Therapeutic Area Data Standards
User Guide for Virology
Version 2.0 (Draft)
Prepared by the
CFAST Virology v2.0 Standards Team
Notes to Readers
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This is the draft version 2.0 of the Therapeutic Area Data Standards User Guide for Virology.
This document is based on SDTM v1.4 and SDTMIG v3.2.
The TAUG-Virology v2draft package includes this user guide and a set of draft standards of interest to this
document.
Revision History
Date
2015-07-21
2012-12-06
Version
2.0 Draft
1.0 Provisional
Summary of Changes
Draft for Public Review
Released
See Appendix E for Representations and Warranties, Limitations of Liability, and Disclaimers.
CDISC Therapeutic Area Data Standards: User Guide for Virology (Version 2.0)
CONTENTS
1
INTRODUCTION ................................................................................................................. 3
1.1 PURPOSE................................................................................................................................................................ 3
1.2 ORGANIZATION OF THIS DOCUMENT......................................................................................................................4
1.3 CONCEPT MAPS .....................................................................................................................................................5
1.4 CONTROLLED TERMINOLOGY ................................................................................................................................ 5
1.5 RELATIONSHIPS TO OTHER STANDARDS ................................................................................................................6
1.5.1
Summary of Changes from Version 1.0 ..................................................................................................6
1.6 KNOWN ISSUES......................................................................................................................................................6
2
OVERVIEW ........................................................................................................................... 7
3
SUBJECT AND DISEASE CHARACTERISTICS ............................................................ 8
3.1 DIAGNOSIS, CONFIRMATION OF INFECTION, AND VIRUS-TYPING...........................................................................8
3.1.1
Examples for Diagnosis, Confirmation of Infection, and Virus-Typing .................................................9
3.2 VIRUS NOMENCLATURE....................................................................................................................................... 10
3.2.1
Example for Virus Nomenclature ......................................................................................................... 11
4
DISEASE ASSESSMENTS ................................................................................................. 15
4.1 RESISTANCE TESTING .......................................................................................................................................... 15
4.1.1
Examples for Resistance Testing .......................................................................................................... 18
4.2 VIRAL LOAD ........................................................................................................................................................ 22
4.2.1
Examples for Viral Load ...................................................................................................................... 23
4.3 HOST IMMUNE RESPONSE .................................................................................................................................... 24
4.3.1
Examples for Host Immunogenic Response ......................................................................................... 24
5
ANALYSIS DATA................................................................................................................ 26
5.1 SUBJECT-LEVEL VARIABLES ............................................................................................................................... 26
5.1.1
Co-existing Diseases and Viral Co-Infections ...................................................................................... 26
5.1.2
Subgroup Variables ............................................................................................................................... 27
5.1.3
Non-Host Taxonomy at Screening and at Subsequent Time Points ...................................................... 27
5.1.4
Subject-Level Variables Associated with Efficacy Response ............................................................... 28
5.2 ANALYSIS DATA FOR PHARMACOGENOMIC FINDINGS.......................................................................................... 29
5.2.1
Descriptive Variables for Non-Host Taxonomy .................................................................................... 29
5.2.2
Derived Variables for Summary of Results at a Given Location .......................................................... 30
5.2.3
Derived-Flag Variables ......................................................................................................................... 30
5.3 ANALYSIS DATA FOR PHENOTYPIC RESULTS ........................................................................................................ 31
5.4 CONSOLIDATION OF PHENOTYPIC AND GENOTYPIC ANALYSIS DATA ................................................................... 32
APPENDICES ............................................................................................................................. 33
APPENDIX A: PROJECT PROPOSAL ................................................................................................................................ 33
APPENDIX B: CFAST VIROLOGY V2.0 STANDARDS TEAM ........................................................................................... 34
APPENDIX C: GLOSSARY AND ABBREVIATIONS ............................................................................................................ 35
APPENDIX D: FURTHER READING ................................................................................................................................. 36
APPENDIX E: REPRESENTATIONS AND WARRANTIES, LIMITATIONS OF LIABILITY, AND DISCLAIMERS ......................... 37
FIGURES
Figure 1: CDISC Industry Wide Data Standards ...........................................................................................................3
Figure 2: Concept Classification Key for Concept Maps .............................................................................................. 5
Figure 3: Presentation of Standard and Non-Standard Variables in This Document .....................................................7
CONCEPT MAPS
Concept Map 1: Drug Sensitivity Testing .................................................................................................................... 16
Concept Map 2: Inhibitory Concentration Assay ......................................................................................................... 17
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Concept Map 3: Inhibitory Concentration Net Assessment ......................................................................................... 17
Concept Map 4: Viral Load Assessment ...................................................................................................................... 22
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1 Introduction
This Therapeutic Area Data Standards User Guide for Virology v2.0 was developed with FDA funding originally
provided to develop an influenza standard. The influenza standard was completed using only part of the funding, and
FDA approved applying the remaining funds to revise the TAUG-Virology Version 1.0 standard to Version 2.0. It
was also developed in collaboration with the Coalition for Accelerating Standards and Therapies (CFAST) initiative.
CFAST, a joint initiative of the Clinical Data Interchange Standards Consortium (CDISC) and the Critical Path
Institute (C-Path), was launched to accelerate clinical research and medical product development by facilitating the
establishment and maintenance of data standards, tools, and methods for conducting research in therapeutic areas
important to public health. CFAST partners include TransCelerate BioPharma Inc. (TCB), the U.S. Food and Drug
Administration (FDA), and the National Cancer Institute Enterprise Vocabulary Services (NCI-EVS), with
participation and input from many other organizations. See http://www.cdisc.org/cfast-0 for a list of CFAST
participating organizations.
CDISC has developed industry-wide data standards enabling the harmonization of clinical data and streamlining
research processes from protocol (study plan) through analysis and reporting, including the use of electronic health
records to facilitate study recruitment, study conduct and the collection of high quality research data. CDISC
standards, implementations and innovations can improve the time/cost/quality ratio of medical research, to speed the
development of safer and more effective medical products and enable a learning healthcare system.
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The goal of the CFAST initiative is to identify a core set of clinical therapeutic area concepts and endpoints for
targeted therapeutic areas and translate them into CDISC standards to improve sematic understanding, support data
sharing and facilitate global regulatory submission.
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1.1 Purpose
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Figure 1: CDISC Industry Wide Data Standards
The purpose of this TAUG-Virology v2.0 is to describe how to use CDISC standards to represent data pertaining to
virology-related studies. The focus is on topics relevant to both interventional and vaccine trials, with emphasis on
concepts that are relevant to multiple virology therapeutic areas. It is intended to serve as a parent guide to more
specific TAUGs such as those currently available for Chronic Hepatitis C and Influenza. See Appendix A for the
project proposal that was approved by the CFAST Steering Committee.
The TAUG-Virology v2.0 provides advice and examples for the Study Data Tabulation Model (SDTM) and the
Analysis Data Model (ADaM), including:
 Guidance on the use of SDTM variables
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Guidance on which domain models and datasets from the SDTM Implementation Guide for Human
Clinical Trials (SDTMIG) to use in representing raw/collected data
Examples of SDTM datasets, with text describing the situational context and pointing out records of note
Discussion of analysis variables, with variable-level metadata
CDISC standards are freely available at www.cdisc.org. It is recommended that implementers consult the
foundational standards prior to implementing these virology-related clinical data standards.
This TAUG-Virology v2.0 describes common kinds of data needed for virology-related studies, so that those
handling the data (e.g., data managers, statisticians, programmers) understand the data and can apply standards
appropriately. These descriptions include the clinical situations from which the data arise, and the reasons these data
are relevant for virology-related studies
The TAUG-Virology v2.0 strives to define research concepts unambiguously, so that consistent terminology can be
used in virology-related studies to enable aggregation and comparison of data across studies and drug programs, and
so that metadata for these research concepts can be likewise defined.
A research concept is a unit of knowledge, created by a unique combination of the characteristics that define
observations of real world clinical research phenomena, and represents clinical research knowledge that borrows
from medical knowledge, statistical knowledge, BRIDG, and the CDISC standards. Metadata for research concepts
include the properties of the data items that are parts of the concepts, controlled terminology for those data items,
and the ways in which the concepts relate to each other.
It is important to note that the research concepts included in this user guide are a sample of the more common
concepts for which data are collected in virology-related studies; they are not intended to influence sponsor
decisions as to what data to collect. The examples included are intended to show how particular kinds of data can be
represented using CDISC standards. This user guide emphasizes that examples are only examples and should not
be over-interpreted. For guidance on the selection of research concepts and endpoints, please refer to the appropriate
clinical and regulatory authorities.
1.2 Organization of this Document
This document is divided into the following sections:
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Section 1, Introduction, provides an overall introduction to the purpose and goals of the Virology project.
Section 2, Overview, provides a general overview and introduction to Virology with specific focus on
content relevant to this user guide.
Section 3, Subject and Disease Characteristics, covers data that are usually collected once at the beginning
of a study.
Section 4, Disease Assessments, covers data that are used to evaluate disease severity, control, or
progression. These are usually collected repeatedly during a study, and may be used as or for the derivation
of efficacy and/or safety endpoints.
Section 5, Statistical Analysis, covers derived variables and analysis datasets that are commonly used for
the analysis of pharmacogenomics data.
Appendices provide additional background material and describe other supplemental material relevant to
virology-related studies.
A list of domains used in the examples in this document, and the sections in which these examples appear, is given
below:
IG
SDTMIG
SDTMIG
SDTMIG
SDTMIG
Class
Special Purpose
Findings
Findings
Findings
Domain
OI - Organism Identification*
IS - Immunogenicity Specimen Assessments
LB - Laboratory Test Results
MB - Microbiology Specimen
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IG
Class
SDTMIG
Findings
SDTMIG-MD Special Purpose
SDTMIG-PGx Findings
* Domain is not final.
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1.3 Concept Maps
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Domain
MS - Microbiology Susceptibility Test
DI - Device Identifier
PF - Pharmacogenomics Findings
Section
3.2.1, 4.1.1
3.1.1,4.1.1
4.1.1
This document uses concept maps to explain clinical processes and research concepts. Concept maps, also
sometimes called mind maps, are diagrams that include “bubbles” representing concepts/ideas/things and labeled
arrows that represent the relationships between the concepts/ideas/things. They are generally easier to draw and
more accessible than more formal modeling diagrams, such as Unified Modeling Language (UML) diagrams.
The diagrams in this document use the following coding for classification of concepts. This classification is based
on classes in the Biomedical Research Integrated Domain Group (BRIDG) model (available at
http://bridgmodel.nci.nih.gov/). These color-symbol pairs have been used to highlight kinds of concepts that occur
commonly in clinical data and therefore give rise to common patterns of data. Some concepts are not coded; they
have a thinner, black outline, and no accompanying symbol. These may include the subject of an observation, as
well as characteristics, or attributes, of the coded concepts.
Figure 2: Concept Classification Key for Concept Maps
1.4 Controlled Terminology
CDISC Controlled Terminology is a set of standard value lists that are used throughout the clinical research process,
from data collection through analysis and submission. Controlled terminology is updated quarterly by the CDISC
Terminology Team and published by the National Cancer Institute’s Enterprise Vocabulary Services (NCI EVS) at:
http://www.cancer.gov/cancertopics/cancerlibrary/terminologyresources/cdisc.
Although the examples in CDISC data standards try to appear plausible, including using controlled terminology
where available, they should not be regarded as a definitive source for controlled terminology. Some codelists and/or
values applicable to research concepts and data elements in this document may still be in development at the time of
publication. Some examples may use values that appear to be controlled terminology, but are actually generic or
"best guess" placeholders. Readers should consult the current CDISC Controlled Terminology (available at the link
above) as the ultimate authority for correct controlled terminology codelists and values.
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1.5 Relationships to Other Standards
This document does not replace the foundational CDISC standards or their implementation guides. Users should
read those standards and implementation guides before applying the advice in this user guide.
Certain types of data have existing CDISC standards that can be used in virology-related studies without additional
development or customization, and so are not covered in special detail in this document.
CDISC data standards are living documents. Due differing update cycles, some of the modeling approaches and
controlled terminology presented in the examples in this document may become outdated before the next version is
released.
Some of the standards used in this document have not yet been published as either final or provisional. These draft
standards include the Non-host Organism Identifiers (OI) Domain and SDTMIG Section 8.4.4, Alternative
Representation of Non-Standard Variables proposal. In order to aid readers in reviewing this document, these
standards are temporarily available at: http://wiki.cdisc.org/display/PUB/Draft+Standards+of+Interest+to+TAUGVirology.
1.5.1 Summary of Changes from Version 1.0
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This guide has been updated to follow a concept-based organization (as opposed to an SDTM domainbased organization) to align with other CFAST TAUGs.
