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Translational Medicine from a Semantic Web Perspective Eric Neumann W3C June 16, 2006

Translational Medicine from a Semantic Web Perspective Eric Neumann W3C June 16, 2006 . Drug Discovery and Medicine. Health Practice Safety Prevention Privacy Knowledge. Hygieia, G. Klimt. Combine. Data Expansion. Large Data Sets Variables >> Samples Many New Data Types

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Translational Medicine from a Semantic Web Perspective Eric Neumann W3C June 16, 2006

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  1. Translational Medicine from a Semantic Web PerspectiveEric NeumannW3C June 16, 2006

  2. Drug Discoveryand Medicine • Health • Practice • Safety • Prevention • Privacy • Knowledge Hygieia, G. Klimt

  3. Combine Data Expansion Large Data SetsVariables >> Samples Many New Data Types Which Formats?

  4. Where Information Advances are Most Needed • Supporting Innovative Applications in R&D • Translational Medicine (Biomarkers) • Molecular Mechanisms (Systems) • Data Provenance, Rich Annotation • Clinical Information • eHealth Records, EDC, Clinical Submission Documents • Safety Information, Pharmacovigilance, Adverse Events, Biomarker data • Standards • Central Data Sources • Genomics, Diseases, Chemistry, Toxicology • MetaData • Ontologies • Vocabularies

  5. Knowledge“--is the human acquired capacity (both potential and actual) to take effective action in varied and uncertain situations.” How does this translate into using Information Systems better in support of Innovation?

  6. Knowledge  Predictiveness Drug Discovery Challenges • Knowledge of Target Mechanisms • Knowledge of Toxicity • Knowledge of Patient-Drug Profiles

  7. Current Challenges: Drug Discovery • Business • Costly, lengthy drug discovery process (12-14 years) • Poor funding to find new uses for existing therapies (ie antibiotics) • Insufficient economic drivers for certain disease areas • Discovery and clinical trials design not well aligned with anticipating adverse effect detection • Post-launch surveillance is weak • Science & Technology • 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

  8. The Big Picture - Hard to understand from just a few Points of View

  9. Complete view tells a very different Story

  10. Distributed Nature of R&D Silos of Data…

  11. R&D Scientist Integrating Data Manually Static, Untagged, Disjoint Dolor Sit Amet Consectetuer Lacreet Dolore Euismod Volutpat Lacreet Dolore Magna Volutpat Nibh Euismod Tincidunt Aliguam Erat Dolor Sit Amet Consectetuer Lacreet Dolore Euismod Volutpat Lacreet Dolore Magna Volutpat Nibh Euismod Tincidunt Aliguam Erat LIMS Bioinformatics Cheminformatics Public Data Sources Existing Web Data Throttles the R&D Potential

  12. Papers Disease Proteins Genes Curation Tools Experiment Ontology Data Integration: Biology Requirements Retention Policy Assays Compounds Audit Trail

  13. Dynamic, Linked, Searchable LIMS Bioinformatics Cheminformatics Public Data Sources Semantic Web Data Integration R&D Scientist

  14. DecisionSupport Raw Data GO BiomarkerQualification MAGE ML BioPAX CDISC TranslationalResearch Psi XML ICH TargetValidation ASN1. NewApplications XLS Safety SAS Tables CSV Toxicity Semantic Bridge

  15. Key Technologies Pharmaceuticals use to Exchanging Knowledge

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

  17. Key Functionality • Ubiquity • Same identifiers for anything from anywhere • Discoverability • Global search on any entity • Interoperability • => Application independence: “Recombinant Data”

  18. Additional Functionality • Provenance • Origin and history of data and annotations • Scalability • Over all potentially relevant data and content • Authentication/Security • Single user and team identity and granular data security • Non-repudiation of authorship • Encryption of graphs • Policy Awareness • Data Preservation • Long-term persistence by minimizing API needs

