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   Sequencing of the human genome was profoundly important science that led to fundamental shifts in our understanding of biology. 30,000 – 40,000 protein coding genes in the human genome and not more than 100,000 previously thought. Thousands of DNA variants have now been associated with traits/diseases. Human Genome Project, International HapMap Project, and Genome wide association studies (GWAS) in the last decade Structure  Mol.  Profiles  Genomic   2 10 3000 30,000
  4. 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!)
  5. 5. 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,   given  a  specific  environment   (PentiumV) (neuronal net visualisation) Genome Phenotype Organism  =  Computer   Genome  &  the  Environment   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. •  The Virtual Physiological Human is a methodological and technological descriptive, integrative and predictive, framework that is intended to enable the investigation of the human body as a single complex system •  Aims •  Enable collaborative investigation of the human body across all relevant scales •  Introduce multiscale methodologies into medical and clinical research Organism Organ Tissue Cell Organelle Interaction Protein Cell Signals Transcript Gene Molecule €207M initiative in EU-FP7 What  is  the  VPH?    
  8. 8.  …pa-ent-­‐tailored  computer   models,  used  for  diagnosis,   preven-on,  drug  treatment   and  surgical  planning  –   assess  treatment  before   administering   Modelling  how  the  human  body  works     Slide Courtesy of S. Kashif Sadiq
  9. 9. Environment Population dimensional  scales   temporal  scales   Organism Organ System Organ Tissue Cell Molecule Atom IntegraLon  across..   organ  systems  
  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. HIV-­‐1  Protease  is  a  common  target  for  HIV  drug  therapy   •  Enzyme  of  HIV  responsible  for  protein   matura-on   •  Target  for  An--­‐retroviral  Inhibitors   •  Example  of  Structure  Assisted  Drug   Design   •  9  FDA  inhibitors  of  HIV-­‐1  protease   So  what’s  the  problem?   •  Emergence  of  drug  resistant   muta-ons  in  protease   •  Render  drug  ineffec-ve   •  Drug  resistant  mutants  have  emerged   for  all  FDA  inhibitors   Monomer B 101 - 199 Monomer A 1 - 99 Flaps Leucine - 90, 190 Glycine - 48, 148 Catalytic Aspartic Acids - 25, 125 Saquinavir P2 Subsite N-terminalC-terminal EU FP6 ViroLab project and EU FP7 CHAIN project PaLent-­‐specific  HIV  Drug  Therapy    
  15. 15. agtgttaccgtactcatcagactcgaggttcaccgta ctcatcagactcgaattcaccgtactcatcagactcg attcaccgtactcatcagactcgsattcaaacccttg gatcaagtgttaccgtactcatcagactcgsattcac cgtactcatcagactcgattcaccgtactcatcagac tcgsattcaccgtactcatcagactcgdsaddttcaa accgggtcacacaagg
  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. Variant of target from patient Array of available drugs Simulator 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. Reference datasets Population view Open Data English Language Low legal involvement Trans-national Research Clinic Individual Patient Closed data National Language High level of legislation National Entities RESEARCH    MEDICINE     Slide Courtesy of Ewan Birney
  21. 21. Bioinformatics in biomedical research (molecular, “omics”, systems biology) Medical informatics In health care & clinical research (EHR) Translational Bioinformatics Research re-use of clinical information Linking Genotype To Phenotype Bridging  gaps  between  BioinformaLcs  and   Medical  InformaLcs  
  22. 22. h[p://www.inbiomedvision.eu    
  23. 23. Challenges  ahead   Biological  challenges   –  Do  we  understand  biology  and   diseases  enough  to  develop   reliable  computa-onal  models?   –  How  to  integrate  growing   knowledge  into  models?   ICT  Challenges   –  Data  quality   –  Data  management   –  Data  security   –  User  interfaces   Societal  challenges   –  Privacy   –  How  to  prevent  inequali-es  in   access  to  health  care?   –  Health  care  economics   –  Implementa-on  in  health  care   –  How  to  prevent  adverse   effects/misuse?   secure management of the clinically-derived data across hospital-university interfaces, via development of large scale data integration warehouses, and back into clinical decision support systems
  24. 24. Data  in  hospitals  
  25. 25. -­‐  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.   Medical  data  
  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   medical  data  from  resource   providers   –  Burn  anonymised  data  on  CDs/ DVDs  and  pass  them  on  to   researchers  vs  electronic   transfer  from  provider  to  data   storage  directly?   –  Network  connecLvity  for  large   simulaLons  and  data   movements   •  Logis-cs   –  IT  infrastructure  handling  vast   amounts  of  data   –  Availability  of  data  in  due  Lme   –  Data  storage/volume   –  Access  to  HPC    
  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   Autonomy   Well-­‐being   JusLce   Scien-sts   Freedom  to   research   Facili-es  and   funding   Appropriate   reward  e.g.  IP   Pa-ents   Right  to  know  or   not  to  know   Improved   treatment  op-ons   Access  to   resources   Vulnerable  groups   Right  to  be  heard   Allevia-on  of   disadvantage   Equality   Professional   groups   Professional   judgment   Increased   burden?   Implica-ons  for   prac-ce   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?  
  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. •  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   IT  Future  of  Medicine   Up  to  €1B  EU  FET  flagship  proposal   h[p://www.ijom.eu        
  32. 32. Medicine  as  driver  of  ICT  innovaLon   Health care & society User needs Personalised medicine Public health ITFoM Industry ICT & Biotech Pharma Computational models of biological systems: cells organs individuals populations Innovation Virtual patient Better drugs, disease prevention, evidence-based decision-making
  33. 33. A  virtual  paLent  integraLon  of  models     Molecules Tissues Anatomy 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. •  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   Conclusions  
  37. 37. Thank  you  for  your  a^enLon!   Nour  Shublaq   Centre  for  Computa-onal  Science   University  College  London,  UK   n.shublaq@ucl.ac.uk  

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