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Pharmaceutical R&D and the role of semantics in information management and decision-making

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. Target ID. Target Val. Screening. Optimize. Pre-clinical. Clinical. Biology. Chemistry.

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Pharmaceutical R&D and the role of semantics in information management and decision-making

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  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. Target ID Target Val. Screening Optimize Pre-clinical Clinical Biology Chemistry Development Drug R&D – complex, costly & risky information-driven enterprise $$ ~ 10 years ~ $1B odds < 1/1000

  3. Reality vs. Ideal State

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

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

  6. 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) Heterosemantic Networks and Decision Support

  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. 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. 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. 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)

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