Modeling changes; additions, and deletions:
o Virus identification records are now consolidated in the Microbiology Specimen domain (MB)
o The Viral Resistance (VR) domain has been deprecated. Drug sensitivity testing is now
consolidated in the Microbiology Susceptibility (MS) domain with the addition of some variables
that had been previously used only in VR.
o Handling of virus nomenclature now makes use of a draft variable (ORGNAMID) and a draft
domain (Non-Host Organism Identifiers—OI). The variable –NSPCES (non-host species) is still
available, but –NSTRN (non-host strain) is deprecated since the concept of “strain” is not relevant
to all viruses. See section 4.2 and the OI domain for more information.
o New examples for immunogenic response to vaccines using the Immunogenicity Specimen (IS)
domain are included.
o PGx domain examples for the Biospecimen Findings (BS), Biospecimen Events (BE),
Pharmacogenomics/genetics Biomarker (PB), and Subject Biomarker (SB) domains have been
removed as they are now covered by the recently published SDTMIG-PGx.
o New examples were added based on development work in the TAUG-Influenza and TAUGChronic Hepatitis C Virus standards.
Concept maps have been updated for clarity and to reflect data modeling changes.
1.6 Known Issues
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The Virology guide makes use of the Microbiology Susceptibility (MS) domain for drug sensitivity testing.
In TAUG-Virology v1.0, these data were modeled in the Virology Resistance (VR) domain. The proposed
modeling shown here requires changes to the MS domain that have not yet been approved by the SDTM
Governance Committee.
This user guide proposes a unified strategy for the identification of viruses (and other microbes) by
representing these data in the Microbiology Specimen (MB) domain, regardless of methods used to identify
these organisms. The previously published TAUG-Chronic Hepatitis C contains an example of sub-species
identification in the PF domain. This disharmony will need to be reconciled, and may result in changes to
either guide.
The modeling strategies outlined in this guide require the deprecation of the VR domain and the "Strain" (-NSTRN) variable. This decision has not yet been approved by the SDTM Governance Committee. This
guide makes use of one new draft domain (Non-host Organism Identifiers- OI) and one new variable
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(ORGNAMID). See Section 3.2 for additional explanation. These additions to SDTM have not yet been
approved by the SDTM Governance Committee.
The variable --CLMETH (collection method) is a draft Findings General Observation Class variable. The
variable has not yet been approved by SDTM Governance Committee.
Presentation of Non-Standard Variables: SDTM places qualifiers of a record that are not among the
standard variables in separate, Supplemental Qualifiers, datasets. Reviewers commonly merge the
Supplemental Qualifiers datasets with the parent domain to create a reviewable dataset. A proposal to
provide an alternate structure for handling these additional, non-standard qualifiers (along with any nonstandard Identifiers and Timing) as variables in the parent domain has been circulated for review, but is not
yet part of the SDTMIG. When non-standard variables (NSVs) are included in SDTM examples in this
document, they are shown in line with the standard variables, but separated slightly from them by a small
gap, and the heading row is shaded black, with white text. This is intended to allow NSVs to be easily
recognizable and distinguishable from standard variables while giving their names as for submission.
Figure 3: Presentation of Standard and Non-Standard Variables in This Document
2 Overview
In contrast to studies of non-infectious diseases, virology studies have unique characteristics that influence data
collection and analysis. The primary ways that virology studies are unique include the collection of taxonomic
identification and the genetic sequencing data of the infecting virus; the scientific need to distinguish between viral
amino acid substitutions that naturally pre-exist versus those that emerge in response to an investigational drug; and
rapidly evolving methodology used to generate and analyze sequence data. These characteristics affect data
collection and analysis as described in the following paragraphs.
In virology studies, data are collected from both the host (the human subject) and the infecting virus (the non-host
species), and it is critical to be able to associate the data from the host subject with the data from their non-host
species. It is of high importance to classify the type of virus that is present in the host. For most species of viruses,
there are different sub-species that have been identified and used to classify the taxonomy of an individual viral
specimen. For example, the hepatitis C virus has up to seven recognized genotypes and there are multiple different
subtypes within most genotypes. Describing the taxonomic levels of the non-host species for a given host is of high
importance because the genotype of the non-host species can influence the severity of the disease and potentially the
drug response. Therefore, the taxonomic data of the non-host species must be captured and clearly linked to the data
of its host.
A characteristic of viruses is their ability to rapidly replicate and evolve in response to selective pressure. This rapid
replication, often with an error-prone viral polymerase, accelerates the probability of genetic mutations that can code
for amino acid substitutions, and in turn can impact viral susceptibility to a drug or the host’s immune response. In
clinical trials of antiviral drugs, measures of viral load within the host are collected over time to understand the
antiviral activity or efficacy of the candidate drug. The collection and analysis of these viral load data from the host
may be temporally associated with genotypic and phenotypic changes in the viral population to identify viral
characteristics that are associated with treatment failure versus success. With respect to clinical trials of vaccines, the
vaccines may need to be redesigned over time to account for genetic drift.
Nucleotide-sequence analyses may be conducted to identify amino acid substitutions in viral populations that are
associated with drug resistance and treatment failure. Over recent years, there has been a surge in new and advanced
technologies that can rapidly sequence microbial populations and generate large amounts of data for analysis.
Regardless of the methodology used, sequence-analysis data are typically compared to a reference sequence to
identify and report amino acid substitutions. It is critical to identify the reference sequence(s) used for the generation
of these results since this must be taken into account when comparing data both within a given trial and across
multiple trials. In addition, viral phenotypic analysis data (e.g., from cell culture or biochemical assays) may be
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collected at multiple time points to measure drug sensitivity that may change over time due to the emergence of
drug-resistance-associated substitutions. Like nucleotide sequence analysis, phenotypic data must be compared to
some reference in order to normalize and report drug sensitivity results. Reference strains used for phenotypic
analyses may include publicly available isolates or internally acquired isolates, such as a proprietary laboratory
strain or the baseline/pre-treatment isolates from individual study participants. Thus, establishing a clear link
between the data collected from the non-host specimen of interest, the data from the reference, and the unique
identification of the host is important.
This introduction provides a very high-level summary of the unique characteristics inherent in the collection and
standard representation of virologic and genomic data. The mock data examples provided below within each section
strive to illustrate some of the complexities. However, readers are encouraged to familiarize themselves in greater
detail with the collection and analysis of these data. Refer to Appendix D for more information.
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3 Subject and Disease Characteristics
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This guide does not focus on any specific viral disease. Users are encouraged to refer to TAUGs for specific viral
diseases (as available at cdisc.org/therapeutic) for information relevant to that disease. The text and examples in the
two subsections below focus on concepts relevant to most viral diseases, including handling of complex sub-species
nomenclature, and general handling of the identification and sub-typing of viruses.
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3.1 Diagnosis, Confirmation of Infection, and Virus-Typing
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Diagnosis of viral diseases or laboratory confirmation of infection by viral diseases starts with the collection of a
subject sample. The type of sample varies depending on the specific virus. These subject samples may then be
cultured for virus amplification before being further tested, or may be directly subjected to testing for virus
identification. Common methods used to confirm the presence of viruses in subject samples include the following:
 Detection and characterization of virus genetic material
 Detection of virus-specific antigens
 Virus culture in permissive cell lines
 Detection of antibodies to the virus
Different methods provide different levels of specificity with regard to identity of the virus present. Users should
refer to TAUGs for specific viral diseases (as available at cdisc.org/therapeutic) for information relevant to that
disease. Regardless of the method used, data on identification of viruses should be represented in the Microbiology
Specimen (MB) domain.
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3.1.1 Examples for Diagnosis, Confirmation of Infection, and Virus-Typing
Example 1
The following is an example from Study P7081-5102 that describes genotyping of the HCV virus in two subjects using a line probe assay (LPA).
Some Required and Expected variables have been omitted in consideration of space and clarity. Controlled terminology is still under development, so some
values in the examples are not CDISC controlled terms. Check terminology shown against current standards before adopting it.
Row 1:
Row 2:
The HCV for this subject has a genotype of 1a.
The HCV for this subject has a genotype of 2b.
mb.xpt
Row STUDYID DOMAIN
USUBJID
MBSEQ MBREFID MBTESTCD
MBTEST
MBORRES MBSTRESC
MBNAM
MBSPEC
MB
P7081-5102-01402
1
DEF-002
VRIDENT Viral Identification
HCV 1a
HCV 1a
Acme Genetics
RNA
1 P7081-5102
MB
P7081-5102-01403
1
DEF-002
VRIDENT Viral Identification
HCV 2b
HCV 2b
Acme Genetics
RNA
2 P7081-5102
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Row
MBMETHOD
MBBLFL VISITNUM VISIT VISITDY MBDTC
Y
1
Baseline
1
2014-01-30
1 (cont) LINE PROBE ASSAY
Y
1
Baseline
1
2014-01-30
2 (cont) LINE PROBE ASSAY
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Example 2
This example shows how to represent data from four different rapid influenza diagnostic tests used to enroll subjects in an influenza treatment trial. Pathogen
identification is represented in the MB domain. Each positive result is confirmed using either viral culture or RT-PCR. The names of the rapid influenza
diagnostic test used are represented in the device domain DI, and linked to the MB domain via SPDEVID.
Some Required and Expected variables have been omitted in consideration of space and clarity. Controlled terminology is still under development, thus some
values in the examples are not CDISC controlled terms. Check terminology shown against current standards before adopting it.
Row 1:
Row 2:
Row 3:
Row 4:
Row 5:
Row 6:
Shows a negative result from a rapid test that is designed to detect only Influenza A.
Shows a negative result from a rapid test that is designed to detect only Influenza B.
Shows a positive result from a rapid test that is designed to detect Influenza A or B but cannot distinguish between the two.
Shows the confirmatory test as determined by RT-PCR for Subject INF01-03 from Row 3. This test is highly specific as indicated by an
identification result to the level of subtype and strain (A/California/7/2009 (H1N1)-like).
Shows the result of Influenza A from a rapid test that is designed to detect Influenza A or B and can distinguish between the two.
Shows the confirmatory test as determined by viral culture for Subject INF01-04 from Row 5. The result is specific to subtype and strain
(A/California/7/2009 (H1N1)-like).
mb.xpt
Row STUDYID DOMAIN USUBJID SPDEVID MBSEQ MBREFID MBTESTCD
MBTEST
INFL123
MB
INF01-01
1
1
SAMP0101
INFAAG
Influenza A Antigen
1
INFL123
MB
INF01-02
2
1
SAMP0102
INFBAG
Influenza B Antigen
2
INFL123
MB
INF01-03
3
1
SAMP0103 INFABAG Influenza A/B Antigen
3
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NEGATIVE
NEGATIVE
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Row STUDYID DOMAIN USUBJID SPDEVID MBSEQ MBREFID MBTESTCD
MBTEST
MBORRES
INFL123
MB
INF01-03
2
SAMP0103 VRIDENT
Viral Identification A/California/7/2009 (H1N1)-like
4
INFL123
MB
INF01-04
4
1
SAMP0104 INFABAG Influenza A/B Antigen
INFLUENZA A VIRUS
5
INFL123
MB
INF01-04
2
SAMP0104 VRIDENT
Viral Identification A/California/7/2009 (H1N1)-like
6
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Row
1 (cont)
2 (cont)
3 (cont)
4 (cont)
5 (cont)
6 (cont)
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MBSTRESC
MPSPEC
LAVAGE
NEGATIVE
FLUID
LAVAGE
NEGATIVE
FLUID
LAVAGE
POSITIVE
FLUID
LAVAGE
A/California/7/2009 (H1N1)-like
FLUID
LAVAGE
INFLUENZA A VIRUS
FLUID
LAVAGE
A/California/7/2009 (H1N1)-like
FLUID
MBLOC
NOSTRIL
NOSTRIL
NOSTRIL
NOSTRIL
NOSTRIL
NOSTRIL
MBMETHOD
MBCLMETH VISITNUM VISIT MBDTC
NASAL
2011-08IMMUNOASSAY
1
VISIT 1
WASH
06
NASAL
2011-08IMMUNOASSAY
1
VISIT 1
WASH
06
NASAL
2011-08IMMUNOASSAY
1
VISIT 1
WASH
06
NASAL
2011-08REVERSE TRANSCRIPTASE POLYMERASE CHAIN REACTION
1
VISIT 1
WASH
06
NASAL
2011-08IMMUNOASSAY
1
VISIT 1
WASH
06
NASAL
2011-08FLUORESCENT IMMUNOASSAY
1
VISIT 1
WASH
06
The type of assay and the commercial kit name used are represented in the DI domain.
di.xpt
Row STUDYID DOMAIN SPDEVID DISEQ DIPARMCD DIPARM
DIVAL
INFL123
DI
1
1
DEVTYPE Device Type Rapid Influenza Diagnostic Test
1
INFL123
DI
1
2
TRADENAM Trade Name
SAS FluAlert A
2
INFL123
DI
2
1
DEVTYPE Device Type Rapid Influenza Diagnostic Test
3
INFL123
DI
2
2
TRADENAM Trade Name
SAS FluAlert B
4
INFL123
DI
3
1
DEVTYPE Device Type Rapid Influenza Diagnostic Test
5
INFL123
DI
3
2
TRADENAM Trade Name
QuickVue Influenza Test
6
INFL123
DI
4
1
DEVTYPE Device Type Rapid Influenza Diagnostic Test
7
INFL123
DI
4
2
TRADENAM Trade Name
BinaxNOW Influenza A&B
8
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280
281
282
283
284
285
286
287
288
289
3.2 Virus Nomenclature
In biology, the term "taxonomy" refers to the science of classifying organisms. Taxonomists assign names to newly discovered organisms through a hierarchy of
taxa (singular: taxon). An example of (and perhaps the most commonly recognized) taxon is "species." The specific values for each taxon in the hierarchy
provide the unique nomenclature for every organism.
