Design and lyophilization of lipid nanoparticles for mRNA vaccine and its robust immune response in mice and nonhuman primates
Bibliographic record
Abstract
mRNA and lipid nanoparticles have emerged as powerful systems for the preparation of vaccines against severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection. The emergence of novel variants or the necessity of cold chain logistics for approved mRNA vaccines undermines the investigation of next-generation systems that could preserve both potency and stability. However, the correlation between lipid nanoparticle composition and activity is not fully explored. Here, we screened a panel of ionizable lipids in vivo and identified lead lipid nanoparticles with a branched-tail lipid structure. Buffer optimization allowed the determination of lyophilization conditions, where lipid nanoparticle-encapsulated mRNA encoding SARS-CoV-2 spike protein could induce robust immunogenicity in mice after 1 month of storage at 5°C and 25°C. Intramuscularly injected lipid nanoparticles distributed in conventional dendritic cells in mouse lymph nodes induced balanced T helper (Th) 1/Th2 responses against SARS-CoV-2 spike protein. In nonhuman primates, two doses of 10 or 100 μg of mRNA induced higher spike-specific binding geometric mean titers than those from a panel of SARS-CoV-2-convalescent human sera. Immunized sera broadly inhibited the viral entry receptor angiotensin-converting enzyme 2 (ACE2) from binding to the spike protein in all six strains tested, including variants of concern. These results could provide useful information for designing next-generation mRNA vaccines.
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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 | 0.000 |
| 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".