Toxicity Of Chemical Mixtures Research Articles

A Machine-Learning-Driven Pathophysiology-Based New Approach Method for the Dose-Dependent Assessment of Hazardous Chemical Mixtures and Experimental Validations.

Environmental chemicals, such as PFAS, exist as mixtures and are frequently encountered at varying concentrations, which can lead to serious health effects, such as cancer. Therefore, understanding the dose-dependent toxicity of chemical mixtures is essential for health risk assessment. However, comprehensive methods to assess toxicity and identify the mechanisms of these harmful mixtures are currently absent. In this study, the dose-dependent toxicity assessments of chemical mixtures are performed in three methodologically distinct phases. In the first phase, we evaluated our machine-learning method (AI-HNN) and pathophysiology method (CPTM) for predicting toxicity. In the second phase, we integrated AI-HNN and CPTM to establish a comprehensive new approach method (NAM) framework called AI-CPTM that is targeted at refining prediction accuracy and providing a comprehensive understanding of toxicity mechanisms. The third phase involved experimental validations of the AI-CPTM predictions. Initially, we developed binary, multiclass classification, and regression models to predict binary, categorical toxicity, and toxic potencies using nearly a thousand experimental mixtures. This empirical dataset was expanded with assumption-based virtual mixtures, compensating for the lack of experimental data and broadening the scope of the dataset. For comparison, we also developed machine-learning models based on RF, Bagging, AdaBoost, SVR, GB, KR, DT, KN, and Consensus methods. The AI-HNN achieved overall accuracies of over 80%, with the AUC exceeding 90%. In the final phase, we demonstrated the superior performance and predictive capability of AI-CPTM, including for PFAS mixtures and their interaction effects, through rigorous literature and statistical validations, along with experimental dose-response zebrafish-embryo toxicity assays. Overall, the AI-CPTM approach significantly improves upon the limitations of standalone AI models, showing extensive enhancements in identifying toxic chemicals and mixtures and their mechanisms. This study is the first to develop a hybrid NAM that integrates AI with a pathophysiology method to comprehensively predict chemical-mixture toxicity, carcinogenicity, and mechanisms.

Two tropical oligochaetes as potential standard test species: a case study evaluating single and combined copper and manganese toxicity

ABSTRACT The lack of toxicity tests using indigenous species in risk assessments of tropical regions has often been discussed. Thus, this study evaluated two tropical oligochaete species (Allonais inaequalis and Dero furcatus) for potential use as test species. To this end, the toxic effects of copper and manganese, both isolated and mixed, were evaluated for both species via acute tests. The test protocol developed by the Organisation for Economic Co-operation and Development (OECD) for temperate sediment-dwelling benthic oligochaetes could be successfully applied for both test species. The 96-h LC50 values (mean ± standard deviation) for D. furcatus were 31 ± 3 µg L−1 and 18 ± 0.061 mg L−1 for copper and manganese, respectively. The corresponding values for A. inaequalis were 26 ± 1 µg L−1 and 28 ± 0.063 mg L−1, respectively. Compared to the toxicity values for temperate arthropods, D. furcatus and A. inaequalis had comparable or greater sensitivity to the evaluated metals. The mixture toxicity tests indicated a dose ratio-dependent deviation of the concentration addition (CA) model, with antagonism occurring especially if the effects in the mixture were provoked by copper. Overall, these findings indicated that these taxa are sensitive candidates for use in tropical ecotoxicity testing. Future studies can elucidate the mechanisms of chemical mixture toxicity.

Macroinvertebrate communities respond strongly but non-specifically to a toxicity gradient derived by effect-based methods

