Mixture Toxicity Models Research Articles

Modeling the Toxicokinetics of Suspensions of Soluble Metallic Nanomaterials.

Proper risk assessment of the many new nanoforms (NFs) that are currently being developed and marketed is hindered by constraints in time and resources for testing their fate and (eco) toxicity profile. This problem has also been encountered in conventional chemical risk assessments, where the definition of related chemical groups can facilitate risk assessment for all class members. Whereas grouping and read-across methods are well established, such approaches are in the early stages of development for NFs. In this study, a modeling framework was developed for grouping NFs into distinct classes regarding the contribution of released ions to suspension-induced toxicity. The framework is based on combining dissolution rate constants of NFs with information about the toxicokinetics of the NFs and the dissolution products formed. The framework is exemplified for the specific case of suspension toxicity of metallic NFs (silver and copper). To this end, principles of mixture toxicity and dose-response modeling are integrated to derive threshold values for the key NF properties determining suspension toxicity: size, shape, and chemical composition. The threshold values thus derived offer a possible solution for the high-throughput screening of NFs according to their morphological and compositional properties in a regulatory context.

Mixtures of organic micropollutants exacerbated in vitro neurotoxicity of prymnesins and contributed to aquatic toxicity during a toxic algal bloom

Prymnesins produced by an algal bloom of Prymnesium parvum led to the death of several hundred tons of freshwater fish in the Oder River in summer 2022. We investigated effects on aquatic life and human cell lines from exposure to extracts of contaminated water collected during the fish kill. We detected B-type prymnesins and >120 organic micropollutants. The micropollutants occurred at concentrations that would cause the predicted mixture risk quotient for aquatic life to exceed the acceptable threshold. Extracts of water and filters (biomass and particulates) induced moderate effects in vivo in algae, daphnids and zebrafish embryos but caused high effects in a human neuronal cell line indicating the presence of neurotoxicants. Mixture toxicity modelling demonstrated that the in vitro neurotoxic effects were mainly caused by the detected B-type prymnesins with minor contributions by organic micropollutants. Complex interactions between natural and anthropogenic toxicants may underestimate threats to aquatic ecosystems.

Equations for estimating binary mixture toxicity: 3-methyl-2-butanone with a series of electrophiles.

Mixture toxicity was determined for 32 binary combinations. One chemical was the non-reactive, non-polar narcotic 3-methyl-2-butanone (always chemical A) and the other was a potentially reactive electrophile (chemical B). Bioluminescence inhibition in Allovibrio fischeri was measured at 15-, 30-, and 45-minutes of exposure for A, B, and the mixture (MX). Concentration-response curves (CRCs) were developed for each chemical and used to develop predicted CRCs for the concentration addition (CA) and independent action (IA) mixture toxicity models. Also, MX CRCs were generated and compared with model predictions using the 45-minute data. Classification of observed mixture toxicity used three specific criteria: 1) predicted IA EC50 vs. CA EC50 values at 45-minutes, 2) consistency of 45-minute MX CRC fit to IA, CA, or otherwise at three effect levels (EC25, EC50 and EC75), and 3) the known/suspected mechanism of toxicity for chemical B. Mixture toxicity was then classified into one of seven groupings. As a result of the predicted IA EC50 being more toxic than the predicted CA EC50, IA represented the greater toxic hazard. For this reason, non-sham MXs having toxicity consistent with CA were classified as being "coincident" with CA rather than mechanistically-consistent with CA. Multiple linear regression analyses were performed to develop equations that can be used to estimate the toxicity of other 3M2B-containing binary mixtures. These equations were developed from the data for both IA and CA, at each exposure duration and effect level. Each equation had a coefficient of determination (r2) above 0.950 and a variance inflation factor <1.2. This approach can potentially reduce the need for mixture testing and is amenable to other model systems and to assays that evaluate toxicity at low effect levels.

Predictive binary mixture toxicity modeling of fluoroquinolones (FQs) and the projection of toxicity of hypothetical binary FQ mixtures: a combination of 2D-QSAR and machine-learning approaches.

