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2015 03-11 Opening EATRIS Finland, Helsinki

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View how to enable personalzied healthcare through integrated technolgies, with great challenges to bridge the innovation gaps in biomarkers.

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2015 03-11 Opening EATRIS Finland, Helsinki

  1. 1. Personalized health(care) through integrated technologies Opening EATRIS Finland Helsinki 11 March 2015 Professor in Personalized Healthcare Head Radboud Center for Proteomics, Glycomics and Metabolomics Coordinator Radboud Technology Centers Head Biomarkers in Personalized Healthcare Prof Alain van Gool
  2. 2. My background 8 years academia (NL, UK) (molecular mechanisms of disease) 13 years pharma (EU, USA, Asia) (biomarkers, Omics) 3 years applied research institute (NL, EU) (biomarkers, personalized health) 3 years university medical center (NL) (personalized healthcare, Omics, biomarkers) 1991-1996 1996-1998 2009-2012 1999-2007 2007-2009 2009-2011 2011-now 2011-now 2
  3. 3. Radboud university medical center • Nijmegen, The Netherlands • Mission: “To have a significant impact on healthcare” • Strategic focus on Participatory and Personalized Healthcare through “the patient as partner” • Core activities: • Patient care • Research • Education • 11.000 colleagues • 52 departments • 3.300 students • 1.000 beds • First academic centre outside US to fully implement EPIC 3
  4. 4. Takehome message • Strategic focus on implementing Personalized Healthcare • Strong technological and methodological infrastructure • Continuous exploration of functional networks 4
  5. 5. Patient Radboud Personalized Healthcare A significant impact on healthcare Molecule Population 5
  6. 6. Personalized Healthcare @ Radboudumc People are different Stratification by multilevel diagnosis +Patient’s preference of treatment Exchange experiences in care communities Select personalized therapy 6
  7. 7. Personalized diagnostics @ Radboudumc 7
  8. 8. Personalized genomic diagnostics {Nature, July 17 2014, 511: 344-} 8
  9. 9. 2012 Patient Targeted Metabolic screen Targeted gene analysis Diagnosis + follow-up 2013 / 2014 Patient Whole exome sequencing Targeted confirmatory metabolite + enzyme testing Diagnosis + follow-up Targeted assays vs holistic approach Next generation metabolic screening Times are changing… add functional genome diagnostics 9
  10. 10. Human samples Plasma, CSF (urine) Controls vs. patient QTOF Mass Spectrometry - Reverse phase liquid chromatography - Positive and negative mode - Features XCMS Alignment Peak comparison > 10,000 Features Personalized metabolic diagnostics Xanthine Uric acid 10 Full metabolite profile: Highly suspected of xanthinuria
  11. 11. Research Biomarkers Diagnostics Department of Laboratory Medicine, Radboudumc Integrated Translational Research and Diagnostic Laboratory, 220 fte, yearly budget ~ 28M euro. Close interaction with Departments of Genetics, Pathology and Medical Microbiology Specialities: • Proteomics, glycomics, metabolomics • Enzymatic assays • Neurochemistry • Cellulair immunotherapy • Immunomonitoring Areas of disease: • Metabolic diseases • Mitochondrial diseases • Lysosomal /glycosylation disorders • Neuroscience • Nefrology • Iron metabolism • Autoimmunity • Immunodeficiency • Transplantation In development: • ~500 Biomarkers • Early and late stage • Analytical development • Clinical validation Assay formats: • Immunoassay • Turbidicity assays • Flow cytometry • DNA sequencing • Mass spectrometry • Experimental human (-ized) invitro and invivo models for inflammation and immunosuppression Validated assays*: • ~ 1000 assays • 3.000.000 tests/year Areas of application: • Personalized healthcare • Diagnosis • Prognosis • Mechanism of disease • Mechanism of drug action *CCKL accreditation/RvA/EFI www.laboratorymedicine.nl 11 Diagnostic power in departments: Example: Department of Laboratory Medicine
  12. 12. Orientation across the spectrum from molecule to man to population Orientationacross thespectrumofdiseases PI Research theme TechnologyCenters Research support by Technology Centers 12
