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Knowledge Management Knowledge Management Issues in a Global Issues in a Global Pharmaceutical R&D Pharmaceutical R&D EnvironmentEnvironment
Ted SlaterTed Slater
Proteomics Center of EmphasisProteomics Center of Emphasis
Pfizer Global R&D MichiganPfizer Global R&D Michigan
W3C Workshop on Semantic Web for Life SciencesW3C Workshop on Semantic Web for Life Sciences27-28 October 200427-28 October 2004Cambridge, Massachusetts USACambridge, Massachusetts USA
About Pfizer Global R&DAbout 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&DIssues with Global R&D
Geographical (time & distance) Language (even if the language is the
same!) Cultural Increased reliance on electronic
communications
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”
RNA Profiling
Proteomics Metabonomics
Current Tools Fall ShortCurrent 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 CrazyHow 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 Add Insult to InjuryHow to Add Insult to Injury
Current State of KMCurrent State of KM
Data TombsData Tombs
Metadata?Metadata?
Experimental protocols Model system descriptions Statistical criteria for data analysis and
acceptability Others
treetree
wallwallfanfan
snakesnake
spearspear
roperope
Hypothesis GenerationHypothesis 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 NeedsClinical 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 DataThe 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 ColumnsClinical 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 AreasOther 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
SummarySummary
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
AcknowledgementsAcknowledgements
John Wilbanks (W3C) Enoch Huang (Pfizer) Eric Neumann (Aventis) Stephen Dobson (Pfizer) Mitch Brigell (Pfizer) Dave Lowenschuss (Pfizer) Ruth VanBogelen (Pfizer)