IBM Technology Day 2013 Keynote B. Dubuis

1 IBM Technology Day 2013, Keynote B. Dubuis 11.6.2013

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© 2013 Benoit Dubuis © 2013 IBM Corporation

Medical revolution and new technologies

Dr. Benoit Dubuis, Directeur Eclosion, Président BioAlps 11 juin 2013

MEDTECH

“Medical revolution and new technologies”

The challenges

Towards an integrated health system

Our contribution

Innovation: The New Currency of

Competition

Crossing Visions

13.06.2013 | 4

PRINCIPAUX DEFIS DANS LE DOMAINE DE LA SANTE

DEFIS SCIENTIFIQUES

AUTRES DEFIS

Démographie

Coûts

Accès

Adoption lente de l’innovation

Utilisation inefficace de l’information

…

Crossing Visions

13.06.2013 | 5

CAUSE DE MORTALITE

TOUTES LES CAUSES 100%

Cardiopathie ischémique

13%

Maladies Cardio-vasculaire

29% Maladies Infectieuses et

parasite 23%

Cancers 12%

Maladies vasculaires cérébrales

9%

Maladies respiratoires

7%

SIDA 4.87%

Crossing Visions

13.06.2013 | 6

P

DEMOGRAPHIE: NOUS VIVONS PLUS

LONGTEMPS 90

80

70

60

50

40

Espéra

nce

de v

ie à

la n

ais

sance

1900 1950 2000

CH: 77

CH: 82

CH: 77

CH: 82

Age 60 – 2 Mia

Age 80 – 380 Mio

Age 100 – 2 Mio

2050

Point de référence:

Espérance de vie durant l’empire Romain était de ~30 ans

Crossing Visions

13.06.2013 | 7

DEMOGRAPHIE: NOUS VIVONS EN MEILLEURE

SANTE Ancien modèle

En bonne santé Malade

Malade

Modèle comprimant la morbidité

En bonne santé Années de vie gagnées

Malade

Modèle actuel

En bonne santé

Crossing Visions

13.06.2013 | 9

LA CHARGE DES MALADIES CHRONIQUES

Près de la moitié d’entre

nous aura un état chronique

d’ici 2030 :

Diabètes

Hypertension

Arthrite

Athéroscléroses

Accident vasculaire cérébrale

Cancer

Obésité

Hyperlipidémie

Maladie Neurodégénérative

Maladie Dégénérative Articulaire

Maladie pulmonaire obstructive

Etc. Dépense dans le

domaine de la santé

pour une personne avec

maladie chronique

Dépense dans le domaine

de la santé pour une

personne sans maladie

chronique

Etats chroniques

représenteront près

83% des coûts de

santé

Crossing Visions

13.06.2013 | 10

COÛT DE LA SANTE

Crossing Visions

13.06.2013 | 11

LES NOUVELLES PISTES

Médecine 3P

Pharmacogénomique

Bioconvergence

Médecine Translationnelle

Télémédecine

Crossing Visions

13.06.2013 | 12

DE LA MEDECINE « PRÊT-A PORTER » à

LA MEDECINE PERSONALISEE

Génomique Protéomique

Crossing Visions

13.06.2013 | 13

PREVENTIVE PREDICTIVE

PERSONNALISEE

Modèle comprimant la morbidité

En bonne santé Années de vie gagnée Malade

Personnalisé

Mortalité

Préventif

Morbidité

Prédictif

Crossing Visions

13.06.2013 | 14

VERS UN SYSTEME DE SANTE INTEGRE

Rx

2009



MEDTECH


The challenges


Our contribution


Competition

Crossing Visions

13.06.2013 | 16

LA FORCE DU

RESEAU

CENTRES

MEDICAUX

OUTILS DE

RECHERCHE

INDUSTRIES

START-UP

CENTRES

ACADEMIQUES

Crossing Visions

13.06.2013 | 17

INNOVATION et INTERDISCIPLINARITE

Crossing Visions

13.06.2013 | 18

INNOVATION et INTERDISCIPLINARITE

Biotechnologie, Systèmes Biologiques

et Biologie Synthétique

Nanotechnologie Science des Matériaux

et Ingénierie de

Miniaturisation

Informatique de pointe

et WIRELESS


Interdisciplinarity (science driven)



