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2014 10-15 LGC Biosciences Autumn seminar Cambridge

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A reflection on the changing role of biomarkers in personalized healthcare.

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2014 10-15 LGC Biosciences Autumn seminar Cambridge

  1. 1. Biomarkers in personalized healthcare, changing perspectives 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 Seminar LGC Biosciences Cambridge, UK 15 Oct 2014
  2. 2. My mixed perspectives in personalized health(care) 8 years academia (NL, UK) (molecular mechanisms of disease) 13 years pharma (EU, USA, Asia) (biomarkers, Omics) 3 years med school (NL) (personalized healthcare, Omics, biomarkers) 3 years applied research institute (NL, EU) (biomarkers, personalized health) A person / citizen / family man (adventures in EU, USA, Asia) 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 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
  4. 4. TNO = Netherlands Organisation for Applied Scientific Research Mission = to drive ideas to reach their full market value. We partner with: Governmental & regulatory organisations Universities Pharma, chemical and food companies International consortia Knowledge development Knowledge application Knowledge exploitation Develop fundamental knowledge With universities With partners With customers Embedded in the market TNO TNO companies 4 Non-for-profit research organisation ~3500 employees 19 sites in Netherlands, 18 countries global 7 main themes (ao Life Sciences)
  5. 5. Biomarkers in Personalized Healthcare an evolving role • From only diagnosis • To Translational Medicine • To Personalized/Stratified/Precision Medicine • To Personalized Healthcare • To Person-centered Health(care) 5
  6. 6. Diagnostic biomarkers in the early days {Kumar and van Gool, RSC, 2013} 1506: The urine wheel Use color, smell and taste of urine to diagnose disease and decide best treatment Ullrich Pinder Epiphanie Medicorum
  7. 7. Biomarkers in Translational Medicine in pharma • Translational medicine  Exposure  Mechanism  Efficacy  Safety • Personalized medicine  Diagnosis  Prognosis  Response prediction • Tools for data-driven decision making  Biologically relevant  Clinically accepted  Quantitative  Different analytes/types  Fit-for-purpose application {Source: Van Gool et al, Drug Disc Today 2010} 7
  8. 8. Biomarker data-driven decisions Target engagement? Effect on disease? yes yes ! no no • No need to test current drug in large clinical trial • Need to identify a more potent drug • Concept may still be correct • Concept was not correct • Abandon approach • Proof-of-Concept • Proceed to full clinical development “Stop early, stop cheap” “More shots on goal” 8 {Kumar and van Gool, RSC, 2013}
  9. 9. Source: John Arrowsmith: Nature Reviews Drug Discovery 2011 • Success rates of clinical proof-of-concept have dropped from 28% to 18% • Insufficient efficacy as the most frequent reason • Targeted therapy through Personalized Medicine may be the solution Promise of Personalized Medicine Analysis of 108 failures in phase II Reason for failure Therapeutic area 9
  10. 10. Biomarkers in Personalized Medicine • Melanoma – targeted medicine • Metabolic health – system medicine 10
  11. 11. Clinical efficacy of Vemurafenib (PLX-4032, Zelboraf) Key biomarkers: Stratification: BRAFV600E mutation Mechanism: P-ERK Cyclin-D1 Efficacy: Ki-67 18FDG-PET, CT Clinical endpoint: progression-free survival (%) {Source: {Source: Chapman et al, NEJM 2011} Flaherty et al, NEJM 2010} 11
  12. 12. Clinical efficacy of Vemurafenib {Wagle et al, 2011, J Clin Oncol 29:3085} Before Rx Vemurafenib, 15 weeks Vemurafenib, 23 weeks • Strong initial effects vemurafenib • Emerging drug resistancy • Reccurence of aggressive tumors 12
  13. 13. Tumor tissue/biomarker heterogeneity • BRAFV600D/E is driving mutation • However, also no BRAFV600D/E mutation found in regions of primary melanomas • Molecular heterogeneity in diseased tissue • Biomarker levels in tissue vary • Biomarker levels in body fluids will vary • Major challenge for (companion) diagnostics {Source: Yancovitz, PLoS One 2012} 13
  14. 14. ‘Complicating’ factors in oncology therapy Source: 11 Sept 2013 @de Volkskrant • Biological clock • Smoking • Pharma-Nutrition • Drug-drug interaction • Alternative medicine • Genetic factors • … Interview with Prof Ron Matthijssen, ErasmusMC, Rotterdam 14
