Achieving scientific and regulatory success in implementing non-animal approaches to human and veterinary rabies vaccine testing: A NICEATM and IABS workshop report
Bibliographic record
Abstract
This two-day workshop, co-sponsored by NICEATM and IABS-NA, brought together over 60 international scientists from government, academia, and industry to advance alternative methods for human and veterinary Rabies Virus Vaccine (RVV) potency testing. On day one, workshop presentations focused on regulatory perspectives related to in vitro potency testing, including recent additions to the European Pharmacopoeia (5.2.14) that provide a scientific rationale for why in vivo methods may be less suitable for vaccine quality control than appropriately designed in vitro methods. Further presentations reviewed the role of the consistency approach to manufacturing and vaccine batch comparison to provide supportive data for the substitution of existing animal-based methods with in vitro assays. In addition, updates from research programs evaluating and validating RVV glycoprotein (G) quantitation by ELISA as an in vitro potency test were presented. On the second day, RVV stakeholders participated in separate human and veterinary vaccine discussion groups focused on identifying potential obstacles or additional requirements for successful implementation of non-animal alternatives to the in vivo potency test. Workshop outcomes and proposed follow up activities are discussed herein.
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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 | 0.141 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.015 | 0.006 |
| Open science | 0.006 | 0.017 |
| Research integrity | 0.014 | 0.015 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".