Download - Stephen Friend WIN Symposium 2011 2011-07-06
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Searching for opportunities for WIN
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
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Personalized Medicine 101: Capturing Single bases pair mutations = ID of responders
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Reality: Overlapping Pathways: 90% Phase I Cpds do not make it
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WHY NOT USE “DATA INTENSIVE” SCIENCE
TO BUILD BETTER DISEASE MAPS?
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Equipment capable of generating massive amounts of data
“Data Intensive Science”- “Fourth Scientific Paradigm” For building: “Better Maps of Human Disease”
Open Information System
IT Interoperability
Evolving Models hosted in a Compute Space- Knowledge Expert
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It is now possible to carry out comprehensive monitoring of many traits at the population level
Monitor disease and molecular traits in populaFons
PutaFve causal gene
Disease trait
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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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50 network papers http://sagebase.org/research/resources.php
List of Influential Papers in Network Modeling
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(Eric Schadt)
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Sage Mission
Sage Bionetworks is a non-profit organization founded in 2009 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
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Sage Bionetworks Collaborators
Pharma Partners Merck, Pfizer, Takeda, Astra Zeneca, Amgen
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Foundations CHDI, Gates Foundation
Government NIH, LSDF
Academic Levy (Framingham) Rosengren (Lund) Krauss (CHORI)
Federation Ideker, Califarno, Butte, Schadt
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Research Platform Commons
Data Repository
Discovery Platform
Building Disease
Maps
Tools & Methods
Commons Pilots
Outposts Federation
CCSB
LSDF-WPP Inspire2Live
POC
Cancer Neurological Disease
Metabolic Disease
Pfizer Merck Takeda
Astra Zeneca CHDI Gates NIH
Curation/Annotation
CTCAP Public Data Merck Data TCGA/ICGC
Hosting Data Hosting Tools
Hosting Models
LSDF
Bayesian Models Co-expression Models
KDA/GSVA 17
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4 Public Breast Cancer Datasets
NKI: van de Vijver et al. A gene-expression signature as a predictor of survival in breast cancer. N Engl J Med. 2002 Dec 19;347(25):1999-2009.
Wang Y et al. Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer. Lancet. 2005 Feb 19-25;365(9460):671-9.
Miller: Pawitan Y et al. Gene expression profiling spares early breast cancer patients from adjuvant therapy: derived and validated in two population-based cohorts. Breast Cancer Res. 2005;7(6):R953-64.
Christos: Sotiriou C et al.. Gene expression profiling in breast cancer: understanding the molecular basis of histologic grade to improve prognosis. J Natl Cancer Inst. 2006 Feb 15;98(4):262-72.
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295 samples
286 samples
159 samples
189 samples
Example 1: Breast Cancer- Generation of Co-expression & Bayesian Networks from published Breast Cancer Studies
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19 Zhang B et al., manuscript
Bayesian Network
Survival Analysis
Coexpression Networks Module combination
Partition BN
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Comparison of Super-‐modules with EGFR and Her2 signaling and resistance pathways
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Key Driver Analysis • IdenFfy key regulators for a list of genes h and a network N • Check the enrichment of h in the downstream of each node in N • The nodes significantly enriched for h are the candidate drivers
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A) Cell Cycle (blue)
C) Pre-mRNA Processing (brown)
B) Chromatin modification (black)
D) mRNA Processing (red)
Global driver
Global driver & RNAi validation
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Signaling between Super Modules
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Recovery of EGFR and Her2 oncoproteins downstream pathways by super modules
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Example 2. The Sage Non-Responder Project in Cancer
Sage Bionetworks • Non-Responder Project
• To identify Non-Responders to approved drug regimens so we can improve outcomes, spare patients unnecessary toxicities from treatments that have no benefit to them, and reduce healthcare costs
• Co-Chairs Stephen Friend, Todd Golub, Charles Sawyers & Rich Schilsky
• AML (at first relapse)-funded NIH • Non-Small Cell Lung Cancer- Started Guangdong General
Hospital Prof Yi-long WU • Colon Cancer Sun Yat Sen Univ-‐Prof WANG • Ovarian Cancer (at first relapse)
• Breast Cancer • Renal Cell
Purpose:
Leadership:
Initial Studies:
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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.