Chemical pollution is one of the most important threats to freshwater ecosystems. The plethora of potentially occurring chemicals and their effects in complex mixtures challenge standard monitoring methods. Effect-based methods (EBMs) are proposed as complementary tools for the assessment of chemical pollution and toxic effects. To investigate the effects of chemical pollution, the ecological relevance of EBMs and the potential of macroinvertebrates as toxicity-specific bioindicators, ecological and ecotoxicological data were linked. Baseline toxicity, mutagenicity, dioxin-like and estrogenic activity of water and sediment samples from 30 river sites in central Germany were quantified with four in vitro bioassays. The responses of macroinvertebrate communities at these sites were assessed by calculating 16 taxonomic and functional metrics and by investigating changes in the taxonomic and trait composition. Principal component analysis revealed an increase in toxicity along a joint gradient of chemicals with different modes of action. This toxicity gradient was associated with a decrease in biodiversity and ecological quality, as well as significant changes in taxonomic and functional composition. The strength of the effects suggested a strong impact of chemical pollution and underlined the suitability of EBMs in detecting ecological relevant effects. However, the metrics, taxa, and traits associated with vulnerability or tolerance to toxicity were found to also respond to other stressors in previous studies and thus may have only a low potential as toxicity-specific bioindicators. Because macroinvertebrates respond integratively to all present stressors, linking both ecological and environmental monitoring is necessary to investigate the overall effects but also isolate individual stressors. EBMs have a high potential to separate the toxicity of chemical mixtures from other stressors in a multiple stressor scenario, as well as identifying the presence of chemical groups with specific modes of action.

Predicting Dose-Dependent Carcinogenicity of Chemical Mixtures Using a Novel Hybrid Neural Network Framework and Mathematical Approach.

This study addresses the challenge of assessing the carcinogenic potential of hazardous chemical mixtures, such as per- and polyfluorinated substances (PFASs), which are known to contribute significantly to cancer development. Here, we propose a novel framework called HNNMixCancer that utilizes a hybrid neural network (HNN) integrated into a machine-learning framework. This framework incorporates a mathematical model to simulate chemical mixtures, enabling the creation of classification models for binary (carcinogenic or noncarcinogenic) and multiclass classification (categorical carcinogenicity) and regression (carcinogenic potency). Through extensive experimentation, we demonstrate that our HNN model outperforms other methodologies, including random forest, bootstrap aggregating, adaptive boosting, support vector regressor, gradient boosting, kernel ridge, decision tree with AdaBoost, and KNeighbors, achieving a superior accuracy of 92.7% in binary classification. To address the limited availability of experimental data and enrich the training data, we generate an assumption-based virtual library of chemical mixtures using a known carcinogenic and noncarcinogenic single chemical for all the classification models. Remarkably, in this case, all methods achieve accuracies exceeding 98% for binary classification. In external validation tests, our HNN method achieves the highest accuracy of 80.5%. Furthermore, in multiclass classification, the HNN demonstrates an overall accuracy of 96.3%, outperforming RF, Bagging, and AdaBoost, which achieved 91.4%, 91.7%, and 80.2%, respectively. In regression models, HNN, RF, SVR, GB, KR, DT with AdaBoost, and KN achieved average R2 values of 0.96, 0.90, 0.77, 0.94, 0.96, 0.96, and 0.97, respectively, showcasing their effectiveness in predicting the concentration at which a chemical mixture becomes carcinogenic. Our method exhibits exceptional predictive power in prioritizing carcinogenic chemical mixtures, even when relying on assumption-based mixtures. This capability is particularly valuable for toxicology studies that lack experimental data on the carcinogenicity and toxicity of chemical mixtures. To our knowledge, this study introduces the first method for predicting the carcinogenic potential of chemical mixtures. The HNNMixCancer framework offers a novel alternative for dose-dependent carcinogen prediction. Ongoing efforts involve implementing the HNN method to predict mixture toxicity and expanding the application of HNNMixCancer to include multiple mixtures such as PFAS mixtures and co-occurring chemicals.

Combined toxicity of acetamiprid and cadmium to larval zebrafish (Danio rerio) based on metabolomic analysis

Emerging contaminants, such as neonicotinoid pesticide acetamiprid (Ace), are frequently detected in the water environment, which can interact with existing heavy metal cadmium (Cd) to produce unpredicted influence. Limited studies have evaluated the effects of multiple pollutant exposures on aquatic animals. Here, we characterized the joint toxicity of Ace and Cd exposure to zebrafish (Danio rerio). The results revealed that Cd and its combined exposure with Ace had an inhibitory effect on the growth of larval zebrafish and induced morphological defects. Combined exposure to high doses of Ace and Cd could significantly reduce the levels of TG, glucose, and pyruvate in larval zebrafish. Untargeted metabolomics revealed that Cd treatment (285) produced more differentially expressed metabolites (DEMs) than Ace treatment (115), and combined treatment produced the most DEMs (294). The KEGG pathway enrichment analysis showed that they could disrupt riboflavin metabolism, amino acid metabolism, and glycolipid metabolism in the larvae of D. rerio. ELISA showed that VB2, FMN, and FAD levels were significantly increased. In addition, gene expression analysis exhibited that the mRNA levels of essential genes related to glycolipid metabolism were substantially affected, such as PK, PEPckc, PPAR-α, and FABP6. Furthermore, targeted amino acid metabolomics confirmed that both single exposure to Cd and combined exposure to Ace and Cd altered the levels of amino acids in larvae, including ALA, ARG, MET, PRO, TYR, VAL, GLY, ORN, and PHE. Taken together, exposure to Ace and Cd, alone or in combination, exerted harmful effects on the individual development, riboflavin metabolism, glycolipid metabolism, and amino acid metabolism disorder of D. rerio. These findings highlighted that more attention should be paid to the compound toxicity of chemical mixtures to aquatic organisms.

