Transcript Young Innovators 2009

YOUNG INNOVATORS 2009
A novel chemogenomic approach to predict
receptor-mediated clinical effects of
chemicals: Applications to anti-Alzheimer’s
agents
Rima Hajjo╫, Bryan L. Roth† and Alexander Tropsha╫
╫Laboratory for Molecular Modeling, Division of Medicinal Chemistry and Natural Products, Eshelman
School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599-7360
†National Institute of Mental Health Psychoactive Drug Screening Program and Departments of Pharmacology, Medicinal
Chemistry, and Psychiatry, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599
ABSTRACT
Modern paradigms in drug discovery are shifting from traditional
receptor-specific studies to multi-receptor profiling to identify
complex mechanisms underlying clinical effects of drugs. These
approaches relate to
chemogenomics,
an emerging
interdisciplinary field that exploits chemical profiling of receptor
panels to identify specific ligands for all targets.
We have developed a novel chemogenomic approach combining
QSAR modeling, model-based virtual screening (VS), and
mining of both biological literature and online databases of gene
signatures and biological profiles.
Young Innovators 2009
ABSTRACT
This approach was applied for the analysis of data pertinent to the
Alzheimer’s disease.
Gene signatures for Alzheimer’s disease were used to query the
Connectivity Map (cmap: http://www.broad.mit.edu/cmap/) database to
formulate testable hypotheses about potential treatments.
Concurrently, QSAR models have been developed for the ligands of 5HT receptors (i.e. 5-HT6 receptors) known to be involved in cognition
enhancement. Externally validated models were used for VS of the
World Drug Index (WDI) database to identify putative ligands. Common
chemical hits from QSAR/VS studies and the cmap were subjected to
radioligand binding assays against 5-HT receptors.
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INTRODUCTION: ALZHEIMER’S DISEASE
Alzheimer's Disease (AD), is the most
common form of dementia. It is incurable
and degenerative.
As of September 2009, there are reported
35 million-plus cases worldwide. This
number is expected to reach 107 million
by 2050.
AD is characterized by the accumulation
of amyloid beta peptide (Abeta), hyperphosphorylation of tau protein, and
increased inflammatory activity in the
hippocampus and cerebral cortex.
The pharmacotherapy of AD consists of
symptomatic
and
disease-modifying
therapies. No Treatments to Prevent,
Delay or Halt Disease Progression are
available today
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Artwork by William Utermohlen (died in 2007
of Alzheimer's Disease): Represents the most
complete and coherent view of a patient's
experience with dementia.
ALZHEIMER’S DISEASE AND THE NEED FOR A NOVEL
APPROACH TO INNOVATE DRUG DISCOVERY
AD has a complex multi-factorial nature and conventional drug discovery
approaches may not be suitable for finding novel therapeutics.
As a computational scientist, I am interested in developing and exploiting, by
the means of computational data analysis, chemogenomic approaches to drug
discovery.
In conventional QSAR studies we typically explore data related to one receptor;
this may be insufficient to understand complex diseases and suggest new
therapeutic options.
OUR HYPOTHESIS : A Novel Integrated Chemogenomic
Approach Could Provide More Comprehensive Answers About
Alzheimer’s Disease Etiology and Innovate Drug Discovery
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INTRODUCTION: CHEMOGENOMICS
Chemogenomics is a new interdisciplinary field, which exploits
multi-receptor chemical profiling to identify specific ligands for all
targets. The term chemogenomics is applied to a multiplicity of
approaches that use chemical compound libraries to probe
biological systems.
Chemogenomic Space:
A complex multidimensional space, where
diseases, molecules, genes, proteins, and
pathways constitute some of its dimensions.
However, few studies attempted to tie together the
data on ligand-protein-disease associations in a
format allowing new discoveries of both drugs
and their targets implicated in the underlying
diseases. Most of the available studies, including
QSAR studies are limited to pharmacological
data (i.e., proteins-molecules subspace)!!
