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From Bench to Bedside: Applications to Drug Discovery and Development

From Bench to Bedside: Applications to Drug Discovery and Development . Eric Neumann W3C HCLSIG co-chair Teranode Corporation HCLSIG F2F Cambridge MA. Knowledge “ --is the human capacity (both potential and actual) to take effective action in varied and uncertain situations. ”.

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From Bench to Bedside: Applications to Drug Discovery and Development

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  1. From Bench to Bedside:Applications to Drug Discovery and Development Eric Neumann W3C HCLSIG co-chair Teranode Corporation HCLSIGF2F Cambridge MA

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

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

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

  5. from Innovation or Stagnation, FDA Report March 2004

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

  7. Translational Medicine • Enable physicians to more effectively translate relevant findings and hypotheses into therapies for human health • Support the blending of huge volumes of clinical research and phenotypic data with genomic research data • 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 D3 Knowledge-base: Drugability and Safety

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

  11. Drug Discovery & Development Knowledge Qualified Targets Molecular Mechanisms Lead Generation Toxicity & Safety Lead Optimization Pharmacogenomics Biomarkers Clinical Trials 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. DiseasePolymorphisms Disease Group Protein Person Chemicalentity Multiple Ontologies Used Together UMLS OMIM SNP Drug target ontology FOAF UniProt BioPAX PubChem Patent ontology Extant ontologies Under development Bridge concept

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

  15. Toxicity Indication Drug Safety Knowledge • 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? Human Hepato-Toxicity Study Hepato-Toxicity Lens

  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 Exchange Implementation Design

  18. Semantic Web-basedSpecifications Developing Standards Design Implementation 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

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