Contrasting toxicity of polystyrene nanoplastics to the rotifer Brachionus koreanus in the presence of zinc oxide nanoparticles and zinc ions

Emerging contaminants such as nanoplastics and nanoparticles likely experience similar environmental behaviours, fate and effects but our knowledge of their combined toxicity is scanty. This study, therefore, investigated the joint toxicity of polystyrene nanoplastics (PNPs) and zinc oxide nanoparticles (ZnO-NPs) to an ecologically important rotifer Brachionus koreanus, and compared with the joint toxicity of PNPs and Zn ions (Zn-IONs from ZnSO4·7H2O). With increasing concentration, ZnO-NPs formed significant agglomeration with PNPs for up to 1.3 times of the original hydrodynamic size of ZnO-NPs, alongside doubling in their sedimentation and thereby losing 58% of their released Zn ions. In contrast, the availability of Zn-IONs was less affected by the agglomeration and sedimentation of PNPs, with only a loss of 18% of Zn ions at the highest concentration of PNPs. Consequently, as suggested by Concentration Addition and Independent Action models and the Model Deviation Ratios, ZnO-NPs and PNPs exerted an antagonistic interaction whereas Zn-IONs and PNPs exhibited an additive effect. We also advocate the use of the Nonparametric Response Surface method, which is more useful to predict the toxicity of chemical mixtures with interacting effects. Our findings suggested a potential difference between particle-particle and particle-ion interactions, especially at higher test concentrations, which may eventually affect their toxicity. We, therefore, call for a more systematic evaluation of commonly coexisting chemical mixtures which consist of nanoplastics and manufactured nanomaterials.

How to analyse and account for interactions in mixture toxicity with toxicokinetic-toxicodynamic models

The assessment of chemical mixture toxicity is one of the major challenges in ecotoxicology. Chemicals can interact, leading to more or less effects than expected, commonly named synergism and antagonism respectively. The classic ad hoc approach for the assessment of mixture effects is based on dose-response curves at a single time point, and is limited to identifying a mixture interaction but cannot provide predictions for untested exposure durations, nor for scenarios where exposure varies in time. We here propose a new approach using toxicokinetic-toxicodynamic modelling: The General Unified Threshold model of Survival (GUTS) framework, recently extended for mixture toxicity assessment. We designed a dedicated mechanistic interaction module coupled with the GUTS mixture model to i) identify interactions, ii) test hypotheses to identify which chemical is likely responsible for the interaction, and finally iii) simulate and predict the effect of synergistic and antagonistic mixtures. We tested the modelling approach experimentally with two species (Enchytraeus crypticus and Mamestra brassicae) exposed to different potentially synergistic mixtures (composed of: prochloraz, imidacloprid, cypermethrin, azoxystrobin, chlorothalonil, and chlorpyrifos). Furthermore, we also tested the model with previously published experimental data on two other species (Bombus terrestris and Daphnia magna) exposed to pesticide mixtures (clothianidin, propiconazole, dimethoate, imidacloprid and thiacloprid) found to be synergistic or antagonistic with the classic approach. The results showed an accurate simulation of synergistic and antagonistic effects for the different tested species and mixtures. This modelling approach can identify interactions accounting for the entire time of exposure, and not only at one time point as in the classic approach, and provides predictions of the mixture effect for untested mixture exposure scenarios, including those with time-variable mixture composition.