  15. 15. Metabolic health and disease Type 2 Diabetes Diabetes complications time 15
  16. 16. Systems view on metabolic health and disease β-cell Pathology gluc Risk factor {Source: Ben van Ommen, TNO} 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 Chronic Stress Disruption circadian rhythm Parasympathetic tone Sympathetic arousal Gut activity Inflammatory response Adrenalin Heart rate Heart rate variability High cortisol α-amylase 16
  17. 17. Systems view on metabolic health and disease β-cell Pathology gluc Risk factor 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 Chronic Stress Disruption circadian rhythm Parasympathetic tone Sympathetic arousal Gut activity Inflammatory response Adrenalin Heart rate Heart rate variability High cortisol α-amylase {Nakatsuji, Metabolism 2009} 17
  18. 18. EC DG for Research and Innovation Alain van Gool Brussels, 11 Sept 2012 Systems view on metabolic health and disease β-cell Pathology gluc Risk factor 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 activity Sweet & 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 Atorvastatin Omega3-fatty acids Pharma Nutrition Lifestyle 18
  19. 19. EC DG for Research and Innovation Alain van Gool Brussels, 11 Sept 2012 Challenging metabolic equilibrium by Pharma-Nutrition Age-matched “healthy” control group t=16 w (sampling) t=9 w t=0 Induction of Diabetes intervention period High-fat (HF) diet High-fat diet “diseased” control group Nutrition/Life style switch HF + Drug 1 HF + Drug 2 HF + Drug 3 …. HF + Drug 10 19
  20. 20. EC DG for Research and Innovation Alain van Gool Brussels, 11 Sept 2012 clinical chemistry System networks Metabolome Transcriptome fluxes Analysis: high throughput, multi organ, multi level High-end data mining and warehousing Extensive histological and molecular phenotyping 20
  21. 21. TNO’s applied biomarker tool box Widely used preclinical translational models Pharma, nutrition and chemical industry, academia Focus on etiology of disease and mechanism of action Human studies Experimental medicine through CRO’s Microdosing Validated analytical platforms Metabolomics profiling and targeted analysis, with focus on lipids, ceramids, cannabinoides Genomics, transcriptomics, proteomics and imaging through a wide network of selected partners Clinical chemistry Data analysis Network biology for mechanistic understanding Multiparameter statistics and chemometrics PK/PD translational modelling Comprehensive system dynamics modelling Biomarker expertise Best practise strategies and approaches A wide network with biomarker academia and industry Metabolic Syndrome • Atherosclerosis • Diabetes • Obesity • Vascular inflammation • NASH, fibrosis 21
  22. 22. EC DG for Research and Innovation Alain van Gool Brussels, 11 Sept 2012 Effects on total adipose tissue weight Full reversal of obese phenotype by Nutrition switch, not by all drug treatments T0901317 (LXR agonist) also reverses obese phenotype 22
  23. 23. EC DG for Research and Innovation Alain van Gool Brussels, 11 Sept 2012 Effects on atherosclerosis Still increased atherosclerosis in Nutrition switch group T0901317 (LXR agonist) strongly induces atherosclerosis 23
  24. 24. EC DG for Research and Innovation Alain van Gool Brussels, 11 Sept 2012 {Nolan, Lancet 2011} A sure need for systems medicine •Multiple interactions and flexibilities in human system (tissues, cells, proteins) •Blocking one pathway will shift equilibrium and create new problems •System medicine approach needed for maximal effect •High value of biomarkers but how to translate to combination therapy? •Pharma-Nutrition? 24
  25. 25. EC DG for Research and Innovation Alain van Gool Brussels, 11 Sept 2012 Relating tissue pharmacology – biomarker - therapy 25
  26. 26. Translating knowledge to field labs 1. Implementation-plan ‘Personalized diagnosis of (pre)diabetic and their lifestyle treatment in Dutch Health care’. 2.Use of Oral Glucose Tolerance Test as a stratification biomarker for (pre)diabetic patients 3. Advice a tailored treatment (lifestyle and/or medical) 4. Monitor added value of stratification 5. Communicate results and lessons learned Being implemented in 1st line care (region Hillegom, Netherlands) Alliance “Expedition Sustainable Care, starting with diabetes”
  27. 27. Year 1 Applying lessons learned across fields e.g. System Biology @TNO Year 2 Year 3
