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

Evaluation of a Proportional Response Addition Approach to Mixture Risk Assessment and Predictive Toxicology Using Data on Four Trihalomethanes from the U.S. EPA's Multiple-Purpose Design Study

In this study, proportional response addition (Prop-RA), a model for predicting response from chemical mixture exposure, is demonstrated and evaluated by statistically analyzing data on all possible binary combinations of the four regulated trihalomethanes (THMs). These THMs were the subject of a multipurpose toxicology study specifically designed to evaluate Prop-RA. The experimental design used a set of doses common to all components and mixtures, providing hepatotoxicity data on the four single THMs and the binary combinations. In Prop-RA, the contribution of each component to mixture toxicity is proportional to its fraction in the mixture based on its response at the total mixture dose. The primary analysis consisted of 160 evaluations. Statistically significant departures from the Prop-RA prediction were found for seven evaluations, with three predications that were greater than and four that were less than the predicted response; interaction magnitudes (n-fold difference in response vs. prediction) ranged from 1.3 to 1.4 for the former and 2.6 to 3.8 for the latter. These predictions support the idea that Prop-RA works best with chemicals where the effective dose ranges overlap. Prop-RA does not assume the similarity of toxic action or independence, but it can be applied to a mixture of components that affect the same organ/system, with perhaps unknown toxic modes of action.

Mechanisms underpinning nonadditivity of global change factor effects in the plant-soil system

Plant-soil systems are key for understanding the effects of factors of global change. Recent work has highlighted the general importance of considering the simultaneous incidence of some factors or stressors. To help mechanistically dissect the possible interactions of such factors, we here propose three broad groups of mechanisms that may generally lead to nonadditivity of responses within a plant-soil system: direct factor interactions (that is one factor directly changing another), within-plant information processing and crosstalk, and effects of factors on groups of soil biota interacting with plants. Interactions are also possible within and across these groups. Factor interactions are very likely to be present in experiments, especially when dealing with an increasing number of factors. Identifying the nature of such interactions will be essential for understanding and predicting global change impacts on plants and soil.

Identification of chemical mixtures to which women are exposed through the diet: Results from the French E3N cohort

Due to the large number of chemical food contaminants, consumers are exposed simultaneously to a wide range of chemicals which can interact and have a negative impact on health. Nevertheless, due to the multitude of possible chemical combinations it is unrealistic to test all combined toxicological effects. It is therefore essential to identify the most relevant mixtures to which the population is exposed through the diet and investigate their impact on heath. The present study aims to identify and describe the main chemical mixtures to which women enrolled in the E3N study, a large French prospective cohort, are chronically exposed through the diet. 74522 women who had answered a validated semi-quantitative food frequency questionnaire in 1993, were included in the present study. Dietary exposure to chemical contaminates was estimated based on the food contamination measured in 186 core food in France collected between 2007 and 2009 by the French agency for food, environment and occupational health, and safety (ANSES) in the framework of the second French total diet study (2TDS). The sparse non-negative matrix under-approximation (SNMU) was used to identify mixtures of chemical substances. A k-means clustering classification of the whole study population was then performed to define clusters with similar co-exposure profiles. Overall, 8 mixtures which explained 83% of the total variance, were retained. The first mixture, entitled "Minerals, inorganic contaminants, and furans", explained the highest proportion of the total variance (38%), and was correlated in particular with the consumption of "Offal" (rho = 0.22), "Vegetables except roots" (rho = 0.20), and "Eggs" (rho = 0.19). The other seven mixtures explained between 17% and 1% of the variance. Finally, 5 clusters were identified based on the adherence to the 8 mixtures. This study, being the largest ever conducted to identify dietary exposure to chemical mixtures, represents a concrete attempt to prioritize mixtures for which it is essential to investigate combined health effects based on exposure.

