Testing an application of a biotic ligand model to predict acute toxicity of metal mixtures to rainbow trout

Extension of a biotic ligand model for predicting the toxicity of neodymium to wheat: The effects of pH, Ca2+ and Mg2

The damage excessive neodymium (Nd) causes to animals and plants should not be underestimated. However, there is little research on the impact of pH and associated ions on the toxicity of Nd. Here, a biotic ligand model (BLM) was expanded to predict the effects of pH and chief anions on the toxic impact of Nd on wheat root elongation in a simulated soil solution. The results suggested that Nd3+ and NdOH2+ were the major ions causing phytotoxicity to wheat roots at pH values of 4.5-7.0. The Nd toxicity decreased as the activities of H+, Ca2+, and Mg2+ increased but not when the activities of K+ and Na+ increased. The results indicated that H+, Ca2+, and Mg2+ competed with Nd for binding sites. An extended BLM was developed to consider the effects of pH, H+, Ca2+, and Mg2+, and the following stability constants were obtained: logKNdBL = 2.51, logKNdOHBL = 3.90, logKHBL = 4.01, logKCaBL = 2.43, and logKMgBL = 2.70. The results demonstrated that the BLM could predict the Nd toxicity well while considering the competition of H+, Ca2+, Mg2+ and the toxic species Nd3+ and NdOH2+ for binding sites.

Interactive Effects of Copper-Silver Mixtures at the Intestinal Epithelium of Rainbow Trout: An In Vitro Approach

While metals are present in mixture in the environment, metal toxicity studies are usually conducted on an individual metal basis. There is a paucity of data in the existing literature regarding specific metal-metal interactions and their effect on metal toxicity and bioavailability. We studied interactions of a silver (Ag)-copper (Cu) mixture at the intestinal epithelium using an intestinal cell line derived from rainbow trout (Oncorhynchus mykiss), the RTgutGC. Exposures were conducted in media containing different chloride concentrations (low chloride, 1 mM; high chloride, 146 mM), thus resulting in different metal speciation. Cytotoxicity was evaluated based on two endpoints, cell metabolic activity and cell membrane integrity. The Ag-Cu mixture toxicity was assessed using two designs: independent action and concentration addition. Metal mixture bioavailability was studied by exposing cells to 500 nM of Ag or Cu as a single metal or a mixture (i.e., 500 nM of Cu plus 500 nM of Ag). We found an antagonistic effect in the low-chloride medium and an additive/synergistic effect in the high-chloride medium. We found that Cu dominates over Ag toxicity and bioavailability, indicating a competitive inhibition when both metals are present as free metal ions in the exposure media, which supports our hypothesis. Our study also suggests different mechanisms of uptake of free metal ions and metal complexes. The study adds valuable information to our understanding of the role of metal speciation on metal mixture toxicity and bioavailability. Environ Toxicol Chem 2024;43:105-114. © 2023 SETAC.

The toxicity of cadmium-copper mixtures on daphnids and microalgae analyzed using the Biotic Ligand Model

For the prediction of metal mixture ecotoxicity, the BLM approach is promising since it evaluates the amount of metals accumulated on the biotic ligand on the basis of water chemistry, i.e., species (major cations) competing with metals, and related toxicity. Based on previous work by Farley et al. (2015) (MMME research project), this study aimed at modeling toxicity of Cd:Cu mixtures (0:1 - 1:1 - 1:0 - 1:2 - 1:3 - 2:1 - 3:1 - 4:1 - 5:1 - 6:1) to the crustacean Daphnia magna(48-h immobilization tests) and the microalga Pseudokirchneriella subcapitata (72-h growth inhibition tests). The 2012 version of the USGS model was chosen, assuming additivity of effects and accumulation of metals on a single site. The assumption that EDTA could contribute to toxicity through metals complexing was also tested, and potential effects due to reduction of ions Ca²⁺ absorption by metals were considered. Modeling started with parameter values of Farley et al. (2015) and some of these parameters were adjusted to fit modeled data on observed data. The results show that toxicity can be correctly predicted for the microalgae and that the hypothesis of additivity is verified. For daphnids, the prediction was roughly correct, but taking into account CuEDTA led to more realistic parameter values close to that reported by Farley et al. (2015). However, It seems that, for daphnids responses, metals interact either antagonistically or synergistically depending on the Cu:Cd ratio. Furthermore, synergy could not be explained by additional effects linked to a reduction of Ca absorption since this reduction, mainly due to Cd, increased inversely to synergy. Finally, the USGS model applied to our data was able to predict Cu:Cd mixture toxicity to microalgae and daphnids, giving rise to estimated EC50s roughly reflecting EC50s calculated from observed toxicity.