All sorts of chemicals get degraded under various environmental stresses, and the degradates coexist with the parent compounds as mixtures in the environment. Antibiotics emerge as an additional concern due to the bioactive nature of both the parent compound and degradation products and their combined exposure to the environment. Therefore, environmental risk assessment of antibiotics and their degradation products is very much necessary. In this direction, we made use of in silico new approach methodologies (NAMs) and machine-learning algorithms. In this study, we have developed a robust and predictive mixture-quantitative structure-activity relationship (QSAR) model with promising quality and predictability (internal: MAETrain = 0.085, QLOO2 = 0.849, external: MAETest = 0.090, and QF12 = 0.859) for predicting the toxicity of the mixtures of a class of antibiotics and their degradation products. To obtain the predictive model, toxicity data of 78 binary fluoroquinolone mixtures in E. coli (endpoint: log 1/IC50 in molar) have been utilized. We have used only 0D-2D descriptors to efficiently encode the structural features of mixture components without any additional complexities. The optimization of the class of mixture descriptors has been performed in this study by using three different mixing rules (linear combination of molecular contributions, the squared molecular contributions, and the norm of molecular contributions). Different machine-learning approaches namely, random forest (RF), ada boost, gradient boost (GB), extreme gradient boost (XGB), support vector machine (SVM), linear support vector machine (LSVM), and ridge regression (RR) have been employed here apart from the conventional partial least squares (PLS) regression to optimize the modeling approach. A rigorous validation protocol has been used for assessing the goodness-of-fit, robustness, and external predictability of the models. Finally, the toxicity of possible untested mixtures of different photodegradation products of fluoroquinolones has been predicted using the best model reported in this study.

“Data fusion” quantitative read-across structure-activity-activity relationships (q-RASAARs) for the prediction of toxicities of binary and ternary antibiotic mixtures toward three bacterial species

Antibiotics are often found in the environment as pollutants. They are usually found as mixtures in the environment and may produce toxicity against different ecological species due to joint exposure in the sub-optimal range. Sometimes the degradation products of parent chemicals also interact with it and cause mixture toxicity. In this study, we have developed three different mixture-Quantitative Structure-Activity Relationship (mixture-QSAR) models for three different bacterial species (Vibrio fischeri, Escherichia coli, and Bacillus subtilis). The toxicity data were collected from a previous experimental report in the literature, which comprised binary and ternary mixtures of sulfonamides (SAs), sulfonamide potentiators (SAPs), and tetracyclines (TCs). We have also explored the interspecies modeling to find inter-correlation among the toxicity of these studied organisms and have developed quantitative structure activity-activity relationship (QSAAR) models by employing the “data fusion” quantitative read-across structure-activity–activity relationship (q-RASAAR) and partial least squares (PLS) regression algorithms. All the models are strictly validated using both internal and external validation tests as suggested in the OECD guidelines. Three different mixing rules have been used in this study for descriptor computations to incorporate the additive and interaction effects among the mixture components. To the best of our knowledge, this is the first report of interspecies mixture toxicity models which can predict the cellular toxicity of binary and ternary mixtures against any of the three above-mentioned organisms.

Prioritization of mixtures of neurotoxic chemicals for biomonitoring using high-throughput toxicokinetics and mixture toxicity modeling

