Drug–drug interaction and doping: Effect of non‐prohibited drugs on the urinary excretion profile of methandienone
Bibliographic record
Abstract
The potential consequences of drug-drug interactions on the excretion profile of the anabolic androgenic steroid methandienone (17β-hydroxy-17α-methylandrosta-1,4-dien-3-one) are discussed. More specifically, we have evaluated by in vitro and in vivo experiments the effects of 7 non-prohibited drugs (fluconazole, ketoconazole, itraconazole, miconazole, fluoxetine, paroxetine, and nefazodone) on the main metabolic pathways of methandienone. These are selected among those most commonly used by the athletes. The in vitro assays were based on the use of human liver microsomes, specific recombinant enzyme isoforms of cytochrome P450 and uridine 5'-diphospho-glucuronosyl-transferase. The in vivo study was performed by analyzing urines collected after the oral administration of methandienone with and without the co-administration of ketoconazole. Methandienone and its metabolites were determined by liquid chromatography-mass spectrometry-based techniques after sample pretreatment including an enzymatic hydrolysis step (performed only for the investigation on phase II metabolism) and liquid/liquid extraction with t-butyl methyl-ether. The results from the in vitro experiments showed that the formation of the hydroxylated and dehydrogenated metabolites was significantly reduced in the presence of itraconazole, ketoconazole, miconazole and nefazodone, whereas the production of the 18-nor-hydroxylated metabolites and glucuronidation reactions was reduced significantly only in the presence of ketoconazole and miconazole. The analysis of the post-administration samples confirmed the in vitro observations, validating the hypothesis that drug-drug interaction may cause significant alterations in the metabolic profile of banned drugs, making their detection during doping control tests more challenging.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".