Lipid-derived nanoparticles for immunostimulatory RNA adjuvant delivery
Bibliographic record
Abstract
The specific activation of Toll-like receptors (TLRs) has potential utility for a variety of therapeutic indications including antiviral immunotherapy and as vaccine adjuvants. TLR7 and TLR 8 may be activated by their native ligands, single-stranded RNA, or by small molecules of the imidazoquinoline family. However the use of TLR7/8 agonists for in vivo therapy is limited by instability, in the case of RNA, or systemic biodistribution and toxicity in the case of small molecule agonists. We hypothesized that unique lipid-like materials, termed "lipidoids," could be designed to efficiently deliver immunostimulatory RNA (isRNA) to TLR-expressing cells to drive innate and adaptive immune responses. A library of lipidoids was synthesized and screened for the ability to induce type I IFN activation in human peripheral blood mononuclear cells when combined with isRNA oligonucleotides. Effective lipidoid-isRNA nanoparticles, when tested in mice, stimulated strong IFN-α responses following subcutaneous injection, had robust antiviral activity that suppressed influenza virus replication, and enhanced antiovalbumin humoral and cell-mediated responses when used as a vaccine adjuvant. Further, we demonstrate that whereas all immunological activity was MyD88-dependent, certain materials were found to engage both TLR7-dependent and TLR7-independent activity in the mouse suggestive of cell-specific delivery. These lipidoid formulations, which are materials designed specifically for delivery of isRNA to Toll-like receptors, were superior to the commonly used N-[1-(2,3-dioleoyloxy)propyl]-N,N,N-trimethylammonium methylsulfate-RNA delivery system and may provide new tools for the manipulation of TLR responses in vitro and in vivo.
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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 | 0.001 |
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".