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

Wrangling Whole Mixtures Risk Assessment: Recent Advances in Determining Sufficient Similarity

Human health risk assessments for complex mixtures can address real-world exposures and protect public health. While risk assessors typically prefer whole mixture approaches over component-based approaches, data from the precise exposure of interest are often unavailable and surrogate data from a sufficiently similar mixture(s) are required. This review describes recent advances in determining sufficient similarity of whole, complex mixtures spanning the comparison of chemical features, bioactivity profiles, and statistical evaluation to determine "thresholds of similarity". Case studies, including water disinfection byproducts, botanical ingredients, and wildfire emissions, are used to highlight tools and methods. Limitations to application of sufficient similarity in risk-based decision making are reviewed and recommendations presented for developing best practice guidelines.

Occurrence and Comparative Toxicity of Haloacetaldehyde Disinfection Byproducts in Drinking Water

The introduction of drinking water disinfection greatly reduced waterborne diseases. However, the reaction between disinfectants and natural organic matter in the source water leads to an unintended consequence, the formation of drinking water disinfection byproducts (DBPs). The haloacetaldehydes (HALs) are the third largest group by weight of identified DBPs in drinking water. The primary objective of this study was to analyze the occurrence and comparative toxicity of the emerging HAL DBPs. A new HAL DBP, iodoacetaldehyde (IAL) was identified. This study provided the first systematic, quantitative comparison of HAL toxicity in Chinese hamster ovary cells. The rank order of HAL cytotoxicity is tribromoacetaldehyde (TBAL) ≈ chloroacetaldehyde (CAL) > dibromoacetaldehyde (DBAL) ≈ bromochloroacetaldehyde (BCAL) ≈ dibromochloroacetaldehyde (DBCAL) > IAL > bromoacetaldehyde (BAL) ≈ bromodichloroacetaldehyde (BDCAL) > dichloroacetaldehyde (DCAL) > trichloroacetaldehyde (TCAL). The HALs were highly cytotoxic compared to other DBP chemical classes. The rank order of HAL genotoxicity is DBAL > CAL ≈ DBCAL > TBAL ≈ BAL > BDCAL>BCAL ≈ DCAL>IAL. TCAL was not genotoxic. Because of their toxicity and abundance, further research is needed to investigate their mode of action to protect the public health and the environment.

An empirical approach to sufficient similarity: combining exposure data and mixtures toxicology data

When assessing risks posed by environmental chemical mixtures, whole mixture approaches are preferred to component approaches. When toxicological data on whole mixtures as they occur in the environment are not available, Environmental Protection Agency guidance states that toxicity data from a mixture considered "sufficiently similar" to the environmental mixture can serve as a surrogate. We propose a novel method to examine whether mixtures are sufficiently similar, when exposure data and mixture toxicity study data from at least one representative mixture are available. We define sufficient similarity using equivalence testing methodology comparing the distance between benchmark dose estimates for mixtures in both data-rich and data-poor cases. We construct a "similar mixtures risk indicator"(SMRI) (analogous to the hazard index) on sufficiently similar mixtures linking exposure data with mixtures toxicology data. The methods are illustrated using pyrethroid mixtures occurrence data collected in child care centers (CCC) and dose-response data examining acute neurobehavioral effects of pyrethroid mixtures in rats. Our method shows that the mixtures from 90% of the CCCs were sufficiently similar to the dose-response study mixture. Using exposure estimates for a hypothetical child, the 95th percentile of the (weighted) SMRI for these sufficiently similar mixtures was 0.20 (i.e., where SMRI <1, less concern; >1, more concern).

Biological and statistical approaches for modeling exposure to specific trihalomethanes and bladder cancer risk

Lifetime exposure to trihalomethanes (THM) has been associated with increased risk of bladder cancer. We explored methods of analyzing bladder cancer risk associated with 4 THM (chloroform, bromodichloromethane, dibromochloromethane, and bromoform) as surrogates for disinfection by-product (DBP) mixtures in a case-control study in Spain (1998-2001). Lifetime average concentrations of THM in the households of 686 incident bladder cancer cases and 750 matched hospital-based controls were calculated. Several exposure metrics were modeled through conditional logistic regression, including the following analyses: total THM (μg/L), cytotoxicity-weighted sum of total THM (pmol/L), 4 THM in separate models, 4 THM in 1 model, chloroform and the sum of brominated THM in 1 model, and a principal-components analysis. THM composition, concentrations, and correlations varied between areas. The model for total THM was stable and showed increasing dose-response trends. Models for separate THM provided unstable estimates and inconsistent dose-response relationships. Risk estimation for specific THM is hampered by the varying composition of the mixture, correlation between species, and imprecision of historical estimates. Total THM (μg/L) provided a proxy measure of DBPs that yielded the strongest dose-response relationship with bladder cancer risk. A variety of metrics and statistical approaches should be used to evaluate this association in other settings.