Modern society continues to pollute the environment with larger quantities of chemicals that have also become more structurally and functionally diverse. Risk assessment of chemicals can hardly keep up with the sheer numbers that lead to complex mixtures of increasing chemical diversity including new chemicals, substitution products on top of still abundant legacy compounds. Fortunately, over the last years computational tools have helped us to identify and prioritize chemicals of concern. These include toxicokinetic models to predict exposure to chemicals as well as new approach methodologies such as in-vitro bioassays to address toxicodynamic effects. Combined, they allow for a prediction of mixtures and their respective effects and help overcome the lack of data we face for many chemicals. In this study we propose a high-throughput approach using experimental and predicted exposure, toxicokinetic and toxicodynamic data to simulate mixtures, to which a virtual population is exposed to and predict their mixture effects. The general workflow is adaptable for any type of toxicity, but we demonstrated its applicability with a case study on neurotoxicity. If no experimental data for neurotoxicity were available, we used baseline toxicity predictions as a surrogate. Baseline toxicity is the minimal toxicity any chemical has and might underestimate the true contribution to the mixture effect but many neurotoxicants are not by orders of magnitude more potent than baseline toxicity. Therefore, including baseline-toxic effects in mixture simulations yields a more realistic picture than excluding them in mixture simulations. This workflow did not only correctly identify and prioritize known chemicals of concern like benzothiazoles, organochlorine pesticides and plasticizers but we were also able to identify new potential neurotoxicants that we recommend to include in future biomonitoring studies and if found in humans, to also include in neurotoxicity screening.

The Next Frontier of Environmental Unknowns: Substances of Unknown or Variable Composition, Complex Reaction Products, or Biological Materials (UVCBs)

Substances of unknown or variable composition, complex reaction products, or biological materials (UVCBs) are over 70 000 “complex” chemical mixtures produced and used at significant levels worldwide. Due to their unknown or variable composition, applying chemical assessments originally developed for individual compounds to UVCBs is challenging, which impedes sound management of these substances. Across the analytical sciences, toxicology, cheminformatics, and regulatory practice, new approaches addressing specific aspects of UVCB assessment are being developed, albeit in a fragmented manner. This review attempts to convey the “big picture” of the state of the art in dealing with UVCBs by holistically examining UVCB characterization and chemical identity representation, as well as hazard, exposure, and risk assessment. Overall, information gaps on chemical identities underpin the fundamental challenges concerning UVCBs, and better reporting and substance characterization efforts are needed to support subsequent chemical assessments. To this end, an information level scheme for improved UVCB data collection and management within databases is proposed. The development of UVCB testing shows early progress, in line with three main methods: whole substance, known constituents, and fraction profiling. For toxicity assessment, one option is a whole-mixture testing approach. If the identities of (many) constituents are known, grouping, read across, and mixture toxicity modeling represent complementary approaches to overcome data gaps in toxicity assessment. This review highlights continued needs for concerted efforts from all stakeholders to ensure proper assessment and sound management of UVCBs.

Synergistic toxicity to the toxigenic Microcystis and enhanced microcystin release exposed to polycyclic aromatic hydrocarbon mixtures

With the continued influx and accumulation of polycyclic aromatic hydrocarbons (PAHs) in eutrophic waters, the effects of PAHs on cyanobacteria bloom need to be clarified. PAHs usually existed as mixtures in aquatic environments, but the combined toxicity of PAH mixtures to toxigenic cyanobacteria remained unknown. This study investigated the effects of phenanthrene (Phe) and benzo [a]pyrene (BaP), alone or in combination, on the growth and physiology of Microcystis aeruginosa. The results showed that a hormesis effect on growth at low doses of the single Phe (≤1 mg/L) or PAH mixtures (≤0.279 mg/L) was observed, whereas the single BaP induced significant growth inhibitions at all concentrations (≥0.2 mg/L). The median effective concentrations (96 h) for Phe, BaP and their mixtures were 4.29, 1.29 and 1.07 mg/L, respectively. Mixture toxicity models showed that Phe and BaP elicited a synergistic interaction on M. aeruginosa. The synergy may be ascribed to the excessive oxidative stress induced by PAH mixtures, which further led to membrane structure damages, photosynthesis inhibitions and decreased metabolic activity. Moreover, the microcystins (MCs) release significantly increased by 25.3% and 31.9% upon exposure to 0.558 and 1.116 mg/L of PAH mixtures. In all, this study suggested that the enhanced release of MCs by PAH mixtures might exacerbate potential risks to the aquatic environment.