Effects of predator cues and pesticide resistance on the toxicity of a (bio)pesticide mixture

BACKGROUND

Populations of target species are typically exposed to pesticide mixtures and natural stressors such as predator cues, and are increasingly developing resistance to single pesticides. Nevertheless, we have poor knowledge whether natural stressors and the presence of pesticide resistance shape mixture toxicity. We tested the single and combined effects of the pesticide chlorpyrifos and the biopesticide Bacillus thuringiensis israelensis (Bti) on the survival of the Southern house mosquito (Culex quinquefasciatus, Say) and whether these effects were magnified by synthetic predator cues of Notonecta water bugs and differed between a chlorpyrifos-resistant (Ace-1R) and non-resistant (S-Lab) strain.

RESULTS

Single exposure to Bti caused mortality in both strains (S-Lab ∼27%, Ace-1R ∼41%) and single exposure to chlorpyrifos caused only mortality in the S-Lab strain (∼33%), while predator cues did not induce mortality. The chlorpyrifos-resistant strain was 1.5-fold more sensitive to Bti, indicating a cost of resistance. The interaction types between chlorpyrifos and Bti (additive), between chlorpyrifos and predator cues (additive), and between Bti and predator cues (synergistic) were consistent in both strains. Despite predator cues making Bti approximately 8% more lethal, they did not change the additive interaction between Bti and chlorpyrifos in their mixture in either strain.

CONCLUSION

These results indicate that the resistance against chlorpyrifos was not partly lifted when chlorpyrifos exposure was combined with Bti and predator cues. Identifying the interaction type within pesticide mixtures and how this depends on natural stressors is important to select control strategies that give a disadvantage to resistant individuals compared to non-resistant individuals. © 2019 Society of Chemical Industry.

Resistance to a chemical pesticide increases vulnerability to a biopesticide: Effects on direct mortality and mortality by predation

Pesticide mixtures are increasingly used to fight pest species that developed resistance to pesticides. To assess the pesticide control efficiency and to reduce ecological damage to non-target species, it is important to quantify the effect of these mixtures and compare them with the effect of their single pesticides on pest species, non-target species and their predator-prey interactions. We studied the effects of the chemical pesticide chlorpyrifos (CPF), the biopesticide Bacillus thuringiensis israelensis (Bti) and their mixture both on the direct mortality and on the mortality by predation. We focused on larvae of a CPF-resistant and a non-resistant strain of the vector mosquito Culex quinquefasciatus and its predator, the pygmy backswimmer Plea minutissima. In the CPF-Bti mixture, both pesticides interacted antagonistically for direct mortality. Exposure to the mixture caused equal direct mortality and equal mortality by predation in both strains. As expected, exposure to CPF resulted in less direct mortality and less mortality by predation in the CPF-resistant mosquito strain compared to the non-resistant strain. Notably, Bti caused a higher mortality in the mosquito larvae of the CPF-resistant strain compared to the non-resistant strain. Furthermore, the predator killed more mosquito larvae of the resistant strain compared to the non-resistant strain when exposed before to Bti alone. These observations identify a novel cost of resistance to a chemical pesticide in terms of increased vulnerability to a biopesticide.

Model construction for estimating potential vulnerability of Japanese soils to cadmium pollution based on intact soil properties

Prediction of heavy metal bioavailability in intact soil is important to manage soil pollution risks. We developed a regression model for representative Japanese soils to judge their potential vulnerability to cadmium (Cd) pollution. We added four rates of Cd to 17 sample soils to mimic artificial contamination. After aging the contaminated soils, we measured Cd's bioavailability using the diffusive gradients in thin-films (DGT) technique. We then evaluated the relationships between bioavailability of Cd ([CdDGT]) and intact soil properties by statistical analyses. Cation exchange capacity (CEC) and pH emerged as significant factors to explain the cadmium bioavailability in Japanese soils. Specifically, lower CEC and lower pH were associated with higher [CdDGT], which poses a higher risk for soil ecosystems. The correlation between pH and [CdDGT] had a high dependence on [CdAdd], whereas that for CEC did not. Regression analysis also showed that the interaction between intact soil pH and spiked concentration ([CdAdd]) had a significant contribution to [CdDGT]. The regression model developed was rationally supported by a biotic ligand model. This simplified but realistic model would be useful in estimating the vulnerability of representative Japanese soils and determining the risk for Japanese soils in relation to Cd contamination.