  19. Translational Research and Personalized Medicine • Two significant areas of HCLS activity • Span most areas of activity Biological Translational Medicine Clinical Research Practice

  20. HCLS Framework:Biomedical Research • Molecular, Cellular and Systems Biology/Physiology • Organism as an integrated an interacting network of genes, proteins and biochemical reactions • Human body as a system of interacting organs • Molecular Cell Biology/Genomic and Proteomic Research • Gene Sequencing, Genotyping, Protein Structures • Cell Signaling and other Pathways • Biomarker Research • Discovery of genes and gene products that can be used to measure disease progression or impacts of drug • Pharmaco-genomics • Impact of genetic inheritance on • Drug Discovery and Translational Research • Use of preclinical research to identify promising drug candidates

  21. HCLS Framework:Clinical Research • Clinical Trials • Determination of efficacy, impact and safety of drugs for particular diseases • Pharmaco-vigilance/ADE Surveillance • Monitoring of impacts of drugs on patients, especially safety and adverse event related information • Patient Cohort Identification and Management • Identifying patient cohorts for drug trials is a challenging task • Translational Research • Test theories emerging from pre-clinical experimentation on disease affected human subjects • Development of EHRs/EMRs for both clinical research and practice • Currently EHRs/EMRs focussed on clinical workflow processes • Re-using that information for clinical research and trials is a challenging task

  22. Translational Research • Improve communication between basic and clinical science so that more therapeutic insights may be derived from new scientific ideas - and vice versa. • Testing of theories emerging from preclinical experimentation on disease-affected human subjects. • Information obtained from preliminary human experimentation can be used to refine our understanding of the biological principles underpinning the heterogeneity of human disease and polymorphism(s). • http://www.translational-medicine.com/info/about • Reference NIH Digital Roadmap activity

  23. Personalized Medicine • Propagation of insights from Genomic research into clinical practice • Impact of new Molecular diagnostic tests hitting the market • How can they be incorporated into clinical care? • How does one update current clinical guidelines to incorporate the use of these tests • How can one enable novel clinical decision support? • How can phenotypic characteristics and genomic markers be used to: • Stratify patient populations • “Personalize” clinical care • Genetic test results as risk factors • Therapeutic use of genomic markers

  24. Ecosystem: Current State Characterized by silos with uncoordinated supply chains leading to inefficiencies in the system Patients, Public Patients FDA National Institutes Of Health Center for Disease Control Pharmaceutical Companies Hospitals Payors Clinical Research Organizations (CROs) Universities, Academic Medical Centers (AMCs) Hospitals Doctors Biomedical Research Clinical Practice Patients Patients Clinical Trials/Research Clinical Practice

  25. Ecosystem: Goal State /* Need to expand this with Biomedical Research + Clinical Practice */ Biomedical Research Clinial Practice /* Need to expand this to include Healthcare and Biomedical Research Players as well… Show an integrated picture with “continuous” information flow */

  26. Qualified Targets Lead Generation Lead Optimization Toxicity & Safety Biomarkers Molecular Mechanisms Pharmacogenomics Clinical Trials Use Case Flow: Drug Discovery and Development KD

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

  28. Semantic Web Drug DD Application Space Therapeutics safety Critical Path Chem Lib manufacturing NDA Production Genomics ClinicalStudies HTS eADME Patent Compound Opt DMPK Biology genes informatics

  29. Opportunities for Semantics in HealthCare • Enhanced interoperability via: • Semantic Tagging • Grounding of concepts in Standardized Vocabularies • Complex Definitions • Semantics-based Observation Capture • Inference on Diseases • Phenotypes • Genetics • Mechanisms • Semantics-based Clinical Decision Support • Guided Data Interpretation • Guided Ordering • Semantics-based Knowledge Management

