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DRAFT GUIDANCE 
 
This guidance document is being distributed for comment purposes only.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publication in the Federal Register of the notice announcing the availability of the draft guidance.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:  EXAMPLESUBMITTING A NONCLINICAL STUDY DATA............21 
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1     
1
Guidance for Industry
2 
Pharmacogenomic Data Submissions  Companion Guidance 
3 
4   
 
5 
6   
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.  
13   
14   
15   
16   
17   
18  I.  INTRODUCTION 
19   
20  This guidance is intended to be used as a companion to the guidance Pharmacogenomic Data 
21  Submissions (March 2005).  It reflects experience gained since the issuance of that guidance with 
22  voluntary genomic data submissions as well as with review by the FDA of numerous protocols 
23  and data submitted under investigational new drug (IND) applications, new drug applications 
24  (NDAs), and biologics license applications (BLAs).  The recommendations are intended to 
25  facilitate scientific progress in the field of pharmacogenomics and to facilitate the use of 
26  pharmacogenomic data in drug development.  The FDA believes that the recommendations made 
27  in this companion guidance, together with the recommendations in the March 2005 guidance, 
28  will benefit sponsors considering the submission of either voluntary genomic data submissions 
29  or marketing submissions containing genomics data.   As technology changes and more 
30  experience is gained, these recommendations may be updated.   
31   
32  FDA's guidance documents, including this guidance, do not establish legally enforceable 
33  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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34  be viewed only as recommendations, unless specific regulatory or statutory requirements are 
35  cited.  The use of the word should in Agency guidances means that something is suggested or 
36  recommended, but not required.  
37   
38   
39  II.  GENE EXPRESSION DATA FROM MICROARRAYS 
40   
41  The following methodological issues should be considered when submitting gene expression 
42  data from microarrays. The recommendations made in this document apply to development of 
43  microarray data that might be submitted in support of INDs, NDAs, and BLAs.  For microarray 
44  data supporting the clearance or approval of a diagnostic device, additional information beyond 
45  these recommendations may be requested.   
46   
47  A.  RNA Isolation, Handling, and Characterization 
48   
49  One of the most critical steps in performing RNA-based experiments such as microarray gene 
50  expression experiments is the isolation of high quality, intact RNA. To achieve this goal and 
51  preserve sample integrity throughout the course of the experiment, some steps before and after 
52  RNA purification should be carefully planned to ensure quality during isolation and confirm 
53  high quality before use in a downstream application. A secondary goal is maximizing the yield 
54  of RNA. In addition, storage and shipping conditions of samples can influence the stability of 
55  RNA. Thus, it is very important to store the RNA under the best conditions to preserve the 
56  integrity of the sample. Finally, we recommend that standard operating procedures (SOPs) be 
57  established to ensure reproducibility of the RNA isolation method and RNA quality (e.g., see 
58  http://www.fda.gov/nctr/science/centers/toxicoinformatics/maqc/docs/MAQC_Sample_Processi
59  ng_Overview_SOP.pdf).  The following recommendations will help achieve these goals.  
60   
61  1.  Pre-RNA Isolation Considerations 
62   
63  RNA is sensitive to degradation by RNase, which is ubiquitously present in living 
64  organisms. Thus, sample-handling issues should be addressed and methods for 
65  sample handling need to be assessed to ensure that the methods and their 
66  associated metrics are suitable for the purpose to which they are applied before 
67  embarking on RNA isolation from samples. We also recommend that any work 
68  areas and equipment to be used to generate data for submission studies be 
69  dedicated specifically for RNA isolation and other RNA-related work. 
70   
71  RNase-free reagents and disposables/glassware: It is imperative to use RNase-free 
72  reagents and glassware for RNA isolation. Commercially available RNA isolation 
73  kits often provide these. It may be of value to confirm that RNase inactivation 
74  methods are functioning as expected prior to launching submission studies. 
75   
76  RNA stabilizer(s): We recommend that the need for adding RNA stabilizing 
77  agents to samples/reagents be assessed and an appropriate RNA stabilizer be 
78  identified, and assessed for suitability in a pilot experiment. 
79   
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80  Batch size:  We recommend that the maximal batch size for sample preparation be 
81  determined to help identify and limit the time taken for the entire RNA isolation 
82  process. Establishing an upper limit for batch size will reduce problems 
83  encountered during the scaling-up process since long processing times can 
84  jeopardize RNA integrity. 
85   
86  Sample collection, storage and shipping conditions: there are numerous variables 
87  that may affect sample reproducibility in microarray studies. We recommend that 
88  the impact of the following variables on RNA quality be assessed. These include: 
89   
90    maximum and minimum sample dimensions 
91    volumes 
92    weights 
93   
94  Additional important parameters include: 
95   
96    correct sampling technique per tissue/organ 
97    timing of sample dissection/processing time 
98    maximum allowed elapsed time between resection and stabilization of the 
99  tissue  
100    stability of specimen in transport under recommended conditions 
101  (temperature, duration, etc) 
102   
103  There may be other study-specific parameters to consider. For example, in 
104  oncology studies we recommend that the percent tumor in the sample be 
105  determined.  
106   
107     
108   
109  2.  RNA Isolation from Tissues or Cells   
110   
111  Treatment of cells or tissue samples prior to RNA isolation and careful handling 
112  are necessary to preserve RNA. Several methods are available for successful 
113  isolation of high quality RNA.  A number of reagents are also available that aid in 
114  preserving the quality of RNA. For example, an RNA stabilizer that is compatible 
115  with RNA isolation procedures may be added to the isolated tissues or cells 
116  before storing the samples. Alternatively, tissues or cells can be quickly frozen in 
117  liquid nitrogen and stored at -80 0C to prevent RNA degradation. Tissues or cells 
118  can also be homogenized in the presence of a strong denaturant that inactivates 
119  RNase, followed by freezing the homogenate at -20 0C or below.  In any case, we 
120  recommend that the manufacturers specifications be followed and that the quality 
121  of the resulting RNA be acceptable for the study. RNase-free reagents, 
122  equipment, materials, and work spaces should be used for subsequent isolation 
123  and analytical steps. 
124   
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125  3.  RNA Isolation from Whole Blood and PBMCs  
126   
127  RNA can be isolated from whole blood or from peripheral blood mononuclear 
128  cells (PBMCs). Most studies conducted so far have used the PBMCs since they 
129  are the most transcriptionally active cells in blood.2 This fraction primarily 
130  consists of lymphocytes and monocytes. RNA isolated from PBMCs and whole 
131  blood should not be used interchangeably in the same study. 
132   
133  RNA isolation from whole blood: RNA may be isolated from whole blood, and 
134  this specimen type is attractive since the blood sample with the RNA stabilizer 
135  can be stored for a long time, presumably without compromising RNA quality or 
136  the stability of expression profiles under manufacturer-suggested conditions. The 
137  storage conditions and the maximum storage durations used to store whole blood 
138  samples selected should meet any acceptance criteria applicable to the selected 
139  platform. One disadvantage of RNA isolation from whole blood is that 
140  reticulocytes (immature red blood cells (RBCs)) in the specimen, while 
141  representing only 0.5-2% of the RBCs, can contribute up to 70% of the mass of 
142  mRNA in total RNA, of which globin mRNA is the major RNA. In microarray 
143  gene expression experiments, the overabundance of globin mRNA can result in 
144  failure to detect some transcripts that are of low abundance.3 While working with 
145  whole blood specimens, the need for protocols for reducing globin mRNA from 
146  whole blood4 or alternative methods to minimize the impact of globin mRNA on 
147  gene expression data should be considered.  If it is determined that such methods 
148  are needed, ensure that they work as intended within the context of your method. 
149   
150  The quality of the microarray data generated from whole blood specimens can be 
151  improved by removing reticulocytes, although this often requires that blood be 
152  processed at the site of blood draw. Any manipulation of the blood sample may 
153  cause a change in the gene expression profiles of some transcripts.5 We 
154  recommend, therefore, that a study to simulate the conditions of the preclinical or 
155  clinical blood specimen collection and manipulation to be employed be 
156  conducted, to assess the impact of key variables on the chosen method. 
157   
158  RNA isolation from PBMCs: RNA may be isolated from PBMCs that have been 
159  isolated from a whole blood specimen using one of several techniques. Some 
                                                 
