Inhibition of Matrix Metalloproteinases as a Feasible Therapeutic Target in Rheumatoid Arthritis
Bibliographic record
Abstract
Rheumatoid arthritis (RA) is a systemic inflammatory disease affecting several joints. Proinflammatory cytokines (IL-1, IL-17, TNF-α and oncostatin M) activate multiple signalling pathways and transcription factors that augment matrix metalloproteinases (MMPs) and aggrecanases (ADAMTSs) expression. These events promote invasion of cartilage by proliferating pannus and ultimate loss of cartilage and bone. MMPs/ADAMTSs can be blocked at the levels of cytokines, signal transduction, transcription factors, mRNA translation and enzyme activity by the small-molecule synthetic and natural inhibitors including tissue inhibitors of metalloproteinases (TIMPs), specific ribozymes and by RNA interference. Overexpression of TIMP-1, TIMP-3 and TIMP-4 inhibits inflammation in RA-like animal models. TIMP-3 uniquely blocks aggrecanases and TNF-α converting enzyme (TACE/ADAM17) in vitro and could diminish synovial proliferation, its invasion of cartilage and inflammation in vivo. TIMP-3 knockout mice display enhanced inflammation and cartilage destruction. TIMPs are being engineered for therapeutic use to reduce RA synovial inflammation, pannus invasion of cartilage and tissue destruction. Because MMPs and aggrecanases inhibition may also interfere with their physiological functions of cytokine and growth factor processing, skeletal development, normal tissue remodelling and repair, extensive research on possible side effects of inhibitors is needed before their therapeutic use for RA. Nevertheless, multiple inhibitory strategies appear promising. Keywords: Aggrecanases, matrix metalloproteinases, rheumatoid arthritis, synthetic inhibitors, tissue inhibitor metalloproteinases
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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 | 0.000 | 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 | 0.001 |
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".