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Stephen Friend WIN Symposium 2011 2011-07-06
Stephen Friend WIN Symposium 2011 2011-07-06
Stephen Friend WIN Symposium 2011 2011-07-06
Stephen Friend WIN Symposium 2011 2011-07-06
Stephen Friend WIN Symposium 2011 2011-07-06
Stephen Friend WIN Symposium 2011 2011-07-06
Stephen Friend WIN Symposium 2011 2011-07-06
Stephen Friend WIN Symposium 2011 2011-07-06
Stephen Friend WIN Symposium 2011 2011-07-06
Stephen Friend WIN Symposium 2011 2011-07-06
Stephen Friend WIN Symposium 2011 2011-07-06
Stephen Friend WIN Symposium 2011 2011-07-06
Stephen Friend WIN Symposium 2011 2011-07-06
Stephen Friend WIN Symposium 2011 2011-07-06
Stephen Friend WIN Symposium 2011 2011-07-06
Stephen Friend WIN Symposium 2011 2011-07-06
Stephen Friend WIN Symposium 2011 2011-07-06
Stephen Friend WIN Symposium 2011 2011-07-06
Stephen Friend WIN Symposium 2011 2011-07-06
Stephen Friend WIN Symposium 2011 2011-07-06
Stephen Friend WIN Symposium 2011 2011-07-06
Stephen Friend WIN Symposium 2011 2011-07-06
Stephen Friend WIN Symposium 2011 2011-07-06
Stephen Friend WIN Symposium 2011 2011-07-06
Stephen Friend WIN Symposium 2011 2011-07-06
Stephen Friend WIN Symposium 2011 2011-07-06
Stephen Friend WIN Symposium 2011 2011-07-06
Stephen Friend WIN Symposium 2011 2011-07-06
Stephen Friend WIN Symposium 2011 2011-07-06
Stephen Friend WIN Symposium 2011 2011-07-06
Stephen Friend WIN Symposium 2011 2011-07-06
Stephen Friend WIN Symposium 2011 2011-07-06
Stephen Friend WIN Symposium 2011 2011-07-06
Stephen Friend WIN Symposium 2011 2011-07-06
Stephen Friend WIN Symposium 2011 2011-07-06
Stephen Friend WIN Symposium 2011 2011-07-06
Stephen Friend WIN Symposium 2011 2011-07-06
Stephen Friend WIN Symposium 2011 2011-07-06
Stephen Friend WIN Symposium 2011 2011-07-06
Stephen Friend WIN Symposium 2011 2011-07-06
Stephen Friend WIN Symposium 2011 2011-07-06
Stephen Friend WIN Symposium 2011 2011-07-06
Stephen Friend WIN Symposium 2011 2011-07-06
Stephen Friend WIN Symposium 2011 2011-07-06
Stephen Friend WIN Symposium 2011 2011-07-06
Stephen Friend WIN Symposium 2011 2011-07-06
Stephen Friend WIN Symposium 2011 2011-07-06
Stephen Friend WIN Symposium 2011 2011-07-06
Stephen Friend WIN Symposium 2011 2011-07-06
Stephen Friend WIN Symposium 2011 2011-07-06
Stephen Friend WIN Symposium 2011 2011-07-06
Stephen Friend WIN Symposium 2011 2011-07-06
Stephen Friend WIN Symposium 2011 2011-07-06
Stephen Friend WIN Symposium 2011 2011-07-06
Stephen Friend WIN Symposium 2011 2011-07-06
Stephen Friend WIN Symposium 2011 2011-07-06
Stephen Friend WIN Symposium 2011 2011-07-06
Stephen Friend WIN Symposium 2011 2011-07-06
Stephen Friend WIN Symposium 2011 2011-07-06
Stephen Friend WIN Symposium 2011 2011-07-06
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Stephen Friend WIN Symposium 2011 2011-07-06

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Stephen Friend, July 6-8, 2011. WIN Annual Symposium, Paris, FR

