Annual Review MIXTURE TOXICITY AND ITS MODELING BY QUANTITATIVE STRUCTURE ACTIVITY RELATIONSHIPS

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1 Environmental Toxicology and Chemistry, Vol. 22, No. 8, pp , SETAC Printed in the USA /03 $ Annual Review MIXTURE TOXICITY AND ITS MODELING BY QUANTITATIVE STRUCTURE ACTIVITY RELATIONSHIPS ROLF ALTENBURGER, MONIKA NENDZA, and GERRIT SCHÜÜRMANN* Department of Chemical Ecotoxicology, UFZ Centre for Environmental Research, Permoserstrasse 15, Leipzig, Germany Analytisches Laboratorium, für Umweltuntersuchungen und Auftragsforschung, Bahnhofstrasse 1, Luhnstedt, Germany (Received 9 August 2001; Accepted 2 January 2003) Abstract Environmental contaminants are frequently encountered as mixtures, and the behavior of chemicals in a mixture may not correspond to that predicted from data on the pure compounds. This paper reviews current quantitative structure activity relationship (QSAR) methodology for the analysis of mixture toxicity. Interactions of components in a mixture can cause complex and substantial changes in the apparent properties of its constituents, resulting in synergistic or antagonistic effects as opposed to the ideal reference case of additive behavior: concentration addition (CA) and independent action (IA) are two prominent reference models for the evaluation of joint activity, and both have mechanistic support from pharmacology. After discussing graphical tools for analyzing binary mixtures and joint effect indices suitable also for multicomponent mixtures, water solubility and hydrophobicity of mixtures are analyzed with respect to the property contributions of the individual components. With the former, small but significant deviations from ideal behavior are observed even for simple organics, whereas in the case of low concentrations, mixture hydrophobicity was found to agree approximately with the fractional contributions of the components. A variety of studies suggest that mixtures of compounds exerting only one (narcotic or specific) mode of action can be modeled satisfactorily by assuming CA, whereas the interaction of differently acting compounds tends to yield a less than CA joint activity. The QSARs have been used to predict concentrations of components in mixtures from joint effects and defined mixture ratios and have been developed to predict narcotic-type mixture toxicity from molecular descriptors that are calculated as composite properties according to the fractional concentrations of the mixture components. In the case of ionogenic compounds, initial results suggest that CA may serve as a firstorder approximation for the joint effect of un-ionized and ionized compound portions. Keywords Quantitative structure activity relationships Mixture toxicity Concentration addition Independent action Review INTRODUCTION There is no such thing as a single chemical exposure, stated Yang et al. [1]. Environmental contaminants are frequently encountered as mixtures, and the behavior of chemicals in a mixture may not correspond to that predicted from data on the pure compounds. Interactions of components in a mixture can cause complex and substantial changes in the apparent properties of its constituents. The cosolutes in a mixture may induce either increased (synergistic) or decreased (antagonistic) effects as compared with ideal (additive) behavior. Most compounds are present in the field at concentrations far below their individual median effective concentration 50% (EC50), possibly also below their individual no observed effect concentration (NOEC), yet still they may contribute to substantial effects. The relevance of joint action of co-contaminants has long been recognized by the regulating authorities [2], and there are approaches to extend risk characterization to mixtures [3 5]. However, in current assessments each chemical is evaluated individually, and mixture effects are considered only via safety/assessment factors. Common to the currently available practical approaches for the hazard assessment of mixtures in the environment is the principal assumption of additive joint activity (i.e., concentration addition [CA]). Although more likely than evaluating multichemical exposures for each contaminant separately [3], and generally supported by empirical observations for a long time [6,7], the additivity * To whom correspondence may be addressed (gs@uoe.ufz.de). assumption is merely a working concept and does not necessarily reflect reality. Analyses of the joint action of chemical mixtures have a long-standing tradition, and reviews are available dealing with different aspects of their pharmacodynamics [8,9], aquatic toxicology [10,11], phyto-pharmacology [12], carcinogenicity [13,14], and environmental toxicology [15]. Depending on the scope of mixture studies, different approaches and methodologies must be applied. Table 1 illustrates the different objectives, foci, and intentions in joint action analyses of mixtures. CONCEPTUAL BASIS Analysis of joint action requires distinguishing different typologies of interaction as related to pharmacological phases. Depending on the reference model, several tools for calculation of expected joint action are available. With regard to nomenclature due to major terminological differences among disciplines being a source of confusion the attempts for harmonization as described [8,16] have been adopted for this review. Classification of joint action Exposure of a biosystem toward chemical mixtures may render different types of joint action (Table 2). The simultaneous presence of two or more substances may alter physicochemical properties of components such as solubility (see Physicochemical properties of mixtures), and this in turn may affect bioavailability and/or responses. Mixtures of chemicals 1900

