Development of an In Vitro Test Method to Replace an Animal-Based Potency Test for Pertactin Antigen in Multivalent Vaccines
Bibliographic record
Abstract
There is increasing interest to replace animal-based potency assays used routinely to test vaccines, since they are highly variable, are costly, and present ethical concerns. The development of relevant in vitro assays is part of the solution. Using pertactin (PRN) antigen as an example in DTaP-IPV (diphtheria, tetanus, acellular pertussis, and inactivated poliovirus) vaccines, a PRN antigenicity ELISA was developed using two monoclonal antibodies with a high affinity to unique PRN epitopes, relevance to human immune responses, and evidence of functionality. The ELISA measured consistent PRN antigenicity between the vaccine lots and was validated to demonstrate its accuracy, precision, linearity, and specificity. Notably, the PRN antigenicity ELISA was more sensitive than the mouse-based potency test and could more effectively differentiate between degraded and intact vaccine lots compared to the in vivo test. From these studies, the PRN antigenicity ELISA is proposed as an in vitro replacement for the in vivo potency test for PRN in DTaP-IPV-based formulations. Important considerations in this study included comprehensive antibody characterization, testing of multiple vaccine lots, method validation, and comparison to animal-based potency. Together, these factors form part of an overall strategy that ensures reliable and relevant in vitro assays are developed to replace animal tests.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".