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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160  commonly used methods include the Ficoll-Hypaque centrifugation and use of 
161  cell preparation tubes with sodium citrate. RNA isolation from PBMCs is the 
162  preferred method for many applications since the RNA is free from globin mRNA 
163  and generally gives better results on microarrays. However, it has been shown 
164  that time delays and temperature changes can affect gene expression profiles of 
165  several genes6, 7 and therefore it is critical to isolate the PBMCs within hours of 
166  blood collection, particularly if any stabilization materials or storage conditions to 
167  stabilize the expression profiles are not used. Regardless of the method chosen, 
168  one should assure, through measurement of quality parameters, that it consistently 
169  and reliably yields RNA with acceptable performance parameters for the selected 
170  analytical method. If more than one RNA isolation method (e.g., methods from 
171  two different manufacturers) are selected, it should be ascertained that both 
172  methods give equivalent results in your system. 
173   
174  4.  RNA Storage  
175   
176  For short term storage, RNA suspended in RNase-free water (with 0.1mM EDTA) 
177  or in TE buffer should be stored at -800 C in aliquots in non-frost-free freezers. 
178  Repeated freeze-thaws should be avoided. Generally, RNA is stable for about a 
179  year at -800 C under the above-mentioned conditions. For long term storage, RNA 
180  samples could be stored at -200 C in ethanol. 
181   
182  5.  RNA QC  
183   
184  The quality of RNA samples can be monitored in several ways. The most 
185  widespread current metric is spectrophotometric analysis using the ratio of 
186  absorbance at 260nm/280nm as a measure of RNA quality and purity.8 Two 
187  common additional methods are agarose gel electrophoresis and analysis using a 
188  dedicated RNA analyzing instrument. Considerations for RNA quality metrics 
189  include the following: 
190   
191    For spectrophotometric analysis, the ratio of absorbance at 260nm and 280 nm 
192  (A /A ) can be used to assess RNA purity and is typically recommended to 
260 280
193  be greater than 1.8.9  
194   
                                                 
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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195    For agarose gel analysis, generally a 1% denaturing agarose gel is used, and 
196  clearly visible 18S and 28S RNA bands are taken as measure of RNA 
197  integrity. Ideally, the intensity of the 28S band should be twice the intensity of 
198  the 18S band. Degraded RNA will have a smeared appearance and lack two 
199  clear bands. 
200   
201    For dedicated RNA analysis instruments, several different metrics may be 
202  useful, e.g., presence of 18S and 28S rRNA peaks, ratio of 28S/18S bands, 
203  and percentage of total RNA represented by the rRNA peaks.8 Specific 
204  recommendations regarding use of dedicated RNA analysis instruments and 
205  the data they generate can be found in manufacturers materials.10 
206   
207  Regardless of the method(s) chosen to assess RNA quality, it should be ascertained 
208  that the acceptance criteria for the RNA samples are consistently appropriate to yield 
209  RNA quality that is suitable for the analytical method selected. The selected RNA 
210  isolation method should minimize genomic contamination of the isolated RNA 
211  because genomic DNA could negatively affect downstream applications. 
212   
213  B.  Labeling Reactions  
214   
215  In genomic submissions, it is important that sponsors use a labeling system that has been 
216  documented to perform well on a given manufacturers array.  It is critical that the sponsor begin 
217  the labeling process with high-quality RNA-free of contaminants that might affect the labeling 
218  efficiency or introduce labeling bias, as compromised RNA quality will affect subsequent steps 
219  of sample processing and ultimately lead to poorer quality microarray data. We recommend that 
220  the use of accepted quality measures (18S/28S ratios) be included in this report and that RNA 
221  samples prepared for labeling be of comparable quality.   
222   
223  We recommend the use of consistent methods of target labeling throughout the particular study 
224  or studies that will be analyzed as a group since dissimilar microarray data could be obtained 
225  when kits from different manufacturers or different types of labeling kits are used. If there is any 
226  change in a critical component in the labeling kit (kit manufacturer, key enzyme or reagent), we 
227  recommend that it be tested to demonstrate comparability of the data generated prior to being 
228  used with samples analyzed as an arm of a study. We recommend that reagent lot acceptance 
229  criteria be developed to ensure the reproducibility of labeling reactions. 
230   
231  The use of standard operating procedures (SOPs) is encouraged, and we recommend that 
232  operators be fully trained on all protocols prior to processing of samples for the study.  
233  Equipment should be on an appropriate maintenance schedule and the laboratory environment 
234  maintained in accordance with the manufacturers recommendations.   
235   
                                                 
