Stephen Friend NIH PPP Coordinating Committee Meeting 2012-02-16
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Transcript of Stephen Friend NIH PPP Coordinating Committee Meeting 2012-02-16
If the physicists do it, the software engineers do it, Why can’t we do it?:
Networked Team Approaches among PPP
Use of Bionetworks to build maps of disease: Moving beyond linear investigations
Integrating layers of omics data models and use of compute spaces
Stephen Friend MD PhD
Sage Bionetworks (Non-Profit Organization) Seattle/ Beijing/ Amsterdam
PPP Coordinating Committee Meeting February 16, 2012
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
29
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).
30
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 45
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 46
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 47
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%) 48
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
49
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
50
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 51
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 52
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
55
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
56
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
57
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
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RMSE=3.35
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Validation Cohort: Utah (n=123)
Chronological Age
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• 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