A Novel Orally Bioavailable Inhibitor of Kinase Insert Domain-Containing Receptor Induces Antiangiogenic Effects and Prevents Tumor Growth <b> <i>in Vivo</i> </b>
Bibliographic record
Abstract
A strategy for antagonizing vascular endothelial growth factor (VEGF) -induced angiogenesis is to inhibit the kinase activity of its receptor, kinase insert domain-containing receptor (KDR), the first committed and perhaps the last unique step in the VEGF signaling cascade. We synthesized a novel ATP-competitive KDR tyrosine kinase inhibitor that potently suppresses human and mouse KDR activity in enzyme (IC(50) = 7.8-19.5 nM) and cell-based assays (IC(50) = 8 nM). The compound was bioavailable in vivo, leading to a dose-dependent decrease in basal- and VEGF-stimulated KDR tyrosine phosphorylation in lungs from naïve and tumor-bearing mice (IC(50) = 23 nM). Pharmacokinetics and pharmacodynamics guided drug dose selection for antitumor efficacy studies. HT1080 nude mice xenografts were treated orally twice daily with vehicle, or 33 or 133 mg/kg of compound. These doses afforded trough plasma concentrations approximately equal to the IC(50) for inhibition of KDR autophosphorylation in vivo for the 33 mg/kg group, and higher than the IC(99) for the 133 mg/kg group. Chronic treatment at these doses was well-tolerated and resulted in dose-dependent inhibition of tumor growth, decreased tumor vascularization, decreased proliferation, and enhanced cell death. Antitumor efficacy correlated with inhibition of KDR tyrosine phosphorylation in the tumor, as well as in a surrogate tissue (lung). Pharmacokinetics and pharmacodynamics assessment indicated that the degree of tumor growth inhibition correlated directly with the extent of inhibition of KDR tyrosine phosphorylation in tumor or lung at trough. These observations highlight the need to design antiangiogenic drug regimens to ensure constant target suppression and to take advantage of PD end points to guide dose selection.
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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 | 0.001 |
| 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".