Combination therapy of zibotentan with cisplatinum and paclitaxel is an effective regimen for epithelial ovarian cancerThis article is one of a selection of papers published in the two-part special issue entitled 20 Years of Endothelin Research.
Bibliographic record
Abstract
In human ovarian carcinoma, the endothelin-1 (ET-1) / endothelin A receptor (ETAR) axis is overexpressed, correlating with tumor grade. Moreover, ETAR activation by ET-1 affects cell proliferation, survival, angiogenesis, and invasion. ETAR blockade with zibotentan (ZD4054), a specific ETAR antagonist, significantly inhibits ovarian cancer growth in vitro and in vivo, underscoring the relevance of this pathway as a target for cancer therapy. Since clinical trial results have defined the combination of platinum and taxane as the standard of care in the management of ovarian cancer, here we explored the therapeutic efficacy of the integration of zibotentan with cytotoxic drugs having different modes of action. We found that the combination of zibotentan with cisplatinum as well as zibotentan with paclitaxel was more effective at inhibiting ovarian cancer HEY cell proliferation induced by endogenous ET-1 than were the single agents alone. However, a significantly enhanced efficacy was observed when we combined zibotentan, cisplatinum, and paclitaxel. Accordingly, in HEY xenografts the coadministration of zibotentan with cisplatinum enhanced the efficacy of the cytotoxic drug alone in controlling tumor growth, associated with reduction in proliferation index and microvessel density. Remarkably, the combination of zibotentan with both cisplatinum and paclitaxel was very effective in inhibiting tumor growth, neovascularization, and cell proliferation, representing a preclinical endpoint to guide combination therapy in clinical trials.
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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".