Liposomal Resiquimod for Enhanced Immunotherapy of Peritoneal Metastases of Colorectal Cancer
Bibliographic record
Abstract
Colorectal cancer with peritoneal metastases is currently treated by cytoreductive surgery and locoregional chemotherapeutics. This standard treatment is associated with high morbidity, mortality, and recurrence rate. To augment the existing therapy, we developed a liposome-based delivery system containing 1,2-stearoyl-3-trimethylammonium-propane chloride (DSTAP), a cationic lipid, to localize a toll-like receptor agonist, resiquimod (R848), in the peritoneal cavity (PerC) for enhancing the immune response against cancer that had spread to the PerC. The liposomes delivered by intraperitoneal injection increased peritoneal retention of R848 by 14-fold while retarding its systemic absorption, leading to a 5-fold decreased peak plasma concentration compared to free R848 in mice. Within the PerC, the DSTAP-liposomes were found in ~40% of the dendritic cells by flow cytometry. DSTAP-R848 significantly upregulated interferon α (IFN-α) in the peritoneal fluid by 2-fold compared to free R848, without increasing the systemic level. Combined with oxaliplatin, a cytotoxic agent inducing immunogenic cell death, DSTAP-R848 effectively inhibited the progression of CT26 murine colorectal tumor in the PerC, while the combination with free R848 only showed a mild effect. Moreover, the combination of oxaliplatin and DSTAP-R848 significantly increased infiltration of CD8+ T cells in the PerC compared to oxaliplatin combined with free R848, indicating enhanced immune response against the tumor. The results suggest that DSTAP-R848 exhibits potential in augmenting existing therapies for treating colorectal cancer with peritoneal metastases via immune activation.
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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".