Vaccine Potentiation by Combination Adjuvants
Bibliographic record
Abstract
Adjuvants are crucial components of vaccines. They significantly improve vaccine efficacy by modulating, enhancing, or extending the immune response and at the same time reducing the amount of antigen needed. In contrast to previously licensed adjuvants, current successful adjuvant formulations often consist of several molecules, that when combined, act synergistically by activating a variety of immune mechanisms. These "combination adjuvants" are already registered with several vaccines, both in humans and animals, and novel combination adjuvants are in the pipeline. With improved knowledge of the type of immune responses needed to successfully induce disease protection by vaccination, combination adjuvants are particularly suited to not only enhance, but also direct the immune responses desired to be either Th1-, Th2- or Th17-biased. Indeed, in view of the variety of disease and population targets for vaccine development, a panel of adjuvants will be needed to address different disease targets and populations. Here, we will review well-known and new combination adjuvants already licensed or currently in development-including ISCOMs, liposomes, Adjuvant Systems Montanides, and triple adjuvant combinations-and summarize their performance in preclinical and clinical trials. Several of these combination adjuvants are promising having promoted improved and balanced immune responses.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".