# Presentation: 686047

## Semantic Interoperability of Time-Oriented and Workflow Information among Clinical Trials Management Applications

**Amar Das, MD, PhD**
**Assistant Professor**
**Stanford Medical Informatics**
**Stanford University**
**das@stanford.edu**

**Notes:**
The increasing complexity of clinical trial has generated an enormous requirement for knowledge and information management at all stages of the clinical trials. In this presentation, I give an overview of the collaborative efforts between our group at Stanford and the Immune Tolerance Network to build a knowledge-based system to manage clinical trials.

## The need for semantic interoperability 

**Epoch ontological framework **

**Illustrative examples of using the ontologies**

**Overview**

## Stanford Medical Informatics
	Ravi Shankar	Martin O’Connor
	Susana Martins	Noah Zimmerman

Immune Tolerance Network 
	Dave Parrish	Kathy McCafferty
	Keith Boyce		Chuck Borromeo
	Manizhe Payton	Trang Gisler
	Peter Sayre		
	... and many others ...

Cimarron Software, Inc.
	Dave Fuhrman	Rob Sargent
	Debi Koltenuk	Chris Council

**Stanford Medical Informatics**
**	****Ravi Shankar****	****Martin O’Connor**
**	****Susana Martins****	****Noah Zimmerman**
**Immune Tolerance Network**** **
**	****Dave Parrish****	****Kathy McCafferty**
**	****Keith Boyce****	****	****Chuck Borromeo**
**	****Manizhe Payton****	****Trang Gisler**
**	****Peter Sayre****	****	**
**	****... ****and many others**** ...**
**Cimarron Software, Inc.**
**	****Dave Fuhrman****	****Rob Sargent**
**	****Debi Koltenuk****	****Chris Council**

**Acknowledgements**

## Semantic interoperation in clinical trials management

**Semantic interoperation in clinical trials management**

- Clinical trials are used to determine whether new drugs or treatments are both safe and effective
- Complex clinical trials involve collaboration among many groups using different software applications
- Lack of standardization and reduced efficiency leads to poor productivity

## Immune Tolerance Network 

**Immune Tolerance Network **

- International collaboration
- Investigator-initiated clinical trials of novel tolerance-promoting therapies in
- Autoimmune diseases
- Transplantation
- Allergy and Asthma
- Provides services to undertake comprehensive mechanistic studies that complement each trial

## Major stages in an ITN clinical trial are study specification, implementation, and assessment

## Personnel and applications interoperate to manage a clinical trial

***CRO***

***Protocol***
***Group***

***Assay ***
***Group***

***Cimarron***

***Operations***
***Group***

***Tubes ***
***Manufacturer***

***Data ***
***Center***

**ImmunoTrak**

**Clinical Trial Data**

***Core***
***Labs***

***Queries***
***Reports***

## Trial information generated by different groups depend heavily on each others information

***CRO***

***Protocol***
***Group***

***Assay ***
***Group***

***Cimarron***

***Operations***
***Group***

***Tubes ***
***Manufacturer***

***Data ***
***Center***

**Clinical Trial Data**

***Core***
***Labs***

***Queries***
***Reports***

## A variety of questions are posed at different stages of clinical trial management

**A variety of questions are posed at different stages of clinical trial management**

**Specification stage**

**Implementation stage**

**Assessment stage**

## Example questions at the specification stage

**Is there another protocol similar to this one?**
**What mechanistic assays are planned?**
**What specimens need to be collected for performing this assay?**
**What tubes can be used to collect the specimens?**

## Example questions at the implementation stage

**Example questions at the implementation stage**

**Where should this specimen container be shipped?**
**What is the next visit for this participant?**
**At which visits are mechanistic assays performed?**
**What specimen containers should be used for this specimen for this assay?**

## Example questions at the assessment stage

**How many participants are in the follow-up period?**
**What are the results of an assay for a specific participant before transplant?**
**What is the enrollment data by sites for these four trials?**
**What are the total number of specimens accessioned?**

## Are reconstituted populations of immune cells following high dose immunosuppression therapy (HDIT) and autologous hematopoietic stem cell transplant (HCT) functionally competent?

