Searching for <i>in silico</i> predicted metabolites and designer modifications of (cortico)steroids in urine by high‐resolution liquid chromatography/time‐of‐flight mass spectrometry
Bibliographic record
Abstract
Glucocorticosteroids are a restricted class of substances and appear on the 'in-competition' prohibited list of the World Anti-Doping Agency (WADA). Analysis of glucocorticosteroids is complicated since they show significant phase 1 and 2 metabolism in the human body and are excreted into urine in concentrations in the microg/L range. Full scan, high-resolution time-of-flight mass spectrometry analysis generates information on all ionisable components in urine, including known and unknown metabolites of steroids and even designer modifications of anabolic steroids. However, evaluation of the data obtained can be difficult and time-consuming because of the need to differentiate between endogenous components and compounds of interest. MetaboLynx, a spectral and chromatographic search program, was modified for the determination of in silico predicted metabolites of glucocorticosteroids and designer modifications of anabolic steroids in human urine. Spiked urine samples were successfully screened for known components in a targeted approach and for unknown species in a non-targeted approach using data filtering to limit potential false-positives. A simplified combined approach of targeted and untargeted screening was used for the detection of metabolites and designer modifications of existing compounds. This approach proved successful and showed its strength in the detection of tetrahydrogestrinone (THG), a designer modification of gestrinone. THG was positively detected in a spiked urine sample and correctly identified as a twofold hydrogenation of gestrinone. The developed screening method can easily be adapted to specific needs and it is envisaged that a similar approach would be amendable to the discovery of metabolites or designer modifications of other compounds of interest.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| 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".