Abstract
Human mPGES-1 is recognized as a promising target for next generation of anti-inflammatory drugs. Although various mPGES-1 inhibitors have been reported in literature, few have entered clinical trials and none has been proven clinically useful so far. It is highly desired for developing the next generation of therapeutics for inflammation-related diseases to design and discover novel inhibitors of mPGES-1 with new scaffolds. Here, we report the identification of a series of new, potent and selective inhibitors of human mPGES-1 with diverse scaffolds through combined computational and experimental studies. The computationally modeled binding structures of these new inhibitors with mPGES-1 provide some interesting clues for rational design of modified structures of the inhibitors to more favorably bind with mPGES-1.
Document Type
Article
Publication Date
8-15-2017
Digital Object Identifier (DOI)
https://doi.org/10.1016/j.bmcl.2017.06.075
Funding Information
This work was supported in part by the funding of the Molecular Modeling and Biopharmaceutical Center at the University of Kentucky College of Pharmacy, the National Science Foundation (NSF grant CHE-1111761), and the National Institutes of Health via the National Center for Advancing Translational Sciences (UL1TR001998) grant. Z.Z. thanks the China Scholarship Council for a scholarship support for his graduate studies at the University of Kentucky.
Related Content
Refer to Web version on PubMed Central for supplementary material.
Repository Citation
Zhou, Ziyuan; Yuan, Yaxia; Zhou, Shuo; Ding, Kai; Zheng, Fang; and Zhan, Chang-Guo, "Selective Inhibitors of Human mPGES-1 from Structure-Based Computational Screening" (2017). Molecular Modeling and Biopharmaceutical Center Faculty Publications. 16.
https://uknowledge.uky.edu/mmbc_facpub/16
Supporting Information
Included in
Medicinal-Pharmaceutical Chemistry Commons, Pharmacy and Pharmaceutical Sciences Commons
Notes/Citation Information
Published in Bioorganic & Medicinal Chemistry Letters, v. 27, issue 16, p. 3739-3743.
© 2017 Elsevier Ltd. All rights reserved.
This manuscript version is made available under the CC‐BY‐NC‐ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/.
The document available for download is the author's post-peer-review final draft of the article.