The slides are from a presentation given by Professor Ravi Iyengar from Mount Sinai School of Medicine at the Drug Forum Meeting #9 that took place in Washington, DC on February 20-21, 2008. The slides describe two projects: one that was published last year, and the other unpublished. These projects used network analysis to explore the relationships between FDA approved drugs and a human protein-protein interaction network

Ravi Iyengar

English

Nature Precedings

Systems Pharmacology
Ravi Iyengar 
Department of Pharmacology and Systems Therapeutics
Mount Sinai School of Medicine
New York NY 
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Network Diagram of FDA Approved Drugs and their Targets 
(Human Gene Products)
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Systems Pharmacology: Global Relationship Between 
Therapeutic Drugs and the Human Genome
Drugs and their 
targets as a 
bipartite graph
Total # of islands
179
Many drug targets are part
of networks, large and small
fEach island is a network o  
interacting proteins 
Avi Ma’ayan and
Ma’ayan, Jenkins, Goldfarb, and Iyengar
Mount Sinai Journal of Medicine (2007) 74: 27-32Sherry Jenkins
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Functional annotation of human drug targets
GO  Gene Ontology
Validates our assumption that
current drug targets are signaling proteins 
Ma’ayan, Jenkins, Goldfarb, and Iyengar
Mount Sinai Journal of Medicine (2007) 74: 27-32
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Relationship between Adverse Effects and 
the Human Genome
There are likely to be multiple mechanisms
underlying adverse effects
Ma’ayan, Jenkins, Goldfarb, and Iyengar
Mount Sinai Journal of Medicine (2007) 74: 27-32
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Systems Pharmacology
Some questions Systems Pharmacology can tackle
H d f h l l iow o we go rom suc  genera  ana ys s to 
specific situations ?
After all  drugs are designed to treat specific pathophysiologies by interacting with specific 
targets !
Can network analysis be used to understand 
complex diseases and adverse events by drugs 
that are currently being used?
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Systems Pharmacology Approaches
Build cellular networks using disease gene 
products as starting points.
Identify boundaries of  functional neighborhoods 
(i e cellular interaction networks) of products of. .       
disease genes. 
Do targets of drugs that cause the same clinical 
phenotype fall within the neighborhood ?
Seth Berger 
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What can we find using Systems 
Pharmacology Approaches?
Predictions for new susceptibility genes 
(mutations and SNPs) for disease origins as well 
as adverse effects   
Ranked list of cellular components that upon        
interaction with drugs can cause clinical 
phenotype (generally adverse effects)   
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Systems Pharmacology of Arrhythmias 
Long QT Syndrome
Prolonged QT Interval on EKG
Delayed repolarization after depolarization of cardiac 
i lventr c es
Syncope,  Sudden death, Ventricular arrhythmias, 
Torsades de Pointes
Congenital
10+ genes with known causative mutations     
Cardiac ion channels and related proteins
Acquired
Metabolic disturbances
Drug-induced
Many thanks to
Dan Roden
Vanderbilt
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Drug Induced LQTS
More than 70 FDA approved drugs
S i l lpann ng severa  c asses
Direct HERG ion channel blockade
Most common 
Degree of blockade is not directly related to risk of arrhythmia
Channel trafficking
Signal crosstalk
Reduced repolarization reserve
S tibilit d i k difi f d i d d LQTSuscep y an  r s  mo er genes or rug n uce  
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Identifying the Disease Gene Neighborhood
Develop an Integrated Mammalian Protein-
Protein Interaction (PPI) Database
Consolidated 9 publicly available protein-protein interaction 
databases
Biogrid HPRD MINT PDZbase Reactome DIP Intact Mips, , , , , , , , 
PhosphoELM
Used gene orthology (Jackson Labs and NCBI homologene) to 
merge non human mammalian proteins and their human orthologs -       
Resulting Network contains:
10 351 nodes (gene products),   
64,411 edges (interactions)
19,810 referencesN
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Identifying the Disease Gene Neighborhood
Identify Functional Node Distance
Usually Shortest Path
Bias for hubs (nodes with many connections)
We have used mean first passage time (MFPT)
To measure functional distance from the disease gene
Distance in a PPI network has been used to:
Identify functional modules
Annotate proteins with Gene Ontology Terms
Identify disease gene candidates   
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Calculating “Functional Distance” 
Between Nodes Using Random Walks
A random walker can 
step from any node to     
any adjacent node with 
equal probability.
Mean First Passage Time 
describes number of 
steps on average it takes 
a random walker to walk 
from a specified node to 
another specified node
p=.5
p=.5 p=.33p=.33
  . p=.33
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Long QT Syndrome Disease Gene List
