2014 10-15 lgc biosciences autumn seminar cambridge
DESCRIPTION
A reflection on the changing role of biomarkers in personalized healthcare.TRANSCRIPT
Biomarkers in personalized healthcare,
changing perspectives
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
Seminar LGC Biosciences Cambridge, UK 15 Oct 2014
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 med school (NL)
(personalized healthcare, Omics, biomarkers)
3 years applied research institute (NL, EU)
(biomarkers, personalized health)
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
Radboud university medical center
• Nijmegen, The Netherlands
• Mission: “To have a significant impact on healthcare”
• Strategic focus on Personalized Healthcare through “the patient as partner”
• Core activities:
• Patient care
• Research
• Education
• 11.000 colleagues
• 52 departments
• 3.300 students
• 1.000 beds
• First academic centre outside US to fully implement EPIC
TNO = Netherlands Organisation for Applied Scientific Research Mission = to drive ideas to reach their full market value.
We partner with:
Governmental & regulatory organisations Universities Pharma, chemical and food companies International consortia
Knowledge
development
Knowledge
application
Knowledge
exploitation
Develop
fundamental
knowledge
With
universities
With
partners
With
customers
Embedded in the
market
TNO TNO companies
4
Non-for-profit research organisation ~3500 employees
19 sites in Netherlands, 18 countries global 7 main themes (ao Life Sciences)
Biomarkers in Personalized Healthcare an evolving role
• From only diagnosis
• To Translational Medicine
• To Personalized/Stratified/Precision Medicine
• To Personalized Healthcare
• To Person-centered Health(care)
5
Diagnostic biomarkers 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
Biomarkers in Translational Medicine in pharma
• Translational medicine
Exposure
Mechanism
Efficacy
Safety
• Personalized medicine
Diagnosis
Prognosis
Response prediction
• Tools for data-driven decision making
Biologically relevant
Clinically accepted
Quantitative
Different analytes/types
Fit-for-purpose application
{Source: Van Gool et al, Drug Disc Today 2010}
7
Biomarker data-driven decisions
Target engagement? Effect on disease?
yes yes !
no no
• No need to test current
drug in large clinical trial
• Need to identify a more
potent drug
• Concept may still be
correct
• Concept was not correct
• Abandon approach
• Proof-of-Concept
• Proceed to full
clinical
development
“Stop early, stop cheap”
“More shots on goal”
8
{Kumar and van Gool, RSC, 2013}
Source: John Arrowsmith: Nature Reviews Drug Discovery 2011
• Success rates of clinical proof-of-concept have dropped from 28% to 18% • Insufficient efficacy as the most frequent reason • Targeted therapy through Personalized Medicine may be the solution
Promise of Personalized Medicine
Analysis of 108 failures in phase II
Reason for failure Therapeutic area
9
Biomarkers in Personalized Medicine
• Melanoma – targeted medicine
• Metabolic health – system medicine
10
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}
11
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
12
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}
13
‘Complicating’ factors in oncology therapy
Source: 11 Sept 2013 @de Volkskrant
• Biological clock
• Smoking
• Pharma-Nutrition
• Drug-drug interaction
• Alternative medicine
• Genetic factors
• …
Interview with Prof Ron Matthijssen, ErasmusMC, Rotterdam
14
Metabolic health and disease
Type 2
Diabetes
Diabetes
complications
time
15
Systems view on metabolic health and disease β-cell Pathology
gluc Risk factor
{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
Chronic Stress Disruption
circadian rhythm
Parasympathetic
tone
Sympathetic
arousal
Gut
activity
Inflammatory
response
Adrenalin
Heart rate Heart rate
variability
High cortisol
α-amylase
16
Systems view on metabolic health and disease β-cell Pathology
gluc Risk factor
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
Chronic Stress Disruption
circadian rhythm
Parasympathetic
tone
Sympathetic
arousal
Gut
activity
Inflammatory
response
Adrenalin
Heart rate Heart rate
variability
High cortisol
α-amylase
{Nakatsuji, Metabolism 2009}
17
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
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
18
EC DG for Research and Innovation
Alain van Gool
Brussels, 11 Sept 2012
