2013 03-21 eTRIKS overview


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2013 03-21 eTRIKS overview

  1. 1. eTRIKS:  A  Knowledge  Management  Platform  for  Translational  Research  Anthony  Rowe,  Janssen  R&D  On  Behalf  of  eTRIKS  
  2. 2. Challenge  of  Drug  Development   Complex  Disease  Phenotypes       2  
  3. 3. Challenge  of  Drug  Development   Complex  Disease  Phenotypes       3  
  4. 4. How  do  we  stratify  these   complex  phenotypes?   Mass   WGS RNAseq Imaging RT  Sensing SpecNext  Genera;on  Pla?orms  -­‐>  Data  Explosion   4  
  5. 5. Typical  Pharma  Biomarker  Program  Ongoing  Drug  Development  Programme   Preclinical Discovery Development Phase I Phase II Phase III Biomarker Biomarker Diagnostic Discovery Validation Development Associated  Biomarker  Programme     5  
  6. 6. Challenges  in  running  internal   biomarker  programs  •  Study  popula;on  is  defined  by  clinical   development  program   –  Does  not  provide  a  cross  sec;onal  view  of  the   popula;on   –  Does  not  enable  early  detec;on  •  Cost  of  running  sufficiently  powered  Phase  0   studies  is  prohibi;ve    •  How  to  overcome  these  challenges  ?  
  7. 7. Collaboration  with  Academia  Industry   Public   Private   Consor;um  Academia  
  8. 8. Sharing  costs  enables  bigger   studies     Organisa(on   Org  1   Org  2   Org  3   Org  4  Organisa(on   Org  5   Org  6   Org  7   Org  1  
  9. 9. Innova(ve  Medicines  Ini(a(ve:  Joining  Forces  in  the  Healthcare  Sector    
  10. 10.      Key  Concepts   Ø     “Non-­‐compe;;ve”  collabora;ve                 research  for  EFPIA  companies       Ø  Compe;;ve  calls  to  select  partners  of     EFPIA  companies  (IMI  beneficiaries)               Ø     Open  collabora;on  in  public-­‐private  consor;a   (data  sharing,  dissemina;on  of  results)      
  11. 11. Challenge  1:   Fixed  Budget  over  5  Years     u cture In frastr e  S cienc
  12. 12. Challenge  2:     Fixed  Time  Line   Org  1   5  Years   Org  2   Org  3   Project  Consor;um   Org  4   Org  n  The  value  of  data  is  long  lived,  virtual  organisa;ons  are  not:      E.G  Framingham  Heart  Study  started  in  1948      Who  stewards  the  data  when  the  consor;um  ends?  
  13. 13. How  do  we  provides  a  cost  effec0ve  model  to  provide  a  Knowledge  management  pla6orm  to  IMI  and  similar  projects?  
  14. 14. Translational  Research  Information  and   Knowledge  Management  Service 20M  Euro   Oct-­‐2012  –  Sept-­‐2017  Sustainable  Open  Platform   2B  Euro  Public  Private  Partnership   The  IMI  Research  Agenda  Requires  an  O2B  Euro  Public  Private  Partnership   pen  Knowledge  Management   Infrastructure  
  15. 15. Consortium  of  16  Partners   Academic/Pharma/Coordination/Standards   EFPIA  Lead   Analytics   Imperial   Universite  Du  Academic  Lead   College   AstraZeneca   Luxembourg   Sanofi   Development   London   Merck   Roche   Pfizer   Lundbeck   Serono   Glaxo   Janssen   IDBS   Lilly   SmithKline   Biosci   Hosting   CNRS   CDISC   Bayer   Consulting   Standards   Coordination  
  16. 16. Requirments  of  the  call  •  Start  with  a  proven  pla?orm,  tranSMART  •  Deliverables  reflec;ng  demands  of  actual   Efficacy  and  Safety  projects  •  Small  consor;um  •  Limit  funding  in  the  first  phase.  •  Explicit  consor;um  capabili;es  &  skills  
  17. 17. DeliverablesPla9orm:    Building  on  open  source  TranSMART  system    a  KM  pla?orm  for   collabora;ve  KM  for  IMI  transla;onal  projects      Services:      Support  for  IMI  (&  other  EU)  TR  Studies  re  KM  data  services   TR  project  KM  consulta;on,  cura;on  support,  historic  data  cura;on   Pla?orm  maintenance,  enhancements  &  code  control   Administra;on,  exploita;on  support,  training,  awareness      Content:                    Populate  with  exis;ng  and  ac;ve  TR  Study  Data   Clinical  Study  Data   Pre-­‐Clinical  Study  Data  (e.g.  in  vivo)   Biomarker  data  associated  with  Studies:  ‘omics,  gene;c,  NGS,  etc.   Background  knowledge  (e.g.  molecular  pathway  data,  literature)    Standards:  Development  and  adop;on  of  TR  informa;on  standards    Research:  Research  &  Development  of  new  analy;cs  methods  and  tools  
