2013-04-23 Top Institute Pharma Spring meeting, Utrecht

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2013-04-23 Top Institute Pharma Spring meeting, Utrecht

  1. 1. Companion Diagnostics: an update Prof. Alain van Gool Netherlands Organisation for Applied Scientific Research (TNO) Radboud University Nijmegen Medical Centre Radboud University Nijmegen TI Pharma Spring Meeting Utrecht, 23rd April 2013
  2. 2. Companion Diagnostics Right drug in right patient at right dose at right time In other words: Apply a well characterized therapy in a biological system you know well to treat a disease you understand well, in a way that you know works. Use (molecular) biomarkers as diagnostic companions of a drug. TI Pharma Spring meeting Utrecht, 23 April 2013 Alain van Gool 2
  3. 3. What type of biomarkers to use? {Biomarkers definition working group, 2001 } Definition: ‘a characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention’ Or ‘Whatever works in adding value’ Molecular biomarkers provide a molecular impression of a biological system (cell, animal, human) Biomarkers can be various sorts of data, or combinations thereof 3 TI Pharma Spring meeting Utrecht, 23 April 2013 Alain van Gool
  4. 4. Companion Diagnostics – some numbers At present in pharmaceutical development: 40.000 clinical trials ongoing 16.000 trials in oncology 8.000 trials in oncology have a companion diagnostic At present on market: 113 Biomarker in drug label (2012; up from 69 in 2010 = +64%) 16 CDx testing needed (2012; up from 4 in 2010 = +400%) Costs of development: >1.000 MUSD per drug ~10 MUSD per diagnostic Source: www.fda.gov TI Pharma Spring meeting Utrecht, 23 April 2013 Alain van Gool 4
  5. 5. Companion Diagnostics Metabolism Efficacy or safety Source: www.fda.gov TI Pharma Spring meeting Utrecht, 23 April 2013 Alain van Gool 5
  6. 6. Companion Diagnostics in Oncology V600D/E Kinase domain {Roberts and Der, 2007} B-RAFV600D/E mutation: constitutively active kinase, oncogenic addiction Overactivate ERK pathway drives cell proliferation RAF inhibitors block growth of tumor xenografts with B-RAFV600D/E mutation Prevalence of B-RAFV600D/E Melanoma (60%), colon (15%), ovarian (30%), thyroid (30%) cancer Develop B-RAF inhibitors with B-RAFV600D/E as companion diagnostic 6 TI Pharma Spring meeting Utrecht, 23 April 2013 Alain van Gool
  7. 7. 7 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: Flaherty et al, NEJM 2010}{Source: Chapman et al, NEJM 2011} 7 TI Pharma Spring meeting Utrecht, 23 April 2013 Alain van Gool
  8. 8. 8 Clinical effects of Vemurafenib {Wagle et al, 2011, J Clin Oncol 29:3085} Before Rx Vemurafenib, 15 weeks Vemurafenib, 23 weeks • Strong initial effects vemurafenib • Drug resistancy • Reccurence of tumors 8 TI Pharma Spring meeting Utrecht, 23 April 2013 Alain van Gool
  9. 9. EC DG for Research and Innovation Alain van Gool Brussels, 11 Sept 2012 9 • BRAFV600D/E is considered the driving mutation • However, varying levels of BRAFV600D/E mutation found in regions of a primary melanoma • Molecular heterogeneity in diseased tissue • Biomarker levels in tissue and body fluids will vary • New biomarkers are needed • Challenge for companion diagnostics {Source: Yancovitz, PLoS One 2012} Tumor tissue heterogeneity TI Pharma Spring meeting Utrecht, 23 April 2013 Alain van Gool
  10. 10. The innovation gap in biomarker development 10 • Imbalance between biomarker discovery and application. • Gap 1: Strong focus on discovery of new biomarkers, few biomarkers progress beyond initial publication to multi-center clinical validation. • Gap 2: Insufficient demonstrated added value of new clinical biomarker and limited development of a commercially viable diagnostic biomarker test. Discovery Clinical validation/ confirmation Diagnostic test Number of biomarkers Gap 1 Gap 2 TI Pharma Spring meeting Utrecht, 23 April 2013 Alain van Gool
