1. Pharmaceutical R&D and the role of semantics in information management and decision- making Otto Ritter AstraZeneca R&D Boston W3C Workshop on Semantic Web for Life Sciences 27-28 October, 2004

2. 2 Drug R&D – complex, costly & risky information-driven enterprise Biology ChemistryDevelopment Target IDTarget Val.ScreeningOptimizePre-clinicalClinical ~ 10 years ~ $1B odds < 1/1000 $$

3. 3 Reality vs. Ideal State

4. 4 A B C benefit cost uncertainty Project vs. Business Perspectives

5. 5 Many Maps, Models, Mappings attributes (some context-dependent) functional & structural spaces models context INDIVIDUAL ENTITY conceptual categories

6. 6 Heterosemantic Networks and Decision Support  Find optimal routes between entities, based on evidence  Extend evidence-based routes with technological options (cost, risk)  Extend optimal plans, based on science and technology, into a lattice of business options (real options valuation)

7. 7 From Molecular and Biomedical Information Pathways to “R&D Pathways”  Typical project routes  Time, cost, attrition & transition probabilities  Model fitting for different contexts (e.g., disease area, target or lead molecular class, …)  Simulation, ranking of options  Joint portfolio & infrastructure optimization

8. 8 Where we need (semantic and syntactic) information integration  Problem statement… definition  Representation… language, formalism  Integration/Implementation… data, methods  Modeling… model, theory  Evaluation of… confidence feasibility  Simulation of… answers consequences  Analysis… options, conclusions  Interpretation… reference to reality  Decisions… impact on reality

9. 9 Lessons learned so far  Decouple form (syntax) from meaning (semantics)  Allow for multiple interpretations & conflicts  Reuse generic (form-oriented) components  Operational definition for identity  Explicit representation of context  Decision support analysis presents a special case of intelligent information integration across the science, technology and business domains

10. 10 Needs & Opportunities  Large-scale and high-throughput data integration, mining, model building and verification, interpretation & reasoning over complex, dynamic, hetero-semantic domains  “Workflows of workflows”, driven by the meaning, sensitive to context, and smart about uncertainty  Stack of high-level declarative languages. Orthogonal representations of concepts, logical and physical structure, UI services and views (extension of the Model-View-Control paradigm)