Current progress towards prevention of <scp>Nipah</scp> and <scp>Hendra</scp> disease in humans: <scp>A</scp> scoping review of vaccine and monoclonal antibody candidates being evaluated in clinical trials
Bibliographic record
Abstract
OBJECTIVES: Nipah and Hendra are deadly zoonotic diseases with pandemic potential. To date, no human vaccine or monoclonal antibody (mAb) has been licensed to prevent disease caused by these pathogens. The aim of this scoping review was to identify and describe all Phase I, II, and III clinical trials of vaccine candidates or mAbs candidates designed to prevent Nipah and Hendra in humans and to compare the characteristics of the vaccine candidates to characteristics outlined in the Target Product Profile drafted by the World Health Organisation as part of the WHO Research & Development Blueprint for Action to Prevent Epidemics. METHODS: We searched 23 clinical trial registries, the Cochrane Central Register of Clinical Trials, and grey literature up to June 2023 to identify vaccine and mAb candidates being evaluated in registered clinical trials. Vaccine candidate and trial characteristics were double-extracted for evaluation and the vaccine candidate characteristics were compared with the preferred and critical criteria of the World Health Organisation's Target Product Profile for Nipah virus vaccine. RESULTS: Three vaccine candidates (Hendra Virus Soluble Glycoprotein Vaccine [HeV-sG-V], PHV02, and mRNA-1215) and one mAb (m102.4) had a registered human clinical trial by June 2023. All trials were phase 1, dose-ranging trials taking place in the United States of America or Australia and enrolling healthy adults. Although all vaccine candidates meet the dose regimen and route of administration criteria of the Target Product Profile, other criteria such as measures of efficacy and reactogenicity will need to be evaluated in the future as evidence becomes available. CONCLUSION: Multiple vaccine candidates and one mAb candidate have reached the stage of human clinical trials and are reviewed here. Monitoring progress during evaluation of these candidates and candidates entering clinical trials in the future can help highlight many of the challenges that remain.
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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 | 0.018 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".