Mie2012 27 aug12-shublaq


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Mie2012 27 aug12-shublaq

  1. 1. Personalised  medicine:  A  legacy  of  promises  without  delivery.  Can  we  get  it  right  today?     Nour  Shublaq   Centre  for  Computa-onal  Science  (CCS)   University  College  London,  UK   n.shublaq@ucl.ac.uk   MIE 2012 – Process, Information, and Data Models, Monday Aug 27, 2012, Pisa
  2. 2. Overview  •  The  Human  Genome  Project  •  The  Virtual  Physiological  Human  (VPH)  ini-a-ve  •  VPH  Simula-on  Case  Studies  –  1)  clinical  decision  support   in  surgery  2)  towards  personalised  drug  design  •  INBIOMEDvision  –  challenges  ahead  •  EU  FET  Flagship  project  IT  Future  of  Medicine  •  Conclusions  
  3. 3. Human  Genome  Project   30,000 3000Sequencing of the human genome was 10profoundly important science that led 2to fundamental shifts in ourunderstanding of biology.30,000 – 40,000 protein coding genesin the human genome and not morethan 100,000 previously thought.Thousands of DNA variants have nowbeen associated with traits/diseases.Human Genome Project, InternationalHapMap Project, and Genome wideassociation studies (GWAS) in the lastdecade Genomic   Mol.  Profiles   Structure  
  4. 4. New  Sequencers   1 Human Genome in: 5 years (2001) 2 years (2004) 4 days (Jan 2008) 16 Hours (Oct 2008) 3 Hours (Nov 2009) 6 minutes (Now!) 4
  5. 5. Organism  =  Computer  Genome  &  the  Environment   Genome Life  is  the  transla-on  of  the   informa-on  in  the  genome   into  the  phenotype  of  the   organism:   The  organism  ‚computes‘  this   phenotype  from  its  genotype,   (PentiumV) (neuronal net visualisation) given  a  specific  environment   Phenotype Slide Courtesy of Hans Lehrach
  6. 6. •  The  Human  Genome  Project  •  The  Virtual  Physiological  Human  (VPH)  ini-a-ve  •  VPH  Simula-on  Case  Studies  –  1)  clinical  decision  support   in  surgery  2)  towards  personalised  drug  design  •  INBIOMEDvision  –  challenges  ahead  •  EU  FET  Flagship  project  IT  Future  of  Medicine  •  Conclusions  
  7. 7. What  is  the  VPH?    •  The Virtual Physiological Human is a methodological and technological Organism descriptive, integrative and predictive, framework that is intended to enable the Organ investigation of the human body as a Tissue single complex system Cell Organelle Interaction•  Aims Protein •  Enable collaborative investigation of Cell the human body across all relevant Signals scales •  Introduce multiscale methodologies Transcript into medical and clinical research Gene Molecule €207M initiative in EU-FP7
  8. 8. Modelling  how  the  human  body  works      …pa-ent-­‐tailored  computer   models,  used  for  diagnosis,   preven-on,  drug  treatment   and  surgical  planning  –   assess  treatment  before   administering   Slide Courtesy of S. Kashif Sadiq
  9. 9. IntegraLon  across..  Environment Population organ  systems   Organism Organ System temporal  scales   Organ Tissue Cell Moleculedimensional  scales   Atom
  10. 10. •  The  Human  Genome  Project  •  The  Virtual  Physiological  Human  (VPH)  ini-a-ve  •  VPH  Simula-on  Case  Studies  –  1)  clinical  decision  support   in  surgery  2)  towards  personalised  drug  design  •  INBIOMEDvision  –  challenges  ahead  •  EU  FET  Flagship  project  IT  Future  of  Medicine  •  Conclusions  
