This document discusses using data-intensive science to build better models of human disease. It argues that advances in data generation, computing power, and open information systems now make it possible to comprehensively monitor disease and molecular traits in populations. This could allow evolving disease models in a shared compute space to develop better understanding of complex biological pathways and their relationship to diseases. The document outlines several initiatives, including the Clinical Trial Comparator Arm Partnership and Sage Bionetworks, that aim to facilitate open sharing of genomic and clinical trial data to generate more powerful models and accelerate progress against human diseases.

1. it is more about how we do science than what
advantages of an open innovation compute space
for building better models of disease
beyond siloed drug discovery- Arch2POCM

2. Autism Transverse Myelitis
Treating Symptoms v.s. Modifying Diseases
Diabetes Cancer
Will it work for me?

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

4. Reality: Overlapping Pathways provide complexity

7. WHY NOT USE
“DATA INTENSIVE” SCIENCE
TO BUILD BETTER DISEASE MAPS?

8. “Data Intensive Science”- “Fourth Scientific Paradigm”
For building: “Better Maps of Human Disease”
Equipment capable of generating
massive amounts of data
IT Interoperability
Open Information System
Evolving Models hosted in a
Compute Space- Knowledge Expert

9. It is now possible to carry out comprehensive
monitoring of many traits at the population level
Monitor disease and molecular traits in
populations
Putative causal gene
Disease trait

10. How is genomic data used to understand biology?
RNA amplification
Tumors
Microarray hybirdization
Tumors
Gene Index
Standard!GWAS Approaches Profiling Approaches
Identifies Causative DNA Variation but Genome scale profiling provide correlates of disease
provides NO mechanism  Many examples BUT what is cause and effect?
 Provide unbiased view of
molecular physiology as it
relates to disease phenotypes
trait
 Insights on mechanism
 Provide causal relationships
and allows predictions
Integrated!Genetics Approaches

11. Genomic Literature
Mol. Profiles Structure
The Evolution of Systems Biology
Model Evolution
Disease Models
Model Topology
Physiologic / Pathologic
Model Dynamics Phenotype Regulation

12. List of Influential Papers in Network Modeling
 50 network papers
 http://sagebase.org/research/resources.php

13. (Eric Schadt)

14. 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
Building Disease Maps Data Repository
Commons Pilots Discovery Platform
Sagebase.org

15. Sage Bionetworks Collaborators
 Pharma Partners
 Merck, Pfizer, Takeda, Astra Zeneca, Amgen
 Foundations
 CHDI, Gates Foundation
 Government
 NIH, LSDF
 Academic
 Levy (Framingham)
 Rosengren (Lund)
 Krauss (CHORI)
 Federation
 Ideker, Califarno, Butte, Schadt 15

16. Platform Commons Research
Cancer
Neurological Disease
Metabolic Disease
Curation/Annotation
Building
Data Disease
Repository Maps
CTCAP
Public Data Pfizer
Merck Data Outposts Merck
TCGA/ICGC Federation Takeda
CCSB Astra Zeneca
CHDI
Commons Pilots Gates
NIH
LSDF-WPP
Inspire2Live
Hosting Data POC
Hosting Tools Hosting Bayesian Models
Co-expression Models
Models
Discovery Tools &
Platform Methods
KDA/GSVA
LSDF

17. 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.

18. Objective Rewards vs Deeper Rewards
AUTONOMY
MASTERY
PURPOSE

19. Bin Zhang
Model of Alzheimer’s Disease Jun Zhu
AD
normal
AD
normal
AD
normal
Cell
cycle
http://sage.fhcrc.org/downloads/downloads.php

20. THE FEDERATION
Butte Califano Friend Ideker Schadt
vs

21. Federated Aging Project :
Combining analysis + narrative
=Sweave Vignette
Sage Bionetworks Lab
R code + PDF(plots + text + code snippets)
narrative
HTML
Data objects
Califano Lab Ideker Lab Submitted
Paper
Shared Data JIRA: Source code repository & wiki
Repository

22. Synapse as a Github for building models of disease

23. Platform for Modeling
SYNAPSE

25. IMPACT ON PATIENTS

27. ANY
CITIZEN/ PATIENT
ANY
MODELER
ANY
FOUNDATION
Or
INDUSTRY PROJECT
ANY
CITIZEN/ PATIENT

28. Assumption that genetic alterations
in human conditions should be owned

30. We still consider much clinical research as if we were
hunter gathers! not sharing soon enough
- .

31. TENURE FEUDAL STATES

33. Engaging Communities of Interest
NEW MAPS
Disease Map and Tool Users-
( Scientists, Industry, Foundations, Regulators...)
PLATFORM
Sage Platform and Infrastructure Builders-
( Academic Biotech and Industry IT Partners...)
RULES AND GOVERNANCE
Data Sharing Barrier Breakers-
(Patients Advocates, Governance
M
and Policy Makers, Funders...)
APS
FOR
M
NEW TOOLS
PLAT
NEW
Data Tool and Disease Map Generators-
(Global coherent data sets, Cytoscape,
RULES GOVERN Clinical Trialists, Industrial Trialists, CROs…)
PILOTS= PROJECTS FOR COMMONS
Data Sharing Commons Pilots-
(Federation, CCSB, Inspire2Live....)