2 QSARs in mixture toxicity Environ. Toxicol. Chem. 22, Table 1. Scope of environmental mixture studies Objective Focus Intention Analysis of joint action Identification of toxic components Description of combined effects Prediction of mixture toxicity Toxicant/target interactions, toxicant/cosolute interactions Effective contributions Effects assessment Hazard assessment Characterization of sites and modes of (inter)action Prioritization of chemicals Hazard identification Risk management/regulation with similar modes of action are expected to behave like dilutions of each other, whereas combinations of dissimilarly acting compounds may reveal statistically independent responses [17], (see Reference models for evaluation). The combined effect may then be without interaction (i.e., the observed effect is as expected in quantitative terms based on the information for the individual activities of the components), or it may be interactive [17] in qualitative or quantitative terms (i.e., synergistic or antagonistic). On the biosystem level, the tolerance distributions to multiple stressors may be or may not be correlated. These so-called correlations of responses may occur from the level of molecular receptors to the assembly of species in communities. For the purpose of this review, a mechanism of action is understood as the primary, molecular interaction between compounds of concern and structures or functions of a biosystem leading to an effect, whereas a mode of action considers the effector chain that propagates a primary interaction into an effect with observable consequences for an organism. Reference models for evaluation Analysis of joint action of chemical mixtures is based on comparing the observed effects to a reference model [18,19] (i.e., calculated effects typically obtained from the activities of the individual components assuming no interaction). Several concepts are available for this purpose, and the two most prominent (Table 3) are pharmacologically substantiated [8]. Figure 1 illustrates the basic pharmacological reasoning: regarding the sham combination of a compound with itself as a theoretical mixture experiment, the plausibility of different expected responses relative to the actual (nonlinear) concentration-response curve may be considered. Simply summing the effects, which is still a popular reference concept in experimental reports on findings of synergistic mixture responses, classifies this compound as acting synergistically with itself. In contrast, CA [20,21] allows the modeling of any type of concentration-response function of single compounds. This has often been taken as a strong argument in favor of CA as a universal reference for mixture responses [18]. There are, however, no straightforward theoretical sham combinations for mixtures of different components. Concentration addition is Table 2. Typologies of joint action of chemical mixtures Level of consideration Chemical properties Mode of action Combined effect Biosystem response Alterations present Similar Interactive Correlated Joint action Alterations absent Dissimilar Noninteractive Noncorrelated thought to be valid for mixtures where the components have similar sites and modes of action [8] (i.e., the activities and effects are not changed if one compound is partially replaced, or diluted, by an equipotent amount of another). The alternative concept favored in human toxicology [9], namely independent action (IA), can be found in the literature under various names. It originates from statistical considerations of independent responses [22] and is currently held appropriate for mixtures where the components have different sites and dissimilar modes of action. Experimental evidence demonstrating the plausibility of these pharmacodynamic assumptions for toxic response levels using mixtures of specifically acting compounds in algal and bacterial biotests has recently been provided for both concepts [23 25]. In the literature there is no consensus as to how strict the requirements for similarity of site, mechanisms, or modes of action for mixture components have to be to adequately employ either reference model [26]. Because detailed pharmacological information is usually absent for most environmental contaminants, no a priori preference can be given. One way of dealing with this assessment dilemma [27] would be to use both reference models simultaneously and take the range of expected responses as a prediction window for combined effects. With any scientific study, rational experimental design is a crucial prerequisite for optimal results. The design strategies for test mixtures (Table 4) depend on underlying hypotheses and reference models. For binary mixtures of two substances (S i ) at different concentrations (C i ), three basic choices are available. First, the n n design [19] is used to determine the concentration-response relationship of one substance in the presence of one or more fixed levels of another compound. This design allows easy interpretation in terms of IA [9]. Second, Ray design [19] provides constant mixture ratios, such as in terms of EC50s. This design is best suited for comparing responses with CA, although it also allows interpretation according to IA if the individual responses are estimated. Third, composite design combines aspects of the n n design and Ray design for covering any possible interactions at various mix- Table 3. Reference models for calculating expected mixture effects Concentration addition (Loewe additivity) Assumption: same site of action; similar mode of action Formula: c 1 /ECX 1 c 2 /ECX 2 1 Independent action (Bliss independence, response addition, effect multiplication) Assumption: different sites of action; dissimilar modes of action Formula: E(c 1,2 ) E(c 1 ) E(c 2 ) E(c 1 )E(c 2 )

3 1902 Environ. Toxicol. Chem. 22, 2003 R. Altenburger et al. Fig. 2. Combination effects of equipotent mixtures of substances A and B (after Frei [20]). The diagonals represent the activities of the individual components A and B at concentration levels corresponding to the respective molar percentage. The horizontal resultants give the joint effects of mixtures with increasing fractions of A being replaced by equipotent amounts of B in the case of additivity (left), synergism (middle), or antagonism (right). Fig. 1. What is synergy? Illustration of the mixture toxicity in the case of effect summation as compared to the actual dose response curve of one compound (sham combination of different concentrations of one component). ture ratios. The mixture compositions are selected by factorial design strategies that allow rational selection of mixtures for a limited number of observation points optimized for the description of mutual interactions. Selection of specific mixture ratios for testing should reflect effect contributions of the individual components. If components in mixtures reveal concentration-response relationships with different slopes, one component may dominate the toxicity of the mixture to such an extent that contributions by other components may not be detectable. To balance the design, a sensitivity analysis of the chosen design points for the mixture responses should be carried out. A persistent misunderstanding is that parallelism of concentration-response relationships of individual mixture components is required for concentration-additive behavior. The respective formula provided in Table 3 reveals, however, that the only requirement concerns the determination of effect concentrations. For investigating mixture responses at a defined effect level, equal fractions of this effect concentration are used for components with parallel concentration-response curves. In the case of the concentration-response relationships of the individual components being not