Usage: This is a domain specific variable, and qualifies the ISTEST variable.
Description: The textual description of the entity secreted by the cells represented in the ISTEST. The combination of ISTEST and ISSCMBCL should describe "the thing, the entity or the analyte" one is measuring, without the need of additional variables.
It is placed right after BDAGNT
No. Free-text description.
--BDAGNT
Binding Agent
Usage: This is a domain specific variable, and qualifies the ISTEST variable.
Description: The textual description of the agent that's binding to the entity in the ISTEST variable. The ISBDAGNT variable is used to indicate that there is a binding relationship between the entities in the ISTEST and ISBDAGNT variables, regardless of direction.
ISBDAGNT is not a method qualifier. It should only be used when the actual interest of the measurement is the binding interaction between the two entities in ISTEST and ISBDAGNT. In other words, the combination of ISTEST and ISBDAGNT should describe "the thing, the entity or the analyte" one is measuring, without the need of additional variables.
The binding agent may be, but not limited to, a test article, a portion of the test article, a related compound, an endogenous molecule, an allergen or an infectious agent.
It is placed right after ISTEST. (this is used more than SCMBCL)
Yes. Two codelists.
-Microorgansim (MICROORG)
-Binding Agent for Immunogenicity Assessments (ISBDAGT).
--TSTOPO
Test Operational Objective
Usage: This is a domain-specific variable for LB and IS that qualifies the --TEST variable. This is a permissible variable.
Description: The textual description of the high-level purpose of the test at the operational level.
It is placed right after ISTSTDTL.
*When getting this ready for internal review, make sure to add TSTDTL into the IS specification table.
Yes. One codelist.
Codelist Rules: this is a non-extensible codelist and the MB SDS/CT team reserves the right to review future term request to this codelist and decide whether addition of new terms are appropriate.
Controlled Terminology:
-SCREEN: A test whose operational purpose is to determine the presence or absence of a substance or organism.
-CONFIRM: A test whose operational purpose is to verify the presence or absence of a substance or organism.
-QUANTIFY: A test whose operational purpose is to determine the amount or concentration of a substance or organism.
use-case examples for "Quantify" are: measurements of any types of antibody titer (titration), viral load, bacterial colony count, drug toxicity concentration, etc.
Analytical purpose vs operational purpose of a test - there is a difference between the two, analytical purpose is about the analyte itself - what specific analyte are you measuring, which is why in lab we tend to have the analyte name as the LBTEST - LBTEST tells the analyte of interest. The operational purpose/objective of an analyte test is to screen, confirm then quantify the analyte - this represents a different level of purpose, and operational purpose can be applied to any analyte hence it is not analyte-specific.
Take ADA as an example, ISTEST = Binding Antidrug Antibody, this test tells you that you are looking for ADAs, not, for example, C-reactive proteins. The Screen, Confirm and Quantify then tell you the operational reason, the whys behind each performed ADA test.
REAS (Currently NSV) : it is been proposed to name this variable as REAS/Reasons Done, or at least re-use this variable. This idea is rejected because we want to control this variable with very specific values and use-cases and REAS has been used in many TAUGs with all kinds of values. We do not want to confuse people who are already using REAS.
Controlled Terminology Notes:
Is "Detection" a valid value for this codelist? We had said before that one should use "detection" when one does not know whether the test is for screening or confirmatory, however is this a synonym to screen? look at the definition for screening, how is it different from detection? Confirm is to verify the previously detected substance is present.
Sponsor feedback is that "detected" is normally reported as a result hence this value does not belong in this codelist, because both screen and confirm are to detect the analyte, they are test qualifiers.
How about "quantify to confirm", what does that mean, is this also a valid value?
Ine mentioned that sometimes you would perform a quantificaiton test to confirm the existence of a substance. In this instance you get a quantitative result which confirms the existence of the substance, from which you normally only report "Positive or Negative" as confirmation. However the PURPOSE of the test is still to confirm the substance, so even though in the process you are using a quantitative method, the high-level purpose is to confirm. In this instance, the correct TSTOPO value to use is still "CONFIRM". Quantitative is an attribute of the method.
Example 1: Tiered Testing of ADA
This example shows the tiered testing of antidrug antibody (ADA). Typical tiered testing scheme for ADA evaluation includes the following steps: screening, confirmatory, and "characterization". In the first tier, all evaluable samples are run in the screen assay. Samples that score positive in the screen assay are then analyzed in a confirmatory assay (tier 2). Samples that are positive for ADA in the screen and confirmatory tiers are reported as positive, while samples that are negative in either tier are reported as negative. Further tiered testing of positive samples frequently includes analysis of antibody titer and neutralizing activity. The variable "TSTOPO" has the following controlled values: SCREEN, CONFIRM and QUANTIFY to describe the operational objective or the reason behind each testing step, and also to provide uniqueness to each row of record. ISGRPID is used in this example to show that the records are related to each other, and in this case, tests are done in a tiered, sequential manner.
Row 1:
Shows the screening of the presence of ADA to DRUG AZ-007.
Row 2:
Shows the confirmation of the previously detected ADA to DRUG AZ-007.
Row 3:
Shows the quantification of the said ADA from the screen and confirmatory steps.
$titleHtml
is.xpt
Row
STUDYID
DOMAIN
USUBJID
ISSEQ
ISREFID
ISGRPID
ISTESTCD
ISTEST
ISBDAGNT
ISTSTOPO
ISCAT
ISSCAT
ISORRES
ISORRESU
ISSTRESC
ISSTRESN
ISSTRESU
ISSPEC
ISMETHOD
VISITNUM
VISIT
ISDTC
1
ABC
IS
ABC-002
1
V555
1
ADA_BAB
Binding Antidrug Antibody
DRUG AZ-007
SCREEN
ANTIDRUG ANTIBODIES
HUMORAL IMMUNITY
POSITIVE
POSITIVE
SERUM
ELECTROCHEMILUMINESCENCE IMMUNOASSAY
1
VISIT 1
2017-07-27
2
ABC
IS
ABC-002
2
V555
1
ADA_BAB
Binding Antidrug Antibody
DRUG AZ-007
CONFIRM
ANTIDRUG ANTIBODIES
HUMORAL IMMUNITY
POSITIVE
POSITIVE
SERUM
ELECTROCHEMILUMINESCENCE IMMUNOASSAY
1
VISIT 1
2017-07-27
3
ABC
IS
ABC-002
3
V555
1
ADA_BAB
Binding Antidrug Antibody
DRUG AZ-007
QUANTIFY
ANTIDRUG ANTIBODIES
HUMORAL IMMUNITY
50
titer
50
50
titer
SERUM
ELECTROCHEMILUMINESCENCE IMMUNOASSAY
1
VISIT 1
2017-07-27
$warningHtml
IS NSV Metadata
Variable
Label
Type
Role
Origin
ISTSTOPO
Test Operational Objective
text
Non-Standard Record Qualifier
CRF
Dataset Wrapper Debug Message
Please add a row column to your dataset.
Example 2: Consolidated Reporting of ADA
The example below shows how to represent the various types of antidrug antibody tests (ADA). Of note, while most ADAs do not inhibit the pharmacodynamic activity of the drug, neutralizing antidrug antibodies (NAbs) can inhibit drug activity soon after the drug is administered. However, most ADAs, or rather, the non-NAbs can lower the drug's systemic exposure just as well by increasing the rate of drug clearance, resulting in a clinically similar outcome to that of Nabs – reduced clinical efficacy.
Rows 1-4 in this example show binding antidrug antibody reaction against the administered analogue drug, whereas rows 5-8 show cross-reactive antidrug antibody reaction against the endogenous protein that's structurally similar to the analogue study drug. Both the study drug and the endogenous protein are represented by the IS domain-specific variable "ISBDAGNT", which only qualifies the ISTEST variable. The variable, "ISTSTOPO", is also used in this dataset to describe the purpose of each testing step, and provides uniqueness to each record. ISGRPID is used to show which records are related.
