Mixtures, toxicity

The following sections describe case studies for which the effects of single chemicals have been ruled out. These studies represent only a fraction of those demonstrating the known effects of toxic mixture exposure. They are meant to illustrate the scope of hazards faced when humans are exposed to toxic chemical mixtures. 

In almost all of the studies presented here (as well as in most others not reported here) the chemical mixtures that produced unanticipated toxic effects contained at least one lipophile (K 2.00) and one hydrophile ( CW 2.00). The octanol water partition coefficients (7T w) are given in parentheses for each of the chemicals identified to point out the lipophilic and hydrophilic species. 

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Patel Y, Kushwah HS, Kushwah A, et al. 1998b. Effect of chronic pesticides mixture toxicity on some enzymes in rats. Indian Vet J 75 698-700. 

Borgert, C.J., QuiU, T.F., McCarty, L.S., Mason, A.M. (2004) Can Mode of Action Predict Mixture Toxicity for Risk Assessment Toxicology and Applied Pharmacology, 210, 85-96. 

For the derivation of the PNEC several approaches have been proposed. Generally these can be categorised into three distinct assessments a conservative, a distributional, and a mixture toxicity approach. In conservative approaches, usually the most (realistic) sensitive endpoint such as LC50 or the known no observed effect concentration (NOEC) is taken and divided by an uncertainty factor (10-100). The selected uncertainty factor value depends on the type of endpoint and the number of available data, and is applied to account for laboratory to field extrapolations, species differences in sensitivities, and similar uncertainties. In distributional approaches, a series of, or all available, literature data are taken and a selected cut-off value is applied to the distribution of these data. The cut-off value may be, e.g., the concentration value that will protect 95% of the species (tested). In general, again an uncertainty factor (usually of 10) is then applied to take into account species differences. In the mixture toxicity approach, a similar mode of action is assumed for the assessment of the combined (additive) effect of the mixture. All relevant mixture components are scaled relative to the most potent one. This results in relative potencies for each component. The total effect of the mixture is then evaluated by... 

Mixture toxicities. As is obvious from Chapters 2 and 6, APEO and their degradation products occur in the environment as complex mixtures. In risk characterisation studies of AP and APEO, the toxicity of individual constituents of such mixtures, whether assayed in acute and chronic toxicity, or in estrogenicity tests (Chapter 7), is being considered to occur, for each separate endpoint, through the same separate mode of action, and consequently to be additive. Thus, relative potencies can be established for each individual... 

The third scheme proposed by Konemann [348] uses a mixture toxicity index (MTI) defined as... 

In summary, the different joint effect models of multicomponent pollutant mixtures (i.e., the toxic unit, additive and mixture toxicity indices) were presented. Using such models to analyze the joint effect of a group of toxic and carcinogenic organic compounds such as polycyclic aromatic hydrocarbons will be presented and evaluated in Sect. 3.2. 

Highest transfection efficiencies were found for Chol/KLl-14-ratios of 0.5 to 0.7. Higher or lower ratios led to lower transfection efficiencies, which were similar to that of the KL-1-14/DOPE mixtures. Toxicity of all... 

Cyanobacteria toxins are toxins produced by certain species of blue-green algae that have become a major environmental and public health concern. The behavior of cyanotoxins during chlorination treatment has been recently reviewed by Merel et al. [129]. Chlorination DBFs have been reported only for the hepatotoxins microcystin-LR and cylindrospermopsin. Other cyanotoxins, such as nodularins, saxitoxins, and anatoxins, have yet to be investigated. Different isomers of six chlorination products of microcystin-LR have been characterized dihydroxy-microcystin, monochloro-microcystin, monochloro-hydroxy-microcystin, monochloro-dihydroxy-microcystin, dichloro-dihydroxy-microcystin, and trichloro-hydroxy-microcystin. Only two chlorination DBFs have been reported so far for cylindrospermopsin 5-chloro-cylindros-permopsin and cylindrospermopsic acid [129]. Chlorination of microcystin, cylindrospermopsin, and nodularins seems to reduce the mixture toxicity however, this aspect has not been extensively studied [129]. 

Altenburger R, Nendza M, Schuurmann G (2003) Mixture toxicity and its modeling by quantitative structure-activity relationships. Environ Toxicol Chem 22 1900-1915... 

At present the risk assessment of contaminated objects is mainly based on the chemical analyses of a priority list of toxic substances. This analytical approach does not allow for mixture toxicity, nor does it take into account the bioavailability of the pollutants present. In this respect, bioassays provide an alternative because they constitute a measure for environmentally relevant toxicity, that is, the effects of a bioavailable fraction of an interacting set of pollutants in a complex environmental matrix [9-12]. 

W. K. Lutz, S. Vamvakas, A. Kopp-Schneider, J. Schlatter and H. Stopper, Deviation from additivity in mixture toxicity relevance of nonlinear dose-response relationships and cell line differences in genotoxicity assays with combinations of chemical mutagens and g-radiation. Environmental Health Perspectives Supplements, 2002,110(6), 915-918. 

Cleuvers M. (2(X)4). Mixture toxicity of the anti-inflammatory drugs diclofenac, ibuprofen, naproxen, and acetylsahcyhc acid. Ecotoxicology and Environmental Safety 59 309-315. 

People are environmentally exposed to PBB and PBDE mixtures of different congeneric composition than the original commercial PBB and PBDE mixtures. Although the toxicity or potency of environmental PBB and PBDE mixtures consequently may be greater or less than that of commercial mixtures, there are insufficient mixture toxicity data on which to directly base MRLs for environmental PBBs and PBDEs. 

