HealthGrid: a Global Initiative to support Biomedical and VPH communities

1. HealthGrid: a Global Initiative to support Biomedical and VPH communities Yannick Legré (Maat-G knowledge) president of HealthGrid (yannick.legre@healthgrid.org/ylegre@maat-g.com) Groupe de Travail GrilleClermont-Fd– April 29th, 2009http://healthgrid.orghttp://roadmap.healthgrid.orghttp://spider.healthgrid.org The Action-Grid project is funded by the European Commission under the 7th framework program

2. Past focus: Linking all the point of care within a region/country Emergency Hospital Health Centre Pharmacy Secure Networks Region 3 Mobile, Wireless &Broadband mobile PC Region 2 Region 1 Home

3. Current R&D focus (since 1999 - )‏ Connecting individuals with Health Information Networks Emergency Hospital Health Centre Pharmacy Secure Networks Region 3 Mobile, Wireless &Broadband mobile PC Region 2 Region 1 Mobility Home ••• 3

4. New and Future Activities Towards full picture of individual’s health status Biochips Biosensors Genomic data Environmental Data Phenomic data ICT Systems

5. Synthesis of all “Health Information levels” Public Health Informatics Medical Informatics Biomedical informatics Medical Imaging Bioinformatics

6. HealthGrid (1/2) The term “HealthGrid” was coined in 2002 The concept of “grids for health” was described in the HealthGrid White Paper in 2005 (http://whitepaper.healthgrid.org). It set out a vision of the opportunities and potential benefits offered by applying grids in different areas of biomedicine and healthcare. A roadmap for the wide adoption and deployment of HealthGridtechnologies in Europe was issued early in 2008 (http://roadmap.healthgrid.org)‏

7. HealthGrid (2/2) The HealthGrid vision relies on the setting up of grid infrastructures for medical research, healthcare, and the life sciences … which implies: the availability of grid services, most notably for dataand knowledge management; the deployment of these services on infrastructures involving healthcare centres (e.g. hospitals), medical research laboratories and public health administrations; and the definition and adoption of international standards and interoperability mechanisms for medical information stored on the grid.

8. A perspective on the present use of grids(1/2)‏ Use of grids for biomedical sciences Life Sciences To address complexity of databases interoperability (e.g. Embrace)‏ To ease the design of data analysis workflow (e.g. MyGrid)‏ Medical Research To store and manipulate large cohorts of medical images (e.g. Mammogrid)‏ To bring together and to correlate patient medical and biological data (e.g. ACGT)‏ Drug Discovery First step of a full in silico drug discovery process successfully proven (e.g. Wisdom)‏ To reduce time and save money in the drug discovery process

9. Example n°1: BiG, BLAST in Grid Scientific objectives Speed-up and Ease the use of a Well-knownApplication for Protein and Nucleotid Alignment. Applications in Drug Development, Phylogeny, etc. Method MPI-Blast. Splitting of Input Sequences and Reference Databases into Multiple Jobs. Deals with Multiple Databases Simultaneously. Enhanced Security Through a MyProxy Server. Fault Tolerant on the Client and Server Side. Embeddable on a Stand-alone Application or Web Portal. Status: Production in EELA. Contact: Vicente Hernández (UPV ), vhernand@dsic.upv.es Ignacio Blanquer (UPV), iblanque@dsic.upv.es

10. Example n°2: OpenGATE Geant4 Application for Emission Tomography (GATE - www.opengatecollaboration.org)‏ Simulation toolkit adapted to nuclear medicine Innovative feature: inclusion of time-dependent effects Grid used to improve and speed up simulations. Requires Geant4: large, complex package. Individual simulations not easily divisible. Simulation of decay of O-15 (green) and C-11 (blue)‏ 0-2 min 7-9 min 14-16 min

11. Example n°3: WISDOM WISDOM (http://wisdom.healthgrid.org)‏ Developing new drugs for neglected and emerging diseases with a particular focus on malaria and H5N1. Reduced R&D costs for neglected diseases Accelerated R&D for emerging diseases Three large calculations: WISDOM-I (Summer 2005)‏ Avian Flu (Spring 2006)‏ WISDOM-II (Autumn 2006)‏ WISDOM calculations used FlexX from BioSolveIT (3-6k free, floating licenses) in addition to Autodock.

