The Toxicity Data Landscape for Environmental Chemicals

Abstract

ObjectiveThousands of chemicals are in common use, but only a portion of them have undergone significant toxicologic evaluation, leading to the need to prioritize the remainder for targeted testing. To address this issue, the U.S. Environmental Protection Agency (EPA) and other organizations are developing chemical screening and prioritization programs. As part of these efforts, it is important to catalog, from widely dispersed sources, the toxicology information that is available. The main objective of this analysis is to define a list of environmental chemicals that are candidates for the U.S. EPA screening and prioritization process, and to catalog the available toxicology information.Data sourcesWe are developing ACToR (Aggregated Computational Toxicology Resource), which combines information for hundreds of thousands of chemicals from > 200 public sources, including the U.S. EPA, National Institutes of Health, Food and Drug Administration, corresponding agencies in Canada, Europe, and Japan, and academic sources.Data extractionACToR contains chemical structure information; physical–chemical properties; in vitro assay data; tabular in vivo data; summary toxicology calls (e.g., a statement that a chemical is considered to be a human carcinogen); and links to online toxicology summaries. Here, we use data from ACToR to assess the toxicity data landscape for environmental chemicals.Data synthesisWe show results for a set of 9,912 environmental chemicals being considered for analysis as part of the U.S. EPA ToxCast screening and prioritization program. These include high-and medium-production-volume chemicals, pesticide active and inert ingredients, and drinking water contaminants.ConclusionsApproximately two-thirds of these chemicals have at least limited toxicity summaries available. About one-quarter have been assessed in at least one highly curated toxicology evaluation database such as the U.S. EPA Toxicology Reference Database, U.S. EPA Integrated Risk Information System, and the National Toxicology Program.

Scientific contestations over “toxic trespass”: health and regulatory implications of chemical biomonitoring

“…Meanwhile, regulatory agencies rely heavily on scientific data to make regulatory decisions more credible to diverse stakeholders (e.g., Jasanoff 1993Jasanoff , 2009Brown 2013). Yet all too often, emergent science can be so uncertain that regulatory decision-making and policy changes are mired in negotiations that are very slow or not forthcoming at all.…”

“…Chemicals to be monitored are defined a priori and only cover about 250-300 (or 10 %) of the chemicals in US commerce. Considerations such as production volume are often used to select chemicals for targeted biomonitoring (Judson et al 2009;Egeghy et al 2012). However, the lack of information about where, how, and the extent to which chemicals are used and substituted in different products makes it difficult to predict and track which chemical exposures are most important with respect to human development and health risks.…”

“…With an increase in the number of biomonitoring studies from diverse stakeholders including academic researchers, government agencies, environmental health and justice movement organizations (EHJM), and industry groups comes the need to better understand the public health consequences of exposures to synthetic chemicals. A growing body of research demonstrates adverse health effects for several chemicals, but there is a paucity of toxicological and human health data for most of the 84,000 chemicals that circulate in commerce (Judson et al 2009;Woodruff et al 2011a;Porta 2012). This new Bvisibility^of ubiquitous chemical exposures in people reveals the shortcomings of the Toxic Substances Control Act of 1976 (TSCA), the policy that guides the regulation of industrial chemicals (Wilson and Schwarzman 2009).…”

Building associations between markers of environmental stressors and adverse human health impacts using frequent itemset mining

“…While there are over 83,000 chemicals in commerce, the vast majority have little human health effect or exposure information [13,19]. There is a critical need for cost-effective identification and prioritization of those compounds most likely contributing to adverse health outcomes, spawning several initiatives [20,24,36].…”

Economic benefits of using adaptive predictive models of reproductive toxicity in the context of a tiered testing program

“…Should such approaches become widely accepted and implemented, it could become practical to significantly reduce the toxicity information data gaps that exist for large numbers of chemicals [Judson et al 2009], while increasing the overall efficiency in resource utilization in chemical testing programs. Increased cost efficiency could derive from multiple factors, including: direct testing costs (studied here); indirect testing costs (e.g., data review and analysis time and personnel); time required to reach a decision; and laboratory animal use.…”