The Thrombospondin-1 Mimetic ABT-510 Increases the Uptake and Effectiveness of Cisplatin and Paclitaxel in a Mouse Model of Epithelial Ovarian Cancer
Bibliographic record
Abstract
Epithelial ovarian cancer (EOC) comprises approximately 90% of ovarian cancers and arises from the surface epithelium. Typical treatment of EOC involves cytoreductive surgery combined with chemotherapy. More recent therapies have targeted the tumor vasculature using antiangiogenic compounds such as thrombospondin-1 (TSP-1). TSP-1 mimetic peptides such as ABT-510 have been created and have been in various clinical trials. We have previously shown that ABT-510 reduces abnormal vasculature associated with tumor tissue and increases the presence of mature blood vessels. It has been hypothesized that treatment with antiangiogenic compounds would allow increased delivery of cytotoxic agents and enhance treatment. In this study, we evaluated the potential role of ABT-510 and various chemotherapeutics (cisplatin and paclitaxel) on tumor progression, angiogenesis, and the benefits of combinational treatments on tissue uptake and perfusion using an orthotopic syngeneic mouse model of EOC. Animals were treated with ABT-510 (100 mg/kg per day) alone or in combination with cisplatin (2 mg/kg per 3 days) or paclitaxel (10 mg/kg per 2 days) at 60 days after tumor induction. Radiolabeled and fluorescently labeled paclitaxel demonstrated a significant increase in tumor uptake after ABT-510 treatment. Combined treatment with ABT-510 and cisplatin or paclitaxel resulted in a significant increase in tumor cell and tumor endothelial cell apoptosis and a resultant decrease in ovarian tumor size. Combined treatment also regressed secondary lesions and eliminated the presence of abdominal ascites. The results from this study show that through vessel normalization, ABT-510 increases uptake of chemotherapy drugs and can induce regression of advanced ovarian cancer.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".