Bio it worldexpoeurope2012_shublaq

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Bio/Healthcare-IT Research Priorities in Europe

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Bio it worldexpoeurope2012_shublaq

  1. 1. Towards  Personal   Health  Records,   Transla3onal   Research,  and  a   Truly  IT  Revolu3on   of  Medicine   Nour  Shublaq,  PhD   Centre  for  Computa-onal  Science   University  College  London,  UK   n.shublaq@ucl.ac.uk  From Drug Discovery Informatics to Personalised Therapeutics – Oct 2012, Vienna
  2. 2. Overview  •  Why  Personalised  Medicine?    •  The  Virtual  Physiological  Human  (VPH)  ini-a-ve  •  VPH  Simula-on  Case  Study  –  towards  personalised   drug  design  •  Infrastructure  suppor-ng  drug  discovery  –  Improving   the  odds  of  the  Medical  LoMery  •  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!)Cost of whole genome sequencing expected to drop to $100 in a few years 4
  5. 5. hMp://www.inbiomedvision.eu    
  6. 6. Challenges  ahead  Biological  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  
  7. 7. 1 Genotype-­phenotype  resources   Complex  disease   networks  Molecular-­level  models   (GWAS,  PPI,  …)   Disease  gene   Oinding   &  sema ing     ntic   2 in Disease   web   Developments   Text  m susceptibility  System-­level  models   (organ  networks,…)   Phenotypic   variation   Pharmacogenomics   Clinical  phenotypes   (EHR,  multi-­scale   physiological  models…)   Exposome   3 Translational  Systems  Biology   (drugs,  diet,  environmental   chemicals,…)   Nour Shublaq et al. (2012) – under review
  8. 8. •  Why  Personalised  Medicine?    •  The  Virtual  Physiological  Human  (VPH)  ini-a-ve  •  VPH  Simula-on  Case  Study  –  towards  personalised   drug  design  •  Infrastructure  suppor-ng  drug  discovery  –  Improving   the  odds  of  the  Medical  LoMery  •  Conclusions  
  9. 9. What  is  the  VPH?    •  The Virtual Physiological Human is a methodological and technological Organism descriptive, integrative and Organ predictive, framework that is Tissue intended to enable the investigation of the human body as a single Cell complex system Organelle Interaction Protein•  Aims Cell •  Enable collaborative Signals investigation of the human Transcript body across all relevant scales Gene •  Introduce multiscale Molecule methodologies into medical €207M initiative and clinical research in EU-FP7
  10. 10. The  challenge:  organs  to  proteins    Environment   → Medical informaticsOrganism  Organ  system   → Personalised medicine Heart Lungs Diaphragm Knee Colon Liver EyeOrgan   x 1million 20 generations Cardiac sheets Acinus Osteon Lymph node Liver lobule NephronTissue  Cell  Network  Protein  Gene  Atom  
  11. 11. •  Why  Personalised  Medicine?    •  The  Virtual  Physiological  Human  (VPH)  ini-a-ve  •  VPH  Simula-on  Case  Study  –  towards  personalised   drug  design  •  Infrastructure  suppor-ng  drug  discovery  –  Improving   the  odds  of  the  Medical  LoMery  •  Conclusions  
  12. 12. Drug  Selec3on  and  Drug  Design      Assessment  of  the  binding  of  small  molecules  to  proteins   key  to  both  drug  discovery  and  treatment  selec-on    Techniques  applicable  to  one  area  can  also  be  used  in   another    Quan-fying  drug  –  protein  binding  strength  requires   atomis-cally  detailed  models    Time  to  comple-on  key  in  both  drug  discovery  and          clinical  applica-ons  
  13. 13. WIREs Syst Biol Med, Aug 2012Chem Biol DrugDes specialtheme, Jan 2013Epub Jul 2012
  14. 14. Pa3ent-­‐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. Clinical  SeSng  –  HIV  drug  ranking   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  Patient sequence for which existing clinical decision support toolsprovide differing resistance assessments
  17. 17. Simulator  for  Personalised  Drug  Ranking   BAC Simulator: a decision support software to assist clinicians for cancer treatment, and to reliably predicts patient-specific drug susceptibility.Array of available drugs BAC 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. High  Throughput  Automa3on  •  Needs a grid or grid- of-grids•  We calculate “many” binding affinities rapidly•  Do not need to manually launch each simulation Technological  environment  accesses   worldwide  Grid  resources  
  19. 19. HIV-­‐1  Protease:    Mul3ple  Drug  Resistance   •  Simulate  5  clinically   relevant  variants  bound  to   inhibitor  lopinavir   •  Reproduce  experimental*   binding  affinity  ranking   •  Require  mul-ple   simula-ons  to  efficiently   explore  relevant  ensemble   of  structures   Sadiq et al. J Chem Inf Model 50(5), 890-905 * Ohtaka et al. Biochemistry 2003, 42 (46), 13659-13666
  20. 20. HIV-­‐1  Protease:    Mul3ple  Drug  Resistance   •  Effect  of   muta-onal   combina-ons   superaddi-ve   •  50  replica   simula-ons   performed  for  each   data  point   •  Results  replicated   to  within  1.3  kcal/ mol    
