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The rise of digitized medicine disrupts current research and business models

This article discusses the rapid progress of technologies for generating data in digitized medicine and how it disrupts current research and business models. It highlights the complexity of biology and the need for digitalization to share and compute medical information. The article also explores the challenges and opportunities in predictive and personalized medicine, as well as the importance of considering biology rules and computing health quality. It concludes by addressing the current challenges in R&D, data sharing, and business models in the emerging data-sharing landscape.

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The rise of digitized medicine disrupts current research and business models

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  1. 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

  2. Observations – rise of digitized medicine • Rapid progress of technologies for generating data

  3. Database growth (2007/2006 %) 122% 100% 211% E-PDB (Structures) 120% 136% 122%

  4. Very large user community A million unique users per year Average Web Hits per Day Including Ensembl

  5. Observations – rise of digitized medicine • Rapid progress of technologies for generating data • Biology rules and its more complex than we ever could imagine !

  6. Structure in Complexity - Nested Networks of: - genes - proteins - metabolites - cells - organs, … Challenge - Identify players (nodes) and interactions (edges) and dynamics

  7. Observations – rise of digitized medicine • Rapid progress of technologies for generating data • Biology rules and its more complex than we ever could imagine ! • Digitalization is a prerequisite for sharing and computing - medicine and health one of the last frontiers

  8. Resources – work in progress

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

  10. VPH- I FP7 projects Parallel VPH projects Industry Grid access CA CV/ Atheroschlerosis IP Liver surgery STREP Breast cancer/ diagnosis STREP Heart/ LVD surgery STREP Osteoporosis IP Oral cancer/ BM D&T STREP Cancer STREP Networking NoE Heart /CV disease STREP Vascular/ AVF & haemodialysis STREP Liver cancer/RFA therapy STREP Alzheimer's/ BM & diagnosis STREP Heart /CV disease STREP Other Clinics Security and Privacy in VPH CA

  11. A special report on health care and technology Medicine goes digital Apr 16th 2009From The Economist print edition

  12. Observations – rise of digitized medicine • Rapid progress of technologies for generating data • Biology rules and its more complex than we ever could imagine ! • Digitalization is a prerequisite for sharing and computing - medicine and health one of the last frontiers • This disrupts current R&D/business models

  13. Current models Biomarkers for diagnostics UNDERSTANDING Mechanisms of disease INFORMATION (correlations) -> -> Develop clever search strategies (algorithms) DATA

  14. 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

  15. Genome Protein Cell Embryo Fruitfly Mouse Development, Ageing, Disease All the good stuff from the wish list requires large-scale data (1) generation, & (2) accessible, computable * Predictive medicine, * Personalized medicine, * biology rules, * compute health quality (patients)

  16. 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.

  17. ”The Computational Unit @ CMM @ SciLifeLab @ KI -- From Molecular Medicine to Health and back Public Health Informatics Population In house Experimental data (expression, SNPs, proteins, lipids, metabolites, images/histology, cells/population of cells, blood, lifestyle medication, environment, …) Patient Medical Informatics Tissue, organ Cell Public databases Data sampled from several levels, different conditions Bioinformatics Systems Biology Computational Biology Molecule

  18. Performing disruptive science 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

  19. Different end-users • The researcher • Pharma & Biotech • The Medical Doctor • The Patient • Society

  20. Your Body, Your Medical Data, Your Health, Your Actions

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