Chemogenomic space
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Functional
data
Disease
related
genes or
proteins
Disease-Target
Association
Target
related
ligands
Binding
data
Text/database
mining
QSAR
Network mining
Disease
related
proteins
PubMed
Predictive models
STUDY DESIGN
Disease
gene
signatures
CTD
cmap
Database mining
Accept common
hits only
Structural hypothesis
“putative drug candidates”
HMDB
ChemoText
New hypothesis about connectivity between
chemicals and diseases
New testable hypothesis
with higher confidence
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INTRODUCTION: STUDY DESIGN
We have developed some elements of the integrated workflow focused
on the discovery of new drug candidates by merging predictions from
independent lines of evidence. Currently, The overall workflow
combines QSAR modeling and model-based virtual screening (VS),
gene expression profiling, and text mining of biomedical literature for
drug discovery.
Predictive QSAR/VS workflow: Rigorous models correlating chemical
features of compounds and their biological effects are developed and
employed for virtual screening of commercially available chemical
compounds to identify hits; CHEMOTEXT, an in-house repository of
chemical entities and terms that annotate compounds in terms of their
biological activities and, if available, diseases they could treat; cmap
(www.broadinstitute.org/cmap/) developed at the Broad Institute,
Cambridge, MA, links the effects of different drugs and diseases using
gene expression signatures. Only common hits from QSAR/VS and
cmap/ChemoText/etc, are considered as high confidence hits to be
confirmed in further experimental studies.
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INTRODUCTION: QSAR MODELING
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Structure
Property
Relationships
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Slide: Courtesy of Dr. Denis Fourches
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INTRODUCTION: QSAR MODELING
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Relationships
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Slide: Courtesy of Dr. Denis Fourches
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INTRODUCTION: QSAR MODELING
CHEMICAL
STRUCTURES
CHEMICAL
DESCRIPTORS
CHEMICAL DATABASE
PREDICTIVE
QSAR MODELS
PRORETY/
ACTIVITY
QSAR
MAGIC
VIRTUAL
SCREENING
~106
HITS
109
–
molecules
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NON HITS
INTRODUCTION: CONNECTIVITY MAP
Genome-wide mRNA
Expression
data for 1,309 bioactive small
molecules in different cell lines
(MCF7, PC3, HL60, SKMEL5)
7,056 genome-wide
expression profiles
representing
6,100 individual treatment
instances were produced
The Connectivity Map is the most comprehensive effort yet
for using genomics in a drug-discovery framework.
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INTRODUCTION: QUERYING THE CONNECTIVITY
MAP
Input
Database
Output
High positive score
Biological state 1
Null
Signature
Control
Step1: upload signature
High negative score
Step2: query the cmap
Step3 : list of
correlated compounds
Lamb, J. et al. Science, 313, 1929-1935 (2006)
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INTRODUCTION: 5-HT6 RECEPTOR AS A NOVEL
TARGET FOR ALZHEIMER’S
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METHODS: 5-HT6 DATASET
Modeling set: 79 B (Ki < 10,000.00 nM), 99 NB (Ki ≥ 10000.00 nM)
Source: PDSP Ki Database
B: Binder
NB: Non-binder
5-HT6 Dataset
Structure
Activity Data
99 NB
79 B
External validation sets:
24 compounds, all are binders to 5-HT6 (Ki < 10,000.00 nM)
Source: Roth Lab, Department of Pharmacology, UNC-Chapel Hill
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METHODS: COMBI QSAR STUDY DESIGN
Dragon
5-HT6
Dataset: 176 cps
79B & 97 NB
B: Binder
NB: Non-binder
SG
Random splitting
of the dataset using 5-fold
external validation
Compound
representation
kNN
Combi
QSAR
CBA
Accept models
with CCRtr & CCRts
≥ 0.70
Split into
multiple
tr & ts sets
Modeling set
External
set
(24 B)
Validation set
Validate by
Predicting
Validate by
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Y-Randomization
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METHODS: QUERYING THE CMAP WITH
ALZHEIMER’S DISEASE GENE SIGNATURES
Step1: upload signature
Step2: query the cmap
Step3 : list of correlated compounds
cmap SCORE
Alzheimer’s disease gene signatures
“Two different signatures” from
hippocampus (S1) and cerebral cortex
(S2) from two independent reports
Positive
Connectivity
“possible causes
for disease
state”
1.00
F
0.00
E
Negative
Connectivity
“possible
treatments for
disease state”
(S1)
(S2)
cmap
Signature database
“Pattern Matching”
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0.00
C
B
A
-1.00
Identification
of possible treatments
(A,B,C) and causes (F)
RESULTS: QSAR MODELING
The performance of different binary QSAR approaches employed as part
of the combinatorial QSAR strategy for 5-HT6 receptor ligands and
based on external set statistics, is summarized in the figure below.
