Synthetic inhibitor of matrix metalloproteases decreases tumor growth and metastases in a syngeneic model of rat prostate cancerin vivo
Bibliographic record
Abstract
Members of the matrix metalloprotease (MMP) family are implicated in the progression of several malignancies including prostate cancer due to their ability to break down extracellular matrix (ECM) components. In this study, we have evaluated the ability of a synthetic MMP inhibitor (A-177430) to block tumor growth and metastases in a syngeneic model of rat prostate cancer. In an in vitro substrate assay, A-177430 exhibited nanomolar potency (IC(50) 2-6 nM) against the enzymatic activity of several MMPs. For in vivo studies, male Copenhagen rats were injected s.c. with Mat Ly Lu rat prostate cancer cells (1 x 10(6) cells ) into the right flank and animals were administered i.p.with different doses (10-100 mg/kg per day) of A-177430 for 16 days. Administration of A-177430 resulted in a dose-dependent decrease in tumor volume as compared to a control group of animals receiving vehicle alone. The maximum dose (100 mg/kg per day) of A-177430 exhibited complete arrest in tumor growth and prevented the development of macroscopic tumor metastases to lungs without exhibiting any noticeable side effects. Histologic examination of primary tumors from experimental animals showed extensive tumor necrosis and decreased tumor angiogenesis as determined by factor VIII staining of primary tumors following A-177430 treatment. These primary tumors from experimental animals also exhibited a significant increase in tumor cell DNA fragmentation as determined by TUNEL assay. Collectively, these results demonstrate the ability of MMP inhibitors to block tumor growth and metastases by blocking ECM degradation and by inhibiting tumor angiogenesis and promotion of prostate cancer cell apoptosis in vivo.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".