Personalized medicine in childhood asthma. Dr Mariëlle Pijnenburg, Erasmus MC Sophia, Rotterdam, NL

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Personalized medicine in childhood asthma Dr Mariëlle Pijnenburg, Erasmus MC Sophia, Rotterdam, NL

Conflict of interest disclosure Dr Mariëlle Pijnenburg do not have any real or perceived, direct or indirect conflicts of interest that relate to this presentation.

Aims After this presentation you are able: To recognize what patients/ caregivers expect from personalized medicine To name the potentials and limitations of systems biology for personalized medicine

Outline Personalized medicine from a patient s perspective: Mrs Kim Price Personalized medicine from the paediatrician s perspective Definitions Why our patients need personalized medicine? Systems biology: metabolomics, genetics, exposome Conclusions MCQ

Personalized medicine from a patient s perspective What do patients/ parents expect from personalized medicine? want from their health care provider in asthma care? feel the future will look like? How want patients/ parents to be involved in personalized medicine?

What is personalized medicine? Personalized/ precision medicine: customization of health care tailored to the individual; uses some kind of technology or discovery enabling a level of personalization not previously feasible or practical; NIH: emerging approach for disease treatment and prevention that takes into account individual variability in genes, environment, and lifestyle for each person. Personal medicine: patient centered care, providing care that is respectful of and responsive to individual patient preferences, needs, and values, and ensuring that patient values guide all clinical decisions. Stratified medicine: classify individuals into subpopulations that differ in their susceptibility to a particular disease or their response to a particular treatment

Personal medicine: patient centered care Patient and family centered care Dignity and respect Information sharing Participation Collaboration

Patient and family centered care: our experience

Asthma asthma Non-atopic vs atopic Eosinophilic, neutrophilic, paucicellular Severe, moderate, mild Difficult to treat, therapy resistant Exacerbation prone Viral wheeze/ multiple trigger wheeze Early transient, persistent, lateonset wheeze Obesity: yes or no Concordant/ discordant disease Responders/ non-responders Side-effects Preschool children school children - adolescents (near) fatal asthma Triggers Co-morbidities..

Asthma asthma Non-atopic vs atopic Obesity: yes or no Different phenotypes Concordant/ discordant disease Eosinophilic, neutrophilic, paucicellular Responders/ non-responders Severe, moderate, mild Side-effects Difficult to treat, therapy Preschool children school resistant children - adolescents Exacerbation prone (near) fatal asthma Viral wheeze/ multiple trigger Triggers wheeze Co-morbidities Early transient, persistent, lateonset.. wheeze

Asthma management

FEV 1 % Change with Mt >7.5% Mt Response Asthma asthma 50 40 30 20 10 0-10 -20-30 -40-50 Szefler S. JACI 2005. Mt alone n=6 (5%) Neither n=69 (55%) Both n=22 (17%) FP alone n=29 (23%) >7.5% FP Response -50-40 -30-20 -10 0 10 20 30 40 50 FEV 1 % Change with FP

FEV 1 % Change with Mt >7.5% Mt Response Asthma asthma 50 40 30 20 10 0-10 -20-30 -40-50 Szefler S. JACI 2005. Mt alone n=6 (5%) Neither n=69 (55%) Both n=22 (17%) Treatment response differs FP alone n=29 (23%) >7.5% FP Response -50-40 -30-20 -10 0 10 20 30 40 50 FEV 1 % Change with FP

Asthma asthma 20-40% of all children with asthma have poor control Asthma is a complex and heterogenous disease We treat on: Symptoms Exacerbations Lung function What is below the iceberg? How to personalize management?

Bunyavanich et al. JACI 2015.

disease

Systems biology Bunyavanich et al. JACI 2015.

Systems biology- Genetics Prediction of asthma: genes involved in asthma development Monitoring: Risk assessment Treatment: Pharmacogenetics

Genetics - prediction of asthma Klaassen et al. PlosOne 2015.

Genetics risk assessment Bønnelykke et al. Nature Genetics 2014.

Genetics risk assessment Risk assessment: follow patients with AA more frequently; treat them with ICS Bønnelykke et al. Nature Genetics 2014.

Genetics pharmacogenetics Adding LABA 1 of 3 treatment options in step 3 conflicting data on safety FDA meta-analysis McMahon et al. Pediatrics 2011.

Genetics pharmacogenetics Gly-to-Arg substitution in the β 2 -adrenoreceptor gene (ADRB2) associated with downregulation of β 2 -receptors Odds ratio exacerbations 1.52 (95% CI 1.17-1.99) per A allel in children on LABA and ICS Not in children on ICS only or ICS+LTRA or ICS + LABA + LTRA Turner et al. JACI 2016.

School absence 62 children homozygous for Arg/arg: Rescue beta-2 ICS + LABA ICS + LTRA Follow up 1 year QOL Lipworth et al. Clin Sci 2013

Pharmacogenetics update 2015 Davis et al. Curr Allergy Asthma Rep 2015.

