Nonclinical Safety Assessment of Lipid Nanoparticle-and Emulsion-Based Self-Amplifying mRNA Vaccines in Rats
Bibliographic record
Abstract
Vaccines containing mRNA with the capacity to self-amplify represent an alternative to the mRNA vaccines that came to prominence during the COVID-19 pandemic. To gain further insights on the safety profile of self-amplifying mRNA- (SAM-) vaccines, this preclinical toxicology study in rats evaluated the effect of (i) the type of delivery system (lipid nanoparticle [LNP] vs cationic nano-emulsion [CNE]); (ii) antigen-encoding sequence (rabies glycoprotein G vs SARS-CoV-2 Spike); and (iii) RNA amplification. Further analyses also evaluated gene expression in peripheral blood after vaccination, and the biodistribution of vaccine RNA. The SAM vaccines administered as two doses 2-weeks apart had acceptable safety profiles in rats, with respect to clinical signs, blood biochemistry, and macroscopic and microscopic pathology. A transient increase in ALT/AST ratio occurred only in female rats and in the absence of muscle and liver damage was dependent on RNA amplification and appeared related to the greater quantities of vaccine RNA in the muscle and livers of female rats vs male rats. The RNA and delivery-vehicle components, but not the nature of the antigen-coding sequence or the requirement for RNA amplification, affected aspects of the stimulation of innate-immune activity, which was consistent with the transient activation of type I and type II interferon signaling. The delivery vehicle, LNP, differed from CNE as vaccine RNA in CNE compositions appeared independently to stimulate innate-immune activity at 4 hours after vaccination. Our analysis supports further studies to assess whether these differences in innate-immune activity affect safety and efficacy of the SAM vaccine.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".