Large Animal Models for Vaccine Development and Testing
Bibliographic record
Abstract
The development of human vaccines continues to rely on the use of animals for research. Regulatory authorities require novel vaccine candidates to undergo preclinical assessment in animal models before being permitted to enter the clinical phase in human subjects. Substantial progress has been made in recent years in reducing and replacing the number of animals used for preclinical vaccine research through the use of bioinformatics and computational biology to design new vaccine candidates. However, the ultimate goal of a new vaccine is to instruct the immune system to elicit an effective immune response against the pathogen of interest, and no alternatives to live animal use currently exist for evaluation of this response. Studies identifying the mechanisms of immune protection; determining the optimal route and formulation of vaccines; establishing the duration and onset of immunity, as well as the safety and efficacy of new vaccines, must be performed in a living system. Importantly, no single animal model provides all the information required for advancing a new vaccine through the preclinical stage, and research over the last two decades has highlighted that large animals more accurately predict vaccine outcome in humans than do other models. Here we review the advantages and disadvantages of large animal models for human vaccine development and demonstrate that much of the success in bringing a new vaccine to market depends on choosing the most appropriate animal model for preclinical testing.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".