1. 2007 CDISC International Interchange Ontologies in Clinical Research: Representation of clinical research data in the framework of formal biomedical ontologies Richard H. Scheuermann Chief, Division of Biomedical Informatics U.T. Southwestern Medical Center

2. Outline Motivation - US NIH Clinical and Translational Science Award (CTSA) Ontologies and the Open Biomedical Ontologies (OBO) Foundry Ontology for Biomedical Investigations (OBI) Ontology for Clinical Investigations (OCI) –Approach –Current status –Future direction

3. Clinical and Translational Science Award (CTSA)  Implementing biomedical discoveries made in the last 10 years demands an evolution of clinical science.  New prevention strategies and treatments must be developed, tested, and brought into medical practice more rapidly.  CTSA awards will help to lower barriers between disciplines, and encourage creative, innovative approaches to solving complex medical problems.  These awards will catalyze change -- breaking silos, breaking barriers, and breaking conventions.

4. Building a National CTSA Consortium

5. Trial Design Advanced Degree-Granting Programs Participant & Community Involvement Regulatory Support Biostatistics Clinical Resources Biomedical Informatics Clinical Research Ethics CTSAHOME NIH & other government agencies Healthcare organizations Industry Each academic health center will create a home for clinical and translational science

6. Data management - to develop a comprehensive controlled information system infrastructure to capture and manage clinical and translational research data Data integration - to integrate clinical and translational research data with data and knowledge from external public database resources Data analysis - to support clinical and translational research data analysis by providing state-of-the-art software analytical tools Support - to provide training and support for CRIS use Clinical Research Information System

7. Clinical Research Information Systems Protocol Design Protocol Design Statistical Endpoint Analysis Case Report Form Development Visit Management Subject Enrollment & Consenting Clinical Data Capture Laboratory Experimentation Specification Phase Implementation Phase Analysis Phase Sample Procurement & Processing Sample Procurement & Processing Consent Form Development Consent Form Development IRB Submission & Approval Grant Proposal Development Grant Proposal Development Enrollment Criteria Specification Subject Identification & Recruitment Agency & Scientific Reporting Laboratory Results Analysis Integrative Data Analysis Workflow Stakeholders Principal Investigator Principal Investigator IRB Grants Management Grants Management Research Coordinator Research Coordinator Study Subject Study Subject Lab Personnel Lab Personnel Principal Investigator Principal Investigator Study Monitor Study Monitor Data & Statistics Analyst Data & Statistics Analyst Database Analyst Database Analyst Study Sponsor Study Sponsor Functions Feasibility Study

8. Requirements Accurate Representation –therapeutic drug as a design variable vs. medical history –DNA as a therapeutic agent vs. analysis specimen Interoperability –unambiguous data exchange between research sites –effective data exchange between software applications Customization –support of study-specific details Dynamics –role changes throughout and between studies –eligibility criteria to relevant clinical phenotype Inference –semantic queries (e.g. patients with autoimmune disease) Meta-analysis –studies with common features (e.g. all studies where flu vaccine was evaluated as a conditional variable)

9. Constraints Essential to build upon and extend, or map to, existing and emerging data standards (e.g. HL7, CDISC) and relevant vocabularies (e.g. ICD-9/10, NCI Thesaurus, SNOMED-CT) Recognize the difference between medical (hospital) IT and biological (science) IT Support wide variety of different clinical and translational study types - reduce complexity by modeling commonalities Support needs of multiple stakeholders - different uses of same data Standards should be easy to implement and use Standards need to be easily and logically extensible Support clinical research data use cases

10. Need for standard representations Data standards + Common vocabularies + Extensible data model = Data interoperability Description Framework

11. Data Standards and Interoperability: Minimum Information Sets - CDISC Codelists, MIBBI Vocabularies & Ontologies - ICD-9/10, SNOMED, LOINC, NCI Thesaurus, OBO Foundry Object Models - CDISC, HL7 RIM, BRIDG, FuGE Exchange Syntaxes - HL7, XML, RDF

