Author ORCID Identifier
Date Available
1-7-2022
Year of Publication
2019
Document Type
Doctoral Dissertation
Degree Name
Doctor of Philosophy (PhD)
College
Pharmacy
Department/School/Program
Pharmaceutical Sciences
Advisor
Dr. Chang-Guo Zhan
Abstract
Ever since the advent of computer-aided drug design (CADD), in silico simulation methods have greatly accelerated the drug discovery process and lead to the discovery of numerous drug candidates. With the exponential growth of computational power, we nowadays simulate biologic systems at a scale unimaginable a decade ago and thus provides perspectives for drug design. In this dissertation research, combining in silico simulation methods like molecular docking and molecular dynamics (MD) simulation with organic synthesis, in vitro/in vivo experiments and clinical data mining, we developed new drug discovery strategies. These strategies were applied in our drug discovery projects and led to the discovery of inhibitors of microsomal prostaglandin E2 synthase 1 (mPGES-1) and butyrylcholinesterase (BChE) as potential drug candidates.
Protein 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. Unfortunately, almost all the previously reported human mPGES-1 inhibitors are inactive (or possess very low activity) against mouse or rat mPGES-1 that prevents using well-established mouse/rat models of inflammation, pain, and other diseases for preclinical studies. It would be extremely challenging for the mPGES-1-based drug development to follow traditional drug discovery and development route. In order to solve this problem, we developed and applied Drug Repurposing Effort Applying Integrated Modeling-in vitro/vivo-Clinical Data Mining (DREAM-in-CDM) strategy in this project. With molecular dynamics simulation, we observed the process of how mPGES-1 adopts an alternative conformation to control the access of co-factor GSH (glutathione) and its impact on the function of the protein. Based on the simulation results, we not only found an explanation for the difference between the X-ray and CryoEM (cryogenic electron microscopy) structure of mPGES-1 but also used molecular docking method to identify FDA approved drug, lapatinib, as an mPGES-1 inhibitor by virtual screening and the subsequent in vitro experiments. By mining the available clinical trial data, we found solid evidence that lapatinib can be used to relieve various types of pain in cancer patients. Since lapatinib is very well tolerated, we expect lapatinib to be repurposed as a new treatment for cancer-related pain.
BChE has been identified as an ideal drug target for the treatment of Alzheimer’s disease (AD) and heroin overdose. The selectivity of a therapeutically useful inhibitor for BChE over AChE is very important. Unfortunately, there is no good selective BChE inhibitor. With a robust and virtual screening strategy combining with in vitro experiments, we identified a series of compounds from the NCI compound depository as BChE inhibitors with novel scaffolds, high activity and selectivity at the same time. The most potent compound was re-synthesized and the enantiomers of the compound were separated for the first time. The binding mode of the most potent compound was also analyzed and the origin of its high activity and selectivity was revealed that will guide the development of BChE selective inhibitors in the future. In addition, a new tacrine-based BChE affinity chromatography resin was developed. The developed new resin has enabled us to more conveniently and efficiently purify the BChE proteins with improved high purity.
In general, we have successfully developed new drug discovery strategies to identify novel inhibitors of different enzymes. With these newly developed strategies, we expect additional drug discoveries to be made in the foreseeable future.
Digital Object Identifier (DOI)
https://doi.org/10.13023/etd.2020.016
Funding Information
NSF CHE-1111761 (09/01/2011-08/31/2016)
NIH R01 DA032910 (03/01/2014-02/28/2017)
NIH R01 DA035552 (03/01/2015-02/28/2018)
NIH UH2/UH3 DA041115 (09/15/2015-07/31/2016)
Recommended Citation
Zhou, Shuo, "COMPUTATIONAL MODELING GUIDED DISCOVERY OF NOVEL INHIBITORS OF MPGES-1 AND BUTYRYLCHOLINESTERASE AS DRUG CANDIDATES" (2019). Theses and Dissertations--Pharmacy. 109.
https://uknowledge.uky.edu/pharmacy_etds/109