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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236  The development of QC or intermediate labeling steps is highly recommended.  If any 
237  intermediate QC step indicates a problem and the RNA is of reasonable quality, the labeling 
238  process can be repeated to produce higher quality input material for hybridization to the 
239  microarray chip.  In addition, it is recommended that reagents be stored under appropriate 
240  conditions. Use of controls and reference standards are recommended to verify consistent 
241  performance throughout the labeling procedure. 
242   
243  We recommend the use of validated standard operating procedures (SOPs) addressing all aspects 
244  of sample collection, storage, and sample and array processing to generate microarray data, and 
245  all operators should be fully trained on all protocols prior to initiating the study. It is also 
246  advisable to establish appropriate maintenance schedules for all equipment, and ensure that the 
247  laboratory environment is maintained in accordance with the SOPs. 
248   
249  C.   Hybridizations for Microarrays 
250   
251  You should include pertinent information on reproducibility and accuracy of array hybridization 
252  in your submission package. In the absence of widely accepted QA/QC control metrics for DNA 
253  microarray technologies or consensus on how to establish the reliability of the results obtained 
254  from a DNA microarray experiment, we recommend you establish and assess internal control 
255  metrics for quality and reliability. For example, some organizations have used QA/QC pass/fail 
256  filters to eliminate outlier arrays and some array manufacturers recommend thresholds for certain 
257  platform-specific QC measurements.   
258   
259  Currently, the ERCC (External RNA Controls Consortium)11 and MAQC (MicroArray Quality 
260  Control Consortium) groups are developing spike-ins and reference standards, which may be 
261  useful in evaluating the quality of a particular microarray experiment when available. Another 
262  recent effort has produced a pair of reference RNA pools for use with rat DNA microarrays that 
263  allow accuracy, reproducibility, and dynamic range assessments.12 Conceptually, this strategy 
264  could be used to produce reference materials for any organism, including human. Until such 
265  independent resources are widely available and consensus quality standards are developed and 
266  implemented by the microarray community, carefully adhering to the microarray manufacturers 
267  recommended procedures offers the best current practice at this time. Detailed protocols have 
268  been prepared by major DNA microarray manufacturers and posted on the MAQC Web site.12 
269  Because the microarray field is evolving, it is important to note that manufacturers occasionally 
270  change probe sequences and protocols, reflecting continuing improvements to this technology. 
271  Regardless of the source of quality control materials and methods, we recommend you describe 
272  how you selected those that you use, and how you determined that they were acceptable for your 
273  purposes. 
274   
275  We recommend that the following be clearly outlined in a figure: 
276   
                                                 
