Cellular and Molecular Surrogate Markers to Monitor Targeted and Non- Targeted Antiangiogenic Drug Activity and Determine Optimal Biologic Dose
Bibliographic record
Abstract
Perhaps the most significant recent advance in oncology therapeutics has been the approval of various "molecularly targeted" anti-cancer drugs. Currently, there are a large number of similar drugs in early or late stage development, including antiangiogenic agents. Clinical development of such drugs suffers from several handicaps including determining whether a patient's cancer expresses the target and is functionally contributing to cancer growth, monitoring biologic activity, and determining optimal biologic dose. The last problem is related to the low frequency of objective tumor responses (tumor shrinkage) caused by such drugs, or the lack of dose limiting toxicities necessary to define a maximum tolerated dose (MTD), or expression of optimal therapeutic activity at doses below the MTD, when one can be defined. These problems necessitate the development of alternative pharmacodynamic surrogate markers. Here we summarize several such promising markers for monitoring targeted antiangiogenic activity, and establishing optimal therapeutic/biologic dosing. The first is molecular--plasma VEGF--levels of which are rapidly and significantly increased in a dose dependent manner after injection of normal or tumor bearing mice with anti-VEGFR-2 antibodies. The second is a cellular marker, and more generic in nature--circulating VEGF receptor-2 positive cells found in peripheral blood, some of which may be circulating endothelial progenitor cells. Levels of such cells are suppressed in a dose dependent manner which correlate with previously determined optimal biologic/therapeutic anti-tumor activity of various antiangiogenic drugs or treatments. Finally, another promising marker we discuss is soluble VEGFR-2.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".