Combating Human Viral Diseases: Will Plant-Based Vaccines Be the Answer?
Bibliographic record
Abstract
Molecular pharming or the technology of application of plants and plant cell culture to manufacture high-value recombinant proteins has progressed a long way over the last three decades. Whether generated in transgenic plants by stable expression or in plant virus-based transient expression systems, biopharmaceuticals have been produced to combat several human viral diseases that have impacted the world in pandemic proportions. Plants have been variously employed in expressing a host of viral antigens as well as monoclonal antibodies. Many of these biopharmaceuticals have shown great promise in animal models and several of them have performed successfully in clinical trials. The current review elaborates the strategies and successes achieved in generating plant-derived vaccines to target several virus-induced health concerns including highly communicable infectious viral diseases. Importantly, plant-made biopharmaceuticals against hepatitis B virus (HBV), hepatitis C virus (HCV), the cancer-causing virus human papillomavirus (HPV), human immunodeficiency virus (HIV), influenza virus, zika virus, and the emerging respiratory virus, severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) have been discussed. The use of plant virus-derived nanoparticles (VNPs) and virus-like particles (VLPs) in generating plant-based vaccines are extensively addressed. The review closes with a critical look at the caveats of plant-based molecular pharming and future prospects towards further advancements in this technology. The use of biopharmed viral vaccines in human medicine and as part of emergency response vaccines and therapeutics in humans looks promising for the near future.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".