Characteristics of concentration-inhibition curves of individual chemicals and applicability of the concentration addition model for mixture toxicity prediction

An artificial intelligence-based model for predicting reproductive toxicity of bisphenol analogues mixtures to the rotifer Brachionus calyciflorus

The joint toxicity effects of mixtures, particularly reproductive toxicity, one of the main causes of aquatic ecosystem degradation, are often overlooked as it is impractical to test all mixtures. This study developed and evaluated the following models to predict the concentration response curve concerning the joint reproductive toxicity of mixtures of three bisphenol analogues (BPA, BPF, BPAF) on the rotifer Brachionus calyciflorus: concentration addition (CA), independent action (IA), and two deep neural network (DNN) models. One applied mixture molecular descriptors as input variables (DNN-QSAR), while the other applied the ratios of chemicals in the mixtures (DNN-Ratio). Descriptors related to molecular mass were found to be of greater importance and exhibited a proportional relationship with toxic effects. The results indicate that the range of correlation coefficients (R2) between predicted and measured values for various mixture rays by CA and IA models is 0.372 to 0.974 and - 0.970 to 0.586, respectively. The R2 values for DNN-Ratio and DNN-QSAR were 0.841 to 0.984 and 0.834 to 0.991, respectively, demonstrating that models developed by DNN significantly outperform traditional models in predicting the joint toxicity of mixtures. Furthermore, DNN-QSAR not only predicts mixture toxicity but also provides accurate toxicity predictions for BPA, BPF, and BPAF, with R2 values of 0.990, 0.616, and 0.887, respectively, while DNN-Ratio yields values of 0.920, 0.355, and - 0.495. The study also found that the joint effects of mixtures are primarily influenced by the total concentration of the mixtures, and an increase in total concentration shifts the joint effects towards addition. This study introduces a novel approach to predict joint toxicity and analyze the influencing factors of joint effects, providing a more comprehensive assessment of the ecological risk posed by mixtures.

Long-term toxicity assessment of antibiotics against Vibrio fischeri: Test method optimization and mixture toxicity prediction

The current luminescent bacteria test for acute toxicity with short contact time was invalid for antibiotics, and the non-uniformed contact times reported in the literature for long-term toxicity assessment led to incomparable results. Herein, a representative long-term toxicity assessment method was established which unified the contact time of antibiotics and Vibrio fischeri within the bioluminescence increasing period (i.e. 10-100% maximum luminescence) of control samples. The effects of excitation and detoxification of antibiotics such as β-lactams were discovered. Half maximal inhibitory concentration (IC50) of toxic antibiotics (0.00069-0.061 mmol/L) obtained by this method was 2-3 orders of magnitude lower than acute test, quantifying the underestimated toxicity. As antibiotics exist in natural water as mixtures, an equivalent concentration addition (ECA) model was built to predict mixture toxicity based on physical mechanism rather than mathematical method, which showed great fitting results (R2 = 0.94). Furthermore, interaction among antibiotics was investigated. Antibiotics acting during bacterial breeding period had strong synergistic inhibition (IC50 relative deviation from 0.1 to 0.6) such as macrolides and quinolones. Some antibiotics produced increasing synergistic inhibition during concentration accumulation, such as macrolides. The discharge of antibiotics with severe long-term toxicity and strong synergistic inhibition effect should be seriously restricted.

Prediction of the toxic effects of (agro) chemical mixtures on organisms using simple time-based models

The lethal effect of a chemical acting alone can be predicted using the simple hyperbolic model, which relies on the chemicals' median lethal time (LT₅₀). However, this model cannot be used to predict mixture toxicity, considering that toxicity in natural ecosystems often results from exposure to mixtures rather than single chemicals. The lethal time addition method was developed to calculate the LT₅₀ of a pesticide mixture from the LT₅₀ of its components. It enables the hyperbolic model to estimate the lethal effects of a mix of pesticides at various exposure times. The hyperbolic model, complemented by the lethal-time addition model, predicted the percentage mortality of Clarias gariepinus and Oreochromis niloticus exposed to binary and quaternary mixtures of atrazine, mancozeb, chlorpyrifos, and lambda-cyhalothrin and estimated the 96 hr LC₅₀ of the pesticide mixture.

