Reduced and rearranged metabolite structures after metandienone administration: New promising metabolites for potential long-term detection
Bibliographic record
Abstract
Metandienone (MD) is a representative of the group of anabolic androgenic steroids and is commonly used in professional and amateur sports despite being a banned substance by the World Anti-Doping Agency (WADA). Metabolites of MD show high structural similarity to related anabolic androgenic steroids (AAS) such as dehydrochloromethyltestosterone (DHCMT). This led to the hypothesis that metabolites of MD with structures similar to long-term metabolites of DHCMT may be detectable. Therefore, a human administration study of MD was carried out and analyzed with the focus on metabolite structures with partly or fully reduced A-rings and eventually with rearranged D-rings with 17ξ-hydroxymethyl-17ξ-methyl substructures. Synthesized diastereomeric reference material allowed the establishment of a confident identification and characterization of excreted targeted compounds by gas chromatography-mass spectrometry. In this way, the excretion of inter alia 17α-methyl-5β-androstane-3α,17β-diol (T3), 17β-methyl-5β-androstane-3α,17α-diol (T7), 17α-hydroxymethyl-17β-methyl-18-nor-5β-androst-13-en-3α-ol (N3), 17α-hydroxymethyl-17β-methyl-18-nor-5β-androsta-1,13-dien-3α-ol (E3) and 17,17-dimethyl-18-nor-5β-androst-13-en-3α-ol (3α5βnorTHMT) was confirmed. Excretion curves of previously known and newly discovered metabolites enabled the assessment of the relationship between the chemical structure and the time of excretion. In addition to further assembling the picture of human metabolism of AAS with newly discovered metabolites, the detection of E3 allows the presumption to be a promising future candidate for a new long-term marker in anti-doping analyses with indications for an increased detection window.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".