Preclinical and Clinical Development of a YFV 17 D-Based Chimeric Vaccine against West Nile Virus
Bibliographic record
Abstract
Substantial success has been achieved in the development and implementation of West Nile (WN) vaccines for horses; however, no human WN vaccines are approved. This review focuses on the construction, pre-clinical and clinical characterization of ChimeriVax-WN02 for humans, a live chimeric vaccine composed of a yellow fever (YF) 17D virus in which the prM-E envelope protein genes are replaced with the corresponding genes of the WN NY99 virus. Pre-clinical studies demonstrated that ChimeriVax-WN02 was significantly less neurovirulent than YF 17D in mice and rhesus and cynomolgus monkeys. The vaccine elicited neutralizing antibody titers after inoculation in hamsters and monkeys and protected immunized animals from lethal challenge including intracerebral inoculation of high dose of WN NY99 virus. Safety, viremia and immunogenicity of ChimeriVax-WN02 were assessed in one phase I study and in two phase II clinical trials. No safety signals were detected in the three clinical trials with no remarkable differences in incidence of adverse events (AEs) between vaccine and placebo recipients. Viremia was transient and the mean viremia levels were low. The vaccine elicited strong and durable neutralizing antibody and cytotoxic T cell responses. WN epidemiology impedes a classical licensure pathway; therefore, innovative licensure strategies should be explored.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".