Joint action of binary mixtures based on parameter k·ECx from concentration-response curves in long-term toxicity assay

A previous acute toxicity study of binary mixtures showed that the combined toxicity can be predicted with the parameter k∙EC_x. To systematically investigate the ability of k∙EC_x to predict the chronic combined toxicity of binary mixtures, the toxicity of six contaminants and five binary mixtures was determined by long-term microplate toxicity analysis (L-MTA) using Aliivibrio fischeri as the test organism. The independent action model (IA) and the relative model deviation ratio (rMDR) were employed to determine the relationship between the Δ(k∙EC_x)% and rMDR_x. The results showed that these two factors conformed to the exponential function in long-term toxicity. Owing to the time-dependence of toxicity, the mixture type of chronic toxicity changes to the relative type of acute toxicity. If the acute toxicity of binary mixtures changes their mode of joint action throughout the concentration range, the chronic toxicity will also change their mode of joint action, and vice versa. This study clarified the change rules of the joint action of binary mixtures in acute and chronic toxicity which can promote research on chronic toxicity of binary mixtures.

Predicting joint toxicity of chemicals by incorporating a weighted descriptor into a mixture model: Cases for binary antibiotics and binary nanoparticles

A prediction method that integrated a mixture descriptor with an established mixture toxicology method was proposed for the joint toxicity of chemical pollutants. A weighted descriptor derived from the single descriptor of each component was employed to calculate a mixture descriptor, which was successfully embedded into the generalized concentration addition (GCA) model named the extended GCA (XGCA) model. To develop and validate the proposed approach, binary antibiotic mixtures (ciprofloxacin and oxytetracycline) and metal-oxide (copper oxide and zinc oxide) nanoparticle mixtures were selected to study their toxicity to freshwater green algae. The results showed that concentration-response curve (CRC) derived from the XGCA model was closer to the observed CRC than those from the GCA, Concentration Addition (CA), and Independent Action (IA) models. The difference between effect concentrations predicted by the XGCA model and observed did not exceed a factor of 1.6. The XGCA model was relatively more accurate at predicting joint toxicity (in terms of effect concentrations and effect errors) than the reference models, independent of component types and mixture ratios. The XGCA model predicts the joint toxicity through molecular structural or nanostructural characters, thus modes of toxic action are not preconditions for predicting the toxicity of the mixtures. This result demonstrates the practicability of using the XGCA method in toxicity assessments of mixture pollutants with unknown modes of action.

Study of the joint action of multi-component mixtures based on parameter σ2(k∙ECx) characterizing the shape difference of concentration-response curves

A previous study has revealed that the parameter k∙EC_x, characterizing the shape of concentration-response curves (CRCs), could predict the combined toxicity of binary mixtures. This study further explored the predictability of multi-component mixtures. Eleven component mixtures were designed using the uniform design ray, and the acute toxicity of the eleven environmental pollutants and their mixtures to Vibrio fischeri was determined using microplate toxicity analysis. We used independent action (IA) and the effect residual ratio (ERR_x) models to evaluate the combined toxicity of multi-component mixtures and ascertain the functional relationship between σ²_(k∙ECx), a parameter characterizing the CRC morphological difference of multi-component mixtures, and combined toxicity. The variance σ²_(k∙ECx) of each component characteristic parameter of multi-component mixtures gradually increased in the concentration range, and the relationship between σ²_(k∙ECx) and ERR_x was consistent with the exponential function. The literature verification showed that this rule is generally applicable to the acute toxicity of multi-component mixtures to luminescent bacteria. The exponential function showed the variation rule of the joint action of multi-component mixtures. In the present study, the joint toxicity of multi-component mixtures can be predicted from single toxicity and small amount of multiple toxicity, circumventing complex multi-component toxicity experiments.

Releasing Characteristics and Biological Toxicity of the Heavy Metals from Waste of Mercury-Thalliummine in Southwest Guizhou of China

In this paper, the releasing characteristics and biological toxicity of Tl, Hg, As and Sb in waste of Lanmuchang mercury-thallium mine were studied. The results indicated that strong acidity can significantly promote the release of Tl from waste. With the increase of pH, the release of Sb grew steadily, while Hg and As showed a trend of first increasing and then decreasing. Fe₂(SO₄)₃ contributed less to the release of As and Sb than to that of Hg and Tl. FeCl₃ significantly inhibited the release of As, Sb and Tl. In the leaching experiments of litter and root exudates, the lixiviums appeared neutral, and the litter and root exudates solution significantly reduced the release of Tl, and showed less toxicity to luminescent bacteria. However, they promoted the release of Hg, As and Sb at different levels.