Daily temperature variation magnifies the toxicity of a mixture consisting of a chemical pesticide and a biopesticide in a vector mosquito

While many studies on the toxicity of pesticides looked at the effects of a higher mean temperature, effects of the realistic scenario of daily temperature variation are understudied. Moreover, despite the increasing interest for the toxicity of pesticide mixtures how this is influenced by temperature has been largely ignored. We tested whether daily temperature variation (DTV) magnifies the toxicity of two pesticides with a different mode of action, the organophosphate pesticide chlorpyrifos (CPF) and the biopesticide Bacillus thuringiensis var. israelensis (Bti), and of their mixture in the vector mosquito Culex pipiens. Single exposure to CPF and Bti increased mortality and reduced female development time, and exposure to CPF also increased female wing length. DTV was not lethal and did not change the toxicity of the individual pesticides. Yet, a key novel finding was that high DTV increased the mortality of the mixture by changing the interaction between both pesticides from additive to synergistic. Given that in nature daily temperature variation is omnipresent, this is important both for vector control and for ecological risk assessment. The higher toxicity of the mixture at high DTV compared to the typically used constant test temperatures in the laboratory urges caution when evaluating the environmental impact of pesticide mixtures.

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.

Phytotoxicity of individual and binary mixtures of rare earth elements (Y, La, and Ce) in relation to bioavailability

Rare earth elements (REEs) are typically present as mixtures in the environment, but a quantitative understanding of mixture toxicity and interactions of REEs is still lacking. Here, we examined the toxicity to wheat (Triticum aestivum L.) of Y, La, and Ce when applied individually and in combination. Both concentration addition (CA) and independent action (IA) reference models were used for mixture toxicity analysis because the toxicity mechanisms of REEs remain obscure. Upon single exposure, the EC50s of Y, La, and Ce, expressed as dissolved concentrations, were 1.73 ± 0.24 μM, 2.59 ± 0.23 μM, and 1.50 ± 0.22 μM, respectively. The toxicity measured with relative root elongation followed La < Y ≈ Ce, irrespective of the dose descriptors. The use of CA and IA provided similar estimates of REE mixture interactions and toxicity. When expressed as dissolved metal concentrations, nearly additive effects were observed in Y-La and La-Ce mixtures, while antagonistic interactions were seen in Y-Ce mixtures. When expressed as free metal activities, antagonistic interactions were found for all three binary mixtures. This can be explained by a competitive effect of REEs ions for binding to the active sites of plant roots. The application of a more elaborate MIXTOX model in conjunction with the free ion activities, which incorporates the non-additive interactions and bioavailability-modifying factors, well predicted the mixture toxicity (with >92% of toxicity variations explained). Our results highlighted the importance of considering mixture interactions and subsequent bioavailability in assessing the joint toxicity of REEs.

Predicting herbicide mixture effects on multiple algal species using mixture toxicity models

The validity of the application of mixture toxicity models, concentration addition and independent action, to a species sensitivity distribution (SSD) for calculation of a multisubstance potentially affected fraction was examined in laboratory experiments. Toxicity assays of herbicide mixtures using 5 species of periphytic algae were conducted. Two mixture experiments were designed: a mixture of 5 herbicides with similar modes of action and a mixture of 5 herbicides with dissimilar modes of action, corresponding to the assumptions of the concentration addition and independent action models, respectively. Experimentally obtained mixture effects on 5 algal species were converted to the fraction of affected (>50% effect on growth rate) species. The predictive ability of the concentration addition and independent action models with direct application to SSD depended on the mode of action of chemicals. That is, prediction was better for the concentration addition model than the independent action model for the mixture of herbicides with similar modes of action. In contrast, prediction was better for the independent action model than the concentration addition model for the mixture of herbicides with dissimilar modes of action. Thus, the concentration addition and independent action models could be applied to SSD in the same manner as for a single-species effect. The present study to validate the application of the concentration addition and independent action models to SSD supports the usefulness of the multisubstance potentially affected fraction as the index of ecological risk. Environ Toxicol Chem 2017;36:2624-2630. © 2017 SETAC.

