Pharmacogenomic Data Submissions — Companion Guidance

Administrative draft guidance, not enacted legislation. This captured publisher edition preserves the listed document and attachments; later developments are not independently consolidated.

Guidance for Industry
Pharmacogenomic Data Submissions —
Companion Guidance
DRAFT GUIDANCE
This guidance document is being distributed for comment purposes only.
Comments and suggestions regarding this draft document should be submitted within 90 days of
publication in the Federal Register of the notice announcing the availability of the draft guidance.
Submit comments to the Division of Dockets Management (HFA-305), Food and Drug
Administration, 5630 Fishers Lane, rm. 1061, Rockville, MD 20852. All comments should be
identified with the docket number listed in the notice of availability that publishes in the Federal
Register.
For questions regarding this draft document contact (CDER) Federico Goodsaid, 301-796-1535 or
(CBER) Raj Puri, 301-827-0471 .
U.S. Department of Health and Human Services
Food and Drug Administration
Center for Drug Evaluation and Research (CDER)
National Center for Toxicological Research (NCTR)
Center for Biologics Evaluation and Research (CBER)
Center for Devices and Radiological Health (CDRH)
August 2007
Procedural
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Guidance for Industry
Pharmacogenomic Data Submissions —
Companion Guidance
Additional copies are available from:
Office of Training and Communication
Division of Drug Information, HFD-240
Center for Drug Evaluation and Research
Food and Drug Administration
5600 Fishers Lane
Rockville, MD 20857
(Tel) 301-827-4573
http://www.fda.gov/cder/guidance/index.htm
and/or
Office of Communication, Training and
Manufacturers Assistance, HFM-40
Center for Biologics Evaluation and Research
Food and Drug Administration
1401 Rockville Pike, Rockville, MD 20852-1448
http://www.fda.gov/cber/guidelines.htm.
(Tel) 800-835-4709 or 301-827-1800
and/or
Office of Health and Industry Programs
Division of Small Manufacturers Assistance (HFZ-220)
Center for Devices and Radiological Health
Food and Drug Administration
1350 Piccard Drive
Rockville, MD 20850-4307 U.S.A.
http://www.fda.gov/cdrh/ggpmain.html
Email: dsma@cdrh.fda.gov
Fax: 301.443.8818
(Tel) Manufacturers Assistance: 800.638.2041 or 301.443.6597
(Tel) International Staff Phone: 301.827.3993
U.S. Department of Health and Human Services
Food and Drug Administration
Center for Drug Evaluation and Research (CDER)
National Center for Toxicological Research (NCTR)
Center for Biologics Evaluation and Research (CBER)
Center for Devices and Radiological Health (CDRH)
August 2007
Procedural
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TABLE OF CONTENTS
I. 	INTRODUCTION............................................................................................................. 1
II. 	GENE EXPRESSION DATA FROM MICROARRAYS ............................................. 2
A. 	RNA Isolation, Handling, and Characterization......................................................................... 2
1. Pre-RNA Isolation Considerations .................................................................................................. 2
2. RNA Isolation from Tissues or Cells................................................................................................ 3
3. RNA Isolation from Whole Blood and PBMCs ................................................................................ 4
4. RNA Storage .................................................................................................................................... 5
5. RNA QC ........................................................................................................................................... 5
B. 	Labeling Reactions......................................................................................................................... 6
C. 	Hybridizations for Microarrays .................................................................................................. 7
D. 	Fluorescence Reader Settings for Microarrays........................................................................... 8
E. 	Differentially Expressed Genes..................................................................................................... 9
F. 	Biological Interpretation of Lists of Differentially Expressed Genes...................................... 10
III. 	GENOTYPING ............................................................................................................... 11
A. 	Genotyping Methods.................................................................................................................... 11
B. 	DNA Isolation, Handling, and Characterization....................................................................... 12
C. 	Genotyping Report....................................................................................................................... 12
IV. 	PROFICIENCY TESTING............................................................................................ 13
V. 	GENOMIC DATA IN CLINICAL STUDY REPORTS ............................................. 15
VI. 	GENOMIC DATA FROM NONCLINICAL TOXICOLOGY STUDIES ................ 16
A. 	Expanding the Selection Process Criteria.................................................................................. 17
B. 	Characterization of a Particular Compound............................................................................. 17
C. 	General Scientific Discussion ...................................................................................................... 17
VII. 	DATA SUBMISSION FORMAT .................................................................................. 18
A. 	Submission Standard ................................................................................................................... 18
B. 	Microarray Gene Expression Data............................................................................................. 18
C. 	Clinical and Nonclinical Data ..................................................................................................... 19
APPENDIX I: 	EXPERIMENTAL SUMMARY TABLE (EXPSUMTABLE)..................... 20
APPENDIX II: EXAMPLE—SUBMITTING A NONCLINICAL STUDY DATA ............ 21
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Guidance for Industry1
Pharmacogenomic Data Submissions — Companion Guidance
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7 	This draft guidance, when finalized, will represent the Food and Drug Administration's (FDA's) current thinking on
8 	this topic. It does not create or confer any rights for or on any person and does not operate to bind FDA or the
9 	public. You can use an alternative approach if the approach satisfies the requirements of the applicable statutes and
10 	regulations. If you want to discuss an alternative approach, contact the FDA staff responsible for implementing this
11 	guidance. If you cannot identify the appropriate FDA staff, call the appropriate number listed on the title page of
12 	this guidance.
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I. 	INTRODUCTION
This guidance is intended to be used as a companion to the guidance Pharmacogenomic Data
Submissions (March 2005). It reflects experience gained since the issuance of that guidance with
voluntary genomic data submissions as well as with review by the FDA of numerous protocols
and data submitted under investigational new drug (IND) applications, new drug applications
(NDAs), and biologics license applications (BLAs). The recommendations are intended to
facilitate scientific progress in the field of pharmacogenomics and to facilitate the use of
pharmacogenomic data in drug development. The FDA believes that the recommendations made
in this companion guidance, together with the recommendations in the March 2005 guidance,
will benefit sponsors considering the submission of either voluntary genomic data submissions
or marketing submissions containing genomics data. 	As technology changes and more
experience is gained, these recommendations may be updated.
FDA's guidance documents, including this guidance, do not establish legally enforceable
responsibilities. Instead, guidances describe the Agency's current thinking on a topic and should
1 This guidance has been prepared by the Center for Drug Evaluation and Research (CDER), the National Center for
Toxicological Research (NCTR) and the Center for Biologics Evaluation and Research (CBER), in cooperation with
the Center for Devices and Radiological Health (CDRH) at the Food and Drug Administration.
For the purposes of this guidance, the term drug or drug product includes human drug and biological products.
Paperwork Reduction Act Public Burden Statement: According to the Paperwork Reduction Act of 1995, a
collection of information should display a valid OMB control number. The valid OMB control number for this
information collection is 0910-0557 (expires 12/31/2007). The time required to complete this information
collection is estimated to average 10 hours per response, including the time to review instructions, search existing
data resources, gather the data needed and complete and review the information collection.
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be viewed only as recommendations, unless specific regulatory or statutory requirements are
cited. The use of the word should in Agency guidances means that something is suggested or
recommended, but not required.
II. 	GENE EXPRESSION DATA FROM MICROARRAYS
The following methodological issues should be considered when submitting gene expression
data from microarrays. The recommendations made in this document apply to development of
microarray data that might be submitted in support of INDs, NDAs, and BLAs. For microarray
data supporting the clearance or approval of a diagnostic device, additional information beyond
these recommendations may be requested.