  28. 28. Personalized interventions by Pharma-Nutrition Ongoing: Shared Innovation Programs through public-private consortia Higher efficacy / less side effects 28
  29. 29. Data mining Models Modelling Analytics (Mx, Px, Tx) Organ-on- a-chip Imaging Academic/ Clinical Industry Shared Innovation Programs 20+ partners Diagnostics Pharma Nutrition 20+ partners Better diagnosis and interventions Personalized ! 20+ partners 10+ partners 29
  30. 30. Biomarkers in Personalized Healthcare an evolving role • From only diagnosis • To Translational Medicine • To Personalized/Stratified/Precision Medicine • To Personalized Healthcare • To Person-centered Health(care) 30
  31. 31. Personalized Healthcare, more than pathways only Source: Barabási 2007 NEJM 357; 4} • People are different • Different networks and influences • Different risk factors • Different preferences 31
  32. 32. Personalized Healthcare in a systems view 32
  33. 33. A changing world: Personalized Medicine@ USA “The term "personalized medicine" is often described as providing "the right patient with the right drug at the right dose at the right time." More broadly, "personalized medicine" may be thought of as the tailoring of medical treatment to the individual characteristics, needs, and preferences of a patient during all stages of care, including prevention, diagnosis, treatment, and follow-up.” (FDA, October 2013) 33
  34. 34. A changing world: Personalized Medicine @Europe European Science Foundation 30 Nov 2012 Innovative Medicine Initiative 2 8 July 2013 EC Horizon2020 10 Dec 2013 34
  35. 35. Most important in Personalized Healthcare: Include the patient as partner 35
  36. 36. Patient Radboud Personalized Healthcare A significant impact on healthcare Molecule Population Personalized Healthcare @ Radboud university medical center 36
  37. 37. Personalized Healthcare @ Radboudumc People are different Stratification by multilevel diagnosis + Patient’s preference of treatment Exchange experiences in care communities Select personalized therapy Population Man Molecule 37
  38. 38. Translational medicine @ Radboudumc
  39. 39. Personalized genomic diagnostics {Nature, July 17 2014, 511: 344-} 39
  40. 40. 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
  41. 41. 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 41 Full metabolite profile: Highly suspected of xanthinuria
  42. 42. Proteomics Glycomics Metabolomics • Mass spectrometry – NMR based, 20 dedicated fte, + guest scientists • Part of diagnostic laboratory (Department of Laboratory Medicine) • Close interaction with Radboudumc scientists and external partners Radboud Center for Proteomics, Glycomics & Metabolomics Ron Wevers, Alain van Gool, Leo Kluijtmans, Dirk Lefeber et al Research Biomarkers Diagnostics
  43. 43. Research Biomarkers Diagnostics Integrated Translational Research and Diagnostic Laboratory, 200 fte, yearly budget ~ 28M euro. Close interaction with Radboudumc scientists and external partners Please visit: www.laboratorymedicine.nl 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 inflamation 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 Department of Laboratory Medicine *CCKL accreditation/RvA/EFI
  44. 44. Genomics Bioinformatics Animal studies Translational neuroscience Image-guided treatment Imaging Microscopy Biobank Health economics Mass Spectrometry Radboudumc Technology Centers Investigational products Clinical EHR-based trials research Statistics Human physiology Data stewardship Molecule Flow cytometry (Aug 2014) 44
  45. 45. 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 200 dedicated people working in 17 Technology Centers, ~1500 users (internal, external), ~130 consortia www.radboudumc.nl/research/technologycenters/ (Aug 2014)
  46. 46. Cross-technology interactions
  47. 47. Biomarkers in Personalized Healthcare • 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 47
  48. 48. Need to change development process for Personalized Healthcare therapies • Randomized Clinical Trials won’t be good enough (= groups) • n=1 clinical trial designs needed whereby: • Multiple monitoring in same person • Use different types of biodata (molecular, non-molecular) • Normalize data per individual • Combine separate data through meta-analysis • Output: • Responders vs non-responders • Tight data per subgroup • Clear conclusions on therapy 48
  49. 49. healthy disease disease + treatment Different trial outcomes in Personalized Healthcare 49 100% Normalisation Subgroups
