Department of Environmental and Molecular Toxicology, North Carolina State University, Raleigh, North Carolina 27695
1 To whom correspondence should be addressed at Campus Box 7633, Raleigh, NC 27695. Fax: (919) 515-7169. E-mail: ga_leblanc{at}ncsu.edu.
Received May 2, 2005; accepted June 24, 2005
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ABSTRACT |
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Key Words: synergy; cumulative toxicity; predictive model; toxicodynamic; hazard assessment; risk assessment.
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INTRODUCTION |
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Concentration addition models rely upon the assumption that mixture components contribute to toxicity through a common mechanism of action. Calculating mixture toxicity based upon concentration addition requires assessing the relative contribution of each constituent to the total toxicant pool. The toxicity of this pool is then modeled as a single toxicant. Concentration addition is the basis of the "toxic equivalency" approach commonly used to assess toxicity of chemicals of the same class such as dioxins (Safe, 1990). Ample evidence supports the use of the concentration addition model for assessing mixtures toxicity of like-acting chemicals (Altenburger et al., 2000
; Deneer et al., 1988
; Könemann, 1981
). The response addition model, also referred to as the independent joint action model, has been used to compute toxicity of mixtures when chemical constituents have different mechanisms of action (Backhaus et al., 2000
; Walter et al., 2002
). 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, 2004). Alternatively, toxicodynamic interactions can occur between chemicals in which one chemical influences the response of the organism to another chemical (Andersen and Dennison, 2004
). Both toxicokinetic and toxicodynamic interactions can significantly impact the toxicity of chemical mixtures. The importance of addressing chemical interactions was highlighted by the US EPA in their recommendations for evaluating risk associated with chemical mixtures (US EPA, 2000
).
Recently, Altenburger et al. (2005) and Olmstead and LeBlanc (2005)
demonstrated that concentration addition and response addition models could be integrated into a comprehensive model for use in evaluating toxicity of non-interacting chemical mixtures. 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.
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MATERIALS AND METHODS |
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Acute toxicity assays.
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. All toxicity assessments were initiated with neonatal (24 h old) daphnids. Each treatment consisted of two 50 ml beakers containing 40 ml of exposure medium and 10 neonates. Selanastrum (7 x 106cells) and fish food homogenate (0.2 mg dry weight) were provided to each beaker as food at the start of each exposure. All beakers, including controls, contained 0.01% carrier (ethanol). 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 analyses.
Acetylcholinesterase activity was measured according to Ellman et al. (1961) as modified for use with microtiter plates (Fisher et al., 2000
) with minor additional modifications. Exposure groups consisted of three 250 ml beakers containing 200 ml solution and 40 neonates (
24 h old). Algae (1.4 x 107 cells) and fish food (0.4 mg dry weight) were added to each beaker once per day. Solutions were renewed at 24 h. Following the 48-h exposure period, neonates were transferred to 1.5 ml microfuge tubes. Media was removed from tubes; neonates were rinsed, and homogenized in 35 µl ice cold 0.02 M phosphate buffer, pH 8.0 with 1% Triton-X-100 using a Teflon pestle. An additional 315 µl phosphate buffer, pH 8.0 without Triton-X-100 was then added and samples were mixed. Samples were centrifuged at 14,000 x g for 4 min at 4°C and supernatant was transferred to a clean pre-cooled microfuge tube. Approximately 100 µl of the supernatant was stored at 20°C for protein analysis. The following solutions were added to each well in a 96-well plate: 100 µl of 8 mM 5,5'-dithio-bis(2-nitrobenzoate) (D-1830 Sigma), 50 µl supernatant (phosphate buffer with 0.1% Triton-X-100 was used for supernatant blanks), 50 µl of 16 mM acetylthiocholine iodide (A-5751 Sigma). Absorbance was measured kinetically for 15 min at 420 nm using a Fusion Universal Microplate Analyzer (PerkinElmer, Boston, MA). Protein was measured according to Bradford (1976)
using Bio-Rad Protein Assay dye concentrate (Hercules, CA) and a standard curve generated with bovine serum albumin. The molar extinction coefficient (13,300 M1·cm1) (Masson et al., 2004
) was used to calculate the amount of yellow anion, 5-thio-2-nitrobenzoate, formed over 15 min and this rate was normalized to the amount of protein added to the assay (nmol/min/mg). Analyses of variance and Tukey-Kramer HSD were used to determine if significant (p
0.05) differences existed between treatments.
Individual chemical toxicity.