  11. 11. 11 Emerging Discovery Clinical validation/ confirmation Diagnostic application Number of biomarkers Experimental Discovery Assay kit development Assay development Early Late – Many new biomarkers are panels (RNA, protein, biochemical, imaging) – Not wise to discover yet an other biomarker – Focus on selecting the best biomarker (panels) among those already found (scientific and patent literature, databases, etc) – Develop those biomarkers tot clinically applicable tests Imbalance between biomarker discovery and application <10 biomarkers Eg prostate cancer May 2011: 2,231 biomarkers Nov 2012: 6,562 biomarkers {Source: Thomson Reuters Biomarkers Module} 11 TI Pharma Spring meeting Utrecht, 23 April 2013 Alain van Gool
  12. 12. Biomarker innovation gap highlighted in topsector Life Sciences & Health {www.rijksoverheid.nl} {http://www.zonmw.nl/nl/roadmaps-lsh/} Roadmap Molecular Diagnostics: • Build an efficient biomarker development pipeline in Netherlands to enable fast progress of biomarkers from discovery to clinical implementation • Bring all stakeholders together in a functional open innovation network based on public-private-partnerships • Have end-users (patients, clinicians) direct biomarker development in beginning 9 TopSectors 11 Roadmaps in TopSector Life Sciences & Health Topsectors: initiative of Netherlands government to re-define the interest and focus of industry in public-private partnerships (2012)
  13. 13. Uptake of new biomarkers in clinical care Research/technology push: Biomarkers can and should provide the molecular part of this healthcare model in monitoring and follow-up Daily practice in clinical assessment: Combination of personal opinion (patient and physician), physical examination, clinical chemistry to generate personal profiles New biomarkers are added where deemed useful by physician Act accordingly in follow-up care (more or less personalized) Medication (a.o. personalized medicine) Nutrition (a.o. individualized diets) Life style (a.o. individualized exercise, counseling) Slow uptake of new biomarkers Limited by careful / conservative attitude of clinicians (added value of new biomarker?) Limited by reimbursement options by insurers (increasingly important) 13 TI Pharma Spring meeting Utrecht, 23 April 2013 Alain van Gool
  14. 14. Personal profiles Source: Barabási 2007 NEJM 357; 4} • People are different • Different networks influences • Different risk factors 14 TI Pharma Spring meeting Utrecht, 23 April 2013 Alain van Gool
  15. 15. BIODATA PERSONALIZED INTERVENTIONS RISK FACTOR PATTERN MOLECULAR LIFESTYLE / ENVIRONMENT Metabolites RNA Protein DNA Biochemical process Enzymatic activity Imaging mDNA Nutrition Environment Social network Attitude in life Stress work / private MULTIPARAMETER PERSONAL PROFILES Statistics Selection Ranking LIFESTYLE NUTRITION PHARMA TI Pharma Spring meeting Utrecht, 23 April 2013 Alain van Gool 15
  16. 16. Example personal profile-based patient assessment {Chen et al, Cell 2012, 148: 1293} Concept: • Continuous monitoring (n=1) • Routine biomarkers to alert • Omics to explain • Early intervention 16 TI Pharma Spring meeting Utrecht, 23 April 2013 Alain van Gool
