Design of a Quantitative LC-MS Method for Residual Toxins Adenylate Cyclase Toxin (ACT), Dermonecrotic Toxin (DNT) and Tracheal Cytotoxin (TCT) in Bordetella pertussis Vaccines
Bibliographic record
Abstract
The antigens for acellular pertussis vaccines are made up of protein components that are purified directly from Bordetella pertussis (B. pertussis) bacterial fermentation. As such, there are additional B. pertussis toxins that must be monitored as residuals during process optimization. This paper describes a liquid chromatography mass spectrometry (LC-MS) method for simultaneous analysis of residual protein toxins adenylate cyclase toxin (ACT) and dermonecrotic toxin (DNT), as well as a small molecule glycopeptide, tracheal cytotoxin (TCT) in a Pertussis toxin vaccine antigen. A targeted LC-MS technique called multiple reaction monitoring (MRM) is used for quantitation of ACT and TCT, which have established limits in drug product formulations. However, DNT is currently monitored in an animal test, which does not have an established quantitative threshold. New approaches for DNT testing are discussed, including a novel standard based on concatenated quantitation sequences for ACT and DNT. Collectively, the method represents a “3-in-1” analytical simplification for monitoring process-related residuals during development of B. pertussis vaccines.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".