Bridging System Biology research to Personalized Healthcare

Prof Alain van Gool

Head Radboud Center for Proteomics, Glycomics and Metabolomics Coordinator Radboud Technology Centers Radboud university medical center, Nijmegen, Netherlands

Mixed perspectives

8 years academia (NL, UK)

(research, methods)

13 years pharma (EU, USA, Asia)

(biomarkers for pharma, Omics)

2 years applied research institute (NL, EU)

(biomarkers in health)

2 years med school (NL)

(personalized health)

A person / citizen / family man

(adventures in EU, USA, Asia)

Message

• System biology = genetics + metabolic activity + mental state + environment

• Yield Personal Profiles

• Basis for Personalized Healthcare solutions by lifestyle, food and/or pharma

Personalized healthcare with patient as partner

People are different Stratification by multilevel diagnosis

Patient’s preference of treatment Care communities

Application of novel technology in clinical care

• Research/technology push:

• “Biomarkers can and should provide the molecular part of the personalized healthcare model in selection of best therapy, monitoring of effect, and follow-up”

• Daily practice in clinical assessment:

• Diagnosis is combination of personal opinion (patient and physician), physical examination, molecular and clinical chemistry tests to generate personal profiles

• New biomarkers are added where deemed useful by physician

• Costs important factor in decision on application

• 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)

5

Personal profiles

Source: Barabási 2007 NEJM 357; 4}

• People are different • Different networks influences • Different risk factors

6

7

Personal profiles

Marijana Radonjic

8

Personal profiles

Marijana Radonjic

Patient participation and empowerment

included !!

Limited view from the outside

Source: Gary Larson

Animal models Patient-related outcome

Source: National University Hospital Singapore

9

EC DG for Research and Innovation

Alain van Gool

Brussels, 11 Sept 2012

Working in complex human biological systems requires a systems biology approach

System biology in:

Diagnosis Prognosis Treatment Monitoring

Lecture LKCH, UMC Utrecht

29 October 2013

Alain van Gool

System Biology and Personalized Healthcare

11

Ho

meo

sta

sis

A

llo

sta

sis

D

isease

Time

Personalized health

Personalized medicine

“Health management”

Focus on resilience

“Disease management”

Focus on symptom(s)

Medical

treatment

or

Disease

Health

Non-health

Translation is key !

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Data

Knowledge

Understanding

Decision

Action

Çase: Biochemistry of metabolic disorder`s

Myocardial

infactions

Heart

failure

Cardiac

dysfunction

dyslipidemia

Metabolically

healthy

High cholesterol High glucose Hypertension

Brain

disorders Nephropathy Atherosclerosis Stroke Retinopathy

Risk factors of the ‘metabolic syndrome’

Pathologies resulting from the ‘metabolic syndrome’

