Effect of non‐prohibited drugs on the phase II metabolic profile of morphine. An in vitro investigation for doping control purposes
Bibliographic record
Abstract
The potential consequences of drug-drug interaction on the strategies adopted by anti-doping laboratories to report an adverse analytical finding for morphine were investigated. We evaluated in vitro the effects of 14 drugs on the principal metabolic pathways of morphine. The selected drugs are among those most commonly used by the athletes, none of them presently included in the World Anti-Doping Agency (WADA) Prohibited List. The non-prohibited drugs included 4 antifungals (fluconazole, itraconazole, ketoconazole, and miconazole), 6 benzodiazepines (alprazolam, bromazepam, clonazepam, lorazepam, lormetazepam, and triazolam), and 4 non-steroidal anti-inflammatory drugs (diclofenac, ibuprofen, ketoprofen, and nimesulide). The in vitro assays were based on the use of either human liver microsomes or uridine 5'-diphospho-glucuronosyl-transferases. Morphine and its glucuronides were determined by developed liquid chromatography-mass spectrometry procedure after dilution with an aqueous solution containing their deuterated isotopologues as internal standards. Morphine is mainly excreted as phase II metabolites: about 70% of the parent compound is found to be biotransformed by UGT2B7 to morphine-3-glucuronide (6065%) and morphine-6-glucuronide (5-10%). A reduction of the enzymatic activity of the UGT2B7 was recorded in the presence of 9 of the 14 drugs under investigation (ketoconazole, miconazole, itraconazole, diclofenac, ibuprofen, clonazepam, lorazepam, lormetazepam, and triazolam), with a consequent significant reduction of the levels of the glucuronide metabolites. This phenomenon in vivo may affect the rate of the urinary excretion of morphine with the risk of reporting "false negative" results, especially in case of results close to the decision limit value set by WADA.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".