Drug Safety and Efficacy Note on CDISC's Study Data Tabulation Model (SDTM)

W3C HCLS Interest Group Note 01 11 2007

This version:

http://www.w3.org/2001/sw/hcls/NOTE_DSE_20071108

Latest version:

http://www.w3.org/2001/sw/hcls/NOTE_DSE_20071108

Editors:

Eric Neumann, Clinical Semantics Group

Authors and Contributors:

see Acknowledgments

Copyright © 2006-2007 W3C^® (MIT, ERCIM, Keio), All Rights Reserved. W3C liability, trademark and document use rules apply.

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Abstract

CDISC's Study Data Tabulation Model (SDTM) is used to define the study components in terms of domains and observations for a given clinical trial study. However, the ability to use it for sets of biomarkers that serve to define surrogate endpoints and/or evidence of mechanism is not currently not possible / or not well described. We intend to propose an augmentation for the SDTM model using RDF-OWL that will support the inclusion of biomarker data and genotyping from subjects, associated with known mechanisms and endpoint descriptors.

Task Force Charge

This HCLSIG task force focuses on the topic of “applying semantics to R&D Informatics efforts in support Drug Safety and Efficacy” within clinial trials, as well as post-market surveillance. We also intend to demonstrate how Semantic Web standards can be applied to issues related to these in the near-term. Specifically, the task force focuses on the following areas for scenarios and activities: Identify/address challenges and needs regarding Biomarkers and Pharmacogenomics in coordination with FDA guidelines Semantic applications around Drug Safety: Signals and Notification Possible applications of Semantic Web in Clinical Trial planning, management, analysis, and reporting (e.g., EDC and EHR Single-Source, data security, integrity) Facilitating electronic submissions as per the Common Technical Document (eCTD) specifications, http://www.fda.gov/cder/guidance/7087rev.htm ) X Use Cases document to illustrate, in detail, the techniques XX provides for associating documents with appropriate instructions for extracting any embedded data.

Introduction

This HCLSIG task force (DSE) focuses on the topic of “applying semantics to R&D Informatics efforts in support Drug Safety and Efficacy” within clinical trials, as well as post-market surveillance. We also intend to demonstrate how Semantic Web standards can be applied to issues related to these in the near-term. Specifically, the task force focuses on the following areas for scenarios and activities [HCLS].

Background

Digital data from both Non-clinical (animal) and Clinical Studies needs to be organized according to the following areas:

• Study (sponsor, phase, design, objectives)
• Subject (selection criteria, background med info)
• Observations (classified as Findings, Events, and Interventions)

Task Objectives

The objectives focused on a few key items related to the SDTM model and possible extensions to it: Develop and document Scenarios for some of the above identified areas Identify and validate some initial Best Practices for handling safety and efficacy information through semantics, which incorporate current vocabulary conventions Create one or more public Semantic Web-based Demonstrations (see Clinical Trial Demo) Coordination and collaboration with relevant organizations, possibly CDISC, ICH, HL7-RCRIM, EMEA, FDA, NCI-caBIG

• CDISC data repository terms from NCI, currently just strings, but should be URIs
• SDTM Code lists should be both human readable AND machine processable
• Consider transforming SDTM into a graph extensible form (SDGM?)
• Classify Observation types in SDTM per Events, Findings, etc.; offer a mechanism to associate patient measurement context (BP- 90/50; while: patient_is_laying_down)
• Define URI for SDTM entities and concepts with appropriate authority association
• SDTM is currently stateless (was data updated?), so the inclusion of state (version) info into SDTM RDF model is important
• Inclusion of provenance data from clinical (non-repudiability?)

The requirement to convert ODM/XML to RDF may not be approach the problem by addressing SDTM elements; data + metadata , codelists and definitions embedded in one study, instead use references to metadata and defs.

Rationale

The use of information to improve the development of Efficacious and Safe Drugs rests on the proper and timely utilization of diverse information sets, and the adoption and compliance of well-defined policies. As information becomes more diverse and policies more central to the pharmaceutical industry, the development of information systems that are better suited to handle multiple information types (data and ontologies) while complying with defined policies (rules and actions) will become essential. Semantic Web technology standards offer potential solutions for: Aggregating Study Datasets, around Biomarkers (and following eCTD guidelines) Enhancing management of non-clinical and clinical controlled vocabularies that will be certainly expanding and evolving (adaptability) Providing fast access to current safety information though semantic-enabled channels (Pharmacovigilance) Applying Rules, Integrity, and Security in support of policy compliance and management (HIPAA, CFR21Part11 and Sarbanes-Oxley)

Use-Case Context

The Study Data Tabulation Model (SDTM) is used to define the study components in terms of domains and observations for a given clinical trial study. However, the ability to use it for sets of biomarkers that serve to define surrogate endpoints and/or evidence ofd mechanism is not currently possible. We intend to propose an augmented SDTM model using RDF-OWL that will support the inclusion of biomarker data from subjects, associated with known mechanisms and endpoint descriptors.

