In Silico discovery of Metabotropic

Glutamate Receptor-3 (mGluR-3)

inhibitors.

Juan E. Maldonado Weng1

Walter I. Silva, PhD.2

Héctor M. Maldonado, PhD.3

1University of Puerto Rico, Cayey2University of Puerto Rico, Medical Science Campus

3Universidad Central del Caribe, Medical School

Outline Presentation:

• Background, Significance and Hypothesis

• Objectives

• Methodology-Drug Discovery Strategy

• Results

• Conclusions

• Future Studies

• Acknowledgements

In Silico discovery of Metabotropic Glutamate Receptor-3 (mGluR-3) inhibitors.

Glutamate Receptors

Ionotropic

NMDA

NR1

NR2A-D

NR3A

AMPA

GluR1-4

Kainate

GluR5-7

KA1,2

Metabotropic

Group I

mGluR1

mGluR5

Group II

mGluR2

mGluR3

Group III

mGluR4

mGluR6

mGluR7

mGluR8

Maurizio Popoli, Zhen Yan, Bruce S. McEwen & Gerard Sanacora.

The stressed synapse: the impact of stress and glucocorticoids on

glutamate transmission. Nature Reviews Neuroscience 13, 22-37

(January 2012)

Background, Significance and Hypothesis:

• The metabotropic glutamate receptor 3 (mGluR3) has been found to be associated

to an increased risk of bipolar disorders, schizophrenia, alcoholism, anxiety

disorders, an a variety of other mental disorders.

• Chemical compounds with potential to exert pharmacological actions as agonists,

antagonists, or allosteric modulators of this receptor are currently been evaluated

for clinical applications.

• Examples include agonists like LY354740 with potential in the treatment of anxiety

and drug addiction (PMID 9046344), and LY-341495 an antagonist with

antidepressant properties (PMID 18164691).

• Clearly, the number and variety of chemical compounds with potential to interact

with this receptor suggested that this receptor belongs to the limited family of

highly “druggable” targets.

• With this in mind we decided to test the hypothesis that: Selective and high

affinity inhibitors of mGluR-3 can be found using our Drug Discovery

Strategy based on an In Silico approach.

Background, Significance and Hypothesis:

• Create a pharmacophore model that combine the chemical

features obtained from the analysis of currently known

inhibitor (LY341495) and the benzene mapping.

• Perform a virtual pre-screening (filtering) of ZINC Drug

Database (>20 million drug-like compounds) with our

pharmacophore model using the web based resource

ZincPharmer (http://zincpharmer.csb.pitt.edu/).

• Perform a secondary screening (virtual docking) to identify

“top-hits” or potential lead compounds (AutoDock Vina).

• Initiate validation of “top-hits” with bioassay, followed by drug

development phase with in silico modification/optimization

and re-testing of “top-hits”.

Objectives

3D Structurewww.pdb.org

PyMol

3SM9

BioAssay

Secondary Screening: (AutoDock)

Primary Screening: Pharmacophore

Model (ZincPharmer)

High AffinityLead

Compounds

Compounds selected

by the model

Identification of Lead Compounds.

(Ranking of binding energies)

Pharmacophore

identification and

Pharmacophore Model

Generation (LigandScout)

Therapeutically

relevant protein

Target:

mGluR3

Biological ProblemmGluR3 associated disorders

Drug-like

Databases

(17 million

drug-like

compounds)

Benzene

Mapping

Identification of

chemical features

from Inhibitor: LY341495

Results:

LigandScout 2.0 Software

LY-341495

Pharmacophore features

Pharmacophore model

generation

Results:

LY-341495

Benzene

Hybrid pharmacophore model

ZincPharmerhttp://zincpharmer.csb.pitt.edu/pharmer.html

Results:

Model

Number of

Compounds fulfilling

pharmacophore models

conditions

A 2,989,147

B 197,655

C 988,798

0

5

10

15

20

25

30

35

40

45

-10.4 -10.3 -10.2 -10.1 -10 -9.9 -9.8 -9.7 -9.6 -9.5

Compounds with Leading BE per Model

Model A Model b Model C

Model

Compounds

with Leading

BE

A B C

-10.4 3 0 0

-10.3 0 0 0

-10.2 2 0 0

-10.1 1 1 1

-10 8 0 0

-9.9 11 3 4

-9.8 18 2 1

-9.7 17 4 9

-9.6 40 1 7

-9.5 42 1 18

Total number of

compounds142 12 40

Results:

Conclusions• Hot-Spots were identified using benzene mapping and

combined with additional chemical features found in previous

reported inhibitors in a new hybrid pharmacophore model.

• A large group of compounds (194) with predicted high binding

energy (≤ -9.5 kcal/mol) were identified in our first In Silico

campaign.

• Use of Pharmacophore model A resulted in a larger number of

compounds with predicted Binding Energy below -9.5 (142

compounds)

Future Studies:

Establish in our laboratory a bioassay for mGluR3 activity in

order to test some of the small chemical compounds identified in

our in silico study.

Acknowledgements

UPR-Cayey RISE Program

In Silico discovery of Metabotropic

Glutamate Receptor-3 (mGluR-3)

inhibitors.

Juan E. Maldonado Weng1

Walter I. Silva, PhD.2

Héctor M. Maldonado, PhD.3

1University of Puerto Rico, Cayey2University of Puerto Rico, Medical Science Campus

3Universidad Central del Caribe, Medical School