Pharmacokinetics of nebulized and oral procaterol in asthmatic and non‐asthmatic subjects in relation to doping analysis
Bibliographic record
Abstract
The purpose of the present study was to investigate pharmacokinetics of procaterol in asthmatics and non-asthmatics after nebulized and oral administration in relation to doping. Ten asthmatic and ten non-asthmatic subjects underwent two pharmacokinetic trials. At first trial, 4 µg procaterol was administered as nebulization. At second trial, 100 µg procaterol was administered orally. Serum and urine samples were collected before and after administration of procaterol. Samples were analyzed by liquid chromatography-tandem mass spectrometry (LC-MS/MS). Serum and urine concentrations of procaterol were markedly higher after oral administration compared to nebulized administration. After oral administration, serum procaterol concentration-time area under the curve (AUC) was higher (P ≤ 0.05) for asthmatics than non-asthmatics. Likewise, urine concentrations were higher (P ≤ 0.01) for asthmatics than non-asthmatics 4 (47 ± 12 vs. 28 ± 9 ng/mL) and 8 h (39 ± 9 vs. 15 ± 5 ng/mL) after oral administration. Detection of serum procaterol was difficult after nebulized administration with 38 samples (27%) below limit of quantification (LOQ) and only trends were observed. No differences were observed between asthmatics and non-asthmatics in the urine concentrations of procaterol after nebulized administration. In summary, our data showed that asthmatics had higher urine concentrations of procaterol than non-asthmatics after oral administration of 100 µg, whereas no difference was observed between the groups after nebulized administration. For doping control purposes, our observations indicate that it is possible to differentiate therapeutic nebulized administration of procaterol from prohibited use of oral procaterol. Copyright © 2016 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 | 0.000 | 0.001 |
| 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".