Influence of ionizable lipid tail length on lipid nanoparticle delivery of <scp>mRNA</scp> of varying length
Bibliographic record
Abstract
RNA-based therapeutics have gained traction for the prevention and treatment of a variety of diseases. However, their fragility and immunogenicity necessitate a drug carrier. Lipid nanoparticles (LNPs) have emerged as the predominant delivery vehicle for RNA therapeutics. An important component of LNPs is the ionizable lipid (IL), which is protonated in the acidic environment of the endosome, prompting cargo release into the cytosol. Currently, there is growing evidence that the structure of IL lipid tails significantly impacts the efficacy of LNP-mediated mRNA translation. Here, we optimized IL tail length for LNP-mediated delivery of three different mRNA cargos. Using C12-200, a gold standard IL, as a model, we designed a library of ILs with varying tail lengths and evaluated their potency in vivo. We demonstrated that small changes in lipophilicity can drastically increase or decrease mRNA translation. We identified that LNPs formulated with firefly luciferase mRNA (1929 base pairs) and C10-200, an IL with shorter tail lengths than C12-200, enhance liver transfection by over 10-fold. Furthermore, different IL tail lengths were found to be ideal for transfection of LNPs encapsulating mRNA cargos of varying sizes. LNPs formulated with erythropoietin (EPO), responsible for stimulating red blood cell production, mRNA (858 base pairs), and the C13-200 IL led to EPO translation at levels similar to the C12-200 LNP. The LNPs formulated with Cas9 mRNA (4521 base pairs) and the C9-200 IL induced over three times the quantity of indels compared with the C12-200 LNP. Our findings suggest that shorter IL tails may lead to higher transfection of LNPs encapsulating larger mRNAs, and that longer IL tails may be more efficacious for delivering smaller mRNA cargos. We envision that the results of this project can be utilized as future design criteria for the next generation of LNP delivery systems for RNA therapeutics.
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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".