Post on 20-Jan-2016
Hiroaki Kitano
The Systems Biology InstituteSony Computer Science Laboratories, Inc.Okinawa Institute of Science and TechnologyDepartment of Cancer Systems Biology, The Cancer Institute
Systems Systems Drug DesignDrug Design
We cure &
Systems Drug Design Coral Reef Systems Biology
We care
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 PipelinesAn early stage list
X-7CDTB with CSIR India
Phase-I Phase-II Phase-IIIDiscovery Preclinical
Breast cancer
Influenza
JSPS & OIST-SBI Project Cardiovascular system related
With ERATO Kawaoka Project
CNS(SZ, PD, AD)
PD-I program is with Univ. Luxembourg
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 PlatformComputational platform for systems drug discovery
Target Market Segments
Premiere Medical and Wellness ServicesHigh income bracketComprehensive medical and wellness serviceHealthcare version of Private Bank
Affordable medical servicesMass marketQuality service at low costTreatments for each patient cluster
Humanitarian Medical SupportMedicare for Bottom BillionsCost and Access
Cost
Personalize
Gefitinib (Iressa: AZ)
Side effects: Interstitial pneumonia (IP) 5.8% of Japanese patientswith 50% mortality rate
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.
Distribution of mutations in NSCLC
Sharma, et al., Nature Reviews Drug Discovery, April, 2010
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10
1111
1212
11
Target Market Segments
Premiere Medical and Wellness ServicesHigh income bracketComprehensive medical and wellness serviceHealthcare version of Private Bank
Affordable medical servicesMass marketQuality service at low costTreatments for each patient cluster
Humanitarian Medical SupportMedicare for Bottom BillionsCost and Access
Cost
Personalize
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 2004Kitano, Nature, 426, 125 2003Kitano and Oda, Biological Theory, 2006
Intra-tumour heterogeneity(Colorectal Cancer)
Baisse, et al., Int. J. Cancer, 93, 346-352, 2001
Robustness Trade-offsSystems that are optimized for certain perturbations inevitably entail extreme fragility elsewhere.
Kitano, Nature Reviews Genetics, 2004, Kitano, Molecular Systems Biology, 2008Cset and Doyle, Science, 2002
Bode Theorem (Bode 1945)
Cset & Doyle, Science, 2002Yi, et al., Basic control theory for biologists, 2002Kitano, Mol. Syst. Biol., 2007
Robustness-Fragility trade-offs in control theory (negative feedback)
Collateral SensitivityFragility
Resi
stan
ce
Multiple genes are involved inmany diseases
Goh, et al., PNAS 2007
２５％～３０％ ofHubs areInvolved in cancer
Goh, et al., PNAS 2007
Inhibiting HUBsmay cause seriousside-effects
Budding Yeast PIN Human PIN
Hase et al., PLoS Computational Biology, Oct. 30. 2009
N > 36
35 > N > 6
5 > N
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
EMBO Symposium
Long Tail Distribution(log-linear graph)
Sig
nifi
cance
(co
nnect
ion,
frequency
, etc
)
Head TailRank
Copyright ©2003 by the National Academy of Sciences
Borisy, Alexis A. et al. (2003) Proc. Natl. Acad. Sci. USA 100, 7977-7982
Multicomponent therapeutics that prevent proliferation of fluconazole-resistant C. albicans
Combinatorial High Throughput Screening
Copyright ©2003 by the National Academy of Sciences
Borisy, Alexis A. et al. (2003) Proc. Natl. Acad. Sci. USA 100, 7977-7982
Chlorpromazine, an antipsychotic agent, and pentamidine, an antiprotozoal agent, together selectively prevent tumor cell growth in vitro and in vivo
Phase 1/2A Stage
Efficacy
Possible reduction of drug price
– Taxol ： １００ mg 43768
• Bristol-Myers Squibb, Paclitaxel
– Contomin ： １００ mg 9.2• Tanabe-Mitsubishi, Chlorpromazine
– Benanbax ： １００ mg 2824• Sanofi-Aventis, Pentamizine
Drug PriceDrug Price
Kummar, et al., Nature Reviews Drug Discovery, Nov. 2010Originally from Houghton, et al., Mol. Cancer Ther. 9, 101-112 (2010)
Rhabdoid tumour xenograft Rhabdomyosarcoma xenograft
Rapamycin: 5mg/kg daily for 5 consecutive days / week = MTDCyclophosphamide: 150mg/kg daily = MTDCombination = MTD for both
Differential RobustnessScreening
Robustness-based target candidate selection
Differential Robustness
k1
cyclinsynthesis
cyclindegradation
Model A （ｗｔ）
cyclinsynthesis
cyclindegradation
k1
k4
k6
Model B (mutant)
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
Genome-wide gTOW Collection
gTOW-6000
Computational approach for combinatorial problems
42
An example workflow of model-driven biology
Ghosh et al., Nature Reviews Genetics, Nov. 2011
Deep Curation
EGF Receptor Cascade
Oda, et al. Molecular Systems Biology, 2005
4747
+ + +
CellDesigner
Modeling tool for biochemical and gene-regulatory network
http://celldesigner.org
“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
Transcriptional activity of ERα
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 proteins256 reactions126 complexes~200 publications
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Reconstructed phospho-proteomics network
Expression profile basedfocusing of genes and pathways
Oyama, et al., JBC 286 (1) 818-829, 2011
Growth-factor mediated pathway
MAPK Erα crosstalk
PI3K-AKT –Erα crosstalk
90 state variables80 reactions (ODE)~150 parameters
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)
PI3K
Akt
Raf Mek
Erk
Adaptermolecules
Ras
ERBBDimers
HRG
ERα@118
ERα@167
10-fold amplified phosphorylation
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
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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
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10-fold amplified phosphorylation
PI3K Akt ERα
Phosphorylation of Akt
1
Activation of ERα 2
De-phosphorylation of ERα3
57
How to develop high precision simulation?
