The adjuvant GLA-AF enhances human intradermal vaccine responses
Bibliographic record
Abstract
Adjuvants are key to shaping the immune response to vaccination, but to date, no adjuvant suitable for human use has been developed for intradermal vaccines. These vaccines could be self-administered and sent through the mail as they do not require long needles or technical expertise in immunization. In the event of a pandemic outbreak, this approach could alleviate the congregation of patients in health centers and thus reduce the potential of these centers to enhance the spread of lethal infection. A reliable and potent vaccine system for self-administration would provide an effective countermeasure for delivery through existing product distribution infrastructure. We report results from preclinical and clinical trials that demonstrate the feasibility of an adjuvanted, intradermal vaccine that induced single shot protection in ferrets and seroprotection in humans against one of the more lethal strains of pandemic flu, Indonesia H5N1. In the human trial, the vaccine was safe and clinical responses were above approvable endpoints for a protective flu vaccine. Inclusion of a modern TLR4 (Toll-like receptor 4) agonist-based adjuvant was critical to the development of the response in the intradermal groups. In humans, this is the first report of a safe and effective intradermal adjuvant, GLA-AF (aqueous formulation of glucopyranosyl lipid adjuvant), and provides a future path for developing a vaccine-device combination for distribution by mail and self-administration in case of a pandemic.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".