12. Definition of “Ontology” Philosophical “The study of that which exists” (ISMB 2005) “The science of what is: of the kinds and structures of the objects, and their properties and relations in every area of reality” (ISMB 2005) Information/computer scientists “A shared, common, backbone taxonomy of relevant entities, and the relationships between them, within an application domain” (ISMB 2005) “A computable representation of biological reality” (ISMB 2005) “A structured vocabulary” “A formal way of representing knowledge in which concepts are described both by their meaning and their relationship to each other” (Bard 2004) “A data model that represents a domain and is used to reason about the objects in that domain and the relations between them” (Wikipedia)

13. Provide clear thinking about how to structure information Support data integration, modeling, query processing, user interface development, data exchange/export To enforce data correctness To be able to map to database management systems To enables a computer to reason over the data To provide the capability to infer relationships that have not been explicitly defined Ontology Goals

14. The OBO Foundry - 2006 The OBO foundry is a set of interoperable ontologies that adhere to a growing set of principles set forth for best practices in ontology development

15. The OBO Foundry

16. 16 RELATION TO TIME GRANULARITY CONTINUANTOCCURRENT INDEPENDENTDEPENDENT ORGAN AND ORGANISM Organism (NCBI Taxonomy / placeholder) Anatomical Entity (FMA, CARO) Organ Function (placeholder) Phenotypic Quality (PaTO) Biological Process (GO) CELL AND CELLULAR COMPONENT Cell (CL) Cellular Component (FMA, GO) Cellular Function (GO) MOLECULE Molecule (ChEBI, SO, RnaO, PrO) Molecular Function (GO) Molecular Process (GO) Initial OBO Foundry Ontologies building out from the original GO

17. 17 Mature OBO Foundry ontologies (now undergoing reform) Cell Ontology (CL) Chemical Entities of Biological Interest (ChEBI) Foundational Model of Anatomy (FMA) Gene Ontology (GO) Phenotypic Quality Ontology (PaTO) Relation Ontology (RO) Sequence Ontology (SO)

18. 18 Ontologies being built to satisfy Foundry principles ab initio Ontology for Clinical Investigations (OCI) Common Anatomy Reference Ontology (CARO) Environment Ontology (EnvO) Ontology for Biomedical Investigations (OBI) Protein Ontology (PRO) RNA Ontology (RnaO) Subcellular Anatomy Ontology (SAO) Disease Ontology (DO)

19. Disease Ontology

20. OBO Foundry provides a suite of basic science Reference Ontologies designed to serve as modules for re-use in Application Ontologies such as: Infectious Disease Ontology Immunology Ontology Multiple Sclerosis Ontology Mammalian Adult Neurogenesis Ontology 20

21. Ontology for Biomedical Investigations - Overview  International collaboration (since 2006) Communities developing ontologies/terminologies - Unambiguous description of how the investigation was performed - Consistent annotation, powerful queries and data integration  Describe the laboratory workflow Set of universal terms - Investigation (organization, intent, design etc) - Material (biological and chemical, manipulation and transformation) - Protocols and instrumentations - Data generated and types of analysis performed on it Set of biological and technological domain-specific terms - To meet the annotation requirements of any given community (e.g. clinical research)  Part of the Open Biomedical Ontology (OBO) Foundry Orthogonality and x-referencing with existing bio-ontologies 'Interoperable by construction' with those under the Foundry -Including Unit, Quality (PATO), Environment and Chemical (ChEBI) ontologies Agree on an initial structure (trunk) with is_a relationship - Rely on Relation Ontology (RO)

22. OBI – Communities and Structure 1. Coordination Committee (CC): Representatives of the communities -> Monthly conferences 2. Developers WG: CC and other communities’ members Weekly conferences calls 3. Advisors: -> cBiO will oversee the Open BioMedical Ontology (OBO) initiative

