Nanoparticle Retinoic Acid-Inducible Gene I Agonist for Cancer Immunotherapy
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Pharmacological activation of the retinoic acid-inducible gene I (RIG-I) pathway holds promise for increasing tumor immunogenicity and improving the response to immune checkpoint inhibitors (ICIs). However, the potency and clinical efficacy of 5′-triphosphate RNA (3pRNA) agonists of RIG-I are 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 the 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 the potential to enhance response to ICIs and other immunotherapeutic modalities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".