2015 10-06 Building Bridges Biomarker symposium FIMM Helsinki, Alain van Gool

Biomarkers in personalized health(care): past, present and future

Professor of Personalized Healthcare Head Radboud Center for Proteomics, Glycomics and Metabolomics Coordinator Radboud Technology Centers

Applied Biomarker Scientist

Prof Alain van Gool

Building Bridges Autumn 2015: Biomarkers in Clinical and Translational Research -‐ the ABCs of Biomarkers Helsinki 6 Oct 2015

My mixed perspectives in personalized health(care)

8 years academia (NL, UK)

(molecular mechanisms of disease)

13 years pharma (EU, USA, Asia)

(biomarkers, Omics)

4 years med school (NL)

(personalized healthcare, Omics, biomarkers)

4 years applied research institute (NL, EU)

(biomarkers, personalized health, nutrition)

A person / citizen / family man

(adventures in EU, USA, Asia)

1991-1996 (PhD)

1996-1998 (post-doc)

2009-2012 (visiting prof)

1999-2007 2007-2009 2009-2011

2011-now

2 Alain van Gool, Building Bridges Biomarker Symposium, Helsinki, 6 October 2015

Biomarkers in personalized health(care) past, present and future

• From Diagnosis

• To Translational Medicine

• To Personalized/Stratified/Precision Medicine

• To Personalized Health(care)

• Some do’s and don’ts

Alain van Gool, Building Bridges Biomarker Symposium, Helsinki, 6 October 2015 3

Biomarkers in the early days

600 BC:

Sushruta, famous Indian surgeon

Diagnosis of diabetes diabetes, then called “sweet urine disease”, by determining if

ants were attracted to a person’s urine.

Biomarkers in the early days

{Kumar and van Gool, RSC, 2013}

The urine wheel

Use color, smell and taste of urine

to diagnose disease and decide

best treatment

Ullrich Pinder

Epiphanie Medicorum

Biomarker need from pharma industry

• In old days little understanding of cause of disease

• Use of natural compounds from plant and animal

• Limited testing in laboratory + trial and error in clinic

• Frequently not effacious and/or serious side effects in patients

• Unacceptable approach (ethical, financial)

• Start rational and translational drug discovery and development

Translational medicine in pharma

Basic Research

In Vitro Studies

Target Validation

Animal Models

Phase I and Phase II

-PoC- Studies

Phase III Studies

Clinical Research

Forward Translation Forward Translation

Reverse Translation Reverse Translation

(View drug development

as customer)

(Feed back clinical needs

and samples)

[van Gool et al, Drug Disc. Today 2010]

Limited view from the outside

Source: Gary Larson

Animal models Patient-related outcomes

Source: National University Hospital Singapore

Key is to have a good view inside

High need for molecular tools that allow a look into the black box

and improve disease management: biomarkers

Drug exposure ?

Diagnosis ?

Cross-species differences ?

Patient classification ? Prognosis ?

Target engagement ?

Modulation of mechanism ?

Off-target drug effects ?

Treatment Phenotype

Mechanism ?

Other (latent) diseases ?

Person

Biomarkers

{Biomarkers definition working group, 2001 }

Definition: ‘a characteristic that is objectively measured and evaluated as

an indicator of normal biological processes, pathogenic processes, or

pharmacologic responses to a therapeutic intervention’

Or ‘Whatever works in adding value’

Molecular biomarkers provide a molecular impression of a biological system

(cell, animal, human)

Biomarkers can be various sorts of data, or combinations thereof

Dutch CC meeting ‘Personalized Health Care”

Ede, 2 October 2013

Alain van Gool

Lecture LKCH, UMC Utrecht

29 October 2013

Alain van Gool

Biomarker-based translational medicine

• Does the compound get to the site of action?

• Does the compound cause its intended

pharmacological/ functional effects?

• Does the compound have beneficial effects on disease

or clinical pathophysiology?

• What is the therapeutic window (how safe is the drug)?

• How do sources of variability in drug response in target

population affect efficacy and safety?

Optimization

Exploratory

Development PoC Lead

Discovery

Target

Discovery

Exposure ?

Mechanism ?

Efficacy ?

Safety ?

Responders ?

