ZD6474, a Novel Tyrosine Kinase Inhibitor of Vascular Endothelial Growth Factor Receptor and Epidermal Growth Factor Receptor, Inhibits Tumor Growth of Multiple Nervous System Tumors
Bibliographic record
Abstract
PURPOSE: Primary central nervous system (CNS) tumors represent a diverse group of tumor types with heterogeneous molecular mechanisms that underlie their formation and maintenance. CNS tumors depend on angiogenesis and often display increased activity of ErbB-associated pathways. Current nonspecific therapies frequently have poor efficacy in many of these tumor types, so there is a pressing need for the development of novel targeted therapies. EXPERIMENTAL DESIGN: ZD6474 is a novel, orally available low molecular weight inhibitor of the kinase activities associated with vascular endothelial growth factor receptor-2 and epidermal growth factor receptor. We hypothesized that ZD6474 may provide benefit in the treatment of several CNS tumor types. RESULTS: In mice bearing established s.c. tumor xenografts of CNS tumors (malignant glioma and ependymoma) or rhabdomyosarcoma, a limited course of ZD6474 treatment produced significant tumor growth delays and a high rate of partial tumor regression in most models examined. Mice with i.c. malignant glioma xenografts treated with ZD6474 experienced a significant prolongation of survival. Tumors from mice treated with ZD6474 displayed a lower proliferative index and disrupted tumor vascularity. Notably, some of these models are insensitive to low molecular weight kinase inhibitors targeting only vascular endothelial growth factor receptor-2 or epidermal growth factor receptor functions, suggesting that the combined disruption of both epidermal growth factor receptor and vascular endothelial growth factor receptor-2 activities may significantly increase tumor control. CONCLUSIONS: In conclusion, ZD6474 shows significant activity against xenograft models of several primary human CNS tumor types. Consideration for clinical development in this disease setting seems warranted.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".