Predicting the combined toxicity of binary metal mixtures (Cu-Ni and Zn-Ni) to wheat

In order to investigate and model toxicity and interactions between metals in mixtures, inhibition of wheat root elongation in response to additions of single-metals of copper (Cu), zinc (Zn), and nickel (Ni) and of binary mixed-metal combinations of Cu-Ni and Zn-Ni was tested, using water culture experiments under different Mg concentrations and pH values. A biotic ligand model (BLM) of single-metal Cu, Zn, and Ni was established. The results showed that the toxicity of Cu, Zn or Ni in isolation decreased with increasing Mg concentration whereas the effects of pH on Cu, Zn, or Ni toxicity were related not only to free Cu²⁺, Zn²⁺, and Ni²⁺ concentrations, but also to inorganic metal complexes. In binary mixtures, the two metals in the Cu-Ni mixture showed a weakly antagonistic effect, whereas the two metals in the Zn-Ni mixture showed greater antagonism. Using data from single-metal Cu, Zn, and Ni BLMs, combined with the toxicity index and the overall amounts of metal ions bound to the biotic ligands, one simple model was developed. This model consisted of the toxic unit (TU_M, no competition included) and two extended BLMs, BLM-TU_f (f as a function of TU, including competition between Mg²⁺ and metal ions) and BLM-f_mix (including the competition between Mg²⁺ and metal ions, as well as between free metal ions). They were then used to predict the joint toxicity of Cu-Ni and Zn-Ni binary mixtures to wheat. Both of the extended BLMs could provide more accurate predictions of toxic effects of Cu-Ni and Zn-Ni than TU_M. BLM-f_mix performed best for the Zn-Ni binary mixture (r² = 0.93; root-mean-square error, RMSE = 9.87). On the other hand, for the Cu-Ni mixture, the predictive effect based on BLM-TU_f (r² = 0.93; RMSE = 9.60) was similar to that of BLM-f_mix (r² = 0.93; RMSE = 9.56). The results provide a theoretical basis for the evaluation and remediation of soils contaminated with mixtures of heavy metals.

Model-based rationalization of mixture toxicity and accumulation in Triticum aestivum upon concurrent exposure to yttrium, lanthanum, and cerium

Rare earth elements (REEs) often co-exist in the environment, but predicting their 'cocktail effects' is still challenging, especially for high-order mixtures with more than two components. Here, we systematically investigated the toxicity and accumulation of yttrium, lanthanum, and cerium mixtures in Triticum aestivum following a standardized bioassay. Toxic effects of mixtures were predicted using the reference model of Concentration Addition (CA), Ternary model, and Ternary-Plus model. Interactions between the REEs in binary and ternary mixtures were determined based on external and internal concentrations, and their magnitude estimated from the parameters deviated from CA. Strong antagonistic interactions were found in the ternary mixtures even though there were no significant interactions in the binary mixtures. Predictive ability increased when using the CA model, Ternary model, and Ternary-Plus model, with R²= 0.78, 0.80, and 0.87 based on external exposure concentrations, and R²= 0.72, 0.73, and 0.79, respectively based on internal concentrations. The bioavailability-based model WHAM-F_TOX explained more than 88 % and 85 % of the toxicity of binary and ternary REE treatments, respectively. Our result showed that the Ternary-Plus model and WHAM-F_TOX model are promising tools to account for the interaction of REEs in mixtures and could be used for their risk assessment.

How Specific Is Site-Specific? A Review and Guidance for Selecting and Evaluating Approaches for Deriving Local Water Quality Benchmarks

Existing prescriptive guidance on the derivation of local water quality benchmarks (WQBs; e.g., guideline values, criteria, standards) for protecting aquatic ecosystems is limited to only 3 to 4 specific approaches. These approaches do not represent the full suite available for deriving local WQBs for multiple types of water quality-related issues. The general lack of guidance is inconsistent with the need for, and benefits of, local WQBs, and can constrain the appropriate selection and subsequent evaluation of derivation approaches. Consequently, the defensibility of local WQBs may not be commensurate with the nature of the issues for which they are derived. Moreover, where local WQBs are incorporated into regulatory requirements, the lack of guidance presents a potential risk to the derivation of appropriate WQBs and the achievement of desired environmental outcomes. This review addresses the deficiency in guidance by 1) defining local WQBs and outlining initial considerations for deciding if one is required; 2) summarizing the existing regulatory context; 3) summarizing existing guidance and identifying gaps; 4) describing strengths, weaknesses, and potential applications of a range of derivation approaches based on laboratory and/or field data; and 5) presenting a conceptual framework for appropriately selecting and evaluating a derivation approach to best suit the need. The guidance incorporates an existing set of guiding principles for deriving local WQBs and reinforces an existing categorization of site-adapted and site-specific WQBs. The conceptual framework recognizes the need to strike an appropriate balance between effort and ecological risk and, thus, embeds the concept of fit-for-purpose by considering both the significance of the issue being assessed and the extent to which the approach provides confidence that the ecosystem will be appropriately protected. The guidance can be used by industry, regulators, and others for both the a priori selection and the post hoc evaluation of appropriate approaches for deriving local WQBs. Integr Environ Assess Manag 2019;15:683-702. © 2019 The Authors.