  11. 11. GENIUS:  Grid  Enabled  Neurosurgical  Imaging  Using  SimulaLon     The  GENIUS  project  aims  to  model  large  scale  pa-ent  specific  cerebral   blood  flow  in  clinically  relevant  -me  frames     ObjecLves:    To  study  cerebral  blood  flow  using  paLent-­‐specific  image-­‐based  models    To  provide  insights  into  the  cerebral  blood  flow  &  anomalies    To  develop  tools  and  policies  by  means  of  which  users  can  be[er  exploit          the  ability  to  reserve  and  co-­‐reserve  HPC  resources    To  develop  interfaces  which  permit  users  to  easily  deploy  and  monitor                  simula-ons  across  mul-ple  computa-onal  resources    To  visualize  and  steer  the  results  of  distributed  simula-ons  in  real  -me  
  12. 12. Clinical  SupercompuLng:  Diagnosis  and  Decision  Support  in  Surgery   •  Provide  simula-on  support  from  within  the  opera:ng  theatre  for   neuroradiologists   •  Provide  new  informa.on  to  surgeons  for  pa.ent  management  and   therapy:   Diagnosis  and  risk  assessment   Predic-ve  simula-on  in  therapy   •  Provide  pa-ent-­‐specific  informa-on  which  can  help  plan  embolisa-on   of  arterio-­‐venous  malforma-ons,  coiling  of  aneurysms,  etc.  
  13. 13. GENIUS  Clinical  Workflow  Book  compu-ng  resources  in  advance  or  have  a      system  by  which  simula-ons  can  be  run  urgently.  Shi^  imaging  data  around  quickly  over      high-­‐bandwidth  low-­‐latency  dedicated  links.  Interac-ve  simula-ons  and  real-­‐-me      visualisa-on  for  immediate  feedback.   15-20 minute turnaround
  14. 14. PaLent-­‐specific  HIV  Drug  Therapy    HIV-­‐1  Protease  is  a  common  target  for  HIV  drug  therapy   Monomer B Monomer A•  Enzyme  of  HIV  responsible  for  protein   101 - 199 1 - 99 matura-on   Flaps Glycine - 48, 148•  Target  for  An--­‐retroviral  Inhibitors  •  Example  of  Structure  Assisted  Drug   Design   Saquinavir•  9  FDA  inhibitors  of  HIV-­‐1  protease  So  what’s  the  problem?  •  Emergence  of  drug  resistant   P2 Subsite Catalytic Aspartic muta-ons  in  protease   Acids - 25, 125•  Render  drug  ineffec-ve   Leucine - 90, 190 C-terminal N-terminal•  Drug  resistant  mutants  have  emerged   for  all  FDA  inhibitors   EU FP6 ViroLab project and EU FP7 CHAIN project
  15. 15. agtgttaccgtactcatcagactcgaggttcaccgtactcatcagactcgaattcaccgtactcatcagactcgattcaccgtactcatcagactcgsattcaaacccttggatcaagtgttaccgtactcatcagactcgsattcaccgtactcatcagactcgattcaccgtactcatcagactcgsattcaccgtactcatcagactcgdsaddttcaaaccgggtcacacaagg
  16. 16. Too  many  muta-ons  to  interpret  by  a  clinician  Support  so^ware  is  used  to  interpret  genotypic  assays  from  pa-ents  Uses  both  in  vivo  and  in  vitro  data  Is  dependent  on   Size  and  accuracy  of  in  vivo   clinical  data  set   Amount  of  in  vitro  phenotypic   informa-on  available  -­‐  e.g.   binding  affinity  data  
  17. 17. Simulator  for  Personalised  Drug  Ranking   Simulator: a decision support software to assist clinicians for cancer treatment, and to reliably predicts patient-specific drug susceptibility.Array of available drugs SimulatorVariant of target from patient Ranking of drug binding The system could be used to rank proteins of different sequence with the same drug Rapid and accurate prediction of binding free energies for saquinavir-bound HIV-1 proteases. Stoica I, Sadiq SK, Coveney PV. J Am Chem Soc. 2008 Feb 27;130(8):2639-48. Epub 2008 Jan 29.
