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Knowledge Management Issues in a Global Pharmaceutical R&D Environment. Ted Slater Proteomics Center of Emphasis Pfizer Global R&D Michigan. W3C Workshop on Semantic Web for Life Sciences 27-28 October 2004 Cambridge, Massachusetts USA. About Pfizer Global R&D.

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knowledge management issues in a global pharmaceutical r d environment

Knowledge Management Issues in a Global Pharmaceutical R&D Environment

Ted Slater

Proteomics Center of Emphasis

Pfizer Global R&D Michigan

W3C Workshop on Semantic Web for Life Sciences

27-28 October 2004

Cambridge, Massachusetts USA

about pfizer global r d
About Pfizer Global R&D
  • The industry’s largest R&D organization
    • >12,500 employees worldwide
    • Estimated R&D budget in 2004:$7.9 billion
    • Hundreds of research projects over 18 therapeutic areas
  • (Not really using Semantic Web technologies just now)
issues with global r d
Issues with Global R&D
  • Geographical (time & distance)
  • Language (even if the language is the same!)
  • Cultural
  • Increased reliance on electronic communications
slide4

5:00

10:00

2:00

18:00

5:00

4:00

what s in a name
What’s in a Name?
  • “Releasing TaqMan® Data” use case from John Wilbanks (17 Aug 2004)
    • GO annotation from a particular gene
    • TaqMan® data from an exon proximal to that gene
    • Annotating the TaqMan® data with GO annotation is not quite right
    • Different perceptions of concept “gene”
slide6

Proteomics

Metabonomics

RNA Profiling

current tools fall short
Current Tools Fall Short
  • 100+ highly-specialized software tools in place for ’omics technologies
  • All query-centric
    • Single user
    • Low bandwidth
    • Ask a question, get a list
how to drive a biologist crazy
gi|84939483 

gi|39893845 

gi|27394934 

gi|18890092 

gi|10192893 

gi|11243007 

gi|20119252 

gi|19748300 

gi|44308356 

gi|50021874 

gi|10003001 

gi|27762947 

gi|24537303 

gi|27284958 

gi|37373499 

How to Drive a Biologist Crazy
metadata
Metadata?
  • Experimental protocols
  • Model system descriptions
  • Statistical criteria for data analysis and acceptability
  • Others
slide13

fan

wall

spear

tree

rope

snake

Physiology

hypothesis generation
Hypothesis Generation
  • Our domain is too big and complex to fit in our heads
    • Browsing and correlation can’t get us there
  • We need our machines to generate testable hypotheses for us based on our experimental results
  • We need knowledge about causation
clinical km needs
Clinical KM Needs
  • Aggregate and analyze:
    • Safety data
    • Efficacy data
    • Genomic data
    • Healthcare data
    • Performance data
      • Study metadata
      • Staff and vendor performance
      • Resource utilization
the shape of clinical data
The Shape of Clinical Data
  • >2 GB each per Phase-2, -3, or -4 protocol, split over >100 different datasets, each with 20-300 columns
  • Metadata complex, hard to combine across studies
  • Sensitive data
    • Project teams can be reluctant to discuss with other groups (e.g. in discovery)
clinical columns
Clinical Columns
  • Dosage and dose response data
  • Product differentiation
  • Patient demographics
  • Concurrent medications
  • Lab data
  • Subject experience & adverse events
  • How fast does it work? How long does it last?
other areas
Other Areas
  • Legal
    • “Patent searching is an art, not a science”
    • New cases, statutes, policies
  • HR
  • Finance
  • Strategic Alliances
    • PGRD has links with >250 partners in academia and industry
  • More
summary
Summary
  • KM needs in discovery and clinical are complex, diverse, and sizeable
  • We need a knowledge architecture that can be used effectively by machines.
    • Ontologies
    • Software
    • Hardware
acknowledgements
Acknowledgements
  • John Wilbanks (W3C)
  • Enoch Huang (Pfizer)
  • Eric Neumann (Aventis)
  • Stephen Dobson (Pfizer)
  • Mitch Brigell (Pfizer)
  • Dave Lowenschuss (Pfizer)
  • Ruth VanBogelen (Pfizer)