Stephen Friend AMIA Symposium 2012-03-21

Why not use data intensive science to build models of disease

Current Reward Structures

Organizational Structures and Tools

Six Pilots

What  is  the  problem?  

     Most  approved  therapies  assume  indica2ons  would  represent  homogenous  popula2ons  

 Our  exis2ng  disease  models  o8en  assume  pathway  knowledge  sufficient  to  infer  correct  therapies  

Personalized Medicine 101: Capturing Single bases pair mutations = ID of responders

Reality: Overlapping Pathways

The value of appropriate representations/ maps

Equipment capable of generating massive amounts of data

“Data Intensive” Science- Fourth Scientific Paradigm

Open Information System

IT Interoperability

Host evolving computational models in a “Compute Space”

WHY  NOT  USE    “DATA  INTENSIVE”  SCIENCE  

TO  BUILD  BETTER  DISEASE  MAPS?  

what will it take to understand disease?

                   DNA    RNA  PROTEIN  (dark  maGer)    

MOVING  BEYOND  ALTERED  COMPONENT  LISTS  

2002 Can one build a “causal” model?

trait

How is genomic data used to understand biology?

“Standard” GWAS Approaches Profiling Approaches

“Integrated” Genetics Approaches

Genome scale profiling provide correlates of disease   Many examples BUT what is cause and effect?

Identifies Causative DNA Variation but provides NO mechanism

  Provide unbiased view of molecular physiology as it

relates to disease phenotypes

  Insights on mechanism

  Provide causal relationships and allows predictions

RNA amplification Microarray hybirdization

Gene Index

Tum

ors

Tum

ors

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Preliminary Probabalistic Models- Rosetta /Schadt

Gene symbol Gene name Variance of OFPM explained by gene expression*

Mouse model

Source

Zfp90 Zinc finger protein 90 68% tg Constructed using BAC transgenics Gas7 Growth arrest specific 7 68% tg Constructed using BAC transgenics Gpx3 Glutathione peroxidase 3 61% tg Provided by Prof. Oleg

Mirochnitchenko (University of Medicine and Dentistry at New Jersey, NJ) [12]

Lactb Lactamase beta 52% tg Constructed using BAC transgenics Me1 Malic enzyme 1 52% ko Naturally occurring KO Gyk Glycerol kinase 46% ko Provided by Dr. Katrina Dipple

(UCLA) [13] Lpl Lipoprotein lipase 46% ko Provided by Dr. Ira Goldberg

(Columbia University, NY) [11] C3ar1 Complement component

3a receptor 1 46% ko Purchased from Deltagen, CA

Tgfbr2 Transforming growth factor beta receptor 2

39% ko Purchased from Deltagen, CA

Networks facilitate direct identification of genes that are

causal for disease Evolutionarily tolerated weak spots

Nat Genet (2005) 205:370

"Genetics of gene expression surveyed in maize, mouse and man." Nature. (2003)

"Variations in DNA elucidate molecular networks that cause disease." Nature. (2008)

"Genetics of gene expression and its effect on disease." Nature. (2008)

"Validation of candidate causal genes for obesity that affect..." Nat Genet. (2009) ….. Plus 10 additional papers in Genome Research, PLoS Genetics, PLoS Comp.Biology, etc

"Identification of pathways for atherosclerosis." Circ Res. (2007)

"Mapping the genetic architecture of gene expression in human liver." PLoS Biol. (2008)

…… Plus 5 additional papers in Genome Res., Genomics, Mamm.Genome

"Integrating genotypic and expression data …for bone traits…" Nat Genet. (2005)

“..approach to identify candidate genes regulating BMD…" J Bone Miner Res. (2009)

"An integrative genomics approach to infer causal associations ...” Nat Genet. (2005)

"Increasing the power to detect causal associations… “PLoS Comput Biol. (2007)

"Integrating large-scale functional genomic data ..." Nat Genet. (2008)

