Quantification of matrix metalloproteinase activity in plasma of patients enrolled in a BAY 12-9566 phase I study
Bibliographic record
Abstract
The expression of matrix metalloproteinases (MMPs) is often associated with invasiveness or grade of tumours. Increased blood levels of MMP proteins, including MMP-1, MMP-2, MMP-3 and MMP-9 have been detected in various types of cancers. With the exception of one study, MMPs in serum and plasma have been determined using ELISA. In the present study we measured the activity of the MMPs found in human plasma samples using gelatin enzymography and fluorimetric degradation assays. We used plasma samples from healthy control subjects and cancer patients enrolled in a dose-finding study for the MMP inhibitor, BAY 12-9566, to assess the activity of MMPs found in plasma and screen for efficacy of the MMP inhibitor. BAY 12-9566 has inhibitory activity toward MMP-2, MMP-3 and MMP-9. Patients with advanced solid tumours were enrolled in our study and plasma was collected on day 1 before dosing and at steady-state of the drug on day 15. Our results show that BAY 12-9566 was effective in lowering the plasma gelatinolytic activity in the group of 29 patients when considering the data obtained from a fluorimetric gelatinase assay. The data obtained from gelatin enzymography, however, did not reach significance. The fluorimetric degradation assay could be a useful tool to screen plasma from cancer patients in other clinical trials assessing MMP inhibitors.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".