Anti-tumor effect of CT-322 as an Adnectin inhibitor of vascular endothelial growth factor receptor-2
Bibliographic record
Abstract
CT-322 is a new anti-angiogenic therapeutic agent based on an engineered variant of the tenth type III domain of human fibronectin, i.e., an Adnectin™, designed to inhibit vascular endothelial growth factor receptor (VEGFR)-2. This PE Gylated Adnectin was developed using an mRNA display technology. CT-322 bound human VEGFR-2 with high affinity (K(D), 11 nM), but did not bind VEGFR-1 or VEGFR-3 at concentrations up to 100 nM, as determined by surface plasmon resonance studies. Western blot analysis showed that CT-322 blocked VEGF-induced phosphorylation of VEGFR-2 and mitogen-activated protein kinase in human umbilical vascular endothelial cells. CT-322 significantly inhibited the growth of human tumor xenograft models of colon carcinoma and glioblastoma at doses of 15-60 mg/kg administered 3 times/week. Anti-tumor effects of CT-322 were comparable to those of sorafenib or sunitinib, which inhibit multiple kinases, in a colon carcinoma xenograft model, although CT-322 caused less overt adverse effects than the kinase inhibitors. CT-322 also enhanced the anti-tumor activity of the chemotherapeutic agent temsirolimus in the colon carcinoma model. The high affinity and specificity of CT-322 binding to VEGFR-2 and its anti-tumor activities establish CT-322 as a promising anti-angiogenic therapeutic agent. Our results further suggest that Adnectins are an important new class of targeted biologics that can be developed as potential treatments for a wide variety of diseases.
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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".