12. A perspective on the present use of grids(2/2)‏ Adoption of grids for healthcare Still in its infancy… For many good reasons The technology is still rapidly evolving and providing new features. Although it is today not possible to implement a full stable operational system as changes are still expected, first implementations can be done and updated providing a primary set of functionalities. All grid infrastructure projects are deployed on national research and education network which are separate from network used by healthcare services. Legal framework in EU member states which has to evolve to allow the transfer of medical data between member states

13. Pharmacokinetic modeling of blood perfusion: Technique provides quantitative assessment of angiogenesis Angiogenesis is important marker for aggressiveness of tumors Time-series of images allows measurement of modelparameters Computationally intensive Images must be aligned Elastic organs make job harder Example n°4: Pharmacokinetics‏ 0.35 Bright (Concentration)‏ 0.30 0.25 0.20 0.15 0.10 0.05 0 50 100 150 200 250 Time (s)‏

14. Pharmacokinetics Results Computing costs for a study involving 20 patients. Significant reduction in real time: Faster research results Could imagine use in clinical setting Understand tumor aggressiveness and response to therapies Sequential (2623h, 1 CPU)‏ HPC (146h, 20 CPUs)‏ Grid (17.5h, 240 CPUs)‏

15. Example n°5: gPTM3D‏ Interactive analysis of 3D data for surgery planning and volumetric analysis. Requires “guiding” from physician to find initial contours, work around noisy data, … Needs unplanned, interactive access to significant computational resources.

16. The Virtual Physiological Human (VPH) concept Based on the ideas of the International Physiome project The Virtual Physiological Human is a methodological and technological framework that once established will enable the investigation of the human body as a single complex system. The VPH research roadmap developed by project STEP in 2007: www.europhysiome.org - Personalised (patient-specific) healthcare solutions - Early diagnostics & predictivemedicine - Understandingdiseases for the first time acrossseveralbiologicallevels

17. VPH from an ICT perspective Computational framework for multi-scale in-silico model(s) of the human physiology and a toolbox for simulation and visualisation. Patient specific model from bio-signals and (multimodal) images including molecular images Technologies involved: biomedical modelling, simulation and visualisation techniques, imaging, data mining, knowledge discovery tool, semantic integration, databanks, HealthGrid (infrastructure and tools)

18. Virtual Physiological Human Network of Excellence (VPH NoE)‏ NoE: team of organisations working in key focus areas to support and enable VPH research, within and beyond the VPH Initiative - Identification of user needs, standards, ontologies, applications and development of VPH ToolKit - VPH training activities and materials - MSc, interdisciplinary study groups, focused journal issues, textbook - Research/news dissemination and international networking Project coord.: Catherine Gale (UCL)‏ Scientific coord.: Peter Coveney (UCL) & Peter Kohl (Oxford)‏ 13 partners (12 universities, 1 company)19 General Members9 Associate Projects3 Associate Members (organisations)5 Associate Members (industry) Budget ~9.7M€ (~8M€ EU funding)‏

19. preDiCT – Computational Predictionof Drug Cardiac Toxicity In silico assessment of population specific drug cardiac toxicity – on multiple scales from ion channel to whole ventricle. • Validated, predictive models • Simulate a heartbeat “faster than real-time” • Virtual research environment (VRE)‏ • New biomarkers • Safer drugs • Reduced drug development costs Project & Scientific coord.: University of Oxford 9 partners (5 companies, 4 universities)‏ Budget ~5.5M€ (~4.1M€ EU funding)‏

20. Mammogrid+A Grid-powered Mammography Database Healthgrid • Second Opinion • Cancer Screening • Education and Training • ReferenceDatabase / Repository Courtesy of D. Manset - www.maat-g.com

21. Health-e-Child, An Healthgrid for Paediatrics Healthgrid • Decision Support • Vertical Data Integration • KnowledgeDiscovery • ReferenceDatabase of Rare Conditions Courtesy of D. Manset - www.maat-g.com

22. neuGRIDA Grid-based e-Infrastructure for Data Archiving and Computing Intensive Applications in neurosciences Healthgrid *Courtesy of Prodema Inf. • BiomarkersAssessment • BiomarkersAuthoring • Extension to OtherMedical Areas • ReferenceDatabase & Infrastructure QuestionA Marker of Alzheimer’s progression? Courtesy of D. Manset - www.maat-g.com

23. Pharmaco-EpidemiologyMulti-centre Pharma Grid-basedData Warehouse for EpidemiologicalStudies Healthgrid • EpidemiologicalStudies • ScalableDataware House • ScalableStatisticalAnalysis • TranslationalResearch • ReferenceDatabase Courtesy of D. Manset - www.maat-g.com

24. SENTINELLE NetworkBreast and Colon Cancer Screening,Surveillance Network Healthgrid • Breast Cancer Screening • Colon Cancer Screening • Surveillance, Alert and Monitoring Network Courtesy of D. Manset - www.maat-g.com

25. ACGTAdvanced ClinicoGenomic Trialson Cancer Healthgrid • PaediatricNephroblastoma • Breast Cancer • Oncosimulator • Master Ontology • Clinical Trials

26. ICT for Health – Summary of eHealth Activities and Plans Basic research Long term R&D Biomedical Infomatics Virtual Physiological Human Mid term R&D HealthGrid Personal Health Systems (wearables)‏ ICT for Patient Safety Support to Deployment eHealth “Action Plan”, Recommendation on Interoperability, Communication on Telemedicine (2009) EHR & interoperability Deployment 5 years 10 years 15 years Time to results