Both QSAR methods: kNNDragon
and
CBA-SG
performed very well with the
external prediction accuracy
characterized by CCRex of
92% and 78%, respectively.
Models built with randomized
activities for the training set
had average CCRex close to
0.50, indicating that the
models built with actual
activities were robust .
Fig. Comparison of the QSAR approaches to classify 5-HT6 receptor
binders vs. non-binders based on CCRex.
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RESULTS: TOP CMAP NEGATIVE CONNECTIONS
WITH S1 FOR AD
Web browser screen shot showing top negative connections from the cmap with S1
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RESULTS: TOP CMAP NEGATIVE CONNECTIONS
WITH S2 FOR AD
Web browser screen shot from showing top negative connections from the cmap with S2
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RESULTS: VIRTUAL SCREENING
Number of
compounds
Removing duplicates
& modeling set
Generate Dragon descr.
59000
WDI Database
46754
37127
Similarity filter
Use acceptable kNN-Dragon
models to classify binders vs.
non-binders
Use CBA-SG classifier
Select compounds that have -ve
connectivity with AD in cmap
1500
600
Binders
300
Binders
34
Potential anti-AD agents
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RESULTS: SELECTED COMMON VS HITS FROM
QSAR AND CMAP
WDI Name
CLOZAPINE
TAMOXIFEN
FLUSPIRILENE
ZUCLOPENTHIXOL
BI-2
CIDOXEPIN
NORTRIPTYLINE
BI-3
ENCLOMIFENE
DO-897
LY-294002
ACEFYLLINE-PRENYLAMINE
NISOXETINE
IFENPRODIL
FENDILINE
NAFTIFINE
RALOXIFENE
MEBEVERINE
LOBELANIDINE
LOBELINE
AZACYCLONOL
cmap Name
clozapine
tamoxifen
fluspirilene
zuclopenthixol
imipramine
doxepin
nortriptyline
clomipramine
clomifene
Prestwick-559
LY-294002
prenylamine
nisoxetine
ifenprodil
fendiline
naftifine
raloxifene
mebeverine
lobelanidine
lobeline
azacyclonol
No. of
Models
Av. Pred.
Value
cmap
Score S1
900
1.00
-0.398
910
0.99
0.358
854
0.99
-0.493
883
0.98
-0.609
898
0.98
-0.503
908
0.97
-0.463
883
0.96
-0.555
893
Selective0.95
Estrogen-0.768
899
0.91
-0.414
858
0.79
-0.741
Receptor
866
0.70
-0.351
Modulators
679
0.69
-0.589
899
0.68
-0.491
900
0.66
-0.541
765
0.66
-0.388
724
0.66
-0.790
809
0.64
-0.378
820
0.57
-0.543
826
0.56
-0.508
882
0.55
-0.514
840
0.54
-0.448
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cmap
Score S2
-0.366
-0.507
-0.551
-0.746
-0.415
-0.777
-0.410
-0.425
-0.611
-0.619
-0.303
-0.457
-0.408
-0.489
-0.683
-0.591
-0.482
-0.798
-0.488
-0.750
-0.556
RESULTS: RADIOLIGAND BINDING ASSAYS
PDSP ID
cmap
score S1
cmap
score S2
%
Inhibition
Ki (nM)
Clomipramie
13494
-0.768
-0.425
98.6
112.00
Doxepin
13495
-0.463
-0.777
98.1
105.00
Fluspirilene
13512
-0.493
-0.551
97.7
2,230.00
Lobeline
13501
-0.514
-0.750
16.6
NB
LY-294002
13502
-0.351
-0.303
-2.3
NB
Nortriptyline
13503
-0.555
-0.410
99.1
214.00
Prestwick-559
13498
-0.741
-0.619
27.9
NB
Raloxifene
13505
-0.378
-0.482
88.2
750.00
Tamoxifen
13506
0.358
-0.507
91.1
1,041.00
Zuclopenthixol
13510
-0.609
-0.746
98.4
169
Compound
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DISCUSSION: COMMON HITS FROM QSAR AND
THE CMAP
We have queried the cmap to identify drugs that might be connected
to AD. Hits with statistically significant negative connectivity scores
can be considered as potential treatments. Although the two gene
signatures used to query the cmap shared no common genes, both
queries resulted in common list of negative connections.