parallel, different mixture ratios apply for different effect levels. Tools for analysis and interpretation Methods for the analysis and interpretation of joint action of chemicals are so numerous that there are attempts to classify them into categories [10,12]. Graphical approaches and the use Table 4. Illustration of mixture design strategies for a binary mixture of substances (S1, S2) at various concentrations (C1,...,C6) a [C1]S2 [C2]S2 [C3]S2 [C4]S2 [C5]S2 [C6]S2 [C1]S1 [C2]S1 [C3]S1 [C4]S1 [C5]S1 [C6]S1 a Theoretical design points for binary mixtures with identical number of observations according to n n design; ray design; composite design. of indices are most popular for experimenters and QSAR approaches, as they do not require sophisticated biometrics. However, shortcomings of these simple techniques have to be realized. Graphical methods. Pharmacological research in the early 20th century produced the graphical methods for joint effect analyses that are still used today. In 1913, Frei [20] extended Henle s approach from 1889 [28] to study effects of graded equivalent combinations of disinfectants relative to the activities of the individual compounds. He introduced a theoretical background for equipotent compounds (i.e., the basis of the toxic unit concept), discriminated CA (isoaddition) and response addition (heteroaddition), regarded the influence of the steepness and shape of dose-response curves, and discussed possible interactions between mixture components. For defining more or less than additive effects, Frei first determined the equipotent concentrations of the individual substances and then measured the effects of their mixtures with one chemical being partly replaced by increasing fractions of the second compound. The activities of the individual substances A and B and their combinations are related to the concentrations or relative fractions of the components in the mixture (e.g., molar fraction or molar percentage, respectively; Fig. 2). The diagonals (which may be linear, but need not be) represent the activities of the individual components A and B at concentration levels corresponding to the respective molar percentage. The horizontal resultants give the joint effects of mixtures with increasing fractions of A being replaced by equipotent amounts of B. For additive joint effects, the resultant equals the sum of the individual curves corresponding to the straight line connecting the respective effects of the individual compounds. More than additive effects are characterized by upward deviations of the resultant, and for the case of less than additive effects, the resultant deviates downward. These graphs correspond to those in physical chemistry for instance, for vapor pressure deviations from Raoult s law and lay the early basis of rational mixture toxicity analysis. Limiting the analysis to one defined effect level yields the classical isobologram method (Fig. 3 [21]). The two-dimensional representation with two linearly scaled concentration axes uses an isobole, a curve connecting all points of different mixture ratios of the same combined effect. The predicted isobole for CA is the straight line between the respective effect concentrations of the individual compounds. Interpretation of this approach is based on deviations from the predicted additivity isobole. It focuses on mixture ratio dependent effects and is limited to binary mixtures [11]. Representation of concentration-response data in a threedimensional plot with two concentration and one effect axes provides a canonical way of analyzing the joint action. Mod-

4 QSARs in mixture toxicity Environ. Toxicol. Chem. 22, Fig. 3. A schematic isobologram. The straight line connects the effect concentrations of the individual compounds and represents the isobole for concentration-additive behavior of binary mixtures of the components S1 and S2 in varying mixture ratios. The upward-bent, dotted isobole illustrates mixtures that require higher concentrations to exert the effect (i.e., less than concentration additive [CA] or antagonistic); the downward-bent, dashed curve represents mixtures with lower concentrations needed to provoke a combined effect (i.e., more than CA or synergistic effects). eling the mixture data by an envelope makes this a so-called surface analysis. This approach is commonly taken to describe a given mixture and quantify its combination effects, e.g., find a mixture of maximum response [8]. Another option is to plot concentration-response data using one common concentration scale for the individual compounds and one for the total molar concentration of the mixtures. Interpretation is then based on curve shifts that compare, depending on experimental design (see Reference models for evaluation), offsets from concentration-response curves of individual compounds [9] or deviations from calculated mixtureresponse functions [29]. As for any method in the analysis of joint action of chemical mixtures, clear reference to an expected response is necessary for meaningful interpretations, as there is no unambiguous terminology available [19]. A major advantage of this approach is the scope for a sensitivity analysis for a chosen mixture design (i.e., visualization of the contribution of individual components to a given combined effect). Indices of mixture toxicity. Indices of mixture toxicity relate expected and observed responses in quantitative terms. A variety of indices is available, such as the sum of toxic units (M TU [7]); the additivity index (AI) and toxicity enhancement index (TEI, available at [30]); the mixture toxicity index (MTI [31]); the index on prediction quality (IPQ [32]); the similarity parameter ( [33,34]); and the factor of error [35], to name but a few. The MTI, TU, AI, and TEI have been used in the context of predicting mixture toxicity, and their definitions as well as their interpretations with respect to the concepts of CA and no addition are summarized in Table 5. Here no addition is defined as the combined effect that equals the activity of the most potent component alone [22,31,36]. The similarity parameter is a measure of how much a given joint effect deviates from CA. For mixtures of two components with toxic units TU 1 and TU 2, it is defined as (1/) (1/) TU TU (1) with 1 indicating joint effects less than additive and 1 indicating more than additive with respect to CA [33,34]. Although can so far not be predicted from molecular structure, QSARs can well be used for predicting the standard effects of the individual components i, ECX i (e.g., EC50 i as 50% effect concentration of compound i). These can be used to derive QSAR estimates of toxic unit based joint toxicities and associated joint effect index values. Note that such applications are not restricted to mixtures of compounds exerting only one mode of action, because variation in the mode of action can Table 5. Joint effect indices a Type of index and type of interaction Index Toxic unit (TU) Joint effect index Additivity index (AI) Mixture toxicity index (MTI) Author Mathematical definition Sprague and Ramsey [118]; Anderson and Weber [119] c i TUi EC50i where Marking [120] Könemann [78] If M 1, then AI M 1; if M 1, then AI 1/M 1; if M 1, then AI M 1, where MTI where log M0 log M log M 0 ci concentration of component i, M TU M TU EC50i EC50 of component i M M 0 max (TU i) i Strictly concentration additive M TU i 1 AI 0 MTI 1 Less than additive M M 0 AI 0 MTI 0 More than additive M 1 AI 0 MTI 1 No addition (independent action M M 0 MTI 0 for completely positive correlation of responses, r 1) Partial addition M0 M 1 1 MTI 0 a Adapted in modified form [86]. The toxicity enhancement index (TEI, [30]) is obtained from the additivity index (AI) by adding 1 if AI is positive, or subtracting 1 and reciprocating if AI is negative. i i

