Real‐Time pH‐Dependent Self‐Assembly of Ionisable Lipids from COVID‐19 Vaccines and <i>In Situ</i> Nucleic Acid Complexation
Bibliographic record
Abstract
Ionisable amino-lipid is a key component in lipid nanoparticles (LNPs), which plays a crucial role in the encapsulation of RNA molecules, allowing efficient cellular uptake and then releasing RNA from acidic endosomes. Herein, we present direct evidence for the remarkable structural transitions, with decreasing membrane curvature, including from inverse micellar, to inverse hexagonal, to two distinct inverse bicontinuous cubic, and finally to a lamellar phase for the two mainstream COVID-19 vaccine ionisable ALC-0315 and SM-102 lipids, occurring upon gradual acidification as encountered in endosomes. The millisecond kinetic growth of the inverse cubic and hexagonal structures and the evolution of the ordered structural formation upon ionisable lipid-RNA/DNA complexation are quantitatively revealed by in situ synchrotron radiation time-resolved small angle X-ray scattering coupled with rapid flow mixing. We found that the final self-assembled structural identity, and the formation kinetics, were controlled by the ionisable lipid molecular structure, acidic bulk environment, lipid compositions, and nucleic acid molecular structure/size. The implicated link between the inverse membrane curvature of LNP and LNP endosomal escape helps future optimisation of ionisable lipids and LNP engineering for RNA and gene delivery.
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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".