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Dr. Weida Tong is Director of the Center for Toxicoinformatics at FDA’s National Center for Toxicological Research (USFDA-NCTR). He also holds several adjunct positions at universities, including that of Associate Professor at UMDNJ. His Center for Toxicoinformatics at FDA is involved in developing bioinformatics projects to support FDA pharmacogenomics data submission and regulation. Two of the most visible projects from his group are (1) development of the FDA genomic tool, ArrayTrack; and (2) leading the effort on the Microarray Quality Control (MAQC) consortium. In addition, his group also specializes in molecular modeling and QSARs with specific interest in estrogen, androgen, and endocrine disruptor. Dr. Tong has published >100 papers and book chapters.
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The FDA’s Endocrine Disruptor Knowledge Base (EDKB) – Lessons Learned in QSAR Modeling and Applications
Weida Tong, Director of Center for Toxicoinformatics, NCTR/FDA
Considerable scientific, regulatory and popular press attention has been devoted to the Endocrine Disrupting Chemicals (EDCs). A larger number of potential estrogenic EDCs are associated with products regulated by the Food and Drug Administration (FDA), including plastics used in food packaging, phytoestrogens, food additives, pharmaceuticals, cosmetics, etc. Given the huge number of chemicals, many commercially important, and the expense of testing, SAR/QSAR has been considered to be an important priority setting strategy for subsequent experimentation. At the U.S. FDA’s National Center for Toxicological Research (NCTR), we have conducted the Endocrine Disruptor Knowledge Base (EDKB) project, of which SAR/QSARs is a major component. We have developed predictive models for estrogen and androgen receptor binding. The strengths and weaknesses of various QSAR methods were assessed to select those most appropriate for regulatory priority setting. This presentation, rather than presenting the work and results of the EDKB program in an exhaustive manner, selectively discusses salient concepts, issues, and challenges, endeavoring to achieve a tutorial outcome. In particular, concepts such as designing training sets, living models, use of QSARs in a regulatory context, predictive model validation, QSAR applicability domain and prediction confidence estimates are among topics to highlight. The concepts are presented and discussed using EDKB program results to provide qualitative and quantitative illustrations and examples. We believe the experience and lessons learned in the EDKB program will prove valuable to practitioners of QSAR should they endeavor to extend predictive systems to real-world regulatory implementations.
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