  21. 21. EGFR  muta3ons  for  lung  cancer   A750P •  Over  expression  of     Epidermal  Growth  Factor   L747-E749 del Receptor  (EGFR)  is   associated  with  cancer   L858RG719S •  Target  for  inhibitory  drugs   •  Important  muta3ons   include  dele3ons   •  Again  binding  affinity   calcula3ons  can  be  used  to   determine  muta3onal   effects   EGFR Tyrosine Kinase Domain
  22. 22. •  Why  Personalised  Medicine?    •  The  Virtual  Physiological  Human  (VPH)  ini-a-ve  •  VPH  Simula-on  Case  Study  –  towards  personalised   drug  design  •  Infrastructure  suppor-ng  drug  discovery  –  Improving   the  odds  of  the  Medical  LoMery  •  Conclusions  
  23. 23. E-­‐infrastructure    -­‐  Collec3on  of  pa3ent  data  &  storage  -­‐  Access  to  high  performance    compu3ng  infrastructure  to  perform    drug  response  simula3ons  based  on    the  characteris3cs  of  an  individual    
  24. 24. IMENSE:  Individualised  Medicine  Simula3on  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.
  25. 25. P-­‐Medicine     Disease Disease Disease Multi-scale therapy Modelling at the Modelling at the G S G M 1 2 G 0 Modelling at the predictions/disease molecular Level cellular Level N A tissue/organ evolution results Level •  Predic-ve  disease  modelling   •  Exploi-ng  the  individual  data  of  the  pa-ent     •  Op-miza-on   of   cancer   treatment   (Wilms   tumor,   breast   cancer   and   acute  lymphoblas-c  leukemia)   •  Scalable  for  any  disease,  as  long  as:   –  predic-ve  modeling  is  clinically  significant  in  one  or  more  levels   –  development  of  such  models  is  feasible   Led  by  a  clinical  oncologist    -­‐  Prof  Norbert  Graf!    €13M,  2011-­‐2013,  EU  FP7  
  26. 26. VPH-­‐Share     HIV Heart Aneurisms Musculoskeletal VPH-­‐Share  will  provide  the  organisa>onal  fabric  realised  as  a  series  of  services,  offered   in  an  integrated  framework,  to  expose  and  to  manage  data,  informa>on  and  tools,  to   enable  the  composi>on  and  opera>on  of  new  VPH  workflows  and  to  facilitate   collabora>ons  between  the  members  of  the  VPH  community.   €11M,  2011-­‐2015,  EU  FP7  –  Promotes  cloud  technologies      
  27. 27. IT  Future  of  Medicine  Up  to  €1B  EU  Future  Emerging  Technologies  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  plakorm.    •  Turn  this  informa-on  into  knowledge  that  assists  in  taking   medical,  clinical  and  lifestyle  decisions   hMp://www.ikom.eu        
  28. 28. 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
  29. 29. Use  Case:  Cancer  Treatment     Mutation Database The Cancer Model Drug Database X 31Tumor sampling GenomeTumor stem cell extraction/ and Transcriptomeexpansion sequencing X X Modeling Drug treatment Drug Response recommendation Patient Specific Model
  30. 30. ICT  Layers  of  ITFoM  
  31. 31. Rela3on  to  EU  Infrastructures    European Sequencing andGenotyping Infrastructure ISBE Integrated Structural Biology InfrastructureInfrastructure for SystemsBiology – Europe Partnership for Advanced Computing in Europe 33
  32. 32. Computational Life and ife  and  Medical  Sciences   UCL  Computa3onal  L Medical Science (CLMS)  Network   hMp://www.clms.ucl.ac.uk           Management:UCL Partners: 14 NHS Supported by the Dean’s CommitteeTrusts and affiliatedhealthcare institutes/clinics Provosts Strategic Fund Steering Committee
  33. 33. CLMS Goals1. Maintain and expand UCL’s world-leading position in life and biomedical sciences2. Improve collaboration with academic institutions: within UCL, with UCLP and the NHS, Francis Crick Institute, Yale, and others3. Take advantage of new initiatives in integrative biomedical systems science from the UK Research Council, EU and others around the world4. Improve collaboration with industry, create business and commercial opportunities, promote UCL IP licensing5. Plan for the next stages of activity in computational life and medical sciences at UCL
  34. 34. •  Why  Personalised  Medicine?    •  The  Virtual  Physiological  Human  (VPH)  ini-a-ve  •  VPH  Simula-on  Case  Study  –  towards  personalised   drug  design  •  Infrastructure  suppor-ng  drug  discovery  –  Improving   the  odds  of  the  Medical  LoMery  •  Conclusions  
  35. 35. Conclusions   •  Medicine  today  is  a  driver  of  ICT  innova-on  and  vice  versa.   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     –  drugs/treatments   –  disease  preven-on   –  evidence-­‐based  decision-­‐making   –  lifestyle  choices  for  global  ci-zens  
  36. 36. Thank  you  for  your  aden3on!   Nour  Shublaq,  PhD   CREST University  College  London,  UK   n.shublaq@ucl.ac.uk   CREST CREST CRESTepcc|crestaVisual Identity Designs

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