  13. 13. Radboudumc Technology Infrastructure Get organised: 1. What technological expertise do we have and should we have ? 2. How should we organise this ? 3. How will we communicate this ? Activities: • Make inventories on current state and desired future state. • Work with technology coordinators + departments (research, clinical, strategy, communication , valorisation). • Include input from research themes. • Organize monthly full team meetings + many 1:1 meetings. • Discussed output with research institutes, executive board. • Implementation structure 1.0 by 1H2014. Improve in version 2.0 1H2015. 13
  14. 14. External role Internal role • Knowledge hub for technological expertise • Maximise use of available technical capabilities and knowledge (‘duurzaamheid’) • Advise scientists with technological expertise • Advise management on strategic investments and opportunities • Drive innovations by working with each other, theme’s and Valorisation • Easy access to Radboudumc’s technological expertise • Represent Radboudumc as one in external technology networks • Increase funding (grants, contract research) with Valorisation Internal / external role Radboudumc Technology Centers
  15. 15. Technology Platforms UMC St Radboud (Potential) Technology Platforms Genomics RPC CMBI PRIME MIC CDL CRCN Radboud BiobankMalaria lab Flow cytometry TR&CT TNU MITeC PDRC December 2013 15 Inventory phase
  16. 16. Radboudumc Technology Centers Genomics Bioinformatics Animal studies Flow cytometry Translational neuroscience Image-guided treatment Imaging Microscopy Biobank Data stewardship Proteomics Glycomics Metabolomics Radboudumc Technology Centers GMP products Clinical trials January 2014 16 Repositioning phase
  17. 17. • Align with the needs of the Research and Education, and contribute to output and quality of those • Organise each Technology Center as a single portal • Add other Technology Centers when needed and useful • Keep improving efficiency and funding Radboudumc Technology Centers Improving phase 17 Feb-Oct 2014
  18. 18. www.radboudumc.nl/research/technologycenters Genomics Bioinformatics Animal studies Stem cells Translational neuroscience Image-guided treatment Imaging Microscopy Biobank Health economics Mass Spectrometry Radboudumc Technology Centers Investigational products Clinical trials EHR-based research Statistics Human physiology Data stewardship Molecule Flow cytometry Mar 2015
  19. 19. 19 • Proteins • Metabolites • Drugs • PK-PD • Preclinical • Clinical • Behavioural • Preclinical • Animal facility • Systematic review • Cell analysis • Sorting • Pediatric • Adult • Phase 1, 2, 3, 4 • Vaccines • Pharmaceutics • Radio-isotopes • Malaria parasites • Management • Analysis • Sharing • Cloud computing • DNA • RNA • Internal • External • Early HTA • Evidence-based surgery • Field lab • Statistics • Biological • Structural • Preclinical • Clinical• Economic viability • Decision analysis • Experimental design • Biostatistical advice • Electronic Health Records • Big Data • Best practice • In vivo • Functional diagnostics About 250 dedicated people working in 18 Technology Centers, ~1600 users (internal, external), ~140 consortia www.radboudumc.nl/research/technologycenters/ • iPSC • Organoids
  20. 20. Cross-technology interactions 20
  21. 21. www.radboudumc.nl/research/technologycenters/ Combination of • Science • Business • Innovation • Impact in health
  22. 22. Working together on the Radboud campus (Spin-out) companies 22
  23. 23. Radboud Research Facilities www.ru.nl/radboudresearchfacilities/ 23
  24. 24. Working with other Technology Networks Region, nation, Europe, world 24
  25. 25. The EATRIS operational strategy Consortia of centres of excellence in a 3D matrix model Experts Product Platforms QA & RA RPM & Clinical Legal & Ethical compliance Training & EducationCom & IT Biomarkers Group Vaccine Group Tracer & Imaging Group ATMP’s Group Small Molecules Group Optimise translational trajectory Maximise spillovers Disease expertise Alain van Gool Marien de Jonge Wim Oyen Carl Figdor