Development of electrostatic-based bioavailability models for interpreting and predicting differential phytotoxicity and uptake of metal mixtures across different soils

Metals are ubiquitous and normally co-occur as mixtures in soil, but there remains much to do regarding the development of appropriate models which incorporate mixture interactions and bioavailability to estimate their phytotoxicity and phytoaccumulation. Here, we developed a probability-based electrostatic toxicity model (ETM) and a Langmuir-type electrostatic uptake model (EUM) to predict and normalize toxicity and uptake of zinc-copper mixtures in Hordeum vulgare L. in different soils. For model development, the electrical potential (ψ₀) and metal ion activities ({M²⁺}₀) at the cell-membrane surface was computed based on plant physiological properties and soil solution chemistry. Single metal toxicity correlated more closely to their corresponding {M²⁺}₀ than to ion activities in soil solution or total soil metal concentrations. The ETM explained up to 89% of the variance in mixture toxicity across different soils. Incorporation of ψ₀ into the EUM improved the model's ability for predicting metal uptake. Besides, cell-surface H⁺ appeared to significantly inhibit copper uptake via competition or other mechanisms, beyond its effect upon ψ₀. Our results for the first time demonstrate that electrostatic theory can be used to predict and reconcile mixture toxicity and uptake data in different soils, indicating the potential of electrostatic-based models in risk assessment of multimetal-contaminated soils.

An Overview of Literature Topics Related to Current Concepts, Methods, Tools, and Applications for Cumulative Risk Assessment (2007-2016)

Cumulative risk assessments (CRAs) address combined risks from exposures to multiple chemical and nonchemical stressors and may focus on vulnerable communities or populations. Significant contributions have been made to the development of concepts, methods, and applications for CRA over the past decade. Work in both human health and ecological cumulative risk has advanced in two different contexts. The first context is the effects of chemical mixtures that share common modes of action, or that cause common adverse outcomes. In this context two primary models are used for predicting mixture effects, dose addition or response addition. The second context is evaluating the combined effects of chemical and nonchemical (e.g., radiation, biological, nutritional, economic, psychological, habitat alteration, land-use change, global climate change, and natural disasters) stressors. CRA can be adapted to address risk in many contexts, and this adaptability is reflected in the range in disciplinary perspectives in the published literature. This article presents the results of a literature search and discusses a range of selected work with the intention to give a broad overview of relevant topics and provide a starting point for researchers interested in CRA applications.

Criteria for deviation from predictions by the concentration addition model

Loewe's additivity (concentration addition) is a well-known model for predicting the toxic effects of chemical mixtures under the additivity assumption of toxicity. However, from the perspective of chemical risk assessment and/or management, it is important to identify chemicals whose toxicities are additive when present concurrently, that is, it should be established whether there are chemical mixtures to which the concentration addition predictive model can be applied. The objective of the present study was to develop criteria for judging test results that deviated from the predictions by the concentration addition chemical mixture model. These criteria were based on the confidence interval of the concentration addition model's prediction and on estimation of errors of the predicted concentration-effect curves by toxicity tests after exposure to single chemicals. A log-logit model with 2 parameters was assumed for the concentration-effect curve of each individual chemical. These parameters were determined by the maximum-likelihood method, and the criteria were defined using the variances and the covariance of the parameters. In addition, the criteria were applied to a toxicity test of a binary mixture of p-n-nonylphenol and p-n-octylphenol using the Japanese killifish, medaka (Oryzias latipes). Consequently, the concentration addition model using confidence interval was capable of predicting the test results at any level, and no reason for rejecting the concentration addition was found. Environ Toxicol Chem 2016;35:1806-1814. © 2015 SETAC.