Rows 1-2:
Show the confirmation and quantification of binding ADA to coagulation factor VIII analogue drug. A binding antidrug antibody is an antibody that binds to a drug.
Rows 3-4:
Show the confirmation and quantification of the neutralizing binding ADA to coagulation factor VIII analogue drug. A neutralizing binding antidrug antibody is a type of ADA that binds to the functional portion of a drug leading to diminished or negated pharmacological activity. The neutralizing ADAs are a subset of the total ADAs.
Rows 5-6:
Show the confirmation and quantification of the cross-reactive binding ADA to the endogenous coagulation factor VIII. A cross-reactive binding antidrug antibody is a type of ADA that binds to endogenous molecules, they are also a subset of the total ADAs.
Rows 7-8:
Show the confirmation and quantification of the neutralizing cross-reactive binding ADA to the endogenous coagulation factor VIII. A neutralizing cross-reactive binding antidrug antibody is a type of ADA that binds to endogenous molecules and diminishes or negates their function; in some cases, they may also bind and negate the function of the study drug. They are a subset of the total ADAs.
Example 3 - ADA Reaction Against Drug Components - Breakdown Product
This example shows the production of antidrug antibody in response to both the prodrug and its active metabolite. A prodrug is a compound that, after administration, is metabolized into a pharmacologically active drug. Please note in this example, even though only confirmatory records are reported and shown, it is assumed that the screening step has also been performed.
Rows 1-2:
Show the confirmation and quantification of the ADA against the prodrug A.
Rows 3-4:
Show the confirmation and quantification of the ADA against the active metabolite of the prodrug A.
$titleHtml
is.xpt
Row
STUDYID
DOMAIN
USUBJID
ISSEQ
ISREFID
ISGRPID
ISTESTCD
ISTEST
ISBDAGNT
ISCAT
ISSCAT
ISORRES
ISORRESU
ISSTRESC
ISSTRESN
ISSTRESU
ISSPEC
ISMETHOD
VISITNUM
VISIT
ISDTC
ISTSTOPO
1
ABC
IS
ABC-004
1
J123
1
ADA_BAB
Binding Antidrug Antibody
PRODRUG A
ANTIDRUG ANTIBODIES
HUMORAL IMMUNITY
POSITIVE
POSITIVE
SERUM
ELECTROCHEMILUMINESCENCE IMMUNOASSAY
1
VISIT 1
2017-07-27
CONFIRM
2
ABC
IS
ABC-004
2
J123
1
ADA_BAB
Binding Antidrug Antibody
PRODRUG A
ANTIDRUG ANTIBODIES
HUMORAL IMMUNITY
30
titer
30
30
titer
SERUM
ELECTROCHEMILUMINESCENCE IMMUNOASSAY
1
VISIT 1
2017-07-27
QUANTIFY
3
ABC
IS
ABC-004
3
J123
2
ADA_BAB
Binding Antidrug Antibody
PRODRUG A ACTIVE METABOLITE
ANTIDRUG ANTIBODIES
HUMORAL IMMUNITY
POSITIVE
POSITIVE
SERUM
ELECTROCHEMILUMINESCENCE IMMUNOASSAY
1
VISIT 1
2017-07-27
CONFIRM
4
ABC
IS
ABC-004
4
J123
2
ADA_BAB
Binding Antidrug Antibody
PRODRUG A ACTIVE METABOLITE
ANTIDRUG ANTIBODIES
HUMORAL IMMUNITY
60
titer
60
60
titer
SERUM
ELECTROCHEMILUMINESCENCE IMMUNOASSAY
1
VISIT 1
2017-07-27
QUANTIFY
$warningHtml
IS NSV Metadata
Variable
Label
Type
Role
Origin
ISTSTOPO
Test Operational Objective
text
Non-Standard Record Qualifier
CRF
Dataset Wrapper Debug Message
Please add a row column to your dataset.
Example 4 - ADA Reaction Against Drug Components: Multiple Epitopes per Molecule
The example below shows the production of antidrug antibody in response to both the study biologic drug and also to different immunogenic epitopes of the biologic drug. This also captures an example for when the tier stops at screening (interferon beta1a assay) and goes straight into NAB from there. While unusual, it reflects the flexibility of these fields to incorporate multiple options.
A biologic drug may be biotechnology-derived therapeutic proteins (including mAbs) and peptides, some plasma-derived products (e.g., coagulation factor replacement products), and naturally derived proteins (e.g. therapeutic enzymes and toxins).
Row 1:
Shows the presence of ADA against the active motabolite (active interferon beta 1a portion) of peginterferon beta-1a.
Rows 2-3:
Show the screening and confirmation of ADA against the PEG epitope portion of peginterferon beta-1a.
Rows 4-5:
Show the presence and quantification of neutralizing ADA against the whole molecule peginterferon beta-1a.
Row 6:
Shows the absence of ADA against the active metabolite (active interferon beta 1a portion) of peginterferon beta-1a.
Rows 7-10:
Show the screening, confirmation and quantification of ADA against the PEG epitope portion of peginterferon beta-1a.
$titleHtml
is.xpt
Row
STUDYID
DOMAIN
USUBJID
ISSEQ
ISREFID
ISTESTCD
ISTEST
ISBDAGNT
ISCAT
ISSCAT
ISORRES
ISORRESU
ISSTRESC
ISSTRESN
ISSTRESU
ISSPEC
ISMETHOD
VISITNUM
VISIT
ISDTC
ISTSTOPO
1
ABC
IS
ABC-007
1
A1
ADA_BAB
Binding Antidrug Antibody
ACTIVE INTERFERON BETA 1A PORTION OF PEGINTERFERON BETA1A
ANTIDRUG ANTIBODIES
HUMORAL IMMUNITY
POSITIVE
POSITIVE
SERUM
IMMUNOASSAY
1
VISIT 1
2017-07-27
SCREEN
2
ABC
IS
ABC-007
2
A1
ADA_BAB
Binding Antidrug Antibody
PEG PORTION OF PEGINTERFERON BETA1A
ANTIDRUG ANTIBODIES
HUMORAL IMMUNITY
POSITIVE
POSITIVE
SERUM
ELISA
1
VISIT 1
2017-07-27
SCREEN
3
ABC
IS
ABC-007
3
A1
ADA_BAB
Binding Antidrug Antibody
PEG PORTION of PEGINTERFERON BETA1A
ANTIDRUG ANTIBODIES
HUMORAL IMMUNITY
NEGATIVE
NEGATIVE
SERUM
ELISA
1
VISIT 1
2017-07-27
CONFIRM
4
ABC
IS
ABC-007
4
A1
ADA_NAB
Neutralizing Antidrug Antibody
PEGINTERFERON BETA1A
ANTIDRUG ANTIBODIES
HUMORAL IMMUNITY
POSITIVE
POSITIVE
SERUM
REPORTER GENE IMMUNOASSAY
1
VISIT 1
2017-07-27
SCREEN
5
ABC
IS
ABC-007
5
A1
ADA_NAB
Neutralizing Antidrug Antibody
PEGINTERFERON BETA1A
ANTIDRUG ANTIBODIES
HUMORAL IMMUNITY
4.7
titer
4.7
4.7
titer
SERUM
REPORTER GENE IMMUNOASSAY
1
VISIT 1
2017-07-27
QUANTIFY
6
ABC
IS
ABC-008
6
V4
ADA_BAB
Binding Antidrug Antibody
ACTIVE INTERFERON BETA 1A PORTION OF PEGINTERFERON BETA1A
ANTIDRUG ANTIBODIES
HUMORAL IMMUNITY
NEGATIVE
NEGATIVE
SERUM
IMMUNOASSAY
1
VISIT 1
2017-08-27
SCREEN
7
ABC
IS
ABC-008
7
V4
ADA_BAB
Binding Antidrug Antibody
PEG PORTION OF PEGINTERFERON BETA1A
ANTIDRUG ANTIBODIES
HUMORAL IMMUNITY
POSITIVE
POSITIVE
SERUM
ELISA
1
VISIT 1
2017-08-27
SCREEN
8
ABC
IS
ABC-008
8
V4
ADA_BAB
Binding Antidrug Antibody
PEG PORTION OF PEGINTERFERON BETA1A
ANTIDRUG ANTIBODIES
HUMORAL IMMUNITY
POSITIVE
POSITIVE
SERUM
ELISA
1
VISIT 1
2017-08-27
CONFIRM
9
ABC
IS
ABC-008
9
V4
ADA_BAB
Binding Antidrug Antibody
PEG PORTION OF PEGINTERFERON BETA1A
ANTIDRUG ANTIBODIES
HUMORAL IMMUNITY
40
titer
40
40
titer
SERUM
ELISA
1
VISIT 1
2017-08-27
QUANTIFY
$warningHtml
IS NSV Metadata
Variable
Label
Type
Role
Origin
ISTSTOPO
Test Operational Objective
text
Non-Standard Record Qualifier
CRF
Dataset Wrapper Debug Message
Please add a row column to your dataset.
