Plant-based vaccines - a new research field in the modern medicine
Bibliographic record
Abstract
Although the concept of producing vaccines in plants originated in the 1980s, significant progress was only achieved when plant cell and gene technologies matured. Plant-based vaccines represent a novel and promising field in modern medicine, offering a cost-effective and scalable alternative to traditional vaccine production methods. These vaccines leverage genetically engineered plants as biofactories to produce recombinant proteins and virus-like particles (VLPs). Since the FDA approval of the plant-derived therapeutic protein taliglucerase alfa in 2012, numerous biologics-including vaccines against cholera, hepatitis B, dengue, HIV, HPV, Ebola, and COVID-19-have been developed via plant cell technology. Canada's approval of a COVID-19 vaccine in 2022 marks a major step forward in plant-based vaccine development. Unlike conventional vaccines, plant-based vaccines can be delivered orally and are less prone to contamination by human pathogens, making them particularly suitable for resource-limited regions. Orally administered plant vaccines benefit from natural bioencapsulation within plant cell walls, protecting antigens from digestive degradation and enabling targeted release in the gut, where mucosal immune responses are activated. Recent examples, such as the MucoRice-CTB cholera vaccine, demonstrate safety, immunogenicity, and potential for practical use. This review paper highlights the important advantages of plant vaccines, their immunogenic mechanisms, and the potential for oral vaccine development. Key production steps, current status, prospects, and challenges of plant-based vaccines are also discussed to provide a foundation for future research and development.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".