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1. Anthony Rowe, IMI Consortium
1. Anthony Rowe, IMI Consortium
1. Anthony Rowe, IMI Consortium
1. Anthony Rowe, IMI Consortium
1. Anthony Rowe, IMI Consortium
1. Anthony Rowe, IMI Consortium
1. Anthony Rowe, IMI Consortium
1. Anthony Rowe, IMI Consortium
1. Anthony Rowe, IMI Consortium
1. Anthony Rowe, IMI Consortium
1. Anthony Rowe, IMI Consortium
1. Anthony Rowe, IMI Consortium
1. Anthony Rowe, IMI Consortium
1. Anthony Rowe, IMI Consortium
1. Anthony Rowe, IMI Consortium
1. Anthony Rowe, IMI Consortium
1. Anthony Rowe, IMI Consortium
1. Anthony Rowe, IMI Consortium
1. Anthony Rowe, IMI Consortium
1. Anthony Rowe, IMI Consortium
1. Anthony Rowe, IMI Consortium
1. Anthony Rowe, IMI Consortium
1. Anthony Rowe, IMI Consortium
1. Anthony Rowe, IMI Consortium
1. Anthony Rowe, IMI Consortium
1. Anthony Rowe, IMI Consortium
1. Anthony Rowe, IMI Consortium
1. Anthony Rowe, IMI Consortium
1. Anthony Rowe, IMI Consortium
1. Anthony Rowe, IMI Consortium
1. Anthony Rowe, IMI Consortium
1. Anthony Rowe, IMI Consortium
1. Anthony Rowe, IMI Consortium
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1. Anthony Rowe, IMI Consortium

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  • 1. eTRIKS: A Knowledge ManagementPlatform for Translational ResearchAnthony Rowe, Janssen R&DOn Behalf of eTRIKS
  • 2. Challenge of Drug Development Complex Disease Phenotypes 2
  • 3. How do we stratify these complex phenotypes? Mass WGS RNAseq Imaging RT Sensing SpecNext Generation Platforms -> Data Explosion 4
  • 4. Typical Pharma Biomarker ProgramOngoing Drug Development Programme Preclinical Discovery Development Phase I Phase II Phase III Biomarker Biomarker Diagnostic Discovery Validation Development Associated Biomarker Programme 5
  • 5. Challenges in running internal biomarker programs• Study population is defined by clinical development program – Does not provide a cross sectional view of the population – Does not enable early detection• Cost of running sufficiently powered Phase 0 studies is prohibitive• How to overcome these challenges ?
  • 6. Collaboration with AcademiaIndustry Public Private ConsortiumAcademia
  • 7. Sharing costs enables bigger studies Organisation Org 1 Org 2 Org 3 Org 4 Org 5Organisation Org 6 Org 7 Org 8 Org 1
  • 8. Innovative Medicines Initiative:Joining Forces in the Healthcare Sector
  • 9. Key Concepts  “Non-competitive” collaborative research for EFPIA companies  Competitive calls to select partners of EFPIA companies (IMI beneficiaries)  Open collaboration in public-private consortia (data sharing, dissemination of results)
  • 10. Challenge 1:Fixed Budget over 5 Years
  • 11. Challenge 2: Fixed Time Line Org 1 5 Years Org 2 Org 3 Project Consortium Org 4 Org nThe value of data is long lived, virtual organisations are not: E.G Framingham Heart Study started in 1948 Who stewards the data when the consortium ends?
  • 12. How do we provides a cost effective model toprovide a Knowledge management platform toIMI and similar projects?