Stephen Friend, July 6-8, 2011. WIN Annual Symposium, Paris, FR

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  • 1. Searching for opportunities for WIN it is more about how we do science than whatadvantages of an open innovation compute space for building better models of disease beyond siloed drug discovery- Arch2POCM
  • 2. Personalized Medicine 101:Capturing Single bases pair mutations = ID of responders
  • 3. Reality: Overlapping Pathways: 90% Phase I Cpds do not make it
  • 4. WHY  NOT  USE     “DATA  INTENSIVE”  SCIENCE  TO  BUILD  BETTER  DISEASE  MAPS?  
  • 5. “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
  • 6. It is now possible to carry out comprehensive monitoring of many traits at the population levelMonitor  disease  and  molecular  traits  in   populaFons   PutaFve  causal  gene   Disease  trait  
  • 7. 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 12 Integrated Genetics Approaches
  • 8. List of Influential Papers in Network Modeling   50 network papers   http://sagebase.org/research/resources.php
  • 9. (Eric Schadt)
  • 10. 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 diseaseBuilding Disease Maps Data RepositoryCommons Pilots Discovery Platform Sagebase.org
  • 11. 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 16
  • 12. Platform Commons Research Cancer Neurological Disease Metabolic DiseaseCuration/Annotation Building Data Disease Repository Maps CTCAP Public Data Pfizer Merck Data Outposts Merck TCGA/ICGC Federation Takeda CCSB Astra Zeneca CHDI Commons Gates NIH Pilots LSDF-WPP Inspire2Live Hosting Data POC Hosting Tools Bayesian Models Co-expression Models Hosting Models Discovery Tools & Platform Methods KDA/GSVA LSDF 17
  • 13. Example 1: Breast Cancer- Generation of Co-expression &Bayesian Networks from published Breast Cancer Studies 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;347 295 samples (25):1999-2009. Wang Y et al. Gene-expression profiles to predict distant metastasis of lymph-node- negative primary breast cancer. Lancet. 286 samples 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 159 samples 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 189 samples histologic grade to improve prognosis. J Natl Cancer Inst. 2006 Feb 15;98(4): 262-72. 18
  • 14. Coexpression Networks Module combination Partition BN Bayesian NetworkSurvival Analysis 19 Zhang B et al., manuscript
  • 15. Comparison  of  Super-­‐modules  with  EGFR  and  Her2   signaling  and  resistance  pathways  
  • 16. 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   21
  • 17. A) Cell Cycle (blue) B) Chromatin modification (black)C) Pre-mRNA Processing (brown) D) mRNA Processing (red) Global driver Global driver & RNAi validation 23
  • 18. Signaling between Super Modules
  • 19. Recovery  of  EGFR  and  Her2  oncoproteins  downstream  pathways  by  super  modules  
  • 20. Example 2. The Sage Non-Responder Project in Cancer •  To identify Non-Responders to approved drug regimens so Purpose: we can improve outcomes, spare patients unnecessary toxicities from treatments that have no benefit to them, and reduce healthcare costs Leadership: •  Co-Chairs Stephen Friend, Todd Golub, Charles Sawyers & Rich Schilsky Initial •  AML (at first relapse)-funded NIH Studies: •  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 CellSage Bionetworks • Non-Responder Project
  • 21. 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.
  • 22. Example 4: THE FEDERATIONButte Califano Friend Ideker Schadt vs
  • 23. Federated  Aging  Project  :     Combining  analysis  +  narraFve     =Sweave Vignette Sage Lab R code + PDF(plots + text + code snippets) narrative HTML Data objectsCalifano Lab Ideker Lab Submitted Paper Shared  Data   JIRA:  Source  code  repository  &  wiki   Repository  
  • 24. Synapse  as  a  Github  for  building  models  of  disease  
  • 25. Platform for Modeling SYNAPSE  
  • 26. IMPACT  ON  PATIENTS  
  • 27.  TENURE      FEUDAL  STATES      
  • 28. … the world is becoming too fast, too complex, and toonetworked for any company to have all the answers inside Y. Benkler, The Wealth of Networks
  • 29. Largest Attrition For Pioneer Targets is at Clinical POC (Ph II) Target ID/ Hit/Probe/ Clinical Toxicolog Phase I Phase Discovery Lead ID Candidate y/ IIa/IIb ID Pharmaco logyAttrition 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.
  • 30. The current pharma model is redundant Target ID/ Hit/Probe/ Clinical Toxicolog Phase I Phase Discovery Lead ID Candidate y/ IIa/IIb Phase Target ID/ Hit/Probe/ Clinical ID Pharmaco Toxicolog Phase I Discovery Lead ID Candidate logy y/ IIa/IIb ID Pharmaco Target ID/ Hit/Probe/ Clinical logy Toxicolog Phase I Phase Discovery Lead ID Candidate y/ IIa/IIb ID Pharmaco Target ID/ Hit/Probe/ Clinical Toxicolog logy Phase I Phase Discovery Lead ID Candidate y/ IIa/IIb ID Pharmaco logy Target ID/ Hit/Probe/ Clinical Toxicolog Phase I Phase Discovery Lead ID Candidate y/ IIa/IIb ID Pharmaco Target ID/ Hit/Probe/ Clinical logy Toxicolog Phase I Phase Discovery Lead ID Candidate y/ IIa/IIb ID Pharmaco Target ID/ Hit/Probe/ Clinical Toxicolog logy Phase I Phase Discovery Lead ID Candidate y/ IIa/IIb ID Pharmaco logyAttrition 50% 10% 30% 30% 90% Negative POC information is not shared
  • 31. 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
  • 32. •  We want to improve health•  New medicines are part of this equation•  In this, we are failing, and we want to find a solution
  • 33. Innovation is the ability to see change as an opportunity – not a threat
  • 34. 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
  • 35. 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
  • 36. 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.
  • 37. 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
  • 38. 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
  • 39. 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
  • 40. 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
  • 41. Arch2POCM and the Power of Crowdsourcing •  Crowdsourcing: the act of outsourcing taskstraditionally performed by an employee to a large groupof people or community- such as WIN• By making Arch2POCM s clinically characterizedprobes available to all, Arch2POCM will seedindependently funded, crowdsourced experimentalmedicine- advantage WIN• Crowdsourced studies on Arch2POCM probes willprovide clinical information about the pioneer targets inMANY indications- opportunity for WIN
  • 42. 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
  • 43. 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
  • 44. ArchPOCM Oncology: Epigenetics selected as the target area of choice Top Targets: • Discovery • Jard1 • Ezh1 • G9A • Lead • Dyrk1 • Pre-Clin • `Brd4
  • 45. ArchPOCM Oncology: Epigenetics selected as the target area of choice
  • 46. ArchPOCM Oncology: Epigenetics selected as the target area of choice
  • 47. Arch2POCM: Next Steps • Oncology and CNS Arch2POCM strategic design teamsto 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 Arch2POCMprograms (Q3-Q4, 2011)• Obtain financial backing and launch operations in early2012
  • 48. Arch2POCM: an idea whose time has comeIn a world of abundant knowledge, hoarding technology is a self-limitingstrategy. Nor can any organization, even the largest, afford any longer toignore the tremendous external pools of knowledge that exist. HenryChesbrough Ideas are only as good as your ability to make them happen.
  • 49. it is more about how we do science than whatadvantages 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

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