KCNQ1 potassium voltage-gated channel, 10 genes with known causative 
(3784) KQT-like subfamily, member 1
KCNH2 
(3757)
potassium voltage-gated channel, 
subfamily H (eag-related), member 2
mutations
Different mutations in some 
of same genes cause Short 
QT d SCN5A 
(6331)
sodium channel, voltage-gated, type 
V, alpha subunit
ANK2 
(287) ANK2 ankyrin 2, neuronal
 syn rome
1 gene (ALG10) annotated at as 
Reduced Susceptibility to LQTS
KCNE1 
(3753)
potassium voltage-gated channel, 
Isk-related family, member 1
KCNE2 potassium voltage-gated channel, 
Mostly Cardiac Ion Channels 
and related Proteins
(9992) Isk-related family, member 2
KCNJ2 
(3759)
potassium inwardly-rectifying 
channel, subfamily J, member 2
CACNA1
C (775)
calcium channel, voltage-dependent, 
L type, alpha 1C subunit
CAV3 
(859) caveolin 3
SCN4B 
(6330)
SCN4B sodium channel, voltage-
gated, type IV, betaKvLQT1=KCNQ1
+ minK=KCNE1
HERG= KCNH2
+ MIRP=KNCE2
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LQTS
Neighborhood
11 LQTS 
Seed Genes
Statistical  
cutoffs
ΔT>10,000 (15 nodes)
from MFPT 
analysis 
to define 
b d i
ΔT>1,000  (100 nodes)
   oun ar es 
of the LQTS  
neighborhood
LQTS neighborhood ΔT>0
ΔT>100  (520 nodes)
Integrated mammalian network 
(10351 nodes)
(1310 nodes)
Even the biggest 
neighborhood 
has only 12 5% of    .   
the total nodes
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Top 50 Ranked Nodes
in the neighborhood
41  Nodes are 1-3 hops from Disease Genes
These include PKA and PKC
(See  figure) And AKAP 9
Hoffman & Warner (2006) Pharm Tox 
Meth 53:87 
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Drug Targets
Do drugs which show qt prolongation as a side effect or 
LQTS and Torsades De Pointes as adverse events have 
targets that are within the LQTS-Neighborhood?
ArizonaCERT qtdrugs database DrugBank database
Wishart DS et al  Nucleic Acid Res 
(2006) 34: D668-D672Kilborn & Woolsey(2001) BMJ 322:672
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LQTS Disease Gene Products Targeted by Drugs
Rank Node Score Drug
1 KCNH2 50904 ibutilide_fumarate; propafenone
2 KCNQ1 34145.7 indapamide
5 KCNE1 23693.8 indapamide
10 KCNJ2 13781.3 levosimendan
11 CACNA1C 13560.9 magnesium_sulfate; ibutilide_fumarate;  nicardipine
quinidine; lamotrigine;  indecainide; cocaine; benzonatate;
14 SCN5A 12792.7
encainide;  tocainide; propafenone; disopyramide;  riluzole;
mephenytoin; mexiletine; procainamide; moricizine; bepridil;
gabapentin;  verapamil;  hexylcaine;  lidocaine;
carbamazepine; flecainide; dibucaine;  oxcarbazepine;
phenytoin; prilocaine; ethotoin  
qt drug from Arizona CERT;  qt prolonging or torsadogenic from pubmed search;  
t i A i CERT li t f d b b d h T t t f TdPno  n r zona  s  or oun  y pu me  searc . rea men  or 
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Top Network Analysis Identified Drug Targets
10 nodes in the networks with ranks from 43-84          
show relevance to  QT  effect
Some nodes are disease genes for other diseases         
that also show QT prolongation as a associated 
pathophysiology
Some nodes are targets for drugs on ArizonaCERT        
QT  drug base
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Initial Findings
MFPT Ranking provides a continuum of scores, 
emphasizing “more specific interactors” as compared 
to highly connected hubs.  
Known modulators of Torsadagenesis ranked highly
Protein kinase C ranks 15
Protein kinase A ranks 20. 
AKAP9 ranks 19 **  Recently found mutation linked to 
LQTS! **
ADRA1D and ADRB1 and ADRB2 rank within top half 
of the neighborhood
NOS1AP (nitric oxide synthase 1 [neuronal] adaptor      
protein ) , a known modulator of QT interval, is ranked 
367.Na
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Initial Findings → Conclusions
Targets of drugs that are associated with acquired 
LQTS are closer to the LQTS disease genes by 2:1 as 
compared to targets of drugs in general      
This suggests a region of protein-protein interaction 
space which when targeted pharmaceutically is ,   ,  
more likely to cause QT interval prolongation or 
Torsade de Points. 
Such a ranked list of genes can be used to screen for LQTS  
susceptibility polymorphisms in further studies.
New drug candidates can be screened for interactions with 
proteins in the list. Interactions may suggest  that drug could 
have LQTS as an adverse effect
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Initial Findings → Conclusions
Such a ranked list of genes can be used to 
screen for LQTS  susceptibility 
polymorphisms in further studies.
Are the gene products found in myocytes ?
New drug candidates can be screened for 
interactions with proteins in the list     . 
Interactions may suggest  that drug could 
have LQTS as an adverse effect
Cell type and tissue type specification of gene products
and cell type specific networksN
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Systems Pharmacology

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http://precedings.nature.com/documents/1749/version/1/files/npre20081749-1.pdf

Systems Pharmacology

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