  50. 50. H2020 PHC1 application - L’Homme Machine: Exploiting Industrial Control Techniques for Personalized Health Partners Biobanks Databank Coordinator: prof Lutgarde Buydens,
  51. 51. Biomarkers in Personalized Healthcare an evolving role • From only diagnosis • To Translational Medicine • To Personalized/Stratified/Precision Medicine • To Personalized Healthcare • To Person-centered Health(care) 51
  52. 52. Selfmonitoring 52
  53. 53. The future is nearly there … 53 Personalized advice Action Selfmonitor Cloud Lifestyle Nutrition Pharma
  54. 54. Biomarkers in Person-centered Health(care) Patient Caregiver Insurer Self-monitoring Patient Caregiver Insurer Participatory research Bas Bloem Marten Munneke et al 54 Central data point
  55. 55. Biomarkers in Personalized Healthcare an evolving role • From only diagnosis • To Translational Medicine • To Personalized/Stratified/Precision Medicine • To Personalized Healthcare • To Person-centered Health(care) 55
  56. 56. However … Knowledge and Innovation gap: 1. What to measure? 2. How much should it change? 3. What should be the follow-up for me? 56
  57. 57. Translation is key in Personalized Healthcare ! Personal profile data Knowledge Understanding Decision Action 57
  58. 58. Translation 1: Data to usable tests • Imbalance between biomarker discovery, validation and application • Many more biomarkers discovered than available as diagnostic test Discovery Clinical validation/confirmation Diagnostic test Number of biomarkers Gap 1 Gap 2 Biomarker Innovation Gap 58
  59. 59. Some numbers Data obtained from Thomson Reuters Integrity Biomarker Module Eg Biomarkers in time: Prostate cancer May 2011: 2,231 biomarkers Nov 2012: 6,562 biomarkers Oct 2013: 8,358 biomarkers 15 Oct 2014: 10,169 biomarkers with 32,093 biomarker uses EU: CE marking USA: LDT, 510(k), PMA
  60. 60. Reasons for biomarker innovation gap • Not one integrated pipeline of biomarker R&D • Publication pressure towards high impact papers • Lack of interest and funding for confirmatory biomarker studies • Hard to organize multi-lab studies • Biology is complex on organism level • Data cannot be reproduced • Bias towards extreme results • Biomarker variability • … {Source: John Ioannidis, JAMA 2011} {Source: Khusru Asadullah, Nat Rev Drug Disc 2011} 60
  61. 61. Way forward: shared innovation network projects Standardisation, harmonisation, knowledge sharing needed in: 1. Assay development 2. Clinical validation 61
  62. 62. Shared Innovation Network models (Next Generation Life Science) (Source: Model TNO’s Holst Center) Old New 62
  63. 63. Good example of multi-center biomarker validation
  64. 64. Biomarker Development Center (Netherlands) STW perspectief grant Biomarker Development Center Public-private partnership 4 years Project grant €4.3M of which € 2.2M government, and € 2.1M industry (€ 0.9M cash/ € 1.2M kind) Close interactions with: - Clinicians (biomarker application) - Industry partners and stakeholders - Patient stakeholder associations Open for partners ! 64
  65. 65. Translation 2: Science to patient “I’m afraid you’re suffering from an increased IL-1β and an aberrant miR843 expression” Adapted from: 65 ?
  66. 66. Need for interdisciplinary team work 66
  67. 67. Personalized Health(care) model Homeostasis Allostasis Disease Time Disease Health Personalized Intervention of patients-like-me Big Data Risk profiles of persons-like-me Molecular Non-molecular Environment … Personal profile Selfmonitoring Adapted from Jan van der Greef (2013) 67
  68. 68. Person-centered Health(care) Ways forward: • Patients included • Participation + collaboration • Personal profiles • System biology • Health informatics • Personal preferences • Personalized therapies in Lifestyle + Nutrition + Pharma 68
  69. 69. Acknowledgements Lucien Engelen Jan Kremer Paul Smits Maroeska Rovers Nathalie Bovy Ron Wevers Jolein Gloerich Hans Wessels Dirk Lefeber Leo Kluijtmans Bas Bloem Marten Munneke and others Lutgarde Buydens Jasper Engel Jeroen Jansen Geert Postma and others Members of the Radboud umc Personalized Healthcare Taskforce (2013) Radboud umc Technology Centers (2014) alain.vangool@tno.nl alain.vangool@radboudumc.nl www.linkedIn.com Many external collaborators Jan van der Greef Ben van Ommen Peter van Dijken Bas Kremer Lars Verschuren Marijana Radonjic Thomas Kelder Robert Kleemann Suzan Wopereis Ton Rullmann William van Dongen and others 69

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