Life history and physiological responses of native and invasive brine shrimps exposed to zinc

Although a substantial amount of research exists on pollution and biological invasions, there is a paucity of understanding of how both factors interact. Most studies show that pollution favours the establishment of invasive species, but pollution may also promote local adaptation of native species and prevent the establishment of new incomers. However, evidence for this is extremely limited because most studies focus on successful invasions and very few on cases where an invasion has been resisted. Here we provide evidence of local adaptation of native species to pollution combining life history and physiological data. We focused on the invasion of the North American brine shrimp Artemia franciscana, which is causing a dramatic biodiversity loss in hypersaline ecosystems worldwide, and one of the last native Artemia populations in SW Europe (A. parthenogenetica from the historically polluted Odiel estuary, SW Spain). Life table response experiments were carried out in the laboratory to compare the demographic responses of A. parthenogenetica and a nearby A. franciscana population to long-term Zn exposure (0.2 mg L^-1). We also evaluated oxidative stress by measuring antioxidant defences (catalase, glutathione reductase and superoxide dismutase) and lipid peroxidation (thiobarbituric acid reactive substances). A high concentration of Zn induced strong mortality in A. franciscana, which also showed high levels of lipid peroxidation, suggesting relatively poor physiological resistance to pollution compared with A. parthenogenetica. The age at maturity was shorter in A. parthenogenetica, which may be an adaptation to the naturally high mortality rate observed in the Odiel population. Exposure to Zn accelerated age at first reproduction in A. franciscana but not in A. parthenogenetica. In contrast, Zn had a stimulatory effect on offspring production in A. parthenogenetica,which also showed higher reproductive parameters (number of broods, total offspring and offspring per brood) than A. franciscana. Overall, the results of this study strongly suggest that native Artemia from Odiel estuary is locally adapted (at both, reproductive and physiological levels) to Zn contamination and that A. franciscana is highly sensitive. This is a good example of how pollution may play a role in the persistence of the last native Artemia populations in the Mediterranean.

Much ado about interaction: A combination of linear processes yields non-linear toxic effects in chemical mixtures

Predicting chemical mixture toxicity is an important issue for risk assessment. Loewe's concentration addition (CA) is a major model for predicting such toxicity. The CA is an additivity-based model, and if the results of toxicity test deviate from the CA prediction, it is considered that the toxic effect of the mixture is non-additive, and that "interaction" has played some role. In the present study, using as an example a biotic ligand model (BLM), which predicts metal toxicities, we theoretically investigated the toxic effect of mixture and found that the effects are almost always non-additive if the effects are evaluated by total metal concentrations, and the non-additivity is not derived by interactions among metals but by a combination of processes of metal kinetics. Once non-additive effects are observed in chemical mixture, it is often expected that there should be some complex toxic mechanisms or some toxic interaction. Our results suggest that the expectation may not be always true. Since at least two processes are entrained in the metal toxicity (metal speciation and binding of metals to biotic ligand in BLM framework), there is a possibility that the non-additivity is generated by the combination of processes and interaction is nothing to do with it. Our results imply that toxic effects of metal mixture can be predicted more easily than we generally expected.

Using the Biotic Ligand Model framework to investigate binary metal interactions on the uptake of Ag, Cd, Cu, Ni, Pb and Zn in the freshwater snail Lymnaea stagnalis

There is growing interest in the development of mechanistically-based models, such as the Biotic Ligand Model (BLM), for assessing the environmental risk of metal mixtures. However, the derivation of such models requires insights into the mechanisms of multimetal interactions that are often lacking for aquatic organisms. In the present study, we investigated how binary mixtures of six metals (Ag, Cd, Cu, Ni, Pb and Zn) interact for uptake in the great pond snail Lymnaea stagnalis, a freshwater species particularly sensitive to metals in chronic exposure. For each metal, short-term (2-3 h) uptake experiments on juvenile snails were performed with the metal alone and in combination with a second metal, at concentrations encompassing the chronic toxicity concentration range. These experiments showed significant binary metal interactions for 7 out of 15 mixtures. Most interactions were inhibitory in nature, not reciprocal and caused by either Ag or Cu. They led to relative changes of uptake that did not exceed 50% within the range of metal chronic toxicity. The BLM proved to be successful at explaining most of the interactions, via competitive inhibition. This study is in support of using bioavailability-based models, such as the BLM, to model metal mixture interactions in L. stagnalis.

The prediction of combined toxicity of Cu-Ni for barley using an extended concentration addition model

Environment pollution often occurs as an obvious combined effect involving two (or more) elements, and this effect changes with the concentrations of the different elements. The effects on barley root elongation were studied in hydroponic systems to investigate the toxicity of Cu-Ni combined at low doses and at a fixed concentration ratio. For low doses of Cu-Ni, the addition of Ni (<0.5 μM) to Cu significantly decreased Cu toxicity for barley, but the addition of Cu (<0.25 μM) had no significant effect on Ni toxicity. At a fixed concentration ratio, according to the single effective concentration (EC) (barley root elongation inhibitory concentration) values of Cu and Ni, five sets of Cu-Ni fixed ratios were used: ECn(Cu)+ECm(Ni) (n + m = 100) (ECn and EC_m indicate toxicity unit value for n% and m% inhibition of barley root length, respectively). The calculated toxicity unit value for 50% inhibition of root length ranged from 0.44 to 0.98 (i.e., <1), indicating a synergistic effect. To consider the interactions between the metal ions, the extended concentration addition model (e-CA) was established by integrating the Cu-Ni interaction into the concentration addition model (CA), and the data of two groups (the low doses of Cu-Ni and at a fixed concentration ratio) were respectively fitted. The e-CA accurately predicted the root length of barley under the Cu-Ni combined action. The correlation coefficient (r) and the root-mean-square error (RMSE) between predicted and observed values were 0.97 and 6.6 (low-dose group) and 0.96 and 8.12 (fixed-ratio group), respectively, and e-CA significantly improved the prediction accuracy compared to the traditional CA model without consideration of the Cu-Ni competition (r = 0.89, RMSE = 14.16). The results provided a theoretical basis for evaluation and remediation of soil contaminated with heavy metal composites.

