A KDR-binding peptide (ST100,059) can block angiogenesis, melanoma tumor growth and metastasis in vitro and in vivo
Bibliographic record
Abstract
A major goal of treatment strategies for cancer is the development of agents which can block primary tumor growth and development as well as the progression of tumor metastasis without any treatment associated side effects. Using mini peptide display (MPD) technology, we generated peptides that can bind to the human vascular endothelial growth factor (VEGF) receptor KDR. These peptides were evaluated for their ability to block angiogenesis, tumor growth and metastasis in vitro and in vivo. A D-amino acid peptide with high serum stability (ST100,059) was found to have the most potent activity in vitro as indicated by inhibition of VEGF stimulation of endothelial cells. It was also found to be the most active of the series in blocking VEGF-mediated activity in vivo, as measured in Matrigel-filled angioreactors implanted in mice. ST100,059 blocks VEGF-induced MAPK phosphorylation, as well as inhibits VEGF-induced changes in gene expression in HUVEC cells. In in vivo studies, treatment of female C57BL/6 mice inoculated with B16 mouse melanoma cells with ST100,059 resulted in a dose-dependent decrease in tumor volume and lung metastasis as compared to control groups of animals receiving vehicle alone. These studies demonstrate that by using MPD, peptides can be identified with enhanced affinity relative to those discovered using phage display. Based on these studies we have identified one such peptide ST100,059 which can effectively block tumor growth and metastasis due to its anti-angiogenic effects and ability to block intracellular signaling pathways involved in tumor progression.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".