Influence of Polyethylene Glycol Lipid Desorption Rates on Pharmacokinetics and Pharmacodynamics of siRNA Lipid Nanoparticles
Bibliographic record
Abstract
Lipid nanoparticles (LNPs) encapsulating short interfering RNAs that target hepatic genes are advancing through clinical trials, and early results indicate the excellent gene silencing observed in rodents and nonhuman primates also translates to humans. This success has motivated research to identify ways to further advance this delivery platform. Here, we characterize the polyethylene glycol lipid (PEG-lipid) components, which are required to control the self-assembly process during formation of lipid particles, but can negatively affect delivery to hepatocytes and hepatic gene silencing in vivo. The rate of transfer from LNPs to plasma lipoproteins in vivo is measured for three PEG-lipids with dialkyl chains 14, 16, and 18 carbons long. We show that 1.5 mol % PEG-lipid represents a threshold concentration at which the chain length exerts a minimal effect on hepatic gene silencing but can still modify LNPs pharmacokinetics and biodistribution. Increasing the concentration to 2.5 and 3.5 mol % substantially compromises hepatocyte gene knockdown for PEG-lipids with distearyl (C18) chains but has little impact for shorter dimyristyl (C14) chains. These data are discussed with respect to RNA delivery and the different rates at which the steric barrier disassociates from LNPs in vivo.Molecular Therapy-Nucleic Acids (2013) 2, e139; doi:10.1038/mtna.2013.66; published online 17 December 2013.
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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) | 0.000 | 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".