The rise of digitized medicine disrupts current research and business models

The rise of digitized medicine disrupts current research and

business models

Jesper Tegnér Director of the Unit for Computational Medicine,

Department of Medicine, Karolinska Institutet

SALSS Bio-networking session August 21, 2009

Observations – rise of digitized medicine

1. Rapid progress of technologies for generating data

Database growth (2007/2006 %)

211% 100% 122%

122% 136% 120%

E-PDB (Structures)

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Including Ensembl

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A million unique users per year

Very large user community

1. Rapid progress of technologies for generating data

2. Biology rules and its more complex than we ever could imagine !

Observations – rise of digitized medicine

Structure in Complexity - Nested Networks of: - genes- proteins- metabolites- cells

- organs, …

Challenge - Identify players (nodes) and interactions (edges) and dynamics

1. Rapid progress of technologies for generating data

2. Biology rules and its more complex than we ever could imagine !

3. Digitalization is a prerequisite for sharing and computing - medicine and health one of the last frontiers

Observations – rise of digitized medicine

Resources – work in progress

Virtual Physiologica Human, FP6, FP7, NIH, ...

VPH- I FP7 projects                   

Networking NoE

Osteoporosis

IP

Alzheimer's/ BM & diagnosis STREPHeart /CV

disease STREP

Cancer STREP

Liver surgery STREP

Heart/ LVD surgery STREP

Oral cancer/ BM D&T STREP

CV/ Atheroschlerosis IP

Breast cancer/ diagnosis STREP

Vascular/ AVF & haemodialysis STREP

Liver cancer/RFA therapy STREP

Security and Privacy in VPH CA

Grid access CA

Heart /CV disease STREP

Industry

ClinicsOther

Parallel VPH projects

A special report on health care and technologyMedicine goes digital

Apr 16th 2009From The Economist print edition

1. Rapid progress of technologies for generating data

2. Biology rules and its more complex than we ever could imagine !

3. Digitalization is a prerequisite for sharing and computing - medicine and health one of the last frontiers

4. This disrupts current R&D/business models

Observations – rise of digitized medicine

Biomarkers for diagnostics

DATA

UNDERSTANDING

INFORMATION (correlations)

Mechanisms of disease

Current models

-> -> Develop clever search strategies (algorithms)

From the wish list

• Predictive medicine (biomarkers for translational medicine – relevance of animal models)

• Personalized medicine – finding therapeutically relevant subgroups in different disease areas

• Biology rules -> taking complexity into account !

• Compute health quality (patients) derived from the health care process and various molecular measurements

All the good stuff from the wish list requires large-scale data (1) generation, & (2) accessible, computable

Genome

EmbryoCell

Fruitfly

Protein

Mouse Development, Ageing, Disease

* Predictive medicine, * Personalized medicine, * biology rules, * compute health quality (patients)

Current challenges/opportunities

• R&D as an ongoing conversation – how to make this process more efficient ?

• Closed data model (->isolated R&D projects) vs open source thinking

• Current publication model (w.r.t. data) vs “just let it go”

• How to create a data-sharing research model ?• Standards for making data/human/health

accessible & computable – think TCP/IP protocols• How to integrate and compute ?• What does the emerging data-sharing landscape

imply for current business models ? – how to create a “win-win” ?

• Hype smells money -> overselling the field• Business models beyond biomarkers & drugs.

”The Computational Unit @ CMM @ SciLifeLab @ KI -- From Molecular Medicine to Health and back

Population

Patient

Tissue, organ

Cell

Molecule

Public Health Informatics

Medical Informatics

BioinformaticsSystems Biology

Computational Biology

In houseExperimental data

(expression, SNPs, proteins, lipids, metabolites, images/histology,

cells/population of cells, blood, lifestyle medication,

environment, …)

Public databasesData sampled from several levels, different conditions

We need to overcome the idea, so prevalent in both academic and bureaucratic circles, that the only work worth taking seriously is highly detailed research in a speciality. We need to celebrate the equally vital contribution of those who dare to take what I call "a crude look at the whole".

Murray Gell-Mann, Nobel Laureate in Physics, 1994

Performing disruptive science

Different end-users

• The researcher

• Pharma & Biotech

• The Medical Doctor

• The Patient

• Society

Your Body, Your Medical Data, Your Health, Your Actions