On the role of helper lipids in lipid nanoparticle formulations of siRNA
Bibliographic record
Abstract
Onpattro, the first RNAi-based therapeutic to receive FDA approval, is enabled by a lipid nanoparticle (LNP) system that facilitates siRNA delivery into the cytoplasm of target cells (hepatocytes) following intravenous (i.v.) administration. These LNP-siRNA systems consist of four lipid components (ionizable cationic lipid, distearolyphosphatidycholine or DSPC, cholesterol, and PEG-lipid) and siRNA. The ionizable cationic lipid has been optimised for RNA encapsulation and intracellular delivery, and the PEG-lipids have been engineered to regulate LNP size and transfection potency. The roles of the other "helper" lipids, DSPC and cholesterol, remain less clear. Here we show that in empty LNP systems that do not contain siRNA, DSPC-cholesterol resides in outer layers, whereas in loaded systems a portion of the DSPC-cholesterol is internalised together with siRNA. It is concluded that the presence of internalised helper lipid is vital to the stable encapsulation of siRNA in the LNP and thus to LNP-siRNA function.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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".