5 1904 Environ. Toxicol. Chem. 22, 2003 R. Altenburger et al. be accounted for by using appropriately selected QSAR models for the individual components of the mixture. As for binary mixtures, most indices are algebraic equivalents of the isobologram method [10] with differences only in scaling of the quantitative deviations; mixture assessment results should be consistent irrespective of a particular method chosen. This has in fact been demonstrated for toxic unit summation, AI, and MTI [35,37]. There are several reasons for the popularity of indices in mixture assessments: (1) a qualitative statement about whether or not an observed mixture effect meets the model expectations is replaced by a number quantifying the degree of deviation, and (2) large amounts of data as well as data for multiple mixtures can be presented in a very condensed form. The same tools, however, may be used to present few data from a poor design, thus hiding lack of quality in the experimental study. Indices in the assessment of the joint action of chemicals are subject to two major limitations: (1) indices provide only point-wise assessments. There is inevitable loss of information, inherent in a concentration-response relationship, when considering EC50 responses only. Models for global additivity, regarding effect-level dependent responses, can be found with response-surface models [8] or may be derived from parametric modeling approaches [38]. (2) There is no systematic consideration of the variability of responses. This aspect concerns questions such as what does an MTI of 0.8 or a sum of toxic units of 1.2 tell about deviation from CA behavior? Due to experimental variability it is unlikely to meet calculated responses precisely. So far, the significance of such deviations can only be estimated by simple rules of error propagation as shown for the case of MTI, deriving an expected error margin of the index from the variability in responses of the individual components [31]. Particular consideration has to be given to different scales of the indices that may result in identical values at different distances from the model predictions, and vice versa. Furthermore, the indices compare responses on a concentration scale, which means, for instance, that a toxic unit summation of 1.2, 0.2 more than expected by CA, may be little on the effect scale of a mixture with a flat concentration-response curve but much for a different mixture with a steep slope. Alternatives to mixture toxicity indices are using parametric models as derived from statistical dose-response modeling [12,38], or the application of response-surface models [8,39], which, however, both need quite a number of experimental joint effect data as input and thus are more useful for interpretation than for prediction. Generic mixture QSARs and their structural descriptors The concepts of joint mixture toxicity, especially with regard to classifying the additivity of effects, correspond to those in physical chemistry for the analysis of ideal behavior of substances in multiphase systems. The CA of toxicants at low concentrations in the environment compares with the ideal behavior of solutes at infinite dilution. In both cases, a loglinear relationship can be established between the concentration of a component in a mixture and its activity. The analogy with Raoult s law suggests the use of alike formalisms also for other mixture effects. The assumption of ideal behavior of mixtures reveals that their effects can be estimated from the activities of the individual components and their relative abundance in the mixture. If the precise composition of the mixture is known, QSARs of the generic form log A a log D1 b log D2... z (2) with A the activity to be modeled; D 1 and D 2 the structural descriptors; and a, b, z the coefficients of the regression function, can be extended on the basis of established physicochemical principles [37,40]. Then the descriptors of the properties of mixtures are D (xd) (3) (mix) i i The hypothetical descriptor value (D (mix) ) is a measure for the contributions of the components of a mixture to the overall activity. It is not a measurable property but only a numerical operator based on theoretical considerations. Substituting Equation 3 in Equation 2 results in a generic QSAR equation for the estimation of activities and properties of mixtures: log A a log (xd ) b log (xd ) z (4) i 1i i 2i This general expression assumes that all mixture components (i) shall contribute to the predictive descriptor value, and hence to the overall activity of the mixture, according to their molar fraction (x i ) in the mixture. This approach is pragmatic and limited to ideal mixtures. It does not consider terms for mutual interactions of mixture components. However, if nonadditivity may apply for toxic effects depending on the structures concerned, the same nonadditivity may be acting also for any other property related to the structure of the compounds (i.e., the QSAR descriptors). As of yet only thermodynamic approaches, using activity coefficients as correction factors, compensate for nonideal behavior. No other satisfactory descriptors accounting for mutual interactions between co-contaminants are currently available. A further problem concerns real-world mixtures (e.g., effluents or chemical preparations including by-products). If the exact composition of the mixture is not known, there is no way of computing QSAR parameter values. Because of the trivial fact that structural descriptors cannot be calculated when the individual structures are unknown, the assessment of mixtures of undefined composition must rely on some experimental determinations. Chemical analyses for complete identification and quantification of components are practically impossible. The