Exposure concentrations for each chemical were selected, based upon preliminary experiments, that would span response levels from 0 to 100%. The percentage response was plotted against exposure concentration on a log scale and fit with a sigmoidal line using Origin software (Microcal Software Inc., Northampton, MA). The logistic equation representing the sigmoidal fit to the data is:
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Mixture Modeling
Concentration addition.
According to Olmstead and LeBlanc's (2005) integrated addition model, like acting chemicals are assigned to a common cassette (i.e., grouping). Toxicity associated with the cassette is then calculated using a concentration addition approach. Accordingly, malathion and parathion were assigned to a common cassette, the organophosphate (OP) cassette. To establish whether the toxicity of the chemicals within the OP cassette conformed to a concentration addition model, five ratios (Table 2) of the chemicals (malathion:parathion) were each tested at six different concentrations. Parathion concentrations were expressed in terms of malathion equivalents. All five ratios were equitoxic based upon characterization of the toxicity of the individual OPs. The six concentrations of each binary mixture used in the experiments were selected to define the concentration-response curve for the mixture. The joint toxicity of these binary mixtures of like-acting chemicals was computed using the following equation (Olmstead and LeBlanc, 2005
):
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Equations 2 and 3 were integrated to establish the response associated with individual cassettes within a mixture and to sum the responses associated with the cassettes (Olmstead and LeBlanc, 2005). The resulting equation is a combination of concentration and response addition equations:
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Chemical interactions.
The ability of one chemical in the mixture to modify the effective concentration of another was defined by coefficients of interactions or K-functions (Finney, 1942; Mu and LeBlanc, 2004
). Specifically, K-functions, defined the degree to which the concentration of PBO in the mixture altered the effective concentration (i.e., oxon metabolite) of either organophosphate in the mixture. K-functions were described by experimentally deriving the effect of concentrations of PBO on the EC50 values derived for each organophosphate. K-functions were calculated for each of the PBO concentrations with the following equation:
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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 (r2; Zar, 1996). An r2 value of 0.70 or greater was considered a good fit of the observed data to the model (Quality America, 2004
).
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RESULTS |
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The common mode of action of the organophosphatesthe inhibition of acetylcholinesterase activitywas confirmed experimentally (Fig. 2). 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.
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DISCUSSION |
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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 (2000). 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, 1995). 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., 2005
; Olmstead and LeBlanc, 2005
) provides a mechanism-based alternative to assessing mixture toxicity. 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. 6). The integrated addition models presented by Altenburger et al. (2005)
and Olmstead and LeBlanc (2005)
are conceptually equivalent and differ only slightly in their methods of calculation. 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.
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Toxicokinetic interactions can be incorporated into mixture assessments via a qualitative "weight of evidence" approach or a quantitative approach. 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. The "weight of evidence" approach (Mumtaz and Durkin, 1992; modified by Hertzberg et al., 1999
) is currently recommended in the EPA mixture toxicity guidelines (2000). 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, 2002
). 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 (2004)
, which is based on the concept of k-values, or K-functions, first introduced by Finney (1942)
. 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.e., K-functions) could be used to augment the integrated addition model to account for chemical interactions that impact toxicity of mixture constituents. 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., 1995). 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. Application of K-functions based on malathion/piperonyl butoxide interactions to the entire organophosphate cassette significantly underestimated mixture toxicity (data not shown). Further, some organophosphates (e.g., dichlorvos) do not require metabolic activation, but are detoxified by P450s. 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 (1939) over 60 years ago are mathematically integrated in the IAI model. The IAI model provided reasonable predictions of the toxicity of a ternary mixture tested at thirty unique formulations. The model represented a significant improvement over basic addition models. The variability that did exist between observed and modeled results may be due to several factors. Inherent biological variability resulting in different responses of organisms between assays may have contributed to some of the observed variability. The assumption that K-functions derived in binary exposures are unaffected when used with higher order chemical mixtures may not be entirely correct. Further testing of the IAI model with increasingly complex mixtures will help to elucidate basic principles and limitations associated with K-function application.
This model is relatively simple in its application and requires input parameters that are typically available from standard concentration-response analyses. However, quantification of interactions among chemicals requires rigorous experimentation. Future studies may reveal whether limited but targeted experimentation can provide the information required to quantify interactions. Additional studies also are required to develop means of describing interactions where the response to a chemical modifies the organism's response to another chemical in the mixture. Such toxicodynamic interactions are less common (Hertzberg and McDonell, 2002), but may still be important contributors to mixture toxicity. The IAI model holds promise to increase the accuracy of hazard and risk assessments of chemical mixtures by reducing uncertainty in estimating mixture toxicity.
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ACKNOWLEDGMENTS |
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