A. 	RNA Isolation, Handling, and Characterization
One of the most critical steps in performing RNA-based experiments such as microarray gene
expression experiments is the isolation of high quality, intact RNA. To achieve this goal and
preserve sample integrity throughout the course of the experiment, some steps before and after
RNA purification should be carefully planned to ensure quality during isolation and confirm
high quality before use in a downstream application. A secondary goal is maximizing the yield
of RNA. In addition, storage and shipping conditions of samples can influence the stability of
RNA. Thus, it is very important to store the RNA under the best conditions to preserve the
integrity of the sample. Finally, we recommend that standard operating procedures (SOPs) be
established to ensure reproducibility of the RNA isolation method and RNA quality (e.g., see
http://www.fda.gov/nctr/science/centers/toxicoinformatics/maqc/docs/MAQC_Sample_Processi
ng_Overview_SOP.pdf). The following recommendations will help achieve these goals.
1. 	Pre-RNA Isolation Considerations
RNA is sensitive to degradation by RNase, which is ubiquitously present in living
organisms. Thus, sample-handling issues should be addressed and methods for
sample handling need to be assessed to ensure that the methods and their
associated metrics are suitable for the purpose to which they are applied before
embarking on RNA isolation from samples. We also recommend that any work
areas and equipment to be used to generate data for submission studies be
dedicated specifically for RNA isolation and other RNA-related work.
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RNase-free reagents and disposables/glassware: It is imperative to use RNase-free
reagents and glassware for RNA isolation. Commercially available RNA isolation
kits often provide these. It may be of value to confirm that RNase inactivation
methods are functioning as expected prior to launching submission studies.
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RNA stabilizer(s): We recommend that the need for adding RNA stabilizing
agents to samples/reagents be assessed and an appropriate RNA stabilizer be
identified, and assessed for suitability in a pilot experiment.
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Batch size: We recommend that the maximal batch size for sample preparation be
determined to help identify and limit the time taken for the entire RNA isolation
process. Establishing an upper limit for batch size will reduce problems
encountered during the scaling-up process since long processing times can
jeopardize RNA integrity.
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Sample collection, storage and shipping conditions: there are numerous variables
that may affect sample reproducibility in microarray studies. We recommend that
the impact of the following variables on RNA quality be assessed. These include:
• 	maximum and minimum sample dimensions
• 	volumes
• 	weights
Additional important parameters include:
• 	correct sampling technique per tissue/organ
• 	timing of sample dissection/processing time
• 	maximum allowed elapsed time between resection and stabilization of the
tissue
• 	stability of specimen in transport under recommended conditions
(temperature, duration, etc)
There may be other study-specific parameters to consider. For example, in
oncology studies we recommend that the percent tumor in the sample be
determined.
2. 	RNA Isolation from Tissues or Cells
Treatment of cells or tissue samples prior to RNA isolation and careful handling
are necessary to preserve RNA. Several methods are available for successful
isolation of high quality RNA. A number of reagents are also available that aid in
preserving the quality of RNA. For example, an RNA stabilizer that is compatible
with RNA isolation procedures may be added to the isolated tissues or cells
before storing the samples. Alternatively, tissues or cells can be quickly frozen in
liquid nitrogen and stored at -80 0C to prevent RNA degradation. Tissues or cells
can also be homogenized in the presence of a strong denaturant that inactivates
RNase, followed by freezing the homogenate at -20 0C or below. In any case, we
recommend that the manufacturer’s specifications be followed and that the quality
of the resulting RNA be acceptable for the study. RNase-free reagents,
equipment, materials, and work spaces should be used for subsequent isolation
and analytical steps.
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3. 	RNA Isolation from Whole Blood and PBMCs	125
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RNA can be isolated from whole blood or from peripheral blood mononuclear
cells (PBMCs). Most studies conducted so far have used the PBMCs since they
are the most transcriptionally active cells in blood.2 This fraction primarily
consists of lymphocytes and monocytes. RNA isolated from PBMCs and whole
blood should not be used interchangeably in the same study.
RNA isolation from whole blood: RNA may be isolated from whole blood, and
this specimen type is attractive since the blood sample with the RNA stabilizer
can be stored for a long time, presumably without compromising RNA quality or
the stability of expression profiles under manufacturer-suggested conditions. The
storage conditions and the maximum storage durations used to store whole blood
samples selected should meet any acceptance criteria applicable to the selected
platform. One disadvantage of RNA isolation from whole blood is that
reticulocytes (immature red blood cells (RBCs)) in the specimen, while
representing only 0.5-2% of the RBCs, can contribute up to 70% of the mass of
mRNA in total RNA, of which globin mRNA is the major RNA. In microarray
gene expression experiments, the overabundance of globin mRNA can result in
failure to detect some transcripts that are of low abundance.
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3 While working with
whole blood specimens, the need for protocols for reducing globin mRNA from
whole blood4 or alternative methods to minimize the impact of globin mRNA on
gene expression data should be considered. If it is determined that such methods
are needed, ensure that they work as intended within the context of your method.
The quality of the microarray data generated from whole blood specimens can be
improved by removing reticulocytes, although this often requires that blood be
processed at the site of blood draw. Any manipulation of the blood sample may
cause a change in the gene expression profiles of some transcripts.5 We
recommend, therefore, that a study to simulate the conditions of the preclinical or
clinical blood specimen collection and manipulation to be employed be
conducted, to assess the impact of key variables on the chosen method.
RNA isolation from PBMCs: RNA may be isolated from PBMCs that have been
isolated from a whole blood specimen using one of several techniques. Some
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2 An Analysis of Blood Processing Methods to Prepare Samples for GeneChip Expression Profiling- Technical Note
from Affymetrix. (http://www.affymetrix.com/support/technical/technotes/blood_technote.pdf)
3 Fan H. (2005) The transcriptome in blood: challenges and solutions for robust expression profiling. Current
Molecular Medicine 5, 3-10.
4 Debey S. et al., (2006) A highly standardized, robust, and cost-effective method for genome-wide transcriptome
analysis of peripheral blood applicable to large-scale clinical trials. Genomics 87, 653-664.
5 Burczynski M.E. and Dorner A.J. (2006) Transcriptional profiling of peripheral blood cells in clinical
pharmacogenomic studies. Pharmacogenomics 7, 187-202.
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commonly used methods include the Ficoll-Hypaque centrifugation and use of
cell preparation tubes with sodium citrate. RNA isolation from PBMCs is the
preferred method for many applications since the RNA is free from globin mRNA
and generally gives better results on microarrays. However, it has been shown
that time delays and temperature changes can affect gene expression profiles of
several genes
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6, 7 and therefore it is critical to isolate the PBMCs within hours of
blood collection, particularly if any stabilization materials or storage conditions to
stabilize the expression profiles are not used. Regardless of the method chosen,
one should assure, through measurement of quality parameters, that it consistently
and reliably yields RNA with acceptable performance parameters for the selected
analytical method. If more than one RNA isolation method (e.g., methods from
two different manufacturers) are selected, it should be ascertained that both
methods give equivalent results in your system.
4. 	RNA Storage
For short term storage, RNA suspended in RNase-free water (with 0.1mM EDTA)
or in TE buffer should be stored at -800 C in aliquots in non-frost-free freezers.
Repeated freeze-thaws should be avoided. Generally, RNA is stable for about a
year at -800 C under the above-mentioned conditions. For long term storage, RNA
samples could be stored at -200 C in ethanol.
