Assessing the skin irritation and sensitizing potential of concentrates of water chlorinated in the presence of iodinated X-ray contrast media.

Chemical disinfection of water provides significant public health benefits. However, disinfectants like chlorine can react with naturally occurring materials in the water to form disinfection byproducts (DBPs). Natural levels of iodine have been reported to be too low in some source waters to account for the levels of iodinated DBPs detected. Iodinated X-ray contrast media (ICM) have been identified as a potential source of iodine. The toxicological impact of ICM present in source water at the time of disinfection has not been fully investigated. Iopamidol, iohexol, iopromide, and diatrizoate are among the ICM most frequently detected in water. In this study, source water containing one of these four ICM was chlorinated; non-chlorinated ICM-containing water samples served as controls. Reactions were conducted at an ICM concentration of 5 µM and a chlorine dose of 100 µM over 72 hr. Water concentrates (20,000-fold) were prepared by XAD-resin/ethyl acetate extraction and DMSO solvent exchange. We used the MatTek® reconstituted human epithelial skin irritation model to evaluate the water concentrates and also assessed the dermal irritation and sensitization potential of these concentrates using the LLNA:BrdU ELISA in BALB/c mice. None of the water concentrates tested (2500X) resulted in a skin irritant response in the MatTek® skin irritation model. Likewise, none of the concentrates (2500X, 1250X, 625X, 312.5X, 156.25X) produced a skin irritation response in mice: erythema was minimal; the maximum increase in ear thickness was less than 25%. Importantly, none of the concentrates produced a positive threshold response for allergic skin sensitization at any concentration tested in the LLNA:BrdU ELISA. We conclude that concentrates of water disinfected in the presence of four different ICM did not cause significant skin irritation or effects consistent with skin sensitization at the concentrations tested.

The Next Generation Blueprint of Computational Toxicology at the U.S. Environmental Protection Agency.

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.

Evaluating the similarity of complex drinking-water disinfection by-product mixtures: overview of the issues.

Humans are exposed daily to complex mixtures of environmental chemical contaminants, which arise as releases from sources such as engineering procedures, degradation processes, and emissions from mobile or stationary sources. When dose-response data are available for the actual environmental mixture to which individuals are exposed (i.e., the mixture of concern), these data provide the best information for dose-response assessment of the mixture. When suitable data on the mixture itself are not available, surrogate data might be used from a sufficiently similar mixture or a group of similar mixtures. Consequently, the determination of whether the mixture of concern is "sufficiently similar" to a tested mixture or a group of tested mixtures is central to the use of whole mixture methods. This article provides an overview for a series of companion articles whose purpose is to develop a set of biostatistical, chemical, and toxicological criteria and approaches for evaluating the similarity of drinking-water disinfection by-product (DBPs) complex mixtures. Together, the five articles in this series serve as a case study whose techniques will be relevant to assessing similarity for other classes of complex mixtures of environmental chemicals. Schenck et al. (2009) describe the chemistry and mutagenicity of a set of DBP mixtures concentrated from five different drinking-water treatment plants. Bull et al. (2009a, 2009b) describe how the variables that impact the formation of DBP affect the chemical composition and, subsequently, the expected toxicity of the mixture. Feder et al. (2009a, 2009b) evaluate the similarity of DBP mixture concentrates by applying two biostatistical approaches, principal components analysis, and a nonparametric "bootstrap" analysis. Important factors for determining sufficient similarity of DBP mixtures found in this research include disinfectant used; source water characteristics, including the concentrations of bromide and total organic carbon; concentrations and proportions of individual DBPs with known toxicity data on the same endpoint; magnitude of the unidentified fraction of total organic halides; similar toxicity outcomes for whole mixture testing (e.g., mutagenicity); and summary chemical measures such as total trihalomethanes, total haloacetic acids, total haloacetonitriles, and the levels of bromide incorporation in the DBP classes.

Evaluating sufficient similarity for disinfection by-product (DBP) mixtures: multivariate statistical procedures.

For evaluation of the adverse health effects associated with exposures to complex chemical mixtures in the environment, the U.S. Environmental Protection Agency (EPA) (2000) states, "if no data are available on the mixture of concern, but health effects data are available on a similar mixture ... a decision must be made whether the mixture on which health effects are available is 'sufficiently' similar to the mixture of concern to permit a risk assessment." This article provides a detailed discussion of statistical considerations for evaluation of the similarity of mixtures. Multivariate statistical procedures are suggested to determine whether individual samples of drinking-water disinfection by-products (DBPs) vary significantly from a group of samples that are considered to be similar. The application of principal components analysis to (1) reduce the dimensionality of the vectors of water samples and (2) permit visualization and statistical comparisons in lower dimensional space is suggested. Formal analysis of variance tests of homogeneity are illustrated. These multivariate statistical procedures are applied to a data set describing samples from multiple water treatment plants. Essential data required for carrying out sensitive analyses include (1) identification and measurement of toxicologically sensitive process input and output characteristics, and (2) estimates of variability within the data to construct statistically efficient estimates and tests.

Thyroid-hormone-disrupting chemicals: evidence for dose-dependent additivity or synergism.

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 polybrominated 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 polyhalogenated 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.