The antiangiogenic agent neovastat (AE-941) inhibits vascular endothelial growth factor-mediated biological effects.
Bibliographic record
Abstract
PURPOSE: Vascular endothelial growth factor (VEGF) is a potent regulator of angiogenesis, which exerts direct effects on vascular endothelial cells, including endothelial cell proliferation and survival, tubulogenesis, and vascular permeability. In this study, we examined whether Neovastat, a naturally occurring multifunctional antiangiogenic drug, could inhibit the endothelial cell response to VEGF stimulation. RESULTS: We demonstrated that Neovastat was able to block the VEGF-dependent microvessel sprouting from Matrigel-embedded rat aortic rings, and it also blocked the VEGF-induced endothelial cell tubulogenesis in vitro. In vivo studies showed that Neovastat was able to specifically inhibit VEGF-induced plasma extravasation in numerous tissues, including pancreas and skin. The mechanism of action of Neovastat on VEGF-mediated effects was also evaluated at the molecular level. Neovastat was shown to compete against the binding of VEGF to its receptor in endothelial cells and significantly inhibited the VEGF-dependent tyrosine phosphorylation of VEGF receptor-2, whereas it had no significant effect on VEGF receptor-1 activity. Moreover, the inhibition of receptor phosphorylation was correlated with a marked decrease in the ability of VEGF to induce pERK activation. Neovastat does not compete against the binding of basic fibroblast growth factor, indicating a preferential inhibitory effect on the VEGF receptor. CONCLUSIONS: Because Neovastat was shown previously to inhibit metalloproteinase activities, these results suggest that Neovastat is able to target multiple steps in tumor neovascularization, further emphasizing its use as a pleiotropic, multifunctional antiangiogenic drug.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".