…… Plus 3 additional papers in PLoS Genet., BMC Genet.

d

Metabolic Disease

CVD

Bone

Methods

Extensive Publications now Substantiating Scientific Approach Probabilistic Causal Bionetwork Models

• >80 Publications from Rosetta Genetics

  50 network papers   http://sagebase.org/research/resources.php

List of Influential Papers in Network Modeling

(Eric Schadt)

Equipment capable of generating massive amounts of data A-

“Data Intensive” Science- Fourth Scientific Paradigm Score Card for Medical Sciences

Open Information System D-

IT Interoperability D

Host evolving computational models in a “Compute Space F

.

We still consider much clinical research as if we were “hunter gathers”- not sharing

 TENURE      FEUDAL  STATES      

Clinical/genomic data are accessible but minimally usable

Little incentive to annotate and curate data for other scientists to use

Mathematical models of disease are not built to be

reproduced or versioned by others

Assumption that genetic alterations in human conditions should be owned

Lack of standard forms for future rights and consentss

sharing as an adoption of common standards.. Clinical Genomics Privacy IP

Publication Bias- Where can we find the (negative) clinical data?

Sage Mission

Sage Bionetworks is a non-profit organization with a vision to create a “commons” where integrative bionetworks are evolved by

contributor scientists with a shared vision to accelerate the elimination of human disease

Sagebase.org

Data Repository

Discovery Platform

Building Disease Maps

Commons Pilots

Sage Bionetworks Collaborators

  Pharma Partners   Merck, Pfizer, Takeda, Astra Zeneca, Amgen, Johnson &Johnson

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  Foundations   Kauffman CHDI, Gates Foundation

  Government   NIH, LSDF

  Academic   Levy (Framingham)   Rosengren (Lund)   Krauss (CHORI)

  Federation   Ideker, Califarno, Butte, Schadt

A) Miller 159 samples B) Christos 189 samples

C) NKI 295 samples

D) Wang 286 samples

Cell cycle

Pre-mRNA

ECM

Immune response

Blood vessel

E) Super modules

Zhang B et al., Towards a global picture of breast cancer (manuscript).

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NKI: N Engl J Med. 2002 Dec 19;347(25):1999.

Wang: Lancet. 2005 Feb 19-25;365(9460):671.

Miller: Breast Cancer Res. 2005;7(6):R953.

Christos: J Natl Cancer Inst. 2006 15;98(4):262.

Model of Breast Cancer: Co-expression Bin Zhang Xudong Dai Jun Zhu

Why not share clinical /genomic data and model building in the ways currently used by the software industry (power of tracking workflows and versioning

Leveraging Existing Technologies

Taverna

Addama

tranSMART

INTEROPERABILITY  

INTEROPERABILITY

Genome Pattern CYTOSCAPE tranSMART I2B2

SYNAPSE  

Watch What I Do, Not What I Say

sage bionetworks synapse project

Reduce, Reuse, Recycle

sage bionetworks synapse project

Most of the People You Need to Work with Don’t Work with You

sage bionetworks synapse project

My Other Computer is Amazon

sage bionetworks synapse project

CTCAP  Arch2POCM  The  FederaMon  Portable  Legal  Consent  Sage  Congress  Project  BRIDGE  

AZ/Merck  CombinaMons  before  approval  Asian  Cancer  Research  Project  BMS/Merck  Imagining  Data  

Six  Pilots  involving  Sage  Bionetworks  

RULES GOVERN

PLAT

FORM

NEW

MAP

S

Clinical Trial Comparator Arm Partnership “CTCAP” Strategic Opportunities For Regulatory Science

Leadership and Action

FDA September 27, 2011

CTCAP  

Clinical Trial Comparator Arm Partnership (CTCAP)

  Description: Collate, Annotate, Curate and Host Clinical Trial Data with Genomic Information from the Comparator Arms of Industry and Foundation Sponsored Clinical Trials: Building a Site for Sharing Data and Models to evolve better Disease Maps.

