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% 136% 120% E-PDB (Structures)

Including Ensembl Average Web Hits per Day 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 STREP Heart /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 technology Medicine goes digital Apr 16th 2009 From 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 MouseDevelopment, 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 KI -- From Molecular Medicine to Health and back Population Patient Tissue, organ Cell Molecule Public Health Informatics Medical Informatics Bioinformatics Systems Biology Computational Biology In house Experimental data (expression, SNPs, proteins, lipids, metabolites, images/histology, cells/population of cells, blood, lifestyle medication, environment, …) Public databases Data 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