Terminology for virus taxonomic nomenclature is controlled down to the species level by the International Committee on Taxonomy of Viruses (ICTV). The
nomenclature for taxa at the sub-species level is not addressed by any globally-accepted standard terminology. Medical microbiologists and virologists are
primarily interested in genus, species, and the various sub-species levels of taxonomy, as the genetic variations that define these sub-species taxa may be
responsible for differences in their responses to anti-infective agents. Sub-species taxa are not "official" ranks like genus and species. Nonetheless, researchers
and clinical scientists do tend to adhere to specific names for these levels of identification, though the names for these taxa vary from virus to virus. The SDTM
variables --NSPCES (non-host species) and --NSTRN (non-host strain) are often inadequate for the purposes of representing the identity of viruses for two
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reasons: 1) these variables allow for identification of only one sub-species taxon (strain) whereas there are often multiple sub-species taxa for a given group of
organisms, and 2) the term “strain” does not always align with the first, or any, sub-species taxon name for a given group of organisms. The table below shows
the variety in both the numbers of sub-species taxa and the nomenclature conventions for those taxa using five species of viruses as examples.
Species
HIV
Influenza A Hepatitis C
Hepatitis B
HPV
Subtype
Genotype
Genotype
Type
Subspecies Level 1 Type
Strain
Subtype
Sub-genotype
Subspecies Level 2 Group
Recombination Type
Subspecies Level 3 Subtype
(or Clade)
Subspecies Level 4 Subclade
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295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
Tests performed on viruses (such as drug-resistance testing) often yield different results for different sub-species. For these and any other tests where a specific
sub-species of virus must be identified to enable interpretation of the results, the data structure must accommodate these various levels of taxonomy by a)
identifying the name of the sub-species taxon (e.g. group, subtype, etc.) and b) identifying the previously determined value for that taxon (e.g., group=M,
subtype=H3N2, etc.). Note that if SDTM were to accommodate specific variables corresponding to the values of the various sub-species levels described in the
table above, a total of 8 unique variable names would be required. Additionally, at least one of those variables (Subtype) would represent different levels of
subspecies nomenclature in different viruses. Consider “Subtype” in the context of Influenza and Hepatitis C - one represents the first level below species, and
the other is the second level below species. A subtype is not the same concept for these two viruses despite the fact that both would share the same variable name.
Ultimately, as more viruses are identified, more nomenclature conventions for these different viruses will likely compound this problem.
The proposed solution to this problem is to contain this hierarchical nomenclature in a separate dataset, thereby removing the burden from any parent domain
where observations about the organism are contained. All unique organisms are assigned a Non-Host Organism Identifier (ORGNAMID) in the Non-Host
Organism Identifiers (OI) domain. Uniqueness is defined by the specific values of the organism’s entire taxonomy (described by pairs of taxon name and taxon
value) to whatever level of taxonomy is known about a given virus. Any domain that contains observations about a given organism can make use of
ORGNAMID. ORGNAMID can then link to the OI domain for the full taxonomic nomenclature of that organism.
It is important to note that tests to identify viruses do not always provide definitive answers with regard to the taxonomic nomenclature of the virus found. Labs
that report a degree of uncertainty in the identity of viruses may report a range of possible identities, such as "Influenza A/B", or "HCV 4a/c/d." To accommodate
virus names as the objects of additional testing conducted on these incompletely or non-specifically identified viruses, the variable ORGNAMID should be used.
This as-reported name should not be used in place of a fully parsed nomenclature in an OI dataset except in cases where the reported name cannot be parsed into
distinct sub-species taxa. Study sponsors should choose to populate both the as-reported name and the individual sub-species taxa names in an OI dataset.
Consider the following example and refer to the draft OI domain for more information.
317
3.2.1 Example for Virus Nomenclature
318
319
320
321
Example 1
This example is intended to highlight the use of ORGNAMID and the OI domain. Therefore, readers are encouraged to focus their review on the use of these
identifier variables and the relationship between ORGNAMID and the OI domain rather than focusing on the specifics of the drug sensitivity testing shown in the
MS domain. Further details and examples on drug sensitivity testing can be found in Section 4.1 This example shows drug sensitivity testing for HIV and
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Hepatitis C at one time point in two subjects participating in a co-infection study. The first dataset example (MS) shows the results of drug sensitivity testing for
both viruses from both subjects. An OI data set example displaying the taxonomy details of the viruses tested in the MS data set example follows. Both viruses
extracted from subject COINF1-01 are explicitly identified and fully parsed in the OI dataset. However, both viruses extracted from subject COINF1-02 are
incompletely identified and the virus names cannot be fully parsed.
Some Required and Expected variables have been omitted in consideration of space and clarity. Controlled terminology is still under development, thus some
values in the examples are not CDISC controlled terms. Check terminology shown against current standards before adopting it.
Row 1:
Row 2:
Row 3:
Rows 4-6:
Rows 7-9:
Rows 10-12:
Shows the drug concentration required to produce 50% inhibition of the virus growth (IC50) for the HIV virus extracted from subject COINF1-0.
The sponsor assigned an ORGNAMID value corresponding to the lab-reported identity. This value is used to fully parse the taxonomy of this
virus in the OI data set that follows this example.
Shows the drug concentration required to produce 50% inhibition of the virus growth (IC50) for the reference HIV virus. Note that ORGNAMID
in Row 2 corresponds to the lab-reported identity for this reference virus.
Shows the fold change in the IC50 of the virus extracted from the subject compared to the reference virus. This fold change value is the subject
sample result (Row 1) divided by the reference result (Row 2). Because this record is derived, MSDRVFL=Y.
Show results that are analogous to Rows 1-3 (respectively) for the same subject. In this case, the organism is a co-infecting Hepatitis C virus
(HCV). Row 4 shows the subject sample IC50 result, Row 5 shows the reference IC50 result, and Row 6 shows the fold change as described in
Row 3 above. Note that in Row 5 (reference virus result) the sponsor used ORGNAMID=HCV1a-H77, corresponding to a known HCV reference
strain. Study sponsors are encouraged to use known reference strain IDs such as this to populate ORGNAMID when applicable (see draft OI
domain assumption 2b).
Show IC50 results that are analogous to Rows 1-3 (respectively) for subject COINF-02. Note that ORGNAMID in Row 5 is incompletely
identified with respect to Group (“M/N”, as opposed to a definitive “M” or “N”).
Show IC50 results for the co-infecting HCV for subject COINF-02 (analogous Rows 4-6 above). Note the same reference strain used as in Row
5—the known HCV1a variant—as indicated by ORGAMID in Row 11.
ms.xpt
Row STUDYID DOMAIN USUBJID MSSEQ MSGRPID ORGNAMID
MSNSPCES
MSTESTCD
MSTEST
COINF1
MS
COINF1-01
1
1
HIV1MC
HUMAN IMMUNODEFICIENCY VIRUS 1
IC50S
IC50 Subject Result
1
COINF1
MS
COINF1-01
2
1
HIV1MB
HUMAN IMMUNODEFICIENCY VIRUS 1
IC50R
IC50 Reference Control Result
2
COINF1
MS
COINF1-01
3
1
IC50FCR
IC50 Fold Change from Reference
3
COINF1
MS
COINF1-01
4
2
HCV2c
HEPATITIS C VIRUS
IC50S
IC50 Subject Result
4
COINF1
MS
COINF1-01
5
2
HCV1a-H77
HEPATITIS C VIRUS
IC50R
IC50 Reference Control Result
5
COINF1
MS
COINF1-01
6
2
IC50FCR
IC50 Fold Change from Reference
6
COINF1
MS
COINF1-02
1
1
HIV1M/N
HUMAN IMMUNODEFICIENCY VIRUS 1
IC50S
IC50 Subject Result
7
COINF1
MS
COINF1-02
2
1
HIV1MB
HUMAN IMMUNODEFICIENCY VIRUS 1
IC50R
IC50 Reference Control Result
8
COINF1
MS
COINF1-02
3
1
IC50FCR
IC50 Fold Change from Reference
9
COINF1
MS
COINF1-02
4
2
HCV4a/c/d
HEPATITIS C VIRUS
IC50S
IC50 Subject Result
10
COINF1
MS
COINF1-02
5
2
HCV1a-H77
HEPATITIS C VIRUS
IC50R
IC50 Reference Control Result
11
COINF1
MS
COINF1-02
6
2
IC50FCR
IC50 Fold Change from Reference
12
348
Row
MSDRUG MSORRES MSORRESU MSSTRESC MSTRESN MSSTRESU MSDRVFL VISITNUM
0.2
nM
0.2
0.2
nM
1
1 (cont) Experimenavir
0.21
nM
0.21
0.21
nM
1
2 (cont) Experimenavir
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VISIT 1
VISIT 1
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3 (cont)
4 (cont)
5 (cont)
6 (cont)
7 (cont)
8 (cont)
9 (cont)
10 (cont)
11 (cont)
12 (cont)
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350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
MSDRUG MSORRES MSORRESU MSSTRESC MSTRESN MSSTRESU MSDRVFL VISITNUM
Experimenavir
0.95
0.95
Y
1
Heprevir
1.35
nM
1.35
1.35
nM
1
Heprevir
1.21
nM
1.21
1.21
nM
1
Heprevir
1.12
1.12
Y
1
Experimenavir
0.21
nM
0.21
0.21
nM
1
Experimenavir
0.21
nM
0.21
0.21
nM
1
Experimenavir
1
1
Y
1
Heprevir
1.42
nM
1.42
1.42
nM
1
Heprevir
1.21
nM
1.21
1.21
nM
1
Heprevir
1.17
1.17
Y
1
VISIT
VISIT 1
VISIT 1
VISIT 1
VISIT 1
VISIT 1
VISIT 1
VISIT 1
VISIT 1
VISIT 1
VISIT 1
The Non-Host Organism Identifiers (OI) dataset used to represent the parsed taxonomic hierarchy for the pathogens identified by ORGNAMID in the MS dataset
above is shown below.
ORGNAMID is a unique non-host organism ID used to link findings on that organism in other datasets with details about its identification in OI. OIPARM shows
the name of the individual taxa identified and OIVAL shows the experimentally determined values of those taxa.
Rows 1-4:
Rows 5-8:
Rows 9-11:
Rows 12-14:
Rows 15-17:
Rows 18-20:
Show the taxonomy for the HIV organism given the ORGNAMID of HIV1MC. This virus has been identified as HIV-1, Group M, Subtype C.
Show the taxonomy for the HIV organism given the ORGNAMID of HIV1MB. This virus has been identified as HIV-1, Group M, Subtype B.
Show the taxonomy for the HCV organism given the ORGNAMID of HCV2c. This virus has been identified as HCV Genotype 2, Subtype c.
Show the taxonomy for the HCV reference strain organism given the ORGNAMID of HCV1a-H77. This virus is known to be a variant of HCV
Genotype 1, Subtype a. including records such as these in an OI dataset may not be necessary for known strains. Sponsors should discuss with
their review division.
Show the taxonomy for the HIV organism given the ORGNAMID of HIV1M/N. This virus has been identified as HIV-1, but the Group has been
non-specifically identified as either M or N.
