Qualitative identification of growth hormone-releasing hormones in human plasma by means of immunoaffinity purification and LC-HRMS/MS
Bibliographic record
Abstract
The use of growth hormone-releasing hormones (GHRHs) is prohibited in sports according to the regulations of the World Anti-Doping Agency (WADA). The aim of the present study was to develop a method for the simultaneous detection of four different GHRHs and respective metabolites from human plasma by means of immunoaffinity purification and subsequent nano-ultrahigh performance liquid chromatography-high resolution/high accuracy (tandem) mass spectrometry. The target analytes included Geref (Sermorelin), CJC-1293, CJC-1295, and Egrifta (Tesamorelin) as well as two metabolites of Geref and CJC-1293, which were captured from plasma samples using a polyclonal GHRH antibody in concert with protein A/G monolithic MSIA™ D.A.R.T.'S® (Disposable Automation Research Tips) prior to separation and detection. The method was fully validated and found to be fit for purpose considering the parameters specificity, linearity, recovery (19-37%), lower limit of detection (<50 pg/mL), imprecision (<20%), and ion suppression/enhancement effects. The analytes' stability and metabolism were elucidated using in vitro and in vivo approaches. EDTA blood samples were collected from rats 2, 4, and 8 h after intravenous administration of GHRH (one compound per test animal). All intact substances were detected for at least 4 h but no anticipated metabolite was confirmed in laboratory rodents' samples; conversely, a Geref metabolite (GHRH3-29) was found in a human plasma sample collected after subcutaneous injection of the drug to a healthy male volunteer. The obtained results demonstrate that GHRHs are successfully detected in plasma using an immunoaffinity-mass spectrometry-based method, which can be applied to sports drug testing samples. Further studies are however required and warranted to account for potential species-related differences in metabolism and elimination of the target analytes.
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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 | 0.000 | 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 | 0.001 |
| 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".