5. 	RNA QC
The quality of RNA samples can be monitored in several ways. The most
widespread current metric is spectrophotometric analysis using the ratio of
absorbance at 260nm/280nm as a measure of RNA quality and purity.8 Two
common additional methods are agarose gel electrophoresis and analysis using a
dedicated RNA analyzing instrument. Considerations for RNA quality metrics
include the following:
• 	For spectrophotometric analysis, the ratio of absorbance at 260nm and 280 nm
(A260/A280) can be used to assess RNA purity and is typically recommended to
be greater than 1.8.9
6 Baechler E.C. (2004) Expression levels for many genes in human peripheral blood cells are highly sensitive to ex
vivo incubation. Genes and Immunity 5, 347-353.
7 Debey S. (2004) Comparison of different isolation techniques prior gene expression profiling of blood derived
cells: impact on physiological responses, on overall expression and the role of different cell types. The
Pharmacologics Journal 4, 193-207.
8 http://arrayconsortium.tgen.org/np2/public/qualitycontrol/jsp.
9 Dumur, C.I. et al., (2004) Evaluation of quality-control criteria for microarray gene expression analysis. Clinical
Chemistry 50(11), 1994-2002.
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• 	For agarose gel analysis, generally a 1% denaturing agarose gel is used, and
clearly visible 18S and 28S RNA bands are taken as measure of RNA
integrity. Ideally, the intensity of the 28S band should be twice the intensity of
the 18S band. Degraded RNA will have a smeared appearance and lack two
clear bands.
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• 	For dedicated RNA analysis instruments, several different metrics may be
useful, e.g., presence of 18S and 28S rRNA peaks, ratio of 28S/18S bands,
and percentage of total RNA represented by the rRNA peaks.8 Specific
recommendations regarding use of dedicated RNA analysis instruments and
the data they generate can be found in manufacturers materials.10
Regardless of the method(s) chosen to assess RNA quality, it should be ascertained
that the acceptance criteria for the RNA samples are consistently appropriate to yield
RNA quality that is suitable for the analytical method selected. The selected RNA
isolation method should minimize genomic contamination of the isolated RNA
because genomic DNA could negatively affect downstream applications.
B. 	Labeling Reactions
In genomic submissions, it is important that sponsors use a labeling system that has been
documented to perform well on a given manufacturer’s array. It is critical that the sponsor begin
the labeling process with high-quality RNA-free of contaminants that might affect the labeling
efficiency or introduce labeling bias, as compromised RNA quality will affect subsequent steps
of sample processing and ultimately lead to poorer quality microarray data. We recommend that
the use of accepted quality measures (18S/28S ratios) be included in this report and that RNA
samples prepared for labeling be of comparable quality.
We recommend the use of consistent methods of target labeling throughout the particular study
or studies that will be analyzed as a group since dissimilar microarray data could be obtained
when kits from different manufacturers or different types of labeling kits are used. If there is any
change in a critical component in the labeling kit (kit manufacturer, key enzyme or reagent), we
recommend that it be tested to demonstrate comparability of the data generated prior to being
used with samples analyzed as an arm of a study. We recommend that reagent lot acceptance
criteria be developed to ensure the reproducibility of labeling reactions.
The use of standard operating procedures (SOPs) is encouraged, and we recommend that
operators be fully trained on all protocols prior to processing of samples for the study.
Equipment should be on an appropriate maintenance schedule and the laboratory environment
maintained in accordance with the manufacturer’s recommendations.
10 Imbeaud S, Graudens E, Boulanger V, Barlet X, Zaborski P, Eveno E, Mueller O, Schroeder A. and Auffray C.
(2005) Towards standardization of RNA quality assessment using user-independent classifiers of microcapillary
electrophoresis traces. Nucleic Acids Research 33(6), e56.
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The development of QC or intermediate labeling steps is highly recommended. If any
intermediate QC step indicates a problem and the RNA is of reasonable quality, the labeling
process can be repeated to produce higher quality input material for hybridization to the
microarray chip. In addition, it is recommended that reagents be stored under appropriate
conditions. Use of controls and reference standards are recommended to verify consistent
performance throughout the labeling procedure.
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We recommend the use of validated standard operating procedures (SOPs) addressing all aspects
of sample collection, storage, and sample and array processing to generate microarray data, and
all operators should be fully trained on all protocols prior to initiating the study. It is also
advisable to establish appropriate maintenance schedules for all equipment, and ensure that the
laboratory environment is maintained in accordance with the SOPs.
C. 	Hybridizations for Microarrays
You should include pertinent information on reproducibility and accuracy of array hybridization
in your submission package. In the absence of widely accepted QA/QC control metrics for DNA
microarray technologies or consensus on how to establish the reliability of the results obtained
from a DNA microarray experiment, we recommend you establish and assess internal control
metrics for quality and reliability. For example, some organizations have used QA/QC pass/fail
filters to eliminate outlier arrays and some array manufacturers recommend thresholds for certain
platform-specific QC measurements.
Currently, the ERCC (External RNA Controls Consortium)11 and MAQC (MicroArray Quality
Control Consortium) groups are developing spike-ins and reference standards, which may be
useful in evaluating the quality of a particular microarray experiment when available. Another
recent effort has produced a pair of reference RNA pools for use with rat DNA microarrays that
allow accuracy, reproducibility, and dynamic range assessments.12 Conceptually, this strategy
could be used to produce reference materials for any organism, including human. Until such
independent resources are widely available and consensus quality standards are developed and
implemented by the microarray community, carefully adhering to the microarray manufacturer’s
recommended procedures offers the best current practice at this time. Detailed protocols have
been prepared by major DNA microarray manufacturers and posted on the MAQC Web site.12
Because the microarray field is evolving, it is important to note that manufacturers occasionally
change probe sequences and protocols, reflecting continuing improvements to this technology.
Regardless of the source of quality control materials and methods, we recommend you describe
how you selected those that you use, and how you determined that they were acceptable for your
purposes.
We recommend that the following be clearly outlined in a figure:
11 External RNA Controls Consortium. (2005) The External RNA Controls Consortium: a progress report. Nature
Methods 2: 731 - 734.
12 http://edkb.fda.gov/MAQC/
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• 	Microarray chip details	277
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A key information in the submission package is the information of micoarray chips used.
There are at least two different categories of microarray chips, commercial chips and
customer chips (array manufactured by sponsor, or contractors).
1. If commercial array chips are used in the study, sponsor should provide the following
information: the name of manufacture, type of array, lot #, manufacture date (or
expiration date), and array QC parameters (QC tests performed by vendor).
2. If customer chips are used for the study, the sponsor should provide: manufacturing
protocol, documents from vendors if any materials purchased from commercial resources,
QC thresholds, and QC testing results.
• 	Microarray experimental design details
We recommend you include sample processing and labeling (e.g., were samples processed in
the same batch or different batches; was the same procedure used for all samples, technical
replication, biological replication and other appropriate information).
• 	How data were generated and analyzed
One approach would be to start with how the primary data were obtained (e.g., laser scanner
settings, software settings for image acquisition). We recommend you explain how the data
from individual microarrays were combined and the normalization method and then provide
data filtering, data analysis, statistical tests, and other appropriate information.
D. 	Fluorescence Reader Settings for Microarrays
Microarray technology uses a multi-step process in which variability at each step must be
reduced to maximize the probability of detecting changes that arise from biology and not from
experimental artifact. Scanners used to collect the microarray signals are a potential source of
variability in data derived from this technology. Recent publications have pointed out the
importance of optimal reader settings for obtaining high-quality microarray data.13 The signal
readout system is often thought of as a black box that quantitates the signal from each DNA
microarray spot. The measurement of the abundance of RNA species by DNA microarray
technology assumes a linear relationship between the signal read-out from the scanner and the
dye concentration, which is further assumed to be linearly correlated with transcript abundance
in the RNA sample.