34. http://sagecongress.org

35. Group A: ACTIVATING ACCESS
!
Group D
LEGAL STACK-ENABLING PATIENTS: John Wilbanks

36. … the world is becoming too
fast, too complex, and too networked for any
company to have all the
answers inside
Y. Benkler, The Wealth of Networks

37. Is the Industry managing itself into irrelevance?
$130 billion of patented drug sales
will face generics in the 2011-2016
decade (55% of 2009 US sales)
Sales exposed to generics will
double in 2012 (to $33 billion)
98% of big pharma sales come
from products 5 years and older
(avg patent life = 11 years)
6 big pharmas were lost in the last
10 years
R&D spending is flattening,
threatening future innovation

38. Are we starting with the right targets?
How to help Science pay more attention to your
disease- Aled Edwards

39. Largest Attrition For Pioneer Targets is at
Clinical POC (Ph II)
Target ID/ Hit/Probe/ Clinical Toxicology/ Phase I
Phase
Discovery Lead ID Candidate ID Pharmacolo IIa/IIb
gy
Attrition 50% 10% 30% 30% 90%
This is killing drug discovery
We can generate effective and safe molecules in animals, but they do not have
sufficient efficacy and/or safety in the chosen patient group.

40. The current pharma model is redundant
Target ID/ Hit/Probe/ Clinical Toxicology/ Phase I
Phase
Discovery Lead ID Candidate ID Pharmacolo IIa/IIb
Phase
Target ID/ Hit/Probe/ Clinical Toxicology/
gy Phase I
Discovery Lead ID Candidate ID Pharmacolo IIa/IIb
gy
Target ID/ Hit/Probe/ Clinical Toxicology/ Phase I
Phase
Discovery Lead ID Candidate ID Pharmacolo IIa/IIb
gy
Target ID/ Hit/Probe/ Clinical Toxicology/ Phase I
Phase
Discovery Lead ID Candidate ID Pharmacolo IIa/IIb
gy
Target ID/ Hit/Probe/ Clinical Toxicology/ Phase I
Phase
Discovery Lead ID Candidate ID Pharmacolo IIa/IIb
gy
Target ID/ Hit/Probe/ Clinical Toxicology/ Phase I
Phase
Discovery Lead ID Candidate ID Pharmacolo IIa/IIb
gy
Target ID/ Hit/Probe/ Clinical Toxicology/ Phase I
Phase
Discovery Lead ID Candidate ID Pharmacolo IIa/IIb
gy
Attrition 50% 10% 30% 30% 90%
Negative POC information is not shared

41. Cost of Negative Ph II POC Estimated at $12.5 Billion Annually
Remember the two
benefits of failure. First if
you do fail, you learn
what doesn t work and
second the failure gives
you the opportunity to try
a new approach.
Roger van Oech

42. • We want to improve health
• New medicines are part of this equation
• In this, we are failing, and we want to find a
solution

43. Innovation is the ability to see change as an opportunity
– not a threat

44. Let s imagine….
• A pool of dedicated, stable funding
• A process that attracts top scientists and clinicians
• A process in which regulators can fully collaborate to solve key
scientific problems
• An engaged citizenry that promotes science and acknowledges risk
• Mechanisms to avoid bureaucratic and administrative barriers
• Sharing of knowledge to more rapidly achieve understanding of human
biology
• A steady stream of targets whose links to disease have been validated
in humans

45. Arch2POCM
A globally distributed public private partnership (PPP) committed to:
• Generate more clinically validated targets by sharing data
• Help deliver more new drugs for patients

46. Arch2POCM: what s in a name?
Arch: as in archipelago and referring to the distributed
network of academic labs, pharma partners and clinical
sites that will contribute to Arch2POCM programs
POCM: Proof Of Clinical Mechanism:
demonstration in a Ph II
setting that the
mechanism of the
selected disease target
can be safely and usefully
modulated.