Various techniques, such as graphic illustrations (e.g., isobolograms), mixture toxicity indices (e.g., an additivity index), formulas, or fully parameterized models, exist for predicting an expected combined effect based on concentration addition or response addition (for review, see Bodeker et al. 1990). The quantitative relationship between the expected combined effect calculated according to concentration addition or response addition depends (in addition to other factors) primarily on the steepness of the concentration response relationship of the individual components (Drescher and Bodeker 1995). Concentration addition predicts a higher combined effect as compared to response addition when the mixture components have steep concentration response relationships, whereas the opposite is true for flat concentration response relationships of the mixture components. 

When a model (e.g., concentration addition or response addition) is considered appropriate for describing the mixture effects observed in experiments, it can serve predictive purposes (such as formulating a scientific null hypothesis for an experiment), or for practical extrapolation and for risk assessment. There are, however, limitations associated with the concepts and the associated models based on pharmacodynamic reasoning. These limitations were first recognized by Plackett and Hewlett (1952), yet have mainly gone unnoticed by followers of the mechanistic school of mixture toxicity. Three main limitations are identified, and extrapolation solutions are provided. 

Second, interactive mixture toxicity is currently not predictable by the available mixture models. Various levels of possible interactions may be distinguished ... 

To address the quality limitations for extrapolation, the available experimental data on observed mixture effects were evaluated with care, and a pragmatic approach for mixture extrapolation was followed. Although mechanistic understanding was often not the purpose of the experiments, the extrapolation approach is based upon mechanistic principles, that is, regarding the choice between mixture toxicity models. Conceptual considerations on biases and mathematical characteristics of the models were included (see Section 5.3.3). 

The largest reviews of mixture toxicity data conducted to date (Ross 1996 Ross and Warne 1997) examined the toxicity of approximately 1000 predominantly binary, tertiary, and quaternary mixtures. This analysis revealed that between 75% and 80% of the mixtures acted according to the concentration-addition model, 10% to 15% showed less response than expected under this model, and 10% to 15% showed a higher response than expected. Five percent of the mixtures had toxicity values that differed from concentration additivity by a factor greater than 2.5, and 1% of the mixtures had toxicity values that differed by more than a factor of 5. 

In light of the large number of interaction levels that may influence the mixture toxicity response at the sites of toxic action, especially in terrestrial systems, it may come as a surprise that we are able to draw the conclusion that mixture extrapolations appear to be justified by the data. Although the interactions may all be relevant and may all require extrapolations according to the methods explained in the other chapters of this book, the net effect is apparently, and often relatively well, predicted by concentration addition. 

Which types and levels of interactions are present that might affect mixture toxicity, and how can these be appropriately addressed For instance, is there an environmental interaction such as precipitation that would influence mixture effects through modification of exposure in a significant way (Note In the rest of this chapter, limited attention is paid to the issues of modifying effects brought about by nonchemical stressors, such as co-exposure to UV rays or pathogens. Those interactions are treated in the other chapters of this book.)... 

As can be deduced from the review in Section 5.3.1, most studies on mixture toxicity have been conducted on single species exposed to binary mixtures. In the case of studies with more complex mixtures, generally, either the toxicants have all been selected to operate by the same mode of action, or they have all been selected to represent different modes of action. In the complex situation of a multiple contaminated environment, it is likely that the biota are exposed to a mixture where several modes of action (some similar, some dissimilar) are represented by a variety of toxicants (Teuschler et al. 2004 Chapin 2004). Predicting the toxicity of a complex mixture of toxicants could build on observations that both concentration addition... 

Experimental data or field observations on mixture toxicity and the responses in species assemblages are rarely available, with some exceptions (Korthals et al. 2000 Backhaus et al. 2004 Arrhenius et al. 2004). Nonetheless, risk assessment and legislation often focus on the protection of community-level endpoints in both prospective and retrospective risk assessments. 

For addressing multispecies risk of mixture toxicity, we propose the following same procedure that is followed in general mixture studies in single-species ecotoxi-cology. That is, one must consider exposure, look at exposed species groups when necessary in view of the assessment endpoints, consider the mode of action of the components, and apply either of the sets of models based on this information. The practical protocols for mixture risk assessment that stem from this choice are worked out and discussed in the section below. 

As summarized in Section 5.3.1, the vast majority of aquatic mixture toxicity studies report that the actual toxicity of mixtures is very close to the toxicity predicted by concentration addition. The application of Tier-3 approaches is mostly restricted... 

This chapter proposes the use of SSD and mixture toxicity models in ecological risk assessment of species assemblages by calculating the multisubstance potentially affected fraction of species on the basis of measured or predicted (biologically active) concentrations of toxic compounds in the environment. The msPAF method has been scrutinized for its conceptual basis. To address this scrutiny, we cite the human toxicology work of Ashford (1981) as a cross-link. 

De Zwart (2005) used a novel method to predict the effects of multiple stressors caused by pesticides based on a GIS map of agricultural land use, comprising 51 crops. Through the application of SSDs for aquatic organisms, in combination with rules for mixture-toxicity calculations, the modeled exposure results were transformed to risk estimates for aquatic species. The majority of the predicted risks were caused by pesticides applied to potato cropland, and approximately 95% of the predicted risk was caused by only 7 of the 261 pesticides currently used in The Netherlands. 

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