Scenario 1: Genetic Diagnostic CT

1. Phase IIb Clinical Trial Design that includes Amplichip CYP 450 and Colon Cancer Diagnostic Chips, used to identify
which CYP alleles present for drug metabolism; used to screen candidates for placement in different CT arms linkage analysis for colon cancer contributors; for population segmentation and responder analyses
2. Candidate samples (for CYP-allele based recruitment) at last candidate screening visit
3. Study Samples at second visit taken for colon marker analysis and possible later genome-wide analysis
4. EDC on a CT study that will use genotype (biomarkers) to track potential tox signals
5. Raw data SDTM bundling and linking of observations with genotype (from GeneChip data)
6. Analysis of SDTM data between interventions and outcomes (and genertation of ADaM ); statistics, correlation, and association
7. Final bundling of analyzed data and CT interpretations
8. Storage of Clinical findings for clinical mining by other investigations

Example in N3 of SDTM Model with context extensions

The following examples are work in progress (collaborative whiteboard) of how to define and organize clinical data ala the SDTM model using an RDF approach. N3 is being used here to make editing and comprehension easier. Some basic syntactical rules are reviewed here:

• a => rdf:type
• Blank Nodes: :subj :prop1 [ :prop2 :obj ; ... ] => blank nodes have no URI!
• Lists: ( :john :sue :kim ) => [ :first :john ; [ :first :sue ; [ :first :kim ]]]

	@prefix cdisc: <http://www.cdisc.org/sdtm/vocab> . 
	@prefix dse: <http://www.w3.org/2001/sw/hcls/dse> . 
	@prefix nci: <http://nci.nih.gov/cadsr/vocabulary> . 
	@prefix nist: <http://nist.gov/units> . 
	@prefix time <http://www.w3.org/2006/time> . 

	//  Sex Text Code: 'MALE', 'FEMALE', 'UNKNOWN', 'Intersex'

	<http://clinic.com/study/T2271>   
	            a cdisc:Study ;
	            cdisc:subject <http://clinic.com/study/T2271/subject/S83221> ;
	            cdisc:subject <http://clinic.com/study/T2271/subject/S74343> ;
	        ...   .

	<http://clinic.com/study/T2271/subject/S83221> 
	            a cdisc:Subject ;
	            nci:sex_code   nci:Female ;
	     //  here I assume cdisc:Diastolic_BP is a subproperty of cdisc:VSTest --
	            cdisc:observation <http://clinic.com/study/T2271/subject/S83221/observation/O6622> ;
	            cdisc:observation <http://clinic.com/study/T2271/subject/S83221/observation/O6561> ;
	    ...   .

	<http://clinic.com/study/T2271/subject/S83221/observation/O6622>
	        a cdisc:Diastolic_BP ;  
	        cdisc:obs_context  cdisc:patient_lying ;
	        cdisc:obs_value  "98" ;
	        cdisc:obs_units  nist:mmHg .

	<http://clinic.com/study/T2271/subject/S83221/observation/O6561>
	        a cdisc:Pulse ;         
	        cdisc:obs_context  cdisc:patient_lying ;
	        cdisc:obs_value  "64";
	        cdisc:obs_units  nist:bpm .	

Bundling Diastolic_BP, Systolic_BP, and Pulse together under one observation could be done in the following way...

	<http://clinic.com/study/T2271/subject/S83221/observation/O6622>
	        a cdisc:Vital_sign ;   // cdisc:Vital_sign is a subclass of cdisc:Observation
	        cdisc:obs_context  [ cdisc:position cdisc:patient_lying ; cdisc:note cdisc:patient_fainted ; cdisc:patient_status cdisc:non_critical . ] ;

	        cdisc:diastolic [ a cdisc:Diastolic_BP ;  
	           cdisc:obs_value  "98" ;
	           cdisc:obs_units  nist:mmHg .
	        ] ;   
	        cdisc:systolic [ a cdisc:Systolic_BP ;  
	           cdisc:obs_value  "152" ;
	           cdisc:obs_units  nist:mmHg .
	        ] ;   
	        cdisc:pulse [ a cdisc:Pulse ;         
	           cdisc:obs_value  "64";
	           cdisc:obs_units  nist:bpm . 
	        ] .
	

