Lassa fever vaccine candidates: A scoping review of vaccine clinical trials
Bibliographic record
Abstract
OBJECTIVE: Lassa fever (LF) is caused by a viral pathogen with pandemic potential. LF vaccines have the potential to prevent significant disease in individuals at risk of infection, but no such vaccine has been licensed or authorised for use thus far. We conducted a scoping review to identify and compare registered phase 1, 2 or 3 clinical trials of LF vaccine candidates, and appraise the current trajectory of LF vaccine development. METHOD: We systematically searched 24 trial registries, PubMed, relevant conference abstracts and additional grey literature sources up to 27 October 2022. After extracting key details about each vaccine candidate and each eligible trial, we qualitatively synthesised the evidence. RESULTS: We found that four LF vaccine candidates (INO-4500, MV-LASV, rVSV∆G-LASV-GPC, and EBS-LASV) have entered the clinical stage of assessment. Five phase 1 trials (all focused on healthy adults) and one phase 2 trial (involving a broader age group from 18 months to 70 years) evaluating one of these vaccines have been registered to date. Here, we describe the characteristics of each vaccine candidate and trial and compare them to WHO's target product profile for Lassa vaccines. CONCLUSION: Though LF vaccine development is still in early stages, current progress towards a safe and effective vaccine is encouraging.
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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 | 0.013 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| 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".