Determination of GnRH and its synthetic analogues' abuse in doping control: Small bioactive peptide UPLC–MS/MS method extension by addition of <i>in vitro</i> and <i>in vivo</i> metabolism data; evaluation of LH and steroid profile parameter fluctuations as suitable biomarkers
Bibliographic record
Abstract
Gonadotropin-releasing hormone (GnRH) and its small peptide synthetic analogues are included in Section S2 of the World Anti-Doping Agency (WADA) Prohibited List as they stimulate pituitary luteinizing hormone (LH) and testicular testosterone (T) secretion. Both the following approaches can be applied for determination of abuse of these peptides: direct identification of intact compounds and their metabolites in athletes' biofluids and evaluation of LH and T concentrations as mediate markers of drug intake. To develop an effective concept for GnRH and its analogues determination in anti-doping control, in vitro and in vivo studies were conducted. A new method was applied to the evaluation of the slow-release profile of buserelin, goserelin, and leuprolide biodegradable microspheres after the intramuscular injection in male volunteers. Eight metabolites of 10 GnRH analogues were identified after incubation with human kidney microsomes, most of them were leuprolide degradation products. Obtained data were added into ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) method for GnRH analogues determination. The detection time windows for administered peptides and their metabolites in urine samples were evaluated with 2 sample preparation techniques: dilute-and-shoot and solid-phase extraction. To support the second hypothesis, the measurement of LH and the main parameters of the steroid profile were performed in urine samples. Just 1 compound among those investigated resulted in the LH concentration dropping to non-physiological levels. Thus, for doping-control purposes, monitoring of hormone levels fluctuations could be applied only together with longitudinal passport steroid profile data.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".