Example 5 - Autoimmune Disease Diagnosis
The example below shows how to represent autoantibody data.
Rows 1-2:
Show the screening and quantification of Antinuclear Antibodies, and ISBDAGNT is not populated. This is because, If an antibody test has multiple, unclearly defined/described targets, a pre-coordinated ISTEST should be created and ISBDAGNT should not be populated. In this case extractable nuclear antigens consist of >100 different soluble cytoplasmic and nuclear antigens, the ISBDAGNT variable cannot clearly and appropriately represent the target(s) of this antibody test, therefore the pre-cooirdinated ISTEST of Antinuclear Antibodies is used.
Rows 3-8:
Show the screening and quantification of various Sjogren's Syndrome specific autoantibodies.
is.xpt
is.xpt
Row
STUDYID
DOMAIN
USUBJID
ISSEQ
ISREFID
ISGRPID
ISTESTCD
ISTEST
ISBDAGNT
ISORRES
ISORRESU
ISSTRESC
ISSTRESN
ISSTRESU
ISSPEC
ISMETHOD
VISITNUM
VISIT
ISDTC
ISTSTOPO
1
XYZ
IS
XYZ1234
1
19283746
1
ANA
Antinuclear Antibodies
POSITIVE
POSITIVE
SERUM
MULTIPLEXED BEAD BASED IMMUNOASSAY
1
SCREENING
2018-06-20
SCREEN
2
XYZ
IS
XYZ1234
2
19283746
1
ANA
Antinuclear Antibodies
340
titer
POSITIVE
340
titer
SERUM
MULTIPLEXED BEAD BASED IMMUNOASSAY
1
SCREENING
2018-06-20
QUANTIFY
3
XYZ
IS
XYZ1234
1
19283746
2
ATAB
Binding Autoantibody
SJOGRENS SS-A60
POSITIVE
POSITIVE
SERUM
MULTIPLEXED BEAD BASED IMMUNOASSAY
1
SCREENING
2018-06-20
SCREEN
4
XYZ
IS
XYZ1234
2
19283746
2
ATAB
Binding Autoantibody
SJOGRENS SS-A60
181
AU/mL
181
181
AU/mL
SERUM
MULTIPLEXED BEAD BASED IMMUNOASSAY
1
SCREENING
2018-06-20
QUANTIFY
5
XYZ
IS
XYZ1234
3
19283746
3
ATAB
Binding Autoantibody
SJOGRENS SS-A52
POSITIVE
POSITIVE
SERUM
MULTIPLEXED BEAD BASED IMMUNOASSAY
1
SCREENING
2018-06-20
SCREEN
6
XYZ
IS
XYZ1234
4
19283882
3
ATAB
Binding Autoantibody
SJOGRENS SS-A52
51
AU/mL
51
51
AU/mL
SERUM
MULTIPLEXED BEAD BASED IMMUNOASSAY
1
SCREENING
2018-06-20
QUANTIFY
7
XYZ
IS
XYZ1234
5
19283882
4
ATAB
Binding Autoantibody
SJOGRENS SS-B
POSITIVE
POSITIVE
SERUM
MULTIPLEXED BEAD BASED IMMUNOASSAY
1
SCREENING
2018-06-20
SCREEN
8
XYZ
IS
XYZ1234
6
19283882
4
ATAB
Binding Autoantibody
SJOGRENS SS-B
169
AU/mL
169
169
AU/mL
SERUM
MULTIPLEXED BEAD BASED IMMUNOASSAY
1
SCREENING
2018-06-20
QUANTIFY
$warningHtml
IS NSV Metadata
Variable
Label
Type
Role
Origin
ISTSTOPO
Test Operational Objective
text
Non-Standard Record Qualifier
CRF
Dataset Wrapper Debug Message
Please add a row column to your dataset.
Example 6 - Vaccine Studies
The example below shows how to represent data about vaccine-induced immunological responses, as well immunological data collected during the trial but are not germane to the study vaccine.
Recommended ISCAT values for Vaccine Studies
For vaccine studies, below are the recommended ISCAT and ISSCAT values to provide extra clarity. ISCAT and ISSCAT are not controlled therefore the below values are not mandated.
For immunological data pertaining to the study vaccine : ISCAT = VACCINE-RELATED IMMUNOGENICITY.
For immunological data that are collected during the trial but are not assessments about the study vaccine: ISCAT= HISTORICAL INFECTION OR PREVIOUS VACCINATION.
For assessments measuring the "induced-antibody response", ISSCAT = HUMORAL IMMUNITY.
For assessments measuring the "induced-cellular response", ISSCAT = CELLULAR IMMUNITY.
Rows 1-2:
Show the screening and quantification of "Binding Microbial-induced IgG Antibody" against the Human Respiratory Syncytial virus (RSV)-epitope B at baseline, prior to the administration of the study vaccine. The ISBDAGNT value of "HUMAN RESPIRATORY SYNCYTIAL VIRUS-EPITOPE B", is the immunogenic target in the study vaccine that could potentially stimulate the production of antibodies. Note the ISCAT value here is STUDY VACCINE-INDUCED IMMUNOGENICITY, even though at this point, study vaccine has not been administered to the subject - this is done purposefully to enable the grouping of baseline and treatment measurements.
Rows 3-4:
Show the screening and quantification of "Binding Microbial-induced IgG Antibody" against the Influenza A virus at baseline. Co-infection of the Human Respiratory Syncytial virus and Influenza A virus is commonly observed in patients hence baseline anti-Influenza A antibody is also measured to see if the subject suffers from influenza infection as well. Please note, since Influenza A is NOT the immunogenic target of interest in the RSV Vaccine Study, the ISCAT is therefore populated with the value "HISTORICAL INFECTION OR PREVIOUS VACCINATION".
