Influence of Cationic Lipid Composition on Gene Silencing Properties of Lipid Nanoparticle Formulations of siRNA in Antigen-Presenting Cells
Bibliographic record
Abstract
Lipid nanoparticles (LNPs) are currently the most effective in vivo delivery systems for silencing target genes in hepatocytes employing small interfering RNA. Antigen-presenting cells (APCs) are also potential targets for LNP siRNA. We examined the uptake, intracellular trafficking, and gene silencing potency in primary bone marrow macrophages (bmMΦ) and dendritic cells of siRNA formulated in LNPs containing four different ionizable cationic lipids namely DLinDAP, DLinDMA, DLinK-DMA, and DLinKC2-DMA. LNPs containing DLinKC2-DMA were the most potent formulations as determined by their ability to inhibit the production of GAPDH target protein. Also, LNPs containing DLinKC2-DMA were the most potent intracellular delivery agents as indicated by confocal studies of endosomal versus cytoplamic siRNA location using fluorescently labeled siRNA. DLinK-DMA and DLinKC2-DMA formulations exhibited improved gene silencing potencies relative to DLinDMA but were less toxic. In vivo results showed that LNP siRNA systems containing DLinKC2-DMA are effective agents for silencing GAPDH in APCs in the spleen and peritoneal cavity following systemic administration. Gene silencing in APCs was RNAi mediated and the use of larger LNPs resulted in substantially reduced hepatocyte silencing, while similar efficacy was maintained in APCs. These results are discussed with regard to the potential of LNP siRNA formulations to treat immunologically mediated diseases.
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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 | 0.001 |
| 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".