1. Knowledge Management Issues in a Global Pharmaceutical R&D Environment Ted Slater Proteomics Center of Emphasis 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 n The industry’s largest R&D organization n >12,500 employees worldwide n Estimated R&D budget in 2004: $7.9 billion n Hundreds of research projects over 18 therapeutic areas n (Not really using Semantic Web technologies just now)

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

4. 18: 00 5:00 4:0 0 2:0 0 10: 00 5:00

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

6. RNA Profiling Proteomics Metabonomics

7. Current Tools Fall Short n 100+ highly-specialized software tools in place for ’omics technologies n All query-centric n Single user n Low bandwidth n Ask a question, get a list

8. How to Drive a Biologist Crazy n gi|84939483  n gi|39893845  n gi|27394934  n gi|18890092  n gi|10192893  n gi|11243007  n gi|20119252  n gi|19748300  n gi|44308356  n gi|50021874  n gi|10003001  n gi|27762947  n gi|24537303  n gi|27284958  n gi|37373499  n …

9. How to Add Insult to Injury

10. Current State of KM

11. Data Tombs

12. Metadata? n Experimental protocols n Model system descriptions n Statistical criteria for data analysis and acceptability n Others

13. tree wallfansnake spear rope

14. Hypothesis Generation n Our domain is too big and complex to fit in our heads n Browsing and correlation can’t get us there n We need our machines to generate testable hypotheses for us based on our experimental results n We need knowledge about causation

15. Clinical KM Needs n Aggregate and analyze: n Safety data n Efficacy data n Genomic data n Healthcare data n Performance data n Study metadata n Staff and vendor performance n Resource utilization

16. The Shape of Clinical Data n >2 GB each per Phase-2, -3, or -4 protocol, split over >100 different datasets, each with 20-300 columns n Metadata complex, hard to combine across studies n Sensitive data n Project teams can be reluctant to discuss with other groups (e.g. in discovery)

17. Clinical Columns n Dosage and dose response data n Product differentiation n Patient demographics n Concurrent medications n Lab data n Subject experience & adverse events n How fast does it work? How long does it last?

18. Other Areas n Legal n “Patent searching is an art, not a science” n New cases, statutes, policies n HR n Finance n Strategic Alliances n PGRD has links with >250 partners in academia and industry n More

19. Summary n KM needs in discovery and clinical are complex, diverse, and sizeable n We need a knowledge architecture that can be used effectively by machines. n Ontologies n Software n Hardware

20. Acknowledgements n John Wilbanks (W3C) n Enoch Huang (Pfizer) n Eric Neumann (Aventis) n Stephen Dobson (Pfizer) n Mitch Brigell (Pfizer) n Dave Lowenschuss (Pfizer) n Ruth VanBogelen (Pfizer)