Rows 5-6:
Show the titer of "Binding Microbial-induced IgG Antibody" against the RSV-epitope B post-vaccination at visit 1 and 2. These two records show the antibody titers had increased post-vaccination, presumably due to the stimulation from the RSV study vaccine. Note the ISCAT is populated with the value "VACCINE-RELATED IMMUNOGENICITY".
is.xpt
is.xpt
Row
STUDYID
DOMAIN
USUBJID
ISSEQ
ISREFID
ISGRPID
ISTESTCD
ISTEST
ISBDAGNT
ISTSTDTL
ISTSTOPO
ISCAT
ISSCAT
ISORRES
ISORRESU
ISSTRESC
ISSTRESN
ISSTRESU
ISSPEC
ISMETHOD
VISITNUM
VISIT
ISDTC
ISTSTOPO
1
RSV1230
IS
RSV1230-011
1
13668
1
Binding Microbial-induced IgG Antibody
HUMAN RESPIRATORY SYNCYTIAL VIRUS-EPITOPE B
VACCINE-RELATED IMMUNOGENICITY
HUMORAL IMMUNITY
POSITIVE
POSITIVE
SERUM
ELISA
1
BASELINE
2017-05-27
SCREEN
2
RSV1230
IS
RSV1230-011
2
13668
1
Binding Microbial-induced IgG Antibody
HUMAN RESPIRATORY SYNCYTIAL VIRUS-EPITOPE B
VACCINE-RELATED IMMUNOGENICITY
HUMORAL IMMUNITY
25
titer
25
25
titer
SERUM
ELISA
1
BASELINE
2017-05-27
QUANTIFY
3
RSV1230
IS
RSV1230-011
1
13668
2
Binding Microbial-induced IgG Antibody
INFLUENZA A VIRUS
HISTORICAL INFECTION OR PREVIOUS VACCINATION
HUMORAL IMMUNITY
POSITIVE
POSITIVE
SERUM
ELISA
1
BASELINE
2017-05-27
SCREEN
4
RSV1230
IS
RSV1230-011
2
13668
2
Binding Microbial-induced IgG Antibody
INFLUENZA A VIRUS
HISTORICAL INFECTION OR PREVIOUS VACCINATION
HUMORAL IMMUNITY
120
titer
120
120
titer
SERUM
ELISA
1
BASELINE
2017-05-27
QUANTIFY
5
RSV1230
IS
RSV1230-011
1
13668
Binding Microbial-induced IgG Antibody
HUMAN RESPIRATORY SYNCYTIAL VIRUS-EPITOPE B
VACCINE-RELATED IMMUNOGENICITY
HUMORAL IMMUNITY
90
titer
90
90
titer
SERUM
ELISA
2
VISIT 1
2017-07-27
QUANTIFY
6
RSC1230
IS
RSV1230-011
2
13668
Binding Microbial-induced IgG Antibody
HUMAN RESPIRATORY SYNCYTIAL VIRUS-EPITOPE B
VACCINE-RELATED IMMUNOGENICITY
HUMORAL IMMUNITY
500
titer
500
500
titer
SERUM
ELISA
3
VISIT 2
2017-08-27
QUANTIFY
Dataset Debug Message
There is a duplicate variable (ISTSTOPO) in the dataset.
IS NSV Metadata
Variable
Label
Type
Role
Origin
ISTSTOPO
Test Operational Objective
text
Non-Standard Record Qualifier
CRF
Dataset Wrapper Debug Message
Please add a row column to your dataset.
Example 7: Testing of Antibody-secreting Cells
Traditional methods such as ELISA that monitor humoral immune responses after immunization or infection typically only quantify specific antibody titers in serum. These methods do not provide any information about the actual number and location of the immune cells that secrete antibodies or cytokines.
The Enzyme-Linked ImmunoSpot (ELISpot) assay is a method to detect and quantify analyte-secreting T or B cells. Generally during Elispot testing, a colored precipitate forms and appears as spots at the sites of analyte localization (analytes typically are cytokines or antibodies), with each individual spot representing an individual analyte-secreting cell. The spots can be counted with an automated ELISpot reader system or manually, using a stereomicroscope. The example below shows how to represent the quantification of antibody-secreting cells (ASCs) as the number of spots per million peripheral blood mononuclear cells (PBMC) as determined by B-cell ELISpot from a vaccine trial.
The IS domain-specific variable, Secreted Molecule by Cells/SCMBCL, is introduced in the example below to allow flexibility in data representation and post-coordination of the various secreted antibody types and their respective ASCs. This approach liberates the ISTEST variable from having to house pre-coordinated and thus hyper-specific values crafted based on secretion and cell types.
Row 1:
Shows the total number of IgG antibody secreting cells (ASCs) from a subject’s blood sample. In this case, ISTEST = Antibody-secreting Cells, where the entity secreted by the cells in ISTEST is represented by the variable, Secreted Molecule by Cells/ISSCMBCL.
Row 2:
Shows the number of H1 specific IgG ASCs from the same subject’s blood sample. In this case, ISTEST = Antibody-secreting Cells, where the entity secreted by the cells in ISTEST is in ISSCMBCL as "IgG antibody specific to H1 antigen".
Row 3:
Shows the number of H3 specific IgG ASCs from the same subject’s blood sample. In this case, ISTEST = Antibody-secreting Cells, where the entity secreted by the cells in ISTEST is in ISSCMBCL as "IgG antibody specific to H3 antigen".
is.xpt
is.xpt
Row
STUDYID
DOMAIN
USUBJID
ISSEQ
ISREFID
ISTESTCD
ISTEST
ISSCMBCL
ISCAT
ISSCAT
ISORRES
ISORRESU
ISSTRESC
ISSTRESN
ISSTRESU
ISSPEC
ISMETHOD
ISDTC
1
INFL456
IS
INF02-01
1
SAMPBL0201
ABSCCL
Antibody-secreting Cells
TOTAL IGG ANTIBODY
VACCINE-RELATED IMMUNOGENICITY
HUMORAL IMMUNITY
2019
SFC/10^6 PBMC
2019
2019
SFC/10^6 PBMC
PERIPHERAL BLOOD MONONUCLEAR CELL
ELISPOT
2011-08-08
2
INFL456
IS
INF02-01
2
SAMPBL0201
ABSCCL
Antibody-secreting Cells
INFLUENZA H1-SPECIFIC IGG ANTIBODY
VACCINE-RELATED IMMUNOGENICITY
HUMORAL IMMUNITY
626
SFC/10^6 PBMC
626
626
SFC/10^6 PBMC
PERIPHERAL BLOOD MONONUCLEAR CELL
ELISPOT
2011-08-08
3
INFL456
IS
INF02-01
3
SAMPBL0201
ABSCCL
Antibody-secreting Cells
INFLUENZA H3-SPECIFIC IGG ANTIBODY
VACCINE-RELATED IMMUNOGENICITY
HUMORAL IMMUNITY
592
SFC/10^6 PBMC
592
592
SFC/10^6 PBMC
PERIPHERAL BLOOD MONONUCLEAR CELL
ELISPOT
2011-08-08
$warningHtml
Dataset Wrapper Debug Message
Please add a row column to your dataset.
Example 8: Testing of Cytokine-secreting Cells
The example below shows data from the vaccine study for Respiratory Syncytial Virus (RSV) where the subject is being vaccinated with a viral vector containing RSV Epitope B. Peripheral blood mononuclear cells are isolated from the the subject and are tested before (as baseline) and after vaccination in order to investigate whether the circulating PBMCs produce increasing amounts of Interferon gamma after re-stimulating with control or RSV-Epitope B in vitro.
The example below shows how to represent the quantification of cytokine-secreting cells as the number of spots per million peripheral blood mononuclear cells (PBMC) as determined by T-Cell ELISpot from a vaccine trial.
Rows 1-2:
Show the measurement of Cytokine-secreting Cells (ISTEST) at baseline after stimulation with placebo (row 1) or RSV Epitope B (Row 2) respectively. The type of cytokine-secreting cells that is measured in this record produces Interferon Gamma, which is represented by the ISSCMBCL variable.
