Talk by Wim de Grave on the 1st Symposium of Big Data and Public Health, 2013

1. Big Data in Life Sciences
1st Symposium on Big Data and
Public Health
FGV 24/10/2013

2. 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

3. 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 realworld use after regulatory approval
In all fields, the amount of data to be collected and managed has massively increased.

4. 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

8. C. Probst, Fiocruz Paraná

9. C. Probst, Fiocruz Paraná

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All you wanted to know,
but were afraid to ask...

11. 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
12

13. General Proline and Arginine Metabolism

14. In silico biochemistry
Metabolism and solute transport of A. fulgidus
Klenk et al, Nature n390, 364 (1997)

15. human
Drosophila
mouse
C. elegans
Vibrio cholera
Plasmodium
Rickettsia
Archaeoglobus
Campilobacter
Aquifex
M. leprae
Neisseria
Chlamidia
M. tuberculosis
Arabidopsis
Xylella
rat
Buchenerasp
Ureaplasma
Helicobacter
E. coli
Thermoplasma
Borrelia
Yersinia
Ralstonia
S. cerevisiae
Thermotoga
Bacillus
Pseudomonas
S. pombe
Salmonella