ERS Annual Congress Vienna

1–5 September 2012

Postgraduate Course 13 AirPROM: patient-specific modelling and systems

biology living labs workshop

Saturday, 1 September 2012 14:00–17:30

Room: Schubert 5

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The need and potential of patient-specific modelling

Prof. Christopher E. Brightling Institute for Lung Health

Glenfield Hospital LE3 9QP Leicester

United Kingdom ceb17@le.ac.uk

Aims 1. Define the current approaches to phenotyping airways disease 2. Describe what is meant by scales of disease 3. Describe our current knowledge of immunopathology across the scales of disease using

asthma as an exemplar 4. Discuss the shortcomings of our current approaches to clinical integration of multi-scale data 5. Propose how systems biology and computational multi-scale modelling might add value to our

understanding of disease and its potential in stratified/personalized medicine Summary The airways diseases asthma and chronic obstructive pulmonary disease affect over 400 million people world-wide and cause considerable morbidity and mortality. Airways disease costs the European Union in excess of €56 billion per annum. Current therapies are inadequate due to our incomplete understanding of the pathophysiology of these diseases and our lack of recognition of the enormous disease heterogeneity, such that our current ‘one size fits all’ approach is inappropriate. We need to fully characterise this heterogeneity in order to identify the ‘right target’ for the ‘right patient’ and measure the ‘right outcome’. To achieve this goal we need sufficient tools to predict disease progression or response to current or future therapies. Current diagnostic criteria based on the presence of airflow obstruction and symptoms do not integrate the complex pathological changes occurring within the lung, do not define different airway inflammatory patterns, nor do they define different physiological changes or differences in structure as can be defined by imaging. Over recent years there has been interest in describing this heterogeneity and using this information to sub-group patients into airway disease phenotypes. Most approaches to phenotyping have considered disease at a single scale and have not integrated information from different scales (e.g. organ-whole person, tissue-organ, cell-tissue and gene-cell) of disease to provide multi-dimensional phenotypes. Integration of disease biology with clinical expression is critical to improve understanding of this disease. When combined with bio-statistical modelling, this information may lead to identification of new drug targets, new end-points for clinical trials and targeted treatment for subgroups of airway disease patients. It is hoped this will ultimately improve COPD outcomes and represent a move towards personalised medicine. In this review, we will consider these aspects of multi-dimensional phenotyping in more detail. References

1. Barker B, Brightling CE. Phenotyping the Heterogeneity of Chronic Obstructive Pulmonary Disease Clinical Science in press 2012

2. Brightling CE, Gupta S, Gonem S, Siddiqui S. Lung damage and airway remodelling in severe asthma. Clin Exp Allergy. 2012;42(5):638-49

3. Walker C, Gupta S, Hartley R, Brightling CE Computed tomography scans in severe asthma: utility and clinical implications Curr Opin Pulm Med. 2012;18(1):42-7.

4. Bousquet J, Anto JM, Sterk PJ, Adcock IM, Chung KF, Roca J, Agusti A, Brightling C, et al. Systems medicine and integrated care to combat non-communicable disease. Genome Med. 2011 6;3(7):43.

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5. Gonem S, Desai D, Siddiqui S, Brightling C. Evidence for phenotype-driven treatment in asthmatic patients. Curr Opin Allergy Clin Immunol. 2011;11(4):381-5.

6. Brightling CE, Gupta S, Hollins F, Sutcliffe A, Amrani Y. Immunopathogenesis of severe asthma Curr Pharm Des. 2011;17(7):667-73.

7. Siddiqui S, Brightling C. Magic bullets: the new goal for asthma Am J Respir Crit Care Med. 2009; 180(5):383-4.

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PG13 Patient specific modelling and systems biology living labs workshop-

‘The need and potential of patient specific modelling’

Professor Chris BrightlingInstitute for Lung Health, Leicester, UK

Conflicts of Interest

• AirPROM- EU FP7 6 industry partners, SMEs– www.airprom.european-lung-foundation.org/

• COPD MAP- MRC/ABPI 4 Pharma Partners

• Research Funding and Consultancy– Roche/ Genentech– AstraZeneca/ MedImmune– Novartis– GlaxoSmithKline– Chiesi

The need and potential of patient specific modeling

• 400 million people affectedwith asthma world-wide

•COPD third leading causeof death by 2030

• Airway disease costs EU>56 billion Euros/annum

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The need and potential of patient specific modeling

• Validated models topredict airways diseaseprogression and response totreatment

• Platform to translatepatient-specific tools

• Personalised managementof airways disease.