Rows 3-4:
Show the measurement of Cytokine-secreting Cells (ISTEST) at Visit 1 after stimulation with placebo (row 3) or RSV Epitope B (row 4) respectively. The type of cytokine-secreting cells that is measured in this record produces Interferon Gamma, which is represented by the ISSCMBCL variable.
is.xpt
is.xpt
Row
STUDYID
DOMAIN
USUBJID
ISSEQ
ISREFID
ISTESTCD
ISTEST
ISSCMBCL
ISTSTDTL
ISTSTOPO
ISCAT
ISSCAT
ISORRES
ISORRESU
ISSTRESC
ISSTRESN
ISSTRESU
ISSPEC
ISMETHOD
VISITNUM
VISIT
ISDTC
ISAGSTIM
1
RSV1230
IS
RSV1230-011
1
13668
CYKSCCL
Cytokine-secreting Cells
INTERFERON GAMMA
VACCINE-RELATED IMMUNOGENICITY
CELLULAR IMMUNITY
5.1
SFC/10^6 PBMC
5.1
5.1
SFC/10^6 PBMC
PERIPHERAL BLOOD MONONUCLEAR CELL
ELISPOT
1
BASELINE
2017-05-27
NEGATIVE CONTROL
2
RSV1230
IS
RSV1230-011
2
13668
CYKSCCL
Cytokine-secreting Cells
INTERFERON GAMMA
VACCINE-RELATED IMMUNOGENICITY
CELLULAR IMMUNITY
40.5
SFC/10^6 PBMC
40.5
40.5
SFC/10^6 PBMC
PERIPHERAL BLOOD MONONUCLEAR CELL
ELISPOT
1
BASELINE
2017-05-27
RSV-EPITOPE B
3
RSV1230
IS
RSV1230-011
3
13668
CYKSCCL
Cytokine-secreting Cells
INTERFERON GAMMA
VACCINE-RELATED IMMUNOGENICITY
CELLULAR IMMUNITY
6.3
SFC/10^6 PBMC
6.3
6.3
SFC/10^6 PBMC
PERIPHERAL BLOOD MONONUCLEAR CELL
ELISPOT
2
VISIT 1
2017-08-27
NEGATIVE CONTROL
4
RSV1230
IS
RSV1230-011
4
13668
CYKSCCL
Cytokine-secreting Cells
INTERFERON GAMMA
VACCINE-RELATED IMMUNOGENICITY
CELLULAR IMMUNITY
260.5
SFC/10^6 PBMC
260.5
260.5
SFC/10^6 PBMC
PERIPHERAL BLOOD MONONUCLEAR CELL
ELISPOT
2
VISIT 1
2017-08-27
RSV-EPITOPE B
$warningHtml
IS NSV Metadata
Variable
Label
Type
Role
Origin
ISAGSTIM
Antigen Stimulation
text
Non-Standard Record Qualifier
CRF
Dataset Wrapper Debug Message
Please add a row column to your dataset.
Example 9 - Microneutralization Assay
In vaccine studies, microneutralization assays are commonly used in vitro assays to quantify viral-specific neutralizing antibodies in the subject’s specimen that can block viral infection in vitro, and so provide output of vaccine efficacy.
Typically, a subject's serum and the virus of interest are added to in vitro cell cultures. If neutralizing antibodies are present in the serum, those antibodies will bind to the virus and thereby "blocking" and preventing the virus from infecting the cells in the culture plates. By vaccination with a vaccine that induces antibody response, one thus assumes that the quantity of viral-specific antibodies that are able to block viral infection are increased. The neutralization titer is the antiviral antibody titer that blocks viral infection of the cells. The NEUTRALIZING TITER 50% (also known as NT50) in the context of microneutralization assays is defined as the antiviral antibody titer that blocks 50% of viral infection of the cells. Please note that some users may also represent "Neutralizing Titer 50%" as "IC50 titer" or other test descriptors. CDISC recommends housing values such as NT50, IC50 neutralizing titer, etc. in the ISTSTDTL variable.
The example below shows data from the same Respiratory Syncytial Virus (RSV) vaccine study where the subject is being vaccinated with a viral vector containing RSV Epitope B. The subject is tested before (baseline) and after vaccination (visits 1 and 2) whether the anti-RSV binding antibodies present in the subject’s serum also have the functionality to neutralize RSV infection in vitro.
*A Neutralizing antibody is defined as antibodies that bind to, block and prevent non-self agents from infecting cells.
is.xpt
is.xpt
Row
STUDYID
DOMAIN
USUBJID
ISSEQ
ISREFID
ISTESTCD
ISTEST
ISBDAGNT
ISTSTDTL
ISTSTOPO
ISCAT
ISSCAT
ISORRES
ISORRESU
ISSTRESC
ISSTRESN
ISSTRESU
ISSPEC
ISMETHOD
VISITNUM
VISIT
ISDTC
1
RSV1230
IS
RSV1230-011
1
13668
Neutralizing Binding Microbial-induced Antibody
RESPIRATORY SYNCYTIAL VIRUS
NEUTRALIZING TITER 50%
VACCINE-RELATED IMMUNOGENICITY
HUMORAL IMMUNITY
40
titer
40
40
titer
SERUM
MICRONEUTRALIZATION ASSAY
1
BASELINE
2017-05-27
2
RSV1230
IS
RSV1230-011
2
13668
Neutralizing Binding Microbial-induced Antibody
RESPIRATORY SYNCYTIAL VIRUS
NEUTRALIZING TITER 50%
VACCINE-RELATED IMMUNOGENICITY
HUMORAL IMMUNITY
80
titer
80
80
titer
SERUM
MICRONEUTRALIZATION ASSAY
2
VISIT 1
2017-07-27
3
RSV1230
IS
RSV1230-011
3
13668
Neutralizing Binding Microbial-induced Antibody
RESPIRATORY SYNCYTIAL VIRUS
NEUTRALIZING TITER 50%
VACCINE-RELATED IMMUNOGENICITY
HUMORAL IMMUNITY
200
titer
200
200
titer
SERUM
MICRONEUTRALIZATION ASSAY
3
VISIT 2
2017-08-27
$warningHtml
Example 10 - Opsonophagocytic Killing Assay
In vaccine trials, the opsonophagocytic killing (OPK) assay is used as a correlate for protection to measure the "functional capacities" of vaccine-induced antibodies. This in vitro assay aids selecting promising vaccines by demonstrating whether the vaccine-induced antibodies drive efficient complement deposition and subsequent opsonophagocytic killing.
Typically this test is performed by incubating post-immunization sera of a subject with the bacterial strain of interest, phagocytes and complement proteins. If anti-bacterial antibodies are present in the subject's serum, those antibodies will bind to the bacteria together with complement proteins, this subsequently targets the bacteria for opsonization, which is the ingestion and destruction of invading non-self agents by phagocytes. By vaccination, one thus assumes that the quantity of bacterial-specific antibodies are increased, leading to a decreased number of viable bacterial cells in the presence of phagocytes, functional antibodies and complement. The assay read-out is expressed in Opsonization Index (OI) which is calculated using linear interpolation of the serum dilution containing functional antibody killing the desired percentage (usually 50%) of the bacteria, using a pre-specified algorithm.
The example below shows data from a vaccine study for Escherichia Coli (E.Coli) where the subject is being vaccinated with a vector containing E.Coli epitope X. The subject is tested before (baseline, row1) and after vaccination (rows 2-3) whether the vaccine-induced antibodies drive efficient complement deposition and subsequent opsonophagocytic killing of the E.Coli in vitro. The assay read-out is expressed in Opsonization Index (ISTSTDTL), which is a unit-less test.
A functional antibody is defined as antibodies that bind to non-self agents and initiate opsonization (destruction by complement and phagocytes) or active killing of the said non-self agent by other types of cells. Being able to recruit and activate the complement system is the key and definitive nature of a functional antibody.
Example 1 for SDTM IS domain. Team suggests that we create 3 examples; 1 for SDTMIG, 1 for SENDIG 3.0, and 1 for SENDIG 3.1. Team to pick 1 METHOD to use for this example: CHEMILLUMINESCENT IMMUNOASSAY or ELECTROCHEMILLUMINESCENT IMMUNOASSAY (ECLIA) or ELISA or Homogenous Mobility Shift Assay
For example for SENDIG3.0 - Update the result values; Add VISITDY, ISDTC, ISDY only for timing variables.
For example for SENDIG3.1 - Update the result values; Add NOMDY, ISDTC, ISDY only for timing variables.
1) a comment on Example 3. There would not be an analyte for the biologic combination.