**For all trials where HDIT and HCT were used as the intervention, return all pre- and post-intervention immunotyping results (absolute numbers of major cell subsets) done on fresh PBMC using flow cytometry analysis.**

**Are reconstituted populations of immune cells following high dose immunosuppression therapy (HDIT) and autologous hematopoietic stem cell transplant (HCT) functionally competent?**

- query

- answer

## Enterprise-wide knowledge is not formally encoded, leading to management challenges

**Verifying data integrity is difficult**
**Significant efforts needed to resolve inconsistencies after a trial has started**
**Performing data integration is even harder**
**Difficult to perform complex analyses across multiple trials **
**Overall inefficiency at every stage of the clinical trial**

## We impose a suite of ontologies upon  these interactions to bring order and standardization 

***CRO***

***Protocol***
***Group***

***Assay ***
***Group***

***Cimarron***

***Operations***
***Group***

***Tubes ***
***Manufacturer***

***Data ***
***Center***

**Clinical Trial Data**

***Core***
***Labs***

***Queries***
***Reports***

**Epoch**** ****Ontologies**

## Epoch ontologies provide the semantic foundation for the groups and applications interoperate

**Epoch ontologies provide the semantic foundation for the groups and applications interoperate**

***Data ***
***Center***

## Our ontological work has three main goals

**Design tools to help acquire and maintain knowledge about protocol and assay designs**
**Use this knowledge to drive data collection during a trial**
**Use this knowledge to support database querying and ad hoc data analysis**

## We are building Epoch ontologies based on prior and ongoing modeling work and on ITN requirements

**Epoch Ontologies**

**ITN Requirements**

## Epoch ontologies encapsulate the clinical trial knowledge

**Protocol**
**Model**

- Protocol
- Schema

- Mechanistic
- Study Plan

**Assay**
**Model**

**Specimen**
**Container**
**Model**

- Specimen
- Workflow

**Site**
**Model**

- Specimen
- Container
- Set

**Clinical**
**Model**

- Activity

- Assay

**Virtual **
**Trial**
**Data**
**Model**

- Visit

**Temporal**
**Model**

## The ontologies are specified in OWL using the Protégé-OWL editor

## SWRL (Semantic Web Rule Language) rules are executed using the JESS rule engine

**SWRL (Semantic Web Rule Language) rules are executed using the JESS rule engine**

**JESS**
**Rule Engine**

**Protégé-OWL**

**SWRL Rule**

**Epoch **
**Knowledge Base**

## Epoch Protocol Model consists of concepts and their attributes in the clinical trial domain

## Using generic forms generated by Protégé, instances of a class can be entered

## We are building custom user interfaces to enter and browse specific clinical trials

## Visits Sequence and Specimen Workflow are encoded as graphs

## A library of assays 

## A catalog of specimen containers 

## Virtual Trial Data Model encapsulates relevant trial data elements that can be mapped to the data base

## Visit time window specifies the relative time period by which a visit should start

**Visit time window specifies the relative time period by which a visit should start**

## Visit time window constraint check is done using a SWRL rule

**Check that a participant’s visit started as specified by the start condition of that visit.**

## Using the temporal model to write a more complex constraint on drug administration

**On days that both immunotherapy and omalizumab are administered, omalizumab will be injected 60 minutes after the immunotherapy****. **

## An example — Configuration of ImmunoTrak, a Specimen Workflow Application

## ImmunoTrak Configuration File

## SWRL rules map the concepts in the knowledge base to elements in  the XML file

**Epoch **
**Knowledge Base**

**ImmunoTrak **
**Configuration File**

**SWRL mapping rules**

## We are extending SWRL to support knowledge-driven queries

**The number of participants per study per site per visit**

## An overview of the Epoch architecture 

## Research topics that have emerged from our usage of OWL and SWRL

**Integration of JESS, Jena, and other rule engines with SWRL **
**User friendly interfaces to acquire clinical trial knowledge in terms of SWRL rules**
**Database mapping solutions to access legacy relational data via our virtual data model **

## Semantic Web Stack

## In conclusion, we are building Epoch, an ontological framework to manage clinical trials

**A centralized, modifiable repository of knowledge to drive applications**
**The ability to use reference ontologies to structure ITN knowledge**
**A scaleable architecture that can lead to computer-supported trial design **
**The ability to use logic for inferring relationships among time-oriented and workflow data**