Unmodified mRNA in LNPs constitutes a competitive technology for prophylactic vaccines
Bibliographic record
Abstract
mRNA represents a promising new vaccine technology platform with high flexibility in regard to development and production. Here, we demonstrate that vaccines based on sequence optimized, chemically unmodified mRNA formulated in optimized lipid nanoparticles (LNPs) are highly immunogenic and well tolerated in non-human primates (NHPs). Single intramuscular vaccination of NHPs with LNP-formulated mRNAs encoding rabies or influenza antigens induced protective antibody titers, which could be boosted and remained stable during an observation period of up to 1 year. First mechanistic insights into the mode of action of the LNP-formulated mRNA vaccines demonstrated a strong activation of the innate immune response at the injection site and in the draining lymph nodes (dLNs). Activation of the innate immune system was reflected by a transient induction of pro-inflammatory cytokines and chemokines and activation of the majority of immune cells in the dLNs. Notably, our data demonstrate that mRNA vaccines can compete with licensed vaccines based on inactivated virus or are even superior in respect of functional antibody and T cell responses. Importantly, we show that the developed LNP-formulated mRNA vaccines can be used as a vaccination platform allowing multiple, sequential vaccinations against different pathogens. These results provide strong evidence that the mRNA technology is a valid approach for the development of effective prophylactic vaccines to prevent 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) | 0.000 | 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 | 0.001 |
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".