Challenging metabolic equilibrium by Pharma-Nutrition
Age-matched “healthy” control group
t=16 w
(sampling)
t=9 w t=0
Induction of Diabetes intervention period
High-fat (HF) diet
High-fat diet “diseased” control group
Nutrition/Life style switch
HF + Drug 1
HF + Drug 2
HF + Drug 3
…. HF + Drug 10
19
EC DG for Research and Innovation
Alain van Gool
Brussels, 11 Sept 2012
clinica
l chem
istry
Syste
m n
etw
ork
s M
eta
bolo
me
Tra
nscrip
tom
e
fluxe
s Analysis: high throughput, multi organ, multi level
High-end data mining and warehousing
Extensive histological and molecular phenotyping
20
TNO’s applied biomarker tool box
Widely used preclinical translational models
Pharma, nutrition and chemical industry, academia
Focus on etiology of disease and mechanism of action
Human studies
Experimental medicine through CRO’s
Microdosing
Validated analytical platforms
Metabolomics profiling and targeted analysis, with focus on
lipids, ceramids, cannabinoides
Genomics, transcriptomics, proteomics and imaging through
a wide network of selected partners
Clinical chemistry
Data analysis
Network biology for mechanistic understanding
Multiparameter statistics and chemometrics
PK/PD translational modelling
Comprehensive system dynamics modelling
Biomarker expertise
Best practise strategies and approaches
A wide network with biomarker academia and industry
Metabolic Syndrome
• Atherosclerosis
• Diabetes
• Obesity
• Vascular inflammation
• NASH, fibrosis
21
EC DG for Research and Innovation
Alain van Gool
Brussels, 11 Sept 2012
Effects on total adipose tissue weight
Full reversal of obese phenotype by Nutrition
switch, not by all drug treatments
T0901317 (LXR agonist) also
reverses obese phenotype
22
EC DG for Research and Innovation
Alain van Gool
Brussels, 11 Sept 2012
Effects on atherosclerosis
Still increased atherosclerosis in Nutrition
switch group
T0901317 (LXR agonist) strongly
induces atherosclerosis
23
EC DG for Research and Innovation
Alain van Gool
Brussels, 11 Sept 2012
{Nolan, Lancet 2011}
A sure need for systems medicine • Multiple interactions and
flexibilities in human
system
(tissues, cells, proteins)
• Blocking one pathway will
shift equilibrium and create
new problems
• System medicine approach
needed for maximal effect
• High value of biomarkers
but how to translate to
combination therapy?
• Pharma-Nutrition?
24
EC DG for Research and Innovation
Alain van Gool
Brussels, 11 Sept 2012
Relating tissue pharmacology – biomarker - therapy
25
Translating knowledge to field labs
1. Implementation-plan ‘Personalized diagnosis of (pre)diabetic and their lifestyle treatment in Dutch Health care’.
2. Use of Oral Glucose Tolerance Test as a stratification biomarker for (pre)diabetic patients
3. Advice a tailored treatment (lifestyle and/or medical)
4. Monitor added value of stratification
5. Communicate results and lessons learned
Being implemented in 1st line care (region Hillegom, Netherlands)
Alliance “Expedition Sustainable Care,
starting with diabetes”
Year 1
Applying lessons learned across fields
e.g. System Biology @TNO
Year 2
Year 3
Personalized interventions by Pharma-Nutrition
Ongoing: Shared Innovation Programs through public-private consortia
Higher efficacy / less side effects
28
Data
mining
Models
Modelling
Analytics
(Mx, Px, Tx)
Organ-on-
a-chip
Imaging
Academic/ Clinical Industry
Shared Innovation Programs
20+ partners
Diagnostics
Pharma Nutrition
20+ partners
Better diagnosis and interventions
Personalized !
20+ partners
10+ partners
29
Biomarkers in Personalized Healthcare an evolving role
• From only diagnosis
• To Translational Medicine
• To Personalized/Stratified/Precision Medicine
• To Personalized Healthcare
• To Person-centered Health(care)
30
Personalized Healthcare, more than pathways only
Source: Barabási 2007 NEJM 357; 4}
• People are different • Different networks and influences • Different risk factors • Different preferences
31
Personalized Healthcare in a systems view
32
A changing world: Personalized Medicine@ USA
“The term "personalized medicine" is often described as providing "the right patient with
the right drug at the right dose at the right time."
More broadly, "personalized medicine" may be thought of as
the tailoring of medical treatment to the individual characteristics,
needs, and preferences of a patient during all stages of care, including prevention, diagnosis,
treatment, and follow-up.”
