The document discusses knowledge management systems used in clinical trials at the Immune Tolerance Network (ITN). It describes the ITN's mission to advance immune tolerance therapies through high-quality clinical trials integrated with mechanistic research. It outlines some of the key challenges around balancing adaptability and reusability and evolving technologies with data longevity. It also describes some of the key disciplines and components of ITN's knowledge management system including knowledge representation, acquisition, and delivery as well as transactional systems, data annotation, and reporting.

1. KNOWLEDGE MANAGEMENT SYSTEMS IN CLINICAL TRIALS David Parrish Director, Biomedical Informatics Immune Tolerance Network

2. TRANSLATIONAL INFORMATICS AT THE ITN The ITN: At the crossroads of Clinical and Translational Science An international research consortium designed to accelerate the clinical development of immune tolerance therapies. ITN Mission: To advance the clinical application of immune tolerance by performing high quality clinical trials of emerging therapeutics integrated with mechanism-based research. In particular, we aim to: establish new tolerance therapeutics develop a better understanding of the mechanisms of immune function and disease pathogenesis identify new biomarkers of tolerance and disease Each ITN clinical trial incorporates a series of “mechanistic” investigations that complement the clinical study and provide biological insight into disease pathogenesis, immunological effects of therapy, etc.

3. INFORMATIONAL MANAGEMENT CHALLENGES (just) Two Key Dichotomies Adaptability verses Reusability (standardization) We encourage and support innovative and diverse design approaches While needing shared systems and uniform methods for managing the implementation of studies and evaluating results Evolving technologies verses Data Longevity (annotation) We include mechanistic assay technologies that are rapidly evolving or being developed as the study progress ..or do not yet exist While expecting the data to be useful in 5 -10 years

4. DISCIPLINES OF INFORMION MANAGEMENT Data Operations vs. Knowledge Management Knowledge Representation Knowledge Acquisition Knowledge Delivery Master Data Management Transactional Systems Data Annotation Reporting Clean Structure Store Collect Data Cleaning

5. DIA PRESENTATION 2009 Knowledge Representation Knowledge Acquisition Expert Knowledge Specification

6. DIA PRESENTATION 2009 Knowledge Representation Knowledge Acquisition Expert Knowledge Specification Reference ontology –OWL/ BRIDG Implementation Model -EF Local Library

7. PROTEGE We have built the Epoch ontologies using Protégé OWL editor

8. Ontologies Epoch Reference Protégé Plug-In

9. ITN KNOWLEDGE MANAGEMENT SYSTEM Trial Designer (Trial Wiz) Epoch– Owl Files Elements and Relationships Required to Described the Study Relational Data Store Act: Activities Org: Organization Assay: Assay Con: Constraint Mes: Measurement Lab: labware CTO: Clinical Trials Tmp: Temporal Swrl: Swrl Trial Designer Specialized OWL file Common Data Structure

10. DIA PRESENTATION 2009 Knowledge Representation Knowledge Acquisition Expert Knowledge Specification Reference Model -BRIDG Implementation Model -EFM Static Library Activities Groups Timings

11. COMPONENTS OF THE APPLICATION User interface Components 1 2 3

12. Specifying Trial Design in the knowledge system – TrialWiz Designer A USER INTERFACE FOR THE CLINICAL RESEARCHER

13. DIA PRESENTATION 2009 Knowledge Representation Knowledge Acquisition Expert Knowledge Specification Reference Model -BRIDG Implementation Model -EFM Static Library Activities Groups Timings

14. IMMUNOTRAK ImmunoTrak Specimen Tracking

15. ITN PVT APPLICATION Patient visits

16. DIA PRESENTATION 2009 Knowledge Representation Knowledge Acquisition Expert Knowledge Specification Data Representation Data Value Set Specification Activities

17. DIA PRESENTATION 2009 Knowledge Representation Knowledge Acquisition Expert Knowledge Specification Data Representation Data Value Set Specification Activities Data Element Concept Conceptual Domain Data Element Value Domain Concepts Data 0..* 1..1 0..* 1..1 0..* 1..1 0..* 1..1

18. More on metadata mgmt

19. DIA PRESENTATION 2009 Knowledge Representation Knowledge Acquisition Expert Knowledge Specification Data Representation Data Value Set Specification CaDSR Metadata Repository Local Metadata Repository Knowledge Acquisition Data Concepts Structure Content BRIDG Implementation Model -EF Local Library

20. DIA PRESENTATION 2009 Knowledge Acquisition Expert Knowledge Specification Data Value Set Specification CaDSR Local Metadata ODM/CDASH ++ BRIDG Implementation Model -EF Local Library

21. DIA PRESENTATION 2009 Knowledge Representation Knowledge Acquisition Expert Knowledge Specification Data Data Representation Data Value Set Specification Concepts Structure Content

22. USE CASE FOR TODAY Patient Tracking and Specimen Collection and Processing Transactional systems , Workflow aware: allow the information in the system to support process automation, the more general the specification of activities, the less detailed the annotation. PVT ImmunoTrak

23. DISCIPLINES OF INFORMION MANAGEMENT Data Operations vs. Knowledge Management Knowledge Representation Knowledge Acquisition Knowledge Delivery Master Data Management Transactional Systems Configuration Data Annotation Reporting Clean Structure Store Collect Data Cleaning

24. USE CASE Clinical Data Integration

28. BRIDG Study Design Activity Epoch Study Cell Arm Arm SOA Protocol Subject Subject Encounter Segment

29. METADATA MANAGEMENT – ISO 1179 Data Element Perception Measure Associated or not Domain Data Element Concept Conceptual Domain Data element Value Domain Perception Representation Concrete Abstract 0..* 1..1 0..* 1..1 0..* 1..1 0..* 1..1

30. Interventional Study Observational Study Expanded Access Study Study Protocol

31. Study Protocol

32. National Institute of Allergy & Infectious Diseases Funded by: Juvenile Diabetes Research Foundation National Institute of Diabetes & Digestive & Kidney Diseases