MEDTECH


The challenges


Our contribution


Competition


Micro / nanotech cluster

Bio-cluster

A unique hub

for Medtech/Biotech R&D in Europe

Crossing Visions

13.06.2013 | 23

INSTITUTIONS R&D DE CLASSE

INTERNATIONALE

Générer des connaissances Créer des opportunités

Crossing Visions

13.06.2013 | 24

QUALITE DES RECHERCHES

Nombre de publication par 1000 habitants

dans tous les domaines de connaissances et dans le domaine des Sciences de la vie


'evolutionary developmental biology'

Electronic health record'

ACCES AUX TECHNOLOGIES,

OUTILS ET CONNAISSANCES

http://www.opt.im/

http://www.opt.im/

D:%5CDIAPOSITIVES%5Cls.ppt

D:%5CDIAPOSITIVES%5Cbs.ppt

http://www.am-institute.ch/about-us/

http://www.frontiers-in-genetics.org/en/index.php

http://www2.unine.ch/nccr/page7229.html

http://www.unibe.ch/

Crossing Visions

13.06.2013 | 26

De l’idée au produit Créer de la valeur économique et des

emplois

Crossing Visions

13.06.2013 | 27

SARAH MARQUIS CONNECTEE

Coupure de journal Sarah Marquis…

Crossing Visions

13.06.2013 | 28

IMPACT SUR LA PRISE EN CHARGE

0

20

40

60

80

100 Chf 13’800

Chf 5’900

DIABETE: COÛT TOTAL vs COÛT DES MEDICAMENTS

Crossing Visions

13.06.2013 | 29

SYSTEME DE SANTE INTEGRE

0

20

40

60

80

100 Chf 13’800

Chf 5’900

Prédiction Personnalisation

Suivi à distance d’un Patient


Remote Patient Monitoring

Worldwide Mobile Network

Carrier Moving data from body to

instrument (Programmer/Patient

Companion) to wireless carrier

worldwide.

Intimate Body Network Device to Device within the body

Personal Body Network Move data from device to

programmer or device to device

Distance: inches to several feet

http://www.google.ch/url?sa=i&rct=j&q=&source=images&cd=&cad=rja&docid=-5FVf_Eqk5jNXM&tbnid=4vMeYa4iKJd7JM:&ved=0CAUQjRw&url=http://www.arkaycareers.com/medical-jobs/2013/01/common-nonverbal-mistakes-made-at-interviews/&ei=WlC0UfS0Is6z0QXi9oGoBA&bvm=bv.47534661,d.ZGU&psig=AFQjCNHE5UKrABEzzOj2w2-YhrXJ4WC31g&ust=1370857859955492

http://www.google.ch/url?sa=i&rct=j&q=receivern%20logo&source=images&cd=&cad=rja&docid=cTviCmOm53kW7M&tbnid=qCQoG3hdsKRJxM:&ved=&url=http://www.zdnet.com/blog/gadgetreviews/citrix-receiver-instant-access-to-windows-applications-on-iphone/4142&ei=E1K0Udq_EOer7Aas34D4Bw&bvm=bv.47534661,d.ZGU&psig=AFQjCNFPhbbm8sKIMp-xIo46OuEU5rPlSw&ust=1370858387881919


Personalized Medicine

Smart Sensing

Intelligent Information Management

Effective Interfaces

Putting the patient at the center of their health system

http://www.google.ch/url?sa=i&rct=j&q=&source=images&cd=&cad=rja&docid=-5FVf_Eqk5jNXM&tbnid=4vMeYa4iKJd7JM:&ved=0CAUQjRw&url=http://www.arkaycareers.com/medical-jobs/2013/01/common-nonverbal-mistakes-made-at-interviews/&ei=WlC0UfS0Is6z0QXi9oGoBA&bvm=bv.47534661,d.ZGU&psig=AFQjCNHE5UKrABEzzOj2w2-YhrXJ4WC31g&ust=1370857859955492


CSEM’s

Technology Progrms

MEMS

Ultra-low-power integrated

systems

Systems

Surface engineering

Non-invasive wearable sensors

Personal biosensors

Environmental sensors

New data streams + machine

learning = “New vital signs”

