1 / 20

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.

alda
Download Presentation

Knowledge Management Issues in a Global Pharmaceutical R&D Environment

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. 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

  2. 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)

  3. Issues with Global R&D • Geographical (time & distance) • Language (even if the language is the same!) • Cultural • Increased reliance on electronic communications

  4. 5:00 10:00 2:00 18:00 5:00 4:00

  5. 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”

  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. 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

  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. fan wall spear tree rope snake Physiology

  14. 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

  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 • >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)

  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 >250 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)

More Related