A Nanoparticle RIG-I Agonist for Cancer Immunotherapy
Bibliographic record
Abstract
Abstract Pharmacological activation of the retinoic acid-inducible gene I (RIG-I) pathway holds promise for increasing tumor immunogenicity and improving response to immune checkpoint inhibitors (ICI). However, the potency and clinical efficacy of 5’-triphosphate RNA (3pRNA) agonists of RIG-I is hindered by multiple pharmacological barriers, including poor pharmacokinetics, nuclease degradation, and inefficient delivery to the cytosol where RIG-I is localized. Here, we address these challenges through the design and evaluation of ionizable lipid nanoparticles (LNPs) for the delivery of 3p-modified stem-loop RNAs (SLRs). Packaging of SLRs into LNPs (SLR-LNPs) yielded surface charge-neutral nanoparticles with a size of ∼100 nm that activated RIG-I signaling in vitro and in vivo . SLR-LNPs were safely administered to mice via both intratumoral and intravenous routes, resulting in RIG-I activation in the tumor microenvironment (TME) and inhibition of tumor growth in mouse models of poorly immunogenic melanoma and breast cancer. Significantly, we found that systemic administration of SLR-LNPs reprogrammed the breast TME to enhance the infiltration of CD8 + and CD4 + T cells with antitumor function, resulting in enhanced response to αPD-1 ICI in an orthotopic EO771 model of triple negative breast cancer. Therapeutic efficacy was further demonstrated in a metastatic B16.F10 melanoma model, with systemically administered SLR-LNPs significantly reducing lung metastatic burden compared to combined αPD-1 + αCTLA-4 ICI. Collectively, these studies have established SLR-LNPs as a translationally promising immunotherapeutic nanomedicine for potent and selective activation of RIG-I with potential to enhance response to ICIs and other immunotherapeutic modalities.
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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".