Model‐based meta‐analysis of salbutamol pharmacokinetics and practical implications for doping control
Bibliographic record
Abstract
Salbutamol was included in the prohibited list of the World Anti-Doping Agency (WADA) in 2004. Although systemic intake is banned, inhalation for asthma is permitted but with dosage restrictions. The WADA established a urinary concentration threshold to distinguish accordingly prohibited systemic self-administration from therapeutic prescription by inhalation. This study aimed at evaluating the ability of the WADA threshold to differentiate salbutamol therapeutic use from violation of antidoping rules. Concentration-time profile of salbutamol in plasma and its excretion in urine was characterized through a model-based meta-analysis of individual and aggregate data collected after administration of a large range of doses following different modes of administration and under a variety of conditions. The developed model adequately fitted salbutamol plasma and urine concentration-time profiles of the 13 selected studies. Model-based simulations confirmed that a wide range of salbutamol urine concentrations might be measured after drug intake. Although violation of the WADA Code can be strongly suspected in individuals showing very high salbutamol urine concentrations, uncertainty remains for values close to the WADA threshold as they can be compatible with both permitted therapeutic use and violation. Although not entirely discriminant, the current WADA rule is globally supported by our appraisal. It could be further improved by a slight and reasonable adjustment of inhaled daily dosages allowed for therapeutic use. Our model might help antidoping experts in the evaluation of suspected doping cases through confronting the athlete's urine measurements with their allegations about salbutamol treatment.
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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 | 0.031 | 0.048 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.029 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".