Focal adhesion kinase inhibitors are potent anti‐angiogenic agents
Bibliographic record
Abstract
Focal adhesion kinase (FAK), a cytoplasmic tyrosine kinase and scaffold protein localized to focal adhesions, is uniquely positioned at the convergence point of integrin and receptor tyrosine kinase signal transduction pathways. FAK is overexpressed in many tumor cells, hence various inhibitors targeting its activity have been tested for anti-tumor activity. However, the direct effects of these pharmacologic agents on the endothelial cells of the vasculature have not been examined. Using primary human umbilical vein endothelial cells (HUVEC), we characterized the effects of two FAK inhibitors, PF-573,228 and FAK Inhibitor 14 on essential processes for angiogenesis, such as migration, proliferation, viability and endothelial cell tube formation. We observed that treatment with either FAK Inhibitor 14 or PF-573,228 resulted in reduced HUVEC viability, migration and tube formation in response to vascular endothelial growth factor (VEGF). Furthermore, we found that PF-573,228 had the added ability to induce apoptosis of endothelial cells within 36 h post-drug administration even in the continued presence of VEGF stimulation. FAK inhibitors also resulted in modification of the actin cytoskeleton within HUVEC, with observed increased stress fiber formation in the presence of drug. Given that endothelial cells were sensitive to FAK inhibitors at concentrations well below those reported to inhibit tumor cell migration, we confirmed their ability to inhibit endothelial-derived FAK autophosphorylation and FAK-mediated phosphorylation of recombinant paxillin at these doses. Taken together, our data indicate that small molecule inhibitors of FAK are potent anti-angiogenic agents and suggest their utility in combinatorial therapeutic approaches targeting tumor angiogenesis.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".