Each array system, scanner type, and signaling dye combination, may have its own linear
dynamic range, which changes with voltage gains. Important recommendations for scanners that
13 Shi, L., Tong, W., Su, Z., Han, T., Han, J., Puri, R.K., Fang, H., Branham, W.S., Chen, J.J., Xu, Z., Harris, S.C.,
Hong, H., Xie, Q., Perkins, R.G., and Fuscoe, J.C. (2005). Microarray scanner calibration curves: characteristics
and implications. BMC Bioinformatics 6 (Suppl 2):S11.
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will help minimize technical variability and improve consistency of data collection include the
following:
1. 	Calibration of scanners as recommended by the manufacturers
2. 	Routine use of standardized scanner reference materials for calibrations to allow for
characterization of concentration-dependent read-outs
3. 	Attention to scanner settings (e.g., laser power and voltage gain). Specifically, we
recommend that scanner settings be set to maximize the linear dynamic range.
4. 	Keeping the scanner laser power and voltage settings constant during a study. Note that
some scanners are not tunable, so that this source of variability is eliminated.
5. 	If the dye-intensity to signal output relationship is defined, possible corrections when
signals fall outside of the linear dynamic range, thus reducing variability in the very high
or very low signal range
6. 	Submission of scanner setting and calibration information as part of the submission
package
E. 	Differentially Expressed Genes
Specific genes sets derived from microarray experiments can be proposed as genomic
biomarkers for a specific endpoint in a defined context. Such specific gene sets should be
reproduced upon review if the analysis protocol is identical to that reported by the sponsor. The
sponsor should include in the submission a clear description of the steps, parameters, and
algorithms leading to the list of differentially expressed genes list in the genomic submission.
Different analysis protocols may yield dissimilar lists of differentially expressed genes, and these
cannot be justified solely through a biological interpretation if they are to be proposed as
genomic biomarkers. To the extent that these genomic biomarker sets become part of a decision-
making process in drug development or therapeutic applications, we recommend that transfer of
genomic biomarker sets from microarrays to other platforms (such as quantitative RT-PCR) be
attempted only after the sponsor concludes that these differentially expressed genes are sensitive,
specific, and reproducible.
Sources of variability in microarray data leading to the step in which the differentially expressed
gene list is determined may be minimized by following the recommendations in this document.
To determine which genes are in fact differentially expressed, a number of factors need to be
considered that may have confounding effects:
• 	The application of platform-specific flags
• 	Rejection criteria for low-intensity transcripts
• 	Rejection criteria for outlier hybridizations
• 	Platform-specific normalization protocols
• 	Data analysis protocol for selection of differentially expressed genes
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There is no consensus at this time regarding the appropriate choices for each of these factors.
The sponsor should exercise care in how parameters and protocols are chosen for each of these
factors and should consult current literature regarding efforts to reach a consensus.
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14, 	, 	, 	, 	,	15 16 17 18
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In principle, several analysis protocols can be used to determine lists of differentially expressed
gene lists for a sufficiently large number of technical and biological replicates. In practice,
constraints on the number of technical and biological replicates are likely to be the norm in
genomic submissions. For example, technical replicates are constrained by the minimum amount
of RNA needed to hybridize each biological sample. Both clinical as well as preclinical samples
may have major constraints in the total amount of RNA available from each biological sample.
Biological replicates are constrained by the total number of subjects to be included in a study.
We recommend that these constraints be considered in the selection of analysis protocols for the
determination of differentially expressed genes.
F. 	Biological Interpretation of Lists of Differentially Expressed Genes
Once the list of differentially expressed genes has been generated via a variety of statistical and
analytical tools, the next step in the process should be to interpret the biological meaning of gene
expression changes and determine whether biological pathways may be of functional relevance
to the mechanism of drug action, or may be correlated to safety and/or efficacy.
A number of questions should be addressed at this point, including, for example:
• 	Are genes from a particular pathway or set of pathways significantly overrepresented in
the list?
• 	How many pathways are affected?
• 	Can the mechanism of action be inferred from the functions of the pathways altered or
from the pattern of expression across the genes within these pathways?
14 Simon R. Development and evaluation of therapeutically relevant predictive classifiers using gene expression
profiling. (2006) J Natl Cancer Inst. 98(17):1169-71.
15 Simon R. (2006) A checklist for evaluating reports of expression profiling for treatment selection. Clin Adv
Hematol Oncol. 4(3):219-24.
16 Dobbin KK, Simon RM. (2007) Sample size planning for developing classifiers using high dimensional DNA
microarray data. Biostatistics. 8(1):101-17.
17 Varma S, Simon R. (2006) Bias in error estimation when using cross-validation for model selection. BMC
Bioinformatics. 7:91.
18 Guo L, Lobenhofer EK, Wang C, Shippy R, Harris SC, Zhang L, Mei N, Chen T, Herman D, Goodsaid FM,
Hurban P, Phillips KL, Xu J, Deng X, Sun YA, Tong W, Dragan YP, Shi L. (2006) Rat toxicogenomic study
reveals analytical consistency across microarray platforms. Nat Biotechnol. 24(9):1162-1169.
19 Canales RD, Luo Y, Willey JC, Austermiller B, Barbacioru CC, Boysen C, Hunkapiller K, Jensen RV, Knight
CR, Lee KY, Ma Y, Maqsodi B, Papallo A, Peters EH, Poulter K, Ruppel PL, Samaha RR, Shi L, Yang W, Zhang
L, Goodsaid FM. (2006) Evaluation of DNA microarray results with quantitative gene expression platforms. Nat
Biotechnol. 24(9):1115-22.
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• 	What is the tissue specificity of the pathways and the gene function in relation to
biological processes?
• 	What are the magnitude and/or pattern of the alteration in a particular pathway in relation
to treatments with other compounds (related or unrelated) with known pharmacological
or toxicological properties?
At present, no single tool can be used to find answers to all these questions, but a combination of
tools can be used to address a particular question of interest as thoroughly as possible. To this
end, a variety of analytical platforms are available, either free on the Web or via purchase of a
commercially available product.
An overlap of the biological interpretations obtained with two or more different databases can
facilitate a consensus on what the interpretation should be. However, this is not always the case.
Consensus can be hindered by many factors including, but not limited to, absence of information
on the compound of interest in the reference databases or a lack of annotation for particular
pathways of interest. For example, subsets of genes may be placed in specific pathways in one
system, but they may not be represented in the same pathways in another pathway analysis tool,
or genes may not have been evaluated in a particular platform. In pathway analysis databases,
the information may differ depending on which content is extracted from the literature and how
that extraction is performed (whether automated or by manual curators). In addition, a critical
distinction is whether all information is extracted, or if only the information supported by direct
experimental evidence included in the publication is extracted. We recommend heavy reliance on
the literature and on reference databases to extract functional information on specific gene lists
and generate hypotheses on the biological significance of the relevant set of genes.
We also recommend that the biological significance of gene sets proposed by a sponsor be
accompanied by a standard set of information that will enable recapitulation of the analysis and
assessment of the validity of the interpretation by regulatory reviewers. In addition, we
recommend that the gene sets proposed by sponsors should be validated by other conventional
techniques, such as Q-PCR, or RT-PCR. Such information should include, but not be limited to:
• 	Type of database used for annotation, including vendor name
• 	Methods and approaches (cut-off, statistical tests) used to identify over-represented
pathways within the database
• 	References used to justify any user-defined annotation
• 	A summary by the sponsor of the interpretation of the pathway annotation results
III. 	GENOTYPING
A. 	Genotyping Methods
Genetic differences among individuals occur in a variety of forms, from alterations in
chromosomal arrangement or copy number to single base-pair changes. Much of the genetic
variation currently used in pharmacogenetics occurs at the level of individual genes (e.g., drug
metabolizing enzymes) on a scale ranging from single base-pair changes to entire gene
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duplications or deletions. Examining genomic DNA is often the most reliable and practical
method for characterizing genetic variation, although methods based on protein or mRNA
expression levels can be preferable in some situations, such as when determining treatment-
sensitivity of cancer or viral infection. Many methods are currently available for characterizing
DNA variations, and new methods are rapidly being developed.