Show the taxonomy for HCV organism given the ORGNAMID of HCV4a/c/d. This virus has been identified as HCV Genotype 4, but the
Subtype has been non-specifically identified as either a, c, or d.
oi.xpt
Row
1
2
3
4
5
6
7
8
9
10
11
12
STUDYID DOMAIN ORGNAMID OISEQ OIPARMCD OIPARM OIVAL
STUDY123
OI
HIV1MC
1
SPCIES
Species
HIV
STUDY123
OI
HIV1MC
2
TYPE
Type
1
STUDY123
OI
HIV1MC
3
GROUP
Group
M
STUDY123
OI
HIV1MC
4
SUBTYP
Subtype
C
STUDY123
OI
HIV1MB
1
SPCIES
Species
HIV
STUDY123
OI
HIV1MB
2
TYPE
Type
1
STUDY123
OI
HIV1MB
3
GROUP
Group
M
STUDY123
OI
HIV1MB
4
SUBTYP
Subtype
B
STUDY123
OI
HCV2c
1
SPCIES
Species
HCV
STUDY123
OI
HCV2c
2
GENTYP
Genotype
2
STUDY123
OI
HCV2c
3
SUBTYP
Subtype
c
STUDY123
OI
HCV1a-H77
1
SPCIES
Species
HCV
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20
STUDYID DOMAIN ORGNAMID OISEQ OIPARMCD OIPARM OIVAL
STUDY123
OI
HCV1a-H77
2
GENTYP
Genotype
1
STUDY123
OI
HCV1a-H77
3
SUBTYP
Subtype
a
STUDY123
OI
HIV1M/N
1
SPCIES
Species
HIV
STUDY123
OI
HIV1M/N
2
TYPE
Type
1
STUDY123
OI
HIV1M/N
3
GROUP
Group
M/N
STUDY123
OI
HCV4a/c/d
1
SPCIES
Species
HCV
STUDY123
OI
HCV4a/c/d
2
GENTYP
Genotype
4
STUDY123
OI
HCV4a/c/d
3
SUBTYP
Subtype
a/c/d
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4 Disease Assessments
This section covers disease assessment-related concepts relevant to most viral diseases, using specific examples
from Influenza and Chronic Hepatitis C. Concepts covered include:
 Drug resistance testing
 Viral load
 Immune response
Users should refer to any available TAUGs for specific viral diseases of interest (http://www.cdisc.org/therapeutic)
for more information relevant to that disease.
4.1 Resistance Testing
As discussed in Section 2, viruses tend to change genetically over time, and some do so quite rapidly. Genetic
variation in viruses may manifest itself clinically as altered drug sensitivity. Therefore, interventional studies of viral
diseases often include drug resistance testing at multiple time points to aid in treatment decisions or to determine if
treatment failure is associated with pre-existence or emergence of drug resistant virus. Drug sensitivity testing may
broadly be viewed in two major categories: phenotypic and genotypic testing.
Phenotypic testing involves the direct exposure of virus or a viral component (e.g., a viral enzyme) to a study drug to
ascertain the effect of the drug on the ability of the virus to infect cells or carry out a particular function necessary
for replication. One form of this type of testing is a cell-culture replication assay, where clinical-specimen-derived
viruses are introduced to permissive cell cultures in the presence of varying concentrations of drug. In these assays,
investigators measure viral replication at each drug concentration; and the concentration of drug that reduces viral
replication by 50% is determined. This is referred to as the 50% “effective concentration” value (EC50 value). Drug
concentrations that inhibit other levels of viral replication may also be determined, such as 90% inhibition of virus
replication (EC90 value). Because EC50 (or EC90) values may vary somewhat between experiments, a standard
reference virus strain is often analyzed at the same time, and a fold-change (FC) in EC50 value is determined,
calculated as the ratio of EC50 values for the clinical specimen and reference viruses. Fold-changes in EC50 values
may also be calculated using results obtained for two different clinical specimens from the same subject, for
example to compare the susceptibility of a virus at the time of treatment failure to that of the virus at baseline; this
would be referred to as a FC in EC50 value from baseline.
Another common type of phenotypic drug susceptibility testing is an enzyme inhibition assay, such as the
neuraminidase (NA) inhibition assay for influenza virus. Similar to cell-culture replication assays, these phenotype
tests involve incubating various concentrations of drug with the virus or specific viral enzyme being targeted.
However, rather than measuring the drug’s impact on viral replication in cell culture these assays measure the drug’s
impact on viral enzyme activity using a biochemical readout. These assays allow for a more direct quantification of
an activity based on a drug's mechanism of action–in this case, inhibition of the activity of the influenza
neuraminidase enzyme which plays an important role in the proliferation of the virus within the host subject.
Investigators measure the concentration of the drug required to inhibit 50% of the activity of the viral enzyme, and
may also report the results as fold-change values relative to a standard reference or baseline isolate. However, in
contrast to cell culture viral phenotype assays that report drug activity as EC50 values, enzyme and other
biochemical assay results are reported as "inhibitory concentrations" or IC50 values.
Genotypic tests usually are nucleic acid sequencing assays that identify genetic changes in a viral genome region of
interest. In most cases these tests report translated sequences (i.e., amino acid coding sequences) since most antiviral
drugs target viral proteins and investigators are primarily interested in the amino acid sequence of the viral drug
target. Sequences are compared to a standard reference sequence to identify and report specific amino acid
differences in the clinical specimen-derived virus relative to the “wild-type” standard. These methods can be used to
identify amino acid differences in a viral isolate that exist prior to any treatment (i.e., natural polymorphisms), and
also to identify amino acid substitutions that emerge in a subject’s viral population as a result of drug exposure or
treatment failure. Baseline polymorphisms and treatment-emergent substitutions in viruses are then analyzed in
conjunction with clinical outcome data, and possibly also with results from phenotypic assays, to identify specific
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amino acid positions or changes that contribute to drug resistance. The specific details of which genes are probed in
these assays depend on the drug’s mechanism of action and also may vary from virus to virus. Users may refer to
TAUGs for a specific viral disease of interest (http://www.cdisc.org/therapeutic) for more information.
The concept maps below start with a basic overview of the process for conducting phenotypic and genotypic assays,
followed by more detailed views of the process for determining IC50 (or EC50) values at various time points, and a
net assessment of fold change in susceptibility.
Concept Map 1: Drug Sensitivity Testing
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Concept Map 2: Inhibitory Concentration Assay
Concept Map 3: Inhibitory Concentration Net Assessment
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4.1.1 Examples for Resistance Testing
Example 1
This example shows a longitudinal assessment of genetic variation in the influenza neuraminidase (NA) gene from two subjects. These assessments look for
changes in the Arginine (R) residue at position 292 in the neuraminidase protein over a period of five days, because this change is known to confer resistance to
NA inhibitor drugs. PFORRES shows the one-letter amino acid abbreviation, which is more commonly used than 3-letter abbreviations. PFSTRESC shows the
result using standard Human Genome Variation Society (HGVS) nomenclature.
Some Required and Expected variables have been omitted in consideration of space and clarity. Controlled terminology is still under development, thus some
values in the examples are not CDISC controlled terms. Check terminology shown against current standards before adopting it.
Row 1:
Rows 2-3:
Rows 4-6:
Shows that the baseline assessment found no variation in R292 for Subject INF01-01. Note that the experimental result (PFORRES) and the
reference result (PFORREF) are the same. The standard result (PFSTRESC) value of “p.(=)” indicates there is no change detected.
Show that the R292 residue mutated to Lysine (K) on Day 2 and remained that way through Day 5 for Subject INF01-01. Note that the
experimental result (PFORRES) has changed to “K.”
Show that the baseline, Day 2, and Day 5 assessments found no variation in R292 for Subject INF01-02.
pf.xpt
Row STUDYID DOMAIN USUBJID PFSEQ ORGNAMID PFGENTYP
PFGENRI
PFTESTCD
PFTEST
PFCAT
PFORRES PFORREF
INF01
PF
INF01-01
1
H3N2
PROTEIN NEURAMINIDASE
AA
AMINO ACID PROTEIN VARIATION
R
R
1
INF01
PF
INF01-01
2
H3N2
PROTEIN NEURAMINIDASE
AA
AMINO ACID PROTEIN VARIATION
K
R
2
INF01
PF
INF01-01
3
H3N2
PROTEIN NEURAMINIDASE
AA
AMINO ACID PROTEIN VARIATION
K
R
3
INF01
PF
INF01-02
1
H3N2
PROTEIN NEURAMINIDASE
AA
AMINO ACID PROTEIN VARIATION
R
R
4
INF01
PF
INF01-02
2
H3N2
PROTEIN NEURAMINIDASE
AA
AMINO ACID PROTEIN VARIATION
R
R
5
INF01
PF
INF01-02
3
H3N2
PROTEIN NEURAMINIDASE
AA
AMINO ACID PROTEIN VARIATION
R
R
6
455
Row PFGENLOC PFSTRESC VISITNUM
VISIT
PFDTC
292
p.(=)
1
BASELINE 2012-03-01
1 (cont)
292
R292K
2
DAY 2
2012-03-02
2 (cont)
292
R292K
3
DAY 5
2012-03-05
3 (cont)
292
p.(=)
1
BASELINE 2012-03-01
4 (cont)
292
p.(=)
2
DAY 2
2012-03-02
5 (cont)
292
p.(=)
3
DAY 5
2012-03-05
6 (cont)
456
457
458
459
460
The table below shows how species and subtype are represented in OI domain. The variable ORGNAMID is used to link this information to findings about the
organism. The records show the taxonomy for the influenza organism given the ORGNAMID of H3N2. This virus has been identified as Influenza A H3N2
oi.xpt
Row STUDYID DOMAIN ORGNAMID OISEQ OIPARMCD OIPARM OIVAL
OI
H3N2
1
SPCIES
Species Influenza A
1 STUDY123
OI
H3N2
2
SUBTYP
Subtype
H3N2
2 STUDY123
461
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474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
Example 2
This example shows how to represent data from an NA inhibition assay assessing influenza susceptibility to a neuraminidase inhibitor during an antiviral
treatment trial. This assessment was done at three time points over a five-day period. Each time point compares a known reference strain to a subject-derived
sample strain that has previously been identified as being of the same lineage based on genetic markers (thus the strain name ending in “-like”). The taxonomy
information for each sample is captured in the OI domain and can be linked to the results in the MS domain using the variable ORGNAMID. In this example,
information about the analysis software and software version used to calculate the IC50 values is represented in non-standard variables for MS, but it could also
be represented as changeable properties of the workstation, in the Device-In-Use (DU) domain. SPDEVID holds the commercial kits used; the full set of
information necessary to identify these is represented in the Device Identifiers domain (DI).
Some Required and Expected variables have been omitted in consideration of space and clarity. Controlled terminology is still under development, thus some
values in the examples are not CDISC controlled terms. Check terminology shown against current standards before adopting it.
Rows 1-3:
Rows 4-6:
Row 7:
Rows 8-10:
Row 11:
Row 12:
Show the drug concentration required to produce 50% inhibition of the virus growth (IC50) for both the influenza virus extracted from subject
INF01-01 (Row 1) and the reference influenza strain (Row 2). In both cases, ORGNAMID represents the organism being tested (subject sample
virus and reference virus, respectively). This value serves as both an intuitive representation of the as-reported name of the virus, and a link to the
OI domain where there fully parsed taxonomic nomenclature is represented. Row 3 shows the fold change in the IC50 of the virus extracted from
the subject compared to the reference virus. This fold change value is the subject sample result (Row 1) divided by the reference result (Row 2).
Because this record is derived, MSDRVFL=Y. These 3 records comprise the baseline visit for this subject.
Show the IC50 values for the same subject and reference strain (Rows 4 and 5 respectively), and the fold change in resistance (Row 6) for the
“Day 2” visit.
Shows the fold change in resistance of the subject virus sample at the Day 2 visit from the baseline visit. Not to be confused with fold change in
resistance as in Rows 3 and 6, fold change from baseline is calculated by dividing the current IC50 Subject Result (Row 4) by the IC50 Subject
Result from the baseline visit (Row 1).
Show the IC50 values for the same subject and reference strain (Rows 8 and 9 respectively), and the fold change in resistance (Row 10) for the
“Day 5” visit.
Shows the fold change in resistance of the subject virus sample at the Day 5 visit from the baseline visit. Fold change from baseline is calculated
by dividing the current IC50 Subject Result (Row 8) by the IC50 Subject Result from the baseline visit (Row 1).
