Mildronate (Meldonium) in professional sports – monitoring doping control urine samples using hydrophilic interaction liquid chromatography – high resolution/high accuracy mass spectrometry
Bibliographic record
Abstract
To date, substances such as Mildronate (Meldonium) are not on the radar of anti-doping laboratories as the compound is not explicitly classified as prohibited. However, the anti-ischemic drug Mildronate demonstrates an increase in endurance performance of athletes, improved rehabilitation after exercise, protection against stress, and enhanced activations of central nervous system (CNS) functions. In the present study, the existing evidence of Mildronate's usage in sport, which is arguably not (exclusively) based on medicinal reasons, is corroborated by unequivocal analytical data allowing the estimation of the prevalence and extent of misuse in professional sports. Such data are vital to support decision-making processes, particularly regarding the ban on drugs in sport. Due to the growing body of evidence (black market products and athlete statements) concerning its misuse in sport, adequate test methods for the reliable identification of Mildronate are required, especially since the substance has been added to the 2015 World Anti-Doping Agency (WADA) monitoring program. In the present study, two approaches were established using an in-house synthesized labelled internal standard (Mildronate-D3 ). One aimed at the implementation of the analyte into routine doping control screening methods to enable its monitoring at the lowest possible additional workload for the laboratory, and another that is appropriate for the peculiar specifics of the analyte, allowing the unequivocal confirmation of findings using hydrophilic interaction liquid chromatography-high resolution/high accuracy mass spectrometry (HILIC-HRMS). Here, according to applicable regulations in sports drug testing, a full qualitative validation was conducted. The assay demonstrated good specificity, robustness (rRT=0.3%), precision (intra-day: 7.0-8.4%; inter-day: 9.9-12.9%), excellent linearity (R>0.99) and an adequate lower limit of detection (<10 ng/mL).
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".