Advisory Committee for Pharmaceutical Science June 10, 2003

1. Submission of Microarray Data:Dealing Effectively with Data Quality Issues and Information Content Necessary to Develop an Expression Database Advisory Committee for Pharmaceutical Science June 10, 2003

2. The Vision Better compounds submitted Safer compounds submitted and approved Lower approval time Vision and Challenge • Challenge: Over 50% of drug failures due to efficacy/tox. • Problem: 20 million US patients exposed to drugs withdrawn between Sept. ’97 and Sept. ’98 • Problem: Only 1 in 10 IND’s become approved drugs -- Our current methods of candidate characterization are only 10% accurate • Solution: Bridge genomics and chemistry to broadly understand a compound’s effects in genomic terms

3. Assumptions -- Sponsor side • Sponsor providing data to support IND or NDA • Data is part of a larger package and not the sole and only evidence provided • Sponsor has on going array efforts with trained staff or will contract with CRO that meets these requirements

4. Assumptions – Agency side • Agency is willing to develop and train staff so that the data is meaningfully interpreted and a balanced view is taken • Sponsor is concerned about overly reactive view • Sponsor is concerned about the future liability from public disclosure • Agency is able to accept data in a community defined standard format and has capacity to assess quality • Agency is prepared for all current technologies • Agency will keep up with future technology improvements • Agency desires to deposit submitted data into an internal database for use by staff during future evaluations • Agency understands that context is essential for a balanced view

5. Array measurements similarities to traditional measurements, but… • ALT elevation is associated with liver damage • Treated group value is 55 ± 7 (SE) [range: 30-75], n=5 • Control group value is 50 ± 4 (SE) [range: 15-110] n=15 • No treated animals outside range of controls • Conclusion: ALT not significantly changed by treatment, consistent with good liver safety. • Gene expression data on 5 RNAs associated with liver damage • No treated animal outside range of controls • Conclusion: 5 RNAs associated with liver toxicity not significantly changed, consistent with good liver safety.

6. … but, microarray data has differences from conventional measurements • Both agency and community has lower familiarity with technology • Will improve with experience • Concern that the survey might uncover “confounding” factors • Sponsor concerned about a “overly reactive view” • FDA concerned about sponsor missing important findings • Less “scientific agreement” about how to interpret and meaning • Pattern matching is less familiar to biological community • Requires a different mindset than single gene focus of the past • Perception that microarray data is lower quality and noisier • Technology has improved • Carefully conducted experiments are accurate and predictive

7. Summary: What should be provided by sponsor to FDA • MIAME/MAGE-ML compliant descriptions of experiment(s) and electronic submission of all data • Minimum experimental design metrics similar to that required for any other biological experiment • “Youth” of technology requires that additional quality data be submitted to demonstrate competency of experimenters • Sponsors interpretation of data in “scientific paper style” format • Comparison to community accepted RNA biomarkers and compared to Bench Mark Drugs and Toxicants • Interpretation vs. context of current drugs failed drugs and toxicants

8. Sponsor provided data to assure QUALITY Themes • Measurement vs. lab historical values • Measurement vs. external standards • RNA external standard • Measurement vs. internal standards • A few spike-in standards • Different than other agency submissions due to “youth” of technology • Need to assure competency of experimenter • Need to assure internal/external consistency • Need to assure consistency with historical values

9. In Vivo Biology RNA Isolation RNA Isolation Target Preparation Target Preparation Hybridization Hybridization Data Loading In Vitro Biology Laboratory Information Management System (LIMS) Laboratory Information Management System (LIMS) Experiment is complex but has several points where critical evaluations needed    286 steps needed to prepare microarray Several quality control check points Independent of platform

10. Experimental design minimums • Minimum of biological triplicate • Minimum of three untreated or mock or vehicle treated controls, processed contemporaneously • Minimum of three external standard RNA’s, processed contemporaneously • Minimum three spike-in RNA’s each sample Treated Untreated - Control RNA standard External Control 3 Spike RNA’s

