Imaging and Modulating Antisense Microdistribution in Solid Human Xenograft Tumor Models
Bibliographic record
Abstract
PURPOSE: The tumor microenvironment is complex and heterogeneous, populated by tortuous irregular vasculature, hypoxic cells, and necrotic regions. These factors can all contribute to the biodistribution difficulties encountered by most cancer therapeutic agents. Antisense oligodeoxynucleotides (ASO) are a class of therapeutics where limited information is available about their distribution within a solid tumor environment. EXPERIMENTAL DESIGN: To assess ASO distribution, a fluorescein-labeled phosphorothionated ASO based on the G3139 mismatch control was injected systemically (i.v.) into tumor-bearing severe combined immunodeficient mice. Hoechst 33342 was injected i.v. to visualize active vasculature. Unstained sections were imaged through tiled fluorescence stereomicroscopy and then quantitated using novel algorithms. Tumor sections from four human tumor models were examined (CaSki, DU-145, C666-1, and C15) for hypoxia, apoptosis/necrosis, and morphology. RESULTS: For all four tumors, ASO accumulated within regions of hypoxia, necrosis, and apoptosis. Scatter plots of ASO versus active vasculature generated for each individual tumor revealed a consistent pattern of distribution of the ASO within each model. In C666-1 xenografts, the slopes of these scatter plots were significantly reduced from 0.41 to 0.16 when pretreated with the antivascular agent ZD6126 48 h before ASO injection. This was accompanied by the formation of large disseminated necrotic regions in the tumor, along with a 13.1 mmHg reduction in interstitial fluid pressure. CONCLUSIONS: These data suggest the possibility that these algorithms might offer a generalizable and objective methodology to describe the distribution of molecular therapeutic agents within a tumor microenvironment and to quantitatively assess distribution changes in response to combination therapies.
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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".