Systems Drug Design
Hiroaki Kitano
The Systems Biology Institute
Sony Computer Science Laboratories, Inc.
Okinawa Institute of Science and Technology
Department of Cancer Systems Biology, The Cancer Institute
We cure &
We care
Systems Drug Design
Coral Reef Systems Biology
SBI Strategy
• Innovation in drug design and systems
medicine
• Faster social and business impacts
• Global strategy (Singapore, India, Shanghai)
• Rolling out business operations
SBI Collaborative Drug Pipelines
An early stage list
Discovery
Preclinical
Phase-I
Phase-II
Phase-III
X-7CD
TB with
CSIR India
Breast
cancer
Influenza
CNS
(SZ, PD, AD)
JSPS & OIST-SBI
Project
With ERATO Kawaoka Project
PD-I program is with Univ. Luxembourg
Cardiovascular system related
Discovery Phase:
Identification of possible molecular targets for a given disease
Translational Phase:
(1) Given a candidate compound, identify what is the best disease
subtype
(2) Given a candidate compound and target disease,
find what other drugs to be used in combination
Software Platform
Computational platform for systems drug discovery
Target Market Segments
Personalize
 Premiere Medical and Wellness Services
 High income bracket
 Comprehensive medical and wellness service
 Healthcare version of Private Bank
 Affordable medical services
 Mass market
 Quality service at low cost
 Treatments for each patient cluster
 Humanitarian Medical Support
 Medicare for Bottom Billions
 Cost and Access
Cost
Gefitinib (Iressa: AZ)
Indication: Non-small cell lung cancer
Efficacy:
For patients with EGFR mutation, overall response rate was 75%
EGFR mutation in 25% of Japanese patients
2% in U.S.A.
Side effects:
Interstitial pneumonia (IP) 5.8% of Japanese patients
with 50% mortality rate
Distribution of mutations in
NSCLC
Sharma, et al., Nature Reviews Drug Discovery, April, 2010
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10
11
12
11
Target Market Segments
Personalize
 Premiere Medical and Wellness Services
 High income bracket
 Comprehensive medical and wellness service
 Healthcare version of Private Bank
 Affordable medical services
 Mass market
 Quality service at low cost
 Treatments for each patient cluster
 Humanitarian Medical Support
 Medicare for Bottom Billions
 Cost and Access
Cost
TB: A Disease Neglected
Robustness
An ability of the system to maintain
its functions even under external and
internal perturbations
Cancer Robustness
• Major sources of robustness
– Feedback loops and crosstalks within cell
– Heterogeneity of mutations
• Due to point mutations, mitotic recombination,
anueploidy
•
– Host-Tumor Entrainment
• Hypoxia Inducible Factors, microenvironment
remodeling
• Self-extending symbiosis: Cell fusion, chromosome
intake, macrophage, etc.
Kitano, Nature Rev. Cancer, 4, 227-35 2004
Kitano, Nature, 426, 125 2003
Kitano and Oda, Biological Theory, 2006
Intra-tumour heterogeneity
(Colorectal Cancer)
Baisse, et al., Int. J. Cancer, 93, 346-352, 2001
Robustness Trade-offs
Systems that are optimized for certain perturbations
inevitably entail extreme fragility elsewhere.
Kitano, Nature Reviews Genetics, 2004, Kitano, Molecular Systems Biology, 2008
Cset and Doyle, Science, 2002
Robustness-Fragility trade-offs in control theory
(negative feedback)
Bode Theorem (Bode 1945)
Cset & Doyle, Science, 2002
Yi, et al., Basic control theory for biologists, 2002
Kitano, Mol. Syst. Biol., 2007
Collateral Sensitivity
Resistance
Fragility
Multiple genes are involved in
many diseases
Goh, et al., PNAS 2007
２５％～３０％ of
Hubs are
Involved in cancer
Inhibiting HUBs
may cause serious
side-effects
Goh, et al., PNAS 2007
N > 36
35 > N > 6
5>N
Budding Yeast PIN
Human PIN
Hase et al., PLoS Computational Biology, Oct. 30. 2009
Hase et al., PLoS Computational Biology, 30 Oct 2009
Internet Router Topology
Human PPI
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Targets of FDA Approved Drugs
Hase et al., PLoS Computational Biology, 30 Oct 2009
Significance (connection, frequency, etc)
Long Tail Distribution
(log-linear graph)
Head
Tail
Rank
EMBO Symposium
Combinatorial High Throughput Screening
Multicomponent therapeutics that prevent proliferation of fluconazole-resistant C. albicans
Borisy, Alexis A. et al. (2003) Proc. Natl. Acad. Sci. USA 100, 7977-7982
Copyright ©2003 by the National Academy of Sciences
Chlorpromazine, an antipsychotic agent, and pentamidine, an antiprotozoal agent, together
selectively prevent tumor cell growth in vitro and in vivo
Efficacy
