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In the response addition model, combined effects of the chemicals are based upon the probability that individual constituents of the mixture will affect the exposed organisms. The concentration addition and response addition models are limited in their application to complex mixtures in that they do not address chemical interactions. Toxicokinetic interactions can occur between chemicals in which one chemical alters the effective concentration of another Andersen and Dennison, Alternatively, toxicodynamic interactions can occur between chemicals in which one chemical influences the response of the organism to another chemical Andersen and Dennison, Both toxicokinetic and toxicodynamic interactions can significantly impact the toxicity of chemical mixtures.
Recently, Altenburger et al. The intent of the present study was to expand this approach to incorporate interactions among chemical constituents when they are predicted to occur. Important issues addressed in this work include: 1 evaluating whether single interaction modifiers can be applied to classes of chemicals and 2 establishing whether clearly defined binary interactions persist in higher order combinations. The strength of the integrated addition and interaction IAI model was assessed by comparing model results to experimentally determined toxicity of 30 different derivations of a ternary mixture.
All toxicological experiments were performed with the daphnid Daphnia magna.
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Adult daphnids were discarded after three weeks and replaced with neonates. Culture daphnids were fed 2. The Selenastrum was cultured in the laboratory using Bold's basal medium. Chemicals used in mixture analyses malathion, parathion, and piperonyl butoxide were acquired from ChemServices West Chester, PA. Absolute ethanol was used as the carrier for all of the chemicals. Each treatment consisted of two 50 ml beakers containing 40 ml of exposure medium and 10 neonates. All beakers, including controls, contained 0.
Beakers were labeled on the bottom and randomly rearranged, so that the exposure concentration in each beaker was not known to the investigator when assessing response of organisms. At 48 h, neonates were evaluated for response. The response endpoint, immobilization, was judged by the inability of the neonate to occupy the water column during 10 s of observation. Acetylcholinesterase activity was measured according to Ellman et al.
Algae 1. Solutions were renewed at 24 h. Following the h exposure period, neonates were transferred to 1. Protein was measured according to Bradford using Bio-Rad Protein Assay dye concentrate Hercules, CA and a standard curve generated with bovine serum albumin. The response to thirty combinations of the three chemicals was computed using the concentration addition model Equation 2 , the response addition model Equation 3 , the integrated addition model Equation 4 , and the IAI model Equation 6.
In addition, the actual toxicity of the 30 mixtures was measured and results were compared to the four model results. The 30 mixture formulations were designed so that the ratio of the three chemicals varied among the mixture formulations. Model predictions were compared to experimental data using coefficients of determination r 2 ; Zar, An r 2 value of 0.
The IAI model requires toxicity description for the individual chemicals within a mixture. Concentration-response curves were generated for malathion, parathion, and piperonyl butoxide Fig. The two organophosphates exhibited similar toxicity characteristics. Piperonyl butoxide was considerably less toxic as compared to the organophosphates and had a power approximately one-half that of the organophosphates. Concentration-response profiles of the individual chemicals used in the mixtures toxicity assessment: malathion A , parathion B , and piperonyl butoxide C.
Data points represent the percentage of immobilized daphnids. Data were fit using a logistic equation Equation 1. Toxicity of chemicals was assessed in h acute toxicity tests measuring immobilization in Daphnia magna. According to the IAI model, the organophosphates would be assigned to the same cassette and toxicity associated with the cassette would be assessed using a concentration addition approach. The validity of using concentration addition to model the toxicity associated with the organophosphate cassette was determined using several combinations of the two organophosphates deemed to be equitoxic based upon concentration additivity.
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Indeed, the concentration-response assessments of these binary mixtures were statistically indistinguishable Table 2. Therefore, the contributions of malathion and parathion to the toxicity of the final mixtures were modeled as a single organophosphate cassette. Concentrations are expressed in malathion equivalents. The common mode of action of the organophosphates—the inhibition of acetylcholinesterase activity—was confirmed experimentally Fig.
In contrast, piperonyl butoxide did not inhibit acetylcholinesterase activity. Piperonyl butoxide was, therefore, assigned to its own cassette where the toxicity of this mixture component was integrated into the toxicity of the mixture using the response addition model. Acetylcholinesterase activity in daphnids following exposure to mixture constituents. A Malathion was evaluated at 0. B Parathion was evaluated at 0. Bars represent the mean and SD for 3 replicate treatments.
We hypothesized that piperonyl butoxide would interact with the constituents of the organophosphate cassette in a manner that would modify the toxicity associated with this cassette. The ability of piperonyl butoxide to abrogate the acetylcholinesterase-inhibiting potential of each organophosphate was demonstrated directly Fig.
The antagonistic effect of piperonyl butoxide on the toxicity of the organophosphates was further demonstrated by the progressive shifting of the concentration-response curves for malathion Fig.
This modifying effect of piperonyl butoxide was quantified as concentration-dependent K-functions Fig. These K-functions were used in the final IAI model to modify the effective concentrations of malathion and parathion as dictated by the concentration of piperonyl butoxide in the mixture. Impact of piperonyl butoxide on organophosphate concentration-response curves. Data were fit using Equation 1. All piperonyl butoxide concentrations tested were below the no observed effect level for piperonyl butoxide alone.
The relationship between piperonyl butoxide concentrations and modifying effects on malathion A and parathion B as defined by K-functions. K-functions were derived for each concentration of PBO evaluated Fig. Parameters used in Equation 5 were derived from the concentration-response curves depicted in Figure 3.
