The haloacetic acids (HAAs) are disinfection by-products (DBPs) that are formed during the disinfection of drinking water, wastewaters and recreational pool waters. Currently, five HAAs [bromoacetic acid (BAA), dibromoacetic acid (DBAA), chloroacetic acid (CAA), dichloroacetic acid (DCAA), and trichloroacetic acid (TCAA); designated as HAA5] are regulated by the U.S. EPA, at a maximum contaminant level of 60 μg/L for the sum of BAA, DBAA, CAA, DCAA, and TCAA. We present a comparative systematic analysis of chronic cytotoxicity and acute genomic DNA damaging capacity of 12 individual HAAs in mammalian cells. In addition to the HAA5, we analyzed iodoacetic acid (IAA), diiodoacetic acid (DiAA), bromoiodoacetic acid (BIAA), tribromoacetic acid (TBAA), chlorodibromoacetic acid (CDBAA), bromodichloroacetic acid (BDCAA), and bromochloroacetic acid (BCAA). Their rank order of chronic cytotoxicity in Chinese hamster ovary cells was IAA > BAA > TBAA > CDBAA > DIAA > DBAA > BDCAA > BCAA > CAA > BIAA > TCAA > DCAA. The rank order for genotoxicity was IAA > BAA > CAA > DBAA > DIAA > TBAA > BCAA > BIAA > CDBAA. DCAA, TCAA, and BDCAA were not genotoxic. The trend for both cytotoxicity and genotoxicity is iodinated HAAs > brominated HAAs > chlorinated HAAs. The use of alternative disinfectants other than chlorine generates new DBPs and alters their distribution. Systematic, comparative, in vitro toxicological data provides the water supply community with information to consider when employing alternatives to chlorine disinfection. In addition, these data aid in prioritizing DBPs and their related compounds for future in vivo toxicological studies and risk assessment.

The U.S. Environmental Protection Agency (EPA) is faced with the challenge of efficiently and credibly evaluating chemical safety often with limited or no available toxicity data. The expanding number of chemicals found in commerce and the environment, coupled with time and resource requirements for traditional toxicity testing and exposure characterization, continue to underscore the need for new approaches. In 2005, EPA charted a new course to address this challenge by embracing computational toxicology (CompTox) and investing in the technologies and capabilities to push the field forward. The return on this investment has been demonstrated through results and applications across a range of human and environmental health problems, as well as initial application to regulatory decision-making within programs such as the EPA’s Endocrine Disruptor Screening Program. The CompTox initiative at EPA is more than a decade old. This manuscript presents a blueprint to guide the strategic and operational direction over the next 5 years. The primary goal is to obtain broader acceptance of the CompTox approaches for application to higher tier regulatory decisions, such as chemical assessments. To achieve this goal, the blueprint expands and refines the use of high-throughput and computational modeling approaches to transform the components in chemical risk assessment, while systematically addressing key challenges that have hindered progress. In addition, the blueprint outlines additional investments in cross-cutting efforts to characterize uncertainty and variability, develop software and information technology tools, provide outreach and training, and establish scientific confidence for application to different public health and environmental regulatory decisions.

Endocrine disruption from environmental contaminants has been linked to a broad spectrum of adverse outcomes. One concern about endocrine-disrupting xenobiotics is the potential for additive or synergistic (i.e., greater-than-additive) effects of mixtures. A short-term dosing model to examine the effects of environmental mixtures on thyroid homeostasis has been developed. Prototypic thyroid-disrupting chemicals (TDCs) such as dioxins, polychlorinated biphenyls (PCBs), and poly-brominated diphenyl ethers have been shown to alter thyroid hormone homeostasis in this model primarily by up-regulating hepatic catabolism of thyroid hormones via at least two mechanisms. Our present effort tested the hypothesis that a mixture of TDCs will affect serum total thyroxine (T4) concentrations in a dose-additive manner. Young female Long-Evans rats were dosed via gavage with 18 different polyyhalogenated aromatic hydrocarbons [2 dioxins, 4 dibenzofurans, and 12 PCBs, including dioxin-like and non-dioxin-like PCBs] for 4 consecutive days. Serum total T4 was measured via radioimmunoassay in samples collected 24 hr after the last dose. Extensive dose–response functions (based on seven to nine doses per chemical) were determined for individual chemicals. A mixture was custom synthesized with the ratio of chemicals based on environmental concentrations. Serial dilutions of this mixture ranged from approximately background levels to 100-fold greater than background human daily intakes. Six serial dilutions of the mixture were tested in the same 4-day assay. Doses of individual chemicals that were associated with a 30% TH decrease from control (ED30), as well as predicted mixture outcomes were calculated using a flexible single-chemical-required method applicable to chemicals with differing dose thresholds and maximum-effect asymptotes. The single-chemical data were modeled without and with the mixture data to determine, respectively, the expected mixture response (the additivity model) and the experimentally observed mixture response (the empirical model). A likelihood-ratio test revealed statistically significant departure from dose additivity. There was no deviation from additivity at the lowest doses of the mixture, but there was a greater-than-additive effect at the three highest mixtures doses. At high doses the additivity model underpredicted the empirical effects by 2- to 3-fold. These are the first results to suggest dose-dependent additivity and synergism in TDCs that may act via different mechanisms in a complex mixture. The results imply that cumulative risk approaches be considered when assessing the risk of exposure to chemical mixtures that contain TDCs.