  18. 18. The  TranSMART  Pla?orm  
  19. 19. The  TranSMART  Pla?orm  tranSMART  is  a  knowledge  management  pla?orm  that  enables  scien;sts  to  develop  and  refine  research  hypotheses  by  inves;ga;ng  correla;ons  between  gene;c  and  phenotypic  data,  and  assessing  their  analy;cal  results  in  the  context  of  published  literature  and  other  work.  •  Data  set  Explorer:   •  Phenotypic  data,  such  as  demographics,  clinical  observa;ons,  clinical  trial     outcomes,  and  adverse  events     •  High  content  biomarker  data,  such  as  gene  expression,  genotyping,   pharmacokine;c  and  pharmaco-­‐dynamics  markers,  metabolomics  data,  and   proteomics  data      •  ‘Search’:   •  Unstructured  text-­‐data,  such  as  published  journal  ar;cles,  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    
  20. 20. TranSMART  Screenshot  
  21. 21. Work  Packages   WP  Number   WP  Name   WP  Leads  Biosci  ConsulJng  (Collabora(on    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 ConsulJng   WP6   Community and Outreach Janssen/BioSci  ConsulJng   WP7   Ethics GSK/CNRS/Bayer/Sanofi  
  22. 22. Supported  Project  Pipeline    at   project  start  Project  Name Project  Contact Therapeu(c  Area Data  Type  Summary IMI  RoundIMI  U-­‐BIOPRED P  Sterk Severe  Asthma Clinical,  Omics 1st Clinical,  Next  Genera;on   Sequencing,  Protein  Arrays  Cell-­‐IMI  OncoTrack D  Henderson Colon  Cancer 2nd based  Assays,  Animal  Models,   Cancer  Stem  Cells Clinical  observa;ons,  Legacy   D  Sikkema   Biopharmaceu;cal    IMI  ABI  RISK cohorts,  Cell-­‐based  assays,  Gene   3rd Julie  Davidson   Risk  Assessment   Expression,    Long-­‐term  studies Prostate,  Breast  and   Tissue  Micro-­‐Arrays,  In  Vitro  Culture  IMI  PREDECT J  Hickman 2nd Lung  Cancer Models,  GEMM  Animal  Models Comba;ng   K  Brown   Pharmacology,  In  vivo,  Clinical,  IMI  ND4BB An;microbial   6th Phil  Gribbon   omics Resistance     J  Issacs  MRC-­‐ABPI  RA-­‐MAP Rheumatoid  Arthri;s Clinical,  Omics Not  IMI S  Brockbank K  Stoller   Depression  &  IMI  NEWMEDS Clinical,  Pre-­‐Clinical 1st S  Kapoor   Schizophrenia P  Bordes  IMI  Predict-­‐TB Tuberculosis Clinical,  Pre-­‐Clinical  PK/PD 3rd G  Davis
  23. 23. What  have  we  done  in  the  first  6  months?  
  24. 24. 6  month  update  •  Building  the  development  community  •  First  supported  project  •  Public  Server  
  25. 25. 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    
  26. 26. 6  month  update  •  Building  the  development  community  •  First  supported  project  •  Public  Server  
  27. 27. U-BIOPRED 
(Unbiased BIOmarkers in PREDiction of respiratory disease outcomes)
 → a 5-year European project to understand more about severe asthma
  28. 28. Hypothesis#The 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.##
  29. 29. 
What UBIOPRED is producing:
 # ü  Large cohort & biobank of deeply phenotyped adult and paediatric patients# # ü  ‘Handprints’: stratification of severe asthma# # ü  Preclinical models more reflective of clinical disease# # ü  A GMP viral challenge exacerbation model #
  30. 30. 40# 210 members# 1.025 subjects# 1.500 variables# 175.000 samples#3.000.000 data points#
  31. 31. 6  month  update  •  Building  the  development  community  •  First  supported  project   –  Next  5  projects  being  scoped  •  Public  Server  -­‐    TBA  
  32. 32. 1.  Ensure  the  legacy  of  project  data/results    2.  Facilitate  dataset  integra;on    3.  Increase  opera;onal  efficiency    4.  Establish  a  common  set  of  standards   www.eTRIKS.org   Linked  In  Discussion  Group:  eTRIKS  Twiper  @etriks1