Development of multi-metal interaction model for Daphnia magna: Significance of metallothionein in cellular redistribution

Despite the great progress made in metal-induced toxicity mechanisms, a critical knowledge gap still exists in predicting adverse effects of heavy metals on living organisms in the natural environment, particularly during exposure to multi-metals. In this study, a multi-metal interaction model of Daphnia manga was developed in an effort to provide reasonable explanations regarding the joint effects resulting from exposure to multi-metals. Metallothionein (MT), a widely used biomarker, was selected. In this model, MT was supposed to play the role of a crucial transfer protein rather than detoxifying protein. Therefore, competitive complexation of metals to MT could highly affect the cellular metal redistribution. Thus, competitive complexation of MT in D. magna with metals like Pb²⁺, Cd²⁺ and Cu²⁺ was qualitatively studied. The results suggested that Cd²⁺ had the highest affinity towards MT, followed by Pb²⁺ and Cu²⁺. On the other hand, the combination of MT with Cu²⁺ appeared to alter its structure which resulted in higher affinity towards Pb²⁺. Overall, the predicted bioaccumulation of metals under multi-metal exposure was consisted with earlier reported studies. This model provided an alternative angle for joint effect through a combination of kinetic process and internal interactions, which could help to develop future models predicting toxicity to multi-metal exposure.

Distribution and speciation of zinc in the gills of rainbow trout (Oncorhynchus mykiss) during acute waterborne zinc exposure: Interactions with cadmium or copper

We utilized micro X-ray fluorescence imaging (μ-XFI) and micro X-ray absorption near-edge spectroscopy (μ-XANES), which are both synchrotron-based techniques to investigate Zn distribution profile, its co-localization patterns with Ca, S, and Fe and speciation in the gills of rainbow trout (RBT). Fish (~100 g) were exposed to acutely toxic levels of waterborne Zn alone and in combination with waterborne Cd or Cu for 24 h (each at 1 × 96 h LC₅₀). Gill sections were prepared and analyzed at the VESPERS beamline of the Canadian Light Source. The primary lamellae of the fish gill were found to be the primary area of Zn accumulation. These regions also correspond to the zones of mitochondria rich cells localization in fish gills, supporting the putative roles of these cells in metal uptake. Zn was also found to predominantly co-localize with Ca and S, but not with Fe, indicating the roles of Ca and S in intracellular Zn handling. Zn distribution in the gill was markedly reduced during co-exposure to Cd, but not to Cu, suggesting a competitive interaction between Zn and Cd for uptake. The speciation of Zn in the gill was dominated by Zn-phosphate, Zn-histidine and Zn-cysteine species; however, the interactions of Zn with Cd or Cu resulted in the loss of Zn-cysteine. Overall, our findings provide important novel insights into the interactions of Zn, Cd and Cu in the fish gill, which may ultimately help to explain the mechanisms underlying the acute toxicity of these metals in binary mixture to fish.

Mixtures of Cu, Ni, and Zn act mostly noninteractively on Pseudokirchneriella subcapitata growth in natural waters

Freshwater biota are usually exposed to mixtures of different metals in the environment, which raises concern because risk-assessment procedures for metals are still mainly based on single-metal toxicity. Because microalgae are primary producers and therefore at the base of the food web, it is of utmost importance to understand the effects of metal mixtures on these organisms. Most studies that have investigated the combined interactive effects of mixtures on microalgae performed tests in only one specific water. The objective of the present study was to test if combined effects of mixtures to Pseudokirchneriella subcapitata were the same or different across natural waters showing diverse water-chemistry characteristics. This was done by performing experiments with ternary Cu-Ni-Zn mixtures in 3 natural waters and with binary Cu-Ni mixtures in 5 natural waters. We showed that the ternary mixture acted noninteractively on algal growth, except in one water in which the mixture acted antagonistically. We suggest that a low-cationic competition situation in the latter water could be the reason for the antagonistic interaction between the metals. On the other hand, the binary mixture acted noninteractively on algal growth in all tested waters. We showed that both the concentration addition and independent action models can serve as accurate models for toxicity of ternary Cu-Ni-Zn and binary Cu-Ni mixtures to P. subcapitata in most cases and as protective models in all cases. In addition, we developed a metal mixture bioavailability model, by combining the independent action model and the single-metal bioavailability models, that can be used to predict Cu-Ni-Zn and Cu-Ni toxicity to P. subcapitata as a function of metal concentration and water characteristics. Environ Toxicol Chem 2018;37:587-598. © 2017 SETAC.