Hits from the cmap are considered potential therapeutics for treating
the AD; they could have different mechanisms of action including
binding to 5-HT6 receptors.
Taking common hits from the QSAR study and the cmap increases
the chances of identifying novel 5-HT6 ligands as effective anti-AD
treatments. This approach also affords the increase in the true hit
rates in the QSAR study.
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DISCUSSION: SERMS IDENTIFIED TO BE 5-HT6
RECEPTOR BINDERS
Binding assays confirmed that 7 out of a total of 10 predicted binders were
true binders for the 5-HT6 receptors. Achieving a success rate of 70% ,
indicates that our QSAR/VS approach was successful in prioritizing
computational hits for biological screening.
Surprisingly, four of the computational hits predicted to bind to the 5-HT6
receptor were found to be known selective estrogen receptor modulators
(SERMs). These hits included tamoxifen, toremifene, clomiphene and
raloxifene. However, further analysis of chemical-protein interaction
networks indicated that raloxifene has distinctive protein partners than the
rest of the SERMs.
Both tamoxifen and raloxifene were proved to be true binders in respective
binding assays suggesting that these SERMs may be potentially re-profiled
as potential anti-AD drugs. Tests will be done soon for toremifene and
clomiphene.
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DISCUSSION: RALOXIFENE IS A 5-HT6 BINDER AND
POTENTIAL ANTI-ALZHEIMER’S
A power of the integrated informatics approach to drug discovery
Raloxifene was one of the common hits
of QSAR and the cmap. Experimental
testing confirmed that raloxifene binds
to 5-HT6 receptor with a Ki= 750 nM.
A recent study indicated that raloxifene
given at a dose of 120 mg/day led to
reduced risk of cognitive impairment in
postmenopausal women. A newlyfunded clinical trial is ongoing to test
whether raloxifene can slow the rate of
AD progression.
This example can be considered as a
proof of concept for our novel
chemogenomic approach. Therefore, the
rest of the 34 hits should be studied
comprehensively in relation to AD.
Fig. Raloxifene (blue triangle) and Chlorpromazine
(square) versus [3H] LSD competition binding at 5-HT6
receptors.
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DISCUSSION: ADDITIONAL STUDIES FOR
DEVELOPING FAMILY-BASED RECEPTOR MODELS
In our effort to understand the mechanisms of actions of drugs predicted
from the cmap as treatments for AD, a new study was conducted where
gene signatures for the Alzheimer’s disease (AD) were used to query the
cmap to identify potential anti-AD agents.
Concurrently, QSAR models were developed for the serotonin,
dopamine, muscarinic and sigma receptor families implicated in the AD.
The models were used for VS of the World Drug Index database to
identify putative ligands.
12 common hits from QSAR/VS and cmap studies were subjected to
parallel binding assays against a panel of GPCRs. All compounds were
found to bind to at least one receptor with binding affinities between 1.7 9000 nM.
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CONCLUSIONS
We have developed a novel integrative chemogenomic
workflow focused on the discovery of new drug candidates by
merging predictions from independent lines of evidence.
The current workflow combines QSAR modeling and modelbased virtual screening (VS), gene expression profiling, and
text mining of biomedical literature for drug discovery.
This approach was shown to be useful in predicting new
experimentally confirmed ligands for 5-HT6 receptors.
