Drug‐drug interaction and doping, part 2: An <i>in vitro</i> study on the effect of non‐prohibited drugs on the phase I metabolic profile of stanozolol
Bibliographic record
Abstract
The present study was designed to provide preliminary information on the potential impact of metabolic drug‐drug interaction on the effectiveness of doping control strategies currently followed by the anti‐doping laboratories to detect the intake of prohibited agents. In vitro assays based on the use of human liver microsomes and recombinant cytochrome P450 isoforms were developed and applied to characterize the phase I metabolic profile of the prohibited agent stanozolol, both in the absence and in the presence of substances (ketoconazole, itraconazole, miconazole, cimetidine, ranitidine, and nefazodone) not included in the World Anti‐Doping Agency (WADA) list of prohibited substances and methods and frequently administered to athletes. The results show that the in vitro model utilized in this study is adequate to simulate the in vivo metabolism of stanozolol. Furthermore, our data showed that ketoconazole, itraconazole, miconazole, and nefazodone caused a marked modification in the production of the metabolic products (3’‐hydroxy‐stanozolol, 4β‐hydroxy‐stanozolol and 16β‐hydroxy‐stanozolol) normally selected by the anti‐doping laboratories as target analytes to detect stanozolol intake. On the contrary, moderate variations were registered in the presence of cimetidine and no significant modifications were measured in the presence of ranitidine. This evidence confirms that the potential effect of drug‐drug interactions is duly taken into account also in anti‐doping analysis. Copyright © 2014 John Wiley & Sons, Ltd.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".