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B. 	DNA Isolation, Handling, and Characterization
Whole blood is commonly used for the extraction of genomic DNA in clinical research settings.
Blood collection tubes generally use anticoagulants such as EDTA, CPD, ACD, Citrate or
Heparin. DNA in a blood sample is susceptible to degradation unless properly stored. Although
manufacturers of blood collection tubes usually recommend appropriate storage conditions for
optimum stability, we recommend you ensure that these conditions yield DNA that is suitable for
your assay, for example, by checking for the presence of full-length DNA.
When DNA is isolated from blood, carryover of contaminants such as salts, phenol, ethanol,
heme (in blood DNA isolation), and detergents from conventional purification procedures can
inhibit performance of DNA in downstream applications. In addition, contamination with the
anticoagulant heparin impairs amplification by PCR.20, 21 Potential for contamination and
interference in isolation procedures should be assessed, and procedures for avoiding these should
be implemented where necessary.
Although DNA is a relatively stable molecule, it should be stored carefully. Degradation of DNA
can have a major effect on any results obtained, generating errors that are both quantitative and
qualitative. There are several factors that can result in DNA degradation including introduction
of enzymatically active nucleases, acid hydrolysis, and degradation due to repeated freeze-thaw
cycles. You should implement DNA handling and storage procedures that limit these and any
other factors that could affect DNA quality. For example:
• 	Avoid exposure of DNA solutions to nucleases that may be present on lab equipment or
in reagents;
• 	Store DNA at a slightly alkaline pH (e.g., Tris EDTA buffer) once isolated;
• 	Maintain long-term storage of DNA at -200 C or at -800 C.
• 	Freeze sample in aliquots to reduce freeze-thaw degradation
C. 	Genotyping Report
We recommend that the following information be included in the genotyping report, regardless
of the genomics submission type (see the Pharamcogenomic Data Submissions guidance for
regulatory requirements):
• 	Description of assay platform or methodology
20 Smythe et al., BMC Infectious Diseases, 2002, 2:13.
21 Yokota et al., Journal of Clinical Laboratory Analysis, 1999, 13: 133 – 140.
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• 	Samples studied, including demographics and sample size justification for
genotype/clinical phenotype correlation and adequate coverage for ethnic/racial
groups; include expected allele frequency in different populations
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• 	Alleles measured and correlation with metabolic status designation
– 	For metabolizing enzymes, how EM (extensive metabolizer), PM (poor
metabolizer), IM (intermediate metabolizer), or UM (ultra rapid
metabolizer) are determined
– 	Sample test report
– 	For new genes, correlation between gene variant and encoded protein
activity
• 	Whether the assay was performed in a CLIA-certified lab or research lab
IV. 	PROFICIENCY TESTING
High-quality data are the foundation for deriving reliable biological conclusions from a
microarray gene expression study. However, large differences in data quality have been
observed in published data sets when the same platform was used by different laboratories.22, 23
In many cases, poor quality of microarray data was due not to the inherent quality problems of a
platform but to the lack of technical proficiency of the laboratory that generated the data. Such a
systematic procedural failure in a laboratory is much more serious than randomly failed
hybridizations that lead to outlying arrays, because the laboratory may not recognize that it has a
procedural failure problem.
The Agency recommends that sponsors provide data that will enable FDA reviewers to
objectively evaluate the competency of the laboratory that generated the data in a genomic
submission. Many studies report quality control metrics or use standards to provide internal
assessments of microarray data. This information is useful for confirming the technical ability to
reproducibly perform a given assay within an individual study.
In addition to within-laboratory testing, an assessment of the overall competence of a facility can
be performed through inter-laboratory comparisons, such as proficiency testing. Laboratory
proficiency can be monitored through a number of approaches.
• 	RNA sources
Two FDA-led initiatives have developed and characterized reference RNA samples for
proficiency testing. Mixed tissue pools of rat RNA samples have been designed with known
22 Shi L, Tong W, Goodsaid F, Frueh FW, Fang H, Han T, Fuscoe JC and Casciano DA (2004) QA/QC: challenges
and pitfalls facing the microarray community and regulatory agencies. Expert Rev Mol Diagn 4:761-77.
23 Shi L, Tong W, Fang H, Scherf U, Han J, Puri RK, Frueh FW, Goodsaid FM, Guo L, Su Z, Han T, Fuscoe JC,
Xu ZA, Patterson TA, Hong H, Xie Q, Perkins RG, Chen JJ and Casciano DA (2005) Cross-platform comparability
of microarray technology: intra-platform consistency and appropriate data analysis procedures are essential. BMC
Bioinformatics 6 Suppl 2:S12.
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differences in tissue-selective genes24 and have been used in the first proficiency testing
program for microarray laboratories.
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25 In addition, the MicroArray Quality Control
(MAQC) Project26 developed two human reference materials and extensively tested them on
multiple gene expression platforms. Data from both initiatives have been deposited in public
databases, and the RNA samples used in the MAQC project are now commercially available
for use by laboratories to assess ability to reproduce MAQC data.
• 	Experimental design for proficiency testing
Most RNA-based genomic assays are designed to detect differentially expressed genes or
profiles. A proficiency testing program for these assays could be centered on testing of
replicates of two biologically different samples with known differences in transcript
abundance, in order to measure the ability to repeatedly detect differential gene expression.
For example, the laboratory could plan to process three or more replicates of sample A
(labeled A1, A2, and A3) and three or more replicates of sample B (labeled B1, B2, and B3),
to evaluate the within-laboratory repeatability both in terms of repeatable intensity
measurements and repeatable detection of differential gene expression. If multiple
laboratories provide data generated using the same RNA samples and the same platform,
site-to-site reproducibility and comparability of sites to detect differences in expression can
be assessed. We recommend that laboratories use a proficiency testing program, and that the
testing be repeated throughout the year so that multiple data sets from the same laboratory
can be compared to confirm the consistency of the laboratory’s performance over time.
• 	Laboratory compliance
The Agency encourages microarray facilities to adhere to the good laboratory practices
outlined in 21 CFR 58. Laboratories may also wish to obtain CMS/CLIA certification if the
microarray data have potential clinical or diagnostic applications. All CLIA-compliant assays
require repeated data comparisons with other providers to verify the competency of
individual laboratories. Participation in a proficiency testing program would fulfill this CLIA
requirement.
24 Thompson KL, Rosenzweig BA, Pine PS, Retief J, Turpaz Y, Afshari CA, Hamadeh HK, Damore MA,
Boedigheimer M, Blomme E, Ciurlionis R, Waring JF, Fuscoe JC, Paules R, Tucker CJ, Fare T, Coffey EM, He Y,
Collins PJ, Jarnagin K, Fujimoto S, Ganter B, Kiser G, Kaysser-Kranich T, Sina J and Sistare FD (2005) Use of a
mixed tissue RNA design for performance assessments on multiple microarray formats. Nucleic Acids Res 33:e187.
25 Reid LH et al. (2006). Proficiency testing program for microarray facilities (in preparation).
http://www.expressionanalysis.com/proficiency_test.html.
26 Shi L, Reid LH et al (2006) MicroArray Quality Control (MAQC) Project: A comprehensive survey
demonstrates concordant results between gene expression technology platforms. Nat Biotechnol 24(9), 1151-1161.