{van Gool et al, Drug Disc Today 2010}

{Kumar, van Gool, RSC biomarkers, 2013}

2ND intl Pharma-Nutrition Conference

Singapore, 17 April 2013

Alain van Gool

Lecture LKCH, UMC Utrecht

29 October 2013

Alain van Gool

One strategy

Biomarker strategy: Data-driven decisions

To be made during testing of drug in preclinical and clinical disease models:

Target engagement? Effect on disease?

yes yes !

• 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”

Rational selection of best targets and drugs works

The 5R’s assessment:

• Right Target

• Right Tissue

• Right Safety

• Right Patients

• Right Commercial Potential

Adopt rational target selection in pharma research CarTarDis = Cardiovascular Target Discovery Public-private partnership, 13 partners, 8 countries, project budget 8.0M Eur Started 1 Oct 2013 for 4 years Adopting AstraZeneca’s 5R strategy in drug target selection

(Coordinator)

CarTarDis.eu

Successes of drug development

Antibiotics Vaccins

Reproductive medicine Oncology

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 • Patient selection using companion diagnostics

A need for Personalized Medicine

(Analysis of 108 failures in phase II)

Reason for failure Therapeutic area

• From Diagnosis

Consider individual differences in life science research

18 Alain van Gool, Pharma-Nutrition 2015, 13 March 2015

{Source: Chakma. Journal of Young Investigators. 2009}

Principle of Personalized/Precision/Targeted Medicine

Personalised Medicine @Europe

European Science Foundation 30 Nov 2012

Innovative Medicine Initiative 2 8 July 2013

EC Horizon2020 10 Dec 2013

Precision medicine @USA

President Obama State of Union 2015

22 Alain van Gool, NanoNext.NL, 3 July 2015

Optimal Personalized / Precision / Targeted Medicine

Case study: B-RAF mutations and melanoma

{Miller and Mihm,

Mechanism of pathophysiology in BRAF mutated tumors

Kinase domain

{Roberts and Der, 2007}

• B-RAFV600E mutation: constitutively active kinase, oncogenic addiction

• Overactivate ERK pathway drives cell proliferation • RAF inhibitors shown to block growth of tumors with B-RAFV600E mutation • Prevalence of B-RAFV600E is base for patient selection:

• Melanoma (60%), colon (15%), ovarian (30%), thyroid (30%) cancer

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}

SelectBio Biomarkers 2014 Cambridge 8 July 2014

Alain van Gool

Development of Vemurafenib (Zelboraf)

{Source: Davis M J , Schlessinger J J Cell Biol 2012}

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 heterogeneity

• BRAFV600D/E is considered as driving mutation in melanoma

• However, also no BRAFV600D/E mutation found in regions of a primary melanoma

• Molecular heterogeneity in diseased tissue

• Biomarker levels in tissue vary

• Biomarker levels in body fluids will vary

• Major challenge for (companion) diagnostics in solid cancers

{Source: Yancovitz, PLoS One 2012}

Exponential technologies

“The only constant is change, and the rate of change is

increasing”

We are at the knee of the exponential curve

Demo room

Exponential developments in biomarker technologies

• Next generation sequencing • DNA, RNA • Risk analysis and therapy selection

• Mass spectrometry

• Proteins, metabolites • Monitoring of disease and treatment effects

• Imaging • Non invasive images, real time • Spatial view of intact organs and organisms

Some examples of innovation:

Next Generation Sequencing

{Nature, July 17 2014, 511: 344-}

The epigenome

The microbiome

Emerging protein biomarkers

Current diagnostic protein assays:

• Mostly protein abundance

Emerging:

• Post-translational modifications

• Ratio protein isoforms

• Protein complexes

Alain van Gool, Building Bridges Biomarker Symposium, Helsinki, 6 October 2015

Glycomics

Intact glycoproteins

Free glycans

Glycopeptides 500

10 15 20 25 30 35 40 Time [min]

PGM1 profile

CID fragmentation spectrum

Discovering new glycoprotein biomarkers

• 1D LC-MS/MS glycoproteomics in plasma • Detection of ~12.000 unique deconvoluted monoisotopic masses per

single analysis (> 50% are glycopeptides)

5 10 15 20 25 30 35 40 Time [min]

Proof of principle study:

Intact protein analysis

Bottum-up proteomics

Top-down proteomics

Intact complexome analysis as new biomarker?

• Native tissue biopsies

• Isolate intact membrane complexes

• Separate and isolate complexes using native gels

• LC-MS/MS analysis of intact proteins

• Data analysis

Tissue 1 (n=3)

Tissue 2 (n=3)

Subunit

Subunit – tissue 1

Subunit – tissue 2

• Identified protein sequence of subunit • Deduce simulated sequences from database • Determine fit with experimental data

m/z Int.