Evaluation of estrogenic and antiestrogenic activity in sludge and explanation of individual compound contributions

Mixture toxicity, including agonistic and antagonistic effects, is an unrevealed environmental problem. Estrogenic endocrine disruptors are known to cause adverse effects for aquatic biota, but causative chemicals and their contributions to the total activity in sewage sludge remain unknown. Therefore, advanced analytical methods, a yeast bioassay and mixture toxicity models were concurrently applied for the characterization of 8 selected sludges with delectable estrogenic activity (and 3 sludges with no activity as blanks) out of 25 samples from wastewater treatment plants (WWTPs). The first applied full logistic model adequately explained total activity by considering the concentrations of the monitored compounds. The results showed that the activity was primarily caused by natural estrogens in municipal WWTP sludge. Nevertheless, activity in a sample originating from a car-wash facility was dominantly caused by partial agonists – nonylphenols – and only a model enabling prediction of all dose-response curve parameters of the final mixture curve explained these results. Antiestrogenic effects were negligible, and effect-directed analysis identified the causative chemicals.

Assessing the Toxicity of Mine-Water Mixtures and the Effectiveness of Water Quality Guideline Values in Protecting Local Aquatic Species.

Six tropical freshwater species were used to assess the toxicity of mine waters from a uranium mine adjacent to a World Heritage area in northern Australia. Key contaminants of potential concern for the mine were U, Mg, Mn, and total ammonia nitrogen (TAN). Direct toxicity assessments were carried out to assess whether the established site-specific guideline values for individual contaminants would be protective with the contaminants occurring as mixtures. Metal speciation was calculated for contaminants to determine which were the major contributors of toxicity, with 84 to 96% of Mg predicted in the free-ion form as Mg2+ , and 76 to 92% of Mn predicted as Mn2+ . Uranium, Al, and Cu were predicted to be strongly bound to fulvic acid. Uranium, Mg, Mn, and Cu were incorporated into concentration addition or independent action mixture toxicity models to compare the observed toxicity in each of the waters with predicted toxicity. For >90% of the data, mine-water toxicity was less than predicted by the concentration addition model. Instances where toxicity was greater than predicted were accompanied by exceedances of individual metal guideline values in all but one case (i.e., a Mg concentration within 10% of the guideline value). This indicates that existing individual water quality guideline values for U, Mg, Mn, and TAN would adequately protect ecosystems downstream of the mine. Environ Toxicol Chem 2021;40:2334-2346. © 2021 Commonwealth of Australia. Environmental Toxicology and Chemistry © 2021 SETAC.

Bioavailable Environmental Pollutant Patterns in Sediments from Passive Equilibrium Sampling.

Sediment-associated risks depend on the bioavailable fraction of organic chemicals and cannot be comprehended by their total concentrations. The present study investigated contamination patterns of bioavailable chemicals in sediments from various sites around the globe by using passive equilibrium sampling. The extracts had been characterized previously for mixture effects by in vitro reporter gene assays and were in this study analyzed using gas chromatography-high resolution mass spectrometry for 121 chemicals including both legacy and emerging contaminants. The spatial distribution of the detected chemicals revealed distinct contamination patterns among sampling sites. We identified compounds in common at the different sites but most contaminant mixtures were site-specific. The mixture effects of the detected chemicals were predicted with a mixture toxicity model from effect concentrations of bioactive single chemicals and detected concentrations, applying a joint model for concentration addition and independent action. The predicted mixture effects were dominated by polycyclic aromatic hydrocarbons, and among the chemicals with available effect data, 17% elicited oxidative stress response and 18% activated the arylhydrocarbon receptor. Except for two sites in Sweden, where 11 and 38% of the observed oxidative stress response were explained by the detected chemicals, less than 10% of effects in both biological end points were explained. These results provide a comprehensive investigation of bioavailable contamination patterns of sediments and may serve as an example of employing passive equilibrium sampling as a monitoring technique to integrate the risk of bioavailable sediment-associated chemicals in aquatic environments.

