Characterization of Lipid Nanoparticles Containing Ionizable Cationic Lipids Using Design-of-Experiments Approach
Bibliographic record
Abstract
Lipid nanoparticles (LNPs) containing short-interfering RNA (LNP-siRNA systems) are a promising approach for silencing disease-causing genes in hepatocytes following intravenous administration. LNP-siRNA systems are generated by rapid mixing of lipids in ethanol with siRNA in aqueous buffer (pH 4.0) where the ionizable lipid is positively charged, followed by dialysis to remove ethanol and to raise the pH to 7.4. Ionizable cationic lipids are the critical excipient in LNP systems as they drive entrapment and intracellular delivery. A recent study on the formation of LNP-siRNA systems suggested that ionizable cationic lipids segregate from other lipid components upon charge neutralization to form an amorphous oil droplet in the core of LNPs. This leads to a decrease in intervesicle electrostatic repulsion, thereby engendering fusion of small vesicles to form final LNPs of increased size. In this study, we prepared LNP-siRNA systems containing four lipid components (hydrogenated soy phosphatidylcholine, cholesterol, PEG-lipid, and 1,2-dioleoyl-3-dimethylammonium propane) by microfluidic mixing. The effects of preparation parameters [lipid concentration, flow rate ratio (FRR), and total flow rate], dialysis process, and complex formation between siRNA and ionizable cationic lipids on the physicochemical properties [siRNA entrapment on the particle size and polydispersity index (PDI)] were investigated using a design of experiments approach. The results for the preparation parameters showed no impact on siRNA encapsulation, but lipid concentration and FRR significantly affected the particle size and PDI. In addition, the effect of FRR on the particle size was suppressed in the presence of anionic polymers such as siRNA as compared to the case of LNPs alone. More intriguingly, unlike empty LNPs, a decrease in the PDI and an increase in the particle size occurred after dialysis in the LNP-siRNA systems. Such changes by dialysis were suppressed at FRR = 1. These findings provide useful information to guide the development and manufacturing conditions for LNP-siRNA systems.
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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".