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Example 4: THE FEDERATION Butte Califano Friend Ideker Schadt
vs
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Federated Aging Project : Combining analysis + narraFve
=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
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Synapse as a Github for building models of disease
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Platform for Modeling
SYNAPSE
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IMPACT ON PATIENTS
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TENURE FEUDAL STATES
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“… 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
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Largest Attrition For Pioneer Targets is at Clinical POC (Ph II)
Target ID/ Discovery
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.
Hit/Probe/Lead ID
Clinical Candidate
ID
Toxicology/
Pharmacology
Phase I Phase IIa/IIb
Attrition
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The current pharma model is redundant
50% 10% 30% 30% 90%
Negative POC information is not shared Attrition
Target ID/ Discovery
Hit/Probe/Lead ID
Clinical Candidate
ID
Toxicology/
Pharmacology
Phase I Phase IIa/IIb
Target ID/ Discovery
Hit/Probe/Lead ID
Clinical Candidate
ID
Toxicology/
Pharmacology
Phase I Phase IIa/IIb
Target ID/ Discovery
Hit/Probe/Lead ID
Clinical Candidate
ID
Toxicology/
Pharmacology
Phase I Phase IIa/IIb
Target ID/ Discovery
Hit/Probe/Lead ID
Clinical Candidate
ID
Toxicology/
Pharmacology
Phase I Phase IIa/IIb
Target ID/ Discovery
Hit/Probe/Lead ID
Clinical Candidate
ID
Toxicology/
Pharmacology
Phase I Phase IIa/IIb
Target ID/ Discovery
Hit/Probe/Lead ID
Clinical Candidate
ID
Toxicology/
Pharmacology
Phase I Phase IIa/IIb
Target ID/ Discovery
Hit/Probe/Lead ID
Clinical Candidate
ID
Toxicology/
Pharmacology
Phase I Phase IIa/IIb
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“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
Cost of Negative Ph II POC Estimated at $12.5 Billion Annually
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• We want to improve health
• New medicines are part of this equation
• In this, we are failing, and we want to find a solution
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”
Innovation is the ability to see change as an opportunity – not a threat
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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
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A globally distributed public private partnership (PPP) committed to:
• Generate more clinically validated targets by sharing data
• Help deliver more new drugs for patients
Arch2POCM
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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.
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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
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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
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Arch2POCM: April San Francisco Meeting
• Selected Disease Areas of Focus: Oncology,, Neuroscience and Opportunistic (O, CNS and 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
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Entry Points For Arch2POCM Programs
Lead identification Phase I Phase II Preclinical
Lead optimisation
Assay in vitro probe
Lead Clinical candidate
Phase I asset
Phase II asset
- genomic/ genetic Pioneer target sources - disease networks
- academic partners - private partners - Sage Bionetworks, SGC,
Early Discovery
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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- such as WIN
• By making Arch2POCM’s clinically characterized probes available to all, Arch2POCM will seed independently funded, crowdsourced experimental medicine- advantage WIN
• Crowdsourced studies on Arch2POCM probes will provide clinical information about the pioneer targets in MANY indications- opportunity for WIN
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ROI for Pharma Partner
• Option to in-licence asset after positive POCM
• Early data for new clinically validated (and invalidated) targets
• Easier access to the crowd of “proven” experts/ centers: leverage the crowd’s learnings to ID the most promising unmet medical need
• Collaborate in more open way with regulatory agencies and patient groups
• Jointly invalidate a larger number of pioneer targets
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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
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ArchPOCM Oncology: Epigenetics selected as the target area of choice
Top Targets:
• Discovery • Jard1 • Ezh1 • G9A
• Lead • Dyrk1
• Pre-Clin • ̀Brd4
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ArchPOCM Oncology: Epigenetics selected as the target area of choice
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ArchPOCM Oncology: Epigenetics selected as the target area of choice
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Arch2POCM: Next Steps • Oncology and CNS Arch2POCM strategic design teams to generate project workflow plans and timelines (September)
• 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 and launch operations in early 2012
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Arch2POCM: ���an idea whose time has come
Ideas are only as good as your ability to make them happen.
"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
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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
Each of these are opportunities For the WIN Consortium