Metronomic Oral Topotecan with Pazopanib Is an Active Antiangiogenic Regimen in Mouse Models of Aggressive Pediatric Solid Tumor
Bibliographic record
Abstract
PURPOSE: Low dose metronomic (LDM) chemotherapy, combined with VEGF signaling pathway inhibitors, is a highly effective strategy to coordinately inhibit angiogenesis and tumor growth in many adult preclinical cancer models. We have tested the efficacies of daily oral LDM topotecan alone and in combination with pazopanib, a VEGF receptor inhibitor, in three pediatric extracranial solid tumor mouse models. EXPERIMENTAL DESIGN: In vitro dose-response study of topotecan and pazopanib was conducted on several neuroblastoma, osteosarcoma, and rhabdomyosarcoma cell lines. In vivo antitumor efficacies of the LDM topotecan and pazopanib as single agents and in combination were tested on 4 subcutaneous xenograft models and on 2 neuroblastoma metastatic models. Circulating angiogenic factors such as circulating endothelial cells (CEC), circulating endothelial pro genitor cells (CEP), and microvessel densities were used as surrogate biomarker markers of antiangiogenic activity. RESULTS: In vitro, topotecan caused a dose-dependent decrease in viabilities of all cell lines, while pazopanib did not. In vivo, combination of topotecan + pazopanib (TP + PZ) showed significant antitumor activity and significant enhancement in survival compared with the respective single agents in all models. Reductions in viable CEP and/or CEC levels and tumor microvessel density were correlated with tumor response and therefore confirmed the antiangiogenic activity of the regimens. Pharmacokinetic studies of both drugs did not reveal any drug-drug interaction. CONCLUSION: Metronomic administration of TP + PZ showed a statistically significant antitumor activity compared with respective single agents in pediatric tumor mouse models and represent a valid option as a maintenance therapy in aggressive pediatric solid tumors.
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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 | 0.000 |
| 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 | 0.000 | 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".