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Phenotyping severe asthma The U-BIOPRED project

Grazie per aver scelto di utilizzare a scopo didattico questo materiale delle Guidelines 2011 libra. Le ricordiamo che questo materiale è di proprietà dell’autore e fornito come supporto didattico per uso personale. Phenotyping severe asthma The U-BIOPRED project. p.j.sterk@amc.nl.

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Phenotyping severe asthma The U-BIOPRED project

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  1. Grazie per aver scelto di utilizzare a scopo didattico questo materiale delle Guidelines 2011 libra.Le ricordiamo che questo materiale è di proprietà dell’autore e fornito come supporto didattico per uso personale.

  2. Phenotyping severe asthma The U-BIOPRED project p.j.sterk@amc.nl Department of Respiratory Medicine Academic Medical Center University of AmsterdamThe Netherlands

  3. Towards phenotyping severe asthma • What is a phenotype? • What is the concept behind unbiased disease-fingerprinting? • Is this needed for the management of severe asthma? • The IMI-EU project U-BIOPRED • The promiss of ‘systems medicine’

  4. What is a phenotype? The composite of observable characteristics of an organism… resulting from interaction between its genetic make-up and environmental influences… that is relatively stable, but not invariable with time.

  5. The three determinants of a phenotype Time Environment Genetics

  6. Complex biological systems:The secret of life • Spontaneous self-organisation • Open systems, importing energy, exporting waste • Not linear: output not simply proportional to input • Sudden emergent phenomena, deterministic chaos • Adaptation by negative feedback loops • Fluctuating: homeokinesis rather than homeostasis Schrödinger E. Lectures Trinity College Dublin; 1944. New York: MacMillan Goldberger AL. Proc Am Thoracic Soc 2006;3:467-472. Macklem PT. J Appl Physiol 2008;104:1844-1846. Macklem PT & Seely A. Perspectives Biol Med 2010;53:330-343.

  7. Genes, environment and time Richards K. Life. Little, Brown & Company, London, 2010

  8. Genes Cell differentiation & activation Organ structure & function Organism health & disease Gene-& post- transcriptional regulation Cell-cell interaction Macro physiology Capturing phenotypes

  9. diagnosis & therapy disease domain Symptoms √ √ Functional ? Cellular ? Molecular

  10. Severe asthma Facts • Despite all our attempts, the clinical course of severe asthma is far from optimal • Unfortunately, the development of new drugs for severe asthma has not been successful during the past years Reasons? • Severe asthma is not a single disease: individual patients are clinically very different • There are multiple and co-existent disease mechanisms • At present the efficacy of new drugs cannot be predicted well enough from preclinical models nor from currently defined patient characteristics

  11. Asthma severity and control Cockcroft & Swystun JACI 1996;98:1016-1018 ATS/ESR Task Force Asthma Control and Exacerbations Taylor et al. ERJ 2008;32:545-554 Reddel et al. AJRCCM 2009;180;58-99

  12. “Severe asthma” Problematic asthma Uncontrolled asthma Non-adherent asthma Fixed obstruction Exacerbation prone Co-morbid asthma Refractory asthma Truly severe asthma Difficult asthma no asthma NAEPP 1997, ERS 1999, GINA 2002, ATS & SARP 2002, ENFUMOSA 2003, BIOAIR 2005 TENOR 2004, Paris 2007, ERS 2008, PSACI 2008, WHO 2009, U-BIOPRED 2011

  13. Consensus Definition and classification • Problematic asthma • All asthma that remains uncontrolled despite prescription of high intensity treatment • Difficult asthma • Mild-moderate asthma that remains uncontrolled • Adherence <50%, VCD, dysfunctional breathing, psychosocial • Persistent exposures • Untreated co-morbidity • Severe asthma • Poor control or >2 exacerbations/year, despite high intensity treatment • > 1000 (adults) or 500 (children) μg FP equivivalent or daily oral steroids, combined with LABA or other add-on medication • Mainted control only achievable by high intensity treatment • Thereby serious risk of adverse effects Bel et al. U-BIOPRED Study. Thorax 2011 EPub

  14. 1 2 5 Moore et al. Am J Respir Crit Care Med 2010;181:315-323

  15. Asthma: complex biology Normal asthma Central Peripheral Mauad, Bel, Sterk. J Allergy Clin Immunol 2007;120:997-1009

  16. Additional phenotypic markers?

  17. When will a disease marker be useful? ● ● ● ● ● ● ● ● ● ● Marker A ● Marker B ● ● ● ● ● ● ● ● ● ● Reference feature Reference feature

  18. Disease markers which provide complementary information in asthma Factor analysis Age FEV1 FVC PC20 Reversibility Sputum - eosinophils - ECP Rosi et al. JACI 1999;103:232

  19. Phenotypic cluster analysis in asthma Discordant Symptoms Obese Late onset Atopic and early onset Concordant symptoms and inflammation Symptoms Controlled Mixed onset Discordant Inflammation Eosinophilic inflammation Haldar et al. Am J Respir Crit Care Med 2008;178:218-224