use of, for instance, high-performance liquid chromatography-mass spectrometer techniques for the detection of lipophilic contaminants and the estimation of their log K ow values in complex mixtures [41] may be biased by the efficacy of the extraction method applied to the samples [42]. As an alternative, surrogate parameters (e.g., for the total lipophilicity content of a mixture) that avoid the need for identifying and quantifying individual compounds (for examples see Hydrophobicity) have been proposed. PHYSICOCHEMICAL PROPERTIES OF MIXTURES Quantitative structure activity relationship modeling and the prediction of properties of mixtures is possible only to a limited extent as of yet. Aqueous solubility The aqueous solubility of chemicals may be greatly affected in mixtures. Their behavior in water is either ideal or nonideal. For ideal mixtures, i.e., when cosolutes do not affect the activity of component i (activity coefficient i 1), the concentration of an individual solute in the aqueous phase is proportional to its molar fraction in the organic (mixture) phase [43 47]. The joint water solubility, such as equilibrium concentrations in saturated solutions, corresponds to the fractional solubilities of the solutes:

6 QSARs in mixture toxicity Environ. Toxicol. Chem. 22, limited database, however, the model could not be validated for its predictive power. Fig. 4. Cosolute interactions on aqueous contaminant concentrations. The water solubility of compounds may either increase or decrease depending on the cosolute (after Fredenslund et al. [47]). W c i cw xo SW i i i (5) the concentration of component i in the water phase, with O x i the molar fraction of component i in the organic phase, and W S i the water solubility of the respective pure compound. For nonideal mixtures, such as when cosolutes affect the activity of component i (activity coefficient i 1), Equation 5 extends to cw (xo O)SW i i i i (6) Models such as UNIFAC [48,49], based on the group contribution concept, can be used to calculate the activity coefficients of solutes in the organic phase. Extension of log K ow based QSARs for single compounds [50 54], according to Equation 3, is not substantiated by experimental validation. The principal limitation to ideal mixtures restricts the applicability of such extended QSARs because, even for apparently simple compounds such as substituted benzenes, aqueous solubility may deviate systematically from ideal behavior [47]. To test cosolute effects on water solubilities (i.e., equilibrium concentrations in water), the 10 binary combinations of benzene, aniline, 2-sec-butylphenol, 2-nitroanisole, and 2-chlorotoluene were studied each at several molar ratios. The experimental results indicate that there are significant cosolute interactions on aqueous contaminant concentrations that appear to depend on the structures of the cosolutes. The solubility of compounds may either increase or decrease depending on the cosolute (Fig. 4). To account for nonideal behavior, modeling the aqueous saturation concentrations by the activity coefficients of the solutes in the organic phase revealed reasonably good agreement with the experimental data. Also, an empirical QSAR model, correlating the observed aqueous equilibrium concentrations of binary mixtures to the solute s solubility and the cosolute s relative polarity depending on its molar fraction in the organic phase, gave a relatively good representation of the measured joint solubilities. Due to the Hydrophobicity W W At low concentrations ( ci K Si ), as in most environmental situations, and assuming an infinite volume ratio of the aqueous/hydrophobic phases (i.e., constant aqueous concentrations of the solutes upon partitioning), mixture hydrophobicity may correspond to the fractional contributions of the components [55,56]. Then, for well-defined mixtures, the weighted average partition coefficient [57] is the logarithm of the sum of the products of the molar fractions of the components (x i ) and their K ow values [(K ow ) i ] log K log [x (K ) ] (7) ow(mix) i ow i If these conditions (well-known composition, ideal behavior, constant aqueous concentrations) are not fulfilled, experimental sum parameters for the total lipophilicity content are an alternative [55,56,58,59]. The procedures are based on a separation of the mixture on a reversed-phase high-performance liquid chromatography (RP-HPLC) C18 column into fractions of increasing hydrophobicity, determination of the total molar concentration in each fraction using, for instance, vapor pressure osmometry or a gas chromatography mass spectrometer, and consecutive extrapolation of a mean log K ow from the retention time and the molar concentration for each fraction. Using a different partitioning system, this approach was applied to assess the total baseline toxicity content of water samples [55,60] and as a tool to identify chemicals with high bioconcentration potential [61]. Thus the method can provide surrogate parameters for the detection of (sub)acute levels of contaminations in mixtures of unknown composition. BIOLOGICAL EFFECTS OF MIXTURES With regard to toxicity, co-contaminants may mutually affect not only the toxicant/target interactions, but also adsorption, distribution and excretion (i.e., kinetics), biotransformation (i.e., metabolism), and bioavailability [62]. The primary impacts may be due to alterations in transport and distribution, changes in membrane properties, effects on metabolizing enzymes, or competition for conjugation reactions. The resulting toxicity of a chemical mixture may be additive, but only if the principal processes are equivalent and if they do not change in the presence of the other compounds. For chemicals with identical modes of interaction, the additive joint activity is plausible, which may be the case for both nonspecifically or specifically acting toxicants. But also with diverse modes of action, additivity can occur within reasonable limits. The baseline toxicity of organic chemicals is defined as resulting from nonspecific effects, and with regard to this particular (but unspecific) mode of action, all components of a mixture are equivalent and hence concentration additive. Although baseline toxicity is related to partitioning into membranes and adsorption to macromolecules, these processes occur not only with so-called narcotic substances, but also with specifically acting chemicals. They also undergo multiple distribution between lipid and aqueous phases, such as membrane passages, until they reach their specific sites of action in an organism. Hence specific toxicants must always have nonspecific effects. The nonspecific partitioning processes take place at any exposure concentration, including concentrations far below the threshold for the specific toxicity. As a consequence, all organic chemicals exert nonspecific tox-

