Knowledge Management Issues in a Global Pharmaceutical R

1
Knowledge Management Issues in a Global
Pharmaceutical RD Environment
W3C Workshop on Semantic Web for Life
Sciences 27-28 October 2004 Cambridge,
Massachusetts USA

• Ted Slater
• Proteomics Center of Emphasis
• Pfizer Global RD Michigan

2
About Pfizer Global RD

• The industrys largest RD organization
• gt12,500 employees worldwide
• Estimated RD budget in 20047.9 billion
• Hundreds of research projects over 18 therapeutic
  areas
• (Not really using Semantic Web technologies just
  now)

3
Issues with Global RD

• Geographical (time distance)
• Language (even if the language is the same!)
• Cultural
• Increased reliance on electronic communications

4
(No Transcript)
5
Whats 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

6
Proteomics
Metabonomics
RNA Profiling
7
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

8
How to Drive a Biologist Crazy

• gi84939483 ?
• gi39893845 ?
• gi27394934 ?
• gi18890092 ?
• gi10192893 ?
• gi11243007 ?
• gi20119252 ?
• gi19748300 ?

• gi44308356 ?
• gi50021874 ?
• gi10003001 ?
• gi27762947 ?
• gi24537303 ?
• gi27284958 ?
• gi37373499 ?

9
How to Add Insult to Injury
10
Current State of KM
11
Data Tombs
12
Metadata?

• Experimental protocols
• Model system descriptions
• Statistical criteria for data analysis and
  acceptability
• Others

13
Physiology
14
Hypothesis Generation

• Our domain is too big and complex to fit in our
  heads
• Browsing and correlation cant get us there
• We need our machines to generate testable
  hypotheses for us based on our experimental
  results
• We need knowledge about causation

15
Clinical KM Needs

• Aggregate and analyze
• Safety data
• Efficacy data
• Genomic data
• Healthcare data
• Performance data
• Study metadata
• Staff and vendor performance
• Resource utilization

16
The Shape of Clinical Data

• gt2 GB each per Phase-2, -3, or -4 protocol, split
  over gt100 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)

17
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?

18
Other Areas

• Legal
• Patent searching is an art, not a science
• New cases, statutes, policies
• HR
• Finance
• Strategic Alliances
• PGRD has links with gt250 partners in academia and
  industry
• More

19
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

20
Acknowledgements

• John Wilbanks (W3C)
• Enoch Huang (Pfizer)
• Eric Neumann (Aventis)
• Stephen Dobson (Pfizer)
• Mitch Brigell (Pfizer)
• Dave Lowenschuss (Pfizer)
• Ruth VanBogelen (Pfizer)