Phase 1/2A Stage
Borisy, Alexis A. et al. (2003) Proc. Natl. Acad. Sci. USA 100, 7977-7982
Copyright ©2003 by the National Academy of Sciences
Drug Price
Possible reduction of drug price
– Taxol
：
１００mg 43768
• Bristol-Myers Squibb, Paclitaxel
– Contomin
：
１００mg
9.2
• Tanabe-Mitsubishi, Chlorpromazine
– Benanbax
：
１００mg
• Sanofi-Aventis, Pentamizine
2824
Rhabdoid tumour xenograft
Rhabdomyosarcoma xenograft
Rapamycin: 5mg/kg daily for 5 consecutive days / week = MTD
Cyclophosphamide: 150mg/kg daily = MTD
Combination = MTD for both
Kummar, et al., Nature Reviews Drug Discovery, Nov. 2010
Originally from Houghton, et al., Mol. Cancer Ther. 9, 101-112 (2010)
Differential Robustness
Screening
Robustness-based target
candidate selection
Differential Robustness
Model A （ｗｔ）
Model B (mutant)
k4
cyclin
degradation
k6
k1
cyclin
synthesis
cyclin
degradation
k1
cyclin
synthesis
Morohashi, et al., J. Theor. Biol., 216, 19-30 2002
Moriya, Shimizu-Yoshida, Kitano, PLoS Genetics, 14 July 2006
Upper-bound dosage of cell cycle related genes
-leucine
-uracil
Moriya, Shimizu-Yoshida, Kitano, PLoS Genetics, 14 July 2006
gTOW-6000
Genome-wide
gTOW Collection
Computational approach for combinatorial problems
An example workflow of model-driven biology
Ghosh et al., Nature Reviews Genetics, Nov. 2011
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Deep Curation
EGF Receptor Cascade
Oda, et al. Molecular Systems Biology, 2005
47
CellDesigner
Modeling tool
for biochemical and gene-regulatory network
+
+
+
http://celldesigner.or
“PAYAO”
Community Tagging System to SBML models
• A community tool to work on the same pathway models
simultaneously, insert tags to the specific parts of the
model, exchange comments, record the discussions
and eventually update the models accurately and
concurrently.
• Reads SBML models, display them with CellDesigner
PAYAO: SBML Models Tagging System
Large Scale Network Map for
Breast Cancer Tamoxifen Resistance

Molecular interactions of ERα interactions in MCF-7 cell lines curated from literature
and represented in SBML format (CellDesigner 4.0.1)
Signaling network interactions
142 proteins
256 reactions
126 complexes
~200 publications
Transcriptional activity of ERα
Reconstructed phosphoproteomics network
Expression profile based
focusing of genes and pathways
Oyama, et al., JBC 286 (1) 818-829, 2011
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Dynamic model construction

•
•
Dynamic model encompassing major players of the ligand-independent ER activation
Model adapted from existing ERBB network models (Chen et.al 2009, Wolf et.al 2007, Birtwistle et.al 2007)
Model abstracts ERBB dimerization states (Birtwistle et.al 2007)
Growth-factor
PI3K
mediated pathway
HRG
90 state variables
80 reactions (ODE)
Akt
~150 parameters
ERBB
Dimers
Adapter
molecules
PI3K-AKT –Erα
crosstalk
Ras
MAPK Erα crosstalk
ERα@167
Raf
Mek
Erk
ERα@118
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Experimental results


Molecular components of MAPK and PI3K-Akt pathways are highly phosphorylated compared to
WT cells
ERα @167 characterized by 10-fold amplification in phosphorylation in TamR cells
10-fold amplified
phosphorylation
Simulation reproducing experimental results


Models based dynamics of the molecular components identify elevated
phosphorylation states, particularly for ERα @167
Sensitivity Analysis: ERα @167 phosphorylation sensitive to PI3K-Akt arm
10-fold amplified
phosphorylation
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PI3K
ERα
Akt
1
2
Phosphorylation
of Akt
Activation of ERα
3
De-phosphorylation of ERα
How to develop high precision
simulation?
57
Comparison of robustness profile and a computational model
Possible causes of differences
•
Treatment of Paralogues
(CLB1-2, CLB3-4, CLB5-6
etc.)
•
Treatment of Stoichiometric
Inhibitor （Clbs-Sic1, Esp1Pds1, Net1-Cdc14)
Moriya, Shimizu-Yoshida, Kitano, PLoS Genetics, 14 July 2006
Cdc14, Net1
Esp1, Pds1
are all essential genes
Kaizu, Moriya, Kitano, PLoS Genetics, 2010
Cleavage of Mcd1 by Caspase-like Protease Esp1 Promotes Apoptosis in Budding Yeast
Hui Yang, Qun Ren, and Zhaojie Zhang, Mol. Biol. Cell, Vol. 19, Issue 5, 2127-2134, May 2008
Kaizu, Moriya, Kitano, PLoS Genetics 2010
Kaizu, Moriya, Kitano, PLoS Genetics 2010
Budding Yeast Cell Cycle and Signaling
64
Kaizu, Moriya, Kitano, PLoS Genetics 2010
Kaizu_Figure S3
A
B
Chen’s model
C
D
ESP1-op
ESP1-op, PDS1-op
Amount (unit)
Esp1total Amount (unit)
Wild type
Time (min.)
