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