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, Esp1-Pds1, Net1-Cdc14)
Moriya, Shimizu-Yoshida, Kitano, PLoS Genetics, 14 July 2006
Cdc14, Net1Esp1, Pds1are all essential genes
Kaizu, Moriya, Kitano, PLoS Genetics, 2010
Cleavage of Mcd1 by Caspase-like Protease Esp1 Promotes Apoptosis in Budding YeastHui 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
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Kaizu, Moriya, Kitano, PLoS Genetics 2010
A Chen’s model
Cell mass Esp1active Esp1total
Time (min.) Time (min.) Time (min.)
B C D
Am
ou
nt
(un
it)
Esp
1to
tal
Am
ou
nt
(un
it)
Wild type ESP1-op ESP1-op, PDS1-op
Kaizu_Figure S3
Kaizu, Moriya, Kitano, PLoS Genetics 2010
A
B C
Transport model
D
Cell mass Esp1active Esp1total
Time (min.) Time (min.) Time (min.)
Am
ou
nt
(un
it)
Esp
1to
tal
Am
ou
nt
(un
it)
Wild type ESP1-op ESP1-op, PDS1-op
Esp1active
Kaizu, Moriya, Kitano, PLoS Genetics 2010
A
Esp1 phosphorylation model
C
Time (min.) Time (min.)
Am
ou
nt
(un
it)
Am
ou
nt
(un
it)
Wild type ESP1-opB
Cell mass Esp1active Esp1total
Kaizu, Moriya, Kitano, PLoS Genetics 2010
Pds1 phosphorylation model
Cell mass Esp1active Esp1total
Wild typeESP1-op
Am
ount
(un
it)
Am
ount
(un
it)
Time (min.) Time (min.)
Kaizu_Figure S6
Phosphorylation isprevented
Kaizu, Moriya, Kitano, PLoS Genetics 2010
WT
Clb2 deletion
No phenotype if Esp1:Psd1 balance is kept normal
Pds1 or Esp1 deletion
Pds1 and Eps1 deletions are both lethal, thus effects Clb2 based buffering cannot be observed
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, Esp1-Pds1, 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/error-
correction
• 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
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
MergePayao
Garuda Vision
iPath KLEIOARENA3D
+ + +
CellDesigner
Modeling tool for biochemical and gene-regulatory network
http://celldesigner.org
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Inheriting in silico IDETight integration with CellDesingerSupports ISML, SBML, etc.Garuda compliant
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
MergePayao
Garuda Vision
iPath KLEIOARENA3D
40
www.garuda-alliance.org
ADME/PK model
Heart m
odelHD-Physiology Project
Inter-layer interactions
drug ADME/PK
Intra-cellular interaction
Cellular model
Electrophysiology
Con
cent
ratio
n at
cel
lula
r lev
el
Action potential
Doz
e, p
atte
rns,
etc
.
Intra-cellular dynamics
Molecular level models
GeneticPolymorphism
115
ADME/PK
Inter-cellular dynamics Action potential Electrophysiology
MD/BD
Off-line computingand visualization
Off-line computing and parameter integration
Loosely coupled real-time computing
EMBO Symposium
Possible application of cell based toxicology
Prediction of QT elongation
Prediction of HepatotoxicityLiver takes central role in the clearance and transformation of chemicalsStep 1 oxidation, reduction, hydrolysis, hydrationStep 2 transferaseHepatotoxicity means the liver damage induced by chemicals. Hepatotoxicity is one of the major cause of drug withdrawal.
QT elongation is one of the major cause of drug withdrawal. HERG channel is the main target of QT elongation.
88EMBO Symposium
Kitano, et al., Nature chemical biology, 2011
The First Molecular Interaction Map of TBOSDD-SBI collaboration
Kitano, Ghosh, Matsuoka, Nature Chemical Biology, May 2011
Kitano, Ghosh, Matsuoka, Nature Chemical Biology, May 2011
Computational Modeling& Simulations
Theories:Robustness, etc
Technology Platform
Data Analysis
Goal-driven project management and decision making
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Universal approach for personalized medicine and unmet medical needs
Universal approach for personalized medicine and unmet medical needs