Combinatorial design of ionizable lipid nanoparticles for muscle-selective mRNA delivery with minimized off-target effects
Bibliographic record
Abstract
Ionizable lipid nanoparticles (LNPs) pivotal to the success of COVID-19 mRNA (messenger RNA) vaccines hold substantial promise for expanding the landscape of mRNA-based therapies. Nevertheless, the risk of mRNA delivery to off-target tissues highlights the necessity for LNPs with enhanced tissue selectivity. The intricate nature of biological systems and inadequate knowledge of lipid structure-activity relationships emphasize the significance of high-throughput methods to produce chemically diverse lipid libraries for mRNA delivery screening. Here, we introduce a streamlined approach for the rapid design and synthesis of combinatorial libraries of biodegradable ionizable lipids. This led to the identification of iso-A11B5C1, an ionizable lipid uniquely apt for muscle-specific mRNA delivery. It manifested high transfection efficiencies in muscle tissues, while significantly diminishing off-targeting in organs like the liver and spleen. Moreover, iso-A11B5C1 also exhibited reduced mRNA transfection potency in lymph nodes and antigen-presenting cells, prompting investigation into the influence of direct immune cell transfection via LNPs on mRNA vaccine effectiveness. In comparison with SM-102, while iso-A11B5C1's limited immune transfection attenuated its ability to elicit humoral immunity, it remained highly effective in triggering cellular immune responses after intramuscular administration, which is further corroborated by its strong therapeutic performance as cancer vaccine in a melanoma model. Collectively, our study not only enriches the high-throughput toolkit for generating tissue-specific ionizable lipids but also encourages a reassessment of prevailing paradigms in mRNA vaccine design. This study encourages rethinking of mRNA vaccine design principles, suggesting that achieving high immune cell transfection might not be the sole criterion for developing effective mRNA vaccines.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
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".