Intramuscular DNA vaccine provides protection in non-human primate and mouse models of SARS-CoV-2
Bibliographic record
Abstract
Nucleic acid vaccine approaches have proven successful in the context of the SARS-CoV-2 pandemic, however challenges with delivery remain. Here we describe PlaCCine, a DNA-based vaccine platform that utilizes a device- and vector-free chemical delivery system. This system includes a DNA plasmid encoding the target antigen and generates robust immune responses, offering significant protection against live viral challenges in both non-human primates and mice. We designed spike plasmid immunogens representing early SARS-CoV-2 strains and found that parental spike PlaCCine vaccination induced SARS-CoV-2 specific cellular and humoral responses in non-human primates and supported significant viral control following challenge. To evaluate immunogenicity and protective efficacy against emerging variants, we further advanced the platform to incorporate the SARS-CoV-2 XBB1.5 variant and observed robust, dose-dependent cellular and humoral responses in mice. When mice were immunized and intranasally challenged with 1×105 TCID50 of SARS-CoV-2 XBB1.5 virus, all immunized animals survived the challenge and displayed undetectable lung viral loads. Together these data demonstrate the efficacy of the PlaCCine platform for the delivery of vaccine antigens and support the continued translation of this platform for infectious diseases.
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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) | 0.000 | 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) | 0.003 | 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".