Engineering ionizable lipids for rapid biodegradation balances mRNA vaccine efficacy and tolerability
Bibliographic record
Abstract
Abstract The optimization of lipid nanoparticles (LNPs) has played a key role in enhancing the efficacy of mRNA vaccines, yet challenges with LNP tolerability remain. The ionizable lipid component within LNPs is critical to the efficient delivery of mRNA. Ionizable lipids can also trigger innate immune activation, which is beneficial for vaccine efficacy but may contribute to adverse inflammatory reactions. Engineering ionizable lipids for rapid biodegradation is a promising, yet underexplored, strategy to dampen inflammation. Here, we report the rational design and optimization of biodegradable ionizable lipids for intramuscular mRNA vaccines in mice. We show that in vivo biodegradability is enhanced by controlling lipid hydrolysis kinetics and that protein output is maximized by tuning the LNP apparent pK a . In an influenza vaccine model, the lead lipid (δO3) generates equivalent neutralizing antibodies and stronger antigen-specific T cell responses compared to a benchmark lipid (SM-102) used in approved mRNA vaccines. Furthermore, by comparing ionizable lipid analogs with similar potency but opposing biodegradation kinetics, we find that faster lipid clearance from tissues coincides with a lower inflammatory response while preserving strong vaccine immunogenicity. These findings demonstrate that fast-degrading ionizable lipids can balance the efficacy and tolerability of mRNA vaccines, with implications for addressing side effects and patient acceptance of new vaccine applications.
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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".