Shows the qualitative net assessment of the overall change in susceptibility of subject sample virus over the 3 visits. MSORRES/MSSTRESC
shows “Reduced Susceptibility”. The variable MSGRPID is used to show all of the results that were used in the “Inhibitory Net Concentration
Assessment”.
ms.xpt
Row STUDYID DOMAIN USUBJID SPDEVID MSSEQ MSGRPID
ORGNAMID
MSTESTCD
INFL123
MS
INF01-01
10
1
1
A/California/7/2009 (H1N1)
IC50S
1
INFL123
MS
INF01-01
10
2
1
A/California/7/2009 (H1N1)-like
IC50R
2
INFL123
MS
INF01-01
3
1
IC50FCR
3
INFL123
MS
INF01-01
12
4
1
A/California/7/2009 (H1N1)
IC50S
4
INFL123
MS
INF01-01
12
5
1
A/California/7/2009 (H1N1)-like
IC50R
5
INFL123
MS
INF01-01
6
1
IC50FCR
6
INFL123
MS
INF01-01
7
1
IC50FCB
7
INFL123
MS
INF01-01
12
8
1
A/California/7/2009 (H1N1)
IC50S
8
INFL123
MS
INF01-01
12
9
1
A/California/7/2009 (H1N1)-like
IC50R
9
© 2015 Clinical Data Interchange Standards Consortium, Inc. All rights reserved
Draft
MSTEST
IC50 Subject Result
IC50 Reference Control Result
IC50 Fold Change from Reference
IC50 Subject Result
IC50 Reference Control Result
IC50 Fold Change from Reference
IC50 Fold Change from Baseline
IC50 Subject Result
IC50 Reference Control Result
MSDRUG
Investigamavir
Investigamavir
Investigamavir
Investigamavir
Investigamavir
Investigamavir
Investigamavir
Investigamavir
Investigamavir
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Row STUDYID DOMAIN USUBJID SPDEVID MSSEQ MSGRPID
INFL123
MS
INF01-01
10
1
10
INFL123
MS
INF01-01
11
1
11
INFL123
MS
INF01-01
12
1
12
ORGNAMID
MSTESTCD
MSTEST
MSDRUG
IC50FCR
IC50 Fold Change from Reference
Investigamavir
IC50FCB
IC50 Fold Change from Baseline
Investigamavir
ICNETAS Inhibitory Concentration Net Assessment Investigamavir
494
Row
MSORRES
MSORRESU
MSSTRESC
MSTRESN MSSTRESU MSSPEC
MSMETHOD
0.20
nM
0.20
0.20
nM
MUCUS NEURAMINIDASE INHIBITION ASSAY
1 (cont)
0.21
nM
0.21
0.21
nM
NEURAMINIDASE INHIBITION ASSAY
2 (cont)
0.95
0.95
3 (cont)
0.21
nM
0.21
0.21
nM
MUCUS NEURAMINIDASE INHIBITION ASSAY
4 (cont)
0.22
nM
0.22
0.22
nM
NEURAMINIDASE INHIBITION ASSAY
5 (cont)
0.95
0.95
6 (cont)
1.05
1.05
7 (cont)
4.18
nM
4.18
4.18
nM
MUCUS NEURAMINIDASE INHIBITION ASSAY
8 (cont)
0.20
nM
0.20
0.20
nM
NEURAMINIDASE INHIBITION ASSAY
9 (cont)
21
21
10 (cont)
21
21
11 (cont)
REDUCED SUSCEPTIBILITY
12 (cont) REDUCED SUSCEPTIBILITY
495
Row
1 (cont)
2 (cont)
3 (cont)
4 (cont)
5 (cont)
6 (cont)
7 (cont)
8 (cont)
9 (cont)
10 (cont)
11 (cont)
12 (cont)
496
497
MSCLMETH
MSANMETH
MSDRVFL VISITNUM
VISIT
MSDTC
NASAL SWAB SOFTWARE ANALYSIS
1
BASELINE 2011-08-01
SOFTWARE ANALYSIS
1
BASELINE 2011-08-01
Y
1
BASELINE 2011-08-01
NASAL SWAB SOFTWARE ANALYSIS
2
DAY 2
2011-08-02
SOFTWARE ANALYSIS
2
DAY 2
2011-08-02
Y
2
DAY 2
2011-08-02
Y
2
DAY 2
2011-08-02
NASAL SWAB SOFTWARE ANALYSIS
3
DAY 5
2011-08-05
SOFTWARE ANALYSIS
3
DAY 5
2011-08-05
Y
3
DAY 5
2011-08-05
Y
3
DAY 5
2011-08-05
3
DAY 5
2011-08-05
SFTWR SFTWRVER
JASPR
1.3
JASPR
1.3
JASPR
1.3
JASPR
1.3
JASPR
1.3
JASPR
1.3
JASPR
1.3
JASPR
1.3
JASPR
1.3
Metadata for non-standard variables:
Variable Name Variable Label Type Controlled Terms, Codelist, or Format Origin
Role
Evaluator Sponsor Comments
SFTWR
Analysis Software Char
CRF
Non-Standard Qualifier
SFTWRVER
Software Version Num
CRF
Non-Standard Qualifier
498
499
500
501
502
503
504
505
The table below shows how influenza species, subtype, and strain are represented in the OI domain. The variable ORGNAMID is used to link this information to
NA inhibition assay results in the MS domain.
Rows 1-3:
Rows 4-6:
Show the taxonomy for the influenza organism extracted from the subject. This virus has been identified as A/California/7/2009 (H1N1) and has
been given the ORGNAMID A/California/7/2009 (H1N1).
Show the taxonomy for the influenza laboratory reference sample. This virus has been identified as A/California/7/2009 (H1N1)-like and has
been given the ORGNAMID A/California/7/2009 (H1N1)-like.
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oi.xpt
Row STUDYID DOMAIN
ORGNAMID
OISEQ OIPARMCD OIPARM
OIVAL
INFL123
OI
A/California/7/2009 (H1N1)
1
SPCIES
Species
Influenza A
1
INFL123
OI
A/California/7/2009 (H1N1)
2
SUBTYP
Subtype
H1N1
2
INFL123
OI
A/California/7/2009 (H1N1)
3
STRAIN
Strain
A/California/7/2009 (H1N1)
3
INFL123
OI
A/California/7/2009 (H1N1)-like
1
SPCIES
Species
Influenza A
4
INFL123
OI
A/California/7/2009 (H1N1)-like
2
SUBTYP
Subtype
H1N1
5
INFL123
OI
A/California/7/2009 (H1N1)-like
3
STRAIN
Strain
A/California/7/2009 (H1N1)-like
6
508
509
510
511
The table below shows how to represent the type of assay and the commercial kit name in the DI domain.
di.xpt
Row STUDYID DOMAIN SPDEVID DISEQ DIPARMCD DIPARM
DIVAL
INFL123
DI
10
1
DEVTYPE Device Type NA Inhibition Assay
1
INFL123
DI
10
2
TRADENAM Trade Name
NA-XTD KIT
2
INFL123
DI
12
1
DEVTYPE Device Type NA Inhibition Assay
3
INFL123
DI
12
2
TRADENAM Trade Name
NA-STAR KIT
4
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518
519
520
4.2 Viral Load
Viral load is a measure of the amount of virus in a given amount of body fluid such as blood or mucus. Viral load is often related to viral infection severity and
antiviral drug efficacy. Viral load can be assessed by a variety of methods including quantification of viral nucleic acid or viral culture. Nucleic acid levels are
usually determined by a quantitative PCR (qPCR) assay (with a reverse transcriptase step in the case of an RNA virus) with the results expressed as log10
copies/mL Quantitative viral cell culture results may be expressed as log10 plaque forming units or 50% tissue culture infectious doses per milliliter of sample
(PFU/mL or TCID50/mL, respectively). The concept map below describes the different methods of viral load assessment.
Concept Map 4: Viral Load Assessment
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531
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540
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4.2.1 Examples for Viral Load
Example 1
This example shows how to represent data from five different assessments that serve as either direct or surrogate viral load measurements for three different
subjects participating in antiviral treatment studies. For viral infection prevention, vaccine trial viral loads would be handled identically, with the caveat that qRTPCR may also amplify virus particles from the vaccine itself.
Some Required and Expected variables have been omitted in consideration of space and clarity. Controlled terminology is still under development, thus some
values in the examples are not CDISC controlled terms. Check terminology shown against current standards before adopting it.
Row 1:
Shows the results of a qR-PCR assay to assess Influenza A viral load in terms of log 10 copies/mL. LBLLOQ shows that this particular test has a
lower limit of quantification of 0.9 log10 copies/mL.
Shows the results of a qRT-PCR assay to assess HIV-1 viral load in terms of log 10 copies/mL. LBLLOQ shows that this particular test has a
lower limit of quantification of 1.3 log10 copies/mL.
Shows the results of a qRT-PCR assay to assess HCV viral load in terms of log10 IU/mL. LBLLQ shows that this particular test has a lower limit
of quantification of 1.6 log10 IU/mL.
Shows the results of a tissue culture based infectivity assay performed to determine the amount of Influenza A required to kill 50% of the cultured
cells (TCID50/mL).
Shows the results of a tissue culture based infectivity assay performed to determine the number of Influenza A plaque-forming units (PFU) per
mL of subject sample.
Row 2:
Row 3:
Row 4:
Row 5:
lb.xpt
Row STUDYID DOMAIN USUBJID LBSEQ
LBREFID
INFL456
LB
INF02-01
1
SAMPMU0201
1
2
HIV456
LB
HIV02-01
1
3
HCV456
LB
HCV02-01
1
LBNSPCES
LBTESTCD
INFLUENZA A VIRUS
VRLOAD
HUMAN IMMUNODEFICIENCY
SAMPMU0301
VRLOAD
VIRUS 1
SAMPMU0401
HEPATITIS C VIRUS
VRLOAD
4
INFL456
LB
INF02-01
1
SAMPMU0202
INFLUENZA A VIRUS
5
INFL456
LB
INF02-01
1
SAMPMU0203
INFLUENZA A VIRUS
LBTEST
Viral Load
LBORRES LBORRESU
6.4
log10 copies/mL
Viral Load
5
log10 copies/mL
Viral Load
7.3
PTCID50
50 Percent Tissue Culture Infective Dose
7.6
VRPLAQ
Viral Plaque Formation
11
log 10 IU/mL
log10
TCID50/mL
PFU/mL
543
Row
LBSTRESC LBSTRESN
1 (cont)
6.4
6.4
2 (cont)
5
5
3 (cont)
7.3
7.3
4 (cont)
5 (cont)
7.6
11
7.6
11
LBSTRESU
LBSPEC
log10 copies/mL
MUCUS
LBMETHOD
LBCLMETH
LBLLOQ VISIT
LBDTC
QUANTITATIVE REVERSE TRANSCRIPTASE
NASAL SWAB
0.9
VISIT 2 2011-08-08
POLYMERASE CHAIN REACTION
QUANTITATIVE REVERSE TRANSCRIPTASE
log10 copies/mL
BLOOD
VENIPUNCTURE
1.3
VISIT 2 2011-08-13
POLYMERASE CHAIN REACTION
QUANTITATIVE REVERSE TRANSCRIPTASE
log10 IU/mL
BLOOD
VENIPUNCTURE
1.6
VISIT 2 2011-08-11
POLYMERASE CHAIN REACTION
log10 TCID50/mL LAVAGE FLUID
END-POINT DILUTION ASSAY
NASAL WASH
VISIT 2 2011-08-08
PFU/mL
LAVAGE FLUID
VIRAL PLAQUE ASSAY
NASAL WASH
VISIT 2 2011-08-08
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4.3 Host Immune Response
Infections by viruses and microorganisms typically cause an immune response within the host organism (study subject). Specifically, the host mounts an immune
defense in the way of antibodies to antigens (foreign proteins recognized by the host as "non-self") presented by the infectious agent. Vaccines work by
introducing antigens or attenuated virus particles to the host in order to evoke an immune response without subjecting them to "live" infectious viruses. Immune
titers, or antibody titers, are a measure of the subject's immune response to an infectious agent, or response to a vaccine. Immune titers are typically performed by
extracting a serum sample from the host subject, creating a dilution series of the serum, and introducing this diluted series of subject serum to wells containing
virus particles and cells that are permissive to infection by the virus. The assay is interpreted visually and is typically expressed as the inverse of the most diluted
serum sample that prevents infection of the cells. The specific dilution factor used and how prevention of infection is interpreted varies from virus to virus and
different types of assays. Refer to TAUGs for a specific viral disease of interest (http://www.cdisc.org/therapeutic) for more information.
Another method for determining host immune response is to quantify cell-mediated immunity by way of measuring antigen-specific T- or B-cells. Although these
assays appear much like those for any other hematology lab tests, because these assessments describe whether a therapy provoked/caused/induced an
immune response, data of this type should be represented in the Immunology Specimen (IS) domain.
4.3.1 Examples for Host Immunogenic Response
Example 1
This example shows two immune response titers from an influenza A vaccine trial: Hemagglutination Inhibition (HI) and Microneutralization (MN). Both of
these assays use a subject’s serum to quantify influenza immune factors circulating in the blood. The subjects’ sera are tested against a laboratory strain of
influenza virus.
Some Required and Expected variables have been omitted in consideration of space and clarity. Controlled terminology is still under development, thus some
values in the examples are not CDISC controlled terms. Check terminology shown against current standards before adopting it.
Row 1:
Row 2:
Shows the results of an HI titer. ISORRES shows that a 1:32 dilution of subject serum was the most dilute sample capable of inhibiting
hemagglutination. The standard result titer is expressed as the inverse of this dilution.
