Modulating the Nature of Ionizable Lipids and Number of Layers in Hyaluronan-Decorated Lipid Nanoparticles for In Vitro Delivery of RNAi
Bibliographic record
Abstract
Lipid nanoparticles (LNPs) have established their position as nonviral vectors for gene therapy. Tremendous efforts have been made to modulate the properties of LNPs to unleash their full clinical potential. Among the strategies being pursued, the layer-by-layer (LbL) technique has gained considerable attention in the biomedical field. Illuminated by our previous work, here we investigate if the LbL approach could be used to modify the LNP cores formulated with three different ionizable lipids: DODMA, MC3, and DODAP. Additionally, we wondered if more than three layers could be loaded onto LNPs without disrupting their gene transfection ability. Taking advantage of physicochemical analysis, as well as uptake and gene silencing studies, we demonstrate the feasibility of modifying the surface of LNPs with the LbL assembly. Precisely, we successfully modified three different LNPs using the layer-by-layer strategy which abrogated luciferase activity in vitro. Additionally, we constructed a 5×-layered HA-LNP containing the MC3 ionizable lipid which outperformed the 3×-layered counterpart in transfecting miRNA-181-5p to the pediatric GBM cell line, as a proof-of-concept in vitro experiment. The method used herein has been proven reproducible, of easy modification to adapt to different ionizable lipid-containing LNPs, and holds great potential for the translation of RNA-based therapeutic strategies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".