(FDA, October 2013)
33
A changing world: Personalized Medicine @Europe
European Science Foundation
30 Nov 2012
Innovative Medicine Initiative 2
8 July 2013
EC Horizon2020
10 Dec 2013
34
Most important in Personalized Healthcare:
Include the patient as partner
35
Patient
Radboud Personalized Healthcare
A significant impact
on healthcare
Molecule
Population
Personalized Healthcare @ Radboud university medical center
36
Personalized Healthcare @ Radboudumc
People are different Stratification by multilevel diagnosis
+ Patient’s preference of treatment
Exchange experiences in care communities
Select personalized therapy
37
Population
Man
Molecule
37
Translational medicine @ Radboudumc
Personalized genomic diagnostics
{Nature, July 17 2014, 511: 344-}
39
2012
Patient Targeted
Metabolic
screen
Targeted
gene
analysis
Diagnosis
+ follow-up
2013 / 2014
Patient
Whole
exome
sequencing Targeted
confirmatory
metabolite +
enzyme
testing
Diagnosis
+ follow-up
Targeted assays vs holistic approach
Next
generation
metabolic
screening
Times are changing… add functional genome diagnostics
Human samples
Plasma, CSF (urine) Controls vs. patient
QTOF Mass Spectrometry
- Reverse phase liquid chromatography - Positive and negative mode - Features
XCMS
Alignment
Peak comparison
> 10,000 Features
Personalized metabolic diagnostics
Xanthine Uric acid
41
Full metabolite profile:
Highly suspected of xanthinuria
Proteomics Metabolomics Glycomics
• Mass spectrometry – NMR based, 20 dedicated fte, + guest scientists • Part of diagnostic laboratory (Department of Laboratory Medicine) • Close interaction with Radboudumc scientists and external partners
Radboud Center for Proteomics, Glycomics & Metabolomics
Ron Wevers, Alain van Gool, Leo Kluijtmans, Dirk Lefeber et al
Research Biomarkers Diagnostics
Research Biomarkers Diagnostics
Integrated Translational Research and Diagnostic Laboratory, 200 fte, yearly budget ~ 28M euro. Close interaction with Radboudumc scientists and external partners Please visit: www.laboratorymedicine.nl
Specialities: • Proteomics, glycomics, metabolomics • Enzymatic assays • Neurochemistry • Cellulair immunotherapy • Immunomonitoring
Areas of disease: • Metabolic diseases • Mitochondrial diseases • Lysosomal /glycosylation disorders • Neuroscience • Nefrology • Iron metabolism • Autoimmunity • Immunodeficiency • Transplantation
In development: • ~500 Biomarkers • Early and late stage • Analytical development • Clinical validation
Assay formats: • Immunoassay • Turbidicity assays • Flow cytometry • DNA sequencing • Mass spectrometry • Experimental human (-ized)
invitro and invivo models for inflamation and immunosuppression
Validated assays*: • ~ 1000 assays • 3.000.000 tests/year
Areas of application: • Personalized healthcare • Diagnosis • Prognosis • Mechanism of disease • Mechanism of drug action
Department of Laboratory Medicine
*CCKL accreditation/RvA/EFI
Genomics
Bioinformatics
Animal studies Translational
neuroscience
Image-guided treatment
Imaging
Microscopy
Biobank
Health economics
Mass Spectrometry
Radboudumc Technology
Centers Investigational
products
Clinical trials EHR-based
research
Statistics
Human physiology
Data stewardship
Molecule
Flow cytometry
(Aug 2014)
44
45
• Proteins • Metabolites • Drugs • PK-PD • Preclinical
• Clinical
• Behavioural • Preclinical
• Animal facility • Systematic review
• Cell analysis • Sorting
• Pediatric • Adult • Phase 1, 2, 3, 4
• Vaccines • Pharmaceutics • Radio-isotopes • Malaria parasites
• Management • Analysis • Sharing • Cloud computing
• DNA • RNA
• Internal • External
• HTA • Evidence-based
surgery • Field lab
• Statistics • Biological • Structural
• Preclinical • Clinical
• Economic viability
• Decision analysis
• Experimental design • Biostatistical advice
• Electronic Health Records • Big Data • Best practice
• In vivo • Functional
diagnostics
About 200 dedicated people working in 17 Technology Centers, ~1500 users (internal, external), ~130 consortia
www.radboudumc.nl/research/technologycenters/
(Aug 2014)
Cross-technology interactions
Biomarkers in Personalized Healthcare
• 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
ChipCube-LC- Q-tof MS
• Outcome 1: Explanation of disease
• Outcome 2: Dietary intervention as succesful personalized therapy