Application of cross-validation strategies to avoid overestimation of performance of 2D-QSAR models for the prediction of aquatic toxicity of chemical mixtures

ABSTRACT The quantitative structure–activity relationship (QSAR) modelling of mixtures is not as simple as that for individual chemicals, and it needs additional care to avoid overestimation of the performance. In this research, we have developed a 2D-QSAR model using only 2D interpretable and reproducible descriptors to predict the aquatic toxicity of mixtures of polar and non-polar narcotic substances present in the environment. Partial least squares (PLS) regression has been used to model the response variable (log 1/EC50 against Photobacterium phosphoreum) and the structural features of 84 binary mixtures of polar and nonpolar narcotic toxicants complying with the Organization of Economic Co-operation and Development (OECD) protocols. The model was cross-validated by mixtures-out and compounds-out cross-validation to nullify the developmental bias. The reliability of prediction of the model has been judged by the Prediction Reliability Indicator (PRI) tool using a newly designed set. The new model is robust, reproducible, extremely predictive, easily interpretable, and can be used for reliable prediction of aquatic toxicity of any untested chemical mixtures within the applicability domain. We have additionally used a machine learning-based chemical read-across algorithm in this study to improve the quality of predictions for the toxicity of the mixtures with the modelled descriptors.

Exposure duration and composition are important variables to predict short-term toxicity of effluents to a tropical copepod, Acartia sinjiensis

Predicting the toxicity of effluent exposures, which vary in duration, composition, and concentration, poses a challenge for ecological risk assessments. Effluent discharges may frequently result in the exposure of aquatic organisms to high concentrations of mixed contaminants for short durations. In the receiving environment effluents will undergo dilution and physical or chemical processes that further reduce contaminant concentrations at varying rates. To date, most studies comparing toxicity risks of continuous and pulsed contaminant exposures have focused on individual contaminants. In this study, the toxicity to the tropical euryhaline copepod Acartia sinjiensis of two complex effluents was assessed, comparing 6- and 18-h pulses and 78-h continuous exposures. Observations of larval development success and population size were completed after a 78-h incubation period, to observe for latent effects after pulse exposures. The chemical compositions of the effluents were assessed over time and different contaminants (i.e., metals, ammonia or organics) declined at differing rates. These were characterized as either a minimal, steady, or rapid decline. Nauplii development and population after 78 h were more impacted by effluent exposures following an 18-h pulse, compared to a 6-h pulse. Based on pulse-exposure concentrations, the 50% effect concentrations (EC50) were similar for continuous and 18-h exposures but up to 3-fold greater (lower toxicity) for the shorter 6-h exposures. Time-weighted average concentrations did not accurately predict toxicity from pulse exposures of the effluents. Concentration-addition toxicity modelling using toxicity data from pulse exposures of single contaminants was useful for predicting the toxicity of chemical mixtures exposed for varying durations. Recommendations for modified approaches to assessing risks of short-term effluent discharges are discussed.

High throughput screening of bisphenols and their mixtures under conditions of low-intensity adipogenesis of human mesenchymal stem cells (hMSCs)

In vitro models of adipogenesis are phenotypic assays that most closely mimic the increase of adipose tissue in obesity. Current models, however, often lack throughput and sensitivity and even report conflicting data regarding adipogenic potencies of many chemicals. Here, we describe a ten-day long adipogenesis model using high content analysis readouts for adipocyte number, size, and lipid content on primary human mesenchymal stem cells (MSC) sensitive enough to compare bisphenol A derivatives quantitatively in a robust and high throughput manner. The number of adipocytes was the most sensitive endpoint capable of detecting changes of 20% and was used to develop a benchmark concentration model (BMC) to quantitatively compare eight bisphenols (tested at 0.1–100 μM). The model was applied to evaluate mixtures of bisphenols obtaining the first experimental evidence of their additive effect on human MSC adipogenesis. Using the relative potency factors (RPFs), we show how a mixture of bisphenols at their sub-active concentrations induces a significant adipogenic effect due to its additive nature. The final active concentrations of bisphenols in tested mixtures reached below 1 μM, which is within the concentration range observed in humans. These results point to the need to consider the toxicity of chemical mixtures.