X1, Y1

m/z Int.

X2, Y1

m/z Int.

X3, Y1

m/z Int.

X4, Y1

m/z Int.

X4, Y2

m/z Int.

X3, Y2

m/z Int.

X2, Y2

‘Nb of pixels’ = ‘nb of mass spectra’

Mass Spectrometry Imaging (MSI)? {Source: Gregory Hamm}

Each m/z signal = One specific MS image

Mass Spectrometry Imaging (MSI)?

‘Nice images’ & Reliable information

H.E. Staining

Molecular images overlay

Toxico

MSI - Q

400 µm resolution

40 µm resolution

20 µm resolution

Mass Spectrometry Imaging (MSI): Spatial Resolution

Spatial resolution = difference between two pixels of the image

Lipid markers distribution:

m/z 538.98

CE = Cholesteryl ester PC = Phosphatidylcholine TG= Triacylglycerol Heme b= Hemoglobine b

MALDI-MSI reveals regionally distinct lipid profiles in atherosclerotic plaque

Biomarker Discovery: CVD Disease

CarTarDis

Discovery of CVD biomarkers using MSI?

Lipids relative quantification in histological regions

Shoulder region

Fibrous cap

Calcified region

Necrotic core

LPC (18:1)

…lysophosphatidylcholine (LPC)

CarTarDis

Biomarker Discovery: CVD Disease

New data !

www.scanadu.com

Personalized advice

Action

Selfmonitor Cloud

Lifestyle Nutrition Pharma

DIY monitoring of vital signs

• 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

DIY sequencing

• Measure your brain waves (EEG)

• Recognize conditions for maximal concentration or relaxation.

• Use device to train.

DIY EEG imaging

DIY protein biomarker analysis

‘insideables’

‘wearables’

BUT … Biomarker innovation gaps

Discovery Clinical

validation/confirmation

Diagnostic

Number of

biomarkers

• Too much biomarker discovery • Too little development to application

Biomarker innovation gaps: some numbers

Data from Thomson Reuters Integrity database, February 2015

Alzheimer’s Disease

Chronic Obstructive

Pulmonary Disease

Type II Diabetes Mellitis

Biomarker innovation gaps: some numbers

5 biomarkers/ working day

1 biomarker/ 1-3 years

1 biomarker/ 3-10 years

Eg Biomarkers in time: Prostate cancer May 2011: n= 2,231 biomarkers Nov 2012: n= 6,562 biomarkers Oct 2013: n= 8,358 biomarkers Nov 2014: n= 10,350 biomarkers 5 Oct 2015: n = 11,856 biomarkers

Discovery Clinical

Diagnostic

Number of

biomarkers

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}

Health Valley Event 2014 Nijmegen

13 March 2014 Alain van Gool

“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

Health Valley Event 2014 Nijmegen

13 March 2014 Alain van Gool

• From Diagnosis

Personalized health(care)

Is more than ‘just’ targeted medicines

It’s personal !

‘I want to stay healthy.’ ‘If not, how do I get healthy?’

The route to Personalized Health

Analogy: route planner

GPS to a location

Amsterdam

Traffic jam

Amsterdam

Route 1 Route 2

= Default Traffic jam near Utrecht Alternative route

Personalized Health(care) planner

GPS to health

Health

Route 1 Route 2

= Default First signs of disease risk

Alternative route

Health risk

Health

Personalized Health(care) model

Analogies to GPS route planner:

• Technology enabled

• Monitoring should be on the background; only alert when risk

• Success through participation of user

• Personal choice to actively monitor or not

• Commercial competition of tool builders to become market leader(s)

• Implementation as standard in society

GPS to health

Know your personal thresholds and intervention options

Personalized Intervention

of patients-like-me Risk profiles

of persons-like-me

Big Biomarker Data

Molecular Non-molecular Environment …

isease

Disease

Health

Selfmonitoring

Adapted from Jan van der Greef, TNO (2013)

Personal profile

Personalized Participatory Pre-emptive

Personalized health

Personalized medicine

Example personal profile-based patient assessment

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

Concept:

• Continuous monitoring (n=1)

• Routine biomarkers to alert

• Omics to explain

• Early intervention

Simulate and visualise health interventions {Albert de Graaf}

• From Diagnosis

• Some do’s and don’ts

How to move forward?