• Outcome 3: Glycoprofile transferrin developed and applied as diagnostic test
• Genetic defect in glycosylation enzyme (PGM1) identified via exome sequencing
{Tegtmeyer et al, NEJM 370;6: 533 (2014)}
Genomics Glycomics Metabolomics
47
Need to change development process for Personalized Healthcare therapies
• Randomized Clinical Trials won’t be good enough (= groups)
• n=1 clinical trial designs needed whereby:
• Multiple monitoring in same person
• Use different types of biodata (molecular, non-molecular)
• Normalize data per individual
• Combine separate data through meta-analysis
• Output:
• Responders vs non-responders
• Tight data per subgroup
• Clear conclusions on therapy
48
healthy disease disease + treatment
Different trial outcomes in Personalized Healthcare
49
100%
Normalisation Subgroups
H2020 PHC1 application - L’Homme Machine: Exploiting Industrial Control Techniques for Personalized Health
Partners Biobanks
Databank
Coordinator: prof Lutgarde Buydens,
Biomarkers in Personalized Healthcare an evolving role
• From only diagnosis
• To Translational Medicine
• To Personalized/Stratified/Precision Medicine
• To Personalized Healthcare
• To Person-centered Health(care)
51
Selfmonitoring
52
The future is nearly there …
53
Personalized advice
Action
Selfmonitor Cloud
Lifestyle Nutrition Pharma
Biomarkers in Person-centered Health(care)
Patient
Caregiver
Insurer
Self-monitoring
Patient
Caregiver
Insurer
Participatory
research
Bas Bloem
Marten Munneke
et al
54
Central
data point
Biomarkers in Personalized Healthcare an evolving role
• From only diagnosis
• To Translational Medicine
• To Personalized/Stratified/Precision Medicine
• To Personalized Healthcare
• To Person-centered Health(care)
55
However …
Knowledge and Innovation gap:
1. What to measure?
2. How much should it change?
3. What should be the follow-up for me?
56
Translation is key in Personalized Healthcare !
Personal profile data
Knowledge
Understanding
Decision
Action
57
Translation 1: Data to usable tests
• 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
Biomarker Innovation Gap
58
Some numbers
Data obtained from Thomson Reuters Integrity Biomarker Module
Eg Biomarkers in time: Prostate cancer
May 2011: 2,231 biomarkers
Nov 2012: 6,562 biomarkers
Oct 2013: 8,358 biomarkers
15 Oct 2014: 10,169 biomarkers with 32,093 biomarker uses
EU: CE marking
USA: LDT, 510(k), PMA
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}
60
Way forward: shared innovation network projects
Standardisation, harmonisation, knowledge sharing needed in:
1. Assay development
2. Clinical validation
61
Shared Innovation Network models (Next Generation Life Science) (Source: Model TNO’s Holst Center)
Old New
62
Good example of multi-center biomarker validation
Biomarker Development Center (Netherlands)
STW perspectief grant
Biomarker Development Center
Public-private partnership 4 years
Project grant €4.3M of which € 2.2M government,
and € 2.1M industry (€ 0.9M cash/ € 1.2M kind)
Close interactions with:
- Clinicians (biomarker application)
- Industry partners and stakeholders
- Patient stakeholder associations
Open for partners !
64
Translation 2: Science to patient
“I’m afraid you’re
suffering from an
increased IL-1β and
an aberrant miR843
expression”
Adapted from:
65
?
Need for interdisciplinary team work
66
Personalized Health(care) model
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)
67
Person-centered Health(care)
Ways forward:
• Patients included
• Participation + collaboration
• Personal profiles
• System biology
• Health informatics
• Personal preferences
• Personalized therapies in
Lifestyle + Nutrition + Pharma
68
Acknowledgements
Lucien Engelen
Jan Kremer
Paul Smits
Maroeska Rovers
Nathalie Bovy
Ron Wevers
Jolein Gloerich
Hans Wessels
Dirk Lefeber
Leo Kluijtmans
Bas Bloem
Marten Munneke
and others
Lutgarde Buydens
Jasper Engel
Jeroen Jansen
Geert Postma
and others
Members of the
Radboud umc Personalized Healthcare Taskforce (2013)
Radboud umc Technology Centers (2014)
www.linkedIn.com
Many external collaborators
Jan van der Greef
Ben van Ommen
Peter van Dijken
Bas Kremer
Lars Verschuren
Marijana Radonjic
Thomas Kelder
Robert Kleemann
Suzan Wopereis
Ton Rullmann
William van Dongen
and others
69