Anionic Lipid Nanoparticles Preferentially Deliver mRNA to the Hepatic Reticuloendothelial System
Bibliographic record
Abstract
Lipid nanoparticles (LNPs) are the leading nonviral technologies for the delivery of exogenous RNA to target cells in vivo. As systemic delivery platforms, these technologies are exemplified by Onpattro, an approved LNP-based RNA interference therapy, administered intravenously and targeted to parenchymal liver cells. The discovery of systemically administered LNP technologies capable of preferential RNA delivery beyond hepatocytes has, however, proven more challenging. Here, preceded by comprehensive mechanistic understanding of in vivo nanoparticle biodistribution and bodily clearance, an LNP-based messenger RNA (mRNA) delivery platform is rationally designed to preferentially target the hepatic reticuloendothelial system (RES). Evaluated in embryonic zebrafish, validated in mice, and directly compared to LNP-mRNA systems based on the lipid composition of Onpattro, RES-targeted LNPs significantly enhance mRNA expression both globally within the liver and specifically within hepatic RES cell types. Hepatic RES targeting requires just a single lipid change within the formulation of Onpattro to switch LNP surface charge from neutral to anionic. This technology not only provides new opportunities to treat liver-specific and systemic diseases in which RES cell types play a key role but, more importantly, exemplifies that rational design of advanced RNA therapies must be preceded by a robust understanding of the dominant nano-biointeractions involved.
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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".