pH‐Dependent Lyotropic Liquid Crystalline Mesophase and Ionization Behavior of Phytantriol‐Based Ionizable Lipid Nanoparticles
Bibliographic record
Abstract
Abstract Self‐assembled lipid nanoparticles (LNPs), serving as essential nanocarriers in recent COVID‐19 mRNA vaccines, provide a stable and versatile platform for delivering a wide range of biological materials. Notably, LNPs with unique inverse mesostructures, such as cubosomes and hexosomes, are recognized as fusogenic nanocarriers in the drug delivery field. This study delves into the physicochemical properties, including size, lyotropic liquid crystalline mesophase, and apparent pK a of LNPs with various lipid components, consisting of two ionizable lipids (ALC‐0315 and SM‐102) used in commercial COVID‐19 mRNA vaccines and a well‐known inverse mesophase structure‐forming helper lipid, phytantriol (PT). Two partial mesophase diagrams are generated for both ALC‐0315/PT LNPs and SM‐102/PT LNPs as a function of two factors, ionizable lipid ratio (α, 0–100 mol%) and pH condition (pH 3–11). Furthermore, the impact of different LNP stabilizers (Pluronic F127, Pluronic F108, and Tween 80) on their pH‐dependent phase behavior is evaluated. The findings offer insights into the self‐assembled mesostructure and ionization state of the studied LNPs with potentially enhanced endosomal escape ability. This research is relevant to developing innovative next‐generation LNP systems for delivering various therapeutics.
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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) | 0.001 | 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".