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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277    Microarray chip details  
278   
279  A key information in the submission package is the information of micoarray chips used.  
280  There are at least two different categories of microarray chips, commercial chips and 
281  customer chips (array manufactured by sponsor, or contractors).  
282   
283  1.  If commercial array chips are used in the study, sponsor should provide the following 
284  information: the name of manufacture, type of array, lot #, manufacture date (or 
285  expiration date), and array QC parameters (QC tests performed by vendor). 
286   
287  2.  If customer chips are used for the study, the sponsor should provide: manufacturing 
288  protocol, documents from vendors if any materials purchased from commercial resources, 
289  QC thresholds, and QC testing results.     
290   
291    Microarray experimental design details 
292   
293  We recommend you include sample processing and labeling (e.g., were samples processed in 
294  the same batch or different batches; was the same procedure used for all samples, technical 
295  replication, biological replication and other appropriate information). 
296   
297    How data were generated and analyzed   
298   
299  One approach would be to start with how the primary data were obtained (e.g., laser scanner 
300  settings, software settings for image acquisition). We recommend you explain how the data 
301  from individual microarrays were combined and the normalization method and then provide 
302  data filtering, data analysis, statistical tests, and other appropriate information. 
303   
304  D.  Fluorescence Reader Settings for Microarrays 
305   
306  Microarray technology uses a multi-step process in which variability at each step must be 
307  reduced to maximize the probability of detecting changes that arise from biology and not from 
308  experimental artifact. Scanners used to collect the microarray signals are a potential source of 
309  variability in data derived from this technology. Recent publications have pointed out the 
310  importance of optimal reader settings for obtaining high-quality microarray data.13 The signal 
311  readout system is often thought of as a black box that quantitates the signal from each DNA 
312  microarray spot. The measurement of the abundance of RNA species by DNA microarray 
313  technology assumes a linear relationship between the signal read-out from the scanner and the 
314  dye concentration, which is further assumed to be linearly correlated with transcript abundance 
315  in the RNA sample.  
316   
317  Each array system, scanner type, and signaling dye combination, may have its own linear 
318  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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319  will help minimize technical variability and improve consistency of data collection include the 
320  following: 
321  1.  Calibration of scanners as recommended by the manufacturers 
322  2.  Routine use of standardized scanner reference materials for calibrations to allow for 
323  characterization of concentration-dependent read-outs  
324  3.  Attention to scanner settings (e.g., laser power and voltage gain). Specifically, we 
325  recommend that scanner settings be set to maximize the linear dynamic range.  
326  4.  Keeping the scanner laser power and voltage settings constant during a study. Note that 
327  some scanners are not tunable, so that this source of variability is eliminated. 
328  5.  If the dye-intensity to signal output relationship is defined, possible corrections when 
329  signals fall outside of the linear dynamic range, thus reducing variability in the very high 
330  or very low signal range 
331  6.  Submission of scanner setting and calibration information as part of the submission 
332  package 
333   
334  E.  Differentially Expressed Genes 
335   
336  Specific genes sets derived from microarray experiments can be proposed as genomic 
337  biomarkers for a specific endpoint in a defined context. Such specific gene sets should be 
338  reproduced upon review if the analysis protocol is identical to that reported by the sponsor.  The 
339  sponsor should include in the submission a clear description of the steps, parameters, and 
340  algorithms leading to the list of differentially expressed genes list in the genomic submission. 
341   
342  Different analysis protocols may yield dissimilar lists of differentially expressed genes, and these 
343  cannot be justified solely through a biological interpretation if they are to be proposed as 
344  genomic biomarkers. To the extent that these genomic biomarker sets become part of a decision-
345  making process in drug development or therapeutic applications, we recommend that transfer of 
346  genomic biomarker sets from microarrays to other platforms (such as quantitative RT-PCR) be 
347  attempted only after the sponsor concludes that these differentially expressed genes are sensitive, 
348  specific, and reproducible. 
349   
350  Sources of variability in microarray data leading to the step in which the differentially expressed 
351  gene list is determined may be minimized by following the recommendations in this document. 
352  To determine which genes are in fact differentially expressed, a number of factors need to be 
353  considered that may have confounding effects: 
354   
355    The application of platform-specific flags 
356    Rejection criteria for low-intensity transcripts 
357    Rejection criteria for outlier hybridizations 
358    Platform-specific normalization protocols 
359    Data analysis protocol for selection of differentially expressed genes 
360   
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361  There is no consensus at this time regarding the appropriate choices for each of these factors.  
362  The sponsor should exercise care in how parameters and protocols are chosen for each of these 
363  factors and should consult current literature regarding efforts to reach a consensus.14, 15, 16, 17, 18, 
364  19 
365   
366  In principle, several analysis protocols can be used to determine lists of differentially expressed 
367  gene lists for a sufficiently large number of technical and biological replicates. In practice, 
368  constraints on the number of technical and biological replicates are likely to be the norm in 
369  genomic submissions. For example, technical replicates are constrained by the minimum amount 
370  of RNA needed to hybridize each biological sample. Both clinical as well as preclinical samples 
371  may have major constraints in the total amount of RNA available from each biological sample. 
372  Biological replicates are constrained by the total number of subjects to be included in a study.  
373  We recommend that these constraints be considered in the selection of analysis protocols for the 
374  determination of differentially expressed genes. 
375   
376  F.  Biological Interpretation of Lists of Differentially Expressed Genes 
377   
378  Once the list of differentially expressed genes has been generated via a variety of statistical and 
379  analytical tools, the next step in the process should be to interpret the biological meaning of gene 
380  expression changes and determine whether biological pathways may be of functional relevance 
381  to the mechanism of drug action, or may be correlated to safety and/or efficacy. 
382   
383  A number of questions should be addressed at this point, including, for example:  
384    Are genes from a particular pathway or set of pathways significantly overrepresented in 
385  the list?   
386    How many pathways are affected?   
387    Can the mechanism of action be inferred from the functions of the pathways altered or 
388  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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389    What is the tissue specificity of the pathways and the gene function in relation to 
390  biological processes?  
391    What are the magnitude and/or pattern of the alteration in a particular pathway in relation 
392  to treatments with other compounds (related or unrelated) with known pharmacological 
393  or toxicological properties? 
394   
395  At present, no single tool can be used to find answers to all these questions, but a combination of 
396  tools can be used to address a particular question of interest as thoroughly as possible. To this 
397  end, a variety of analytical platforms are available, either free on the Web or via purchase of a 
398  commercially available product.  
399   
400  An overlap of the biological interpretations obtained with two or more different databases can 
401  facilitate a consensus on what the interpretation should be. However, this is not always the case. 
402  Consensus can be hindered by many factors including, but not limited to, absence of information 
403  on the compound of interest in the reference databases or a lack of annotation for particular 
404  pathways of interest. For example, subsets of genes may be placed in specific pathways in one 
405  system, but they may not be represented in the same pathways in another pathway analysis tool, 
406  or genes may not have been evaluated in a particular platform. In pathway analysis databases, 
407  the information may differ depending on which content is extracted from the literature and how 
408  that extraction is performed (whether automated or by manual curators). In addition, a critical 
409  distinction is whether all information is extracted, or if only the information supported by direct 
410  experimental evidence included in the publication is extracted. We recommend heavy reliance on 
411  the literature and on reference databases to extract functional information on specific gene lists 
412  and generate hypotheses on the biological significance of the relevant set of genes. 
413   
414  We also recommend that the biological significance of gene sets proposed by a sponsor be 
415  accompanied by a standard set of information that will enable recapitulation of the analysis and 
416  assessment of the validity of the interpretation by regulatory reviewers. In addition, we 
417  recommend that the gene sets proposed by sponsors should be validated by other conventional 
418  techniques, such as Q-PCR, or RT-PCR. Such information should include, but not be limited to: 
419   
420    Type of database used for annotation, including vendor name 
421    Methods and approaches (cut-off, statistical tests) used to identify over-represented 
422  pathways within the database 
423    References used to justify any user-defined annotation 
424    A summary by the sponsor of the interpretation of the pathway annotation results 
425   
426   
427  III.  GENOTYPING  
428   
429  A.  Genotyping Methods 
430   
431  Genetic differences among individuals occur in a variety of forms, from alterations in 
432  chromosomal arrangement or copy number to single base-pair changes.  Much of the genetic 
433  variation currently used in pharmacogenetics occurs at the level of individual genes (e.g., drug 
434  metabolizing enzymes) on a scale ranging from single base-pair changes to entire gene 
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435  duplications or deletions.  Examining genomic DNA is often the most reliable and practical 
436  method for characterizing genetic variation, although methods based on protein or mRNA 
437  expression levels can be preferable in some situations, such as when determining treatment-
438  sensitivity of cancer or viral infection.  Many methods are currently available for characterizing 
439  DNA variations, and new methods are rapidly being developed. 
440   
441  B.  DNA Isolation, Handling, and Characterization 
442   
443  Whole blood is commonly used for the extraction of genomic DNA in clinical research settings.  
444  Blood collection tubes generally use anticoagulants such as EDTA, CPD, ACD, Citrate or 
445  Heparin. DNA in a blood sample is susceptible to degradation unless properly stored.  Although 
446  manufacturers of blood collection tubes usually recommend appropriate storage conditions for 
447  optimum stability, we recommend you ensure that these conditions yield DNA that is suitable for 
448  your assay, for example, by checking for the presence of full-length DNA. 
449   
450  When DNA is isolated from blood, carryover of contaminants such as salts, phenol, ethanol, 
451  heme (in blood DNA isolation), and detergents from conventional purification procedures can 
452  inhibit performance of DNA in downstream applications. In addition, contamination with the 
453  anticoagulant heparin impairs amplification by PCR.20, 21 Potential for contamination and 
454  interference in isolation procedures should be assessed, and procedures for avoiding these should 
455  be implemented where necessary.  
456   
457  Although DNA is a relatively stable molecule, it should be stored carefully. Degradation of DNA 
458  can have a major effect on any results obtained, generating errors that are both quantitative and 
459  qualitative.  There are several factors that can result in DNA degradation including introduction 
460  of enzymatically active nucleases, acid hydrolysis, and degradation due to repeated freeze-thaw 
461  cycles.  You should implement DNA handling and storage procedures that limit these and any 
462  other factors that could affect DNA quality.  For example: 
463    Avoid exposure of DNA solutions to nucleases that may be present on lab equipment or 
464  in reagents; 
465    Store DNA at a slightly alkaline pH (e.g., Tris EDTA buffer) once isolated; 
466    Maintain long-term storage of DNA at -200 C or at -800 C. 
467    Freeze sample in aliquots to reduce freeze-thaw degradation 
468   
469  C.  Genotyping Report 
470   
471  We recommend that the following information be included in the genotyping report, regardless 
472  of the genomics submission type (see the Pharamcogenomic Data Submissions guidance for 
473  regulatory requirements): 
474   
475    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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476    Samples studied, including demographics and sample size justification for 
477  genotype/clinical phenotype correlation and adequate coverage for ethnic/racial 
478  groups; include expected allele frequency in different populations 
479    Alleles measured and correlation with metabolic status designation 
480    For metabolizing enzymes, how EM (extensive metabolizer), PM (poor 
481  metabolizer), IM (intermediate metabolizer), or UM (ultra rapid 
482  metabolizer) are determined 
483    Sample test report 
484    For new genes, correlation between gene variant and encoded protein 
485  activity 
486    Whether the assay was performed in a CLIA-certified lab or research lab 
487   
488   
489  IV.  PROFICIENCY TESTING 
490   
491  High-quality data are the foundation for deriving reliable biological conclusions from a 
492  microarray gene expression study.  However, large differences in data quality have been 
493  observed in published data sets when the same platform was used by different laboratories.22, 23  
494  In many cases, poor quality of microarray data was due not to the inherent quality problems of a 
495  platform but to the lack of technical proficiency of the laboratory that generated the data. Such a 
496  systematic procedural failure in a laboratory is much more serious than randomly failed 
497  hybridizations that lead to outlying arrays, because the laboratory may not recognize that it has a 
498  procedural failure problem. 
499   
500  The Agency recommends that sponsors provide data that will enable FDA reviewers to 
501  objectively evaluate the competency of the laboratory that generated the data in a genomic 
502  submission. Many studies report quality control metrics or use standards to provide internal 
503  assessments of microarray data. This information is useful for confirming the technical ability to 
504  reproducibly perform a given assay within an individual study.  
505   
506  In addition to within-laboratory testing, an assessment of the overall competence of a facility can 
507  be performed through inter-laboratory comparisons, such as proficiency testing.  Laboratory 
508  proficiency can be monitored through a number of approaches. 
509   
510    RNA sources 
511   
512  Two FDA-led initiatives have developed and characterized reference RNA samples for 
513  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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514  differences in tissue-selective genes24 and have been used in the first proficiency testing 
515  program for microarray laboratories.25  In addition, the MicroArray Quality Control 
516  (MAQC) Project26 developed two human reference materials and extensively tested them on 
517  multiple gene expression platforms. Data from both initiatives have been deposited in public 
518  databases, and the RNA samples used in the MAQC project are now commercially available 
519  for use by laboratories to assess ability to reproduce MAQC data. 
520   
521    Experimental design for proficiency testing 
522   
523  Most RNA-based genomic assays are designed to detect differentially expressed genes or 
524  profiles.  A proficiency testing program for these assays could be centered on testing of 
525  replicates of two biologically different samples with known differences in transcript 
526  abundance, in order to measure the ability to repeatedly detect differential gene expression.  
527  For example, the laboratory could plan to process three or more replicates of sample A 
528  (labeled A1, A2, and A3) and three or more replicates of sample B (labeled B1, B2, and B3), 
529  to evaluate the within-laboratory repeatability both in terms of repeatable intensity 
530  measurements and repeatable detection of differential gene expression. If multiple 
531  laboratories provide data generated using the same RNA samples and the same platform, 
532  site-to-site reproducibility and comparability of sites to detect differences in expression can 
533  be assessed.  We recommend that laboratories use a proficiency testing program, and that the 
534  testing be repeated throughout the year so that multiple data sets from the same laboratory 
535  can be compared to confirm the consistency of the laboratorys performance over time. 
536   
537    Laboratory compliance 
538   
539  The Agency encourages microarray facilities to adhere to the good laboratory practices 
540  outlined in 21 CFR 58. Laboratories may also wish to obtain CMS/CLIA certification if the 
541  microarray data have potential clinical or diagnostic applications. All CLIA-compliant assays 
542  require repeated data comparisons with other providers to verify the competency of 
543  individual laboratories. Participation in a proficiency testing program would fulfill this CLIA 
544  requirement. 
545   
546   
                                                 
