Incorporation of a polyamine into lipid nanoparticles increases the endosomal release and transfection of nucleic acids without toxicity
Bibliographic record
Abstract
Efficient cytosolic delivery of RNA therapeutics remains a major challenge. In this study, we developed a polymer-incorporated lipid nanoparticle ( p -LNP) platform by integrating a low-molecular-weight polyamine (NS102) into standard LNP formulations. Systematic evaluation revealed that the molecular weight and amine composition of the polymer significantly influenced the delivery performance and biocompatibility, with NS102 (Mw ~1000 Da, exclusively tertiary amines) emerging as the optimal candidate. Incorporating 4.5 mol% NS102 into LNPs enhanced their pH-buffering capacity, endosomal escape, and RNA stability without altering key physicochemical properties. These improvements resulted in superior RNA delivery, evidenced by increased mRNA transfection efficiency and siRNA activity in mice following both intravenous and intramuscular administration. Specifically, the p -LNPs achieved up to a 100-fold increase in luciferase bioluminescence expression in the liver and reduced the effective dose (ED 50 ) of siRNA against factor VII produced by the liver by 50 %, compared to standard LNPs. Moreover, the p -LNP formulations exhibited excellent biocompatibility, with no significant cytotoxicity or liver toxicity in mice. These findings highlight the potential of the p -LNP platform for advancing RNA-based therapeutics, offering a promising strategy to overcome existing delivery barriers.
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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".