title: Computational toxicology approaches at the US Food and Drug Administration.
abstract: For over a decade, the United States Food and Drug Administration (US FDA) has been engaged in the applied research, development, and evaluation of computational toxicology methods used to support the safety evaluation of a diverse set of regulated products. The basis for evaluating computational toxicology methods is multi-factorial, including the potential for increased efficiency, reduction in the numbers of animals used, lower costs, and the need to explore emerging technologies that support the goals of the US FDA's Critical Path Initiative (e.g. to make decision support information available early in the drug review process). The US FDA's efforts have been facilitated by agency-approved data-sharing agreements between government and commercial software developers. This commentary review describes former and current scientific initiatives at the agency, in the area of computational toxicology methods. In particular, toxicology-based QSAR models, ToxML databases and knowledgebases will be addressed. Notably, many of the computational toxicology tools available are commercial products - however, several are emerging as non-commercial products, which are freely-available to the public, and which will facilitate the understanding of how these programs work and avoid the "black box" paradigm. Through productive collaborations, the US FDA Center for Drug Evaluation and Research, and the Center for Food Safety and Applied Nutrition, have worked together to evaluate, develop and apply these methods to chemical toxicity endpoints of regulatory interest.
authors.affiliation: Office of Food Additive Safety, Center for Food Safety and Applied Nutrition, US Food and Drug Administration, College Park, MD, USA. chihae.yang@fda.hhs.gov
authors.forename: Chihae
authors.initials: C
authors.lastname: Yang
authors.forename: Luis G
authors.initials: LG
authors.lastname: Valerio
authors.forename: Kirk B
authors.initials: KB
authors.lastname: Arvidson
mesh_terms: D019295:Computational Biology / Q000379:methods*; D003628:Database Management Systems; D016208:Databases, Factual; D006801:Humans; D051188:Knowledge Bases; D004364:Pharmaceutical Preparations / Q000737:chemistry; D021281:Quantitative Structure-Activity Relationship; D014116:Toxicology / Q000379:methods*; D014481:United States; D014486:United States Food and Drug Administration
chemical_list: D004364:Pharmaceutical Preparations