  Public-Private Partnership of leading pharmaceutical companies, clinical trial groups and researchers.

  Neutral Conveners: Sage Bionetworks and Genetic Alliance [nonprofits].

  Initiative to share existing trial data (molecular and clinical) from non-proprietary comparator and placebo arms to create powerful new tool for drug development.

Started Sept 2010

Shared clinical/genomic data sharing and analysis will maximize clinical impact and enable discovery

•  Graphic  of  curated  to  qced  to  models  

Arch2POCM  

Restructuring  the  PrecompeMMve  Space  for  Drug  Discovery  

How  to  potenMally  De-­‐Risk      High-­‐Risk  TherapeuMc  Areas  

Arch2POCM: Highlights A PPP To De-Risk Novel Targets That The Pharmaceutical Industry Can

Then Use To Accelerate The Development of New and Effective Medicines •  The Arch2POCM will be a charitable Public Private Partnership (PPP) that will file no patents and

whose scientific plan (including target selection) will be endorsed by its pharmaceutical, private and public funders

•  Arch2POCM will de-risk novel targets by developing and using pairs of test compounds (two different chemotypes) that interact with the selected targets: the compounds will be developed through Phase IIb clinical trials to determine if the selected target plays a role in the biology of human disease

•  Arch2POCM will work with and leverage patient groups and clinical CROs to enable patient recruitment, and with regulators to design novel studies and to validate novel biomarkers

•  Arch2POCM will make its GMP test compounds available to academic groups and foundations so they can use them to perform clinical studies and publish on a multitude of additional indications

•  Arch2POCM will release all reagents and data to the public at pre-defined stages in its drug development process. To ensure scientific quality, data and reagents will be released once they have been vetted by an independent scientific committee

•  Arch2POCM will publish all negative POCM data immediately in order to reduce the number of ongoing redundant proprietary studies (in pharma, biotech and academia) on an invalidated target and thereby –  minimize unnecessary patient exposure –  provide significant economic savings for the pharmaceutical industry

•  In the rare instance in which a molecule achieves positive POCM, Arch2POCM will ensure that the compound has the ability to reach the market by arranging for exclusive access to the proprietary IND database for the molecule 44

Arch2POCM: scale and scope

•  Proposed Goal: Initiate 2 programs. One for Oncology/Epigenetics/Immunology. One for Neuroscience/Schizophrenia/Autism. Both programs will have 8 drug discovery projects (targets) - ramped up over a period of 2 years

–  It is envisioned that Arch2POCM’s funding partners will select targets that are judged as slightly too risky to be pursued at the top of pharma’s portfolio, but that have significant scientific potential that could benefit from Arch2POCM’s crowdsourcing effort

•  These will be executed over a period of 5 years making a total of 16 drug discovery projects

–  Projected pipeline attrition by Year 5 (assuming 12 targets loaded in early discovery)

•  30% will enter Phase 1 •  20% will deliver Ph 2 POCM data 45

Arch2POCM: proposed funding strategy –  $160-200M over five years is projected as necessary to advance

up to 8 drug discovery projects within each of the two therapeutic programs

–  Arch2POCM funding will come from a combination of public funding from governments and private sector funding from pharmaceutical and biotechnology companies and from private philanthropists

–  By investing $1.6 M annually into one or both of Arch2POCM’s selected disease areas, partnered pharmaceutical companies:

1.  obtain a vote on Arch2POCM target selection 2.  have the opportunity to donate existing compounds from their

abandoned clinical programs for re-purposing on Arch2POCM’s  targets  

3.  gain real time data access to Arch2POCM’s 16 drug discovery projects

4.  have the strategic opportunity to expand their overall portfolio 46

Lead identification Phase I Phase II Preclinical

Lead optimisation

Assay in vitro probe

Lead Clinical candidate

Phase I asset

Phase II asset

Pioneer targets - genomic/ genetic - disease networks - academic partners - private partners - SAGE, SGC,

