Characterization of <i>in vitro</i> generated metabolites of selected peptides <2 kDa prohibited in sports
Bibliographic record
Abstract
With an increasing number of prohibited substances in doping controls, knowledge about their metabolism is crucial for efficient analysis. While for low molecular mass molecules, standard protocols for in vitro metabolism experiments are well established, the situation with peptidic drugs has been shown to be substantially more heterogeneous and complex. Two principle strategies aiming at simulating the metabolism of lower molecular mass peptides in vitro are presented within this study. The prohibited peptides ARA-290, GHRP-3, and Peforelin, with a to-date unknown metabolism, were chosen as model compounds for these experiments and metabolism after incubation with different blood specimens (EDTA-, heparin-, citrate-plasma, and serum) and exposure to recombinant amidase were investigated. The characterization of in vitro generated drug-derived peptidic analytes was accomplished by means of liquid chromatography coupled to high resolution mass spectrometry. Identification of the generated metabolites was ensured by dedicated high resolution product ion experiments after liquid chromatographic separation. While extensive exopeptidase-driven metabolism was observed for ARA-290 (with one main metabolite PyrEQLERALN), GHRP-3 and Peforelin were found to exhibit a considerable metabolic stability with a low tendency for deamidation only. Copyright © 2017 John Wiley & Sons, Ltd.
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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) | 0.000 | 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 | 0.000 | 0.000 |
| 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".