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V. 	GENOMIC DATA IN CLINICAL STUDY REPORTS	547
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There are many possible sources of data for genomic data submissions. Genomic data from
clinical studies may result from microarray expression profiling experiments, genotyping or
single-nucleotide polymorphism (SNP) experiments, or from other evolving analytical
methodologies pertaining to drug dosing or metabolism, safety assessments, or efficacy
evaluations. Genomic data may also be reported from studies where other data are also reported,
such as with efficacy or safety data from clinical or nonclinical studies. However, these data can
be reviewed only if the content of the clinical data report included in the submission contains
sufficient detail regarding the sample selection.
The following describes FDA’s current thinking about what data should be submitted with
genomics data in a submission to the Agency (including a voluntary submission). Regulatory
applications for these data are described in detail in FDA’s Pharmacogenomic Data Submissions
guidance in the context of different algorithms for the submission of pharmacogenomic data
consistent with FDA requirements for INDs, NDAs, and BLAs, as well as for Voluntary
Genomic Data Submissions (VGDS). Throughout the following discussion, we suggest that you
refer to the Pharmacogenomic Data Submissions guidance for in-depth background on this
discussion.
In all genomic submissions, a full clinical study report is very helpful to Agency reviewers. The
report should provide a clear explanation of how the critical design features of the study were
chosen as well as enough information on the plan, methods, and conduct of the study to
eliminate ambiguity in how the study was carried out. The report with its appendices should also
provide individual patient data relevant to pharmacogenomics, including demographic and
baseline data, and details of analytical methods such as validation reports to allow replication of
the critical analyses. It is also particularly important that all analyses, tables, and figures carry
clear identification of the set of patients from which they were generated.
To improve the usefulness of the submission, we recommend that the content of the clinical
section describing a genomic experiment contain the following information:
– 	Title page
– 	Table of contents
– 	Synopses and summary of findings
– 	Background and scientific rationale
– 	Primary and secondary study objectives
– 	Study design, sample collection and storage, and pharmacogenomic methods
– 	Clinical study protocol ., including minimally27:
- inclusion and exclusion criteria
- demographic data
- listing of individual experimental measurements by patient, including
pharmacokinetic/pharmacodynamic datasets and lab results; and explanation of
missing data
27 ICH guidance E3 Structure and Content of Clinical Study Reports.
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- disposition of patients	590
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- protocol deviations
- individual adverse events or laboratory abnormalities
- pharmacogenomic and other biomarker datasets as necessary
- correlation between clinical and pharmacogenomic data
- discussion of the comprehensiveness of genomic data in all randomized patients
and the impact of loss of such information, if any, on inferences and associations,
especially with outcomes
- additional discussion and conclusions
–References and any supplementary materials.
The specific sequence and grouping of topics may change if alternatives are more logical for a
particular study. The Pharmacogenomics Data Submissions guidance and other Agency
regulations and guidance contain detailed discussions on specific regulatory requirements.
The preferred submission standard for clinical data is the Clinical Data Interchanges Standards
Consortium (CDISC) Study Data Tabulation Model (SDTM) standard. Please see the FDA Data
Standards Council Web site28 for more information on the standard.29
VI. 	GENOMIC DATA FROM NONCLINICAL TOXICOLOGY STUDIES
Genomic data can be collected in nonclinical studies, such as toxicogenomic studies. This
section describes how to submit nonclinical toxicology data with a genomic data submission.
How the data should be submitted depends on the purpose of the submission. Three general
types of submissions can be identified:
• 	The first type of submission might have the objective of expanding the selection process
criteria (i.e., screening to aid in the selection of a lead compound for clinical
development or to eliminate compounds with certain characteristics).
• 	The second might present the characterization of a particular compound.
• 	The third might present a general scientific discussion that might not be related to the
development of a compound and/or compound class.
28 See http://www.fda.gov/oc/datacouncil/.
29 The SDTM can be obtained from the CDISC Web site at http://www.cdisc.org/models/sds/v3.1/index.html .
SDTM Implementation Guides:
• 	The Study Data Tabulation Model Implementation Guide (SDTM-IG) for clinical study data can be
obtained from the CDISC web site at: http://www.cdisc.org/models/sds/v3.1/index.html
• 	The Study Data Specification for submitting SDTM datasets to CDER can be obtained at
http://www.fda.gov/cder/regulatory/ersr/Studydata-v1.1.pdf
PK/PD data submission should be in SAS.XPT-compatible format.
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A. 	Expanding the Selection Process Criteria	624
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When a submission is intended to expand the selection process criteria and precede the
development of a compound (i.e., screening for lead compounds or to eliminate certain
characteristics), we recommend the inclusion of the following information:
1. 	General narrative about the objective of the submitted application, brief narrative about the
compound(s), intended use, and mechanism of action
2. 	Objective of the submitted study with its experimental design (treatment, duration, replicates,
drug formulation, route of administration, rationale for dose selection). As applicable,
information about species, strain, sex, genetic background, age, weights, developmental
stage, organ/tissue where sample originated, cell type can be included. We recommend that a
brief description of sample handling, storage and preparation methodology also be included.
3. Toxicology parameters including clinical pathology (serum chemistry and hematology) and
histopathology data consistent with STP guidelines (Toxicologic Pathology, 32, 126-131
(2004)), preferably in an electronic format). When applicable, the correlation between
pathology findings and genetic variation or gene or protein expression should be explained.
4. 	Correlations of individual animal data to genetic variation or gene or protein expression
should be explained.
5. 	Pharmacokinetic parameters and ADME properties of the compound should be provided if
known. When applicable, correlation between pharmacokinetic findings and genetic
variation or gene or protein expression should be highlighted.
6. 	Reference should be made to scientific and analytical methods for genetic variation or gene
or protein expression, including genotyping or expression profiling methods, statistical
methods, and software packages used.
B. 	Characterization of a Particular Compound
If the intent of a submission is to characterize a particular compound, it is generally
recommended that the toxicology portion of the submission be reported in a similar format to a
toxicology report. These reports follow the good review practices template (Section 4.1 m (1 to
6)). If the template is not used, a copy of the study protocol should accompany the line listings
and generally include clinical signs, mortality, body weight, food consumption, hematology,
clinical chemistry, urinalysis, gross pathology, organ weights, histopathology, and
pharmaco/toxicokinetics (as available) with a full tabulation of data suitable for detailed review.
These data contain line listings of the individual data points, including laboratory data points, for
each animal along with summary tabulations of data points. A copy of the study protocol is
expected to accompany the line listings.
C. 	General Scientific Discussion
When a submission contains data to support a general scientific discussion that is not necessarily
related to the development of a compound and/or compound class, the minimal amount of
nonclinical data to be submitted should be similar to the previously described scenarios.
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However, it is up to the sponsor to provide adequate information to clarify and support the
scientific issues discussed. The data submitted will probably not be detailed, but we recommend
that it be tabulated in a form that will be concise and adequately descriptive for the specific
purpose of the submission.
VII. 	DATA SUBMISSION FORMAT
A general description of clinical and non-clinical data associated with genomic data submissions
is included in Sections III and IV of this guidance.. This section provides details on electronic
data submission formats for genomic and associated non-clinical or clinical data.
A. 	Submission Standard
For any type of genomic data submission, we encourage you to submit the data electronically in
a tab-delimited file conforming to the Clinical Data Interchange Standards Consortium (CDISC)
Study Data Tabulation Model (SDTM) standard or the Standard for Exchange of Nonclinical
Data (SEND) SDTM format per the CDISC guidelines (http://www.cdisc.org/ ).30
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B. 	Microarray Gene Expression Data
When a microarray gene expression experiment is included in a genomic data submission, both
raw and normalized gene expression data as well as the gene lists that are used to support the
biological conclusions in the submission should be submitted electronically.