Assessment of mixture toxicity of (tri)azoles and their hepatotoxic effects in vitro by means of omics technologies

Consumers are constantly exposed to chemical mixtures such as multiple residues of different pesticides via the diet. This raises questions concerning potential combination effects, especially because these substances are tested for regulatory purposes on an individual basis. With approximately 500 active substances approved as pesticides, there are too many possible combinations to be tested in standard animal experiments generally requested for regulatory purposes. Therefore, the development of in vitro tools and alternative testing strategies for the assessment of mixture effects is extremely important. As a first step in the development of such in vitro tools, we used (tri)azoles as model substances in a set of different cell lines derived from the primary target organ of these substances, the liver (human: HepaRG, rat: H4IIE). Concentrations were reconciled with measured tissue concentrations obtained from in vivo experiments to ensure comparable effect levels. The effects of the substances were subsequently analyzed by transcriptomics and metabolomics techniques and compared to data from corresponding in vivo studies. The results show that similar toxicity pathways are affected by substances and combinations, thus indicating a similar mode of action and additive effects. Two biomarkers obtained by the approach, CAR and Cyp1A1, were used for mixture toxicity modeling and confirmed the concentration-additive effects, thus supporting the selected testing strategy and raising hope for the development of in vitro methods suitable to detect combination effects and prioritize mixtures of concern for further testing.

Unraveling the ecotoxicity of deep eutectic solvents using the mixture toxicity theory

The interest on deep eutectic solvents (DES) has been increasing. However, the ecotoxicological profile of DES is scarcely known. Also, despite previous studies showed that DES components dissociate in water, none assessed DES toxicity using the classical and adequate models for mixture toxicity prediction - concentration addition (CA) and independent action (IA). This study evaluates the ecotoxicological profile of DES based on [N1111]Cl, [N2222]Cl and [N3333]Cl as hydrogen bond acceptors (HBA) combined with hydrogen-bond donors (HBD) vis. ethylene glycol and 1-propanol, through the Microtox® Acute Toxicity Test. CA and IA with deviations describing synergism/antagonism, dose-ratio and dose-level effects were fitted to the toxicity data. Neither the starting materials nor DES were found hazardous to Aliivibrio fischeri, in this specific case agreeing with the claimed “green character” of DES. Among the starting materials, ethylene glycol was the least toxic, whereas [N3333]Cl was the most toxic (30 min-EC50 = 96.49 g L−1 and 0.5456 g L−1, respectively). DES toxicity followed the same trend as observed for the salts: [N1111]Cl-based DES < [N2222]Cl-based DES < [N3333]Cl-based DES. The IA model, with specific deviations, adjusted better in 5 out of 6 DES. Antagonism was observed for [N1111]Cl-based DES, and synergism for [N3333]Cl-based DES and for 1-propanol:[N2222]Cl. The application of the mixture toxicity models represents a breakthrough in the problematic of assessing the toxicity of the countless number of DES that can be created with the same starting materials, since they provide the expected toxicity of any virtual combination between HBA and HBD.

Potential Toxicity of Dissolved Metal Mixtures (Cd, Cu, Pb, Zn) to Early Life Stage White Sturgeon ( Acipenser transmontanus) in the Upper Columbia River, Washington, United States.

The Upper Columbia River (UCR) received historical releases of smelter waste resulting in elevated metal concentrations in downstream sediments. Newly hatched white sturgeon hide within the rocky substrate at the sediment-water interface in the UCR for a few weeks before swim-up. Hiding behavior could expose them to metal contaminants, and metal toxicity could contribute to population declines in white sturgeon over the past 50 years. This study evaluates whether there is a link between the toxicity of dissolved metals across the sediment-water interface in the UCR and the survival of early life stage (ELS) white sturgeon. Toxicity of dissolved metal mixtures is evaluated using a combination of previously collected laboratory and field data and recently developed metal mixture toxicity models. The laboratory data consist of individual metal (Cd, Cu, Pb, and Zn) toxicity studies with ELS white sturgeon. The field data include the chemical composition of surface and pore water samples that were collected across the sediment-water interface in the UCR. These data are used in three metal accumulation and two response models. All models predict low toxicity in surface water, whereas effects concentrations greater than 20% are predicted for 60-72% of shallow pore water samples. The flux of dissolved metals, particularly Cu, from shallow pore water to surface water likely exposes prime ELS sturgeon habitat to toxic conditions.

