Investigations Into the Human Metabolism of Trestolone (7α‐Methyl‐19‐Nortestosterone)
Bibliographic record
Abstract
Already in the 1960s, the anabolic properties of Trestolone (7α-methyl-19-nortestosterone, MENT) were investigated in the context of cancer research, and MENT was found to be 10 times more potent regarding its anabolic properties compared to testosterone. The human metabolism of MENT was only investigated once in an antidoping context, and three urinary metabolites were identified, corroborating earlier findings from in vitro and animal experiments. Based on these metabolites, no doping control sample was reported to contain MENT or its metabolites in the last two decades albeit MENT is readily available via online distributors. One reason for the lack of adverse analytical findings in doping controls could be analytical challenges originating from the chromatographic properties of MENT and its urinary metabolites. Therefore, the human metabolism of MENT was reinvestigated employing an excretion study with deuterated MENT and metabolite detection based on hydrogen isotope ratio mass spectrometry in combination with high accuracy/high resolution mass spectrometry. Considering unconjugated, glucuronidated, and sulfated metabolites, 50 potential candidates were detected. In order to identify those metabolites suitable for sports drug testing, three volunteers administered a single oral dose of nondeuterated MENT, and all postadministration samples were investigated using triple quadrupole mass spectrometry-based determinations routinely employed in doping controls. From the 50 metabolites detected, two showed promising results with respect to their detection windows and suitability under routine measurement conditions. The specificity of the novel metabolites was ensured by the reanalysis of 200 routine doping control samples demonstrating the absence of potential coeluting compounds.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.002 |
| 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) | 0.000 | 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 teacher head, 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".