2) updated Example 4b to be the peginterferon example where there are two different epitopes being evaluated in BAB but only the intact molecule in NAB. It is also an example of a somewhat unusual testing scheme because part of it predates the consolidation around the 3-tier assay format. (free access to manuscript - https://www.ncbi.nlm.nih.gov/pubmed/16541956)
Also recommend adding a fourth variable to Test Detail: quasi-quantitation for non-titer methods.
Classic example of prodrugs are peptides (such as proinsulin) or proenzymes where the larger molecule may be administered for better PK profile, depending on the body machinery to process into active form. While early in development, the processed molecule may be most relevant, a full immunogenicity assessment would eventually also include the prodrug.
For the PEGINTERFERON BETA1A example, I used the methods that I would be most likely have used when creating the SDTM based on my knowledge of the methods. For all of the other examples, I stuck with ELECTROCHEMILUMINESCENCE IMMUNOASSAY (published) since a very frequent selection. Other common methods that would be used include: EIA (published), ELISA (published), ENZYME MULTIPLIED IMMUNOASSAY TECHNIQUE (published) FLUORESCENT IMMUNOASSAY (published), IMMUNOASSAY (published) IMMUNOCHEMILUMINOMETRIC ASSAY (published), MICROPARTICLE ENZYME IMMUNOASSAY (published), SINGLE ANTIGEN BEAD-BASED MULTIPLEX ASSAY (published). Honestly, a lot of them feel redundant to me, so it may be helpful to discuss what the subtle differences are with the terminology team. (Depending on how that goes, I might request a more generic MICROPARTICLE IMMUNOASSAY for non-enzyme based).
For NAB assays, ligand-binding assays would be similar. There are also enzyme assays that might use: ENZYME LINKED COLORIMETRY (New - don't need this one) (although fluorogenic substrates are more common). For cell-based assays, I am working on getting a list for the terminology team to help identify existing or draft new methods. Obviously clotting factors use the HEMAGGLUTINATION INHIBITION ASSAY.
Other methods that I have seen used infrequently are: FLOW CYTOMETRY (published), FLOW MICROSCOPY (published), MULTIPLEXED BEAD BASED IMMUNOASSAY (published), RIA (published). Others I didn't see on the list are Biacore (or Surface Plasmon Resonance), immunoassays using Europium detection, and some of the immunoassay chip methods being developed for multiplex assays. However, I just might not have know what words I was looking for. And of course they could all be viewed as variants of immunoassay.
With regard to units, here are some that could apply to assays I've seen that would fall under the quasi-quantification umbrella (using surrogate reference standard): % inhibition, Absorbance U/mL, AFU, anti-Xa IU/mL (clotting factors again), Antibody Unit, Arbitrary U, variants of Bq/L, BU/mL, cpm, EIA unit, ELISA unit/mL, Enzyme U, variants of IU/mL, variants of mg/mL and ug/mL, Ratio (it is in the list), Titer, variants of U/mL.
Miniature descriptions of assays that would want to cover somehow
Surface plasmon resonance (generic for BIAcore)
Biolayer interferometry (generic for Octet)
Enzyme assay using a fluorogenic substrate (generate signal after cleavage)
Cell proliferation in response to stimulus
Cell death in response to stimulus
Variants on the reporter gene theme
PCR of the induced mRNA transcription (either endogenous under native promoter or synthetic reporter gene)
Immunoassay of the translated protein (either endogenous or synthetic)
Fluorescence of GFP (synthetic reporter gene only)
Luminescence of luciferase (synthetic reporter gene only)
The peginterferon beta1a example is a reporter gene immunoassay of translation of an endogenous molecule (specifically MxA) under native promoter region. Citation: White et al, Bioanalysis, 2015, 7:2801-11
ADA background information and modeling challenges:
When a study drug is administered to a subject, the drug may stimulate the production of anti-drug antibodies (ADA). It is important to know the presence/absence of the ADA as well as its quantification. If the ISTEST is a generic value: ISTEST = Binding Anti-drug Antibody, how do you represent the name of the drug to which the ADA is reacting to? Or should the drug name be pre-coordinated into the TEST?
A subject produces a certain endogenous molecule (e.g. glucagon), when you administer an analogue drug (e.g. Glucagon Analogue) that is structurally similar to the endogenous antigen, the ADA evoked by the analogue drug may cross-react to the endogenous molecule in the subject. There needs to be a variable to represent the specificity of the endogenous molecule an ADA is reacting to as well. The IS domain currently has no standard variables to represent this type of INFOCT-357
-
Getting issue details...STATUS.
Microbiology team comments
AZ, BMS, Merck, and Covance comments: for representing ADAs, these companies have currently pre-coordinated the name of the drug into the TEST, such as "anti-AZ007 antibody". The problem is that the "AZ007" part will change from a company internal compound number, to the drug's generic name then to the trade mark name, hence there are at least 3 naming variations for a single drug, and therefore 3 "anti-xxx antibody" tests. Results would be presence/absence of the ADA and also the titer. However pre-coordinating the drug name into the TEST is NOT preferred.CT-356
-
Getting issue details...STATUS
From the clinical perspective, the ADA measurements should be represented by the IS domain because immune responses (i.e. antibody production by subject) toward administered drugs are measured, so technically we are assessing the immunogenicity of the drug, and therefore the ADA data should stay in IS instead of LB.
The clinical LB/Microbiology team agrees that this data belongs in IS. However, the ADA request is being made by a non-clinical sponsor, and SEND does not recognize the IS domain as a valid domain in SENDIG 3.1 nor 3.0. For the purposes of non-clinical users, they could report ADA data in LB but CDISC will NOT publish controlled terminology for ADA tests in the LBTEST-CD codelist. The codelist is extensible so this non-clinical extension of CT at a company is valid for submission in LB.
The MB team suggests to post-coordinate the name of the test article and ISTEST; generic ISTEST-CDs should be created.
Will NOT use ISSCAT to house the name of the test article; a NSV "Binding Agent/BDAGNT" is proposed to house the name of the test article.
FYI, the PhUSE team is recommending use of LB domain for SENDIG 3.0 and IS domain for SENDIG 3.1 (which allows custom domains). That white paper is under preparation for distribution in the relatively near future.
Many examples include the non-standard variable "Test Mode" which has values like "DETECTION", "CONFIRMATORY TEST" and "QUASI-QUANTIFICATION". I think the non-standard variable (aka supplemental qualifier) "–REAS", which is already in SDTMIG Appendix C2, might be a better place to represent this information.
If the Test Mode is in supplemental qualification, it would create the appearance of duplicate records in the main table for each sample ID that proceeds past the screening assay. Since knowing this value is essential to integrate the 1-3 results into a single reported value and a key identifier to show which variant of the assay was used, keeping in the main data table is still my preferred approach.
In the currently published model either variable would be a non-standard variable and either variable could be proposed as a new standard variable. I was questioning the name of the variable. From the values given, it seemed that the variable was being used to indicate why the test was being done, rather than to give a property of the test.
When any new standard variable is proposed, I think that best practice would be to also proposed a definition for the variable.
Thank you for your feedback. For ADA testing, we frequently use the word "Tier" to designate which step in the process to the final result, but it seemed very field specific. We also considered "Step" but thought that was more vague than "Mode". Do you have any recommendations for the variable name that would be less confusing than "Mode"?
The values in question, Screening Test, Confirmatory Test and Qasi-quantification, were originally housed in TSTDTL and were later moved into a different variable TSTMOD (Test mode). The TSTMOD NSV is developed by the Cell Phenotyping (CP) team and i believe, this was also a recommendation originally made by the METHOD subteam. We obtained permission from Craig Zwickl (Method and CP team lead) to use this NSV for the IS domain, its usage in IS is consistent to how it is used in the new CP domain. The CP domain and its new variables are still under development which is why TSTMOD remains as a NSV. The CP team is getting ready to have their examples, new variables, and other domain-related information for GGG review and approval. I am hesitant to ask GGG to approve making TSTMOD standard for IS until the CP team has a chance to show and discuss this variable with the governance team first.