  17. 17. From clinical Omics to personalized treatment: • 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 • Genetic defect in glycosylation enzyme identified via exome sequencing • Outcome: Explanation of disease • Outcome: Dietary intervention as succesful personalized therapy • Outcome: Glycoprofile being developed as diagnostic test by mass spectrometry Example from rare diseases Dietary intervention {Dirk Lefeber et al, submitted} 17 Incomplete glycosylation Complete glycosylation TI Pharma Spring meeting Utrecht, 23 April 2013 Alain van Gool
  18. 18. EC DG for Research and Innovation Alain van Gool Brussels, 11 Sept 2012 Oncology CVD, neuro, immune Diabetes Personal profiles differ per disease phenotype TI Pharma Spring meeting Utrecht, 23 April 2013 Alain van Gool 18
  19. 19. EC DG for Research and Innovation Alain van Gool Brussels, 11 Sept 2012 • Obesity • Diabetes type 2 HEALTH DISEASE COMPLICATIONS • Atherosclerosis • Nephropathy fibrosis • Osteoarthritis • Stroke • etc Metabolic syndrome metabolic disturbance local inflammation Not a single cause but complex multifactorial diseases Disturbed equilibrium between multiple pathways and key components A system biology approach is needed For discovery research, diagnosis and treatment Continuous monitoring really pays off Most effective therapy is ‘eat better, move more’ (lifestyle change) Nutriceuticals / Lifestyle Food Pharma 19 TI Pharma Spring meeting Utrecht, 23 April 2013 Alain van Gool
  20. 20. EC DG for Research and Innovation Alain van Gool Brussels, 11 Sept 2012 Each organ has its own characteristics in maintaining/loosing flexibility and this determines the health to diabetes transition. {Nolan, Lancet 2011} A sure need for system biology High need to study the effect of drugs/nutrition on each of these organs and their interaction within the whole system of each person. TI Pharma Spring meeting Utrecht, 23 April 2013 Alain van Gool 20
  21. 21. EC DG for Research and Innovation Alain van Gool Brussels, 11 Sept 2012 21 Working in complex human biological systems requires a systems biology approach Way forward: 1. Focus on key processes 2. Measure key node biomarkers 3. Convert to a functional fingerprint assay panel 4. Make actionable personalized decision on health / disease management 5. Test added value in real life through field labs
  22. 22. EC DG for Research and Innovation Alain van Gool Brussels, 11 Sept 2012 Important processes in T2D Diagnosis Potential interventions Dietary/LS Pharma 1.Pancreatic β-cell function (impaired insulin secretion) *OGTT: I/∆G and DI(0) *PYY, Arg, His, Phe, Val, Leu Lifestyle; β-cell protective nutrients (MUFA/isoflavonoids); β -cell protective medication (TZDs, GLP-1 analogs, DPP4-inhibitors) 2.Muscle insulin resistance (decreased glucose uptake) *OGTT: Muscle insulin resistance index, Insulin secretion/insulin resistance index *Val, Ile, Leu, Gamma-glutamylderivates, Tyr, Phe, Met PUFA/SFA balance; Physical activity; Weight loss; TZDs (e.g.PPARγ) 3.Hepatic insulin resistance (decreased glucose uptake and increased hepatic glucose production-HGP) *Hepatic insulin resistance index *OGTT: Hepatic insulin sensitivity index *ALAT, ASAT, bilirubine, GGT, ALP, ck-18 fragments, lactate, α-hydroxybutyrate, β-hydroxybutyrate Decrease SFA and n- 6 PUFA, and increase n-3 PUFA; Weight loss; Metformin; TZDs; Exenatide (GLP-1 analog); DPP4 inhibitors 4. Adipocyte insulin resistance and lipotoxicity *basal adipocyte insulin resistance index *FFA platform, glycerol α-lipoic acid; PUFA/SFA balance; Omega 3 fatty acids; Chitosan/plantsterols; TZDs; Acipimox 5. GI tract (incretin deficiency/resistance) *ivGTT vs OGTT *GLP-1, GIP, glucagon, galzuren MUFA; Dietary fibre (pasta/rye bread); Exenatide 6. Pancreatic α-cell (hyperglucagonemia) *fasting plasma glucagon ? Glucagon receptor antagonists; Exenatide; DPP4 inhibitors 7A.Chronic low-grade inflammation in pancreas, muscle, liver, adipose tissue, hypothalamus 7B. Vascular inflammation *CRP, total leucocytes * V-CAM, I-CAM, Oxylipids, cytokines Fish oil/n-3 fatty acids; Vit. C/Vit. E/Carotenoids; Salicylates; TNF-α inhibitors and others TI Pharma Spring meeting Utrecht, 23 April 2013 Alain van Gool 22
