Hiroaki Kitano The Systems Biology Institute Sony Computer Science Laboratories, Inc. Okinawa...

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

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

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


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

+ + +

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

52

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

79

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

80

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)


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

Budding Yeast Cell Cycle and Signaling

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


A

B C

Transport model

D


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


A

Esp1 phosphorylation model

C

Time (min.) Time (min.)

Am

ou

nt

(un

it)

Am

ou

nt

(un

it)

Wild type ESP1-opB



Pds1 phosphorylation model


Wild typeESP1-op

Am

ount

(un

it)

Am

ount

(un

it)

Time (min.) Time (min.)

Kaizu_Figure S6

Phosphorylation isprevented


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)


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

28

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

Computational Modeling& Simulations

Theories:Robustness, etc

Technology Platform

Data Analysis

Goal-driven project management and decision making

94

Universal approach for personalized medicine and unmet medical needs

Universal approach for personalized medicine and unmet medical needs

Hiroaki Kitano The Systems Biology Institute Sony Computer Science Laboratories, Inc. Okinawa...

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