In some of the examples, there seems to be redundancy between TSTMOD values and other data. For example, "QUASI-QUANTIFICATION" seems to go with tests whose results are expressed as titers, "SCREENING TEST" and "DETECTION" seem to go with tests whose results are "POSITIVE" or "NEGATIVE", although those results are also used for a test with TSTMOD = "CONFIRMATORY TEST" in examples 1, 2, 3, 4, and 5. In Example 2, rows 1 and 2 seem to be exactly the same except for the value of TSTMOD. If the same test can be used for two different purposes ("SCREENING TEST" and "CONFIRMATORY TEST"), then this is not a property of the test, but rather of the purpose for which the test is being used. If that is so, then the decision not to represent this information in TSTDTL is correct.
It sounds as though there are often protocols that prescribe a series of tests to establish antibody status. This reminds me of the series of tests described in Section 6.3.3, Vaccine-induced seropositivity, in the TAUG-HIV. If the variable TSTMOD is intended to designate a step in a defined sequence of tests, then I think the definition of the variable will need careful attention. I think that this sequence of tests would also have to be explained in something like the SDRG, since I assume that these testing protocols vary by analyte and also evolve over time.
The proposed domain-specific variables need some additional metadata, such as their order within the findings qualifiers.
Should the example be presented showing these as standard variables, rather than supplemental qualifiers? If these become domain-specific standard variables in some version of the SDTM, then in versions of hte SDTMIG and SENDIG based on that version of the SDTM, they would be standard variables.
Jordan, the above proposal now lacks the test mode variable (or a different name such as TIER). Could you please add back in to ensure that all key variables are captured in the request for public comment?
I am not sure if you were at the GGG meeting last November where the MB team sought for overall modeling approval for the IS domain updates, during that meeting, the governance team advised against the use of ISTSTDTL to house these three values: Screening Test, Confirmatory Test and Qusi-quantification, the rational being: these values describe testing conditions/steps rather than actual further details of the test, they are inappropriate for TSTDTL. Therefore it was recommended to the MB team to use a different NSV to house the "tier" values. TSTDTL variable definition and usage still need clarification, i think this may also contribute to people's hesitance to put important and distinguishing information in this variable.
The ISTSTMOD (test mode) NSV is developed by the Cell Phenotyping (CP) team and i believe, this was also a recommendation originally made by the METHOD subteam. We obtained permission from Craig Zwickl (Method and CP team lead) to use this NSV for the IS domain, its usage in IS is consistent to how it is used in the new CP domain. The Cell Phenotyping domain and its new draft variables are still under development which is why TSTMOD remains as a NSV, unfortunately, we can't move NSVs into the main dataset table until they become standard.
My remaining concern is that this is a key piece of information needs to differentiate between rows of data. In the absence of a standard variable, the data sets will continue to flag errors for duplicate rows. This is a concern when the absence of the duplicate rows would indicate the true problem. Could we still include that there will be a standard variable, but the name is still being harmonized with the new Cell Phenotyping domain?
You mean add as a known issue? something like "the TSTMOD" variable remains as a NSV currently but efforts are being made to standardize this variable for the IS domain for a future SDTMIG vXXX?"
I think this is more a process issue rather than modeling, given the CP domain may still change, we'd like to standardize TSTMOD for IS after it is relatively stable (i.e. defined and approved by GGG for CP first at least).
The formatting for the variable ISBDAGT in the header row of IS datasets is currently causing error messages. Please see the section on highlighting here WIKI | Macros | Dataset to see how to highlight using the macro. That will eliminate the error messages.
I spoke to Darcy, who says that highlighting a cell in the header row may be a little tricky. Consult with her if it's not clear from the documentation how to do that.
Thanks Diane! Actually this is the working page for all our historical records and notes. For internal/public review, i will move the examples to the SDTMIG 3.4 WIKI space and make them WIKI/SDTM-complaint - no more highlighting and such. But thanks for checking with Darcy
Adding a note from Diane about the use of "NULL" in ISBDAGNT when there isn't a single binding agent: I think it should just be blank. When SDTMIG Section 4.2.5 says missing data should be represented by nulls, it means blank, not the text string "NULL".
22 Comments
Jordan Li
Example 1 for SDTM IS domain. Team suggests that we create 3 examples; 1 for SDTMIG, 1 for SENDIG 3.0, and 1 for SENDIG 3.1. Team to pick 1 METHOD to use for this example: CHEMILLUMINESCENT IMMUNOASSAY or ELECTROCHEMILLUMINESCENT IMMUNOASSAY (ECLIA) or ELISA or Homogenous Mobility Shift Assay
For example for SENDIG3.0 - Update the result values; Add VISITDY, ISDTC, ISDY only for timing variables.
For example for SENDIG3.1 - Update the result values; Add NOMDY, ISDTC, ISDY only for timing variables.
Joleen White
I updated several things.
1) a comment on Example 3. There would not be an analyte for the biologic combination.
2) updated Example 4b to be the peginterferon example where there are two different epitopes being evaluated in BAB but only the intact molecule in NAB. It is also an example of a somewhat unusual testing scheme because part of it predates the consolidation around the 3-tier assay format. (free access to manuscript - https://www.ncbi.nlm.nih.gov/pubmed/16541956)
Also recommend adding a fourth variable to Test Detail: quasi-quantitation for non-titer methods.
Classic example of prodrugs are peptides (such as proinsulin) or proenzymes where the larger molecule may be administered for better PK profile, depending on the body machinery to process into active form. While early in development, the processed molecule may be most relevant, a full immunogenicity assessment would eventually also include the prodrug.
Joleen White
For the PEGINTERFERON BETA1A example, I used the methods that I would be most likely have used when creating the SDTM based on my knowledge of the methods. For all of the other examples, I stuck with ELECTROCHEMILUMINESCENCE IMMUNOASSAY (published) since a very frequent selection. Other common methods that would be used include: EIA (published), ELISA (published), ENZYME MULTIPLIED IMMUNOASSAY TECHNIQUE (published) FLUORESCENT IMMUNOASSAY (published), IMMUNOASSAY (published) IMMUNOCHEMILUMINOMETRIC ASSAY (published), MICROPARTICLE ENZYME IMMUNOASSAY (published), SINGLE ANTIGEN BEAD-BASED MULTIPLEX ASSAY (published). Honestly, a lot of them feel redundant to me, so it may be helpful to discuss what the subtle differences are with the terminology team. (Depending on how that goes, I might request a more generic MICROPARTICLE IMMUNOASSAY for non-enzyme based).
For NAB assays, ligand-binding assays would be similar.
There are also enzyme assays that might use: ENZYME LINKED COLORIMETRY (New - don't need this one) (although fluorogenic substrates are more common).
For cell-based assays, I am working on getting a list for the terminology team to help identify existing or draft new methods. Obviously clotting factors use the HEMAGGLUTINATION INHIBITION ASSAY.
Other methods that I have seen used infrequently are: FLOW CYTOMETRY (published), FLOW MICROSCOPY (published), MULTIPLEXED BEAD BASED IMMUNOASSAY (published), RIA (published). Others I didn't see on the list are Biacore (or Surface Plasmon Resonance), immunoassays using Europium detection, and some of the immunoassay chip methods being developed for multiplex assays. However, I just might not have know what words I was looking for. And of course they could all be viewed as variants of immunoassay.
Joleen White
With regard to units, here are some that could apply to assays I've seen that would fall under the quasi-quantification umbrella (using surrogate reference standard): % inhibition, Absorbance U/mL, AFU, anti-Xa IU/mL (clotting factors again), Antibody Unit, Arbitrary U, variants of Bq/L, BU/mL, cpm, EIA unit, ELISA unit/mL, Enzyme U, variants of IU/mL, variants of mg/mL and ug/mL, Ratio (it is in the list), Titer, variants of U/mL.
