3Rs in Quality Control of Human Vaccines: Opportunities and Barriers
Bibliographic record
Abstract
The 3Rs principles – Replacement, Reduction, Refinement – were established in 1959 and since then have been adopted widely and particularly in Europe with the European Directive 2010/63/EU. The vaccine industry in Europe has been committed to the 3Rs principles for several years, including animal welfare as well as for reducing and replacing animal use in research, non-clinical safety and analytical testing. Whereas animal testing has been successfully removed from lot release testing of well-characterized human vaccines, large numbers of laboratory animals continue to be used for safety and potency quality control testing for established inactivated vaccines such as rabies, pertussis, diphtheria, and tetanus vaccines. Moreover, specifications for human vaccine batch approval often differ between various parts of the world, resulting in either duplication of animal testing or partial implementation of 3Rs for some vaccines when distributed worldwide. This reinforces the need for enhancing international harmonization and cooperation efforts. In this chapter, we review the use of laboratory animals in human vaccines research and quality control and describe the vaccine manufacturing industry commitments and its concrete programs for implementing 3Rs principles in R&D and industrial operations processes. We highlight the successes as well as the barriers that are encountered when implementing 3Rs principles, as well as the ongoing efforts that include international collaborations with other industries, public organizations and Health Authorities for the acceptance of alternative methods.
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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 | 0.016 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.009 | 0.007 |
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".