Use of Animal Models in the Development of Human Vaccines
Bibliographic record
Abstract
Over the past 100 years, animal infectious disease research has played a crucial role in the development of human vaccines. In fact, many of today's vaccines are based on utilizing animal pathogens, either in the form of an attenuated vaccine or as a vaccine vector. Vaccine development has become increasingly complex with chronic and newly emerging diseases, a demand for therapeutic vaccines for noninfectious diseases, extended vaccine in the neonate and the elderly, and increasing concerns regarding vaccine safety. Furthermore, the evaluation of quantity and quality of immune responses and the ability to efficiently translate the results of basic research into the clinic are critical to ensure that vaccines meet their therapeutic potential. Here, we review the importance of animal models for developing and testing novel human vaccines, discuss the limitations of existing animal models in knowledge translation, and summarize the needs and criteria for future animal models. We argue that efficient translation of basic vaccine research to clinical therapies will depend upon the availability of appropriate animal models to address each of the questions which arise during vaccine development.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".