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2015 2-23 Oxford Global 2015 Manchester

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View how to bridge the gap between scientific biomarker research in academia and point of care diagnostic apps in society.

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2015 2-23 Oxford Global 2015 Manchester

  1. 1. Biomarkers in Personalized Health(care), moving beyond Targeted Medicine Professor of 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. 3 Personalized advice Action Selfmonitor Cloud Lifestyle Nutrition Pharma
  3. 3. ‘insideables’ ‘wearables’
  4. 4. 5 “Selfmonitoring = Trend of 2014”The future of medicine
  5. 5. 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 8
  6. 6. But … Knowledge and Innovation gap: 1. What to measure? 2. How much should it change? 3. What should be the follow-up for me?
  7. 7. 10
  8. 8. Exponential technologies “The only constant is change, and the rate of change is increasing” We are at the knee of the exponential curve
  9. 9. Buzzwords Progress and beyond You are the CEO of your own healthcare team Exponential technologies Disruptive innovation Digitalizing yourself Sitting is the new smoking Uber-ization of healthcare The future is already here, it’s just not evenly distributed … What’s normal for me is not normal for you Do things different Don’t think out of the box, just think there is no box !
  10. 10. Demo room
  11. 11. Exponential developments in biomarker technologies • Next generation sequencing • Large level of detail on genome level (DNA, RNA) • Sequencing per patient is becoming practice • Allows risk analysis and therapy selection • Mass spectrometry • Large level of detail on metabolic level (proteins, metabolites) • Analysis of blood, urine, cells, tissues, hair, etc all possible • Allows monitoring of disease and treatment effects • Imaging • Large level of detail on intact in vivo level • Analysis of any tissue, real time • Allows spatial view of intact organs and organisms
  12. 12. Genome sequencing will become much cheaper !
  13. 13. Next in Next Generation Sequencing • Trends: ₋ Further reduction in sequencing costs ₋ Computational power ₋ Machine learning to analyse (big) data ₋ Link molecular diagnosis to cellular therapies Also beyond the oncology field: • Volker: Intestinal surgery → XIAP → Cord blood • Beery twins: Cerebral palsy → SPR → Diet 5HTP • Wartman: Leukemia → FLT3 → Sunitinib • Gilbert: Healthy → BRCA → Mas/Ovarectomy • Snyder: T2Diabetes → GCKR, KCNJ11 → Diet, exercise • Lauerman: Scotoma, leg → JAK2 → Aspirin • Bradfield: Healthy → CDH1 → Gastrectomy Georg Church, Craig Venter
  14. 14. The microbiome
  15. 15. The epigenome
  16. 16. Consider individual differences in biomarker research 19
  17. 17. 21
  18. 18. But … Knowledge and Innovation gap: 1. What to measure? 2. How much should it change? 3. What should be the follow-up for me?
  19. 19. Most important for biomarkers in Personalized Healthcare: Focus on the end user: the patient 23
  20. 20. 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
  21. 21. Translation is key in Personalized Healthcare ! “I’m afraid you’re suffering from an increased IL-1β and an aberrant miR843 expression” Adapted from: 25 ?
  22. 22. Translation is key in Personalized Healthcare ! Personal profile data Knowledge Understanding Decision Action 26
  23. 23. Translation is key in Personalized Healthcare ! Select personalized therapy Treatment options Successrates Example from Prostate cancer patient guide
  24. 24. Translation is key in Personalized Healthcare ! Treatment options Pro’sCon’s Select personalized therapy
  25. 25. Biomarker innovation gaps Discovery Clinical validation/confirmation Diagnostic test Number of biomarkers Gap 1 Gap 2 29 Gap 3
  26. 26. Biomarker innovation gaps: some numbers 30 Data from Thomson Reuters Integrity database, February 2015 Alzheimer’s Disease Chronic Obstructive Pulmonary Disease Type II Diabetes Mellitis
  27. 27. Biomarker innovation gaps: some numbers Discovery Clinical validation/confirmation Diagnostic test Number of biomarkers Gap 1 Gap 2 31 Gap 3 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
  28. 28. How to move forward? 1. Focus on the end user 2. Validate more biomarkers in one go 3. System biology 4. Define, share and act on “Good Biomarker Practices” 5. Build biomarker development pipelines 6. Develop translational DIY technologies 7. Interpret data with self-normalisation 8. Interdisciplinary teamwork 32