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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547  V.  GENOMIC DATA IN CLINICAL STUDY REPORTS   
548   
549  There are many possible sources of data for genomic data submissions. Genomic data from 
550  clinical studies may result from microarray expression profiling experiments, genotyping or 
551  single-nucleotide polymorphism (SNP) experiments, or from other evolving analytical 
552  methodologies pertaining to drug dosing or metabolism, safety assessments, or efficacy 
553  evaluations. Genomic data may also be reported from studies where other data are also reported, 
554  such as with efficacy or safety data from clinical or nonclinical studies. However, these data can 
555  be reviewed only if the content of the clinical data report included in the submission contains 
556  sufficient detail regarding the sample selection.  
557   
558  The following describes FDAs current thinking about what data should be submitted with 
559  genomics data in a submission to the Agency (including a voluntary submission).  Regulatory 
560  applications for these data are described in detail in FDAs Pharmacogenomic Data Submissions 
561  guidance in the context of different algorithms for the submission of pharmacogenomic data 
562  consistent with FDA requirements for INDs, NDAs, and BLAs, as well as for Voluntary 
563  Genomic Data Submissions (VGDS). Throughout the following discussion, we suggest that you 
564  refer to the Pharmacogenomic Data Submissions guidance for in-depth background on this 
565  discussion.  
566   
567  In all genomic submissions, a full clinical study report is very helpful to Agency reviewers. The 
568  report should provide a clear explanation of how the critical design features of the study were 
569  chosen as well as enough information on the plan, methods, and conduct of the study to 
570  eliminate ambiguity in how the study was carried out.  The report with its appendices should also 
571  provide individual patient data relevant to pharmacogenomics, including demographic and 
572  baseline data, and details of analytical methods such as validation reports to allow replication of 
573  the critical analyses. It is also particularly important that all analyses, tables, and figures carry 
574  clear identification of the set of patients from which they were generated.  
575    
576  To improve the usefulness of the submission, we recommend that the content of the clinical 
577  section describing a genomic experiment contain the following information:  
578    Title page 
579    Table of contents 
580    Synopses and summary of findings 
581    Background and scientific rationale 
582    Primary and secondary study objectives 
583   Study design, sample collection and storage, and pharmacogenomic methods 
   