1. Focus biomarker research on the end user

Plan biomarker studies based on needs of end user

Discovery Clinical

Diagnostic

Number of

biomarkers

• Don’t do it because you can • Do it because it is needed

Connect patient to clinical lab to patient

https://www.youtube.com/watch?v=yhLbuX0H7rg

Case: diagnostic glycoprotein biomarker • Rare metabolic disease cases

• Combination glycoproteomics and exome sequencing

• Outcome 1: Explanation of disease

• Outcome 2: Dietary intervention as succesful personalized therapy

• Outcome 3: Glycoprofile transferrin developed and applied as diagnostic test

{Tegtmeyer et al, NEJM 370;6: 533 (2014)}

Genomics Glycomics Metabolomics

{Monique van Scherpenzeel, Dirk Lefeber}

Lab values Clinical outcomes

Pain Mobility Fatigue

INTEGRATE-HTA

Objectives patient and clinican may be different

R van Hoorn, W Kievit, M Tummers, GJ van der Wilt

How to do optimal shared decision making?

Intervention

Translation is key in Personalized Healthcare !

Personal profile data

Knowledge

Understanding

Decision

Action

“I’m afraid you’re

suffering from an

increased IL-1β and

an aberrant miR843

expression”

Adapted from:

Treatment options

Pro’s

Con’s

Select personalized therapy

2. System biology

System Biology β-cell Pathology

gluc Risk factor

{Source: Ben van Ommen, TNO}

therapy

Visceral

adiposity

LDL elevated

Glucose toxicity

Fatty liver

inflammation

endothelial

inflammation

systemic

Insulin resistance

Systemic

inflammation

Hepatic IR

Adipose IR

Muscle metabolic

inflexibility

adipose

inflammation

Microvascular

damage

Myocardial

infactions

failure

Cardiac

dysfunction

disorders

Nephropathy

Atherosclerosis

β-cell failure

High cholesterol

High glucose

Hypertension

dyslipidemia

ectopic

lipid overload

Hepatic

inflammation

Stroke

fibrosis

Retinopathy

Physical inactivity Caloric excess

Chronic Stress Disruption

circadian rhythm

Parasympathetic

Sympathetic

arousal

Worrying

Hurrying

Endorphins Gut

activity Sweet & fat foods

Sleep disturbance

Inflammatory

response

Adrenalin

Challenge

stress

Heart rate Heart rate

variability

High cortisol

α-amylase

Lipids, alcohol, fructose

Carnitine, choline

Stannols, fibre

Low glycemic index

Epicathechins

Anthocyanins

Quercetin, Se, Zn, …

Metformin

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”

Explore personalized interventions by Pharma-Nutrition

Shared Innovation Programs through public-private consortia

Higher efficacy / less side effects

2. System biology

3. Build biomarker development pipelines based on

“Good Biomarker Practices”

Build biomarker validation pipelines

Standardisation, harmonisation, knowledge sharing needed in:

1. Assay development

2. Clinical validation

3. Regulatory acceptance

NL Roadmap Molecular Diagnostics (2012) NL Grant 4.3M Eur (2014)

Move towards EU funding (2016)

Define, share and act on“Good Biomarker Practices”

Some items in need of standardisation:

• Reproducibility, quality requirements

• Study design & statistical power

• Variability & heterogeneity

• Specimen acquisition & pre-analytics

• Sample preparation

• Patient & associated clinical data

• Analytical standards & quality control

Not reinvent the wheel.

Standardisation, harmonisation, knowledge sharing needed in:

1. Assay development

2. Clinical validation

3. Biomarker qualification

• Assay/platform development

• Quality system manufacturing

• Data analysis & management

• Regulatory requirements

• Ethical, IP & legal aspects

• Early HTA

• Quality in documentation & publication

Good example of multi-center biomarker validation

2. System biology

4. Validate more biomarkers in one go

Validate more biomarkers in one go

1. Determine the context of change in a biomarker. 2. Drive validation of multiple biomarkers at once

Multiple measures

Patient 1 Patient 2

Technologies are already out there: • Next generation sequencing • Microarrays • Multiplex immunoassays

Single measure

• Targeted mass spectrometry • Binding assays • Mass spec imaging

2. System biology

5. Value negative results

Case: Tissue proteomics profiling project Protein expression (positive controls)