Asthma UK Asthma Audit 1999

191 209319

634

3793

152

305 310

1,1331,200

1,000

800

600

400

200

0% 2.5/17 8/ 32 8/ 18 5/ 7 1.5/ 1

Stage I II III IV V

Esti

mat

ed t

otal

cos

t of

trea

tmen

t pe

r pe

rson

(£) Exacerbations

No exacerbations

Towards Personalised Medicine

Traditional Asthma Classification

•Extrinsic / Intrinsic asthma Rackemann 1927

•Occupational asthma

•Exercise induced asthma

•Catamenal asthma

Aetiology

Patterns of Variable Airflow Obstruction•Turner-Warwick 1977- Brittle asthma- The morning dipper- Irreversible asthma

•Ayres 1998- Types 1 & 2 brittle asthma

Pathology•Pavord 1999, Wenzel1999, Green 2002- Eosinophilic Vs Non-eosinophilic

•BTS

•ATS Refractory

•ERS ‘Difficult’ asthma

•GINA

•NAEPP III

Severity/Control

•No uniformity•Descriptive•Focus on few dimensions of disease

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Unravelling Complexity

“If you can not measure it, you can not improve it”.

Lord Kelvin

Unravelling Complexity

High Resolution Complex Phenotyping

Resolution

SymptomsSignsLung function

What is a Phenotype?

A phenotype is any observablecharacteristic that results from thegenetic background as well as theinfluence of environmental factors andpossible interactions between the two.

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AIRWAY DISEASE

CHRONIC INFLAMMATION

Pathology

AIRFLOW OBSTRUCTION

AIRWAY HYPERRESPONSIVENESS

Physiology

SYMPTOMS

EXACERBATIONS

Clinical Expression

Airway Disease Domains

Inflammatory Phenotypes

BrightlingChest 2006;129(5):1344-8 COPD Asthma Normal

Sp

utu

m eo

sin

oph

ils (

%)

Brightling et al. Lancet 2000

Green et alLancet 2002

Asthma

Variable Airflow Obstruction

Allergy

Eosinophilic airway inflammation

Symptoms

- PEF Variability

- Modified JACS - Atopic status (SPT)

- Induced sputum

Demographics BMI

- Age of onset- Gender

Phenotyping Heterogeneity

PC20

10

Summary

INFLAMMATION PREDOMINANTLate onset

Greater proportion of malesFew daily symptoms

Concordant disease

Discordant Inflammation

Discordant Symptoms

OBESE FEMALE NON EOSINOPHILIC

High symptom expression

EARLY SYMPTOM PREDOMINANTNon-eosinophilic

Normal BMIHigh symptom expression

Refractory Asthma

Eos Inflammation

Sym

pto

ms

Haldar et al 2008 AJRCCM

Summary

INFLAMMATION PREDOMINANTLate onset

Greater proportion of malesFew daily symptoms

Discordant Inflammation

Discordant Symptoms

OBESE FEMALE NON EOSINOPHILIC

High symptom expression

EARLY SYMPTOM PREDOMINANTNon-eosinophilic

Normal BMIHigh symptom expression

Eos Inflammation

Sym

pto

ms

Haldar et al 2008 AJRCCM

Refractory Asthma

120

100

80

60

40

20

0

Severe exacerbations (cumulative

number)

0 1 2 3 4 5 6 987 10 11

Time (months)