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CONCLUSIONS
Our approach led to the identification of 34 computational hits
as potential treatments for AD. 10 hits with higher confidence
level were tested in binding assays and 7 compounds were
confirmed as 5-HT6 receptor ligands achieving a success rate
of 70%. Mining of the biological literature indicated possible
anti-AD effects (for raloxifene, fluspirilene, nortriptyline and
doxepin) or neuroprotective effects (for clomipramine) for
most of the predicted hits.
This approach could be extended to many similar receptor
systems serving as a cost-effective in silico tool for the
discovery of novel biologically active compounds acting via
clinically relevant targets.
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ACKNOWLEDGMENTS
ADVISOR
MML MEMBERS
Prof. Alexander TROPSHA
Research Professors
Graduate Students
Alexander GOLBRAIKH
Clark JEFFRIES
M. KARTHIKEYAN
Simon WANG
Hao ZHU
System Administrator
Chris GRULKE
Nancy BAKER
Kun WANG
Tanarat KIETSAKORN
Tong-Ying WU
Jui-Hua HSIEH
Hao TANG
Liying ZHANG
Stephen BUSH
Man LUO
Guiyu Zhao
Andrew FANT
Dongqiuye PU
Tiago MODA
Joyce CHANDARAJOT
Mihir SHAH
Administrative Assistant
Adjunct Members:
Paula PRESS
Postdoctoral Fellows
Prof. Bryan ROTH
Denis FOURCHES
Denise TEOTIKO
Eugene MURATOV
Georgiy Abramochkin
CONSULTATION
Visiting Research Scientist
Dr. Justin LAMB
BROAD INSTITUTE
Alex SEDYKH
COLLABORATORS
FUNDING
NIH
EPA
University of Jordan
Research Programmer
Theo WALKER
Weifan ZHENG, Shubin LIU
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REFERENCES
Kubinyi, H. Chemogenomics in drug discovery. Ernst. Schering. Res. Found.
Workshop 2006, 1-19.
Roth, B. L.; Sheffler, D. J.; Kroeze, W. K. Magic shotguns versus magic bullets:
selectively non-selective drugs for mood disorders and schizophrenia. Nat. Rev. Drug
Discov. 2004, 3, 353-359.
Keiser, M. J.; Roth, B. L.; Armbruster, B. N.; Ernsberger, P.; Irwin, J. J.; Shoichet, B. K.
Relating protein pharmacology by ligand chemistry. Nature Biotechnology 2007, 25,
197-206.
Hood, L.; Perlmutter, R. M. The impact of systems approaches on biological
problems in drug discovery. Nature Biotechnology 2004, 22, 1215-1217.
Hopkins, A. L. Network pharmacology. Nature Biotechnology 2007, 25, 1110-1111.
Cavalli, A.; Bolognesi, M. L.; Minarini, A.; Rosini, M.; Tumiatti, V.; Recanatini,
M.; Melchiorre, C. Multi-target-directed ligands to combat neurodegenerative
diseases. J. Med. Chem. 2008, 51, 347-372.
Rosenbaum, D. M.; Rasmussen, S. G. F.; Kobilka, B. K. The structure and function
of G-protein-coupled receptors. Nature 2009, 459, 356-363.
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REFERENCES
Tropsha, A. Predictive QSAR (Quantitative Structure Activity Relationships)
Modeling. In Comprehensive Medicinal Chemistry II; Martin, Y. C. Ed.; Elsevier:
2006; pp 113-126.
Tropsha, A.; Golbraikh, A. Predictive QSAR Modeling workflow, model
applicability domains, and virtual screening. Current Pharmaceutical Design 2007,
13, 3494-3504.
Olsson, T.; Oprea, T. I. Cheminformatics: a tool for decision-makers in drug
discovery. Curr. Opin. Drug Discov. Devel. 2001, 4, 308-313.
Baker, N. Extracting Drug Activity Terms from Medline Annotations. Proceedings:
Summit on Translational Bioinformatics; March, 2008; : American Medical
Informatics Association; 2008. Summit on Translational Bioinformatics. 2008.
American Medical Informatics Association. Ref Type: Conference Proceeding.
Jacoby, E. A novel chemogenomics knowledge-based ligand design strategy Application to G protein-coupled receptors. Quantitative Structure-Activity
Relationships 2001, 20, 115-123.