A curve was fit to the plot using Equation 1. The toxicity of 30 combinations of the ternary mixture Table 3 was experimentally determined and compared to predicted toxicity using the concentration addition model Equation 2 , the response addition model Equation 3 , the integrated addition model Equation 4 , and the IAI model Equation 6. Rather, all models grossly overestimated mixture toxicity Figs. Comparison of observed data to results generated from concentration addition A , response addition B , integrated addition C , and integrated addition and interaction models D.
The solid line represents a relationship between modeled and predicted data. Observed data were generated in toxicity tests with Daphnia magna using thirty concentrations of the ternary mixture of malathion, parathion, and piperonyl butoxide described in Table 3. Concentration addition, response addition, integrated addition, and integrated addition and interaction results were calculated with Equations 2 , 3 , 4 , and 6 respectively.
The results of this study demonstrate that toxicokinetic interactions can be incorporated into an integrated addition model to assess mixture toxicity. Recent studies have shown that concentration and response addition models can be used in combination to create a comprehensive additive model to calculate the toxicity of non-interacting chemical mixtures Altenburger et al. Here, we build upon that modeling framework by incorporating toxicokinetic interactions between mixture constituents. By definition, chemical interactions represent a deviation from simple additivity when modeling mixture toxicity.
To quantify these interactions, the expected additive toxicity of the mixture must first be determined. Choosing the appropriate model to assess additivity is essential for accurate interpretation of interaction results. US EPA guidelines for assessing mixture toxicity suggest a default model of concentration addition This recommendation is based on a tendency towards more conservative estimates of mixture toxicity with concentration addition than with response addition modeling Drescher and Boedecker, However, indiscriminate application of concentration addition lacks a sound mechanistic basis and therefore increases the uncertainty associated with predicting mixture toxicity.
The integrated addition model described in recent works Altenburger et al. Initially, chemicals with similar mechanisms of action are placed into groups, or cassettes. The toxicity within each cassette is modeled with concentration addition and overall toxicity of the different cassettes is then modeled with response addition Fig.
The integrated addition models presented by Altenburger et al. The integrated addition model represents a significant advance in assessing toxicity of non-interacting chemical mixtures. This model, however, is not equipped to manage interactions among chemicals that impact toxicity of the mixture. Schematic representation of the integrated addition and interaction model.
R mixture represents the response to the mixture. Similar-acting chemicals are placed in a cassette. Toxicity associated with the cassette is calculated using the concentration addition model. Toxicokinetic interactions between chemicals are incorporated as modifiers of concentrations of chemicals within the cassettes. Total mixture toxicity associated with all cassettes is calculated using the response addition model. The possibility of significant synergistic interactions occurring between two or more chemicals in the environment is perhaps the most compelling reason to study mixture toxicity.
Well-defined examples of synergy include enhanced hepatotoxicity of carbon tetrachloride with pre-exposure to kepone Klingensmith and Mehendale, and interactions involving hormone receptor antagonists and hormone synthesis inhibitors Mu and LeBlanc, Interactions often can be predicted based on mechanisms of action of constituent chemicals.
For example, the P inhibitor piperonyl butoxide used in the present study was hypothesized to antagonize the toxicity of malathion and parathion by decreasing their metabolic activation. However, some interactions will not be apparent from constituent mechanisms of action. The integrated addition model has the potential to identify these unexpected interactions. In effect, significant deviation of experimental results from model predictions implies interaction. Once the source of the interaction is identified, either through inference or experimentation, quantification and incorporation of the interaction into the model follow.
The two approaches are conceptually quite similar in that both modify the effective concentrations of chemicals in an effector concentration-dependent manner. However, the approaches differ significantly in their application. Briefly, interaction terms that define the effect of one chemical upon another are generated based upon the predicted magnitude of interaction experimentally determined or default value as a function of the concentrations of the interacting chemicals. Hazard quotients exposure level divided by reference dose or reference concentration of individual chemicals in the mixture are multiplied by the interaction term.
The modified hazard quotients are then summed to arrive at the hazard index of the mixture Hertzberg and MacDonell, The hazard index is dimensionless and simply provides a general estimate of the hazard associated with the mixture. It is useful for identifying potentially hazardous mixtures, but it does not provide an accurate calculation of mixture toxicity. Alternatively, a strictly quantitative approach was described by Mu and LeBlanc , which is based on the concept of k-values, or K-functions, first introduced by Finney This approach involves quantification of the progressive shift in the concentration-response curve of a chemical elicited by increasing concentrations of the effector chemical.
The primary goal of this work was to establish whether modifying functions i. A secondary aim of this work was to increase our understanding of how mechanism-based classes of chemicals, or cassettes, function in mixtures. For example, evidence suggests that certain classes of chemicals display consistent patterns of interaction Durkin et al. Such consistency raises the possibility that K-functions could be generated that describe the effect of one cassette of chemicals upon another cassette.
However, displaying the same type of interaction does not imply that the chemicals exhibit the same magnitude of interaction. In the present work, piperonyl butoxide demonstrated substantial antagonism with both malathion and parathion; however, the degree of antagonism was significantly different between the two organophosphates necessitating the generation of K-functions specific to each organophosphate.
Further, some organophosphates e. These compounds might appropriately be assigned to the organophosphate cassette to calculate joint organophosphate toxicity, but they would require K-functions that describe a synergistic, and not antagonistic, interaction with piperonyl butoxide. The three concepts describing mixture behavior originally identified by Bliss over 60 years ago are mathematically integrated in the IAI model.