Matrix Metalloproteinases: From Molecular Mechanisms to Physiology, Pathophysiology, and Pharmacology
Bibliographic record
Abstract
The first matrix metalloproteinase (MMP) was discovered in 1962 from the tail of a tadpole by its ability to degrade collagen. As their name suggests, matrix metalloproteinases are proteases capable of remodeling the extracellular matrix. More recently, MMPs have been demonstrated to play numerous additional biologic roles in cell signaling, immune regulation, and transcriptional control, all of which are unrelated to the degradation of the extracellular matrix. In this review, we will present milestones and major discoveries of MMP research, including various clinical trials for the use of MMP inhibitors. We will discuss the reasons behind the failures of most MMP inhibitors for the treatment of cancer and inflammatory diseases. There are still misconceptions about the pathophysiological roles of MMPs and the best strategies to inhibit their detrimental functions. This review aims to discuss MMPs in preclinical models and human pathologies. We will discuss new biochemical tools to track their proteolytic activity in vivo and ex vivo, in addition to future pharmacological alternatives to inhibit their detrimental functions in diseases. SIGNIFICANCE STATEMENT: Matrix metalloproteinases (MMPs) have been implicated in most inflammatory, autoimmune, cancers, and pathogen-mediated diseases. Initially overlooked, MMP contributions can be both beneficial and detrimental in disease progression and resolution. Thousands of MMP substrates have been suggested, and a few hundred have been validated. After more than 60 years of MMP research, there remain intriguing enigmas to solve regarding their biological functions in diseases.
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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) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".