The advantages of linear concentration-response curves for in vitro bioassays with environmental samples.

In vitro assays and high-throughput screening (HTS) tools are increasingly being employed as replacements for animal testing, but most concentration-response curves are still evaluated with models developed for animal testing. We argue that application of in vitro assays, particularly reporter gene assays, to environmental samples can benefit from a different approach to concentration-response modeling. First, cytotoxicity often occurs at higher concentrations, especially for weakly acting compounds and in complex environmental mixtures with many components. In these cases, specific effects can be masked by cytotoxicity. Second, for many HTS assays, low effect levels can be precisely quantified because of the low variability of controls in cell-based assays and the opportunity to run many concentrations and replicates when using high-density well-plate formats (e.g., 384 or more wells per plate). Hence, we recommend focusing concentration-response modeling on the lower portion of the concentration-response curve, which is approximately linear. Effect concentrations derived from low-effect level linear concentration-response models facilitate simple derivation of relative effect potencies and the correct application of mixture toxicity models in the calculation of bioanalytical equivalent concentrations. Environ Toxicol Chem 2018;37:2273-2280. © 2018 SETAC.

Deciphering the Combined Effects of Environmental Stressors on Gene Transcription: A Conceptual Approach.

The use of classical mixture toxicity models to predict the combined effects of environmental stressors based on toxicogenomics (OMICS) data is still in its infancy. Although several studies have made attempts to implement mixture modeling in OMICS analysis to understand the low-dose interactions of stressors, it is not clear how interactions occur at the molecular level and how results generated from such approaches can be better used to inform future studies and cumulative hazard assessment of multiple stressors. The present work was therefore conducted to propose a conceptual approach for combined effect assessment using global gene expression data, as illustrated by a case study on assessment of combined effects of gamma radiation and depleted uranium (DU) on Atlantic salmon ( Salmo salar). Implementation of the independent action (IA) model in reanalysis of a previously published microarray gene expression dataset was performed to describe gene expression patterns of combined effects and identify key gene sets and pathways that were relevant for understanding the interactive effects of these stressors. By using this approach, 3120 differentially expressed genes (DEGs) were found to display additive effects, whereas 279 (273 synergistic, 6 antagonistic) were found to deviate from additivity. Functional analysis further revealed that multiple toxicity pathways, such as oxidative stress responses, cell cycle regulation, lipid metabolism, and immune responses were enriched by DEGs showing synergistic gene expression. A key toxicity pathway of DNA damage leading to enhanced tumorigenesis signaling is highlighted and discussed in detail as an example of how to take advantage of the approach. Furthermore, a conceptual workflow describing the integration of combined effect modeling, OMICS analysis, and bioinformatics is proposed. The present study presents a conceptual framework for utilizing OMICS data in combined effect assessment and may provide novel strategies for dealing with data analysis and interpretation of molecular responses of multiple stressors.

Development of a bioanalytical test battery for water quality monitoring: Fingerprinting identified micropollutants and their contribution to effects in surface water