Visceral

adiposity

LDL elevated

Glucose toxicity

Fatty liver

gut

inflammation

endothelial

inflammation

systemic

Insulin resistance

systemic

inflammation

Hepatic IR

Adipose IR

Muscle metabolic

inflexibility

adipose

inflammation

Microvascular

damage

Myocardial

infactions

Heart

failure

Cardiac

dysfunction

Brain

disorders

Nephropathy

Atherosclerosis

β-cell failure

Reversible process

β-cell Pathology

High cholesterol

High glucose

gluc Risk factor

Hypertension

dyslipidemia

ectopic

lipid overload

Ìrreversible process

Hepatic

inflammation

Stroke

IBD

fibrosis

Retinopathy

Metabolically

healthy

{Nakatsuji, Metabolism 2009} {Source: Ben van Ommen, TNO}

Visceral

adiposity

LDL elevated

Glucose toxicity

Fatty liver

Gut

inflammation

endothelial

inflammation

systemic

Insulin resistance

Systemic

inflammation

Hepatic IR

Adipose IR

Muscle metabolic

inflexibility

adipose

inflammation

Microvascular

damage

Myocardial

infactions

Heart

failure

Cardiac

dysfunction

Brain

disorders

Nephropathy

Atherosclerosis

β-cell failure

High cholesterol

High glucose

Hypertension

dyslipidemia

ectopic

lipid overload

Hepatic

inflammation

Stroke

IBD

fibrosis

Retinopathy

Physical inactivity Caloric excess

Chronic Stress Disruption

circadian rhythm

Parasympathetic

tone

Sympathetic

arousal

Worrying

Hurrying

Endorphins Gut

activity Sweet & fat

foods

Sleep disturbance

Inflammatory

response

Adrenalin

Fear

Challenge

stress

β-cell Pathology

gluc Risk factor

Heart rate Heart rate

variability

High cortisol

α-amylase

Systems view on metabolic health and disease

Lipids, alcohol, fructose

Carnitine, choline

Stannols, fibre

Low glycemic index

Epicathechins

Anthocyanins

Soy

Quercetin, Se, Zn, …

Metformin

Vioxx

Salicylate

LXR agonist

Fenofibrate Rosiglitazone

Pioglitazone

Sitagliptin

Glibenclamide

Atorvastatin

Omega3-fatty acids

Pharma

Nutrition Lifestyle

{Source: Ben van Ommen, TNO}

EC DG for Research and Innovation

Alain van Gool

Brussels, 11 Sept 2012

Important processes in

T2D

Diagnosis

Potential interventions

Dietary/Lifestyle Pharma 1.Pancreatic β-cell function

(impaired insulin secretion)

*OGTT: I/ΔG 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

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How personal is personalized?

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Emerging concept:

A chronic disease = a collection of rare diseases

Opportunity to learn lessons from rare disease field.

Personalized Healthcare in rare diseases

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• 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 1: Explanation of disease

• Outcome 2: Dietary intervention as succesful personalized therapy

• Outcome 3: Glycoprofile developed as diagnostic test by mass spectrometry

Dietary intervention

Incomplete glycosylation Complete glycosylation

{Dirk Lefeber et al,

NEJM 2013}

Use of shared data in Personalized Health(care)

N=1 health care Following P4 medicine: Participatory, Personalized, Predictive, Preventive

Disease

Health

Ho

meo

sta

sis

A

llo

sta

sis

D

isease

Time

Personalized Intervention

Public Big Data

Personal Risk profiles

Molecular Non-molecular Environment …

Use of shared data in Personalized Healthcare

{Chen et al, Cell 2012, 148: 1293}

Concept: • Continuous monitoring (n=1) • Routine biomarkers to alert • Omics to explain • Early intervention

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Challenge 1: biomarker innovation gap

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• 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

Biomarker innovation gap: some numbers

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Data from Thomson Reuters Integrity, April 2013

Alzheimer’s Disease

Chronic Obstructive

Pulmonary Disease

Type II Diabetes Mellitis

Eg Biomarkers in time: Prostate cancer

May 2011: 2,231 biomarkers

Nov 2012: 6,562 biomarkers

Oct 2013: 8,358 biomarkers

Challenge 2: Visualization of health

{Source: Albert de Graaf, TNO}

Open call to build bridges together to join forces

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• Biomarker development

• Data visualization

• Extraction of knowledge from EPD

• Best practice in supporting patients in decision making

• Radboud umc as field lab for global innovations

• Etc etc

Acknowledgements

Jan van der Greef

Ben van Ommen

Peter van Dijken

Robert Kleemann

Lars Verschuren

Bas Kremer

Ton Rullmann

Marijana Radonjic

Thomas Kelder

Suzan Wopereis

and others

Lucien Engelen

Ron Wevers

Jolein Gloerich

Dirk Lefeber

Monique Scherpenzeel

Leo Kluijtmans

Udo Engelke

and others

Lutgarde Buydens

Jasper Engel

Lionel Blanchet

Jeroen Jansen

and others

Radboud UMC Personalized Healthcare Taskforce:

Andrea Evers, Alain van Gool, Joris Veltman, Jan Kremer, Bas

Bloem, Maroeska Rovers, Jack Schalken, Paul Smits + Gerdi

Egberink, Viola Peulen, Martijn Hoogboom, Martijn Gerretsen

alain.vangool@tno.nl

alain.vangool@radboudumc.nl