  23. 23. EC DG for Research and Innovation Alain van Gool Brussels, 11 Sept 2012 Field labs: test health care concepts in real life • Build field lab with pre-diabetic patients, physicians, dietitians, insurers, etc • Measure individual ‘risk’ parameters for metabolic syndrome +/- challenge • phenotypes, clinical chemistry, specific Omics, etc • Convert data into a personal profile + personalized health advice • life style +/- nutrition +/- pharmaceutical drugs • Test personalized health concept in field lab following P4 medicine principle • Alliance “Expedition Sustainable Care, starting with diabetes” TI Pharma Spring meeting Utrecht, 23 April 2013 Alain van Gool 23
  24. 24. EC DG for Research and Innovation Alain van Gool Brussels, 11 Sept 2012 Oncology CVD, neuro, immune Diabetes Personal profiles differ per disease phenotype TI Pharma Spring meeting Utrecht, 23 April 2013 Alain van Gool 24
  25. 25. High attrition in most chronic diseases {Source: Kola, 2008, Nature 83, 2: 227} • Multifactorial causes of disease, mostly not well understood • Risk factors include both molecular as lifestyle/environmental factors • Treatment is often symptom-based, not mechanism-based • System approach in diagnosis and treatment (systems medicine) • Need improved disease definitions and understanding 25 TI Pharma Spring meeting Utrecht, 23 April 2013 Alain van Gool
  26. 26. EC DG for Research and Innovation Alain van Gool Brussels, 11 Sept 2012 Redefining disease {Nature Reviews Drug Discovery 2011, 10: 641} 26 8th IMI call: Joined effort in EU to improve disease definitions and define best potential therapies 1. RA, SLE 2. AD, PD TI Pharma Spring meeting Utrecht, 23 April 2013 Alain van Gool
  27. 27. EC DG for Research and Innovation Alain van Gool Brussels, 11 Sept 2012 Human Diseasomes From Barabási 2007 NEJM 357:4 Redefining disease 27 TI Pharma Spring meeting Utrecht, 23 April 2013 Alain van Gool
  28. 28. EC DG for Research and Innovation Alain van Gool Brussels, 11 Sept 2012 Redefining disease: Medicine 3.0 Concept: • Target causes of disease rather than symptoms • Identify and quantify common mechanisms of chronic diseases • Identify new targets for intervention NL: Proposal submitted (10 yrs, 30MEur) EU: Align with IMI and Horizon2020 28 TI Pharma Spring meeting Utrecht, 23 April 2013 Alain van Gool
  29. 29. EC DG for Research and Innovation Alain van Gool Brussels, 11 Sept 2012 Network medicine Proposed procedure for network-based drug discovery for personalized therapy Source: Schadt et al, 2009, Nature, 8:268} 29 TI Pharma Spring meeting Utrecht, 23 April 2013 Alain van Gool
  30. 30. Personalized Health = Food + Lifestyle + Pharma 30 TI Pharma Spring meeting Utrecht, 23 April 2013 Alain van Gool
  31. 31. Acknowledgements Jan van der Greef Ben van Ommen Peter van Dijken Robert Kleemann Bas Kremer Tom Rullmann Suzan Wopereis Marijana Radonjic Thomas Kelder and others Ron Wevers Jolein Gloerich Dirk Lefeber Monique Scherpenzeel Udo Engelke and others Lutgarde Buydens Jasper Engel Lionel Blanchet Jeroen Jansen and others Radboud UMC Personalized Medicine Taskforce: Andrea Evers, Alain van Gool, Joris Veltman, Jan Kremer, Maroeska Rovers, Jack Schalken, Bas Bloem, Gerdi Egberink, Viola Peulen, Martijn Hoogboom, Martijn Gerretsen 31 alain.vangool@tno.nl TI Pharma Spring meeting Utrecht, 23 April 2013 Alain van Gool

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