Shows the results of an MN titer. ISORRES shows that a 1:64 dilution of subject serum was the most dilute sample capable of neutralizing
Influenza virus in the assay. The standard result titer is expressed as the inverse of this dilution.
is.xpt
Row STUDYID DOMAIN USUBJID ISSEQ
ISREFID
ISNSPCES ISTESTCD
ISTEST
ISCAT
ISORRES ISORRESU ISSTRESC
INFL456
IS
INF02-01
1
SAMPBL0201 Influenza A INFAHIT Hemagglutination Inhibition Antibody Titer SEROLOGY
1:32
dilution
32
1
INFL456
IS
INF02-02
2
SAMPBL0202 Influenza A INFAMNT
Microneutralization Antibody Titer
SEROLOGY
1:64
dilution
64
2
572
Row ISSTRESN ISSTRESU ISSPEC
ISMETHOD
ISDTC
32
titer
SERUM HEMAGGLUTINATION INHIBITION ASSAY 2011-08-08
1 (cont)
64
titer
SERUM
MICRONEUTRALIZATION ASSAY
2011-08-08
2 (cont)
573
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581
582
583
584
585
586
Example 2
The Enzyme-Linked ImmunoSpot (ELISpot) assay is a method for monitoring cell-mediated immunity. It is used to detect antigen-specific T or B cells. This
example shows how to represent the quantification of antibody-secreting cells (ASCs) as the number of spots per million peripheral blood mononuclear cells
(SFC/10^6 PBMC) as determined by ELISpot from a vaccine trial. A spot represents a single reactive cell as a result of running an ELISpot assay.
Some Required and Expected variables have been omitted in consideration of space and clarity. Controlled terminology is still under development, thus some
values in the examples are not CDISC controlled terms. Check terminology shown against current standards before adopting it.
Row 1:
Row 2:
Row 3:
Shows the total number of IgG ASCs from a subject’s blood sample.
Shows the number of H1 specific IgG ASCs from the same subject’s blood sample.
Shows the number of H3 specific IgG ASCs from the same subject’s blood sample.
is.xpt
Row STUDYID DOMAIN USUBJID ISSEQ
ISREFID
ISTESTCD
ISTEST
ISORRES ISORRESU ISSTRESC ISSTRESN
INFL456
IS
INF02-01
1
SAMPBL0201 TIGGASC
Total IgG Antibody Secreting Cells
2019
SFC/10^6 cells
2019
2019
1
INFL456
IS
INF02-01
2
SAMPBL0201 H1IGGASC H1 Specific IgG Antibody Secreting Cells
626
SFC/10^6 cells
626
626
2
INFL456
IS
INF02-01
3
SAMPBL0201 H3IGGASC H3 Specific IgG Antibody Secreting Cells
592
SFC/10^6 cells
592
592
3
587
Row
ISSTRESU
1 (cont)
SFC/10^6
PBMC
2 (cont)
SFC/10^6
PBMC
3 (cont)
SFC/10^6
PBMC
ISSPEC
ISMETHOD ISDTC
PERIPHERAL
BLOOD
ELISPOT 2011-08-08
MONONUCLEAR
CELL
PERIPHERAL
BLOOD
ELISPOT 2011-08-08
MONONUCLEAR
CELL
PERIPHERAL
BLOOD
ELISPOT 2011-08-08
MONONUCLEAR
CELL
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5 Analysis Data
This section provides an introduction to how the data associated with viruses and their hosts are used to analyze if
and how the reported genotypic and phenotypic changes are related to investigational products and to subject level
characteristics. The data represented in the MS domain may be analyzed in tandem with data that is represented in
other pharmacogenomics domains, such as PF. Therefore, discussion below includes analysis issues related to data
sourced from the PF domain.
It should be noted that much of the data in the MS and PF domains are, in most respects, derived data. In PF, the
records themselves and the standard SDTM variables are essentially obtained using computer intensive algorithms
that distill the terabytes of sequence data into derived results. In MS, the findings summarize the results of inhibitory
and effective concentration that are derived at specialized laboratories., The inclusion of additional derived variables
described below, along with the original SDTM data and selected subject level variables, could yield a functional
analysis dataset or be viewed as an intermediate dataset. Whereas a complete discussion of the analysis of
pharmacogenomic and virologic data is beyond the scope of this TAUG, the sections below describe analysis
variables and concepts that are needed for typical analyses of these data. Discussion below focuses on the derived
variables themselves regardless of how or where in the analysis process they are created and whether these datasets
are used as a final analysis dataset or an intermediate dataset. Standard variable names and associated metadata for
these derived variable concepts are presented below.
606
5.1 Subject-Level Variables
607
608
609
610
611
612
613
614
615
616
617
618
Individual subject characteristics are important for the analysis of pharmacogenomic data since there is great interest
in understanding genotypic changes in relationship to co-morbidities, response to current treatment, response to
previous treatments, and other subject-level subgroups. However, the need for the variables presented here is not
unique to the analysis of genomic data, and many of them would prove useful in many therapeutic areas. At present,
the ADaM model does not define standard variables for these important TA-agnostic research concepts other than
recommending the default method of creating sponsor-defined flag variables coupled with corresponding
informative metadata. Whereas sponsor-defined variables may work well within a given study or submission, they
do not foster industry-wide standardization. The sections below aim to bridge this gap with the proposal of standard
methodology and variables to use for these concepts. The variables are described in terms of use in
pharmacogenomic analyses, yet they easily can be extended to other analyses. Many of these variable concepts may
be represented within the Subject-Level Analysis Dataset (ADSL) but the discussion below presents a methodology
to be used regardless of the analysis dataset in which they are derived.
619
5.1.1 Co-existing Diseases and Viral Co-Infections
620
621
622
623
624
In virology studies, identifying whether a subject had co-morbidities of interest and whether they had co-infections
with other viral agents is important. The following approach uses two paired and standard variables to represent that
description of the co-morbidity and co-infection and the corresponding subject level response. The following table
presents the variable level metadata for these variables.
Variable
Name
COMORy
Variable
Label
Description
of CoMorbidity y
Type
Controlled
Terms or
Format
Text
COMORyFL Presence of
Text Y / N
Co-Morbidity
y
COINFy
Description
Text
Notes
This is a text description of the y'th co-morbidity of interest. At present there
is no proposed controlled terminology for this text description. Sponsors
should strive for consistency across all studies within a submission. For
example: COMOR1='Compensated Cirrhosis'
This is a flag variable to indicate that the subject had or did not have the comorbidity described in the companion variable COMORy. The variable level
metadata for this variable should clearly describe how the presence of the
co-morbidity was established. This may simply be a reference to a given
value or set of terms in MHTERM. Understanding how sponsors identified a
particular co-morbidity is important for regulatory reviewers.
This is a text description of the y'th co-infection of interest. For virology, the
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Name
Variable
Label
Type
Controlled
Terms or
Format
of CoInfection y
COINFyFL
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
A common analysis need for any therapeutic area is to identify subgroups of subjects. In the ADaM model, this can
be achieved by using the variable name fragment *GRy to create a sponsor defined ‘y’th’grouping variable. For
example, a sponsor may define PTRTGR1 to contain information about whether the subject had been exposed to a
previous antiviral treatment, for example, PTRTGR1 might have values of 'Treatment Naive', ‘Direct Acting Antiviral Agent Experienced', or ‘Peginterferon plus Ribavirin Experienced'. As with co-morbidities, such sponsor
defined grouping variables work well within a given study but they do not foster a standard methodology across the
industry. The following suggests a similar approach used above to create pairs of variables that describe the
subgroup and the subject-level value. The recommendation is to use these paired variables only for subgroups that
are important for the primary analysis rather than every possible subgroup of interest. Subject subgroups used for
secondary or exploratory analysis can utilize the current ADaM convention. By utilizing these variables for the
subgroups of primary interests, helps the consumer of the analysis data clearly identify the primary subgroups of
interest. Note also that co-morbidities or co-infections may themselves be subgroups of interest. Therefore, these
genetic subgroup variables should not be used for these concepts.
SBGRyVAL
641
642
643
644
645
646
647
648
649
650
651
652
Text Y/N
co-infections of interest will almost always be another viral entity. For most
situations, the genotype of the co-infecting virus is often sufficient For
example, COINF1='HIV'
This is a flag variable to indicate that the subject was or was not infected
with the virus described by the companion variable COINFy. The variable
level metadata for this variable should also clearly describe how the
presence of the co-infection was established.
5.1.2 Subgroup Variables
Variable
Name
SBGRyDSC
640
Presence of
Co-Infection
y
Notes
Variable Label
Description of
Subgroup y
Value of
Subgroup y
Type
Notes
Text This is a text description of the y'th primary subject level subgroup.
Text This is the subject level value for the subgroup defined by SBGRyDSC. The number
of different values of SBGRyVAL should be consistent with values used for analysis
5.1.3 Non-Host Taxonomy at Screening and at Subsequent Time Points
In virologic studies, knowledge of the taxonomy of the non-host species is important. This taxonomy may be used as
a stratification factor or as inclusion/ exclusion criteria. Taxonomy of the non-host species is often determined at
screening or baseline but then may be repeated at later time points. Due to differences in methodology used to
determine the taxonomy, there may be differences between the results of taxonomy for a given subject. Therefore,
the following variables are proposed for use in distinguishing what could be described as the initial taxonomy results
versus subsequent taxonomy results. Because of the finite number of viruses of interest, virus specific variable
names are utilized as opposed to the more generic variable naming seen above for co-morbidities, co-infections, and
subgroups.
All variables below are of Type ‘Text’ and share similar discussion regarding Source /Derivation. Thus variable
names and labels are presented below and are followed by a discussion on issues related to source derivations.
Variable Name
HCVPRE
HCVPOST
HIVPRE
HIVPOST
HBVPRE
HBVPOST
FLUPRE
FLUPOST
Variable Label
HCV Taxonomy Pre-treatment
HCV Taxonomy Post-treatment
HIV Taxonomy Pre-treatment
HIV Taxonomy Post-treatment
HBV Taxonomy Pre-treatment
HBV Taxonomy Post-treatment
Influenza Taxonomy Pre-Treatment
Influenza Taxonomy Post-Treatment
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The source derivation of these variables should be traceable to one or more records in the OI domain. Within the MB
domain, the method used for establishing the non-host species taxonomy is available (MBMETHOD). Differences
of the taxonomy determined at pre-treatment versus after randomization are often dependent on the methodology
used for sequencing at these two time points. For example, sponsors may use a less sensitive method at screening to
establish one level of taxonomy but more precise methods may be used at later time-points. For example, a
screening value may be reported as ‘HCV 1 a/c/d’, indicating the lack of precision with respect to the subtype of
HCV Genotype 1 whereas the subsequent value may be reported as ‘HCV 1d’, indicating that more precise
sequencing was conducted to fully characterize the taxonomy. Each unique taxonomic result of the non-host species
should be referenced by a unique ORGNAMID and be associated one or more records in the OI domain to represent
the individual levels of taxonomy as reported. It is recommended that the value used to populate the above variables
be the value of ORGNAMID.
If there is a common methodology used for taxonomic assessments across all subjects, it is advisable to include this
within the variable level metadata for these variables. This will allow the reviewer to quickly understand whether the
method used for determining the taxonomy contributed to the differences between the two values. When different
methodologies are used for different subjects, specifying this within the variable level metadata is not feasible.
However, indicating that there were differences in methodology between subjects will alert reviewers to investigate
values of MBMETHOD.
5.1.4 Subject-Level Variables Associated with Efficacy Response
The analysis of phenotypic and genotypic changes is often conducted by comparing frequency of these changes
between subjects who did or did not achieve the protocol defined efficacy endpoint. In addition, further analyses
stratified by the reasons for failure to achieve the protocol defined efficacy endpoint may be of interest. For many
virology studies, the primary efficacy endpoint is based on the measurement of the viral load within the subject.
These data would be found in the SDTM LB domain. The content and structure of the primary efficacy analysis
dataset is not in the scope of this TAUG but as a generality, one could expect the analysis dataset to support primary
efficacy analysis would contain a record and/or variables that would easily identify whether a subject met the
definition of an efficacy responder. For example, if the ADaM Basic Data Structure (BDS) is used, then a dedicated
parameter (PARAM) and/or a CRITy/CRITyFL pair could identify efficacy responder status. The reason for not
achieving efficacy response would be available in a supportive variable for any subject that was classified as an
efficacy non-responder.
As a generality, these efficacy response variables often need to be combined with other important subject level
variables or covariates. Creation of these subject level variables for efficacy response, however, poses a process
issue for ADaM since they would be difficult, if not impossible, to include in ADSL since the final efficacy analyses
would have to be conducted in order to accurately define them. In general, this presents a problem with circularity in
programming process given that ADSL is often the first analysis dataset created and subsequent analysis datasets are
created afterwards. The need for a subject level variables that defines efficacy responder status and reason for not
achieving efficacy response is not unique to virology and is an analysis need for many protocols. The two variables
proposed in this section are defined as subject level variables but how they are made available to combine with other
ADSL subject level variables is an open issue.