  20. Exhaled nitric oxide + FEV1predict lung function decline in severe asthmatics during 5 years prospective follow-up Baseline FEV1≤ 80% Baseline FEV1 > 80% Change in FEV1 (ml) Change in FEV1 (ml) Exhaled NO (ppb) Exhaled NO (ppb) Van Veen et al, ERJ 2008;32:344-349

  21. Heatmap for molecular phenotyping from cytokines in BAL of severe asthma Brasier et al. J Allergy Clin Immunol 2008;121:30-37

  22. Transcriptomic phenotypes from sputum in asthma Baines et al. J Allergy Clin Immunol 2011;127:153-160

  23. Protein expression profiling in serum in asthma, COPD, cystic fibrosis and controls(SELDI-TOF-MS signatures) Gomes-Alves et al. Clin Biochemistry 2010;43:168-177

  24. Electronic nose analysis Fens et al. Am J Respir Crit Care Med 2009:180:1076-82

  25. Training and validation sets by eNose: asthma versus COPD Accuracy: 85% AUC: 0.93 ●Training set COPD●Training set asthma ▄Validation set COPD ▄Validation set fixed asthma Fens et al. ATS 2010, submitted

  26. disease phenotype diagnosis & therapy disease domain Symptoms √ √ Functional ! Cellular ! Molecular

  27. Continuous recording and fluctuations of respiratory resistance in asthma Slats et al. Am J Respir Crit Care Med 2007;176:121-128

  28. Respiratory impedance in asthma and COPD COPD Asthma Muskulus et al. J Appl Physiol 2010;109:1582-1591

  29. Multi-dimensional, non-parametric fluctuation analysis of the dynamics of respiratory impedance in asthma and COPD Muskulus et al. J Appl Physiol 2010;109:1582-1591

  30. ROC curve using 5-dimensional reconstruction of respiratory impedance dynamics in discriminating asthma and COPD Discriminant score Asthma COPD Muskulus et al. J Appl Physiol 2010;109:1582-1591

  31. disease phenotype diagnosis & therapy disease domain Symptoms √ √ Functional ! Cellular ! Molecular

  32. Novartis GlaxoSmithKline AstraZeneca Chiesi Pfizer Roche UCB Boehringer Ingelheim Johnson & Johnson Almirall University of Amsterdam University of Southampton Imperial College London University of Manchester University of Nottingham Fraunhofer institute Hannover Centr Nat Recherche Sc Villejuif Paris Université de Méditerranee Montpellier Karolinska Institute Stockholm University Umea UniversityTor Vergata Rome Università Cattolica del Sacro Cuore Rome University of Catania Hvidore Hospital Copenhagen University Hospital Copenhagen Haukeland University Bergen Semmelweis University Budapest Jagiellonan University Krakow University Hospital Bern University of Ghent Netherlands Asthma Foundation Asthma UK European Lung Foundation EFA Int Primary Care Respir Group Lega Italiano Anti Fumo Biosci Aerocrine Synairgen Philips Research

  33. Hypothesis U-BIOPRED study Biomarker profiles from multi-scale molecular, physiological, and clinical data integrated by an innovative systems biology approach into distinct handprints will enablethe prediction of clinical course and therapeutic efficacy and identification of novel targets in the treatment of severe asthma www.ubiopred.eu

  34. 1025 subjectsincluding adults ánd children

  35. Study design • Severe asthma consensus and diagnostic algorithm (Bel et al. Thorax 2011 EPub) • Cross-sectional comparitive handprint discovery • Longitudinal follow-up during 30 months • Iterative comparison handprints from preclinical models (human ex-vivo, animal in vivo) • Proof of concept intervention by randomized controlled trial www.ubiopred.eu

  36. Study design exacerbations tele-monitoring bronchoscopy screening baseline follow-up 1 Follow-up 2 24-30 3-6 -1 0 Months

  37. U-BIOPREDWorkpackages

  38. ‘Systems Medicine’ Patient reported Clinical Functional Cellular Molecular Auffray, Adcock, Chung, Djukanovic, Pison, Sterk. Chest 2010;137:1410-1416. www.ubiopred.eu

  39. Unresolved disease problem Formalize questions Ensure quality Cytology Histology Quantitative morphology (imaging) Organ function and dynamics Clinical expression and patient perception Genomics Transcriptomics Proteomics Metabolomics Integrate data knowledge repository biobanking Perturb the system Refine unbiased computational model by iteration Add open source public data Generate hypotheses Kaminsky, Irvin, Sterk. J Appl Physiol 2011: EPub.

  40. Kaminsky, Irvin, Sterk. J Appl Physiol 2011, EPub.

  41. Conclusions • Phenotypes are integral descriptions of biological systems from the molecular to organism level • They are not stable, being modulated by genes, time and environment • In asthma and COPD there is increasing evidence that multi-dimensional biomarker signals are complementary to clinical characteristics • Unbiased cluster- and time-series analysis by using a systems medicine approach can make a step-change from traditional diagnoses to “phenotype-handprints” • U-BIOPRED is validating this strategy for severe asthma p.j.sterk@amc.nl

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