  18. 18. The  Life  Science  Problem   ExponenLal  development  of  science,   discovery,  and  engineering,  yet   This  does  not  seem  to  empower  medicine!     Promises  without  Delivery  
  19. 19. •  The  Human  Genome  Project  •  The  Virtual  Physiological  Human  (VPH)  ini-a-ve  •  VPH  Simula-on  Case  Studies  –  1)  clinical  decision  support   in  surgery  2)  towards  personalised  drug  design  •  INBIOMEDvision  –  challenges  ahead  •  EU  FET  Flagship  project  IT  Future  of  Medicine  •  Conclusions  
  20. 20. RESEARCH    MEDICINE     Research Clinic Reference datasets Population view Individual Patient Open Data Closed data English Language National Language Low legal involvement High level of legislation Trans-national National Entities Slide Courtesy of Ewan Birney
  21. 21. Bridging  gaps  between  BioinformaLcs  and  Medical  InformaLcs   Translational Bioinformatics Bioinformatics Linking Medical informatics in biomedical research Genotype In health care & (molecular, “omics”, To clinical research systems biology) Phenotype (EHR) Research re-use of clinical information
  22. 22. h[p://www.inbiomedvision.eu    
  23. 23. Challenges  ahead  secure management of the clinically-derived data across hospital-universityinterfaces, via development of large scale data integration warehouses, andback into clinical decision support systemsBiological  challenges   Societal  challenges   –  Do  we  understand  biology  and   –  Privacy   diseases  enough  to  develop   –  How  to  prevent  inequali-es  in   reliable  computa-onal  models?   access  to  health  care?   –  How  to  integrate  growing   –  Health  care  economics   knowledge  into  models?   –  Implementa-on  in  health  care   –  How  to  prevent  adverse  ICT  Challenges   effects/misuse?   –  Data  quality   –  Data  management   –  Data  security   –  User  interfaces  
  24. 24. Data  in  hospitals  
  25. 25. Medical  data  -­‐  Medical  imaging  (MRI,  CT,  etc.)  in  various  formats  (JPEG,  DICOM,  .xls  …)  -­‐  Pseudonymised  pa-ent  informa-on  (therapy  details,  follow-­‐up  diagnosis,   treatments,  etc.)  -­‐  Genomic,    DNA,  RNA,  protein/proteomics  data,  etc.  
  26. 26. Data  integraLon  &  management   •  How  to  store  heterogeneous  data  in  one  environment?   •  How  to  interface  with  the  various  types  of  data  to  understand  and  use?   (interoperability)   •  How  to  deal  with  the  large  size  of  data  resul-ng  from  complex   simula-ons,  e.g.  terabytes  and  petabytes?   •  How  to  acquire  and  transfer  •  Logis-cs   medical  data  from  resource   –  IT  infrastructure  handling  vast   providers   amounts  of  data   –  Burn  anonymised  data  on  CDs/ –  Availability  of  data  in  due  Lme   DVDs  and  pass  them  on  to   –  Data  storage/volume   researchers  vs  electronic   –  Access  to  HPC     transfer  from  provider  to  data   storage  directly?   –  Network  connecLvity  for  large   simulaLons  and  data   movements  
  27. 27. IMENSE:  Individualised  Medicine  SimulaLon  Environment   •  Central  integrated  repository  of  pa-ent  data  for  project  clinicians  &   researchers   –  Storage  of  and  audit  trail  of  computa-onal  results   –  Interfaces  for  data  collec-on,  edi-ng  and  display   –  Provides  a  data  environment  for  integra-on  of  mul--­‐scale  data  &   decision  support  environment  for  clinicians   •  Cri-cal  factors  for  Success  and  longevity   –  Use  Standards  and  Open  Source  solu-ons   –  Use  pre-­‐exis-ng  EU  FP6/FP7  solu-ons  and  interac-on  with  VPH-­‐ NoE  Toolkit   S. J. Zasada et al., “IMENSE: An e-Infrastructure Environment for Patient Specific Multiscale Modelling and Treatment, Journal of Computational Science, In Press, Available online 26 July 2011, ISSN 1877-7503, DOI: 10.1016/j.jocs. 2011.07.001.