• 	Raw data – It is recommended that one file be submitted per array. For example, CEL
files would be submitted for the Affymetrix GeneChip platform, while the tab delimited
spreadsheet format could be used for other platforms with the gene ID (e.g., GenBank
Acc#, manufacturer ID), in the first column.
• 	Normalized data – It is recommended that one file be submitted per array. The tab
delimited spreadsheet format should be used with the gene ID (e.g., GenBank Acc#,
manufacturer ID) in the first column.
• 	Gene lists – Lists of genes supporting a biological interpretation in the submission should
be included. Probeset IDs in each array should identify each entry in these lists. The lists
should be submitted along with parameters such as fold change and p-value for each gene
of interest in a tab delimited format.
• 	Besides the parameters mentioned above, the gene lists (or Results) submission should
also include following information:
o 	software used for data analysis
30 More information can be found at FDA Data Standards Council Web site, http://www.fda.gov/oc/datacouncil/.
The Standard for Exchange of Nonclinical Data (SEND) Implementation Guide for Animal Toxicology Studies can
be obtained from the CDISC Web site at:
http://www.cdisc.org/models/send/v2.3/SENDV2.3ImplementationGuide.pdf.
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o 	filtering conditions (such as intensity filter, spot flag filter, spot size filter, and
detection call filter)
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o 	normalization method selected for data analysis (there are several different
normalization methods available, such as median, Lowess, and housekeeping
gene normalizations)
o 	methods selected for statistical analyses.
In addition to the data files, an experimental summary table (called ExpSumTable, Appendix I)
should be prepared to summarize the key experimental parameters investigated in the microarray
study. The experimental parameters should be prepared in accordance to the MIAME (Minimum
Information About a Microarray Experiment) guidelines.
C. 	Clinical and Nonclinical Data
The Study Data Tabulation Model (SDTM) that encompasses both CDISC and SEND has been
developed to guide the organization, structure, and format for both clinical and nonclinical data
submissions. For genomic data submissions, clinical and nonclinical data should be prepared in
accordance to the SDTM. CDISC/SEND organizes the study data under the concept of domains.
Each domain summarizes a collection of observations with a topic-specific commonality. At this
point, we ask that each domain be prepared as a separate file in a tab-delimited format. Appendix
II provides examples of data formatted for a nonclinical data submission.
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APPENDIX I: 	EXPERIMENTAL SUMMARY TABLE (EXPSUMTABLE)	729
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The ExpSumTable summarizes key experimental parameters investigated in a microarray study.
The first three columns are required. The first two columns provide the subject ID (e.g., animal
ID) and Array ID respectively. The microarray raw data file is specified in the third column. The
remaining columns provide the key experimental parameters that could be used to group array
data for analysis. Sponsors should consider including parameters in the ExpSumTable useful in
data analysis.
SubID 	ArrayID 	File 	dose(ppk) 	Tissue 	Chemical 	…
1 	Ctl_1 	Ctl_1.cel 	0 	Liver 	Corn Oil 	…
2 	Ctl_2 	Ctl_2.cel 	0 	Liver 	Corn Oil 	…
3 	Ctl_3 	Ctl_3.cel 	0 	Liver 	Corn Oil 	…
4 	Ctl_4 	Ctl_4.cel 	0 	Liver 	Corn Oil 	…
5 	Ctl_5 	Ctl_5.cel 	0 	Liver 	Corn Oil 	…
6 	Ctl_6 	Ctl_6.cel 	0 	Liver 	Corn Oil 	…
7 	Ctl_7 	Ctl_7.cel 	0 	Liver 	Corn Oil 	…
8 	Ctl_8 	Ctl_8.cel 	0 	Liver 	Corn Oil 	…
9 	Ctl_9 	Ctl_9.cel 	0 	Liver 	Corn Oil 	…
10 	Ctl_10 	Ctl_10.cel 	0 	Liver 	Corn Oil 	…
11 	Ctl_11 	Ctl_11.cel 	0 	Liver 	Corn Oil 	…
12 	Treat_1 	Treat_1.cel 	10 	Liver 	Cmpd_1 	…
13 	Treat_2 	Treat_2.cel 	50 	Liver 	Cmpd_1 	…
14 	Treat_3 	Treat_3.cel 	100 	Liver 	Cmpd_1 	…
15 	Treat_4 	Treat_4.cel 	10 	Liver 	Cmpd_2 	…
16 	Treat_5 	Treat_5.cel 	50 	Liver 	Cmpd_2 	…
17 	Treat_6 	Treat_6.cel 	100 	Liver 	Cmpd_2 	…
18 	Treat_7 	Treat_7.cel 	10 	Liver 	Cmpd_3 	…
19 	Treat_8 	Treat_8.cel 	50 	Liver 	Cmpd_3 	…
20 	Treat_9 	Treat_9.cel 	100 	Liver 	Cmpd_3 	…
21 	Treat_10 	Treat_10.cel 	10 	Liver 	Cmpd_4 	…
22 	Treat_11 	Treat_11.cel 	50 	Liver 	Cmpd_4 	…
23 	Treat_12 	Treat_12.cel 	100 	Liver 	Cmpd_4 	…
739
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The preparation of nonclinical study data included in a genomic data submission is illustrated
through the hypothetical example below. You can find more details on data preparation in the
SEND format at: http://www.cdisc.org/models/send/v2.3/SENDV2.3ImplementationGuide.pdf.	744
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The objective of the example experiment is to identify gene expression patterns that might be
related to liver toxicity. Ten rats were used in the study, five for control and five dosed by oral
gavage with Drug X in a 6-day repeated-dose experiment. Microarray gene expression and
clinical pathology data were reported for each rat in the study. For the genomic data submission,
domains 1-6 are required. Refer to the SEND implementation guide noted above regarding
which domains apply to the study. It is important to use a short name starting with the two-letter
domain code for the column names (variables).
