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 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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".