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

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Drug R&D – complex, costly & risky information-driven enterprise

Biology Chemistry Development

Target ID Target Val. Screening Optimize Pre-clinical Clinical

~ 10 years~ $1Bodds < 1/1000

$$

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Reality vs. Ideal State

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AB

C

benefit

cost

uncertainty

Challenge Problem Knowledge

TechnologicalKnowledge

ScientificKnowledge

BusinessKnowledge

Project vs. Business Perspectives

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Many Maps, Models, Mappings

attributes(some context-dependent)

functional& structuralspaces

modelscontext

INDIVIDUALENTITY

conceptualcategories

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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)

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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

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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

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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

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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)