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Domain 1: Study Design Summary
SSPARMC
D 	SSPAR 	SSVAL
SSSE
Q
STTYP 	Study Type 	Repeat Dose Toxicity 	1
LBNAM 	Laboratory Name 	Company XYZ 	2
LBLOC
Laboratory
Location 	City, State 	3
SPECIES 	Species 	Rat 	4
STRAIN 	Strain 	Sprague-Dawley 	5
DESIGN 	Study Design 	Parallel 	6
TRMSAC
Terminal Sacrifice
Period 	1-6 days 	7
GLPTYP 	GLP Type 	FDA 	8
QARPT 	QA Report 	Yes 	9
DURDOS
Duration of
Dosing 	6 days 	10
STTITL 	Study Title
6-Day Oral Toxicity Study in Male Sprague-Dawley Rats
treated with a drug 	11
ALTSTDID 	Alternate Study Id 	Submission ID 123456 	12
SENDVER 	SEND Version 	2.3 	13
STDTC 	In-Life Start Date 	5/1/2001 	14
ENDTC 	In-Life End Date 	7/1/2001 	15
Domain 2: Subject Characteristics
USUBJI
D
ARMC
D
SCTESTC
D
SCORRE
S 	SCTEST
SCSTRES
C 	SCSEQ
1 	1 	SEX 	Male 	Sex 	Male 	1
2 	1 	SEX 	Male 	Sex 	Male 	2
3 	1 	SEX 	Male 	Sex 	Male 	3
4 	1 	SEX 	Male 	Sex 	Male 	4
5 	1 	SEX 	Male 	Sex 	Male 	5
6 	2 	SEX 	Male 	Sex 	Male 	6
7 	2 	SEX 	Male 	Sex 	Male 	7
8 	2 	SEX 	Male 	Sex 	Male 	8
9 	2 	SEX 	Male 	Sex 	Male 	9
10 	2 	SEX 	Male 	Sex 	Male 	10
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Domain 3: Group Characteristics
ARMCD 	GCTESTCD 	GCORRES 	GCTEST 	GCSTRESC 	GCSEQ
1 	GRPNAM 	Low-dose 	Group Name 	Low-dose 	1
1 	CTLGRPDFL 	N 	Control Group Flag 	N 	2
2 	GRPNAM 	Control 	Group Name 	Control 	3
2 	CTLGRPDFL 	Y 	Control Group Flag 	Y 	4
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Domain 4: Exposure
USUBJID 	ARMCD 	EXTRT 	EXTRTV 	EXDOSE EXDOSU 	EXDOSFRQ 	EXDOSFRM EXDOSTOT EXROUTE EXDUR EXGRPID EXSEQ STDY* 	ENDY*
1 	1 	X 	Labrafil 	10 	mg/kg 	once daily 	liquid 	10 	oral 	P6D 	1 	1 	1 	6
2 	1 	X 	Labrafil 	10 	mg/kg 	once daily 	liquid 	10 	oral 	P6D 	1 	2 	1 	6
3 	1 	X 	Labrafil 	10 	mg/kg 	once daily 	liquid 	10 	oral 	P6D 	1 	3 	1 	6
4 	1 	X 	Labrafil 	10 	mg/kg 	once daily 	liquid 	10 	oral 	P6D 	1 	4 	1 	6
5 	1 	X 	Labrafil 	10 	mg/kg 	once daily 	liquid 	10 	oral 	P6D 	1 	5 	1 	6
6 	2 	Vehicle 	Labrafil 	0 	mg/kg 	once daily 	liquid 	0 	oral 	P6D 	2 	6 	1 	6
7 	2 	Vehicle 	Labrafil 	0 	mg/kg 	once daily 	liquid 	0 	oral 	P6D 	2 	7 	1 	6
8 	2 	Vehicle 	Labrafil 	0 	mg/kg 	once daily 	liquid 	0 	oral 	P6D 	2 	8 	1 	6
9 	2 	Vehicle 	Labrafil 	0 	mg/kg 	once daily 	liquid 	0 	oral 	P6D 	2 	9 	1 	6
10 	2 	Vehicle 	Labrafil 	0 	mg/kg 	once daily 	liquid 	0 	oral 	P6D 	2 	10 	1 	6
766
767 	*General SDTM timing fields, always permissible (see 2.2.5 of the SDTM document at
http://www.fda.gov/cder/regulatory/ersr/Studydata-v1.1.pdf)	768
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Domain 5: Clinical Pathology
USUBJID CPTESTCD 	CPSTRESN 	CPSTRESU 	CPSPEC 	CPCAT 	CPSCAT 	CPTEST 	CPSTRESC 	CPORRES 	CPORRESU CPGRPID 	CPSEQ 	DY*
1 	MONO 	0.484 	10E+9/L 	Blood 	HEM
Chemical
Analysis
Monocyte
count 	0.484 	0.484 	10E+9/L 	1 	1 	6
2 	MONO 	0.418 	10E+9/L 	Blood 	HEM
Chemical
Analysis
Monocyte
count 	0.418 	0.418 	10E+9/L 	1 	2 	6
3 	MONO 	0.429 	10E+9/L 	Blood 	HEM
Chemical
Analysis
Monocyte
count 	0.429 	0.429 	10E+9/L 	1 	3 	6
4 	MONO 	0.447 	10E+9/L 	Blood 	HEM
Chemical
Analysis
Monocyte
count 	0.447 	0.447 	10E+9/L 	1 	4 	6
5 	MONO 	0.471 	10E+9/L 	Blood 	HEM
Chemical
Analysis
Monocyte
count 	0.471 	0.471 	10E+9/L 	1 	5 	6
6 	MONO 	0.441 	10E+9/L 	Blood 	HEM
Chemical
Analysis
Monocyte
count 	0.441 	0.441 	10E+9/L 	2 	6 	6
7 	MONO 	0.407 	10E+9/L 	Blood 	HEM
Chemical
Analysis
Monocyte
count 	0.407 	0.407 	10E+9/L 	2 	7 	6
8 	MONO 	0.448 	10E+9/L 	Blood 	HEM
Chemical
Analysis
Monocyte
count 	0.448 	0.448 	10E+9/L 	2 	8 	6
9 	MONO 	0.408 	10E+9/L 	Blood 	HEM
Chemical
Analysis
Monocyte
count 	0.408 	0.408 	10E+9/L 	2 	9 	6
10 	MONO 	0.418 	10E+9/L 	Blood 	HEM
Chemical
Analysis
Monocyte
count 	0.418 	0.418 	10E+9/L 	2 	10 	6
1 	ALT 	57 	U/L 	Blood 	CHEM
Chemical
Analysis 	ALT 	57 	57 	U/L 	1 	11 	6
2 	ALT 	44 	U/L 	Blood 	CHEM
Chemical
Analysis 	ALT 	44 	44 	U/L 	1 	12 	6
3 	ALT 	42 	U/L 	Blood 	CHEM
Chemical
Analysis 	ALT 	42 	42 	U/L 	1 	13 	6
4 	ALT 	39 	U/L 	Blood 	CHEM
Chemical
Analysis 	ALT 	39 	39 	U/L 	1 	14 	6
5 	ALT 	45 	U/L 	Blood 	CHEM
Chemical
Analysis 	ALT 	45 	45 	U/L 	1 	15 	6
6 	ALT 	39 	U/L 	Blood 	CHEM
Chemical
Analysis 	ALT 	39 	39 	U/L 	2 	16 	6
7 	ALT 	40 	U/L 	Blood 	CHEM
Chemical
Analysis 	ALT 	40 	40 	U/L 	2 	17 	6
8 	ALT 	40 	U/L 	Blood 	CHEM
Chemical
Analysis 	ALT 	40 	40 	U/L 	2 	18 	6
9 	ALT 	39 	U/L 	Blood 	CHEM
Chemical
Analysis 	ALT 	39 	39 	U/L 	2 	19 	6
10 	ALT 	38 	U/L 	Blood 	CHEM
Chemical
Analysis 	ALT 	38 	38 	U/L 	2 	20 	6	771
772 	* General SDTM timing fields, always permissible (see 2.2.5 of the SDTM document at
http://www.fda.gov/cder/regulatory/ersr/Studydata-v1.1.pdf)	773
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Domain 6: Microscopic Findings
USUBJI
D
MITESTC
D 	MITEST 	MIORRES
MISTA
T
MIREASN
D
MIGRPI
D
MISE
Q 	DY
1 	LIVER 	Liver 	Normal 	1 	1 	6
2 	LIVER 	Liver 	Normal 	1 	2 	6
3 	LIVER 	Liver 	Normal 	1 	3 	6
4 	LIVER 	Liver 	Normal 	1 	4 	6
5 	LIVER 	Liver 	Normal 	1 	5 	6
6 	LIVER 	Liver
Mild periportal
vacuolation 	2 	6 	6
7 	LIVER 	Liver
Mild periportal
vacuolation 	2 	7 	6
8 	LIVER 	Liver
Mild periportal
vacuolation 	2 	8 	6
9 	LIVER 	Liver
Mild periportal
vacuolation 	2 	9 	6
10 	LIVER 	Liver
Mild periportal
vacuolation 	2 	10 	6
25