candidates has been growing over the last decade. The SARM drug candidates S-22 and S-23 belong to one of the most advanced groups of androgen receptor modulators and are based on an arylpropionamide-derived core structure. Due to their anabolic effects, SARMs have been prohibited in elite sports and have been a subject of sports drug testing programmes since January 2008. Consequently, the structure of analytically useful urinary metabolites should be elucidated to provide targets for sensitive and retrospective analysis. In the present study, the phase-I and -II metabolism of S-22 and S-23 was simulated using hepatic human enzymes, and resulting metabolites were characterized by means of state-of-the-art mass spectrometric approaches employing high resolution/high accuracy Orbitrap mass spectrometry. Subsequently, the newly defined target compounds including the glucuronic acid conjugates of S-22 and S-23, their corresponding monohydroxylated and bishydroxylated analogs, as well as their B-ring depleted counterparts were implemented into an existing routine doping control procedure, which was examined for its specificity for the added substances. In order to obtain proof-of-concept data for authentic urine specimens, canine urine samples collected up to 72 h after oral administration of S-22 to dogs were analyzed using the established approach outlining the capability of the presented assay to detect the glucuronide of S-22 as well as the B-ring-depleted metabolite (M3) in all samples following therapeutic (31.4 µg/kg) dosing. Finally, M3 was chemically synthesized, characterized by nuclear magnetic resonance spectroscopy and high resolution/high accuracy mass spectrometry, and chosen as primary target for future doping control analyses.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".