Monoclonal antibody produced in plants efficiently treats West Nile virus infection in mice
Bibliographic record
Abstract
Over the past decade, West Nile virus (WNV) has spread to all 48 of the lower United States as well as to parts of Canada, Mexico, the Caribbean, and South America, with outbreaks of neuroinvasive disease occurring annually. At present, no therapeutic or vaccine is available for human use. Epidemics of WNV and other emerging infectious disease threats demand cost-efficient and scalable production technologies that can rapidly transfer effective therapeutics into the clinical setting. We have previously reported that Hu-E16, a humanized anti-WNV mAb, binds to a highly conserved epitope on the envelope protein, blocks viral fusion, and shows promising postexposure therapeutic activity. Herein, we generated a plant-derived Hu-E16 mAb that can be rapidly scaled up for commercial production. Plant Hu-E16 was expressed at high levels within 8 days of infiltration in Nicotiana benthamiana plants and retained high-affinity binding and potent neutralizing activity in vitro against WNV. A single dose of plant Hu-E16 protected mice against WNV-induced mortality even 4 days after infection at rates that were indistinguishable from mammalian-cell-produced Hu-E16. This study demonstrates the efficacy of a plant-produced mAb against a potentially lethal infection several days after exposure in an animal challenge model and provides a proof of principle for the development of plant-derived mAbs as therapy against emerging infectious diseases.
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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 | 0.000 | 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".