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)