Increased Plasma Vascular Endothelial Growth Factor (VEGF) as a Surrogate Marker for Optimal Therapeutic Dosing of VEGF Receptor-2 Monoclonal Antibodies
Bibliographic record
Abstract
A major obstacle compromising the successful application of many of the new targeted anticancer drugs, including angiogenesis inhibitors, is the empiricism associated with determining an effective biological/therapeutic dose because many of these drugs express optimum therapeutic activity below the maximum tolerated dose, if such a dose can be defined. Hence, surrogate markers are needed to help determine optimal dosing. Here we describe such a molecular marker, increased plasma levels of vascular endothelial growth factor (VEGF), in normal or tumor-bearing mice that received injections of an anti-VEGF receptor (VEGFR)-2 monoclonal antibody, such as DC101. Rapid increases of mouse VEGF (e.g., within 24 hours) up to 1 order of magnitude were observed after single injections of DC101 in non-tumor-bearing severe combined immunodeficient or nude mice; similar increases in human plasma VEGF were detected in human tumor-bearing mice. RAFL-1, another anti-VEGFR-2 antibody, also caused a significant increase in plasma VEGF. In contrast, increases in mouse VEGF levels were not seen when small molecule VEGFR-2 inhibitors were tested in normal mice. Most importantly, the increases in plasma VEGF were induced in a dose-dependent manner, with the maximum values peaking when doses previously determined to be optimally therapeutic were used. Plasma VEGF should be considered as a possible surrogate pharmacodynamic marker for determining the optimal biological dose of antibody drugs that block VEGFR-2 (KDR) activity in a clinical setting.
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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 | 0.001 | 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) | 0.000 | 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".