Joleen White
Miniature descriptions of assays that would want to cover somehow
Surface plasmon resonance (generic for BIAcore)
Biolayer interferometry (generic for Octet)
Enzyme assay using a fluorogenic substrate (generate signal after cleavage)
Cell proliferation in response to stimulus
Cell death in response to stimulus
Variants on the reporter gene theme
The peginterferon beta1a example is a reporter gene immunoassay of translation of an endogenous molecule (specifically MxA) under native promoter region. Citation: White et al, Bioanalysis, 2015, 7:2801-11
Jordan Li
ADA background information and modeling challenges:
Microbiology team comments
AZ, BMS, Merck, and Covance comments: for representing ADAs, these companies have currently pre-coordinated the name of the drug into the TEST, such as "anti-AZ007 antibody". The problem is that the "AZ007" part will change from a company internal compound number, to the drug's generic name then to the trade mark name, hence there are at least 3 naming variations for a single drug, and therefore 3 "anti-xxx antibody" tests. Results would be presence/absence of the ADA and also the titer. However pre-coordinating the drug name into the TEST is NOT preferred. CT-356 - Getting issue details... STATUS
Jordan Li
Microbiology team suggestions
Joleen White
FYI, the PhUSE team is recommending use of LB domain for SENDIG 3.0 and IS domain for SENDIG 3.1 (which allows custom domains). That white paper is under preparation for distribution in the relatively near future.
Diane Wold
Many examples include the non-standard variable "Test Mode" which has values like "DETECTION", "CONFIRMATORY TEST" and "QUASI-QUANTIFICATION". I think the non-standard variable (aka supplemental qualifier) "–REAS", which is already in SDTMIG Appendix C2, might be a better place to represent this information.
Joleen White
If the Test Mode is in supplemental qualification, it would create the appearance of duplicate records in the main table for each sample ID that proceeds past the screening assay. Since knowing this value is essential to integrate the 1-3 results into a single reported value and a key identifier to show which variant of the assay was used, keeping in the main data table is still my preferred approach.
Diane Wold
In the currently published model either variable would be a non-standard variable and either variable could be proposed as a new standard variable. I was questioning the name of the variable. From the values given, it seemed that the variable was being used to indicate why the test was being done, rather than to give a property of the test.
When any new standard variable is proposed, I think that best practice would be to also proposed a definition for the variable.
Joleen White
Thank you for your feedback. For ADA testing, we frequently use the word "Tier" to designate which step in the process to the final result, but it seemed very field specific. We also considered "Step" but thought that was more vague than "Mode". Do you have any recommendations for the variable name that would be less confusing than "Mode"?
Jordan Li
Hi Diane Wold, Joleen White
The values in question, Screening Test, Confirmatory Test and Qasi-quantification, were originally housed in TSTDTL and were later moved into a different variable TSTMOD (Test mode). The TSTMOD NSV is developed by the Cell Phenotyping (CP) team and i believe, this was also a recommendation originally made by the METHOD subteam. We obtained permission from Craig Zwickl (Method and CP team lead) to use this NSV for the IS domain, its usage in IS is consistent to how it is used in the new CP domain. The CP domain and its new variables are still under development which is why TSTMOD remains as a NSV. The CP team is getting ready to have their examples, new variables, and other domain-related information for GGG review and approval. I am hesitant to ask GGG to approve making TSTMOD standard for IS until the CP team has a chance to show and discuss this variable with the governance team first.
Diane Wold
In some of the examples, there seems to be redundancy between TSTMOD values and other data. For example, "QUASI-QUANTIFICATION" seems to go with tests whose results are expressed as titers, "SCREENING TEST" and "DETECTION" seem to go with tests whose results are "POSITIVE" or "NEGATIVE", although those results are also used for a test with TSTMOD = "CONFIRMATORY TEST" in examples 1, 2, 3, 4, and 5. In Example 2, rows 1 and 2 seem to be exactly the same except for the value of TSTMOD. If the same test can be used for two different purposes ("SCREENING TEST" and "CONFIRMATORY TEST"), then this is not a property of the test, but rather of the purpose for which the test is being used. If that is so, then the decision not to represent this information in TSTDTL is correct.
It sounds as though there are often protocols that prescribe a series of tests to establish antibody status. This reminds me of the series of tests described in Section 6.3.3, Vaccine-induced seropositivity, in the TAUG-HIV. If the variable TSTMOD is intended to designate a step in a defined sequence of tests, then I think the definition of the variable will need careful attention. I think that this sequence of tests would also have to be explained in something like the SDRG, since I assume that these testing protocols vary by analyte and also evolve over time.
Diane Wold
The proposed domain-specific variables need some additional metadata, such as their order within the findings qualifiers.
Should the example be presented showing these as standard variables, rather than supplemental qualifiers? If these become domain-specific standard variables in some version of the SDTM, then in versions of hte SDTMIG and SENDIG based on that version of the SDTM, they would be standard variables.
Joleen White
Jordan, the above proposal now lacks the test mode variable (or a different name such as TIER). Could you please add back in to ensure that all key variables are captured in the request for public comment?
Jordan Li
Hi Joleen,
I am not sure if you were at the GGG meeting last November where the MB team sought for overall modeling approval for the IS domain updates, during that meeting, the governance team advised against the use of ISTSTDTL to house these three values: Screening Test, Confirmatory Test and Qusi-quantification, the rational being: these values describe testing conditions/steps rather than actual further details of the test, they are inappropriate for TSTDTL. Therefore it was recommended to the MB team to use a different NSV to house the "tier" values. TSTDTL variable definition and usage still need clarification, i think this may also contribute to people's hesitance to put important and distinguishing information in this variable.
The ISTSTMOD (test mode) NSV is developed by the Cell Phenotyping (CP) team and i believe, this was also a recommendation originally made by the METHOD subteam. We obtained permission from Craig Zwickl (Method and CP team lead) to use this NSV for the IS domain, its usage in IS is consistent to how it is used in the new CP domain. The Cell Phenotyping domain and its new draft variables are still under development which is why TSTMOD remains as a NSV, unfortunately, we can't move NSVs into the main dataset table until they become standard.
Please let me know if you have more questions,
Jordan
Joleen White
My remaining concern is that this is a key piece of information needs to differentiate between rows of data. In the absence of a standard variable, the data sets will continue to flag errors for duplicate rows. This is a concern when the absence of the duplicate rows would indicate the true problem. Could we still include that there will be a standard variable, but the name is still being harmonized with the new Cell Phenotyping domain?
Jordan Li
You mean add as a known issue? something like "the TSTMOD" variable remains as a NSV currently but efforts are being made to standardize this variable for the IS domain for a future SDTMIG vXXX?"
I think this is more a process issue rather than modeling, given the CP domain may still change, we'd like to standardize TSTMOD for IS after it is relatively stable (i.e. defined and approved by GGG for CP first at least).
Diane Wold
The formatting for the variable ISBDAGT in the header row of IS datasets is currently causing error messages. Please see the section on highlighting here WIKI | Macros | Dataset to see how to highlight using the macro. That will eliminate the error messages.
I spoke to Darcy, who says that highlighting a cell in the header row may be a little tricky. Consult with her if it's not clear from the documentation how to do that.
Jordan Li
Thanks Diane! Actually this is the working page for all our historical records and notes. For internal/public review, i will move the examples to the SDTMIG 3.4 WIKI space and make them WIKI/SDTM-complaint - no more highlighting and such. But thanks for checking with Darcy
Jordan Li
Adding a note from Diane about the use of "NULL" in ISBDAGNT when there isn't a single binding agent: I think it should just be blank. When SDTMIG Section 4.2.5 says missing data should be represented by nulls, it means blank, not the text string "NULL".