Smart sensing


CSEM: Human vital signs Multiparameter

Sensing

SpO2 / CBT

NIBP

ECG respiration

HR / HRV CO2 / O2


Geophysical ExperimentWal

kin

g

Sitting

Donning

Sitting Wal

kin

g

DoffingTaking sample

Fall

• CBT

• HR

• BR

• SpO2

• 3D-ACC

CSEM : LTMS clinical study sample


Intelligent Information Management

Longitudinal data

Personal baseline

Detect trends & deviations from norm

Personal health records

Privacy

Sharing

Anonymous aggregation

Patient-centered decision support


Effective Interfaces

Multimodal

GUI, touch, gesture, speech, …

Mobile

Portable, networked, wearable

Intuitive

Easy to learn, use, maintain

Adaptive

Proactive

Source: Henry Kautz Department of Computer Science, Rochester University


Ten Targets for Wireless Medicine

Alzheimer’s 5M Vital signs, location, activity, balance

Asthma 23M RR, FEV1, Air quality, oximetry, pollen

count

Breast Cancer 3M Ultrasound self-exam Web

COPD* 10M RR, FEV1, Air quality, oximetry

Depression 21M Med Compliance, Activity, Communication

Diabetes 24M Glucose, Hemoglobin A1C

Heart Failure 5M Cardiac pressures, weight, BP, fluid status

Hypertension 74M Continuous BP, Med compliance

Obesity 80M Smart scales, Glucose, Caloric in/out,

Activity

Sleep Disorders 40M Sleep phases, quality, apnea, vital signs

Disease No. Affected Wireless Solutions Disease Wireless Solutions No. Affected Disease Wireless Solutions

* Chronic obstructive pulmonary disease

Source: Burrill


The shift to patient-empowering,

information-leveraging technologies

“PI” (patient-empowering and information-leveraging) technologies


Source: George Poste

Access,

cost and

quality

of care

Molecular

and

personalized

medicine

Proficient

use of

information

(e.health)

Link between Genomic and physiology


Biocorpora –Drugdesigntech SA

Collaborative Drug Discovery

Sourc

e:

Dr.

Cédric M

erlo

t, D

rug D

esig

n T

ech

Information matters – Data lifecycle


Sharing information

Huge amount of data produced, but not shared

Produced by HT technologies: screening, -omics

Stored in data silos, only useable by specialists

Need for one-stop-shop for all data for everybody

Provide a single point-of-entry to all project data

Explain raw data to non-specialists scientists

Share data across multi disciplinary research teams

Enable re-analysis, reuse data

Combine information from multiple sources

Drill down from summarized data to raw data

Sourc

e:

Dr.

Cédric M

erlo

t, D

rug D

esig

n T

ech


Collaborative Drug Discovery

publication

molecules High troughput

project leaders data analysis labs

in-context

reporting

Biocorpora

proposals CRO

Sourc

e:

Dr.

Cédric M

erlo

t, D

rug D

esig

n T

ech


The application of various 'omic'-technologies is

leading to the generation of vast amounts of data

Systems Biology studies assemblies of interacting biomolecular

components at multiple levels of biological abstraction.

The current challenge is to develop new computational tools and

analytical procedures that allow the integration of these various

datasets


- omics Data Management

Microarray Flow cytometry NMR Mass spectrometry

analysis

project leaders data analysis labs

in-context

reporting

Biocorpora

Sourc

e:

Dr.

Cédric M

erlo

t, D

rug D

esig

n T

ech


Megaclust: SIB, Vital-IT & Genebio

Enabling flow cytometry data analysis

Rethinking Enabling

Data

acquisition

Result Analysis and

decision making Identification of cell types

Leveraging the power of flow cytometry in clinical trials is a

great challenge due to the size and complexity of the data

generated.

MegaClust thoroughly and accurately identifies the different

cell types present in the dataset

Sourc

e:

megaclu

st.

vital-

it.c

h


Megaclust: SIB, Vital-IP & Genebio

Enabling flow cytometry data analysis

MegaClust is an integrated service for the analysis of large and

complex flow cytometry datasets

- Proprietary algorithm

- Optimized for massive parallelization

- Deployed on 256 processors

- Multidisciplinary

expertise

Sourc

e:

megaclu

st.

vital-

it.c

h


Megaclust: Example of identification of AML

treated patients among 50 individuals

MegaClust

5 markers

per sample

...

