1. From Bench to Bedside: Applications to Drug Discovery and Development Eric Neumann W3C HCLSIG co-chair Teranode Corporation HCLSIG F2F Cambridge MA

2. Knowledge “ --is the human capacity (both potential and actual) to take effective action in varied and uncertain situations. ”

3. Drug Innovation and the Technology Gap Need to utilize Knowledge more effectively

4. Drug R&D Trends from Innovation or Stagnation, FDA Report March 2004

6. New Regulatory Issues Confronting Pharmaceuticals from Innovation or Stagnation, FDA Report March 2004 Tox/Efficacy ADME Optim

7. Translational Medicine translate Enable physicians to more effectively translate relevant findings and hypotheses into therapies for human health blending Support the blending of huge volumes of clinical research and phenotypic data with genomic research data knowledge Apply that knowledge to patients and finally make individualized, preventative medicine a reality for diseases that have a genetic basis

8. Role of Informatics John Glaser, CIO Partners Healthcare Providing high quality and efficient health care isn't possible anymore without a sophisticated marriage of information technology and state-of-the-art science. Bringing these together to inform patient care is a tremendous undertaking… the full array of new information provided by genomic research must be harnessed and made real for doctors and patients A Framework for conducting clinical research in and across large multidisciplinary academic medical centers is designed to establish a "new" biomedicine to "fully exploits the fruit of the genomic revolution for clinical practice and allows clinical care to be leveraged to advance basic biological research.

9. Challenges for Drug D&D Counteracting the legacy of “Silos” How to break away from the DD “conveyor belt model” to the “Translation model” –gaining and sharing insights throughout the process The Benefit of New Targets for New Diseases How to best identify safety and efficacy issues early on, so that cost and failure are reduced A D 3 Knowledge-base: Drugability and Safety

10. Drug Discovery & Development Knowledge Qualified Targets Lead Generation Toxicity & Safety Biomarkers Pharmacogenomics Clinical Trials Molecular Mechanisms Lead Optimization KDKD

11. Drug Discovery & Development Knowledge Qualified Targets Lead Generation Toxicity & Safety Biomarkers Pharmacogenomics Clinical Trials Molecular Mechanisms Lead Optimization Launch

12. Communities and Interoperability Semantic interoperability is directly tied to CoP:  “Within a community or domain, relative homogeneity reduces interoperability challenges. Heterogeneity increases as one moves outside of a focal community/domain, and interoperability is likely [to be] more costly and difficult to achieve” Moen, 2001  Meanings encoded in a schema are usually useful for only one (original) community - difficult to extend to others!  Database utility more difficult if group is heterogeneous

13. Multiple Ontologies Used Together Drug target ontology FOAF Patent ontology OMIM Person Group Chemical entity Disease SNP BioPAX UniProt Extant ontologies Protein Under development Bridge concept UMLS Disease Polymorphisms PubChem

14. Potential Linked Clinical Ontologies Clinical Trials ontology RCRIM (HL7) Genomics CDISC IRB Applications Molecules Clinical Obs ICD10 Pathways (BioPAX) Disease Models Extant ontologies Mechanisms Under development Bridge concept SNOMED Disease Descriptions Tox

15. Drug Safety Knowledge Human Hepato-Toxicity Study Toxicity Indication Hepato-Toxicity Lens Genomic Profile Standards set by Regulatory Agencies To be part of NDA (New Drug Applications) How will Reviewers be empowered to handle such large amaount sof new data?

16. CDISC and the Semantic Web? Reduce the need to write data parsers to any CDISC XML Schema Make use of ontologies and terminologies directly using RDF Easier inclusion of Genomic data Use Semantic Lenses for Reviewers Easier acceptance by industry with their current technologies

17. Developing Standards Design Implementation Exchange

18. Developing Standards Design Implementation Semantic Web-based Specifications Exchange

19. Support Full Information Integration Integration: integrate and manage data from sources, EDC systems, Clinical Data Management Systems, labs and CROs Analysis and reporting: Accurately and timely analytical reports from study data, for use in decision making; easier results sharing with researchers and reviewers Discovery: Use expanding research information as a knowledge base for rapid investigations into critical drug safety issues, new marketing claims, and identify product-line extensions.

20. Thank You