  • 13. Translational Research Information and Knowledge Management Service 20M Euro Oct-2012 – Sept-2017Sustainable Open Platform 2B Euro Public Private Partnership The IMI Research Agenda Requires an Open Knowledge Management 2B Euro Public Private Partnership Infrastructure
  • 14. Consortium of 16 Partners Academic/Pharma/Coordination/Standards EFPIA Lead Analytics ImperialAcademic Lead Universite Du College AstraZeneca Luxembourg Sanofi Development London Merck Roche Pfizer Lundbeck Serono Glaxo Janssen IDBS Lilly SmithKline Biosci Hosting CNRS CDISC Bayer Consulting Standards Coordination
  • 15. Requirments of the call• Start with a proven platform, tranSMART• Deliverables reflecting demands of actual Efficacy and Safety projects• Small consortium• Limit funding in the first phase.• Explicit consortium capabilities & skills
  • 16. DeliverablesPlatform: Building on open source TranSMART system a KM platform for collaborative KM for IMI translational projectsServices: Support for IMI (& other EU) TR Studies re KM data services TR project KM consultation, curation support, historic data curation Platform maintenance, enhancements & code control Administration, exploitation support, training, awarenessContent: Populate with existing and active TR Study Data Clinical Study Data Pre-Clinical Study Data (e.g. in vivo) Biomarker data associated with Studies: ‘omics, genetic, NGS, etc. Background knowledge (e.g. molecular pathway data, literature)Standards: Development and adoption of TR information standardsResearch: Research & Development of new analytics methods and tools
  • 17. The TranSMART Platform
  • 18. The TranSMART PlatformtranSMART is a knowledge management platform that enables scientists to develop andrefine research hypotheses by investigating correlations between genetic and phenotypicdata, and assessing their analytical results in the context of published literature and otherwork.• Data set Explorer: • Phenotypic data, such as demographics, clinical observations, clinical trial outcomes, and adverse events • High content biomarker data, such as gene expression, genotyping, pharmacokinetic and pharmaco-dynamics markers, metabolomics data, and proteomics data• ‘Search’: • Unstructured text-data, such as published journal articles, conference abstracts and proceedings, and internal studies and white papers • Reference data from sources such as MeSH, UMLS, Entrez, etc. • Metadata providing context about datasets, allowing users to assess the relevance of results delivered by tranSMART
  • 19. TranSMART Screenshot
  • 20. Work Packages WP Number WP Name WP LeadsBiosci Consulting (Collaboration Management) WP1 Platform Deployment CNRS/JPNV WP2 Platform Development Imperial/Sanofi/Pfizer WP3 Data Standards Roche/IDBS/Merck/CDISC WP4 Curation and Analysis Luxembourg/Sanofi Management and AstraZeneca/BioSci WP5 Sustainability Consulting WP6 Community and Outreach Janssen/BioSci Consulting WP7 Ethics GSK/CNRS/Bayer/Sanofi
  • 21. Supported Project Pipeline at project startProject Name Project Contact Therapeutic Area Data Type Summary IMI RoundIMI U-BIOPRED P Sterk Severe Asthma Clinical, Omics 1st Clinical, Next Generation Sequencing, Protein Arrays Cell-IMI OncoTrack D Henderson Colon Cancer 2nd based Assays, Animal Models, Cancer Stem Cells Clinical observations, Legacy D Sikkema BiopharmaceuticalIMI ABI RISK cohorts, Cell-based assays, Gene 3rd Julie Davidson Risk Assessment Expression, Long-term studies Prostate, Breast and Tissue Micro-Arrays, In Vitro CultureIMI PREDECT J Hickman 2nd Lung Cancer Models, GEMM Animal Models Combating K Brown Pharmacology, In vivo, Clinical,IMI ND4BB Antimicrobial 6th Phil Gribbon omics Resistance J IssacsMRC-ABPI RA-MAP Rheumatoid Arthritis Clinical, Omics Not IMI S Brockbank K Stoller Depression &IMI NEWMEDS Clinical, Pre-Clinical 1st S Kapoor Schizophrenia P BordesIMI Predict-TB Tuberculosis Clinical, Pre-Clinical PK/PD 3rd G Davis
  • 22. What have we done in the first 6 months?
  • 23. 6 month update• Building the development community• First supported project• Public Server
  • 24. TM Hackathon/Tech Strategy• ~50 Developers, 3 days in London, Feb 25-27• June 2013 - tranSMART 1.1 – Stable Postgres version – Data services • Security • Export • Plugin framework• September 2013 - tranSMART 1.2 – Faceted Search – SOLR Indexing (unified search)• TBD - Research branch – Mongo Db – NGS
  • 25. 6 month update• Building the development community• First supported project• Public Server
  • 26. U-BIOPRED(Unbiased BIOmarkers in PREDiction of respiratory disease outcomes) → a 5-year European project to understand more about severe asthma
  • 27. HypothesisThe use of biomarker profiles comprised of various types ofhigh-dimensional data, integrated with an innovativesystems biology approach into distinct phenotypehandprints, will enable significantly better prediction oftherapeutic efficacy than single or even clusteredbiomarkers of one data type, and will identify novel targets.
  • 28. What UBIOPRED is producing:  Large cohort & biobank of deeply phenotyped adult and paediatric patients  ‘Handprints’: stratification of severe asthma  Preclinical models more reflective of clinical disease  A GMP viral challenge exacerbation model
  • 29. 40 210 members 1.025 subjects 1.500 variables 175.000 samples3.000.000 data points
  • 30. 6 month update• Building the development community• First supported project – Next 5 projects being scoped• Public Server - TBA
  • 31. 1. Ensure the legacy of project data/results2. Facilitate dataset integration3. Increase operational efficiency4. Establish a common set of standards www.eTRIKS.org Linked In Discussion Group: eTRIKS Twitter @etriks1

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