Liposomal lipid nanoparticles for extrahepatic delivery of mRNA
Bibliographic record
Abstract
Long-circulating, transfection-competent lipid nanoparticle (LNP)-mRNA delivery systems are critical for achieving efficient transfection in extrahepatic tissues. Here we investigate the properties of LNP mRNA systems containing high proportions of bilayer forming lipids, using equimolar egg sphingomyelin and cholesterol as the bilayer-forming components. We show that LNP mRNA systems prepared at bilayer lipid to ionizable lipid molar ratios of 4-0.67 exhibit high mRNA encapsulation efficiencies (90–100%) and excellent transfection potencies in vitro. Systems with bilayer lipid to ionizable lipid molar ratios equating to 4 exhibit a liposomal morphology with a solid core suspended in an aqueous interior surrounded by a lipid bilayer. These liposomal LNPs exhibit longer circulation lifetimes than LNP systems with Onpattro-like lipid compositions and have enhanced extrahepatic transfection properties. The prolonged blood circulation lifetime is attributed to reduced plasma protein adsorption. The transfection competency of liposomal LNP systems is attributed to export of the solid core containing mRNA from the LNP as the endosomal pH is lowered. Long-circulating, transfection-competent LNP-mRNA systems are key for effective extrahepatic delivery. Here, authors show that LNPs with high bilayer lipid ratios yield high mRNA encapsulation, prolonged circulation, and enhanced transfection in extrahepatic tissues.
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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".