2014 12-11 Skipr99 masterclass Arnhem
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Personalized Healthcare, a molecular view in the (near) future Impressions from the Exponential Medicine 2014 conference Professor of Personalized Healthcare Head Radboud Center for Proteomics, Glycomics and Metabolomics Coordinator Radboud Technology Centers Head Biomarkers in Personalized Healthcare Prof Alain van Gool Skipr-99 Masterclass ‘Disruptive innovatie & e-health’ Arnhem, 11 Dec 2014
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Review of the molecular innovations in personalized healthcare, reporting back from the Exponential Medicine 2014 conference in San Diego.
Transcript of 2014 12-11 Skipr99 masterclass Arnhem
Personalized Healthcare, a molecular view in the (near) future
Impressions from the Exponential Medicine 2014 conference
Professor of Personalized Healthcare Head Radboud Center for Proteomics, Glycomics and Metabolomics Coordinator Radboud Technology Centers
Head Biomarkers in Personalized Healthcare
Prof Alain van Gool
Skipr-99 Masterclass
‘Disruptive innovatie & e-health’ Arnhem, 11 Dec 2014
My mixed perspectives in Personalized Healthcare
8 years academia (NL, UK)
(molecular mechanisms of disease)
13 years pharma (EU, USA, Asia)
(biomarkers, Omics)
3 years university medical center (NL)
(personalized healthcare, Omics, biomarkers)
3 years applied research institute (NL)
(biomarkers, personalized health)
A family man (NL, Europe, Asia, USA)
1991-1996 1996-1998 2009-2012
1999-2007 2007-2009 2009-2011
2011-now
2011-now
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My professional passions
Personalized Health(care)
Biomarkers
Molecular Profiling (Omics)
Future of medicine
3
Patient
Radboud Personalized Healthcare
A significant impact
on healthcare
Molecule
Population
4
Working in complex human biological systems requires a systems biology approach
System biology in:
Diagnosis Prognosis Treatment Monitoring
6
Exponential technologies
“The only constant is change, and the rate of change is
increasing”
We are at the knee of the exponential curve
Buzzwords Progress and beyond
You are the CEO of your own healthcare team
Exponential technologies
Disruptive innovation
Digitalizing yourself
Sitting is the new smoking
Uber-ization of healthcare
The future is already here, it’s just not evenly distributed …
What’s normal for me is not normal for you
Do things different
Exponential developments in biomarker technologies
• Next generation sequencing • Large level of detail on genome level (DNA, RNA) • Sequencing per patient is becoming practice • Allows risk analysis and therapy selection
• Mass spectrometry
• Large level of detail on metabolic level (proteins, metabolites)
• Analysis of blood, urine, cells, tissues, hair, etc all possible • Allows monitoring of disease and treatment effects
• Imaging
• Large level of detail on intact in vivo level • Analysis of any tissue, real time
• Allows spatial view of intact organs and organisms
Next in Next Generation Sequencing • Trends:
₋ Further reduction in sequencing costs
Georg Church, Craig Venter
Exponential development of Next Generation Sequencing
E Mardis. Nature 2011
Costs per base (towards a $1,000 dollar genome - 2015?)
2001: 1.5 genome/year 300.000.000 USD/genome
Nov 2014: 100.000 genomes/year
1.400 USD/genome
Genome sequencing will become much cheaper !
Next in Next Generation Sequencing • Trends:
₋ Further reduction in sequencing costs ₋ Computational power ₋ Machine learning to analyse (big) data ₋ Link molecular diagnosis to cellular therapies
Georg Church, Craig Venter
Also beyond the oncology field:
• Volker: Intestinal surgery → XIAP → Cord blood
• Beery twins: Cerebral palsy → SPR → Diet 5HTP
• Wartman: Leukemia → FLT3 → Sunitinib
• Gilbert: Healthy → BRCA → Mas/Ovarectomy
• Snyder: T2Diabetes → GCKR, KCNJ11 → Diet, exercise
• Lauerman: Scotoma, leg → JAK2 → Aspirin
• Bradfield: Healthy → CDH1 → Gastrectomy
Next in Next Generation Sequencing • Trends:
₋ Further reduction in sequencing costs ₋ Computational power ₋ Machine learning to analyse (big) data ₋ Link molecular diagnosis to cellular therapies
• Sequence 1 billion people in 2020
• Sharing genomes through Personalgenomes.org
• Longevity (sequencing very old people to identify rare protective alleles)
• Synthetic life, teleporting for vaccinations at remote places
• Micobiome sequencing
Georg Church, Craig Venter
The gut microbiome
The future?
• DIY sequence your genome and/or your microbiome genome • at a provider, at a pharmacy, at home
• Take your genome to the doctor • Have a personalized healthcare advice
Selfmonitoring
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Digital medicine
Demo room
The future is nearly here …
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Personalized advice
Action
Selfmonitor Cloud
Lifestyle Nutrition Pharma
The future is nearly here …
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Measure your brain waves (EEG)
Recognize conditions for maximal concentration or relaxation.
Use device to train.
But …
Knowledge and Innovation gap:
1. What to measure?
2. How much should it change?
3. What should be the follow-up for me?
Translation is key in Personalized Healthcare !
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Personal profile data
Knowledge
Understanding
Decision
Action
Translation is key in Personalized Healthcare !
“I’m afraid you’re
suffering from an
increased IL-1β and
an aberrant miR843
expression”
Adapted from:
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?
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
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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 Nov 2014: 10,350 biomarkers with 33,863 biomarker uses
How to move forward? 1. System Biology
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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
EC DG for Research and Innovation
Alain van Gool
Brussels, 11 Sept 2012
Relating tissue pharmacology – biomarker - therapy
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”
Personalized interventions by Pharma-Nutrition
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Ongoing: Shared Innovation Programs through public-private consortia
Higher efficacy / less side effects
How to move forward? 2. Interdisciplinary team work
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www.radboudumc.nl/research/technologycenters
How to move forward? 2. Interdisciplinary team work
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• 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 240 dedicated people working in 17 Technology Centers, ~1500 users (internal, external), ~130 consortia
www.radboudumc.nl/research/technologycenters/
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
Biomarker Development Center
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
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