Defence mechanisms: the role of physiology in current and future environmental protection paradigms

Ecological risk assessments principally rely on simplified metrics of organismal sensitivity that do not consider mechanism or biological traits. As such, they are unable to adequately extrapolate from standard laboratory tests to real-world settings, and largely fail to account for the diversity of organisms and environmental variables that occur in natural environments. However, an understanding of how stressors influence organism health can compensate for these limitations. Mechanistic knowledge can be used to account for species differences in basal biological function and variability in environmental factors, including spatial and temporal changes in the chemical, physical and biological milieu. Consequently, physiological understanding of biological function, and how this is altered by stressor exposure, can facilitate proactive, predictive risk assessment. In this perspective article, existing frameworks that utilize physiological knowledge (e.g. biotic ligand models, adverse outcomes pathways and mechanistic effect models), are outlined, and specific examples of how mechanistic understanding has been used to predict risk are highlighted. Future research approaches and data needs for extending the incorporation of physiological information into ecological risk assessments are discussed. Although the review focuses on chemical toxicants in aquatic systems, physical and biological stressors and terrestrial environments are also briefly considered.

Characterization of the effects of binary metal mixtures on short-term uptake of Cd, Pb, and Zn by rainbow trout (Oncorhynchus mykiss)

Biotic Ligand Models (BLMs) for individual metals improve our ability to regulate metals in the aquatic environment by considering the effects of water quality parameters (ionic composition, pH, DOC) on metal bioavailability. However, in natural aquatic systems, organisms are often simultaneously exposed to multiple metals and these interactions are not currently considered in BLMs or most environmental regulations. Recently, several different mixture BLMs (mBLMs) have been developed to begin assessing this issue. Some of these models assume competitive interactions between all metals, while others assume only metals with similar modes of action (e.g., Na⁺ or Ca²⁺ antagonists) will competitively interact. In this study, we used standard in vivo 3-h gill metal binding assays to characterize the uptake of Cd, Pb, and Zn individually and in binary mixtures with Ag, Cd, Cu, Pb, Ni, and Zn across a range of concentrations that encompassed the 96-h LC50 for each metal. Inhibition of Cd, Pb, and Zn uptake at the gill by introduction of a second metal was consistent with mode of action in some cases, but not others. Further, contrary to expectations, inhibition was always either non-competitive or could not be defined statistically. We also observed one example of stimulated metal uptake (Ni stimulated Zn uptake). Consistent with our previous experiments on Ag, Cu, and Ni, these studies suggest that current mBLM frameworks will need revision to better reflect the mechanisms underlying metal mixture interactions.

Modeling interactions and toxicity of Cu-Zn mixtures to zebrafish larvae

Quantitative predictions of metal-metal interactions and toxicity in aquatic organisms meet a unique challenge. Accumulation and toxicity of Cu and Zn mixtures in zebrafish larvae has been quantified in binary metal system with variable combinatorial concentrations in order to understand the interactions between essential trace metals and assess availability of the toxicokinetic-toxicodynamic (TK-TD) model which simulated the uptake of metals over time as well as metal toxicity after 24h of exposure. Competitive uptake experiments showed a straightforward antagonistic competition, as would be predicted by Michaelis-Menten competitive equilibrium model. Zn uptake decreased significantly in the presence of Cu²⁺ concentrations higher than 10^-6M. Cu²⁺ was shown to compete strongly with Zn for uptake, having a higher affinity constant to biotic ligand (BL) sites (K_CuBL=10^5.42M^-1) than Zn (K_ZnBL=10^4.13M^-1). TK-TD model considering potential metal-metal antagonism interactions showed good predictive power in predicting accumulation and toxicity of Cu-Zn mixtures in zebrafish larvae with the high coefficient of determination (r²) and significant level (p). In particular, with the elevated Zn concentrations in mixtures, the TD model showed better predictive power in predicting toxicity of 10^-6M Cu concentration in Cu-Zn mixtures. The TK-TD analysis provided some new insights into the interactive mechanism of binary Cu and Zn exposure in aquatic animals and may have important implications for our understanding of quantitative predictions of metal-metal interactions and toxicity in a field where animals are simultaneously exposed to several metals.

Toxicity Assessment of Binary Metal Mixtures (Copper-Zinc) to Nitrification in Soilless Culture with the Extended Biotic Ligand Model

Metals are always found in the environment as mixtures rather than as solitary elements. Only a limited number of studies have developed appropriate models that incorporate bioavailability to estimate the toxicity of heavy-metal mixtures. In the present study, we explored the applicability of two extended biotic ligand model (BLM) approaches-BLM-f _mix and BLM-toxicity unit (TU)-to predict and interpret mixture toxicity with the assumption that interactions between metal ions obey the BLM theory. Exposure assays of single and mixed metals were performed with inoculums of an ammonia-oxidizing bacterium SD5 isolated from soil. Nitrification of the cultures was the end point used to quantify the toxic response. The results indicated that the developed BLM-f _mix approach could well estimate the single toxicity of Cu²⁺ and Zn²⁺ as well as their binary mixture toxicity to nitrification with >90% of toxicity variation explained. Assuming that metal ions compete with each other for binding at a single biotic ligand, the BLM-f _mix approach (root-mean-square error [RMSE] = 19.66, R ² = 0.8879) showed better predictive power than the BLM-TU approach (RMSE = 31.12, R ² = 0.6892). The present study supports the use of the accumulation of metal ions at the biotic ligands as predictor of toxicity of single metals and metal mixtures.