Variable level metadata for two subject level efficacy related variables are presented below.
Variable Name
Variable Label
EFFPRSFL
Primary Efficacy Responder Flag
NEFFREA
Reason For Not Achieving Efficacy Response
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EFFPRSFL should be populated with 'Y' or 'N' for all intent-to-treat subjects. NEFFREA will remain blank for
subjects who achieved and maintained efficacy response. There may be situations where efficacy analyses are
repeated over time. For example, suppose a protocol defined the primary efficacy to be established at 12 weeks but a
subsequent analysis was to be conducted at 24 weeks. In this situation, there may be interest to compare, on a
subject level, the response at 12 weeks versus the response at 24 weeks. Identifying subjects who either maintained
their efficacy response over time or relapsed may be an important analysis need. To accommodate repeated efficacy
analyses, additional flag variables are needed. It is recommended that the variable name above be reserved for the
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primary efficacy analyses and, when needed, the variables, EFFRyFL, where y is an integer, be created for
subsequent analyses. Sponsors could consider using a value of 'y' that indicates the timing of the repeated efficacy
analysis. For example, EFFR12FL and EFFR24FL would indicate efficacy analyses conducted at 12 and 24 weeks,
respectively. Optional variables that describe the definitions of the efficacy endpoint, EFFPDEF and EFFRyDF, etc.,
may be added although this information should first and foremost be included in the variable level metadata for the
flag variable. The variable NEFFREA is populated just once and indicates the reason for not achieving efficacy
response. Therefore, this reason will be associated with the first efficacy responder flag variable that is 'N' in the
situation where EFFPRSFL and EFFRyFL are used.
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5.2 Analysis Data for Pharmacogenomic Findings
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The data represented in the PF domain are derived from software utilized by the sequencing laboratory. Each row
represents a comparison of the findings at a given genetic location for a reference strain versus the subject’s nonhost species. In brief, the analysis of pharmacogenomic findings data often begins with a determination of the
frequency of changes present at given locations within the genetic regions of interest and a comparison of the
incidence of subjects with these changes between treatment groups and/or other subgroups based on subject level
characteristics. Such comparisons are done at multiple time points within the study in order to investigate whether
the genetic changes are related to duration or other characteristics of therapy.
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This discussion of the analysis needs for pharmacogenomic data will focus on three areas; 1) the representation of
variables related to the taxonomy of the non-host species and of the species used for reference; 2) the representation
of multiple findings at a single genetic location for a given sample; and 3) the derivation of variables useful for
calculating the incidence of changes at a given location. The analysis dataset described here is created from records
in the PF domain coupled with a series of derived variables in each of the three areas above. For purposes of this
document, this analysis data set will be referred to as ADPF but this is not meant to be a standard naming
convention nor is it meant to imply that such an individual analysis dataset always would be created.
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5.2.1 Descriptive Variables for Non-Host Taxonomy
There are two sets of variables relating to taxonomy that are useful for inclusion in ADPF. The first sets are those
that describe the reference strain used by the sequencing laboratory. This reference strain is often pre-specified. The
particular reference strain utilized may be selected due to regulatory request, since having sponsors use the same
reference strain enables the comparison of results across submissions. The description of the reference strain(s) used
for a given study should be included in the OI domain and associated with a unique ORGNAMID. See Section 3.2.1
above for a depiction of capturing the use of the reference strain ‘H77’ in the OI domain. However, at present there
is no standard record level variable in the PF domain to use to indicate the reference strain used in the generation of
the results of a particular record. Note that if one subject is infected with multiple non-host species, their results in
PF will be generated using different reference strains. Absence of a standard record level reference strain variable in
PF may be considered a gap but the current solution is to create a SUPPQUAL variable to indicate the reference
strain and it is assumed that this SUPPQUAL value would be traceable to the OI domain.
For analysis purposes, this text description of the reference strain needs to be present on each record in analysis data
created from the PF domain. For purposes of analysis, it is recommended that the following variable be included in
any pharmacogenomics findings analysis dataset.
Variable
Name
PFREF
Variable
Label
Type
Reference
Text
Strain Used
for PF Results
Controlled
Terms or
Format
Source / Derivation Notes
Derived. The value of this variable should be directly traceable to the OI domain,
via ORGNAMD. The variable name is prefixed with ‘PF’ to indicate this was the
reference strain used for PF results. Such a distinction will be important if
pharmacogenomics (PF) data is ever merged with resistance testing (MS) data,
where reference strains are used as well.
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The second sets of variables related to taxonomy are those associated with the taxonomy of the non-host species.
Again, the taxonomy of each non-host species should be present in the OI domain and referenced by a unique
ORGNAMID. Because one host species can be infected with more than one non-host species, it is important to have
record level variables in ADPF that associate the results with the non-host species. At a minimum, the OI variable
ORGNAMID should be included on each record in the PF analysis dataset. Having this value of full path as reported
by the sequencing laboratory allows for the creation of additional variables for individual levels of taxonomy that
may be important for the analysis. For example, if the value of ORGNAMID is ‘HCV 4a’, then additional variables
for each level may be desired, such as: ‘4’, and ‘a”. The suggested variable naming conventions for these optional
variables the value used for OIPARM. For example, “TYPE” and “SUBTYPE”.
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5.2.2 Derived Variables for Summary of Results at a Given Location
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761
762
763
For a given sample and genetic location, there may be more than one genetic difference reported. Each difference
will be present as a separate record in PF. For analysis purposes, it is useful to have a variable that summarizes all
of the changes observed at a given location for a given sample. This variable effectively concatenates the individual
findings identified at the location for the given subject. See below for an example.
Row USUBJID PFSEQ PFTESTCD PFTEST PFGENRI PFGENLOC PFORREF PFORRES PFSTRESC PFRESALL
17C0154
90
AA
Amino Acid
NS3
80
Q
R
Q80R
Q80R/K
1
17C0154
91
AA
Amino Acid
NS3
80
Q
K
Q80K
Q80R/K
2
17C0154
92
AA
Amino Acid
NS3
168
D
E
D168E
D168E
3
17C0154
93
AA
Amino Acid
NS3
67
P
S
P67S
P67S
4
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In this example, there were two amino acid differences reported at location PFGENLOC=80 relative to reference for
this given specimen. These are reported by the lab as separate records and reported as such in the SDTM PF domain.
For ADPF, the addition of the derived PFRESALL variable summarizes all reported differences for the subject at the
given location. The same value of PFRESALL is presented on each contributing record for that location. The label
for this text variable is ‘All Reported Changes at Location’.
Another use case for this variable is the situation where the results for a given location include a difference relative
to reference as well as a wild-type result (no change from reference). In general, it is recommended that wild type
record be excluded from the SDTM PF domain due to issues with file size. However, in the case that there are
multiple results reported at the same location but one indicates a wild-type result (that is, no change from reference),
the value of PFRESALL should indicate this result. For example, a value of ‘Q80K/Q’ will indicate that there are
two results for this subject and location, one of which is a wild-type result. However, in SDTM implementation of
PF, only the record for the 'K' substitution would be present in the submitted data.
5.2.3 Derived-Flag Variables
Of particular importance in the analysis of pharmacogenomic results is whether a given polymorphism was present
at baseline or whether it appeared during treatment. Upon first consideration, this sounds like a use case for the
ADaM treatment emergent flag variable, TRTEMFL However, there are subtle conceptual differences that exist and
for this reason a different flag variable is proposed The difference is that in a classic treatment emergent definition, a
given record (event) is flagged as treatment emergent (TRTEMFL='Y') if the start date of the event is on or after the
date of first dose of investigational product. If the start date of the event precedes the first dose, then TRTEMFL='N'.
However, in PF, each record indicates the presence of a polymorphism but this is independent of when this
polymorphism may have first appeared. If the same polymorphism persists over time, it will be reported as multiple
records in PF for each sample that was processed. Therefore, there may be polymorphisms that are reported at
baseline (pre-treatment) but are reported again and again at each time point. It is important to know that these are
baseline polymorphisms and not new events. Thus the analysis need is to distinguish the baseline polymorphisms
from changes that occur post-baseline. The flag that is proposed here is as follows:
Variable
Name
BLPMFL
Variable Label
Baseline
Polymorphism
Type
Text
Controlled
Terms or
Source / Derivation Notes
Format
Y/N
A value of 'Y' indicates that the polymorphism described by PFSTRESC was
present at baseline A value of 'N' indicates that the polymorphism was not
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Variable
Name
Variable Label
Flag
Type
Controlled
Terms or
Format
Source / Derivation Notes
present at baseline, and by definition, is then considered to be a genetic
change that occurred after exposure to treatment All records that are pre-dose
should have a value of BLPMFL='Y'
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801
In addition to identifying polymorphisms that appear after initial exposure, there may be the need to identify
polymorphisms that revert back to wild type during the study. In a sense, these polymorphisms appear for a period
of time but then are 'dropped'. Note that evidence of dropping is only established by the absence of the
polymorphism. As discussed above, there will be repeated records for the same polymorphism that persists over time
but if the genetic change reverts back to normal, there will be no record in PF since the recommendation is for these
'no-change' records to not be submitted (yet they exist in the original sequencing data). There are complexities with
respect to defining and deriving a flag that indicates that a polymorphism on a given record disappears at some time
in the future. At this time, there is no proposal for a standard variable to use for this concept, but sponsors should be
aware of this analysis need.
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5.3 Analysis Data for Phenotypic Results
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In addition to the analysis of pharmacogenomics changes, the analysis of data from in-vitro resistance testing is
conducted. These data are represented in the MS domain and the results of the in-vitro resistance testing are derived
by the laboratory at the time of testing. The discussion below is not complete in respect to a full description of an
intermediate or final analysis dataset to support summary of in-vitro resistance testing. What is presented below
should be considered an introduction and not a complete specification for an analysis dataset for MS. In many
situations, the variables discussed below should provide guidance for how a fully functional analysis dataset for MS
should be structured.
What is needed for analysis is essentially a transpose of the three related MS records that correspond to the results
from the reference strain, the subject-level sample, and the resulting fold change calculation.
Below are three example records from an MS domain, showing only selected variables
ms.xpt
Row USUBJID SPDEVID ORGNAMID MSSEQ MSGRPID VISIT MSTESTCD
MSTEST
MSSTRESN MSSTRESU
DAY
IC50 Subject
INF01-01
10
HCV4a
1
1
IC50S
0.2
nM
1
2
Result
DAY
IC50 Reference
INF01-01
10
HCV4a-ED43
2
1
IC50R
0.21
nM
2
2
Control Result
DAY
IC50 Fold Change
INF01-01
HCV4a
3
1
IC50FCR
0.95
3
2
from Reference
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For analysis it is advantageous to have these three results on the same record. The recommended variable names for
these three derived columns are the values of MSTESTCD while the values of MSTEST are proposed to be used for
the label of each transposed variable. Therefore, the resulting analysis record would include the following variables:
Row USUBJID SPDEVID ORGNAMID MSGRPID VISIT IC50S IC50R IC50FCR MSSTRESU
INF01-01
10
HCV4a
1
DAY 2 0.2
0.21
0.95
nM
1
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Note that in this transposed data, the value of the variable ORGNAMID represents the subject's non-host species,
not the taxonomy of the reference strain. All other MS columns, such as VISIT, MSGRPID, MSREFID,
MSGENTYP, MSGENRI, etc. that are common to the three related records should be retained. Note that MSSEQ
cannot be retained since it does not share a common value across the three related records. However, MSGRPID and
MSREFID should provide the necessary traceability. It is assumed that MSTRESN will always be populated and
that all three records share the same units for the result under normal conditions, MSTRESN will be present and it
would be unusual for the units to differ between these related records. Note that different concentrations may be
reported. Therefore, for a given visit, the transposed record may contain multiple 'ICxxS', 'ICxxR', and 'ICxxFCR'
results. A similar approach would be used if effective concentration values (e.g. EC50S, 'EC50R, and EC50FCR) are
reported in the MS domain.
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For the in-vitro testing, a reference strain is used in order to calculate the records associated with the reference
control result (eg. IC50R). In the MS example above, the reference strain is indicated by the ORGNAMID of
"HCV4a-ED43". Since ORGNAMID is used above to capture the subject’s non-host species, a new variable must be
created to retain the taxonomic description of the reference strain. The following variable is proposed for capturing
the value of the reference strain and this variable should be added to each record in the analysis dataset.