GO Protein distributions

Cellular compartments

LFQ scatter plot Biological replicates

y= 0.9834x + 130390 R2=0.9842

Q: downstream effects of transgene? Hippocampus tissue of Transgenic mice 4 Conditions: WT, TG, WT treated, TG treated with drug 5 Biological replicates; 2D LC-MS/MS analysis (20 fractions, 1 hour gradient) Label-Free Quantitation (LFQ – MaxQuant) • LC-MS/MS analyses: 400 • MS spectra: 1.937.394 • MS/MS spectra: 2.323.458 • Detected isotope patterns: 66.602.271 • Isotope patterns sequenced: 1.295.489 • Average absolute mass deviation: 1,38 ppm • 1,3 Terrabyte data

PCA analysis – loading plot

• Matched MS/MS spectra to peptides: 500.317 • Identified proteins: 3.187 • Quantified proteins: 2.365 (≥2 peptides/protein) • Differential proteins: 276 (p<0.05) • Average CV < 21%* * Combining biological and technical reproducability

Transgene

Downstream

2. System biology

6. Interpret data in personalized way

healthy disease disease + treatment

Interpret data with self-normalisation

Subgroups

Normalisation of responders

2. System biology

7. Use available resources

Research Biomarkers Diagnostics

Department of Laboratory Medicine, Radboudumc Integrated Translational Research and Diagnostic Laboratory, 220 fte, yearly budget ~ 28M euro. Close interaction with Dept of Genetics, Pathology and Medical Microbiology

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 inflammation 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

Biomarker development pipeline @ Radboudumc

*CCKL accreditation/RvA/EFI

www.laboratorymedicine.nl Alain van Gool, Building Bridges Biomarker Symposium, Helsinki, 6 October 2015 98

www.radboudumc.nl/research/technologycenters

Radboudumc Technology Centers

2. System biology

8. Seek interdisciplinary team work

Interdisciplinary team work

Alain van Gool, 2nd Precision Medicine Congress, London, 15 Sept 2015 102

Try-outs at REshape Innovation Center

Lucien Engelen

Multi-partner collaborations in Health Informatics

Speak each other’s language

Teach how to work inter-disciplinary

2. System biology

3. Build biomarker development pipelines based on “Good Biomarker Practices”

9. Copy best practice

Copy best practice

• Nation wide coverage

• 66 regional networks

• 3000 trained experts

• 12 disciplines

prof Bas Bloem dr Marten Munneke

5. Supportive technology

1. Network of experts

2. The patient as partner

4. Transparant quality controle

3. Integral reward for outcome, not production

5 key components of ParkinsonNet

Demonstrated added value

Regular care

ParkinsonNet care

% Hip fracture Cost per patient*

*Hospitals, medication, care at home

Dutch export product …

King Willem Alexander

Bas Bloem

Marten Munneke

Queen Maxima

2. System biology

3. Build biomarker development pipelines based on “Good Biomarker Practices”

9. Copy best practice

10. Spread the word

Spread the word

Acknowledgements

Ron Wevers

Jolein Gloerich

Hans Wessels

Monique Scherpenzeel

Dirk Lefeber

Leo Kluijtmans

Lucien Engelen

Paul Smits

Maroeska Rovers

Nathalie Bovy

Bas Bloem

and many others

www.radboudumc.nl/personalizedhealthcare

www.radboudumc.nl/research/technologycenters

www.radboudresearchfacilities.nl

alain.vangool@tno.nl

alain.vangool@radboudumc.nl

www.linkedIn.com

www.slideshare.net/alainvangool

Many collaborators

Jan van der Greef

Ben van Ommen

Bas Kremer

Lars Verschuren

Ivana Bobeldijk

Marjan van Erk

Carina de Jongh

Peter van Dijken

Peter Wielinga

Robert Kleemann

Suzan Wopereis

and many others And funders

CarTarDis

2015 10-06 Building Bridges Biomarker symposium FIMM Helsinki, Alain van Gool

Health & Medicine

Transcript of 2015 10-06 Building Bridges Biomarker symposium FIMM Helsinki, Alain van Gool

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2016 01-19 University Twente, Enschede, Alain van Gool

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2015 05-20 Radboudumc REshape breakfast meeting Alain van Gool

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porphyrin biomarker

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FIMM - Biblioteca digitala - Misu

2015 04-13 Pharma Nutrition 2015 Philadelphia Alain van Gool

2015 07-03 Nanonext NL Alain van Gool, Amsterdam