  28. 28. Legal  and  ethical  issues  Data  breach  is  the  unauthorised  acquisi-on,  access,  use,  or  disclosure  of  protected  health  informa-on    ownership  of  data,  compliance,  what  are  the  applicable  laws  and  regula-ons    governing  the  data?  Audi-ng  in  the  cloud?   Autonomy   Well-­‐being   JusLce   Scien-sts   Freedom  to   Facili-es  and   Appropriate   research   funding   reward  e.g.  IP   Pa-ents   Right  to  know  or   Improved   Access  to   not  to  know   treatment  op-ons   resources   Vulnerable  groups   Right  to  be  heard   Allevia-on  of   Equality   disadvantage   Professional   Professional   Increased   Implica-ons  for   groups   judgment   burden?   prac-ce  
  29. 29. PaLent  Empowerment  
  30. 30. •  The  Human  Genome  Project  •  The  Virtual  Physiological  Human  (VPH)  ini-a-ve  •  VPH  Simula-on  Case  Studies  –  1)  clinical  decision  support   in  surgery  2)  towards  personalised  drug  design  •  INBIOMEDvision  –  challenges  ahead  •  EU  FET  Flagship  project  IT  Future  of  Medicine  •  Conclusions  
  31. 31. IT  Future  of  Medicine   h[p://www.ijom.eu        Up  to  €1B  EU  FET  flagship  proposal  •  Exploit  unprecedented  amounts  of  detailed   biological  data  being  accumulated  for  individual   people  •  Harness  the  latest  developments  in  ICT   –  large  scale  data  integra-on  and  mining,   cloud  compu-ng,  high  performance   compu-ng,  advanced  modelling  and   simula-on,     –  all  brought  together  in  a  highly  flexible   plajorm.    •  Turn  this  informa-on  into  knowledge  that   assists  in  taking  medical,  clinical  and  lifestyle   decisions  
  32. 32. Medicine  as  driver  of  ICT  innovaLon   ITFoM Health care Industry & society Computational models of Innovation User needs biological systems: cells ICT organs & individuals BiotechPersonalised medicine populations Pharma Public health Virtual patient Better drugs, disease prevention, evidence-based decision-making
  33. 33. A  virtual  paLent  integraLon  of  models     Tissues Anatomy Molecules Statistics 35
  34. 34. ICT  Layers  of  ITFoM  
  35. 35. •  The  Human  Genome  Project  •  The  Virtual  Physiological  Human  (VPH)  ini-a-ve  •  VPH  Simula-on  Case  Studies  –  1)  clinical  decision  support   in  surgery  2)  towards  personalised  drug  design  •  INBIOMEDvision  –  challenges  ahead  •  EU  FET  Flagship  project  IT  Future  of  Medicine  •  Conclusions  
  36. 36. Conclusions   •  Data-­‐intensive  projects,  and  more  future  projects  will  be.   –  biomedicine  community  is  starving  for  storage;     –  network  bandwidth  now  limi-ng:  a  faster  network  is  needed  for   data  movement.   •  Advanced  IT  allows  us  to  analyse  pa-ents  all  the  way  up   from  their  own  DNA  sequences   •  A  personalised  approach  is  expected  to  lead  to  improved     –  health  outcomes     –  treatments   –  lifestyle  choices  for  global  ci-zens  
  37. 37. Thank  you  for  your  a^enLon!   Nour  Shublaq   Centre  for  Computa-onal  Science   University  College  London,  UK   n.shublaq@ucl.ac.uk