Polymeric and lipid nanoparticles for delivery of self-amplifying RNA vaccines
Bibliographic record
Abstract
Self-amplifying RNA (saRNA) is a next-generation vaccine platform, but like all nucleic acids, requires a delivery vehicle to promote cellular uptake and protect the saRNA from degradation. To date, delivery platforms for saRNA have included lipid nanoparticles (LNP), polyplexes and cationic nanoemulsions; of these LNP are the most clinically advanced with the recent FDA approval of COVID-19 based-modified mRNA vaccines. While the effect of RNA on vaccine immunogenicity is well studied, the role of biomaterials in saRNA vaccine effectiveness is under investigated. Here, we tested saRNA formulated with either pABOL, a bioreducible polymer, or LNP, and characterized the protein expression and vaccine immunogenicity of both platforms. We observed that pABOL-formulated saRNA resulted in a higher magnitude of protein expression, but that the LNP formulations were overall more immunogenic. Furthermore, we observed that both the helper phospholipid and route of administration (intramuscular versus intranasal) of LNP impacted the vaccine immunogenicity of two model antigens (influenza hemagglutinin and SARS-CoV-2 spike protein). We observed that LNP administered intramuscularly, but not pABOL or LNP administered intranasally, resulted in increased acute interleukin-6 expression after vaccination. Overall, these results indicate that delivery systems and routes of administration may fulfill different delivery niches within the field of saRNA genetic medicines.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".