Development and validation of a metal mixture bioavailability model (MMBM) to predict chronic toxicity of Ni-Zn-Pb mixtures to Ceriodaphnia dubia

Recently, several bioavailability-based models have been shown to predict acute metal mixture toxicity with reasonable accuracy. However, the application of such models to chronic mixture toxicity is less well established. Therefore, we developed in the present study a chronic metal mixture bioavailability model (MMBM) by combining the existing chronic daphnid bioavailability models for Ni, Zn, and Pb with the independent action (IA) model, assuming strict non-interaction between the metals for binding at the metal-specific biotic ligand sites. To evaluate the predictive capacity of the MMBM, chronic (7d) reproductive toxicity of Ni-Zn-Pb mixtures to Ceriodaphnia dubia was investigated in four different natural waters (pH range: 7-8; Ca range: 1-2 mM; Dissolved Organic Carbon range: 5-12 mg/L). In each water, mixture toxicity was investigated at equitoxic metal concentration ratios as well as at environmental (i.e. realistic) metal concentration ratios. Statistical analysis of mixture effects revealed that observed interactive effects depended on the metal concentration ratio investigated when evaluated relative to the concentration addition (CA) model, but not when evaluated relative to the IA model. This indicates that interactive effects observed in an equitoxic experimental design cannot always be simply extrapolated to environmentally realistic exposure situations. Generally, the IA model predicted Ni-Zn-Pb mixture toxicity more accurately than the CA model. Overall, the MMBM predicted Ni-Zn-Pb mixture toxicity (expressed as % reproductive inhibition relative to a control) in 85% of the treatments with less than 20% error. Moreover, the MMBM predicted chronic toxicity of the ternary Ni-Zn-Pb mixture at least equally accurately as the toxicity of the individual metal treatments (RMSE_Mix = 16; RMSE_{Zn only} = 18; RMSE_{Ni only} = 17; RMSE_{Pb only} = 23). Based on the present study, we believe MMBMs can be a promising tool to account for the effects of water chemistry on metal mixture toxicity during chronic exposure and could be used in metal risk assessment frameworks.

Characterization of the effects of binary metal mixtures on short-term uptake of Ag, Cu, and Ni by rainbow trout (Oncorhynchus mykiss)

Single metal Biotic Ligand Models (BLMs) have been developed for a number of metals and model organisms. While these BLMs improve our ability to regulate metals in the aquatic environment, in reality, organisms are often simultaneously exposed to metal mixtures. Recently, several attempts have been made to develop mixture BLMs (mBLMs). Some of these models assume competitive interactions between all metals, while others assume only metals with a similar mode of action (e.g., Na⁺ or Ca²⁺ antagonists) will competitively interact. To begin testing these assumptions in the mBLM framework, standard 3-h gill metal binding assays with Ag, Cu, and Ni (primary metals), were performed in vivo on freshwater rainbow trout. Fish were exposed across a range of concentrations encompassing the 96-h LC50 for that metal to characterize uptake kinetics for each of these three primary metals (radiolabelled) in the presence and absence of a secondary metal (Ag, Cd, Cu, Ni, Pb, or Zn; not radiolabelled). We observed a complex series of interactions in binary mixtures that frequently contradicted theoretical expectations. Metals with similar modes of action did competitively interact in some instances, but not others, and when they did compete the competition was not necessarily reciprocal (e.g., Cu inhibited Ag uptake but Ag did not inhibit Cu uptake). We also observed examples of interactions between metals with dissimilar modes of action and several examples of metals stimulating the uptake of other metals. The underlying mechanisms for these unexpected interactions are unclear, but suggest that many of the current assumptions in mBLMs regarding the number and types of metal uptake sites and corresponding metal interactions are not correct. Careful characterization of metal mixture interactions is clearly needed before a reliable mBLM can be developed.

Mixture toxicity and interactions of copper, nickel, cadmium, and zinc to barley at low effect levels: Something from nothing?

Metal contamination is mostly a mixture of different metals, and these multicomponent mixtures can produce significant mixture effects. The present study was set up to investigate the toxicity of multiple metal mixtures of Cu, Ni, Cd, and Zn to plants at metal doses individually causing low-level phytotoxic effects. Barley (Hordeum vulgare L.) root elongation toxicity tests were performed in resin-buffered nutrient solutions to control metal speciation. Treatments included single-metal concentrations and binary, ternary, and quaternary mixtures. Mixtures of different metals at free ion concentrations, each causing <10% inhibition of root elongation, yielded significant mixture effects, with inhibition reaching up to 50%. The independent action (IA) model predicted mixture toxicity statistically better than the concentration addition (CA) model, but some synergisms relative to the IA model were observed. These synergisms relative to IA were most pronounced in quaternary mixtures and when the dose-response curves had steep slopes. Generally, antagonistic interactions relative to the CA model were observed. Increasing solution Zn concentrations shifted metal interactions (CA based) from additive or slightly synergistic at background Zn concentrations to antagonistic at higher Zn concentrations, suggesting a protective effect of Zn. Overall, the present study shows that the CA model can be used as a conservative model to predict metal mixture toxicity to barley. Environ Toxicol Chem 2016;35:2483-2492. © 2016 SETAC.

