Plant-made vaccines against viral diseases in humans and farm animals
Bibliographic record
Abstract
Plants provide not only food and feed, but also herbal medicines and various raw materials for industry. Moreover, plants can be green factories producing high value bioproducts such as biopharmaceuticals and vaccines. Advantages of plant-based production platforms include easy scale-up, cost effectiveness, and high safety as plants are not hosts for human and animal pathogens. Plant cells perform many post-translational modifications that are present in humans and animals and can be essential for biological activity of produced recombinant proteins. Stimulated by progress in plant transformation technologies, substantial efforts have been made in both the public and the private sectors to develop plant-based vaccine production platforms. Recent promising examples include plant-made vaccines against COVID-19 and Ebola. The COVIFENZ ® COVID-19 vaccine produced in Nicotiana benthamiana has been approved in Canada, and several plant-made influenza vaccines have undergone clinical trials. In this review, we discuss the status of vaccine production in plants and the state of the art in downstream processing according to good manufacturing practice (GMP). We discuss different production approaches, including stable transgenic plants and transient expression technologies, and review selected applications in the area of human and veterinary vaccines. We also highlight specific challenges associated with viral vaccine production for different target organisms, including lower vertebrates (e.g., farmed fish), and discuss future perspectives for the field.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".