Predicting cytotoxicity of binary pollutants towards a human cell panel in environmental water by experimentation and deep learning methods

Biological assays are useful in water quality evaluation by providing the overall toxicity of chemical mixtures in environmental waters. However, it is impossible to elucidate the source of toxicity and some lethal combination of pollutants simply using biological assays. As facile and cost-effective methods, computation model-based toxicity assessments are complementary technologies. Herein, we predicted the human health risk of binary pollutant mixtures (i.e., binary combinations of As(III), Cd(II), Cr(VI), Pb(II) and F(I)) in water using in vitro biological assays and deep learning methods. By employing a human cell panel containing human stomach, colon, liver, and kidney cell lines, we assessed the human health risk mimicking cellular responses after oral exposures of environmental water containing pollutants. Based on the experimental cytotoxicity data in pure water, multi-task deep learning was applied to predict cellular response of binary pollutant mixtures in environmental water. Using additive descriptors and single pollutant toxicity data in pure water, the established deep learning model could predict the toxicity of most binary mixtures in environmental water, with coefficient of determination (R2) > 0.65 and root mean squared error (RMSE) < 0.22. Further combining the experimental data on synergistic and antagonistic effects of pollutant mixtures, deep learning helped improve the predictive ability of the model (R2 > 0.74 and RMSE <0.17). Moreover, predictive models allowed us identify a number of toxicity source-related physiochemical properties. This study illustrates the combination of experimental findings and deep learning methods in the water quality evaluation.

Status Quo in Data Availability and Predictive Models of Nano-Mixture Toxicity.

Co-exposure of nanomaterials and chemicals can cause mixture toxicity effects to living organisms. Predictive models might help to reduce the intensive laboratory experiments required for determining the toxicity of the mixtures. Previously, concentration addition (CA), independent action (IA), and quantitative structure–activity relationship (QSAR)-based models were successfully applied to mixtures of organic chemicals. However, there were few studies concerning predictive models for toxicity of nano-mixtures before June 2020. Previous reviews provided comprehensive knowledge of computational models and mechanisms for chemical mixture toxicity. There is a gap in the reviewing of datasets and predictive models, which might cause obstacles in the toxicity assessment of nano-mixtures by using in silico approach. In this review, we collected 183 studies of nano-mixture toxicity and curated data to investigate the current data and model availability and gap and to derive research challenges to facilitate further experimental studies for data gap filling and the development of predictive models.

Modeling the toxicity of pollutants mixtures for risk assessment: a review

The occurrence of contaminants in natural waters is a potential threat to the environment. Since contaminants are commonly present as mixtures, numerous interactions may occur, resulting in lower or, more dangerously, higher toxicity, by comparison with single substances. The toxicity of multicomponent systems can be determined experimentally, but toxicity prediction by suitable models is faster, environmentally friendly and less expensive. Here we review approaches and models, which can be utilized in assessing toxicity of chemical mixtures. In the first part, the assessment of toxicity of chemical mixtures and possible interactions between mixture constituents are discussed. The second part covers conventional modeling, including the simplest, and most common toxicity models, namely concentration addition and independent action models, and derived integrated models. The third part presents advanced toxicity modeling. We review the quantitative structure–activity relationship (QSAR) approach and its elements: calculation of molecular descriptors and their selection with principal component analysis and genetic algorithm. Modeling with artificial neural networks is also discussed. We present hybrid models which combine the fuzzy set theory approach with the conventional concentration addition and independent action models. We conclude that conventional models: concentration addition and independent action model, are still most commonly used; integrated models are more accurate compared to conventional ones, even though their application requires more data; advanced numerical methods such as genetic algorithm, neural networks, and fuzzy set theory give a new perspective on toxicity prediction, and no universal tool for toxicity assessment has been developed so far.

Exposure Duration and Composition are Important Variables to Predict Short-Term Toxicity of Effluents to a Tropical Copepod, Acartia Sinjiensis

Predicting the toxicity of effluent exposures, which vary in duration, composition, and concentration, poses a challenge for ecological risk assessments. Effluent discharges may frequently result in the exposure of aquatic organisms to high concentrations of mixed contaminants for short durations. In the receiving environment effluents will undergo dilution and physical or chemical processes that further reduce contaminant concentrations at varying rates. To date, most studies comparing toxicity risks of continuous and pulsed contaminant exposures have focused on individual contaminants. In this study, the toxicity to the tropical euryhaline copepod Acartia sinjiensis from 6- and 18-h pulse and 78-h continuous exposures to two complex effluents was assessed. Observations of larval development success and population size were completed after a 78-h incubation period, to observe for latent effects after pulse exposures. The chemical compositions of the effluents were assessed over time and contaminant-specific declines were observed. These were characterised as either a minimal, steady, or rapid decline. Nauplii tolerated greater effluent concentrations for 6-h compared to 18-h pulses. Based on pulse-exposure concentrations, the 50% effect concentrations (EC50) were similar for continuous and 18-h exposures but up to 3-fold greater (lower toxicity) for the shorter 6-h exposures. Time-weighted average concentrations did not predict toxicity from pulse exposures of the effluents. Concentration-addition toxicity modelling using toxicity data from pulse exposures of single contaminants was useful for predicting the toxicity of chemical mixtures exposed for varying durations. Recommendations for modified approaches to assessing risks of short-term effluent discharges are discussed.