12

‡p=0.01

1 asthma admission

6 asthma admissions

PhenotypeOutcomes at 12

monthsStudy Group Sig

Symptom predominant

Clinical Protocol

N=7

Sputum management

N=4

Change in ICS Dose

+1428.57 -400 0.022

Severe exacerbation frequency

5.43 2.50 0.198

Number commenced on OCS

6 (85.7%)

0 -

Cluster Specific Outcomes

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PhenotypeOutcomes at 12

monthsStudy Group Sig

Inflammation predominant

Clinical Protocol

N=15

Sputum management

N=24

Change in ICS Dose +753.33 +241.67 0.22

Severe exacerbation frequency

3.53 0.38 0.002

Number commenced on OCS

2 (13.3%) 9 (37.5%) 0.167

Cluster Specific Outcomes

INFLAMMATION PREDOMINANTLate onset

Greater proportion of malesFew daily symptoms

OBESE FEMALE NON EOSINOPHILIC

High symptom expression

EARLY SYMPTOM PREDOMINANTNon-eosinophilic

Normal BMIHigh symptom expression

Refractory Asthma

Eos Inflammation

Sym

pto

ms

Specific Anti-eosinophil

Therapy

Right Patient, Right Target, Right Outcome

HaldarNEJM 2009

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Airway geometry: response to mepolizumab

Siddiqui et alAllergy 2009

Haldar et al NEJM 2009

At Exacerbations:

Are there biological clusters?

Study Outline

Bafadhel et al AJRCCM 2011

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Biological Clustering COPD Exacerbations

Factor

1 2 3

TNF α 0.80 -0.36

TNF R1 0.88

TNF R2 0.93

CCL13 0.82

CCL17 0.90

CXCL10 0.92

CXCL11 0.93

IL 1β 0.89 -0.36

IL 5 0.76

IL 6R 0.89

IL 8 0.83 Bafadhel et al AJRCCM 2011

Bacterial Exacerbation

IL1β 125pg/ml : 90% sensitive80% specific

AUC=0.89

Bacterial exacerbation if bacteria in stable state

OR 4.9 (2.4 to 9.9)

Predictors for Sputum Eosinophilia

AUC = 0.852% cut off 90% sensitivity, 60%

specificity

Eosinophilic exacerbationif eosinophilic in stable state

OR 2.7 (1.3 to 5.7)

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At Exacerbations:

Can biomarkers direct therapy?

COPD Exacerbations: Targeted Therapy

Bafadhel et al AJRCCM 2012

Targeted Corticosteroid Therapy at Exacerbation

Bafadhel et al AJRCCM 2012

Health Status

Lung Function

Symptom Scores

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Targeted Corticosteroid Therapy at Exacerbation

Bafadhel et al AJRCCM 2012

Complex Systems-homeokinesis

Clinical Exacerbation

Persistent disequilibrium

High risk

Day to day variation

Low risk

modulators

predictors

prevent

triggers

AirPROM‐ Airway Disease Predicting Outcomes 

Through Patient Specific Computational Modelling

16

Large Airway: Structure-Function

Healthy Control

Severe Asthma

Large Airway: Structure-Function

CT Imaging Beyond Geometry

17

CFD: Therapeutic Response

Small Airway: Structure-FunctionSmall Airway Impedence

Ventilation Heterogeneity

Alveolar structure

Air Trapping

Inflammation

Small airway Domains

Air Trapping: Expiratory CT

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Small airway obstruction in Asthma & COPD

Hogg et al NEJM 2004

Airway Remodelling

Immunopathology Asthma versus COPD

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Gene Protein Cell Tissue Organ Patient

PATHOGENS

Bacteria/ Viruses/ Moulds

ALLERGENSPOLLUTION &

Physical Damage

Host SusceptibilityImpaired Innate immunity

TIME

The need and potential of patient specific modeling

Patient-Specific Multi-Scale Models

Benayoun 2003 AJRCCM

Politi J Thero Biol2010

ORMDL3Moffat NEJM 2010, Nature 2007

Cantero-Recaseus Hum Mol Gen 2010

Mahn 2009 PNAS

Personalised Healthcare / Target Identification

UP & Down the Scales

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Acknowledgements

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