  29. 29. How to move forward? 2. Validate more biomarkers in one go 33 1. Determine the context of change in a biomarker. 2. Drive validation of multiple biomarkers at once Multiple measures Patient 1 Patient 2 Technologies are already out there: • Next generation sequencing • Microarrays • Multiplex immunoassays Single measure • Targeted mass spectrometry • Binding assays • Mass spec imaging
  30. 30. How to move forward? 3. System Biology 34 β-cell Pathology gluc Risk factor {Source: Ben van Ommen, TNO} therapy Visceral adiposity LDL elevated Glucose toxicity Fatty liver Gut inflammation endothelial inflammation systemic Insulin resistance Systemic inflammation Hepatic IR Adipose IR Muscle metabolic inflexibility adipose inflammation Microvascular damage Myocardial infactions Heart failure Cardiac dysfunction Brain disorders Nephropathy Atherosclerosis β-cell failure High cholesterol High glucose Hypertension dyslipidemia ectopic lipid overload Hepatic inflammation Stroke IBD fibrosis Retinopathy Physical inactivity Caloric excess Chronic Stress Disruption circadian rhythm Parasympathetic tone Sympathetic arousal Worrying Hurrying Endorphins Gut activitySweet & fat foods Sleep disturbance Inflammatory response Adrenalin Fear Challenge stress Heart rate Heart rate variability High cortisol α-amylase Lipids, alcohol, fructose Carnitine, choline Stannols, fibre Low glycemic index Epicathechins Anthocyanins Soy Quercetin, Se, Zn, … Metformin Vioxx Salicylate LXR agonist Fenofibrate Rosiglitazone Pioglitazone Sitagliptin Glibenclamide Atorvastati n Omega3-fatty acids Pharma Nutrition Lifestyle
  31. 31. How to move forward? 4. Define, share and act on“Good Biomarker Practices” 35 Some items in need of standardisation: • Reproducibility, quality requirements • Study design & statistical power • Variability & heterogeneity • Specimen acquisition & pre-analytics • Sample preparation • Patient & associated clinical data • Analytical standards & quality control Not reinvent the wheel. Standardisation, harmonisation, knowledge sharing needed in: 1. Assay development 2. Clinical validation 3. Biomarker qualification • Assay/platform development • Quality system manufacturing • Data analysis & management • Regulatory requirements • Ethical, IP & legal aspects • Early HTA • Quality in documentation & publication
  32. 32. How to move forward? 5. Build biomarker development pipelines Standardisation, harmonisation, knowledge sharing needed in: 1. Assay development 2. Clinical validation Example: Biomarker Development Center Open Innovation Network ! Roadmap Molecular Diagnostics PPP Grant 4.3M Euro 36 2015/2016
  33. 33. Good example of multi-center biomarker validation
  34. 34. Research Biomarkers Diagnostics Department of Laboratory Medicine, Radboudumc Integrated Translational Research and Diagnostic Laboratory, 220 fte, yearly budget ~ 28M euro. Close interaction with Dept 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 Use available resources: Biomarker development pipeline @ Radboudumc *CCKL accreditation/RvA/EFI www.laboratorymedicine.nl 38
  35. 35. • 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 How to move forward? 6. Develop translational DIY technologies
  36. 36. 40 • Measure your brain waves (EEG) • Recognize conditions for maximal concentration or relaxation. • Use device to train. How to move forward? 6. Develop translational DIY technologies
  37. 37. How to move forward? 6. Develop translational DIY technologies
  38. 38. healthy disease disease + treatment How to move forward? 7. Interpret data with self-normalisation 42 Subgroups 100% Normalisation of responders
  39. 39. How to move forward? 8. Interdisciplinary team work 43
  40. 40. www.radboudumc.nl/research/technologycenters How to move forward? 8. Interdisciplinary team work
  41. 41. 45 • 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 • 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 240 dedicated people working in 17 Technology Centers, ~1500 users (internal, external), ~130 consortia www.radboudumc.nl/research/technologycenters/
  42. 42. How to move forward? Collaboration in Health Informatics 46
  43. 43. How to move forward? Start small, think big 47
  44. 44. Finally, be passionate ! My professional passions: Personalized Health(care) Biomarkers Molecular Profiling (Omics) Future of medicine 48
  45. 45. 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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