Prediction of aquatic toxicity of chemical mixtures by the QSAR approach using 2D structural descriptors

The rapid industrialization has led to the generation of various organic chemicals and multi-component mixtures which affect the environment adversely. Although organic chemicals are often exposed to the environment as a form of chemical mixtures rather than individual compounds, there is insufficient toxicity data available for the chemical mixtures due to the associated complexities. Most importantly, the nature of toxicity of mixtures is completely different from the individual chemicals, which makes the evaluation more difficult and challenging. In this paper, we have developed QSAR models for various individual and mixture data sets for the prediction of the aquatic toxicity. We have used Partial Least Squares (PLS) regression as a statistical tool to build the models. The various structural features of the individual chemicals and the mixture components have been modeled against the toxicity end point pEC50 (negative logarithm of median effective concentration in molar scale) of the aquatic organisms Photobacterium phosphoreum (marine bacterium) and Selenastrum capricornutum (freshwater algae). The mixture descriptors have been calculated by the weighted descriptor generation approach. The models were developed in accordance with OECD guidelines, and the quality of each model has been adjudged by strict validation parameters. The final models are robust, extremely predictive and interpretable mechanistically which can be used for the prediction of toxicity of untested chemical mixtures under the domain of applicability of the developed models.

Development of predicitve models to distinguish metals from non-metal toxicants, and individual metal from one another

BackgroundEvaluating the toxicity of chemical mixture and their possible mechanism of action is still a challenge for humans and other organisms. Microarray classifier analysis has shown promise in the toxicogenomic area by identifying biomarkers to predict unknown samples. Our study focuses on identifying gene markers with better sensitivity and specificity, building predictive models to distinguish metals from non-metal toxicants, and individual metal from one another, and furthermore helping understand underlying toxic mechanisms.ResultsBased on an independent dataset test, using only 15 gene markers, we were able to distinguish metals from non-metal toxicants with 100% accuracy. Of these, 6 and 9 genes were commonly down- and up-regulated respectively by most of the metals. 8 out of 15 genes belong to membrane protein coding genes. Function well annotated genes in the list include ADORA2B, ARNT, S100G, and DIO3. Also, a 10-gene marker list was identified that can discriminate an individual metal from one another with 100% accuracy. We could find a specific gene marker for each metal in the 10-gene marker list. Function well annotated genes in this list include GSTM2, HSD11B, AREG, and C8B.ConclusionsOur findings suggest that using a microarray classifier analysis, not only can we create diagnostic classifiers for predicting an exact metal contaminant from a large scale of contaminant pool with high prediction accuracy, but we can also identify valuable biomarkers to help understand the common and underlying toxic mechanisms induced by metals.

An improved overall risk probability-based method for assessing the combined health risks of chemical mixtures: An example about mixture of aflatoxin B1 and microcystin LR by dietary intake

Previous studies on the risk assessment of chemicals with respect to human health have focused mainly on the safety of individual substances. Recently, public health policy emphasizes the combined effects of mixtures. An overall risk probability (ORP)-based method long with the combined toxicity factor (cuv) can be used to evaluate the combined toxicity of chemical mixtures from the environment and foods on human health. However, the procedure for calculating the cuv accurately and quantitatively in the ORP method is yet unclear. In this study, an improved ORP-based method (IORP) was developed by introducing a variable time t, and the cuv was analyzed quantitatively using simultaneous equations and based on the principle of least squares regression. This phenomenon can be explained based on the example of the mixture of aflatoxin B1 (AFB1) and microcystin LR (MC-LR) by dietary intake in order to understand the application of this method. The IORP approach makes it possible for estimating the combined effects of mixtures for human health by dietary pathway.

Mathematical Modeling Approaches for Assessing the Joint Toxicity of Chemical Mixtures Based on Luminescent Bacteria: A Systematic Review.