23. OBI – Top Level Classes  Continuant: an entity that endure/remains the same through time Independent Continuant: stands on its own E.g. All physical entities (instrument, technology platform, document etc.) E.g. Biological material (organism, population etc.) Dependent Continuant: inheres from another entity E.g. Environment (depend on the set of ranges of conditions, e.g. geographic location) E.g. Characteristics (entity that can be measured, e.g. temperature, unit) - Realizable: an entity that is realized through a process (executed/run) E.g. Software (a set of machine instructions) E.g. Design (the plan that can be realized in a process) E.g. Role (the part played by an entity within the context of a process)  Occurrent: an entity that occurs/unfolds in time E.g. Temporal Regions, Spatio-Temporal Regions (single actions or Event) Process E.g. Investigation (the entire ‘experimental’ process) E.g. Assay (process of performing some tests and recording the results)

24. Ontology for Clinical Investigations Approach Transparency and inclusivity (http://www.bioontology.org/wiki/index.php/OCI:Main_Pa ge; Google “OCI wiki”)http://www.bioontology.org/wiki/index.php/OCI:Main_Pa ge Combined top down/bottom up approach (prospective standardization) –Assembled term lists –Combine terms –Separate homonyms –Combine synonyms –Assigned membership into BFO/OBI branches –Position terms within branches –Define terms Testing

25. OCI Wiki

26. Term lists

27. Homonyms sample size: 1. A subset of a larger population, selected for investigation to draw conclusions or make estimates about the larger population. 2. The number of subjects in a clinical trial. 3. Number of subjects required for primary analysis.

28. Study Design Descriptive research – research in which the investigator attempts to describe a group of individuals based on a set of variable in order to document their characteristics –Case study – description of one or more patients –Developmental research – description of pattern of change over time –Normative research – establishing normal values –Qualitative research – gathering data through interview or observation –Evaluation research – objectively assess a program or policy by describing the needs for the services or policy, often using surveys or questionnaires Exploratory research –Cohort or case-control studies – establish associations through epidemiological studies –Methodological studies – establish reliability and validity of a new method –Secondary analysis – exploring new relationships in old data –Historical research – reconstructing the past through an assessment of archives or other records Experimental research –Randomized clinical trial – controlled comparison of an experimental intervention allowing the assessment of the causes of outcomes Single-subject design Sequential clinical trial Evaluation research – assessment of the success of a program or policy –Quasi-experimental research –Meta-analysis – statistically combining findings from several different studies to obtain a summary analysis

29. Assign membership into BFO/OBI branches

30. Biological marker (CDISC) Study populations (CDISC) Trial coordinator (CDISC) Study variable (CDISC) Drug (RCT) Subject (MUSC)

31. Case report form (CDISC) Patient file (CDISC) Consent form (CDISC) New drug application (MUSC) Investigational new drug application (MUSC)

32. Meta-analysis (CDISC) Quality assurance (CDISC) Quality control (CDISC) Baseline assessment (CDISC) Validation (CDISC) Coding (MUSC) Permuted block randomization (MUSC) Secondary-study-protocol (RCT) Intervention-step (RCT) Blinding-method (RCT) Study design Development plan (CDISC) Standard operating procedures (CDISC) Statistical analysis plan (CDISC)

35. Future directions Engage more stakeholders Direct collaboration with organizations such as CDISC Continue development Evaluation approaches and metrics –Based on scientific use cases –Categories of use cases Interoperability –Data exchange –Accuracy of representation –Homonyms and context; ontology helps us do that Reasoning and inference –Test with CTSA IT Project (trial registration)

36. OCI Working Group Jennifer Fostel Richard Scheuermann Cristian Cocos W. Jim Zheng Wenle Zhao Herb Hagler Jamie Lee Matthias Brochhausen Amar K. Das Dave Parrish Barry Smith Trish Whetzel