Synthetic nucleic acid antibody prophylaxis confers rapid and durable protective immunity against Zika virus challenge
Bibliographic record
Abstract
Significant concerns have arisen over the past 3 y from the increased global spread of the mosquito-borne flavivirus, Zika. Accompanying this spread has been an increase in cases of the devastating birth defect microcephaly as well as of Guillain–Barré syndrome in adults in many affected countries. Currently there is no vaccine or therapy for this infection; however, we sought to develop a combination approach that provides more rapid and durable protection than traditional vaccination alone. A novel immune-based prophylaxis/therapy strategy entailing the facilitated delivery of a synthetic DNA consensus prME vaccine along with DNA-encoded anti-ZIKV envelope monoclonal antibodies (dMAb) were developed and evaluated for antiviral efficacy. This immediate and persistent protection strategy confers the ability to overcome shortcomings inherent with conventional active vaccination or passive immunotherapy. A collection of novel dMAbs were developed which were potent against ZIKV and could be expressed in serum within 24–48 h of in vivo administration. The DNA vaccine, from a previous development, was potent after adaptive immunity was developed, protecting against infection, brain and testes pathology in relevant mouse challenge models and in an NHP challenge. Delivery of potent dMAbs protected mice from the same murine viral challenge within days of delivery. Combined injection of dMAb and the DNA vaccine afforded rapid and long-lived protection in this challenge model, providing an important demonstration of the advantage of this synergistic approach to pandemic outbreaks.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".