Proteosome-adjuvanted intranasal influenza vaccines: advantages, progress and future considerations
Bibliographic record
Abstract
The development of a safe and effective non-live intranasal influenza vaccine has been an elusive target in vaccinology for many decades. It is perceived that intranasal immunization, by offering a more convenient and less invasive vaccination modality, will boost vaccination rates against influenza, a disease that continues to inflict a significant annual health and economic burden worldwide. Intranasal immunization may also confer additional immunoprotective benefits by eliciting mucosal secretory antibodies at the site of entry of the virus, which are typically more broadly cross-reactive and cross-protective compared with those induced by systemic routes of vaccination. This property is highly desirable for confering improved protection against variant strains of influenza virus. Here we review the current status of intranasal proteosome-based influenza vaccines that comprise commercial detergent-split influenza antigens and proteosome adjuvants derived from purified bacterial outer membrane proteins. We demonstrate that these vaccines exhibit the desired advantages expected from immunization via the intranasal route. Furthermore, in clinical trials proteosome-based influenza vaccines were shown to be safe and protective in humans. The future possibilities for commercializing intranasal proteosome-influenza vaccines are also discussed.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".