Bunyavanich et al. JACI 2015.

Biomarkers (metabolomics) Prediction of asthma Monitoring: risk management Treatment: response to treatment Identify new pathways and possible therapeutic targets

Biomarkers Serum Urine Sputum BAL Exhaled breath Exhaled breath condensate (EBC)

Serum biomarkers - eosinophils Increased risk of later asthma Predict risk of exacerbation (Trung 2014) Predict response to Omalizumab (Busse et al. JACI 2013), mepolizumab (Ortega et al. Ann Am Thor Soc 2014) and reslizumab (Corren et al. Chest 2016)

Muraro et al. JACI 2016.

Biomarkers - Exhaled breath and exhaled breath condensate (EBC) Single markers: e.g. ph, FeNO Combination of (known) markers Profiles of (unknown) markers

Biomarkers - Volatile organic compounds Exhaled air contains thousands of VOC Reflect respiratory and systemic disease May be detected by different technology

Prediction of treatment response with e-nose Prediction steroid response (FEV1 >12% or PC20AMP>2dd): VOC better than FeNO and sputum eosinophils Van der Schee et al, CEA 2013

Prediction of exacerbations prospective study, 40 children, follow up 1 year 2-months intervals: FeNO, VOCs, lung function and symptoms 16/40 exacerbation 6 VOCs optimal predictive value for exacerbations (correct classification 96%, sens 100%, spec 93%) FeNO and lung function not predictive Robroeks et al. ERJ 2013.

Predicting asthma: combinations 200 children, 2-4 yrs Astma diagnosis at 6 yrs AUC combination VOCs, genetics and API 0.95 PPV 90% NPV 89% Klaassen/ van de Kant. AJRCCM 2014.

Exhaled breath condensate Extracted from Am J Respir Crit Care Med 2001;164(5):731-7 TURBO DECCS 09

Acidity of EBC: reduced ph in asthmatic children EBC of asthmatics more acid, especially in steroid naive children Significant overlap Acid-base balance disturbed in asthmatic airways? Carraro et al, Allergy 2005

Leukotrienes and 8-isoprostane in EBC EBC leukotrienes E 4, B 4 elevated in asthma and similar in ICS treated and ICS naive asthmatics Mondino JACI 2004 8-isoprostane = marker of oxidative stress Elevated in asthma with and without steroids Shahid, Respir Res 2005

Biomarkers - EBC Systematic review of 46 papers on EBC in asthma, atopy in children (Thomas et al. Pediatr Pulmonol 2013) lower EBC ph values in asthmatics, even lower if poorly controlled higher levels of aldehydes, reduced glutathione during exacerbations eicosanoids and TH2 cytokines more variable results, often elevated

Biomarkers EB and EBC Systematic review on EB (9) and EBC (84) in respiratory diseases ( Van Mastrigt et al. Clin Exp Allergy. 2014) - Metabolomics may have important advantages over detecting single markers - VOC profiles/ biomarkers EBC able to discern asthma form healthy (AUC 0.94) and respond to treatment - Some correlation with asthma control, less with asthma severity - Lack of standardization of collection and analysis methods - Lack of longitudinal studies and external validation

EB and EBC

Bunyavanich et al. JACI 2015.

Exposome Vrijheid. Thorax 2014.

Exposome 50% of worldwide mortality attributable to a few environmental factors: air pollution, smoking, diet Vrijheid. Thorax 2014.

Exposome Measurements of exposure are not very accurate Measure only 1 exposure Real time individual monitoring needed Human Exposome Project: environment (diet, lifestyle, behavior) genetics and medication HELIX project: pre- and postnatal exposures EXPOsOMICS project: monitoring of individual exposure with sensors, smartphones, georeferencing and satellites HEALS project: individual exposure measured with apps coupled to DNA sequencing, epigenetic DNA modifications, and gene expression

Do not forget Most children with asthma are well controlled with step 1-2 treatment, guided by symptoms and/or lung function. Improving inhaler technique and adherence to treatment may improve asthma control in poorly controlled children

Precision medicine challenges Handling of large, complex data sets computational challenge Integration of data sets and integrated analysis Translation in format for clinical decision making Cost-benefit implications

Barriers for personalized medicine Electronic medical records Greater number of genes identified for each asthma drug response pathway Ability of genomic information to predict drug treatment response in individual patients Better phenotyping and endotyping More targeted treatments

Conclusions Patients want personal medicine : patient and family centered care Personalized medicine for children with asthma is a developing field Treatment and monitoring on genomics/ metabolomics/exposomics may benefit selected children with asthma Pharmacogenetics may help in choosing the right medication for the right child, preventing adverse effects Metabolomics in EB and EBC remain promising but still a research tool Exposomics new dimension which has to be developed