The role of helper lipids in optimising nanoparticle formulations of self-amplifying RNA
Bibliographic record
Abstract
Lipid nanoparticle (LNP) formulation plays a vital role in RNA vaccine delivery. However, further optimisation of self-amplifying RNA (saRNA) vaccine formulation could help enhance seroconversion rates in humans and improve storage stability. Altering either the ionisable or helper lipid can alter the characteristics and performance of formulated saRNA through the interplay of the phospholipid's packing parameter and the geometrical shape within the LNP membrane. In this study, we compared the impact of three helper lipids (DSPC, DOPC, or DOPE) used with two different ionisable lipids (MC3 and C12-200) on stability, transfection efficiency and the inflammation and immunogenicity of saRNA. While helper lipid identity altered saRNA expression across four cell lines in vitro, this was not predictive of an ex vivo or in vivo response. The helper lipid used influenced LNP storage where DSPC provided the best stability profile over four weeks at 2-8 °C. Importantly, helper lipid impact on LNP storage stability was the best predictor of expression in human skin explants, where C12-200 in combination with DSPC provided the most durable expression. C12-200 LNPs also improved protein expression (firefly luciferase) and humoral responses to a SARS-CoV-2 spike saRNA vaccine compared to MC3 LNPs, where the effect of helper lipids was less apparent. Nevertheless, the performance of C12-200 in combination with DSPC appears optimal for saRNA when balancing preferred storage stability requirements against in vivo and ex vivo potency. These data suggest that helper lipid influences the stability and functionality of ionisable lipid nanoparticle-formulated saRNA.
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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 | 0.001 |
| 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 | 0.001 |
| 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".