Tumor‐Tailored Ionizable Lipid Nanoparticles Facilitate IL‐12 Circular RNA Delivery for Enhanced Lung Cancer Immunotherapy
Bibliographic record
Abstract
Abstract The advancement of message RNA (mRNA) ‐based immunotherapies for cancer is highly dependent on the effective delivery of RNA (Ribonucleic) payloads using ionizable lipid nanoparticles (LNPs). However, the clinical application of these therapies is hindered by variable mRNA expression among different cancer types and the risk of systemic toxicity. The transient expression profile of mRNA further complicates this issue, necessitating frequent dosing and thus increasing the potential for adverse effects. Addressing these challenges, a high‐throughput combinatorial method is utilized to synthesize and screen LNPs that efficiently deliver circular RNA (circRNA) to lung tumors. The lead LNP, H1L1A1B3, demonstrates a fourfold increase in circRNA transfection efficiency in lung cancer cells over ALC‐0315, the industry‐standard LNPs, while providing potent immune activation. A single intratumoral injection of H1L1A1B3 LNPs, loaded with circRNA encoding interleukin‐12 (IL‐12), induces a robust immune response in a Lewis lung carcinoma model, leading to marked tumor regression. Immunological profiling of treated tumors reveals substantial increments in CD45 + leukocytes and enhances infiltration of CD8 + T cells, underscoring the ability of H1L1A1B3 LNPs to modulate the tumor microenvironment favorably. These results highlight the potential of tailored LNP platforms to advance RNA drug delivery for cancer therapy, broadening the prospects for RNA immunotherapeutics.
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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 | 0.000 |
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".