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Cited by 504

The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.

Targeting matrix metalloproteinases in cancer: Bringing new life to old ideas

Open access
Cathcart, Jillian, Pulkoski-Gross, Ashleigh, Cao, Jian·2015·Genes & Diseases

Abstract

Since the identification of matrix metalloproteinases (MMPs), a family of zinc-dependent endopeptidases, as being a driving factor for cancer progression and patient prognosis, MMPs have been studied extensively. Although early programs targeting MMPs were largely unsuccessful in clinical trials, they remain a viable and highly desirable therapeutic target based on preclinical studies and their role in disease progression. As information regarding the structure and function of these proteinases is compiled and biotechnology evolves, tools to develop better inhibitors are within our grasp. Improved methods for high throughput screening and in silico drug design programs have identified compounds which are highly potent, have high binding affinities, and exhibit favorable pharmacokinetic profiles. More recently, advances in drug delivery methods or compounds which bind outside the active site have brought new light to the field. In this review, we highlight the role of MMPs in cancer, clinical trials for MMP inhibitors, and novel approaches to targeting MMPs in cancer.

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Crossref · 10.1016/j.gendis.2014.12.002

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Metadata

Authors
Cathcart, J., Pulkoski-Gross, A., & Cao, J.
Venue
Genes & Diseases
Publisher
Elsevier BV
Published
2015-03
Volume
2
Issue
1
Pages
26-34
ISSN
2352-3042 (ISSN-L)
OpenAlex
W2061723012
Cited by
504
References
6
FWCI
16.78
Type
journal article
Language
en
Open access
gold

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CoChat. (2026). Citation check: Targeting matrix metalloproteinases in cancer: Bringing new life to old ideas. https://app.cochat.ai/papers/10.1016/j.gendis.2014.12.002
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Cathcart, J., Pulkoski-Gross, A., & Cao, J. (2015). Targeting matrix metalloproteinases in cancer: Bringing new life to old ideas. Genes & Diseases. https://doi.org/10.1016/j.gendis.2014.12.002

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.