Reproductive toxicity of binary and ternary mixture combinations of nickel, zinc, and lead to Ceriodaphnia dubia is best predicted with the independent action model

Metals occur as mixtures in the environment. Risk assessment procedures for metals currently lack a framework to incorporate chronic metal mixture toxicity. In the present study, the toxicity of binary and ternary mixture combinations of Ni, Zn, and Pb was investigated in 3 large-scale experiments using the standard chronic (7-d) Ceriodaphnia dubia reproductive toxicity test. These metals were selected because of anticipated differences in mode of action. The toxicity of the metals in most mixtures, expressed as either free metal ion activities or as dissolved metal concentrations, were antagonistic relative to the concentration addition model, whereas no significant (p < 0.05) interactive effects were observed relative to the independent action model. The only exception was the binary Pb-Zn mixture, for which mixture effects were noninteractive based on the dissolved concentrations, but antagonistic based on free ion activities all relative to the independent action model. Overall, the independent action model fitted the observed toxicity better than the concentration addition model, which is consistent with the different modes of action of these metals. The concentration addition model mostly overestimated toxicity. Finally, the present study warns against extrapolation of the type of interactive effects between species, even when they are closely related. Environ Toxicol Chem 2016;35:1796-1805. © 2015 SETAC.

Explanation of non-additive effects in mixtures of similar mode of action chemicals

Many models have been developed to predict the combined effect of drugs and chemicals. Most models are classified into two additive models: independent action (IA) and concentration addition (CA). It is generally considered if the modes of action of chemicals are similar then the combined effect obeys CA; however, many empirical studies report nonlinear effects deviating from the predictions by CA. Such deviations are termed synergism and antagonism. Synergism, which leads to a stronger toxicity, requires more careful management, and hence it is important to understand how and which combinations of chemicals lead to synergism. In this paper, three types of chemical reactions are mathematically modeled and the cause of the nonlinear effects among chemicals with similar modes of action was investigated. Our results show that combined effects obey CA only when the modes of action are exactly the same. Contrary to existing knowledge, combined effects are generally nonlinear even if the modes of action of the chemicals are similar. Our results further show that the nonlinear effects vanish out when the chemical concentrations are low, suggesting that the current management procedure of assuming CA is rarely inappropriate because environmental concentrations of chemicals are generally low.

Modeling and interpreting biological effects of mixtures in the environment: introduction to the metal mixture modeling evaluation project

The fate and biological effects of chemical mixtures in the environment are receiving increased attention from the scientific and regulatory communities. Understanding the behavior and toxicity of metal mixtures poses unique challenges for incorporating metal-specific concepts and approaches, such as bioavailability and metal speciation, in multiple-metal exposures. To avoid the use of oversimplified approaches to assess the toxicity of metal mixtures, a collaborative 2-yr research project and multistakeholder group workshop were conducted to examine and evaluate available higher-tiered chemical speciation-based metal mixtures modeling approaches. The Metal Mixture Modeling Evaluation project and workshop achieved 3 important objectives related to modeling and interpretation of biological effects of metal mixtures: 1) bioavailability models calibrated for single-metal exposures can be integrated to assess mixture scenarios; 2) the available modeling approaches perform consistently well for various metal combinations, organisms, and endpoints; and 3) several technical advancements have been identified that should be incorporated into speciation models and environmental risk assessments for metals.

Testing WHAM-FTOX with laboratory toxicity data for mixtures of metals (Cu, Zn, Cd, Ag, Pb)

The Windermere humic aqueous model using the toxicity function (WHAM-FTOX ) describes cation toxicity to aquatic organisms in terms of 1) accumulation by the organism of metabolically active protons and metals at reversible binding sites, and 2) differing toxic potencies of the bound cations. Cation accumulation (νi , in mol g(-1) ) is estimated through calculations with the WHAM chemical speciation model by assuming that organism binding sites can be represented by those of humic acid. Toxicity coefficients (αi ) are combined with νi to obtain the variable FTOX (= Σ αi νi ) which, between lower and upper thresholds (FTOX,LT , FTOX,UT ), is linearly related to toxic effect. Values of αi , FTOX,LT , and FTOX,LT are obtained by fitting toxicity data. Reasonable fits (72% of variance in toxic effect explained overall) were obtained for 4 large metal mixture acute toxicity experiments involving daphnids (Cu, Zn, Cd), lettuce (Cu, Zn, Ag), and trout (Zn, Cd, Pb). Strong nonadditive effects, most apparent in results for tests involving Cd, could be explained approximately by purely chemical competition for metal accumulation. Tentative interpretation of parameter values obtained from these and other experimental data suggests the following order of bound cation toxicity: H < Al < (Cu Zn Pb UO2 ) < (Cd Ag). Another trend is a strong increase in Cd toxicity relative to that of Zn as organism complexity increases (from bacteria to fish).

