<i>In vitro</i> Procoagulant Activity Induced in Endothelial Cells by Chemotherapy and Antiangiogenic Drug Combinations: Modulation by Lower-Dose Chemotherapy
Bibliographic record
Abstract
One of the emerging problems concerning the use of antiangiogenic drugs, when used in combination with certain chemotherapy regimens, is enhanced rates and severity of adverse clotting events. For as yet unknown reasons, certain drugs and particular combinations can induce an elevated incidence of thromboembolic events in treated cancer patients [e.g., SU5416, a vascular endothelial cell growth factor receptor-2 (VEGFR-2) antagonist, when combined with gemcitabine and cisplatin (CDDP)]. Such results highlight the need to develop assays capturing the essence of enhanced clot formation under such combination treatment and which may have predictive potential as well. Here, we report the possibility of such an assay (i.e., the ratio of tissue factor over tissue factor pathway inhibitor expression or activity in cultured human endothelial cells calculated as a coagulation index). A marked increase in coagulation index was observed after exposure to SU5416 and the CDDP/gemcitabine chemotherapy combination in contrast to either of these treatments used alone. Substitution of SU5416 with any one of ZD6474, SU6668, IMC-1121, a monoclonal antibody to VEGFR-2, or an antibody to VEGF (bevacizumab) did not cause a marked increase in the coagulation index, nor did the combination of SU5416 with 5-fluorouracil and leucovorin. Finally, we noted that reducing the concentrations of gemcitabine and CDDP (i.e., use of "metronomic dosing" in vitro) significantly attenuated the coagulation index increase induced by these drugs, suggesting that use of low-dose chemotherapy regimens might be an approach to consider for reducing the incidence of adverse clotting events associated with chemotherapy alone or in conjunction with antiangiogenic drug combination therapies.
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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 | 0.000 |
| 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) | 0.002 | 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".