Developments in industrial applications inevitably accelerate the discharge of enormous substances into the environment, whereas multi-component mixtures commonly cause joint toxicity which is distinct from the simple sum of independent effect. Thus, ecotoxicological assessment, by luminescent bioassays has recently brought increasing attention to overcome the environmental risks. Based on the above viewpoint, this review included a brief introduction to the occurrence and characteristics of toxic bioassay based on the luminescent bacteria. In order to assess the environmental risk of mixtures, a series of models for the prediction of the joint effect of multi-component mixtures have been summarized and discussed in-depth. Among them, Quantitative Structure-Activity Relationship (QSAR) method which was widely applied in silico has been described in detail. Furthermore, the reported potential mechanisms of joint toxicity on the luminescent bacteria were also overviewed, including the Trojan-horse type mechanism, funnel hypothesis, and fishing hypothesis. The future perspectives toward the development and application of toxicity assessment based on luminescent bacteria were proposed.

Genetically modified Caenorhabditis elegans may lead to inaccurate toxicity evaluation of mixtures

BackgroundOne of the major challenges in environmental science is how to assess the toxicity and risk of complex pollutant mixtures. However, only a few studies have pointed out that there is a significant difference between the toxicities of chemicals on genetically modified strains and wild-type organisms and there are few reports of the differences in the toxicity of chemical mixtures. Therefore, six chemicals, two substituted phenols (4-chlorophenol and 4-nitrophenol), two pesticides (dichlorvos and glyphosate) and two ionic liquids (1-butylpyridinium chloride and 1-butylpyridinium bromide), were selected to construct a six-component mixture system, the lethality of various mixtures on the genetically modified Caenorhabditis elegans strain mtl-2::GFP (MTL-2) at 12 and 24 h were determined, and differences in toxicity to other strains were compared.ResultsAlthough the toxicity of 4-chlorophenol on MTL-2 was not significantly different from that on N2 wild-type Caenorhabditis elegans (N2), the toxicities of the other five chemicals on MTL-2 were greater than those on N2. The toxicities of six single chemicals and nine mixture rays on MTL-2 increased with time, which is consistent with the effect on N2 and on the genetically modified strain sod-3::GFP Caenorhabditis elegans (SOD-3). The toxicological interactions of various mixtures in MTL-2 at 12 h were half antagonistic (ANT) and half additive (ADD), while at 24 h, they were mainly synergistic (SYN). The toxicological interactions of various mixtures in MTL-2 change from ANT/ADD to primarily SYN with time, which is different from the change from ANT to ADD in SOD-3 and from SYN to ADD in N2.ConclusionsThe toxicity and toxicity interactions of chemical mixtures on different Caenorhabditis elegans strains are different. Therefore, it is necessary to examine the effect of genetic factors on the toxicological interaction of mixtures to avoid underestimating or overestimating the mixture risk.

Exploring Mechanistic Toxicity of Mixtures Using PBPK Modeling and Computational Systems Biology.

Mixtures risk assessment needs an efficient integration of in vivo, in vitro, and in silico data with epidemiology and human studies data. This involves several approaches, some in current use and others under development. This work extends the Agency for Toxic Substances and Disease Registry physiologically based pharmacokinetic (PBPK) toolkit, available for risk assessors, to include a mixture PBPK model of benzene, toluene, ethylbenzene, and xylenes. The recoded model was evaluated and applied to exposure scenarios to evaluate the validity of dose additivity for mixtures. In the second part of this work, we studied toluene, ethylbenzene, and xylene (TEX)-gene-disease associations using Comparative Toxicogenomics Database, pathway analysis and published microarray data from human gene expression changes in blood samples after short- and long-term exposures. Collectively, this information was used to establish hypotheses on potential linkages between TEX exposures and human health. The results show that 236 genes expressed were common between the short- and long-term exposures. These genes could be central for the interconnecting biological pathways potentially stimulated by TEX exposure, likely related to respiratory and neuro diseases. Using publicly available data we propose a conceptual framework to study pathway perturbations leading to toxicity of chemical mixtures. This proposed methodology lends mechanistic insights of the toxicity of mixtures and when experimentally validated will allow data gaps filling for mixtures' toxicity assessment. This work proposes an approach using current knowledge, available multiple stream data and applying computational methods to advance mixtures risk assessment.