  30. Data Semantics in the Life Sciences Pathways, Biomarkers Publications Complex Objects with Categorical/Taxonomic Data Items Systems Biology Gene expression Publications + data Categorical Taxonomic Data Items Image + Text Data Items Data Items Text Text + data items Composite Objects with Embedded “process” Complex Objects Histology Profiling genomics Clinical Findings Clinical trials Unstructured Data Types Structured and Complex Data Types

  31. RDB => RDF Virtualized RDF

  32. Use-Case: COSA Column Semantic <rdf:type Gene> Data Set Row Semantic <rdf:type Subject>

  33. Use-Case:Experimental Design Definition Treatment W VisibleMicroscopy Cultured Cells Control Time Points Staining ImageAnalysis FluorescentMicroscopy Treatment Z

  34. Case Study: Drug Safety ‘Safety Lenses’ • Lenses can ‘focus data in specific ways • Hepatoxicity, genotoxicity, hERG, metabolites • Can be “wrapped” around statistical tools • Aggregate other papers and findings (knowledge) in context with a particular project • Align animal studies with clinical results • Support special “Alert-channels” by regulators for each different toxicity issue • Integrate JIT information on newly published mechanisms of actions

  35. Example:Knowledge Aggregation Courtesy of BG-Medicine

  36. Case Study: Omics ApoA1 … … is produced by the Liver … is expressed less in Atherosclerotic Liver … is correlated with DKK1 … is cited regarding Tangier’s disease … has Tx Reg elements like HNFR1 Subject  Verb  Object

  37. Scenario: Biomarker Qualification • Biomarker Roles • Disease • Toxicity • Efficacy • Molecular and cytological markers • Tissue-specific • High content screening derived information • Different sets associated with different predictive tools • Statistical discrimination based on selected samples • Predictive power • Alternative cluster prediction algorithms • Support qualifications from multiple studies (comparisons) • Causal mechanisms • Pathways • Population variation

  38. Pathways Disease +Samples -Samples Significance &Strength BioMarker Semantics Biomarker Set

  39. Scenario: Toxicity • Mechanisms • Tissue-selective, Species-specific • Pathways, Off-Targets • Metabolites, PK sensitivity • Evidence • Biomarkers • In vitro assays (cell lines), Animal models, Clinical Phase 1 • Literature • Population Variation • Drug Metabolism to toxic forms (CYP, SULT, UGT) • Target interaction variability • Potential vs. Demonstrated • Predictions • Data Mining Patterns • Computational Modeling • Working Solutions • Chemical modifications • Dosing, Reformulation • Documented animal <=> human similarity and variation

  40. Knowledge Mining using Semantic Web “Gene Prioritization through Data Fusion” • Aerts et al, 2006, Nature • Use of quantitative and qualitative information for statistical ranking. • Can be used to identify novel genes involved in diseases

  41. Case Study: BioPAX (Pathways) • <bp:PATHWAYSTEP rdf:ID="xDshToXGSK3bPathwayStep"> • <bp:next-step rdf:resource="#xGSK3bToBetaCateninPathwayStep"/> • <bp:step-interactions> • <bp:MODULATION rdf:ID="xDshToXGSK3b"> • <bp:keft rdf:resource="#xDsh"/> • <bp:right rdf:resource="#xGSK-3beta"/> • <bp:participants rdf:resource="#xGSK-3beta"/> • <bp:name rdf:datatype="http://www.w3.org/2001/XMLSchema#string"> Dishevelled to GSK3beta</bp:name> • <bp:direction rdf:datatype="http://www.w3.org/2001/XMLSchema#string"> IRREVERSIBLE-LEFT-TO-RIGHT</bp: direction > • <bp:control-type rdf:datatype="http://www.w3.org/2001/XMLSchema#string"> INHIBITION</bp: control-type > • <bp: participants rdf:resource="#xDsh"/> • </bp: MODULATION > • </bp: step-interactions > • </bp: PATHWAYSTEP >