584    Clinical study protocol including minimally27:  
., 
585  - inclusion and exclusion criteria 
586  - demographic data 
587  - listing of individual experimental measurements by patient, including 
588  pharmacokinetic/pharmacodynamic datasets and lab results; and explanation of 
589  missing data 
                                                 
27 ICH guidance E3 Structure and Content of Clinical Study Reports. 
 
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590  - disposition of patients 
591  - protocol deviations  
592  - individual adverse events or laboratory abnormalities 
593  - pharmacogenomic and other biomarker datasets as necessary 
  
594  - correlation between clinical and pharmacogenomic data 
595  - discussion of the comprehensiveness of genomic data in all randomized patients 
596  and the impact of loss of such information, if any, on inferences and associations, 
597  especially with outcomes 
598  - additional discussion and conclusions 
599  References and any supplementary materials.  
600   
601  The specific sequence and grouping of topics may change if alternatives are more logical for a 
602  particular study.  The Pharmacogenomics Data Submissions guidance and other Agency 
603  regulations and guidance contain detailed discussions on specific regulatory requirements.  
604   
605  The preferred submission standard for clinical data is the Clinical Data Interchanges Standards 
606  Consortium (CDISC) Study Data Tabulation Model (SDTM) standard.  Please see the FDA Data 
607  Standards Council Web site28 for more information on the standard.29
608   
609   
610  VI.  GENOMIC DATA FROM NONCLINICAL TOXICOLOGY STUDIES 
611   
612  Genomic data can be collected in nonclinical studies, such as toxicogenomic studies.  This 
613  section describes how to submit nonclinical toxicology data with a genomic data submission. 
614  How the data should be submitted depends on the purpose of the submission. Three general 
615  types of submissions can be identified:   
616   
617    The first type of submission might have the objective of expanding the selection process 
618  criteria (i.e., screening to aid in the selection of a lead compound for clinical 
619  development or to eliminate compounds with certain characteristics).  
620    The second might present the characterization of a particular compound.  
621    The third might present a general scientific discussion that might not be related to the 
622  development of a compound and/or compound class. 
623   
                                                 
