The Us Food And Drug Administration'S (Fda) Endocrine Disruptor Knowledge Base (Edkb) Lessons Learned In Qsar Modeling And Applications

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, and cosmetics. Given the huge number of chemicals, many commercially important, and the expense of testing, Structure-Activity Relationship/Quantitative Structure-Activity Relationship (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 conducted the Endocrine Disruptor Knowledge Base (EDKB) project, of which SAR/QSARs is a major component. We 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 chapter, 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 the authors have chosen 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.