  42. Case Study: BioPAX (Pathways) • <bp:PATHWAYSTEP rdf:ID="xDshToXGSK3bPathwayStep"> • <bp:next-step rdf:resource="#xGSK3bToBetaCateninPathwayStep"/> • <bp:step-interactions> • <bp:MODULATION rdf:ID="xDshToXGSK3b"> • <bp:keft rdf:resource="#xDsh"/> • <bp:right rdf:resource="#xGSK-3beta"/> • <bp:participants rdf:resource="#xGSK-3beta"/> • <bp:name rdf:datatype="http://www.w3.org/2001/XMLSchema#string"> Dishevelled to GSK3beta</bp:name> • <bp:direction rdf:datatype="http://www.w3.org/2001/XMLSchema#string"> IRREVERSIBLE-LEFT-TO-RIGHT</bp: direction > • <bp:control-type rdf:datatype="http://www.w3.org/2001/XMLSchema#string"> INHIBITION</bp: control-type > • <bp: participants rdf:resource="#xDsh"/> • </bp: MODULATION > • </bp: step-interactions > • </bp: PATHWAYSTEP >

  43. Modulation affectedBy CHIR99102 Case Study: BioPAX (Pathways) • <bp:PATHWAYSTEP rdf:ID="xDshToXGSK3bPathwayStep"> • <bp:next-step rdf:resource="#xGSK3bToBetaCateninPathwayStep"/> • <bp:step-interactions> • <bp:MODULATION rdf:ID="xDshToXGSK3b"> • <bp:keft rdf:resource="#xDsh"/> • <bp:right rdf:resource="#xGSK-3beta"/> • <bp:participants rdf:resource="#xGSK-3beta"/> • <bp:name rdf:datatype="http://www.w3.org/2001/XMLSchema#string"> Dishevelled to GSK3beta</bp:name> • <bp:direction rdf:datatype="http://www.w3.org/2001/XMLSchema#string"> IRREVERSIBLE-LEFT-TO-RIGHT</bp: direction > • <bp:control-type rdf:datatype="http://www.w3.org/2001/XMLSchema#string"> INHIBITION</bp: control-type > • <drug:affectedBy rdf:resource=”http://pharma.com/cmpd/CHIR99102"/> • <bp: participants rdf:resource="#xDsh"/> • </bp: MODULATION > • </bp: step-interactions > • </bp: PATHWAYSTEP >

  44. 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

  45. Case Study: Drug Discovery Dashboards • Dashboards and Project Reports • Next generation browsers for semantic information via Semantic Lenses • Renders OWL-RDF, XML, and HTML documents • Lenses act as information aggregators and logic style-sheets add { ls:TheraTopic hs:classView:TopicView }

  46. Topic: GSK3beta Topic Disease: DiabetesT2 Alt Dis: Alzheimers Target: GSK3beta Cmpd: SB44121 CE: DBP Team: GSK3 Team Person: John Related Set Path: WNT Drug Discovery Dashboard http://www.w3.org/2005/04/swls/BioDash

  47. Bridging Chemistry and Molecular Biology Semantic Lenses: Different Views of the same data BioPax Components Target Model urn:lsid:uniprot.org:uniprot:P49841 Apply Correspondence Rule:if ?target.xref.lsid == ?bpx:prot.xref.lsidthen ?target.correspondsTo.?bpx:prot

  48. Bridging Chemistry and Molecular Biology • Lenses can aggregate, accentuate, or even analyze new result sets • Behind the lens, the data can be persistently stored as RDF-OWL • Correspondence does not need to mean “same descriptive object”, but may mean objects with identical references

  49. Non-synonymous polymorphisms from db-SNP Pathway Polymorphisms • Merge directly onto pathway graph • Identify targets with lowest chance of genetic variance • Predict parts of pathways with highest functional variability • Map genetic influence to potential pathway elements • Select mechanisms of action that are minimally impacted by polymorphisms

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