Topotecan and Doxorubicin Combination to Treat Recurrent Ovarian Cancer: The Influence of Drug Exposure Time and Delivery Systems to Achieve Optimum Therapeutic Activity
Bibliographic record
Abstract
PURPOSE: To provide proof-of-concept data to support use of Doxil-liposomal topotecan (Topophore C) combinations to treat ovarian cancer. EXPERIMENTAL DESIGN: ES-2, OVCAR-3, and SKOV-3 ovarian cancer cell lines were treated with doxorubicin-topotecan combinations by exposing the cells to drugs from 1 to 72 hours. Pharmacokinetic analysis was conducted following administration of liposomal formulations of these drugs alone and in combination. Efficacy assessments were completed in ES-2 and SKOV-3 ovarian cancer models. RESULTS: On the basis of drug doses capable of achieving 50% reduction in cell viability over 72 hours, doxorubicin-topotecan combinations were additive in SKOV-3 but highly synergistic in ES-2 and OVCAR-3 cells. Favorable drug-drug interactions increased with increased drug exposure time. Topophore C pharmacokinetic remained unaffected when co-administered with Doxil. In the ES-2 model, Doxil at maximum tolerated dose (MTD 7.5 mg/kg) in combination with free topotecan (MTD 15 mg/kg) did not enhance median survival time (MST) over that achieved with topotecan alone. In contrast, MST was increased to 52 days with combination of Topophore C (MTD 2.5 mg/kg) and Doxil (7.5 mg/kg) compared with untreated animals (MST 18 days) or those treated with Topophore C alone (MTD 5 mg/kg, MST 40 days). In the SKOV-3 model, combination treatments showed better therapeutic efficacy than the individual drugs. CONCLUSIONS: Topotecan-doxorubicin combinations produced additive or synergistic effects which were best achieved when the tumor cells were exposed to drugs over extended time. Doxil-Topophore C combinations are therapeutically superior as judged in two ovarian cancer models. Clin Cancer Res; 19(4); 865-77. ©2012 AACR.
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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 | 0.001 | 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) | 0.000 | 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".