QA/QC: challenges and pitfalls facing the microarray community and regulatory agencies. (1st November 2004)

• Record Type: Journal Article
• Authors: Shi, Leming; Tong, Weida; Goodsaid, Federico; Frueh, Felix W; Fang, Hong; Han, Tao; Fuscoe, James C; Casciano, Daniel A
• Is Part Of: Expert review of molecular diagnostics. Volume 4:Number 6(2004)
• Page Start: 761
• Page End: 777
• Publication Date: 2004-11-01
• Subjects: crosslaboratory and crossplatform comparison -- meta-analysis -- microarray -- protocol optimization and standardization -- QA/QC -- quality metrics and thresholds -- regulatory review -- reproducibility and accuracy; Molecular diagnosis -- Periodicals; 616.0758205
• DOI: 10.1586/14737159.4.6.761
• Languages: English
• ISSNs: 1473-7159

Abstract

The scientific community has been enthusiastic about DNA microarray technology for pharmacogenomic and toxicogenomic studies in the hope of advancing personalized medicine and drug development. The US Food and Drug Administration has been proactive in promoting the use of pharmacogenomic data in drug development and has issued a draft guidance for the pharmaceutical industry on data submissions. However, many challenges and pitfalls are facing the microarray community and regulatory agencies before microarray data can be reliably applied to support regulatory decision making. Four types of factors (i.e., technical, instrumental, computational and interpretative) affect the outcome of a microarray study, and a major concern about microarray studies has been the lack of reproducibility and accuracy. Intralaboratory data consistency is the foundation of reliable knowledge extraction and meaningful crosslaboratory or crossplatform comparisons; unfortunately, it has not been seriously evaluated and demonstrated in every study. Profound problems in data quality have been observed from analyzing published data sets, and many laboratories have been struggling with technical troubleshooting rather than generating reliable data of scientific significance. The microarray community and regulatory agencies must work together to establish a set of consensus quality assurance and quality control criteria for assessing and ensuring data quality, to identify critical factors affecting data quality, and to optimize and standardize microarray procedures so that biologic interpretation and decision-making are not based on unreliable data. These fundamental issues must be adequately addressed before microarray technology can be transformed from a research tool to clinical practices.