• Treatment
• Vital Sign
• Adverse Event

	<http://clinic.com/study/T2271/subject/4183542663506> 
	            a cdisc:Subject ;
	            nci:sex_code   nci:Female ;
	            cdisc:treatment <http://clinic.com/study/T2271/subject/4183542663506/observation/O2241> ;
	            cdisc:vitalSigns <http://clinic.com/study/T2271/subject/4183542663506/observation/O6561> ;
	            cdisc:adverseEvent <http://clinic.com/study/T2271/subject/4183542663506/observation/O6622> ;


	// ROUTE        DRGGROUP        DOSE    pid     treatment       tpfday tptday
	// IV   B       7 MG    4183542663506   7mg then 14mg SEMWEB 6/11/84 7/11/84
	<http://clinic.com/study/T2271/subject/S83221/observation/O2241 >
	        a cdisc:Treatment ;   // cdisc:Treatment is a subclass of cdisc:Observation
	        cdisc:design_arm  <http://clinic.com/study/T2271/treated_B/double_dose> ;
	        dse:route cdisc:IV_route ;
	        dse:drug_group "B" ;
	        cdisc:dose "7" ;
	        cdisc:dose_units nist:mg ;
	        cdisc:treatment "7mg then 14mg SEMWEB" ;
	        cdisc:first_date "6/11/84" ;
	        cdisc:term_date "7/11/84" ;

	... // How best to define Treatments and Experimental Design ? using cdisc:design_arm to link back to design graph?


	// VTLTEXT      VTLRES  VISIT_ID        pid     collday related
	// Standing Diastolic BP (mmHg) 75      BASELINE 4183542663506 6/11/84 1
	<http://clinic.com/study/T2271/subject/S83221/observation/O6561 >
	        a cdisc:Vital_sign ;   // cdisc:Vital_sign is a subclass of cdisc:Observation
	        cdisc:visit_id cdisc:BASELINE ;
	        cdisc:visit_date "6/11/84" ;
	        dse:obs_context  [ cdisc:position cdisc:patient_standing  . ] ;

	        cdisc:diastolic [ a cdisc:StandingDiastolic_BP ;  
	           dse:vtltext  "Standing Diastolic BP (mmHg)" ;
	           dse:related_measure  "1" ;
	           dsecdisc:obs_value  "75" ;
	           dse:obs_units  nist:mmHg .
	        ] .   


	// pid  AEFDAY  AETDAY  AESEV   AESEVT  AESER   AESERT PREFTEXT BODYTEXT
	// 4183542663506        6       9       2       MODERATE        2       NO ABDOMEN ENLARGED BODY AS A WHOLE
	<http://clinic.com/study/T2271/subject/S83221/observation/O6622 >
	        a cdisc:Adverse_Event ;   // cdisc:Adverse_Event is a subclass of cdisc:Observation
	        cdisc:visit_id cdisc:BASELINE ;
	        time:first_date "6" ;
	        time:term_date "9" ;
	        time:duration_days "2" ;
	        dse:severity AE:MODERATE ;
	        dse:rating "2" ;
	        dse:RT "NO" ;
	        dse:prefText "NO ABDOMEN ENLARGED" ;
	        dse:bodyText "BODY AS A WHOLE" ;
	        dse:obs_context  [ cdisc:position cdisc:patient_standing  . ] .
		

Discussion regarding CDISC SDTM Code lists

Below, in the related resources section, two examples are attached on how the current XML output looks like from CDISC usage of NCI caDSR for the so called SDTM Controlled Terminologies. These inlude the permissable values as strings to be incorporated in SDTM datasets, e.g:

• Sex Text Code: 'MALE', 'FEMALE', 'UNKNOWN', 'Intersex'
• Vital Signs Test Name Text Name: 'BMI', 'DIABP', 'TEMP', ...

• Today CDISC SDTM standard use short names/terms as strings such as 'DIABP' for "the topic for the focus of the observation" in SDTM datasets. And also for the categorical observation results such as 'FEMALE' for the observation of Sex. These strings are in NCI caDSR linked to concept codes in NCI Thesaurus, such as C25299 for the concept with the name diastolic_pressure as a sub-class of the super-class 'Personal Attribute' with a defintion from UMLS.
• Currently NCI publish the content in NCI Thesaurus as a large OWL file with URI:s assigned for all NCI concepts. For example this one: http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#Diastolic_Pressure (NB links to a very large owl file). In the future this will probably be changed to: http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#C25299 (NB links to a very large owl file). Potentially this could be used as a URI for the concept describing the medical phenomena of diastolic blood pressure
• In the future I would like to have namespaces with URIs assigned for groups of different Types of Observations as recordings of medical phenomena, but also as recordings of actions such as treatments and recordings of adverse events. For basic ones such as the observation of blood pressure and other kinds of vital signs, but also for other more interesting "surrogate endpoints and/or evidence of mechanism".