Stage-gate 1: Early Discovery and PCC Compounds (75%)

Stage-gate 2: Pharma’s re-purposed clinical assets (25%) 47

Entry points for Arch2POCM programs: Two compounds (different chemotypes) will be advanced per target

Five Year Objective: Initiate ≈ 8 drug discovery projects with 6 entering in Early Discovery, one entering in pre-clinical and one entering in PH I

Months → 0-6 7-12 13-18 19-24 25-30 31-36 37-42 43-48 49-54 55-60

Pipeline flow for Arch2POCM

Early discovery (45% PTRS) Pre-clinical (70% PTRS) Ph I (65% PTRS)

Ph II (10% PTRS)

1.3

1

Ph 1 (1)

1

Year #2 Arch2POCM Target Load

Arch2POCM Snapshot at Year 5

Year #1 Arch2POCM Target Load

Early discovery (2)

1

Targets  Loaded   8  

Projected  INDs  filed   3-­‐4  

Ph  1  or  2  Trials  In  Progress   2  

Projected  Complete  Ph  2  (POCM)  Data  Sets  

1  

*PTRS = Probability of technical and regulatory success

Pre-clinical (1)

Early discovery (4)

Pre-clinical

Pre-clinical

Ph 1

Ph 1

Ph 1

Ph 2

Ph 2

Ph 2

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The case for epigenetics/chromatin biology

1.  There are epigenetic oncology drugs on the market (HDACs)

2.  A growing number of links to oncology, notably many genetic links (i.e. fusion proteins, somatic mutations)

3.  A pioneer area: More than 400 targets amenable to small molecule intervention - most of which only recently shown to be “druggable”, and only a few of which are under active investigation

4.  Open access, early-stage science is developing quickly – significant collaborative efforts (e.g. SGC, NIH) to generate proteins, structures, assays and chemical starting points

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Domain Family Typical substrate class* Total Targets

Histone Lysine demethylase

Histone/Protein K/R(me)n/ (meCpG) 30  

Bromodomain Histone/Protein K(ac) 57  

R O Y A L

Tudor domain Histone Kme2/3 - Rme2s 59  

Chromodomain Histone/Protein K(me)3 34  

MBT repeat Histone K(me)3 9  

PHD finger Histone K(me)n 97  

Acetyltransferase Histone/Protein K 17  

Methyltransferase Histone/Protein K&R 60  

PARP/ADPRT Histone/Protein R&E 17  

MACRO Histone/Protein (p)-ADPribose 15  

Histone deacetylases Histone/Protein KAc 11  

395  

The current epigenetics universe

Now known to be amenable to small molecule inhibition 50

Required activities and possible participants for Arch2POCM in Year 1

Ac2vity     Loca2on/Inves2gator  

Target  Structure   SGC  and  academia  

Compound  libraries   Partner/SGC/academia  

Assay  development  for  epigeneMc  screens  and  biomarkers   Partner/SGC/academia/CRO  

HTP  screens  for  epigeneMc  hits   Partner/SGC/academia/CRO  

Med  Chem  SAR  To  ID  Two  Suitable  Binding  Arch2POCM  Test  Compounds  

WuXI,/Axon,/ChemDiv/Amira/UNC,  Dana  Farber,  Oxford,  

Non-­‐GLP  scaleup  of  Arch2POCM  Test  Compounds  and  associated  analyMcs  

???  

