Poly(lactic‐co‐glycolic acid) Nanoparticles for IL‐12 Self‐Amplifying RNA Delivery in Glioblastoma Models
Bibliographic record
Abstract
ABSTRACT Glioblastoma (GBM) remains one of the most lethal brain cancers, with median survival rarely exceeding 15 months after diagnosis. Interleukin‐12 (IL‐12) is a potent immunostimulatory cytokine capable of reshaping the tumor microenvironment (TME), yet its clinical translation is hindered by systemic toxicity and short half‐life. RNA‐based local immunotherapies offer a promising alternative, with self‐amplifying RNA (saRNA) enabling sustained protein expression at low doses. However, its large size and susceptibility to degradation demand a delivery system capable of providing protection, stability, and sustained activity. Here, we report a poly(lactic‐co‐glycolic acid) (PLGA) nanoparticle platform engineered to deliver intact, large saRNA encoding IL‐12. This innovative PLGA NP platform preserved the saRNA structural integrity and drove strong IL‐12 expression in SB28 glioma monolayers and 3D spheroids for at least 72 h. In a zebrafish xenograft model, a single in situ administration of saRNA‐loaded PLGA nanoparticles induced significant macrophage recruitment to the tumor site after 92 h, demonstrating an early immunomodulatory response to the RNA therapeutic. This work establishes a robust PLGA‐based platform for the delivery of large saRNA molecules and provides a foundation for next‐generation RNA immunotherapeutic strategies against GBM.
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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) | 0.000 | 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".