Profiling Chemicals Based on Chronic Toxicity Results from the U.S. EPA ToxRef Database

Background Thirty years of pesticide registration toxicity data have been historically stored as hardcopy and scanned documents by the U.S. Environmental Protection Agency (EPA). A significant portion of these data have now been processed into standardized and structured toxicity data within the EPA’s Toxicity Reference Database (ToxRefDB), including chronic, cancer, developmental, and reproductive studies from laboratory animals. These data are now accessible and mineable within ToxRefDB and are serving as a primary source of validation for U.S. EPA’s ToxCast research program in predictive toxicology. Objectives We profiled in vivo toxicities across 310 chemicals as a model application of ToxRefDB, meeting the need for detailed anchoring end points for development of ToxCast predictive signatures. Methods Using query and structured data-mining approaches, we generated toxicity profiles from ToxRefDB based on long-term rodent bioassays. These chronic/cancer data were analyzed for suitability as anchoring end points based on incidence, target organ, severity, potency, and significance. Results Under conditions of the bioassays, we observed pathologies for 273 of 310 chemicals, with greater preponderance (> 90%) occurring in the liver, kidney, thyroid, lung, testis, and spleen. We observed proliferative lesions for 225 chemicals, and 167 chemicals caused progression to cancer-related pathologies. Conclusions Based on incidence, severity, and potency, we selected 26 primarily tissue-specific pathology end points to uniformly classify the 310 chemicals. The resulting toxicity profile classifications demonstrate the utility of structuring legacy toxicity information and facilitating the computation of these data within ToxRefDB for ToxCast and other applications.

[1]  J. H. Ward.  Hierarchical Grouping to Optimize an Objective Function , 1963 .

[2]  T H Shepard,et al.  National toxicology program. , 1981, Teratology.

[3]  P. Stolpman,et al.  Environmental Protection Agency , 2020, The Grants Register 2022.

[4]  U. Epa.  Guidelines for carcinogen risk assessment , 1986 .

[5]  Ross Ihaka,et al.  Gentleman R: R: A language for data analysis and graphics , 1996 .

[6]  S. Cohen,et al.  Cell Proliferation and Carcinogenesis , 2008 .

[7]  D. Hanahan,et al.  The Hallmarks of Cancer , 2000, Cell.

[8]  D. Wolf,et al.  Confounders in interpreting pathology for safety and risk assessment. , 2005, Toxicology and applied pharmacology.

[9]  R Daniel Benz,et al.  Landscape of current toxicity databases and database standards. , 2006, Current opinion in drug discovery & development.

[10]  D. Dix,et al.  The ToxCast program for prioritizing toxicity testing of environmental chemicals. , 2007, Toxicological sciences : an official journal of the Society of Toxicology.

[11]  Ann Richard,et al.  ACToR--Aggregated Computational Toxicology Resource. , 2008, Toxicology and applied pharmacology.

[12]  Steven K. Gibb.  Toxicity testing in the 21st century: a vision and a strategy. , 2008, Reproductive toxicology.

[13]  F. Collins,et al.  Transforming Environmental Health Protection , 2008, Science.

[14]  Zhen Li,et al.  A comparison of machine learning algorithms for chemical toxicity classification using a simulated multi-scale data model , 2008, BMC Bioinformatics.