11. RNA used in experiments • Ratio of 28S to 18S • Mean • Standard Deviation • Range • Traces for all RNA samples Data provided for each the following: • Samples in dataset • Historical for similar tissue/cell prepared in lab • RNA external standard, contemporaneous with dataset

12. Hybridization and array Q.C. Data for experimental samples • Array average signal to background ratio • Array average background • Array average raw signal • Log dynamic range • Average Raw signal intensity for each of three spike-in RNA’s Comparative data (Mean, SD and range) • Historical for similar samples (match for tissue or cell type) • Historical average for RNA standard • Historical average for each of spike-in RNA’s • Average for contemporaneous RNA standard • Average for contemporaneous controls

13. Experiment internal and external consistency • Experimental samples, correlation coefficient (CC) with • each other • contemporaneous controls • contemporaneous external standard RNA • historical external RNA standards • historical similar tissue or cell line samples Mean, SD and range in all cases

14. Sponsor provided “Scientific literature style interpretation” • Abstract • Significance relative to application • Brief methods • Summary of quality evidence described earlier • Results • Discussion • Conclusion relative the application under consideration • Conclusions in context with other drugs and toxicants Report helps the agency help you

15. Summary: What should be provided by sponsor to FDA • MIAME/MAGE-ML compliant descriptions of experiment(s) and electronic submission of all data • Minimum experimental design metrics similar to that required for any other biological experiment • “Youth” of technology requires that additional quality data be submitted to demonstrate competency of experimenters • Sponsors interpretation of data in “scientific paper style” format • Comparison to community accepted RNA biomarkers and compared to Bench Mark Drugs and Toxicants • Interpretation vs. context of current & failed drugs and toxicants

16. Internal FDA contextual database Micoarray technology requires stepping into the coming age of electronic data submissions • Paper submission of microarray data is not useful • Used by agency to develop a better understanding of technology • Used by the agency to allow a look at the data in the context of other submissions • Contextual database is highly useful to provide meaning and balance to interpretation • Same biology is present regardless of data preparation technology

17. Once the data is available to the agency how should they evaluate? Objectives • Promote balanced view of the data • React to truly significant events • Ground the analysis in the context of real world effects of drugs, failed drugs, toxicants and standards Need a reference database for these purposes

18. What is necessary to provide contextual grounding? Database containing … • Wide diversity of successful drugs, failed relatives, toxicants and standards • Needed to understand pharmacologic mechanism • Needed to understand toxicity mechanism • Multiple tissues with dose and time context • Linkage of expression data to orthogonal data including: • Pharmacology including on and off target sites of action • Histology • Clinical Chemistry • Hematology • Chemical Structure • In vivo and in vitro data for successful bridging domains

19. Benefits of using a reference database to the agency • Provides context to interpret events seen with candidates – An example drawn from DrugMatrix Oncogenes elevated by approved drugs in liver Context promotes balanced view of data

20. Summary: What should be provided by sponsor to FDA • MIAME/MAGE-ML compliant descriptions of experiment(s) and electronic submission of all data • Minimum experimental design metrics similar to that required for any other biological experiment • “Youth” of technology requires that additional quality data be submitted to demonstrate competency of experimenters • Additional quality data beyond that required of established technologies • Sponsors interpretation of data in “scientific paper style” format • Comparison to community accepted RNA biomarkers and compared to Bench Mark Drugs and Toxicants • Interpretation vs. context of current drugs and toxicants

21. Conclusions Looking forward • Micro Array technology is ready to contribute today • Simple assurances of quality are needed • Contextual databases to allow meaningful interpretation are available • Developing understanding and consensus around meaningful RNA biomarkers • Requirements beyond normal verification of data quality will diminish as community sophistication using technology improves • Analysis of data collected off several platforms is quite possible • Same biology is found regardless of the platform • Clinical applications to accessible human tissues will become common

22. Helps realize the Vision Better compounds submitted Safer compounds submitted and approved Lower approval time Result • Improved predictive power of animal studies making truly predictive animals models a reality • Addresses the Problem: Patients exposure to drugs which are subsequently withdrawn • Addresses the Problem: Only 1 in 10 IND’s become approved drugs -- Our current methods of candidate characterization are only 10% accurate