DistribuMon  of  Arch2POCM  Test  Compounds   AAI  pharma  

PK,  PD,  ADME,  Tox  TesMng    PPD,  CRL  

GMP  Manufacturing  of  Arch2POCM  Test  Compounds   AAI  Pharma,  Ipsen,  Camrus,  Durect,  Patheon,  Catalent  

GMP  FormulaMon   Camrus,  Durect,  Patheon,  Catalent,  SwissCo,  Pharmatec  

GMP  Drug  Storage  and  DistribuMon   AAI  Pharma,  SwissCo,  Pharmatec  

IND  PreparaMon  Support   Accurate  FDA  consult./Parexel/QuinMles  

Clinical  Assay  Development  and  QualificaMon   GenopMx/Caris  

Ph  I-­‐II  Clinical  Trials   ICON/  Parexel/Covance/QuinMles  

Ph  I-­‐II  Database  Management  and  CSR  ProducMon   ICON/Parexel/Covance/QuinMles/AAAH   51

Some possible Arch2POCM Third Parties: funded or in-kind contributions

General benefits of Arch2POCM for drug development

1.  Arch2POCM’s use of test compounds to de-risk previously unexplored biology enables drug developers to initiate proprietary drug development starting from an array of unbiased, clinically validated targets

2.  Arch2POCM’s crowdsourced research and trials provides the pharmaceutical industry with “parallel shots on goal: by aligning test compounds to most promising unmet medical need”

3.  The positive and negative clinical trial data that Arch2POCM and the crowd produce and publish will increase clinical success rates (as one can pick targets and indications more smartly) and will save the pharmaceutical industry money by reducing redundant proprietary efforts on failed targets

Why is Arch2POCM a “smart bet” for Pharma investment?

Arch2POCM:  an  external  epigeneMc  think  tank  from  which  Pharma  can  load  the  most  likely  to  succeed  targets  as  proprietary  programs  or  leverage  Arch2POCM  results  for  its  other  internal  efforts  •  A  front  row  seat  on  the  progression  of  8  epigeneMc  targets  means  that:  

•  Pharma  can  select  the  epigeneMc  targets  that  best  compliment  their  internal  porholio  and  for  which  there  is  the  greatest  interest  

•  Pharma  can  structure  Arch2POCM’s  projects  so  that  key  objecMves  line  up  with  internal  go/no-­‐go  decisions  

•  Pharma  can  use  Arch2POCM  data  to  trigger  its  internal  level  of  investment  on  a  parMcular  target  

•  Pharma  can  use  Arch2POCM  resources  to  enrich  their  internal  epigeneMcs  effort:  acMve  chemotypes,  assays,  pre-­‐clinical  models,  biomarkers,  geneMc  and  phenotypic  data  for  paMent  straMficaMon,  relaMonships  to  epigeneMc  experts  

•   Pharma  can  use  Arch2POCM’s  lead  compound  chemotypes  to:  •   inform  their  proprietary  medicinal  chemistry  efforts  on  the  target  

•   idenMfy  chemical  scaffolds  that  impact  epigeneMc  pathways:  a  proprietary  combinaMon  therapy  opportunity  

•   Toxicity  screening  of  Arch2POCM  compounds  with  FDA  tools  can  be  used  to  guide  internal  proprietary  chemistry  efforts  in  oncology,  inflammaMon  and  beyond    

•  Arch2POCM’s  crowd  of  scienMsts  and  clinicians  provides  its  Pharma  partners  withparallel  shots  on  goal  at  the  best  context  for  Arch2POCM’s  compounds/targets  

Arch2POCM: current partnering status •  Pharmaceutical Funding Partners

–  Three companies are considering a potential role as industry anchors for Arch2POCM

–  Two companies have demonstrated interest in Arch2POCM and their company leadership wants to go to next step- awaiting face to face discussions to go over agreement

•  Public Funding Partners

–  Good progress is being made to obtain financial backing for Arch2POCM from public funders in a number of countries (Canada, United Kingdom and Sweden) for both epigenetics and for CNS

–  Ontario Brain Institute, Canada has allocated $3M to the development of an autism clinical network that is committed to work with Arch2POCM

•  Philanthropic Funding Partners: awaiting designation of anchor partners

•  In kind partners –  GE Healthcare (imaging): lead diagnostics partner and willing to share its

experimental oncology biomarkers –  Cancer Research UK: through some of its drug discovery and development

resources participating in Arch2POCM

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Arch2POCM: current partnering status •  Academic partners

