Kin Sing Stephen Lee, Ph.D. Seminar
Drug Discovery Seminar
Date: Friday, February 21
Time: 9:00am
Location: B448 Life Science Building
Design of improved sEH inhibitors through understanding the structure-kinetic-relationship
Kin Sing Stephen Lee, Ph.D. • Assistant Professor • Michigan State University
Soluble epoxide hydrolase (sEH) is a cytosolic enzyme that degrades epoxy-polyunsaturated
fatty acids (PUFAs). Epoxy-PUFAs are key lipid signaling molecules in mammals. Stabilization
of epoxy-PUFAs by inhibiting sEH is anti-inflammatory, anti-hypertensive, analgesic
and antifibrotic. Recent studies also demonstrate that sEH inhibitors are neuroprotective
against neurodegenerative diseases. Thus, sEH inhibitor is an important therapeutic
target. In fact, two sEH inhibitors are currently in clinical trials. Therefore, designing
sEH inhibitors with improved in vivo activity is highly desired for treating other
diseases such as neurodegenerative diseases. Research suggests that drugs with long
drug-target residence time have better in vivo efficacy and are desired properties
for CNS drugs. However, the design principle for improving drugtarget residence time
of drug candidates remains elusive and Structure-Kinetics-Relationship (SKR) of sEH
inhibitors are not known. To better understand the SKR of sEH inhibitors and the effect
of drug-target residence time on in vivo activity of the sEH inhibitors, we will take
a multidisciplinary approach by combining organic chemistry, high-throughput screening
assay, new computational model, machine learning, PK modeling and novel in vivo assay.
We will 1) screen a library of sEH inhibitors for their inhibition constant and drug-
target residence time; 2) use computational model named WExplore to identify key inhibitor-sEH
dissociation pathway; 3) implement a machine learning model based on the screening
dataset to better predict drug-target residence time of new sEH inhibitor and 4) novel
in vivo displacement assay which measures the target occupancy of testing sEH inhibitor
at different post-dosing time by using a highly potent sEH inhibitor to displace sEH-bound
testing inhibitor at specific time point. In this presentation, we will present our
screening data of a sEH library and the SKR of sEH inhibitor obtained from this screening
dataset. We will also report newly designed sEH inhibitors based on the WExplore modeling
results. We will discuss our preliminary machine learning model in predicting sEH
inhibitor’s drug-target residence time. Lastly, we will demonstrate how drugtarget
residence time affects in vivo activity of sEH inhibitors
Join Zoom Meeting: https://msu.zoom.us/j/834293898
