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Personalized healthcare, a view in the near future
Professor in Personalized Healthcare Head Radboud Center for Proteomics, Glycomics and Metabolomics Coordinator Radboud Technology Centers
Head Biomarkers in Personalized Healthcare
Prof Alain van Gool
My mixed perspectives in personalized health(care)
8 years academia (NL, UK)
(molecular mechanisms of disease)
13 years pharma (EU, USA, Asia)
(biomarkers, Omics)
3 years applied research institute (NL, EU)
(biomarkers, personalized health)
3 years med school (NL)
(personalized healthcare, Omics, biomarkers)
A person / citizen / family man
(adventures in EU, USA, Asia)
1991-1996 1996-1998 2009-2012
1999-2007 2007-2009 2009-2011
2011-now
2011-now
2
Personalized Healthcare in the early days
{Kumar and van Gool, RSC, 2013}
1506:
The urine wheel
Use color, smell and taste of
urine to diagnose disease and
decide best treatment
Ullrich Pinder
Epiphanie Medicorum
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
Patient
Radboud Personalized Healthcare
A significant impact
on healthcare
Molecule
Population
5
Personalized Healthcare @ Radboudumc
People are different Stratification by multilevel diagnosis
+ Patient’s preference of treatment
Exchange experiences in care communities
Select personalized therapy
6
Population
Man
Molecule
Translation is key in Personalized Healthcare !
7
Personal profile data
Knowledge
Understanding
Decision
Action
Translation in Personalized Healthcare
“I’m afraid you’re
suffering from an
increased IL-1”
Adapted from:
EC DG for Research and Innovation
Alain van Gool
Brussels, 11 Sept 2012
Systems view on metabolic health and disease β-cell Pathology
gluc Risk factor
{Source: Ben van Ommen, TNO}
therapy
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
Heart rate Heart rate
variability
High cortisol
α-amylase
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
9
EC DG for Research and Innovation
Alain van Gool
Brussels, 11 Sept 2012
Relating tissue pharmacology – biomarker - therapy
Personalized interventions by Pharma-Nutrition
11
Ongoing: Shared Innovation Programs through public-private consortia
Higher efficacy / less side effects
Clinical efficacy of Vemurafenib (PLX-4032, Zelboraf)
Key biomarkers: Stratification: BRAFV600E mutation Mechanism: P-ERK Cyclin-D1 Efficacy: Ki-67 18FDG-PET, CT Clinical endpoint: progression-free survival (%)
{Source: Flaherty et al, NEJM 2010} {Source: Chapman et al, NEJM 2011}
Clinical efficacy of Vemurafenib
{Wagle et al, 2011, J Clin Oncol 29:3085}
Before Rx Vemurafenib, 15 weeks Vemurafenib, 23 weeks
• Strong initial effects vemurafenib • Emerging drug resistancy • Reccurence of aggressive tumors
Tumor tissue/biomarker heterogeneity
• BRAFV600D/E is driving mutation
• However, also no BRAFV600D/E mutation found in regions of primary melanomas
• Molecular heterogeneity in diseased tissue
• Biomarker levels in tissue vary
• Biomarker levels in body fluids will vary
• Major challenge for (companion) diagnostics
{Source: Yancovitz, PLoS One 2012}
Knowledge and Innovation gap:
1. What to measure?
2. How much should it change?
3. What should be the follow-up for me?
Personalized Health(care) model
N=1 personalized health / healthcare Following P4 medicine: Participatory, Personalized, Predictive, Preventive
Ho
meo
sta
sis
A
llo
sta
sis
D
isease
Time
Disease
Health
Personalized Intervention
of patients-like-me
Big Data
Risk profiles of persons-like-me
Molecular Non-molecular Environment …
Personal profile
Selfmonitoring Adapted from Jan van der Greef (2013)
16
Personalized Healthcare model (2)
{Chen et al, Cell 2012, 148: 1293}
Concept: • Continuous monitoring (n=1) • Routine biomarkers to alert • Omics to explain • Early intervention
17
Selfmonitoring
18
The future is nearly there …
19
Personalized advice
Action
Selfmonitor Cloud
Lifestyle Nutrition Pharma
Biomarker innovation gap
• Imbalance between biomarker discovery, validation and application
• Many more biomarkers discovered than available as diagnostic test
Discovery Clinical
validation/confirmation
Diagnostic
test
Number of
biomarkers
Gap 1
Gap 2
Some numbers
Data obtained from Thomson Reuters Integrity Biomarker Module
(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
24 Feb 2014: 9,240 biomarkers with 28,538 biomarker uses
EU: CE marking
USA: LDT, 510(k), PMA
21
Way forward: shared innovation network projects
Standardisation, harmonisation, knowledge sharing needed in:
1. Assay development
2. Clinical validation
22
Example: Biomarker Development Center Dutch PPP grant 4.3M Eur
Personalized Healthcare
Ways forward:
• Patients included
• Participation + collaboration
• Selfmonitoring
• Personal profiles
• System biology
• (Big) Data sharing
• Personal preferences
• Personalized therapies
• Lifestyle + Nutrition + Pharma
Reasons for biomarker innovation gap
• Not one integrated pipeline of biomarker R&D
• Publication pressure towards high impact papers
• Lack of interest and funding for confirmatory biomarker studies
• Hard to organize multi-lab studies
• Biology is complex on organism level
• Data cannot be reproduced
• Bias towards extreme results
• Biomarker variability
• …
{Source: John Ioannidis, JAMA 2011} {Source: Khusru Asadullah, Nat Rev Drug Disc 2011}
24
“It is simply no longer possible to believe much of the clinical
research that is published, or to rely on the judgment of trusted
physicians or authoritative medical guidelines.
I take no pleasure in this conclusion, which I reached slowly and
reluctantly over my two decades as an editor of The New
England Journal of Medicine.”
Marcia Angell, MD Former Editor-in-Chief NEJM Oct 2010
25