This would enable the publication of observation types ontologies. As a formal descriptions of the required patient and measurement context such as these for the measurement of blood pressure:

• the position of the patient at the time of measuring (sitting, lying, etc.),
• the tilt of the surface on which the person is lying,
• the variation in measured blood pressure with respiration,
• the instrument used to measure the blood pressure,
• the size of the cuff if a sphygmomanometer is used,
• The above list is taken from a HL7 Watch blog posting by Barry Smith

Such observation types ontologies could also be the place to describe the different classifications such as this type of observation 'is-a-Metabolic_Marker', according to nciOncology and 'is-a-Finding', according to SDTM general classes.

Clinical Trial Data View Demo

This demo illustrates how clinical trials data formatted as RDF can be visualized. It takes advantage of MIT's SIMILE's Exhibit technology, and shows how easy it is to merge and visualize aggregated data (graphs) through the Web. The data was obtained from four separate SDTM generated Excel documents, that were converted and merged using SIMILE's BABEL utility.

Strategy and Technology

• RDF mappings
• URI syntax and definitions
• OWL Ontologies or SKOS Vocabularies
• Data and Type Validators

Related resources

• CDISC Controlled Terminilogies published in NCI caDSR , examples of the human-readable values as strings
• CDISC_CT_Sex.xml Sex Text Code
• CDISC_CT_VitalSigns.xml Vital Signs Test Name Text Name
• NCI_Thesaurus_Sex.owl Sex OWL Classes
• NCI_Thesaurus_VitalSigns.owl Vital Signs OWL Classes

Summary

CDISC's SDTM model can be mapped into an RDF based model, provided that some key data entities (e.g., Subjects, Observations, Interventions) are specified using URIs. There are clear advantages of using such a model, since additional links and metadata can easily be extended to any study set.

The more complex issues we have not yet addressed include an appropriate mapping of terminology codes and strings into OWL or SKOS defined URI entities.

HCLSIG/Drug Safety and Efficacy/SDTM Notes Draft 1.0 (last edited 2007-11-05 08:40:14 by 212)

Further Information

This concludes the SDTM Note. Further DSE discussions can be found at http://esw.w3.org/topic/HCLSIG/Drug_Safety_and_Efficacy.

References

[CDISC]

Clinical Data Interchange Standards Consortium (CDISC) is an open, multidisciplinary, non-profit organization that has established worldwide industry standards to support the electronic acquisition, exchange, submission and archiving of clinical trials data and metadata for medical and biopharmaceutical product development. The mission of CDISC is to develop and support global, platform-independent data standards that enable information system interoperability to improve medical research and related areas of healthcare.

[NCI Thesaurus]

NCI Thesaurus as a large OWL file , Mindswap, Maryland Information and Network Dynamics Lab Semantic Web Agents Project.

[NCICB]

NCI Center for Bioinformatics , Welcome to the NCI Center for Bioinformatics.

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Acknowledgements

The editor would like to thank the following Working Group members for authoring this document:

• Eric Neumann (formerly Teranode)
• ted.slater@pfizer.com Ted Slater (Pfizer)
• jmcgurk@daiichisankyo-us.com Jim McGurk (Daiichi Sankyo)
• Bo.H.Andersson@astrazeneca.com Bo Andersson (AstraZeneca)
• Kerstin.L.Forsberg@astrazeneca.com Kerstin Forsberg (AstraZeneca)
• Stephen.Dobson@pfizer.com Stephen Dobson (Pfizer)

This document is a product of the Drug Safety and Efficacy Task Force of the HCLS Interest Group.

Glossary

• Biomarkers: A biological characteristic that is objectively measured and evaluted as an indicator of normal biological or pathologenic processes or pharmacological responses to a therapeutic intervention; these can be obtained from microarray analysis, protein assays, or even clinical signs. (see FDA definition and EMEA definition)
• Pharmacogenomics: The branch of pharmaceutics which deals with the influence of genetic variation on drug response, efficacy or toxicity.
• Bench-to-Bedside: A proposed vision for healthcare research that would accelerate the development of new treatements through more direct use of R&D knowledge.
• Translational medicine: A proposed vision of Drug R&D that fosters better association of animal and human studies in order to make better decision in the development process taht would reduce costs and increase time-to-market.
• Safety Signals: Any solid indicators that can provide early warning to adverse drug reactions.
• Pharmacovigilance: The pharmacological practice of the detection, assessment and prevention of adverse effects, particularly long term and short term side effect, through the monitoring and analysis of data, even after a product hase been launched (Postmarketing pharmacovigilance). For more info see http://en.wikipedia.org/wiki/Pharmacovigilance

Change Log

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