7 1906 Environ. Toxicol. Chem. 22, 2003 R. Altenburger et al. icity and can contribute also at very low concentrations to the effects of a mixture. Thus any mixture should be at least as toxic as corresponds to the cumulative sum of the fractional baseline toxic concentrations of the components [31,63 69]. A prerequisite for the latter, however, is the absence of a net antagonism that could result from the metabolic detoxification of some components induced by other components of the mixture. In a mixture of chemicals that all act by different mechanisms, the specific effects may not be triggered due to the very low concentrations of the individual components, but their contributions to the baseline toxicity will persist and may markedly add up to an overall significant response. It follows that even for chemicals with demonstrated nonadditive interactions at higher concentrations, a low-concentration mixture may yield an overall additive response, which would then reflect CA of the baseline toxicities of the individual components [55,67]. Quantitative structure activity relationships approaches have been used in the analysis of joint toxicity of chemicals to (1) provide evidence for similar modes of action and CA mixture toxicities; (2) predict effect concentrations of untested components; (3) describe specific mixture effects deviating from expected responses; (4) discriminate between congeneric structures of dissimilar biological activity; (5) model exposure concentrations; and (6) to derive mixture properties for the prediction of joint toxicity. Similar mode of action and CA mixture toxicity Based on the pioneering work by Overton [70] and introduced to aquatic ecotoxicology by Könemann [31,71], nonspecific toxicity has been identified as the mode of action common to most environmental toxicants. Acute and prolonged toxicity to fish and other aquatic species of various industrial chemicals has since then been shown to be correlated to the 1-octanol/water partition coefficient of the compounds. For assessing the behavior of such compounds in mixtures, Könemann [31] formulated the MTI as a measure of the difference between the expected mixture toxicity based on CA and the actually observed effects. QSAR and mixture toxicity studies have been extended on the same principles for more reactive compounds, different organisms and toxicity parameters, and further structural descriptors [63,64,66,72]. The rationale of this approach has been summarized as follows [73]: (1) the joint effect of mixtures with similar modes of action can be predicted by CA; and (2) chemicals that are adequately described by a particular QSAR may exhibit a similar mode of action. Note, however, that the second statement does not hold true in the strict sense. An example is given by phenols that may contribute to both polar narcosis and oxidative uncoupling (see [74,75], such that a QSAR based on log K ow and pk a could, in principle, cover a range of compound toxicities that differ significantly in their relative contributions to the two modes of toxic action, and may even include derivatives that act only through polar narcosis. Conversely, pentachlorophenol as a known oxidative uncoupler may also fit to simple QSARs employing log K ow as only a descriptor, and in this respect join narcotic chemicals that are well described by baseline toxicity models [75,76]. Recently, the suitability of the first assumption was shown to also hold for mixtures of specifically acting components as opposed to the combined effects from mixtures specifically but dissimilarly acting components in bacteria [23,24] and algae, respectively [25]. Slight but systematic overestimation of mixture toxicity by CA for three-component mixtures of narcotic chemicals was found in a 7-d reproductive assay with Ceriodaphnia dubia [77]. The individual toxicities of benzene, trichloroethylene, toluene, ethylbenzene, xylene, and tetrachloroethylene corresponded well with findings reported earlier, showing a good correlation with log K ow and a reasonably stable acute to chronic toxicity ratio. Their mixture toxicity assessment, directed by a central composite design for the experimentation, was based on response-surface analysis using linear regression models that assume parallel individual concentration-response functions. Although the authors consider this point as a minor issue, the possible error in the predicted mixture toxicities due to differing slopes needs to be carefully evaluated before conclusions on altered mixture toxicity on chronic exposure may be drawn. Furthermore, parallel concentration-response curves are no implicit requirement of the concept of CA, although a re-occurring notion in the literature. QSAR-based prediction of mixture toxicity An early example of the use of QSAR models to predict the joint effect of chemicals was given along with the introduction of the MTI parameter ([31]; see Table 5). For five mixtures of 3 to 50 components in equitoxic concentrations [c i (1/n) LC50 i for all n components i; LC50 indicates lethal concentration 50%], application of the baseline toxicity model for guppies exposed for 14 d log LC50 [mol/l] 0.87 log Kow 1.13 (8) (n 50, r , standard error [SE] 0.24 [78]), or of a QSAR for phenols at ph 6.1 [79] log LC50 (mol/l) 0.71 log Kow 0.03 pka 2.80 (9) (n 11, r , SE 0.10) led to predictions of M (TU i ) 1.1 to 1.0 and MTI 0.96 to 1.0 as opposed to M 1.5 to 0.9 and MTI 0.69 to 1.02 when using experimental LC50 i values. It shows that even for apparently narcotic chemicals like chlorobenzenes, CA may slightly overestimate the joint effect (MTI 1 indicates a less than additive joint effect; Table 5). For the 50-compounds mixture including chlorobenzenes, MTI and MTI(QSAR) agreed quite well (0.9 vs 1.0), demonstrating that a large number of compounds may mask individual deviations from CA. Note that in this mixture, the components were present at concentrations of 0.02 times their individual LC50 values, thus demonstrating that even very small fractions of individual components may be relevant in terms of combined effects when different compounds act on (at least also) the same site. In mixtures with 3 and 10 compounds, the surprisingly large difference between MTI and MTI(QSAR) probably results from a somewhat larger overestimation of the LC50 of 1,2,3,4-tetrachlorobenzene by the baseline narcosis QSAR (0.3 log units difference [78]). Two other mixture toxicity studies with guppy (14-d LC50) showed both agreement and disagreement with the joint effects as expected from CA [80,81]. Equitoxic mixtures of 8 and 24 compounds covering different modes of action resulted in MTI values of 0.74 to Comparison with the associated MTI SDs of 0.08 to 0.11 showed that in most of these cases, joint toxicity followed a slightly less than additive (partial additive) pattern [80]. It was argued that in such mixtures with more specifically acting compounds, the nonspecific (hydrophobic)

