1 / 10

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.

maja
Download Presentation

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

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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)

More Related