Surface waters can contain a diverse range of organic pollutants, including pesticides, pharmaceuticals and industrial compounds. While bioassays have been used for water quality monitoring, there is limited knowledge regarding the effects of individual micropollutants and their relationship to the overall mixture effect in water samples. In this study, a battery of in vitro bioassays based on human and fish cell lines and whole organism assays using bacteria, algae, daphnids and fish embryos was assembled for use in water quality monitoring. The selection of bioassays was guided by the principles of adverse outcome pathways in order to cover relevant steps in toxicity pathways known to be triggered by environmental water samples. The effects of 34 water pollutants, which were selected based on hazard quotients, available environmental quality standards and mode of action information, were fingerprinted in the bioassay test battery. There was a relatively good agreement between the experimental results and available literature effect data. The majority of the chemicals were active in the assays indicative of apical effects, while fewer chemicals had a response in the specific reporter gene assays, but these effects were typically triggered at lower concentrations. The single chemical effect data were used to improve published mixture toxicity modeling of water samples from the Danube River. While there was a slight increase in the fraction of the bioanalytical equivalents explained for the Danube River samples, for some endpoints less than 1% of the observed effect could be explained by the studied chemicals. The new mixture models essentially confirmed previous findings from many studies monitoring water quality using both chemical analysis and bioanalytical tools. In short, our results indicate that many more chemicals contribute to the biological effect than those that are typically quantified by chemical monitoring programs or those regulated by environmental quality standards. This study not only demonstrates the utility of fingerprinting single chemicals for an improved understanding of the biological effect of pollutants, but also highlights the need to apply bioassays for water quality monitoring in order to prevent underestimation of the overall biological effect.

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.

Applying mixture toxicity modelling to predict bacterial bioluminescence inhibition by non-specifically acting pharmaceuticals and specifically acting antibiotics

Pharmaceuticals and antibiotics co-occur in the aquatic environment but mixture studies to date have mainly focused on pharmaceuticals alone or antibiotics alone, although differences in mode of action may lead to different effects in mixtures. In this study we used the Bacterial Luminescence Toxicity Screen (BLT-Screen) after acute (0.5 h) and chronic (16 h) exposure to evaluate how non-specifically acting pharmaceuticals and specifically acting antibiotics act together in mixtures. Three models were applied to predict mixture toxicity including concentration addition, independent action and the two-step prediction (TSP) model, which groups similarly acting chemicals together using concentration addition, followed by independent action to combine the two groups. All non-antibiotic pharmaceuticals had similar EC50 values at both 0.5 and 16 h, indicating together with a QSAR (Quantitative Structure-Activity Relationship) analysis that they act as baseline toxicants. In contrast, the antibiotics’ EC50 values decreased by up to three orders of magnitude after 16 h, which can be explained by their specific effect on bacteria. Equipotent mixtures of non-antibiotic pharmaceuticals only, antibiotics only and both non-antibiotic pharmaceuticals and antibiotics were prepared based on the single chemical results. The mixture toxicity models were all in close agreement with the experimental results, with predicted EC50 values within a factor of two of the experimental results. This suggests that concentration addition can be applied to bacterial assays to model the mixture effects of environmental samples containing both specifically and non-specifically acting chemicals.

Combined toxic effect of airborne heavy metals on human lung cell line A549.

Many studies have demonstrated that heavy metals existing as a mixture in the atmospheric environment cause adverse effects on human health and are important key factors of cytotoxicity; however, little investigation has been conducted on a toxicological study of a metal mixture from atmospheric fine particulate matter. The objective of this study was to predict the combined effects of heavy metals in aerosol by using in vitro human cells and obtain a suitable mixture toxicity model. Arsenic, nickel, and lead were selected for mixtures exposed to A549 human lung cancer cells. Cell proliferation (WST-1), glutathione (GSH), and interleukin (IL)-8 inhibition were observed and applied to the prediction models of mixture toxicity, concentration addition (CA) and independent action (IA). The total mixture concentrations were set by an IC10-fixed ratio of individual toxicity to be more realistic for mortality and enzyme inhibition tests. The results showed that the IA model was statistically closer to the observed results than the CA model in mortality, indicating dissimilar modes of action. For the GSH inhibition, the results predicted by the IA and CA models were highly overestimated relative to mortality. Meanwhile, the IL-8 results were stable with no significant change in immune reaction related to inflammation. In conclusion, the IA model is a rapid prediction model in heavy metals mixtures; mortality, as a total outcome of cell response, is a good tool for demonstrating the combined toxicity rather than other biochemical responses.