Unmodified mRNA in LNPs constitutes a competitive technology for prophylactic vaccines
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it