27. SHARE Roadmap • EU-FP6 eHealth Portfolio Survey • A LeapToward a FullyOperationalEuropean • Healthgrid: SPIDeR

28. is a roadmap for the wide adoption & deployment of grid technologies for healthcare and biomedical research in Europe Not focused only on technical challenges but has explored the wide range of ELSE issues (Ethical, Legal & Socio-Economic) Duration: January 2006 – March 2008 www.eu-share.org for more information Share

29. phase 1 phase 2 Sustainable data grid Sustainable knowledge grid Generalized use of knowledge grids Sustainable computing grid Reference implementation of grid services Agreed medical informatics & grid standards Agreed open source medical ontologies Toward a Roadmap Reference distribution of grid services

30. The Computational example: Innovative Medicine The Data example: Epidemiology The Collaboration examples: Breast Cancer screening, Paediatrics, Neurology & VPH The Knowledge example: General Healthcare Starting from Users’ Requirements

31. Computational Grids

32. Research challenges for: Computing grids Data grids Knowledge grids RCCG2 RCCG4 TIME Interoperability of Infrastructures Usability RCCG8 RCCG5 RCCG6 RCCG3 Quality of service RCCG9 On demand access RCCG7 RCCG10 RCCG1 Computational Grids

33. Data Grids

34. Research challenges for: Computing grids Data grids Knowledge grids TIME RCDG4 RCDG2 RCDG6 Improved distributed data management Distributed data models RCDG5 RCDG7 RCDG1 Quality of service RCDG3 Data Grids

35. Collaboration Grids

36. Workflow management Medical protocols and guidelines RCLG2 RCLG5 Data integration RCLG9 RCLG6 RCLG1 RCLG3 Coordination and communication COMPLEXITY RCLG8 RCLG4 Provenance management RCLG10 RCLG7 Collaboration Grids

37. Knowledge Grids

38. Research challenges for: Computing grids Data grids Knowledge grids TIME RCKG1 RCKG5 RCKG2 RCKG3 Grid technology challenges Defining standards and ontologies Research area challenges RCKG4 RCKG6 RCKG7 Knowledge Grids

39. Liability for Products & Services RCCG2 RCCG4 Interoperability of Infrastructures Usability RCCG8 RCCG5 RCCG6 Trust & Acceptance Assurance RCCG3 Quality of service RCCG9 On demand access RCCG7 RCCG10 Legal/Ethical Issues Cost-Benefit Estimation RCCG1 Socio-Economic Issues TIME ELSE Concerns in Computational Grids

40. Patient consent RCDG4 Improved distributed data management RCDG5 RCDG2 RCDG6 Predefined processing purposes Distributed data models RCDG7 Data security harmonization measures RCDG1 Quality of service Legal/Ethical Issues RCDG3 Sustainability guarantees Socio-Economic Issues TIME ELSE Concerns in Data Grids

41. Intellectual property New policies and codes of conduct Legal/Ethical Issues Dissemination programmes Socio-Economic Issues TIME RCKG1 RCKG5 RCKG2 RCKG3 Grid technology challenges Defining standards and ontologies Research area challenges RCKG4 RCKG6 RCKG7 ELSE Concerns in Knowledge Grids

42. FP6 eHealth Survey FP6 eHealth Portfolio A Survey

43. eHealth Projects Overview

44. Medical Areas Overview

45. Projects Waves 2nd Wave 1st Wave Share

46. FP6 eHealth Survey Projects Technologies Regular Technologies

47. Biomedicalresearch in Europeisheterogeneous, • fragmented and geographicallydispersed •  BUT richin results… a goldmine! • ProductionqualityInfrastructures are veryfew, non interoperable norsustainable •  oftensuchInfrastructures stop existing once projects are over… • At least 80% of thecommunityisdevelopingserviceorientedtools • No EuropeanInfrastructuredeployed in hospitalsnor in (bio)medical centres •  Technologies are oftentoocomplexoradvancedtobeadopted…

48. Astrophysics VO WeatherForecast VO Biomedics VO . . . . . . . e-Infrastructure -Vision Connecting researchers Sharing the best scientific resources Building global virtual communities Sharing and federating scientific data Sharing computers, instruments and applications Linking at the speed of the light *Courtesy of Maria Ramalho Natario

49. GRID . INFRASTRUCTURE e-Infrastructure - Reality Commodity Data Services Community Data Services CONNECTIVITY. INFRASTRUCTURE

50. SPIDeR Objectives • (1) Strengthen Existing & Support New Scientific Data Infrastructures • Data transfer, storage, curation, archiving • Data access, interpret, preservation, certification • (2) Do not reinvent the wheel... • Crystallise research result • Promote technology reuse • Capitalize knowledge • (3) Assess Infrastructure OfferTogetherwithConcreteProjects • Pairs/Tuples of existing/new projects to validateSPIDeRoffer • Connection of projects to SPIDeRanticipated in budget • SPIDeRWorkplan to follow and support projectswaves