The rVSV-EBOV vaccine provides limited cross-protection against Sudan virus in guinea pigs
Bibliographic record
Abstract
Abstract Recombinant vesicular stomatitis viruses (rVSVs) engineered to express heterologous viral glycoproteins have proven to be remarkably effective vaccines. Indeed, rVSV-EBOV, which expresses the Ebola virus (EBOV) glycoprotein, recently received clinical approval in the United States and Europe for its ability to prevent EBOV disease. Analogous rVSV vaccines expressing the glycoproteins of different human-pathogenic filoviruses have also been developed and shown to be effective in pre-clinical evaluations, yet these vaccine candidates have not progressed far beyond research laboratories. As the current outbreak of Sudan virus (SUDV) intensifies in Uganda, the need for proven countermeasures is rendered even more acute. Here we demonstrate that an rVSV-based vaccine expressing the SUDV glycoprotein (rVSV-SUDV) generates a potent humoral immune response that completely protects guinea pigs from SUDV disease and death. Although the cross-protection generated by rVSV vaccines for different filoviruses is thought to be limited, we wondered whether rVSV-EBOV might also provide protection against SUDV, which is closely related to EBOV. Surprisingly, nearly 60% of guinea pigs that were vaccinated with rVSV-EBOV and challenged with SUDV survived, suggesting that rVSV-EBOV offers limited protection against SUDV, at least in the guinea pig model. These results were confirmed by a back-challenge experiment in which animals that had been vaccinated with rVSV-EBOV and survived EBOV challenge were inoculated with SUDV and survived. Whether these data are applicable to the efficacy of rVSV-EBOV in humans is unknown, and they should therefore be interpreted cautiously. Nevertheless, this study confirms the potency of the rVSV-SUDV vaccine, and it highlights the potential for rVSV-EBOV to elicit a cross-protective immune response.
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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 | 0.001 | 0.001 |
| 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".