47. Arch2POCM: a new drug development model?
• Pool public and private sector funding into an independent organization
• Public sector provides stability and new ideas
• Private sector brings focus and experience
• Funding can focus explicitly on high-risk targets
• Pre-competitive model to test hypotheses from financial gain
• Will attract top scientists and clinicians
• Will allow regulators to participate as scientists
• Will reduce perceived conflicts of interests – engages citizens/patients
• Will reduce bureaucratic and administrative overhead
• Will allow rapid dissemination of information without restriction - informs
public and private sectors and reduces duplication

48. • Is there sufficient incentive?
• Will universities forego IP ownership?
• Can we protect compounds that make it ?

49. Pharma
Public funders
Patient groups
PPP
Academics
Regulators
Toronto Feb-2011 meeting:
consensus among 5 pillars
CROs

50. Toronto Feb-2011 meeting:
output on Arch2POCM Feasibility
Pharma
- 6 organisations supportive
Academic Labs
- access to discovery biology and test compounds
Patient groups
- access to patients more quickly and cheaply
- access to “personal data”
Regulators
- access to historical data
- want to help with new clinical endpoints and study designs

51. Arch2POCM: April San Francisco Meeting
• Selected Disease Areas of Focus: Oncology,, Neuroscience and
Opportunistic (Oncology, CNS-Autism/Schizophrenia and Project X,
respectively)
• Defined primary entry points of Arch2POCM test compounds into overall
development pipeline
• Committed academic centers identified: UCSF, Toronto, Oxford
• CROs engaged
• Evaluated Arch2POCM business model
• Two Science Translational Medicine manuscripts published

53. Entry Points For Arch2POCM Programs
- genomic/ genetic
Pioneer target sources - disease networks
- academic partners
- private partners
- Sage Bionetworks, SGC,
Lead Lead
Preclinical Phase I Phase II
identification optimisation
Assay
in vitro
probe
Lead Clinical Phase I Phase II
candidate asset asset
Early Discovery

54. Arch2POCM and the Power of
Crowdsourcing
• Crowdsourcing: the act of outsourcing tasks
traditionally performed by an employee to a large group
of people or community
• By making Arch2POCM s clinically characterized
probes available to all, Arch2POCM will seed
independently funded, crowdsourced experimental
medicine
• Crowdsourced studies on Arch2POCM probes will
provide clinical information about the pioneer targets in
MANY indications

55. Arch2POCM
Communities of Interest
• Arch2POCM Strategic Design Teams
• Currently in place for oncology and CNS disease areas
• Multiple pharmas represented in leadership
• Charged to define detailed project workflow and timeline
• Private Foundations
• Opportunity to seed an Arch2POCM Strategic Design
Team
• Opportunity to leverage the release of patient data for
sponsored trials

56. Arch2POCM Strategic Design Teams:
Target Selection Criteria
Pioneer
May be “high risk”
High patient value
POCM study must provide learnings

57. ArchPOCM Oncology Disease Area
Focus:
Unprecedented targets and mechanisms
Novelty  MOA and clinical findings
Arc2POCM Capacity:
5 targets/year for ~ 4 years
Gate 1: ~75% effort
• New target with lead and Sage bionetworks insights on MOA (increase
likelihood of success), or
• New target (enabled by Sage) with assay
Gate 2: ~25% effort
• Pharma failed or deprioritized/parked compounds
• Compound ID is followed by a Sage systems biology effort to define MOA and
clinical entry point

58. ArchPOCM Oncology: Epigenetics selected as
the target area of choice
Top Targets:
• Discovery
• Jard1
• Ezh1
• G9A
• Lead
• Dyrk1
• Pre-Clin
• `Brd4

59. Arch2POCM: Next Steps
• Oncology and CNS Arch2POCM strategic design teams to
generate project workflow plans and timelines (September)
• Seed Arch2POCM strategic design team around a disease area
of high interest to private foundation(s) to generate project
workflow and timelines (Q4, 2011)
• Define critical details of Arch2POCM leadership, organizational
and decision-making structures (Q3-Q4, 2011)
• Develop business case to support Arch2POCM programs (Q3-
Q4, 2011)
• Obtain financial backing in order to launch operations in early
2012 in at least one disease area

60. Arch2POCM Strategic Design Teams:
(One of the Breakout Groups for this Afternoon )
Which disease areas?
Which pathways?
How will we select targets?
Costs/ Timelines/ Deliverables?
Strengths and Weaknesses?

61. Arch2POCM:
an idea whose time has come
"In a world of abundant knowledge, hoarding technology is a self-limiting strategy. Nor
can any organization, even the largest, afford any longer to ignore the tremendous
external pools of knowledge that exist. Henry Chesbrough
Ideas are only as good as your ability to make them
happen.

63. it is more about how we do science than what
advantages of an open innovation compute space
for building better models of disease
beyond siloed drug discovery- Arch2POCM