Big Data in Life Sciences

1st Symposium on Big Data and Public Health

FGV 24/10/2013

Big Data “Big Data in the life sciences sector is now a strategic and operational issue for almost all stakeholders. Capturing, storing, data flow and analysis of information-rich processes affect all aspects of the pharmaceutical and medical device industries but particularly the discovery, research & development stages”. “A strategic shift towards big data and data-driven approaches must be implemented from senior managers and thoroughly rolled-out across the organization”. Capturing Storing Data flow Analysis Summarize Represent

Life Sciences – drug development - Drug discovery process - lead / target identification and research follow-up - Translation to clinical stages - Why & How of implementing big data approaches - Genomic & personalized medicines - combination of biomarkers research and retrospective data analysis, and analysis of current clinical outcomes - Systems Biology & detailed modeling & simulation processes - Better design NMEs, re-engineer and re-initiate previously failed drug programs - Design, collect & manage clinical stage data - Selecting EDC technologies, outsourcing Data Management responsibilities - Retain control over data quality - Real-world big data (Real World Evidence - RWE) for drug safety & surveillance for regulators in post-marketing, feeding back vital insights into mechanisms of action and real-life prescription and use - Understand product health outcome benefits for regulators, payers & other stakeholders: Product’s effectiveness, associated health outcomes and cost effectiveness endpoints - Manage and integrate data generated at all stages of the value chain, from discovery to real-world use after regulatory approval In all fields, the amount of data to be collected and managed has massively increased.

Life Sciences – R&D Genomics – metagenomics: generation of genetic code data Clinical genomics: pharmacogenomics, disease marker genes etc

Expression studies: transcriptomics and micro-array

Protein structure, function and protein-protein interaction (also RNA/DNA/protein;

saccharide, lipids etc)

System Biology – metabolic systems and their regulation, synthetic biology

Mass spectrometry analysis (biomarkers; complex mixtures)

Metabolomics; biodiversity extracts and fractionation

Phylogeny, Networks of life

Clinical Research, epidemiology, models Public health

Scientific Literature and Patents – data and text mining

C. Probst, Fiocruz Paraná

C. Probst, Fiocruz Paraná

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All you wanted to know,

but were afraid to ask...

Adams, M.D., Kelley, J.M., Gocayne, J.D., Dubnick, M., Polymeropoulos, M.H., Xiao, H., Merril, C.R., Wu, A., Olde, B.,

Moreno, R., Kerlavage, A.R., McCombie, W.R., and Venter, J.C. Complementary DNA sequencing: "expressed sequence tags"

and the human genome project. Science 252, 1651-1656 (1991).

12

T. Otto

General Proline and Arginine Metabolism

In silico biochemistry

Metabolism and solute transport of A. fulgidus

Klenk et al, Nature n390, 364 (1997)

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human

M. leprae

Drosophila

Vibrio cholera

Plasmodium

Neisseria

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Rickettsia

Archaeoglobus

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Helicobacter

Borrelia

Bacillus

Campilobacter

Aquifex

Chlamidia

Ureaplasma

Thermoplasma

Thermotoga

Pseudomonas

Buchenerasp E. coli

S. cerevisiae

Salmonella

Yersinia

Ralstonia S. pombe