Nanoparticle encapsulation enables systemic IGF-Trap delivery to inhibit intracerebral glioma growth
Bibliographic record
Abstract
BACKGROUND: Glioblastoma is an aggressive brain cancer with a 5-year survival rate of 5-10%. Current therapeutic options are limited, due in part to drug exclusion by the blood-brain barrier, restricting access of targeted drugs to the tumor. The receptor for the type 1 insulin-like growth factor (IGF-1R) was identified as a therapeutic target in glioblastoma. We previously reported that the intracerebral growth of glioma cells with reduced IGF-1R levels was inhibited. The objectives of this study were to evaluate the sensitivity of glioma cells to a novel IGF-axis inhibitor, the IGF-Trap, and optimize its delivery to the brain. METHODS: We tested the effect of the IGF-Trap on the growth of the human glioma stem cells MES-1123 and U87 MG cells, and of murine GL261 cells in vivo, using subcutaneous and orthotopic implantation. RESULTS: We show that the growth of glioma cells implanted subcutaneously or orthotopically in the brain was inhibited by systemic and direct intracerebral administration of IGF-Trap, respectively, resulting in increased survival. To increase the efficiency of systemic delivery to the brain, we encapsulated the IGF-Trap in trimethyl chitosan (TRIOZAN™) nanoparticles prior to intravenous injection. We found that nanoparticle encapsulation increased the uptake and retention of the IGF-Trap in the brain and resulted in an improved therapeutic effect against intra-cerebrally growing tumors. CONCLUSION: Our results identify the IGF-Trap as a potent inhibitor of intracerebral glioma growth and show that encapsulation in nanoparticles can improve delivery of biologics such as the IGF-Trap to the brain, thereby enhancing the therapeutic response.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".