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 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) | 0.000 | 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) | 0.001 | 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".