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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624  A.  Expanding the Selection Process Criteria  
625   
626  When a submission is intended to expand the selection process criteria and precede the 
627  development of a compound (i.e., screening for lead compounds or to eliminate certain 
628  characteristics), we recommend the inclusion of the following information:  
629   
630  1.  General narrative about the objective of the submitted application, brief narrative about the 
631  compound(s), intended use, and mechanism of action 
632  2.  Objective of the submitted study with its experimental design (treatment, duration, replicates, 
633  drug formulation, route of administration, rationale for dose selection).  As applicable, 
634  information about species, strain, sex, genetic background, age, weights, developmental 
635  stage, organ/tissue where sample originated, cell type can be included.  We recommend that a 
636  brief description of sample handling, storage and preparation methodology also be included. 
637  3.  Toxicology parameters including clinical pathology (serum chemistry and hematology) and 
638  histopathology data consistent with STP guidelines (Toxicologic Pathology, 32, 126-131 
639  (2004)), preferably in an electronic format). When applicable, the correlation between 
640  pathology findings and genetic variation or gene or protein expression should be explained. 
641  4.  Correlations of individual animal data to genetic variation or gene or protein expression 
642  should be explained. 
643  5.  Pharmacokinetic parameters and ADME properties of the compound should be provided if 
644  known. When applicable, correlation between pharmacokinetic findings and genetic 
645  variation or gene or protein expression should be highlighted. 
646  6.  Reference should be made to scientific and analytical methods for genetic variation or gene 
647  or protein expression, including genotyping or expression profiling methods, statistical 
648  methods, and software packages used. 
649   
650  B.  Characterization of a Particular Compound 
651   
652  If the intent of a submission is to characterize a particular compound, it is generally 
653  recommended that the toxicology portion of the submission be reported in a similar format to a 
654  toxicology report. These reports follow the good review practices template (Section 4.1 m (1 to 
655  6)).  If the template is not used, a copy of the study protocol should accompany the line listings  
656  and generally include clinical signs, mortality, body weight, food consumption, hematology, 
657  clinical chemistry, urinalysis, gross pathology, organ weights, histopathology, and 
658  pharmaco/toxicokinetics (as available) with a full tabulation of data suitable for detailed review. 
659  These data contain line listings of the individual data points, including laboratory data points, for 
660  each animal along with summary tabulations of data points.  A copy of the study protocol is 
661  expected to accompany the line listings. 
662    
663  C.  General Scientific Discussion  
664   
665  When a submission contains data to support a general scientific discussion that is not necessarily 
666  related to the development of a compound and/or compound class, the minimal amount of 
667  nonclinical data to be submitted should be similar to the previously described scenarios. 
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668  However, it is up to the sponsor to provide adequate information to clarify and support the 
669  scientific issues discussed.  The data submitted will probably not be detailed, but we recommend 
670  that it be tabulated in a form that will be concise and adequately descriptive for the specific 
671  purpose of the submission.    
672   
673   
674  VII.  DATA SUBMISSION FORMAT 
675   
676  A general description of clinical and non-clinical data associated with genomic data submissions 
677  is included in Sections III and IV of this guidance.. This section provides details on electronic 
678  data submission formats for genomic and associated non-clinical or clinical data.  
679   
680  A.  Submission Standard 
681   
682  For any type of genomic data submission, we encourage you to submit the data electronically in 
683  a tab-delimited file conforming to the Clinical Data Interchange Standards Consortium (CDISC) 
684  Study Data Tabulation Model (SDTM) standard or the Standard for Exchange of Nonclinical 
685  Data (SEND) SDTM format per the CDISC guidelines (http://www.cdisc.org/ ).30  
686   
687  B.  Microarray Gene Expression Data 
688   
689  When a microarray gene expression experiment is included in a genomic data submission, both 
690  raw and normalized gene expression data as well as the gene lists that are used to support the 
691  biological conclusions in the submission should be submitted electronically. 
692   
693    Raw data  It is recommended that one file be submitted per array. For example, CEL 
694  files would be submitted for the Affymetrix GeneChip platform, while the tab delimited 
695  spreadsheet format could be used for other platforms with the gene ID (e.g., GenBank 
696  Acc#, manufacturer ID), in the first column. 
697    Normalized data  It is recommended that one file be submitted per array. The tab 
698  delimited spreadsheet format should be used with the gene ID (e.g., GenBank Acc#, 
699  manufacturer ID) in the first column. 
700    Gene lists  Lists of genes supporting a biological interpretation in the submission should 
701  be included. Probeset IDs in each array should identify each entry in these lists. The lists 
702  should be submitted along with parameters such as fold change and p-value for each gene 
703  of interest in a tab delimited format. 
704    Besides the parameters mentioned above, the gene lists (or Results) submission should 
705  also include following information: 
706  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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707  o  filtering conditions (such as intensity filter, spot flag filter, spot size filter, and 
708  detection call filter) 
709  o  normalization method selected for data analysis (there are several different 
710  normalization methods available, such as median, Lowess, and housekeeping 
711  gene normalizations) 
712  o  methods selected for statistical analyses. 
713   
714  In addition to the data files, an experimental summary table (called ExpSumTable, Appendix I) 
715  should be prepared to summarize the key experimental parameters investigated in the microarray 
716  study. The experimental parameters should be prepared in accordance to the MIAME (Minimum 
717  Information About a Microarray Experiment) guidelines.              
718   
719  C.  Clinical and Nonclinical Data 
720   
721  The Study Data Tabulation Model (SDTM) that encompasses both CDISC and SEND has been 
722  developed to guide the organization, structure, and format for both clinical and nonclinical data 
723  submissions. For genomic data submissions, clinical and nonclinical data should be prepared in 
724  accordance to the SDTM. CDISC/SEND organizes the study data under the concept of domains. 
725  Each domain summarizes a collection of observations with a topic-specific commonality. At this 
726  point, we ask that each domain be prepared as a separate file in a tab-delimited format. Appendix 
727  II provides examples of data formatted for a nonclinical data submission.   
728   
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729  APPENDIX I:   EXPERIMENTAL SUMMARY TABLE (EXPSUMTABLE) 
730   
731   
732  The ExpSumTable summarizes key experimental parameters investigated in a microarray study. 
733  The first three columns are required. The first two columns provide the subject ID (e.g., animal 
734  ID) and Array ID respectively. The microarray raw data file is specified in the third column. The 
735  remaining columns provide the key experimental parameters that could be used to group array 
736  data for analysis. Sponsors should consider including parameters in the ExpSumTable useful in 
737  data analysis.   
738   
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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740  APPENDIX II:  EXAMPLESUBMITTING  NONCLINICAL STUDY DATA 
741   
742  The preparation of nonclinical study data included in a genomic data submission is illustrated 
743  through the hypothetical example below. You can find more details on data preparation in the 
744  SEND format at: http://www.cdisc.org/models/send/v2.3/SENDV2.3ImplementationGuide.pdf. 
745   
746  The objective of the example experiment is to identify gene expression patterns that might be 
747  related to liver toxicity. Ten rats were used in the study, five for control and five dosed by oral 
748  gavage with Drug X in a 6-day repeated-dose experiment. Microarray gene expression and 
749  clinical pathology data were reported for each rat in the study. For the genomic data submission, 
750  domains 1-6 are required. Refer to the SEND implementation guide noted above regarding 
751  which domains apply to the study. It is important to use a short name starting with the two-letter 
752  domain code for the column names (variables). 
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753        
754  Domain 1: Study Design Summary 
755   
SSPARMC SSSE
D  SSPAR  SSVAL  Q 
STTYP  Study Type  Repeat Dose Toxicity  1 
LBNAM  Laboratory Name  Company XYZ  2 
Laboratory 
LBLOC  Location  City, State  3 
SPECIES  Species  Rat  4 
STRAIN  Strain  Sprague-Dawley  5 
DESIGN  Study Design  Parallel  6 
Terminal Sacrifice 
TRMSAC  Period  1-6 days  7 
GLPTYP  GLP Type  FDA  8 
QARPT  QA Report  Yes  9 
Duration of 
DURDOS  Dosing  6 days  10 
6-Day Oral Toxicity Study in Male Sprague-Dawley Rats 
STTITL  Study Title  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 
756   
757   
758  Domain 2: Subject Characteristics  
759   
USUBJI ARMC SCTESTC SCORRE SCSTRES
D  D  D  S  SCTEST  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 
G:\7735dft.doc  22
8/24/2007  Draft 
Not for Implementation 
 