50

individuals

1.5 M cells (30 K cells * 50 ) and 5 dimensions ( ) AML: acute myeloid leukemia

Sourc

e:

megaclu

st.

vital-

it.c

h


Megaclust: Each individual has unique amounts

of each identified cell types (unique fingerprint)

50 individuals

16

ide

ntifie

d c

ell ty

pe

s

Each column represents a patient specific cellular fingerprint

A9

A172

A37

A95

A58

A33

C167A5

A174

A49

C62

C287

C330

C84

C201

A219

C200

C321

C35

C184

C206

C213

C77

C241

C86

C356

C315

C229

C41

C182

C320

C85

C317

C272

C282

C277

C178

C80

C91

C129

C254

C343

C251

C193

C265

C23

C217

C341

C78

C17

cellu

lar finge

rpri

nt

Sourc

e:

megaclu

st.

vital-

it.c

h


Megaclust: Fingerprints have the power to

stratify individuals (separate controls from AML)

AML

Control

- No a priori assumptions →

unrestricted analysis

- No human bias

- Reproducible

- Handles very large data sets

- Fast

Sourc

e:

megaclu

st.

vital-

it.c

h


Megaclustenables stratification and therefore

provides key insight at each phase of the drug

development pipeline

toxicity/effectiveness assessment

Stratification

design of focused patient group

markers selection

responders characterization

adverse effects

phase1 phase 2 phase3 preclinical

animals healthy

volunteers small group of patients large group of patients

Sourc

e:

megaclu

st.

vital-

it.c

h


IBM BlueGene/L

IBM Systems & Technology Group

EPFL / IBM Confidential | 6/13/2013

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52

EPFL’s Brain & Mind Institute: Application Needs

NeuroInformatics

• 10++ different databases, ~ 100TB of Storage required

• 1000 to 10’000 Cells structure information

NeuroSimulation

• BioChemical Networks

• Molecular Interactions underlying Synaptic Processes

• Microcircuits

NeuroVisualization

• Synaptic Structure

• Neuronal Structure

• Microcircuit Structure


Praesalio – Drugdesigntech SA

Predictive Toxicology

Lack of safety is a major cause of attrition

Raised during clinical phases, incl. Phase IV

Loss of all investments

Reduces therapeutic window (lack of efficacy)

Identify and fix potential issues in drug discovery

Structural modifications are still allowed

Deliver fully characterized compounds

Need for focused, fast evaluation of liabilities

Computer predictions to focus on relevant experiments

1000’s of endpoints evaluated fast and at low cost

Sourc

e:

Dr.

Cédric M

erlo

t, D

rug D

esig

n T

ech


From data to information

Activity DB Models Project

information

Crunching Predictions

1M+ structures

10M+ activities

3000 models

6Tb storage

1 answer

what are the liability in my

compound ?

Sourc

e:

Dr.

Cédric M

erlo

t, D

rug D

esig

n T

ech



MEDTECH


The challenges


Our contribution


Competition

Crossing Visions

13.06.2013 | 56

COMMUNAUTE INDUSTRIELLE

http://www.lilly.com/

http://www.debiopharm.com/

http://www.gsk.com/index.htm

http://www.novartis.com/

http://www.bienair.com/default.asp

http://www.cla.ch/default.asp

http://www.edwards.com/

http://www2.dupont.com/DuPont_Home/en_US/index.html

http://www.brandsoftheworld.com/download/brand/46163.html

http://www.serono.com/

Crossing Visions

13.06.2013 | 57

CHAMPION DU MONDE DE LA CROISSANCE

Vitesse moyenne de croissance annuelle 95-00, 00-06

Source: BAK, Basel Economics


A Successfull biocluster

UN RESEAU

D’EXCELLENCE

…et tant d’autres!

© 2013 IBM Corporation

Find the slides on:

www.republic-of-innovation.ch

Dr. Benoit Dubuis Fondation Eclosion 14, Ch. Des Aulx CH-1228 Plan-les-Ouates Tel 022 880 10 10 [email protected]

IBM Technology Day 2013 Keynote B. Dubuis

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