in silico discovery of metabotropic glutamate receptor-3 (mglur-3) inhibitors presentation
TRANSCRIPT
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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
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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.
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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:
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• 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:
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• 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
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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
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Results:
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LigandScout 2.0 Software
LY-341495
Pharmacophore features
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Pharmacophore model
generation
Results:
LY-341495
Benzene
Hybrid pharmacophore model
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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
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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:
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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.
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Acknowledgements
UPR-Cayey RISE Program
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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