DREAM-in-CDM Approach and Identification of a New Generation of Anti-inflammatory Drugs Targeting mPGES-1

Shuo Zhou, Ziyuan Zhou, Kai Ding, Yaxia Yuan, Charles Loftin, Fang Zheng, Chang Guo Zhan

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

Microsomal prostaglandin E2 synthase-1 (mPGES-1) is known as an ideal target for next generation of anti-inflammatory drugs without the side effects of currently available anti-inflammatory drugs. However, there has been no clinically promising mPGES-1 inhibitor identified through traditional drug discovery and development route. Here we report a new approach, called DREAM-in-CDM (Drug Repurposing Effort Applying Integrated Modeling-in vitro/vivo-Clinical Data Mining), to identify an FDA-approved drug suitable for use as an effective analgesic targeting mPGES-1. The DREAM-in-CDM approach consists of three steps: computational screening of FDA-approved drugs; in vitro and/or in vivo assays; and clinical data mining. By using the DREAM-in-CDM approach, lapatinib has been identified as a promising mPGES-1 inhibitor which may have significant anti-inflammatory effects to relieve various forms of pain and possibly treat various inflammation conditions involved in other inflammation-related diseases such as the lung inflammation caused by the newly identified COVID-19. We anticipate that the DREAM-in-CDM approach will be used to repurpose FDA-approved drugs for various new therapeutic indications associated with new targets.

Original languageEnglish
Article number10187
JournalScientific Reports
Volume10
Issue number1
DOIs
StatePublished - Dec 1 2020

Bibliographical note

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 and the National Science Foundation (NSF grant CHE-1111761). The authors also acknowledge the Computer Center at the University of Kentucky for supercomputing time on a Dell Supercomputer Cluster consisting of 388 nodes or 4,816 processors.

Publisher Copyright:
© 2020, The Author(s).

ASJC Scopus subject areas

  • General

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