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Identifying Tumor Cell Growth Inhibitors by Combinatorial Chemistry and Zebrafish Assays

Overview of attention for article published in PLOS ONE, February 2009
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Title
Identifying Tumor Cell Growth Inhibitors by Combinatorial Chemistry and Zebrafish Assays
Published in
PLOS ONE, February 2009
DOI 10.1371/journal.pone.0004361
Pubmed ID
Authors

Jing Xiang, Hongbo Yang, Chao Che, Haixia Zou, Hanshuo Yang, Yuquan Wei, Junmin Quan, Hui Zhang, Zhen Yang, Shuo Lin

Abstract

Cyclin-dependent kinases (CDKs) play important roles in regulating cell cycle progression, and altered cell cycles resulting from over-expression or abnormal activation of CDKs observed in many human cancers. As a result, CDKs have become extensive studied targets for developing chemical inhibitors for cancer therapies; however, protein kinases share a highly conserved ATP binding pocket at which most chemical inhibitors bind, therefore, a major challenge in developing kinase inhibitors is achieving target selectivity. To identify cell growth inhibitors with potential applications in cancer therapy, we used an integrated approach that combines one-pot chemical synthesis in a combinatorial manner to generate diversified small molecules with new chemical scaffolds coupled with growth inhibition assay using developing zebrafish embryos. We report the successful identification of a novel lead compound that displays selective inhibitory effects on CDK2 activity, cancer cell proliferation, and tumor progression in vivo. Our approaches should have general applications in developing cell proliferation inhibitors using an efficient combinatorial chemical genetic method and integrated biological assays. The novel cell growth inhibitor we identified should have potential as a cancer therapeutic agent.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 50 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 2%
Colombia 1 2%
China 1 2%
Unknown 47 94%

Demographic breakdown

Readers by professional status Count As %
Student > Master 12 24%
Student > Bachelor 9 18%
Student > Ph. D. Student 8 16%
Researcher 7 14%
Student > Postgraduate 4 8%
Other 7 14%
Unknown 3 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 18 36%
Biochemistry, Genetics and Molecular Biology 9 18%
Chemistry 7 14%
Medicine and Dentistry 5 10%
Engineering 3 6%
Other 3 6%
Unknown 5 10%