8 QSARs in mixture toxicity Environ. Toxicol. Chem. 22, portions of toxicity would add according to CA, whereas the specific (and more potent) toxicity components may become less important due to the low individual concentrations. Interestingly, application of the baseline model for guppy LC50 yielded QSAR predictions for M of 2.3 to 1.1, which can be converted to MTI(QSAR) values of 1.36 to At first sight, the latter data (which were not calculated in the original study [80]) would indicate a substantial synergism with respect to CA. However, this result is simply caused by the fact that the QSAR-estimated baseline LC50 values are much larger than the experimental LC50 data, such that the toxic units c i /LC50 i become too large when combining c i (the actual equitoxic concentrations) with LC50 i (QSAR). In the other mixture toxicity study with guppy, four groups of chemicals (50 nonreactive chlorinated hydrocarbons, 11 chloroanilines, 11 chlorophenols, 9 reactive organic halides) were tested in different combinations [81]. Within-group MTI values were in agreement with CA (MTI ), whereas between-group MTI values of mixtures containing 18 to 33 compounds showed a partial additive to additive behavior (MTI ). Significant deviations from CA were seen for between-group mixtures when taking only one compound per group (MTI ). The type of joint effect may vary between species due to their variations in response pattern, and within species with the type of endpoint. The latter was demonstrated by investigating the joint effect of chemicals on the immobilization concentration 50% (48-h IC50); mortality (48-h LC50 or 16- d LC50); growth (16-d EC50, 16-d NOEC); and reproduction (16-d EC50) with Daphnia magna [63,65,68,81 83]. In extension of the idea that fitting the same QSAR may indicate a similar mode of action of compounds and thus concentrationadditive mixture behavior (and disregarding the above-mentioned peculiarities associated with this assumption), for some of the compounds estimated effect concentrations were used to predict mixture toxicity while testing the combined effect only. Using the following QSAR models: log IC50 (mol/l) 0.91 log K 1.28 (10) ow log LC50 (mol/l) 0.64 log K 2.73 (11) ow log EC50 (mol/l) 0.72 log Kow 2.95 (12) (Eqn. 10: n 19, r , SE 0.24; Eqn. 11: n 5, r , SE 0.08; Eqn. 12: n 5, r , SE 0.08 [63]) that were derived for subsets of a total of 50 narcotictype chemicals analyzed in mixture toxicity experiments, the resultant MTI values of 0.95 to 1.00 (48-h IC50 immobilization) and 0.87 to 1.05 (16-d LC50 lethality) with MTI error margins of 0.06 to 0.14 indicated an approximate agreement with CA [63]. For the joint effect on reproduction (16-d EC50), however, MTI values of 0.57 to 0.70 indicated a less than additive interaction of the toxicants. With 14 chemicals selected to cover different modes of action, acute daphnid mortality (48-h LC50) appeared to be in line with CA (MTI 0.95), whereas a clearly less than additive mixture toxicity result was obtained for the inhibition of reproduction (16-d EC50) [84]. Joint effects on daphnid growth by mixtures of 10 and 25 narcotic-type compounds were investigated by the same group [65] using 16-d NOEC as endpoint. Experimental determination of 10 NOEC values lead to log NOEC [mol/l] log Kow 2.04 (13) (n 10, r , SE 0.46 [65]) that was applied to predict the NOEC values of an additional 15 compounds. The respective MTI values of 0.74 (10 compounds with experimental NOEC data only) and 1.16 (25 compounds) are somewhat difficult to interpret, since their deviation from ideal CA is moderate but significant (MTI error margins 0.11 and 0.07, respectively) and, for the two mixtures tested, in opposite directions. For another mixture of 9 compounds selected to cover different modes of toxic action, the joint effect on growth inhibition (16-d EC10) was far below CA [83]. Evaluation of the MTI from the EC10 toxic units as given by the authors yields a value of 0.15, which would indicate a joint effect below the reference case of no addition (Table 5). Using a log K ow -dependent QSAR for NOEC/EC50 for reproduction and growth of D. magna based on 10 nonreactive organic compounds, individual toxicities of an additional 15 chlorohydrocarbons were estimated [82]. Again, the measured joint toxicities of multiple mixtures of these compounds were in good agreement with predictions for concentration-additive behavior (MTI 0.92 to 0.99), with similar results for the 10-compound subset using experimental data only as well as for the QSAR-supported 25-compound set. There are also examples in the literature where multicomponent mixtures of narcosis-type compounds show significant deviations from CA. A corresponding result was obtained for 22 organic pollutants in the 15-min Microtox test [66]. Although the EC50 variation of the individual compounds could be satisfactorily described by a simple log K ow -dependent QSAR equation log EC50 [mol/l] log Kow 0.86 (14) (n 22, r , SE 0.53 [66]), the MTI value of 0.77 (based on experimental EC50 data) suggested a clearly less than additive joint effect of an equitoxic mixture of 21 compounds (the most hydrophilic compound diethyleneglycol was excluded from the mixture experiment). Another investigation addressed the mixture toxicity of shale oil components in a 16-h assay with marine bacteria using EC10 and EC50 as endpoints for quantifying growth inhibition [85]. The compound set covered benzene, naphthalene, phenol, pyridine, and hexene as well as respective alkyl derivatives. While for the individual compounds linear relationships with log K ow were achieved log EC50 (mol/l) log K (15) ow log EC10 (mol/l) log K (16) ow (Eqn. 15: n 18, r ; Eqn. 16: n 18, r [85]), the MTI values of mixtures containing 2 to 5 components (derived from experimental toxicity data) ranged from 0.97 (benzene group) to 3.70 (pyridine plus hexene). Even structural isomers like 1- and 2-ethylnaphthalene yielded an apparently synergistic joint effect as indicated by an MTI value of 2.6. Besides MTI, the TEI (Table 5) was also evaluated (TEI ), and QSAR studies revealed that TEI could be approximately predicted from single-compound toxicity and the first principal moment of inertia of the three-dimensional molecular structures. Another type of analysis associated with the toxic unit model and CA as reference concept is the prediction of individual component concentrations in mixtures of known joint toxicity and defined mixture ratios. Note that for the reference case of CA in an equitoxic mixture yielding a standard response, each of the n