  26. 26. Apply to personalized healthcare 26
  27. 27. Example: Personalized Healthcare in rare disease • 12 families with liver disease and dilated cardiomyopathy (5-20 years) • Initial clinical assessment didn’t yield clear cause of symptoms • Specific sugar loss of serum transferrin identified via glycoproteomics ChipCube-LC- Q-tof MS • Outcome 1: Explanation of disease • Outcome 2: Dietary intervention as succesful personalized therapy • Outcome 3: Glycoprofile transferrin developed and applied as diagnostic test • Genetic defect in glycosylation enzyme (PGM1) identified via exome sequencing {Tegtmeyer et al, NEJM 370;6: 533 (2014)} Genomics Glycomics Metabolomics 27
  28. 28. Biomarkers in Personalized Health(care) an evolving role • From only diagnosis • To Translational Medicine • To Personalized/Precision/Targeted Medicine • To Personalized Healthcare • To Person-centered Health(care) present 28
  29. 29. 29 “Selfmonitoring = Trend of 2014”The future of medicine
  30. 30. ‘insideables’ ‘wearables’
  31. 31. • DIY sequence your genome and/or your microbiome genome • at a provider, at a pharmacy, at home • Take your genome to the doctor • Have a personalized healthcare advice DIY sequencing
  32. 32. 32 • Measure your brain waves (EEG) • Recognize conditions for maximal concentration or relaxation. • Use device to train. DIY brainwave monitoring
  33. 33. DIY blood biomarker analysis
  34. 34. But … Knowledge and Innovation gap: 1. What to measure? 2. How much should it change? 3. What should be the follow-up for me?
  35. 35. Most important for biomarkers in Personalized Healthcare: Focus on the end user: the patient 38
  36. 36. Translation is key in Personalized Healthcare ! “I’m afraid you’re suffering from an increased IL-1β and an aberrant miR843 expression” Adapted from: 39 ?
  37. 37. Lab values Clinical outcomes Patient important outcomes Pain Pubmed Search query Critical appraisal tool Mobility Fatigue INTEGRATE-HTA Intervention Focus on the end user R van Hoorn, W Kievit, M Tummers, GJ van der Wilt Clinical outcomes
  38. 38. Translation is key in Personalized Healthcare ! Personal profile data Knowledge Understanding Decision Action 41
  39. 39. Translation is key in Personalized Healthcare ! Select personalized therapy Treatment options Successrates Example from Prostate cancer patient guide
  40. 40. Translation is key in Personalized Healthcare ! Treatment options Pro’sCon’s Select personalized therapy
  41. 41. Biomarker innovation gaps Discovery Clinical validation/confirmation Diagnostic test Number of biomarkers Gap 1 Gap 2 44 5 biomarkers/ working day 1 biomarker/ 1-3 years 1 biomarker/ 3-10 years ? Eg Biomarkers in time: Prostate cancer May 2011: n= 2,231 biomarkers Nov 2012: n= 6,562 biomarkers Oct 2013: n= 8,358 biomarkers Nov 2014: n= 10,350 biomarkers Gap 3
  42. 42. How to move forward? Way forward: shared innovation Standardisation, harmonisation, knowledge sharing needed in: 1. Assay development 2. Clinical validation and qualification 45
  43. 43. How to move forward? Start small, think big 46
  44. 44. How to move forward? Collaboration in Health Informatics 47 Lucien Engelen et al, Radboud Reshape Center for Innovation
  45. 45. How to move forward? Be passionate ! My personal drivers: Personalized Health(care) Biomarkers Molecular Profiling (Omics) Future of medicine 48
  46. 46. Acknowledgements Lucien Engelen Jan Kremer Paul Smits Maroeska Rovers Nathalie Bovy Ron Wevers Jolein Gloerich Hans Wessels Dirk Lefeber Leo Kluijtmans Bas Bloem and others Lutgarde Buydens Jasper Engel Jeroen Jansen Geert Postma and others www.radboudumc.nl/personalizedhealthcare www.radboudumc.nl/research/technologycenters www.Radboudresearchfacilities.nl alain.vangool@tno.nl alain.vangool@radboudumc.nl www.linkedIn.com Many external collaborators Jan van der Greef Ben van Ommen Bas Kremer Lars Verschuren Ivana Bobeldijk Marjan van Erk Peter van Dijken Marijana Radonjic Thomas Kelder Robert Kleemann Suzan Wopereis and others 49 And funders

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