Gene expression arrays are being used nonclinically to explore associations with clinical outcomes to predict drug response, to better understand mechanisms of drug action, and to identify biomarkers of drug response that may be used to assess effectiveness or toxicity in clinical settings. The unique aspect of microarray gene expression technology is the ability to explore "genome-wide" expression in a given sample. A huge number of measurable analytes from each experiment, even with the application of 99% statistical confidence limits, could yield a significant number of false-positive and false-negative signals. Gene expression microarray technology is evolving, and its performance is not standardized among numerous platforms now available with various probes designed from different gene sequences for the same targets. The scope of information that microarrays provide requires data reduction applications to analyze, visualize, comprehend, and communicate the data output. To use these data effectively and correctly, we need reliable experimental data, sound data reduction algorithms, and publicly available biologically linked reference information. With regard to validation, each feature of an array would not necessarily need validation, but subsets of features should be evaluated with respect to performance. The development of a reference standard, such as mixed-tissue aliquots of species-specific RNA, was recognized as important for increasing confidence in this technology.
There are varying interpretations as to whether, when, or what type of microarray data on lead compounds in drug development would need to be submitted to regulatory agencies as part of an IND. The format for presentation of such data is undefined. It is not clear if the data quality is sufficiently high and convincingly reliable, given the state of the technology, for regulatory authorities to make decisions on microarray data submitted under an investigational new drug application. Many of the workshop participants suggested that all microarray data conducted under either good laboratory practice (GLP) or non-GLP conditions should be submitted to the FDA if the lead compound is covered by an IND. If microarray databases are needed for regulatory scientists to place individual sponsor microarray experimental results into proper perspective, it is not clear how this database can be built since, to date, very little data have been submitted to the agency and publicly available databases have not matured. As databases become more populated with examples, and scientific knowledge and interpretation of gene expression responses expand, there is concern that the microarray data generated today may become more informative over time and might, therefore, affect assessments of products later during development, prior to making a final regulatory marketing approval decision. There was a strong suggestion, with general agreement, that the FDA needs to be transparent in collecting microarray data and in using it for regulatory decisions. By sharing collective knowledge gained from reviewing across applications, the FDA can improve the quality of microarray use in drug development.
Even before gene expression microarray results can be interpreted with confidence, some critical assessment of the integrity of gene expression data is required. Concerns exist about the reliability, precision, accuracy, and inter-laboratory reproducibility of data derived from global gene expression technologies. Numerous statistical, image analysis, pattern recognition, and data reduction clustering algorithms are being applied to microarray data. For screening compounds and improving understanding of drug effects on a target tissue, applying such approaches will provide a "big-picture" overview of patterns on the basis of drug class similarities but could also draw attention to the discriminating details that will distinguish among individual agents within a class. The biological interpretations, regulatory implications, and potential legal ramifications of such evaluations of product performance using global gene expression data are not well articulated at the present time.
Although there may be efforts by end users to establish standard procedures to consistently ensure and evaluate sample quality, as well as to calibrate microarrays and microarray instrumentation to ensure the integrity of their complete data sets, there is little regulatory experience to document the analytical reliability of these methods. There is a need for consensus on a standardized set of information required to fully annotate the data generated from microarray experiments. The degrees of quality control and validation that microarray manufacturers apply to their products, as well as the extent to which good manufacturing practices (GMPs) are relevant, are not always apparent. Information on manufacturing controls and postmanufacturing lotto-lot quality control functional performance/pass-fail measures by microarray providers is important but lacking for many but not all manufacturers. There are little data from well-controlled clinical trials showing that gene expression microarray-based tests are appropriately precise, accurate, and reproducible between laboratories to be clinically valid for patient selection. Communication at the early stages of clinical development between regulatory agencies, diagnostic companies that are developing genome-based tests, and pharmaceutical companies developing drugs will enhance the possibility that test performance can be validated during clinical trials for utility in the clinical setting.
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