Detection of the Human Anti‐ActRII Antibody Bimagrumab in Serum by Means of Affinity Purification, Tryptic Digestion, and LC‐HRMS
Bibliographic record
Abstract
PURPOSE: Inhibitors of the ActRII signaling pathways represent promising therapeutics for the treatment of muscular diseases, but also pose risks as performance-enhancing agents in sports. Bimagrumab is a human anti-ActRII antibody which was found to increase muscle mass and function by blocking ActRII signaling. As it has considerable potential for being misused as doping agent in sports, the aim of this study was to develop a mass spectrometric detection assay for doping control serum samples. EXPERIMENTAL DESIGN: Within this study, a detection method for Bimagrumab in human serum was developed, which combines ammonium sulfate precipitation and affinity purification with proteolytic digestion and LC-HRMS. To facilitate the unambiguous identification of the diagnostic peptides, an orthogonal IM separation was additionally performed. RESULTS: The assay was successfully validated and the analysis of clinical samples demonstrated its fitness for purpose for an application in routine doping control analysis. CONCLUSIONS AND CLINICAL RELEVANCE: Although no myostatin inhibitors have obtained clinical approval yet, the proactive development of detection methods for emerging doping agents represents a key aspect of preventive doping research. The presented approach will expand the range of available tests for novel protein therapeutics and can readily be modified to include further target analytes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".