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RIMA HAJJO: BIOS/CONTACT INFO
CONTACT
E-mail: [email protected]
http://www.unc.edu/~hajjo/
http://www.hajjo.info/
SCHOOL ADDRESS
Laboratory for Molecular Modeling, 2069 Genetic Medicine,
UNC Eshelman School of Pharmacy ,University of North
Carolina at Chapel Hill ,Chapel Hill, NC 27599, USA
RESEARCH INTERESTS
Rima
is
interested
in
cheminformatics,
chemogenomics, datamining, polypharmacology,
GPCRs and CNS-Diseases. She is working on
developing and exploiting, by the means of
computational data analysis, chemogenomics
approaches to drug discovery. Her PhD thesis project
is focusing on polypharmacology, network
pharmacology and computational screening of the
GPCR receptorome.
Young Innovators 2009
Rima Hajjo
RIMA HAJJO/BIOS
EDUCATION
 2005-present, Graduate Program in Pharmaceutical Sciences, University of North
Carolina at Chapel Hill, Chapel Hill, NC
 1999-2003 M.Sc., Pharmaceutical Sciences, University of Jordan, Amman, Jordan
 1994-1999 B.Sc., PharmD, University of Jordan, Amman, Jordan
HONORS AND AWARDS
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2009 Awarded AAPS Young Innovator Award.
2008 Awarded ADDF Young Investigator Scholarship.
2005 Nominated for king Abdullah’s award for best thesis writing.
2004 Awarded a scholarship from the University of Jordan to proceed with PhD studies.
1994 Awarded a scholarship from the Jordanian Ministry of Higher Education to get a PharmD.
1993 Awarded an academic prize for the scientific creativity of school students, from the
Ministry of Education in Jordan.
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RIMA HAJJO/PUBLICATIONS
RECENT PUBLICATIONS
 Hajjo, Rima; Grulke, Christopher; Golbraikh, Alexander; Huang, Xi-Ping; Roth, Bryan L.; and Alexander Tropsha.
Predictive QSAR models of 5-HT2B receptor ligands identify potentially valvulopathic compounds.
(Submitted to J. Med. Chem.)
 Hajjo, Rima; Roth, Bryan L.; and Tropsha Alexander. In silico strategies to identify 5-HT6 receptor ligands as
potential anti-Alzheimer’s treatments. (In preparation)
 Hajjo, Rima; Setola, Vincent; Roth, Bryan L.; and Tropsha Alexander. A novel chemogenomics approach to
identify receptor mediated clinical effects of chemicals. (In preparation)
 Fourches, Denis; Hajjo, Rima; Wang, Xiang; and Tropsha, Alexander. Multi-task versus single task modeling
studies on dopamine receptor ligands. (In preparation)
 Hajjo, Rima; Roth, Bryan L.; and Tropsha, Alexander. Modeling promiscuity of GPCR ligands. (In preparation)
ABSTRACTS/POSTERS
 Hajjo, Rima; Roth, Bryan L.; and Tropsha Alexander. A novel chemogenomics approach to identify receptor
mediated clinical effects of chemicals. Accepted for the 2009 AAPS Annual Meeting and Exposition, November,
2009.
 Hajjo, Rima; Fourches, Denis; Roth, Bryan L.; Tropsha, Alexander. In silico strategies to identify novel 5-HT6
receptor ligands as potential anti-Alzheimer's and anti-obesity treatments. Abstracts of Papers, 238th ACS
National Meeting, Washington, DC, United States, August 16-20, 2009.
 Wang, Xiang S.; Hajjo, Rima; Tropsha, Alexander. A computational workflow to identify and validate the
druggable allosteric binding sites. Abstracts of Papers, 238th ACS National Meeting, Washington, DC, United
States, August 16-20, 2009.
 Hajjo, Rima; Grulke, Christopher; Golbraikh, Alexander; Roth, Bryan L.; Tropsha, Alexander. QSAR models of 5HT2B receptor ligands and their application to predicting compounds that could cause valvulopathy.
Abstracts of Papers, 237th ACS National Meeting, Salt Lake City, UT, United States, March 22-26, 2009
Young Innovators 2009