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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
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
Asthma ≠ asthma
Non-atopic vs atopic
Eosinophilic, neutrophilic, pauci-
cellular
Severe, moderate, mild
Difficult to treat, therapy
resistant
Exacerbation prone
Viral wheeze/ multiple trigger
wheeze
Early transient, persistent, late-
onset 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
Eosinophilic, neutrophilic, pauci-
cellular
Severe, moderate, mild
Difficult to treat, therapy
resistant
Exacerbation prone
Viral wheeze/ multiple trigger
wheeze
Early transient, persistent, late-
onset 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
Eosinophilic, neutrophilic, pauci-
cellular
Severe, moderate, mild
Difficult to treat, therapy
resistant
Exacerbation prone
Viral wheeze/ multiple trigger
wheeze
Early transient, persistent, late-
onset wheeze
Obesity: yes or no…
Concordant/ discordant disease
Responders/ non-responders
Side-effects
Preschool children – school
children - adolescents
(near) fatal asthma
Triggers
Co-morbidities
…..
Different phenotypes
Asthma ≠ asthma
FEV1 % Change with FP
FE
V1 %
Ch
an
ge
wit
h M
t
Mt alone
n=6 (5%)
Neither
n=69 (55%)
FP alone
n=29 (23%)
Both
n=22 (17%)
>
7.5
% M
t R
esp
on
se
>7.5% FP Response
Szefler S. JACI 2005.
-50
-40
-30
-20
-10
0
10
20
30
40
50
-50 -40 -30 -20 -10 0 10 20 30 40 50
Asthma ≠ asthma
FEV1 % Change with FP
FE
V1 %
Ch
an
ge
wit
h M
t
Mt alone
n=6 (5%)
Neither
n=69 (55%)
FP alone
n=29 (23%)
Both
n=22 (17%)
>
7.5
% M
t R
esp
on
se
>7.5% FP Response
Szefler S. JACI 2005.
-50
-40
-30
-20
-10
0
10
20
30
40
50
-50 -40 -30 -20 -10 0 10 20 30 40 50
Treatment response differs
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?
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: genetics, metabolomics, exposomics
Conclusions
MCQ
Bunyavanich et al. JACI 2015.
Bunyavanich et al. JACI 2015.
Systems biology
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
Bønnelykke et al. Nature Genetics 2014.
Risk assessment: follow patients with AA
more frequently; treat them with ICS
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 β2-adrenoreceptor gene (ADRB2)
associated with downregulation of β2-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.
62 children homozygous for
Arg/arg:
ICS + LABA
ICS + LTRA
Follow up 1 year
Lipworth et al. Clin Sci 2013
School absence
Rescue beta-2
QOL
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)
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.
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 steroid response (FEV1 >12% or PC20AMP>2dd): VOC
better than FeNO and sputum eosinophils
Van der Schee et al, CEA 2013
Prediction of treatment response with e-Nose
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 E4, B4 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
Vrijheid. Thorax 2014.
50% of worldwide mortality
attributable to a few environmental
factors: air pollution, smoking, diet
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
