Data from Potentiation of Taxol Efficacy by Discodermolide in Ovarian Carcinoma Xenograft-Bearing Mice
Bibliographic record
Abstract
Abstract Purpose: To evaluate the drug combination of discodermolide and Taxol in human ovarian cancer cells and in an in vivo model of ovarian carcinoma. Experimental Design: The combination index method was used to evaluate the interaction of Taxol and discodermolide in human ovarian SKOV-3 carcinoma cells. Data were correlated with alterations in cell cycle distribution and caspase activation. In addition, SKOV-3 xenograft-bearing mice were treated with either Taxol, discodermolide, or a combination of both drugs given concurrently to evaluate the antitumor efficacy and toxicity of this combination. The Matrigel plug assay and CD31 immunohistochemistry were done to assess antiangiogenic effects. Results: Taxol and discodermolide interact synergistically over a range of concentrations and molar ratios that cause drug-induced aneuploidy in ovarian carcinoma cells. In SKOV-3 xenograft-bearing mice, the combination is significantly superior to either single agent, and induces tumor regressions without notable toxicities. Immunohistochemical analysis of CD31 and Matrigel plug analysis show decreased vessel formation in mice treated with the combination relative to either drug alone. Conclusions: The synergistic activity of Taxol and discodermolide in cells is most potent at drug concentrations that result in drug-induced aneuploidy rather than mitotic arrest. Moreover, in an animal model of ovarian carcinoma, this is a well-tolerated combination that induces tumor regressions and suppresses angiogenesis. These data confirm the potency of this combination and support the use of concurrent low doses of Taxol and discodermolide for potential use in cancer therapeutics.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".