Time (min.)
Cell mass
Esp1active
Time (min.)
Esp1total
Kaizu, Moriya, Kitano, PLoS Genetics 2010
A
Transport model
Esp1active
B
C
D
ESP1-op
Time (min.)
Time (min.)
ESP1-op, PDS1-op
Amount (unit)
Esp1total Amount (unit)
Wild type
Cell mass
Esp1active
Time (min.)
Esp1total
Kaizu, Moriya, Kitano, PLoS Genetics 2010
A
Esp1 phosphorylation model
C
ESP1-op
Amount (unit)
Wild type
Amount (unit)
B
Time (min.)
Cell mass
Time (min.)
Esp1active
Esp1total
Kaizu, Moriya, Kitano, PLoS Genetics 2010
Kaizu_Figure S6
Pds1 phosphorylation model
Phosphorylation is
prevented
ESP1-op
Amount (unit)
Amount (unit)
Wild type
Time (min.)
Time (min.)
Cell mass
Esp1active
Esp1total
Kaizu, Moriya, Kitano, PLoS Genetics 2010
WT
Pds1 or Esp1 deletion
Pds1 and Eps1 deletions are both lethal,
thus effects Clb2 based buffering cannot be observed
Clb2 deletion
No phenotype if Esp1:Psd1 balance is kept normal
Clb2 deletion + Esp1 over-expression
Comparison of robustness profile and a computational model
Possible causes of differences
•
Treatment of Paralogues
(CLB1-2, CLB3-4, CLB5-6
etc.)
•
Treatment of Stoichiometric
Inhibitor （Clbs-Sic1, Esp1Pds1, Net1-Cdc14)
Moriya, Shimizu-Yoshida, Kitano, PLoS Genetics, 14 July 2006
Software problems
• Software for biomedical research is the critical
components for success of research
• Nobody can develop entire software systems alone
• However …..
– Tools are developed independently
– Different GUI, different operating procedure, different APIs,
etc.
– Need to launch tools independently
– No direct data sharing, etc
• Inter-operability is missing!!!!
• Extra work needed for users and developers
(C) Hiroaki Kitano, 2010 ***
LIMITED CIRCULATION ***
Data and Knowledge base
Problems
• Too many fragmented DBs and KBs.
• Inconsistency/maintenance/errorcorrection
• Users are forced to integrate by them self.
• Poor feedback mechanism exists that
prevents DB/KB to improve their quality
(C) Hiroaki Kitano, 2010 ***
LIMITED CIRCULATION ***
The Garuda Alliance
• Developer Benefits
– Consistent GUI, APIs, and other development
framework
– Enables efficient and quality software
development
– Effective dissemination of tools and resources
• User Benefits
–
–
–
–
One Stop Service
A consistent user experience
Highly interoperable software tools
Stable software platform
Garuda Vision
A common platform of tools that supports applications
Garuda modules can be tailored to leverage functions across disparate
tools which otherwise do not inter-operate, while integrating public
domain knowledge spread across isolated databases
Payao
Merge
iPath
ARENA3D
KLEIO
CellDesigner
Modeling tool
for biochemical and gene-regulatory network
+
+
+
http://celldesigner.or
Inheriting in silico IDE
Tight integration with CellDesinger
Supports ISML, SBML, etc.
Garuda compliant
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Garuda Vision
A common platform of tools that supports applications
Garuda modules can be tailored to leverage functions across disparate
tools which otherwise do not inter-operate, while integrating public
domain knowledge spread across isolated databases
Payao
Merge
iPath
ARENA3D
KLEIO
40
www.garuda-alliance.org
HD-Physiology Project
Heart model
ADME/PK model
drug
Doze, patterns, etc.
Inter-layer interactions
ADME/PK
Action potential
Genetic
Polymorphism
Intra-cellular interaction
Cellular model
Electrophysiology
Molecular level models
ADME/PK
Loosely coupled real-time computing
Inter-cellular dynamics
Action potential
Electrophysiology
Off-line computing
and visualization
MD/BD
Off-line computing and
parameter integration
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Possible application of cell based toxicology
Prediction of QT elongation
QT elongation is one of the major
cause of drug withdrawal. HERG
channel is the main target of QT
elongation.
Prediction of Hepatotoxicity
Liver takes central role in the clearance and transformation of
chemicals
Step 1 oxidation, reduction, hydrolysis, hydration
Step 2 transferase
Hepatotoxicity means the liver damage induced by chemicals.
Hepatotoxicity is one of the major cause of drug withdrawal.
EMBO Symposium
Kitano, et al., Nature chemical biology, 2011
EMBO Symposium
88
The First Molecular
Interaction Map of TB
OSDD-SBI
collaboration
Kitano, Ghosh, Matsuoka, Nature Chemical Biology, May 2011
Kitano, Ghosh, Matsuoka, Nature Chemical Biology, May 2011
Theories:
Robustness, etc
Computational Modeling
& Simulations
Data Analysis
Technology Platform
Goal-driven project management and decision making
Universal approach for
personalized medicine and unmet medical needs
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