Hypoxia-Targeting Drug Evofosfamide (TH-302) Enhances Sunitinib Activity in Neuroblastoma Xenograft Models
Bibliographic record
Abstract
Antiangiogenic therapy has shown promising results in preclinical and clinical trials. However, tumor cells acquire resistance to this therapy by gaining ability to survive and proliferate under hypoxia induced by antiangiogenic therapy. Combining antiangiogenic therapy with hypoxia-activated prodrugs can overcome this limitation. Here, we have tested the combination of antiangiogenic drug sunitinib in combination with hypoxia-activated prodrug evofosfamide in neuroblastoma. In vitro, neuroblastoma cell line SK-N-BE(2) was 40-folds sensitive to evofosfamide under hypoxia compared to normoxia. In IV metastatic model, evofosfamide significantly increased mice survival compared to the vehicle (P=.02). In SK-N-BE(2) subcutaneous xenograft model, we tested two different treatment regimens using 30 mg/kg sunitinib and 50 mg/kg evofosfamide. Here, sunitinib therapy when started along with evofosfamide treatment showed higher efficacy compared to single agents in subcutaneous SK-N-BE(2) xenograft model, whereas sunitinib when started 7 days after evofosfamide treatment did not have any advantage compared to treatment with either single agent. Immunofluorescence of tumor sections revealed higher number of apoptotic cells and hypoxic areas compared to either single agent when both treatments were started together. Treatment with 80 mg/kg sunitinib with 50 mg/kg evofosfamide was significantly superior to single agents in both xenograft and metastatic models. This study confirms the preclinical efficacy of sunitinib and evofosfamide in murine models of aggressive neuroblastoma. Sunitinib enhances the efficacy of evofosfamide by increasing hypoxic areas, and evofosfamide targets hypoxic tumor cells. Consequently, each drug enhances the activity of the other.
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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) | 0.001 | 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 | 0.001 |
| 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".