Pollution trees: identifying similarities among complex pollutant mixtures in water and correlating them to mutagenicity

There are relatively few tools available for computing and visualizing similarities among complex mixtures and in correlating the chemical composition clusters with toxicological clusters of mixtures. Using the "intersection and union ratio (IUR)" and other traditional distance matrices on contaminant profiles of 33 specific water samples, we used "pollution trees" to compare these mixtures. The "pollution trees" constructed by neighbor-joining (NJ), maximum parsimony (MP), and maximum likelihood (ML) methods allowed comparison of similarities among these samples. The mutagenicity of each sample was then mapped to the "pollution tree". The IUR-distance-based measure proved effective in comparing chemical composition and compound level differences between mixtures. We found a robust "pollution tree" containing seven major lineages with certain broad characteristics: treated municipal water samples were different from raw water samples and untreated rural drinking water samples were similar with local water sources. The IUR-distance-based tree was more highly correlated to mutagenicity than were other distance matrices, i.e., MP/ML methods, sampling group, region, or water type. IUR-distance-based "pollution trees" may become important tools for identifying similarities among real mixtures and examining chemical composition clusters in a toxicological context.

Concentrations and correlations of disinfection by-products in municipal drinking water from an exposure assessment perspective

Although disinfection by-products (DBPs) occur in complex mixtures, studies evaluating health risks have been focused in few chemicals. In the framework of an epidemiological study on cancer in 11 Spanish provinces, we describe the concentration of four trihalomethanes (THMs), nine haloacetic acids (HAA), 3-chloro-4-(dichloromethyl)-5-hydroxy-2(5H)-furanone (MX), four haloacetonitries, two haloketones, chloropicrin and chloral hydrate and estimate correlations. A total of 233 tap water samples were collected in 2010. Principal component analyses were conducted to reduce dimensionality of DBPs. Overall median (range) level of THMs and HAAs was 26.4 (0.8-98.1) and 26.4 (0.9-86.9) μg/l, respectively (N=217). MX analysed in a subset (N=36) showed a median (range) concentration of 16.7 (0.8-54.1)ng/l. Haloacetonitries, haloketones, chloropicrin and chloral hydrate were analysed in a subset (N=16), showing levels from unquantifiable (<1 μg/l) to 5.5 μg/l (dibromoacetonitrile). Spearman rank correlation coefficients between DBPs varied between species and across areas, being highest between dibromochloromethane and dibromochloroacetic acid (r(s)=0.87). Principal component analyses of 13 DBPs (4 THMs, 9 HAAs) led 3 components explaining more than 80% of variance. In conclusion, THMs and HAAs have limited value as predictors of other DBPs on a generalised basis. Principal component analysis provides a complementary tool to address the complex nature of the mixture.

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 drinking-water disinfection by-product (DBP) mixtures with bootstrap hypothesis test procedures

In chemical mixtures risk assessment, the use of dose-response data developed for one mixture to estimate risk posed by a second mixture depends on whether the two mixtures are sufficiently similar. While evaluations of similarity may be made using qualitative judgments, this article uses nonparametric statistical methods based on the "bootstrap" resampling technique to address the question of similarity among mixtures of chemical disinfectant by-products (DBP) in drinking water. The bootstrap resampling technique is a general-purpose, computer-intensive approach to statistical inference that substitutes empirical sampling for theoretically based parametric mathematical modeling. Nonparametric, bootstrap-based inference involves fewer assumptions than parametric normal theory based inference. The bootstrap procedure is appropriate, at least in an asymptotic sense, whether or not the parametric, distributional assumptions hold, even approximately. The statistical analysis procedures in this article are initially illustrated with data from 5 water treatment plants (Schenck et al., 2009), and then extended using data developed from a study of 35 drinking-water utilities (U.S. EPA/AMWA, 1989), which permits inclusion of a greater number of water constituents and increased structure in the statistical models.

Determinants of whether or not mixtures of disinfection by-products are similar

Reactive chemicals have been used to disinfect drinking waters for over a century. In the 1970s, it was first observed that the reaction of these chemicals with the natural organic matter (NOM) in source waters results in the production of variable, complex mixtures of disinfection by-products (DBP). Because limited toxicological and epidemiological data are available to assess potential human health risks from complex DBP mixture exposures, methods are needed to determine when health effects data on a specific DBP mixture may be used as a surrogate for evaluating another environmental DBP mixture of interest. Before risk assessors attempt such efforts, a set of criteria needs to be in place to determine whether two or more DBP mixtures are similar in composition and toxicological potential. This study broadly characterizes the chemical and toxicological measures that may be used to evaluate similarities among DBP mixtures. Variables are discussed that affect qualitative and quantitative shifts in the types of DBP that are formed, including disinfectants used, their reactions with NOM and with bromide/iodide, pH, temperature, time, and changes in the water distribution system. The known toxicological activities of DBP mixtures and important single DBPs are also presented in light of their potential for producing similar toxicity. While DBP exposures are associated with a number of health effects, this study focuses on (1) mutagenic activity of DBP mixtures, (2) DBP cancer epidemiology, and (3) toxicology studies to evaluate similarity among DBP mixtures. Data suggest that further chemical characterization of DBP mixtures and more systematic study of DBP toxicology will improve the quality and usefulness of similarity criteria.