Metal mixtures modeling evaluation project: 1. Background

Despite more than 5 decades of aquatic toxicity tests conducted with metal mixtures, there is still a need to understand how metals interact in mixtures and to predict their toxicity more accurately than what is currently done. The present study provides a background for understanding the terminology, regulatory framework, qualitative and quantitative concepts, experimental approaches, and visualization and data-analysis methods for chemical mixtures, with an emphasis on bioavailability and metal-metal interactions in mixtures of waterborne metals. In addition, a Monte Carlo-type randomization statistical approach to test for nonadditive toxicity is presented, and an example with a binary-metal toxicity data set demonstrates the challenge involved in inferring statistically significant nonadditive toxicity. This background sets the stage for the toxicity results, data analyses, and bioavailability models related to metal mixtures that are described in the remaining articles in this special section from the Metal Mixture Modeling Evaluation project and workshop. It is concluded that although qualitative terminology such as additive and nonadditive toxicity can be useful to convey general concepts, failure to expand beyond that limited perspective could impede progress in understanding and predicting metal mixture toxicity. Instead of focusing on whether a given metal mixture causes additive or nonadditive toxicity, effort should be directed to develop models that can accurately predict the toxicity of metal mixtures.

Application of a generalized linear mixed model to analyze mixture toxicity: survival of brown trout affected by copper and zinc

Increased concerns about the toxicity of chemical mixtures have led to greater emphasis on analyzing the interactions among the mixture components based on observed effects. The authors applied a generalized linear mixed model (GLMM) to analyze survival of brown trout (Salmo trutta) acutely exposed to metal mixtures that contained copper and zinc. Compared with dominant conventional approaches based on an assumption of concentration addition and the concentration of a chemical that causes x% effect (ECx), the GLMM approach has 2 major advantages. First, binary response variables such as survival can be modeled without any transformations, and thus sample size can be taken into consideration. Second, the importance of the chemical interaction can be tested in a simple statistical manner. Through this application, the authors investigated whether the estimated concentration of the 2 metals binding to humic acid, which is assumed to be a proxy of nonspecific biotic ligand sites, provided a better prediction of survival effects than dissolved and free-ion concentrations of metals. The results suggest that the estimated concentration of metals binding to humic acid is a better predictor of survival effects, and thus the metal competition at the ligands could be an important mechanism responsible for effects of metal mixtures. Application of the GLMM (and the generalized linear model) presents an alternative or complementary approach to analyzing mixture toxicity.

Metal mixture modeling evaluation project: 2. Comparison of four modeling approaches

As part of the Metal Mixture Modeling Evaluation (MMME) project, models were developed by the National Institute of Advanced Industrial Science and Technology (Japan), the US Geological Survey (USA), HDR|HydroQual (USA), and the Centre for Ecology and Hydrology (United Kingdom) to address the effects of metal mixtures on biological responses of aquatic organisms. A comparison of the 4 models, as they were presented at the MMME workshop in Brussels, Belgium (May 2012), is provided in the present study. Overall, the models were found to be similar in structure (free ion activities computed by the Windermere humic aqueous model [WHAM]; specific or nonspecific binding of metals/cations in or on the organism; specification of metal potency factors or toxicity response functions to relate metal accumulation to biological response). Major differences in modeling approaches are attributed to various modeling assumptions (e.g., single vs multiple types of binding sites on the organism) and specific calibration strategies that affected the selection of model parameters. The models provided a reasonable description of additive (or nearly additive) toxicity for a number of individual toxicity test results. Less-than-additive toxicity was more difficult to describe with the available models. Because of limitations in the available datasets and the strong interrelationships among the model parameters (binding constants, potency factors, toxicity response parameters), further evaluation of specific model assumptions and calibration strategies is needed.

Metal mixture modeling evaluation project: 3. Lessons learned and steps forward

A comparison of 4 metal mixture toxicity models (that were based on the biotic ligand model [BLM] and the Windermere humic aqueous model using the toxicity function [WHAM-FTOX ]) was presented in a previous paper. In the present study, a streamlined version of the 4 models was developed and applied to multiple data sets and test conditions to examine key assumptions and calibration strategies that are crucial in modeling metal mixture toxicity. Results show that 1) a single binding site on or in the organism was a useful and oftentimes sufficient framework for predicting metal toxicity; 2) a linear free energy relationship (LFER) for bidentate binding of metals and cations to the biotic ligand provided a good first estimate of binding coefficients; 3) although adjustments in metal binding coefficients or adjustments in chemical potency factors can both be used in model calibration for single-metal exposures, changing metal binding coefficients or chemical potency factors had different effects on model predictions for metal mixtures; and 4) selection of a mixture toxicity model (based on concentration addition or independent action) was important in predicting metal mixture toxicity. Moving forward, efforts should focus on reducing uncertainties in model calibration, including development of better methods to characterize metal binding to toxicologically active binding sites, conducting targeted exposure studies to advance the understanding of metal mixture toxicity, and further developing LFERs and other tools to help constrain the model calibration.