Protection and durable humoral and cellular immune responses in cynomolgus macaques following administration of a Zaire Ebola virus (EBOV) GP DNA vaccine delivered by intramuscular or intradermal electroporation
Bibliographic record
Abstract
Abstract The 2013–2016 Ebola virus disease (EVD) was the first time that the spread of the virus reached epidemic status. There are no licensed vaccines; however the most advanced candidate rVSV-ZEBOV vaccine, is a live-attenuated vesicular stomatitis virus encoding the Ebola virus glycoprotein (GP), and has demonstrated efficacy in a ring-vaccination trial. However, several EVD viral vector vaccine trials have reported adverse events that could limit administration to certain populations. The establishment of robust anamnestic responses has yet to be fully understood with these candidates and may be limited by potential anti-vector immunity. We designed EVD DNA vaccines as an alternative platform with a remarkable safety profile that is serology independent, allowing for possible repeat vector administration. We designed 3 novel synthetic Zaire Ebola virus (EBOV) GP DNA sequences which were tested alone or as multivalent formulations delivered by in vivo intramuscular (IM) or intradermal (ID) electroporation (EP). The EBOV-GP DNA vaccines were highly protective against lethal EBOV-Makona challenge in cynomolgus macaques, with 100% protection in NHPs receiving vaccine by ID-EP delivery and 75% protection in NHPs receiving 2 doses IM-EP. Vaccinated NHPs had no detectable viremia following challenge. Animals (n=4–5/group) injected with different IM-EP or ID-EP DNA regimens were followed to monitor long-term immunogenicity. NHPs have durable total IgG antibody titers and T cells responses to EBOV GP antigen, including polyfunctional CD4 and CD8 T cells and responses in memory subset populations. Together, the data strong support EBOV-GP DNA vaccine delivery for protection and the generation of robust memory immune responses.
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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 | 0.001 |
| 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".