Variable
Name
MSREF
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Variable Label
Type
Reference Strain
Text
Used for MS Results
Source / Derivation Notes
Derived. See above in Section 5.2 regarding traceability of reference strains from the
OI domain. For analysis purposes, it is recommended that the value of MSREF be the
value of ORGNAMID
Note that the reference strain used for in-vitro testing may not be the same reference strain used for the analysis of
the pharmacogenomic findings. This is why two derived variables, PFREF and MSREF, are specified.
5.4 Consolidation of Phenotypic and Genotypic Analysis
Data
In Sections 5.2 and 5.3 above, derived variables required for the analysis of genotypic and phenotypic data,
respectively, are presented. The derived variables were presented in the context of separate analysis datasets for
these analysis areas. Such separate analysis datasets may be useful for sponsors to create for the creation of their
analysis summaries of these data. However, from a review perspective, a thorough analysis that compares the
genotypic and phenotypic results across time is often desired. In other words, there is analysis interest in being able
to correlate the presence of genetic changes with values of resistance testing. Therefore, there may be the need to
perform what is essentially a merge of the PF and transposed MS data, along with the derived PF, MS and subjectlevel variables described above. The proposed terminology for such a dataset is a ‘curated view’ because it
combines data from multiple SDTM domains and adds additional derived variables useful for analyses. Whether or
not this curated view is produced directly from PF and MS or from pre-processed ADPF and ADMS is a sponsor
decision and does not affect the resulting curation of data. If created, it is recommended that the consolidated data be
merged using the SDTM VISIT variable (and other key variables such as xxGENLOC) rather than sample date. This
is because there may be variation in the date of the sample taken for PF and MS results, but the intent was for them
to be done during the same planned visit window. In addition, for review purposes the full join of data is desired;
this may result in curated view records missing either the PF or the MS component.
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Appendices
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Appendix A: Project Proposal
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CFAST is proposing development of v2.0 of the CDISC Virology Therapeutic Area Data Standard. This standard
would build on v1.0 of the Virology standard and existing CDISC standards to facilitate the collection and use of
data relevant to clinical trials involving virology concepts and/or endpoints. There is a need to update the Virology
guide in the context of multiple new virology related standards recently developed and based on lessons learned
during those development efforts, with the goal of a harmonized virology supplement to SDTM.
This project will focus on several key items including updating the document format and layout, harmonizing an
approach to key endpoints (such as viral load and immune titers) and updating domain structures as necessary to
accommodate diverse viral nomenclature that exceeds the current available variables of “species” and “strain”.
Finally, any necessary terminology updates will also be addressed.
The workgroup proposes developing a CDISC Therapeutic Area User Guide including concept maps, examples and
controlled terminology.
The standardization effort is expected to evaluate the following specific areas of interest.
1. Species/strain, taxonomy
2. Viral resistance domain
3. Commonalities in study endpoints and assessments across virologic disease treatment and prophylaxis
Deliverables will consist of the following:
 CDISC Virology Therapeutic Area Data Standard v2.0
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Appendix B: CFAST Virology v2.0 Standards Team
Name
Laura Butte, Co-Lead
Jon Neville, Co-Lead
Joyce Hernandez
Gloria Jones
Susan Kenny
Bess LeRoy
Jordan Li
Anna Pron-Zwick
Institution/Organization
C-Path
C-Path
Joyce Hernandez Consulting
Johnson & Johnson
Maximum Likelihood
C-Path
NIH, NCI-EVS
Astrazeneca
Patrick Harrington
Damon Deming
Helena Sviglin
FDA Liaison
FDA
FDA
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Appendix C: Glossary and Abbreviations
ADaM
ADaMIG
BRIDG
CDASH
CDISC
CFAST
Collected
Controlled
Terminology
Domain
eCRF
Foundational
Standards
MedDRA
NCI EVS
NIH
Patient
Research
Concept
SDS
SDTM
SDTMIG
SHARE
Subject
TCID50
Analysis Data Model
ADaM Implementation Guide
Biomedical Research Integrated Domain Group
Clinical Data Acquisition Standards Harmonization
Clinical Data Interchange Standards Consortium
Coalition for Accelerating Standards and Therapies
“Collected” refers to information that is recorded and/or transmitted to the sponsor. This
includes data entered by the site on CRFs/eCRFs as well as vendor data such as core lab
data. This term is a synonym for “captured”.
A finite set of values that represent the only allowed values for a data item. These values
may be codes, text, or numeric. A code list is one type of controlled terminology.
A collection of observations with a topic-specific commonality about a subject.
Electronic case report form
Used to refer to the suite of CDISC standards that describe the clinical study protocol
(Protocol), design (Study Design), data collection (CDASH), laboratory work (Lab),
analysis (ADaM), and data tabulation (SDTM and SEND). See http://www.cdisc.org/ for
more information on each of these clinical data standards.
Medical Dictionary for Regulatory Activities. A global standard medical terminology
designed to supersede other terminologies (such as COSTART and ICD9) used in the
medical product development process.
National Cancer Institute (NCI) Enterprise Vocabulary Services
National Institutes of Health
A recipient of medical treatment.
A high-level building block of clinical research information that encapsulates lower level
implementation details like variables and terminologies.
Submission Data Standards. Also the name of the team that maintains the SDTM and
SDTMIG.
Study Data Tabulation Model
SDTM Implementation Guide (for Human Clinical Trials)
Shared Health and Clinical Research Electronic Library. CDISC’s metadata repository.
A participant in a study.
Half maximal Tissue Culture Infectious Dose- The amount of virus required to kill 50% of
infected hosts or to produce a cytopathic effect in 50% of inoculated tissue culture cells
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Appendix D: Further Reading
The following works are of interest to this document, but not actively referenced within it.

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
Clinical Data Interchange Standards Consortium. Virology Therapeutic Area Data Standards User Guide.
2012.. Available at:
http://www.cdisc.org/system/files/all/standard_category/application/pdf/vr_ug_v1_0_prov.pdf. Accessed
June 23, 2015.
Clinical Data Interchange Standards Consortium. Therapeutic Area Data Standards User Guide for
Chronic Hepatitis C Virus Infection: Version 1.0 (Provisional). 2015. Available at:
http://www.cdisc.org/system/files/members/standard/TAUG-CHCV%20v1.zip. Accessed June 23, 2015.
Clinical Data Interchange Standards Consortium. Therapeutic Area Data Standards User Guide for
Influenza: Version 1.0 Provisional. 2014. Available at:
http://www.cdisc.org/system/files/members/standard/PDF/TAUGInfluenza%20v1%20Provisional%2024Nov2014%20Final.pdf. Accessed June 23, 2015.
U.S. Department of Health and Human Services, Food and Drug Administration, Center for Drug
Evaluation and Research (CDER). Antiviral Product Development--Conducting and Submitting Virology
Studies to the Agency : Guidance for Submitting HCV Resistance Data. 2006. Available at:
http://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/UCM07096
4.pdf. Accessed June 23, 2015.
U.S. Department of Health and Human Services, Food and Drug Administration, Center for Drug
Evaluation and Research (CDER). Antiviral Product Development--Conducting and Submitting Virology
Studies to the Agency : Guidance for Submitting Influenza Resistance Data. 2006. Available at:
http://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/ucm070959.
pdf. Accessed June 23, 2015.
U.S. Department of Health and Human Services, Food and Drug Administration, Center for Drug
Evaluation and Research (CDER). Antiviral Product Development--Conducting and Submitting Virology
Studies to the Agency : Guidance for Submitting HBV Resistance Data. 2006. Available at:
http://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/UCM07096
7.pdf. Accessed June 23, 2015.
U.S. Department of Health and Human Services, Food and Drug Administration, Center for Drug
Evaluation and Research (CDER). Antiviral Product Development--Conducting and Submitting Virology
Studies to the Agency : Guidance for Submitting HIV Resistance Data. 2006. Available at:
http://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/UCM07095
5.pdf. Accessed June 23, 2015.
U.S. Department of Health and Human Services, Food and Drug Administration, Center for Drug
Evaluation and Research (CDER). Chronic Hepatitis C Virus Infection: Developing Direct Acting Antiviral
Drugs for Treatment. 2013. Draft. Available at:
http://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/UCM22533
3.pdf. Accessed June 23, 2015.
U.S. Department of Health and Human Services, Food and Drug Administration, Center for Drug
Evaluation and Research (CDER). Human Immunodeficiency Virus-1 Infection: Developing Antiretroviral
Drugs for Treatment. 2013. Draft. Available at:
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3.pdf. Accessed June 23, 2015.
© 2015 Clinical Data Interchange Standards Consortium, Inc. All rights reserved
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CDISC Therapeutic Area Data Standards User Guide for Virology
Appendix E: Representations and Warranties, Limitations of
Liability, and Disclaimers
CDISC Patent Disclaimers
It is possible that implementation of and compliance with this standard may require use of subject matter covered by
patent rights. By publication of this standard, no position is taken with respect to the existence or validity of any
claim or of any patent rights in connection therewith. CDISC, including the CDISC Board of Directors, shall not be
responsible for identifying patent claims for which a license may be required in order to implement this standard or
for conducting inquiries into the legal validity or scope of those patents or patent claims that are brought to its
attention.
Representations and Warranties
“CDISC grants open public use of this User Guide (or Final Standards) under CDISC’s copyright.”
Each Participant in the development of this standard shall be deemed to represent, warrant, and covenant, at the time
of a Contribution by such Participant (or by its Representative), that to the best of its knowledge and ability: (a) it
holds or has the right to grant all relevant licenses to any of its Contributions in all jurisdictions or territories in
which it holds relevant intellectual property rights; (b) there are no limits to the Participant’s ability to make the
grants, acknowledgments, and agreements herein; and (c) the Contribution does not subject any Contribution, Draft
Standard, Final Standard, or implementations thereof, in whole or in part, to licensing obligations with additional
restrictions or requirements inconsistent with those set forth in this Policy, or that would require any such
Contribution, Final Standard, or implementation, in whole or in part, to be either: (i) disclosed or distributed in
source code form; (ii) licensed for the purpose of making derivative works (other than as set forth in Section 4.2 of
the CDISC Intellectual Property Policy (“the Policy”)); or (iii) distributed at no charge, except as set forth in
Sections 3, 5.1, and 4.2 of the Policy. If a Participant has knowledge that a Contribution made by any Participant or
any other party may subject any Contribution, Draft Standard, Final Standard, or implementation, in whole or in
part, to one or more of the licensing obligations listed in Section 9.3, such Participant shall give prompt notice of the
same to the CDISC President who shall promptly notify all Participants.
No Other Warranties/Disclaimers. ALL PARTICIPANTS ACKNOWLEDGE THAT, EXCEPT AS PROVIDED
UNDER SECTION 9.3 OF THE CDISC INTELLECTUAL PROPERTY POLICY, ALL DRAFT STANDARDS
AND FINAL STANDARDS, AND ALL CONTRIBUTIONS TO FINAL STANDARDS AND DRAFT
STANDARDS, ARE PROVIDED “AS IS” WITH NO WARRANTIES WHATSOEVER, WHETHER EXPRESS,
IMPLIED, STATUTORY, OR OTHERWISE, AND THE PARTICIPANTS, REPRESENTATIVES, THE CDISC
PRESIDENT, THE CDISC BOARD OF DIRECTORS, AND CDISC EXPRESSLY DISCLAIM ANY
WARRANTY OF MERCHANTABILITY, NONINFRINGEMENT, FITNESS FOR ANY PARTICULAR OR
INTENDED PURPOSE, OR ANY OTHER WARRANTY OTHERWISE ARISING OUT OF ANY PROPOSAL,
FINAL STANDARDS OR DRAFT STANDARDS, OR CONTRIBUTION.
Limitation of Liability
IN NO EVENT WILL CDISC OR ANY OF ITS CONSTITUENT PARTS (INCLUDING, BUT NOT LIMITED
TO, THE CDISC BOARD OF DIRECTORS, THE CDISC PRESIDENT, CDISC STAFF, AND CDISC
MEMBERS) BE LIABLE TO ANY OTHER PERSON OR ENTITY FOR ANY LOSS OF PROFITS, LOSS OF
USE, DIRECT, INDIRECT, INCIDENTAL, CONSEQUENTIAL, OR SPECIAL DAMAGES, WHETHER UNDER
CONTRACT, TORT, WARRANTY, OR OTHERWISE, ARISING IN ANY WAY OUT OF THIS POLICY OR
ANY RELATED AGREEMENT, WHETHER OR NOT SUCH PARTY HAD ADVANCE NOTICE OF THE
POSSIBILITY OF SUCH DAMAGES.
Note: The CDISC Intellectual Property Policy can be found at
http://www.cdisc.org/about/bylaws_pdfs/CDISCIPPolicy-FINAL.pdf.
© 2015 Clinical Data Interchange Standards Consortium, Inc. All rights reserved
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July 21, 2015