Profiling chemicals based on chronic toxicity results from the U.S. EPA ToxRef Database

Abstract

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.

Keywords: cancer; chronic toxicity; pesticides; relational database; toxicity profile.

Figure 1

Unsupervised two-way hierarchical clustering of 207 effects in rat (A) and 112 effects in mouse (B) with incidence > 5, for 310 chemicals with chronic/cancer toxicity data in ToxRefDB. Specific clusters or classes based on associated toxicities are indicated by the color-coded chemical dendrogram: seven clusters for rat, and six for mouse.

Figure 2

ToxRefDB chronic/cancer incidence data summarized by effect type (A) and by target organ pathology (B) for 310 chemicals with rat or mouse studies. Blue bars, total percentage of chemicals with that observed effect; black bars, percentage of chemicals for which that effect was used to derive systemic NOAEL/LOAEL levels.

Figure 3

(A) ToxRefDB systemic toxicity and cancer outcomes represented along an end-point progression continuum. This schema was used to derive a severity score for each chemical based on the maximum value within a target organ. (B) Based on end-point progression, 310 chemicals were scored for liver and kidney pathology in rat and mouse chronic/cancer studies. Clinical chemistry used in this analysis is limited to target-organ–specific analytes (e.g., alanine aminotransferase for liver, and urea nitrogen for kidney).

Figure 4

(A) The 16 rat and 9 mouse ToxRefDB end points from chronic/cancer studies selected for ToxCast predictive modeling. Two-way hierarchical clustering of the rat (B) and mouse (C) end points based on log-transformed potency values. Dose and potency values for all chemicals relative to these 25 end points are provided on the ToxRefDB home page (U.S. EPA 2008c).