Improving animal welfare and reducing animal use for human vaccine potency testing: state of the science and future directions
Bibliographic record
Abstract
NICEATM and ICCVAM convened an international workshop to review the state of the science of human and veterinary vaccine potency and safety testing methods and to identify opportunities to advance new and improved methods that can further reduce, refine, and replace animal use. Topics were addressed in detail by speakers and workshop participants and are reported in a series of six reports. This workshop report, the third in the series, addresses methods and strategies for human vaccine potency testing that can refine animal use to lessen pain and distress, improve animal welfare, and reduce animal use. Workshop participants agreed that the following potency tests for human vaccines should have the highest priority for development of reduction and/or refinement methods: (1) potency tests for vaccines that are most commonly used, (2) potency tests that require the largest number of animals, (3) potency tests that cause severe animal pain and distress, (4) tests where the knowledge base of each antigen is advanced, and (5) potency tests for which alternative methods already exist or are in development. Based on these criteria, the highest-priority human vaccines were identified as diphtheria and tetanus vaccines, pertussis vaccines (whole cell and acellular), rabies vaccine, anthrax vaccine, and complex combination vaccines (containing diphtheria, tetanus, and pertussis together with other antigens such as IPV, Hib, and HepB). For successful implementation of reduction and refinement alternatives, further research is required into the development and broader use of humane endpoints, serological potency methods, and approaches that would reduce the number of animals used in currently approved potency assays. Because the workshop focused on both human and veterinary vaccines, workshop participants also recommended that human vaccine potency testing methods for rabies and tetanus be reviewed for their potential application to the corresponding veterinary vaccines. Participants agreed that achieving broader acceptance and use of alternative methods, requires that the general principles and procedures for the validation of alternative methods for vaccines be standardized/harmonized internationally. The research, development, validation, and harmonization activities recommended at this workshop are expected to lead to new reduction and/or refinement of animal use in human vaccine potency testing methods and more widespread adoption of existing methods that can reduce animal use and improve animal welfare while ensuring the continued safety and efficacy of human vaccines.
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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 | 0.001 |
| 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) | 0.000 | 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".