The production of hemagglutinin‐based virus‐like particles in plants: a rapid, efficient and safe response to pandemic influenza
Bibliographic record
Abstract
During the last decade, the spectre of an influenza pandemic of avian origin has led to a revision of national and global pandemic preparedness plans and has stressed the need for more efficient influenza vaccines and manufacturing practices. The 2009 A/H1N1 (swine flu) outbreak has further emphasized the necessity to develop new solutions for pandemic influenza vaccines. Influenza virus-like particles (VLPs)-non-infectious particles resembling the influenza virus-represent a promising alternative to inactivated and split-influenza virions as antigens, and they have shown uniqueness by inducing a potent immune response through both humoral and cellular components of the immune system. Our group has developed a plant-based transient influenza VLP manufacturing platform capable of producing influenza VLPs with unprecedented speed. Influenza VLP expression and purification technologies were brought to large-scale production of GMP-grade material, and pre-clinical studies have demonstrated that low doses of purified, plant-produced influenza VLPs induce a strong and broad immune response in mice and ferrets. This review positions the recent developments towards the successful production of influenza VLPs in plants, including the production of VLPs from other human viruses and other forms of influenza antigens. The platform developed for large-scale production of VLPs is also presented along with an assessment of the speed of the platform to produce the first experimental vaccine lots from the identification of a new influenza strain.
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".