Fusion-dependent formation of lipid nanoparticles containing macromolecular payloads
Bibliographic record
Abstract
The success of Onpattro™ (patisiran) clearly demonstrates the utility of lipid nanoparticle (LNP) systems for enabling gene therapies. These systems are composed of ionizable cationic lipids, phospholipid, cholesterol, and polyethylene glycol (PEG)-lipids, and are produced through rapid-mixing of an ethanolic-lipid solution with an acidic aqueous solution followed by dialysis into neutralizing buffer. A detailed understanding of the mechanism of LNP formation is crucial to improving LNP design. Here we use cryogenic transmission electron microscopy and fluorescence techniques to further demonstrate that LNP are formed through the fusion of precursor, pH-sensitive liposomes into large electron-dense core structures as the pH is neutralized. Next, we show that the fusion process is limited by the accumulation of PEG-lipid on the emerging particle. Finally, we show that the fusion-dependent mechanism of formation also applies to LNP containing macromolecular payloads including mRNA, DNA vectors, and gold nanoparticles.
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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) | 0.000 | 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".