–  Institutions that have indicated willingness to let their scientists participate without patent filing: UCSF, Massachusetts General Hospital, University of North Carolina, University of Toronto, Oxford University, Karolinska Institute

–  Academic community of epigenetic experts/resources already identified

•  Regulatory partners: Because the objective of the Arch2POCM PPP is to probe and elucidate disease biology as opposed to develop new proprietary products, FDA and EMEA are ready to play an active role (toxicity screens, and legacy clinical trial data)

•  Patient group partners: leaders from Genetic Alliance, Inspire2Live and the Love Avon Army of Women are actively engaged

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Snapshot of Arch2POCM at Year 5: Clinical Trials On Cancer and Immunology Targets In Progress

and Data-sharing Network Established •  3-4 Arch2POCM clinical trials will be in progress or complete

•  Ph 2 POCM data for at least one novel target will be available and released so that either:

–  Others can stop their proprietary studies on the same target to protect patient safety and save money (negative POCM)

–  Others can focus proprietary development efforts onto a validated oncology/immunology target, and Arch2POCM partners can gain exclusive access to the Arch2POCM IND and test compound for further development (positive POCM)

•  Arch2POCM’s disease biology network teams will be conducting discovery through Ph II clinical trials and sharing all the data

–  Arch2POCM’s openly shared data will establish a common stream of knowledge on Arch2POCM’s targets

–  Safe Arch2POCM test compounds will be distributed to qualified labs and centers –  Crowdsourced experimental studies will be underway and providing information

about Arch2POCM targets and test compounds in a range of indications

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The  FederaMon  

2008   2009   2010   2011  

How can we accelerate the pace of scientific discovery?

Ways to move beyond “traditional” collaborations?

Intra-lab vs Inter-lab Communication

Colrain/ Industrial PPPs Academic Unions

(Nolan  and  Haussler)  

human aging: predicting bioage using whole blood methylation

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Training Cohort: San Diego (n=170)

Chronological Age

Bio

logic

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RMSE=3.35

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Validation Cohort: Utah (n=123)

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RMSE=5.44

•  Independent training (n=170) and validation (n=123) Caucasian cohorts •  450k Illumina methylation array •  Exom sequencing •  Clinical phenotypes: Type II diabetes, BMI, gender…

sage federation: model of biological age

Faster Aging

Slower Aging

Clinical Association -  Gender -  BMI -  Disease Genotype Association Gene Pathway Expression Pr

edicted  Age  (liver  expression)  

Chronological  Age  (years)  

Age Differential

Reproducible  science==shareable  science  

Sweave: combines programmatic analysis with narrative

Sweave.Friedrich Leisch. Sweave: Dynamic generation of statistical reports using literate data analysis. In Wolfgang Härdle and Bernd Rönz,editors, Compstat 2002 –

Proceedings in Computational Statistics,pages 575-580. Physica Verlag, Heidelberg, 2002. ISBN 3-7908-1517-9

Dynamic generation of statistical reports using literate data analysis

Federated  Aging  Project  :    Combining  analysis  +  narraMve    

=Sweave Vignette Sage Lab

Califano Lab Ideker Lab

Shared  Data  Repository  

JIRA:  Source  code  repository  &  wiki  

R code + narrative

PDF(plots + text + code snippets)

Data objects

HTML

Submitted Paper

Portable  Legal  Consent  

(AcMvaMng  PaMents)  

John  Wilbanks  

Sage  Congress  Project  April  20  2012  

RealNames  Parkinson’s  Project  RevisiMng  Breast  Cancer  Prognosis  

Fanconi’s  Anemia  

(Responders  CompeMMons-­‐  IBM-­‐DREAM)  

BRIDGE  

DemocraMzaMon  of  Medicinal  Research  

(Social  Value  Chain)  ASHOKA  

Why not use data intensive science to build models of disease

Current Reward Structures

Organizational Structures and Tools

Six Pilots

Networking  Disease  Model  Building