760  Domain 3: Group Characteristics 
761   
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 
762   
763   
764  Domain 4: Exposure 
765   
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
766  10 2 Vehicle Labrafil 0 mg/kg once daily liquid 0 oral P6D 2 10 1 6  
767  *General SDTM timing fields, always permissible (see 2.2.5 of the SDTM document at 
768  http://www.fda.gov/cder/regulatory/ersr/Studydata-v1.1.pdf) 
  23 Draft 
Not for Implementation 
 
769  Domain 5: Clinical Pathology 
770   
USUBJID CPTESTCD CPSTRESN CPSTRESU CPSPEC CPCAT CPSCAT CPTEST CPSTRESC CPORRES CPORRESU CPGRPID CPSEQ DY*
Chemical  Monocyte 
1 MONO 0.484 10E+9/L Blood HEM Analysis count 0.484 0.484 10E+9/L 1 1 6
Chemical  Monocyte 
2 MONO 0.418 10E+9/L Blood HEM Analysis count 0.418 0.418 10E+9/L 1 2 6
Chemical  Monocyte 
3 MONO 0.429 10E+9/L Blood HEM Analysis count 0.429 0.429 10E+9/L 1 3 6
Chemical  Monocyte 
4 MONO 0.447 10E+9/L Blood HEM Analysis count 0.447 0.447 10E+9/L 1 4 6
Chemical  Monocyte 
5 MONO 0.471 10E+9/L Blood HEM Analysis count 0.471 0.471 10E+9/L 1 5 6
Chemical  Monocyte 
6 MONO 0.441 10E+9/L Blood HEM Analysis count 0.441 0.441 10E+9/L 2 6 6
Chemical  Monocyte 
7 MONO 0.407 10E+9/L Blood HEM Analysis count 0.407 0.407 10E+9/L 2 7 6
Chemical  Monocyte 
8 MONO 0.448 10E+9/L Blood HEM Analysis count 0.448 0.448 10E+9/L 2 8 6
Chemical  Monocyte 
9 MONO 0.408 10E+9/L Blood HEM Analysis count 0.408 0.408 10E+9/L 2 9 6
Chemical  Monocyte 
10 MONO 0.418 10E+9/L Blood HEM Analysis count 0.418 0.418 10E+9/L 2 10 6
Chemical 
1 ALT 57 U/L Blood CHEM Analysis ALT 57 57 U/L 1 11 6
Chemical 
2 ALT 44 U/L Blood CHEM Analysis ALT 44 44 U/L 1 12 6
Chemical 
3 ALT 42 U/L Blood CHEM Analysis ALT 42 42 U/L 1 13 6
Chemical 
4 ALT 39 U/L Blood CHEM Analysis ALT 39 39 U/L 1 14 6
Chemical 
5 ALT 45 U/L Blood CHEM Analysis ALT 45 45 U/L 1 15 6
Chemical 
6 ALT 39 U/L Blood CHEM Analysis ALT 39 39 U/L 2 16 6
Chemical 
7 ALT 40 U/L Blood CHEM Analysis ALT 40 40 U/L 2 17 6
Chemical 
8 ALT 40 U/L Blood CHEM Analysis ALT 40 40 U/L 2 18 6
Chemical 
9 ALT 39 U/L Blood CHEM Analysis ALT 39 39 U/L 2 19 6
Chemical 
771  10 ALT 38 U/L Blood CHEM Analysis ALT 38 38 U/L 2 20 6
772  * General SDTM timing fields, always permissible (see 2.2.5 of the SDTM document at 
773  http://www.fda.gov/cder/regulatory/ersr/Studydata-v1.1.pdf) 
  24 Draft 
Not for Implementation 
 
774  Domain 6: Microscopic Findings 
775   
USUBJI MITESTC MISTA MIREASN MIGRPI MISE
D  D  MITEST  MIORRES  T  D  D  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 
Mild periportal 
6  LIVER  Liver  vacuolation        2  6  6 
Mild periportal 
7  LIVER  Liver  vacuolation        2  7  6 
Mild periportal 
8  LIVER  Liver  vacuolation        2  8  6 
Mild periportal 
9  LIVER  Liver  vacuolation        2  9  6 
Mild periportal 
10  LIVER  Liver  vacuolation        2  10  6 
776   
777   
778   
  25