9 1908 Environ. Toxicol. Chem. 22, 2003 R. Altenburger et al. components would be present in concentrations of (1/n) times their individual standard effect concentrations: ck (1/n) ECXk (17) For nonuniform mixtures where the toxic units of the individual components vary, the effect concentration of the kth component can be calculated as c ECX 1 TU (18) k k i ik [35] if CA holds true. Corresponding analyses have been performed in more recent mixture toxicity investigations using the respiration rate of an assay of 12 different bacterial strains as a response (6- h IC50 [35,86]). First, compound-class specific QSARs based on molecular connectivity indices were derived for series of alcohols, ketones and esters, alkanes, amines and acids, aromatics, and halogenated aliphatics. Then these QSARs were used to estimate the concentrations of individual components in mixtures with identical and varied fractions of toxic units jointly causing 50% inhibition. Observed mixture toxicities were mostly within a factor of two for CA, as in earlier reports for specifically acting pesticides in algae [87]. For the total of 610 data points, statistical analysis of actual c k (compound concentration in mixture, selected according to toxic unit approach) versus c k calculated from the toxic unit approach with QSAR estimates of IC50 k (compare with equations above) yielded the following regression equation log c k(qsar) log c k (exp) (19) (n 610, r , SE 0.27 [35]) for the individual compound concentrations in the mixtures. For another data set of 50 mainly narcotic compounds, investigation of the individual component and joint responses in the same bacteria assay (6-h IC50) resulted in MTI values of 0.88 to 1.05 (ten 10-component mixtures) and 0.92 to 1.04 (six 8-component mixtures), respectively [86]. Here evaluation of the joint effects was extended in two ways: first, actual c k (concentration of component k in mixture) yielding a joint standard response was compared with c k calculated from the toxic unit approach of all other components employing experimental IC50 k according to Equation 18, thus testing the extent of agreement of the joint response with CA. Second, a corresponding comparison between actual and toxic unit model derived concentrations was performed using QSAR-predicted IC50 data. This latter approach yields predictions of compound concentrations in mixtures exerting a standard response (here: IC50) based on molecular structure information only, under the assumption of CA. The overall r 2 was for the first approach (experimental IC50), indicating a quite high extent of agreement of the joint responses with CA. Application of the QSAR models resulted in a significantly lower but still acceptable r 2 of 0.799, reflecting a partial inadequacy of the QSAR equations involved. The authors argued further that the deviations between measured and QSAR-estimated concentrations of components in mixtures elucidating the standard effect were comparable with those between training set and validation set data in QSAR modeling. Interestingly, binary mixtures again tended to show larger deviations from CA than multiple mixtures [34]. Possible reasons include a greater experimental variability as well as an increasing degree of compensation of deviations with increasing number of components in the mixture (see above). Note that with multicomponent mixtures, strict CA implies TU 1 (and MTI 1) at the level of standard joint response, whereas the reverse is not true but may simply hold by chance. As mentioned above, chemicals may contribute to joint toxicity at concentrations significantly below their individual NOEC values, provided that for the endpoint of interest CA holds true. This was demonstrated nicely with the joint effect of 50 narcotic chemicals using the 48-h immobilization (IC50) of D. magna as endpoint [68]. The mixtures were nonuniform, with 1,2-dichlorobenzene as the major compound, and 49 components present at concentration levels of 0.01, 0.005, and toxic units. In all three mixture toxicity experiments, the concentration of the major component needed for the joint standard response was in good agreement with the value expected from CA. The mixture contributions of yet untested polycyclic aromatic hydrocarbons (PAHs) to lethality (LC50) in amphipods after 10-d spiked-sediment exposure were estimated using a log K ow based QSAR and fed into a toxic unit summation model (PAH model) [88]. In a multistep procedure, starting from field analytical data on PAH concentrations in sediments, interstitial water concentrations were calculated as exposure concentrations, followed by toxic unit estimation for the identified components. These were summed and translated to a graded response. This last step, a concentration-response model relating the sum of toxic units to observed joint effects, implicitly assumes constant efficacy ratios of the individual components or parallel concentration-response functions of the constituents. Although this is not demonstrated, and probably difficult to prove, it seems to suffice for predicting the combined effects of multiple mixtures of similar compounds. The predictive capability of this approach was satisfactory for sediment samples with PAHs constituting the contaminants of concern, whereas in cases where PAHs were not the principal contaminants, the predicted mortalities underestimated the observed responses. Mixture effects deviating from expected responses Binary mixtures of octanol with various compounds at four different concentration ratios were analyzed with respect to their joint effects on the respiration of multistrain bacterial cultures (see QSAR-based prediction of mixture toxicity) employing the isobolographic technique, which revealed some deviations from CA behavior [35]. The shape of the isoboles was described by a fit parameter accounting for nonideal behavior (see Eqn. 1 in Tools for analysis and interpretation). This mixture ratio dependent parameter may be used like a structural property in QSARs. The procedure is formally equivalent to other attempts of describing the shape of isoboles (for review see Boedeker et al. [12]). Relationships between the composition of binary mixtures and their toxicities (e.g., EC50s for the immobilization of Tubifex tubifex) have been formalized in a QSAR-like manner [62,89,90]. Deviations from additive (ideal) behavior are accounted for by a polynomial dependence of the joint EC50s on the normalized molar ratios (R j ) of compound j in the binary combination of i and j. EC50 1 ar ar2 ar3 ar4 ar5 ij 1 j 2 j 3 j 4 j 5 j (20) Such quantitative composition-activity relationships were derived for binary mixtures of metals and organics, respectively. The comparison of experimental and modeled joint ef-