<i>In vitro</i> evaluation of the effects of anti‐fungals, benzodiazepines and non‐steroidal anti‐inflammatory drugs on the glucuronidation of 19‐norandrosterone: implications on doping control analysis
Bibliographic record
Abstract
We have studied whether the phase II metabolism of 19-norandrosterone, the most representative metabolite of 19-nortestosterone (nandrolone), can be altered in the presence of other drugs that are not presently included on the Prohibited List of the World Anti-Doping Agency. In detail, we have evaluated the effect of non-prohibited drugs belonging to the classes of anti-fungals, benzodiazepines, and non-steroidal anti-inflammatory drugs on the glucuronidation of 19-norandrosterone. In vitro assays based on the use of either pooled human liver microsomes or specific recombinant isoforms of uridine diphosphoglucuronosyl-transferase were designed and performed to monitor the formation of 19-norandrosterone glucuronide from 19-norandrosterone. Determination of 19-norandrosterone (free and conjugated fraction) was performed by gas chromatography - mass spectrometry after sample pretreatment consisting of an enzymatic hydrolysis (performed only for the conjugated fraction), liquid/liquid extraction with tert-butylmethyl ether, and derivatization to form the trimethylsilyl derivative. In parallel, a method based on reversed-phase liquid chromatography coupled to tandem mass spectrometry in positive electrospray ionization with acquisition in selected reaction monitoring mode was also developed to identify the non-prohibited drugs considered in this study. Incubation experiments have preliminarily shown that the glucuronidation of 19-norandrosterone is principally carried out by UGT2B7 (39%) and UGT2B17 (31%). Inhibition studies have shown that the yield of the glucuronidation reaction is reduced in the presence of the anti-fungals itraconazole, ketoconazole, and miconazole, of the benzodiazepine triazolam and of the non-steroidal anti-inflammatory drugs diclofenac and ibuprofen, while no alteration was recorded in the presence of all other compounds considered in this study. Copyright © 2015 John Wiley & Sons, Ltd.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".