Prediction of the joint action of binary mixtures based on characteristic parameter k∙ECx from concentration-response curves

The evaluation of joint toxicity of mixtures is an important topic in toxicology. Previous studies have found that the parameter k∙EC_x of concentration response curves (CRCs) can be used to assess the applicability of concentration addition model (CA). This study further assesses the predictability of k∙EC_x on the joint toxicity evaluation. The toxicities of the twelve environmental pollutants, as well as those of binary mixtures with an equivalent-effect concentration ratio, to Vibrio fischeri were determined by using the microplate toxicity analysis. The toxicity evaluation of mixtures was conducted by CA and independent action model (IA). The relationship between the joint toxicity (measured by the relative model deviation ratio (rMDR)) and the k∙EC_x was studied. The results shows that the k∙EC_x could reflect the shape of CRCs in the whole concentration range. According to the IA and CA, 65% of the mixtures produce strong antagonistic or synergistic effect due to the significant difference of k∙EC_x. The percentage of the relative difference of k∙EC_x of components and the rMDR_x can be fitted by an exponential function. Different types of interactions could be described using this function. It is suggested that the joint toxicity of binary mixtures can be assessed with the parameter k∙EC_x, which can quickly get very important data when planning experiments, but also reduce the number of experiments.

Cadmium and molybdenum co-induce pyroptosis via ROS/PTEN/PI3K/AKT axis in duck renal tubular epithelial cells

Cadmium (Cd) and excess molybdenum (Mo) are harmful to animals, but the combined nephrotoxic mechanism of Cd and Mo in duck remains poorly elucidated. To assess joint effects of Cd and Mo on pyroptosis via ROS/PTEN/PI3K/AKT axis in duck renal tubular epithelial cells, cells were cultured with 3CdSO₄·8H₂O (4.0 μM), (NH₄)₆Mo₇O₂₄·4H₂O (500.0 μM), MCC950 (10.0 μM), BHA (100.0 μM) and combination of Cd and Mo or Cd, Mo and MCC950 or Cd, Mo and BHA for 12 h, and the joint cytotoxicity was explored. The results manifested that toxicity of non-equitoxic binary mixtures of Mo and Cd exhibited synergic interaction. Mo or/and Cd elevated ROS level, PTEN mRNA and protein levels, and decreased PI3K, AKT and p-AKT expression levels. Simultaneously, Mo or/and Cd upregulated ASC, NLRP3, NEK7, Caspase-1, GSDMA, GSDME, IL-18 and IL-1β mRNA levels and Caspase-1 p20, NLRP3, ASC, GSDMD protein levels, increased the percentage of pyroptotic cells, LDH, NO, IL-18 and IL-1β releases as well as relative conductivity. Moreover, NLRP3 inhibitor MCC950 and ROS scavenger BHA could ameliorate the above changed factors induced by Mo and Cd co-exposure. Collectively, our results reveal that combination of Mo and Cd synergistically cause oxidative stress and trigger pyroptosis via ROS/PTEN/PI3K/AKT axis in duck tubular epithelial cells.

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.

Analysis of individual and combined estrogenic effects of bisphenol, nonylphenol and diethylstilbestrol in immature rats with mathematical models

BACKGROUND

Traditional toxicological studies focus on individual compounds. However, this single-compound approach neglects the fact that the mixture exposed to human may act additively or synergistically to induce greater toxicity than the single compounds exposure due to their similarities in the mode of action and targets. Mixture effects can occur even when all mixture components are present at levels that individually do not produce observable effects. So the individual chemical effect thresholds do not necessarily protect against combination effects, an understanding of the rules governing the interactive effects in mixtures is needed. The aim of the study was to test and analyze the individual and combined estrogenic effects of a mixture of three endocrine disrupting chemicals (EDCs), bisphenol A (BPA), nonylphenol (NP) and diethylstilbestrol (DES) in immature rats with mathematical models.

METHOD

In the present study, the data of individual estrogenic effects of BPA, NP and DES were obtained in uterotrophic bioassay respectively, the reference points for BPA, NP and DES were derived from the dose-response ralationship by using the traditional no observed adverse effect (NOAEL) or lowest observed adverse effect level (LOAEL) methods, and the benchmark dose (BMD) method. Then LOAEL values and the benchmark dose lower confidence limit (BMDL10) of single EDCs as the dose design basis for the study of the combined action pattern. Mixed prediction models, the 3 × 2 factorial design model and the concentration addition (CA) model, were employed to analyze the combined estrogenic effect of the three EDCs.

RESULTS

From the dose-response relationship of estrogenic effects of BPA, NP and DES in the model of the prepuberty rats, the BMDL10(NOAEL) of the estrogenic effects of BPA, NP and DES were 90(120) mg/kg body weight, 6 mg/kg body weight and 0.10(0.25) μg/kg body weight, and the LOAEL of the the estrogenic effects of three EDCs were 240 mg/kg body weight, 15 mg/kg body weight and 0.50 μg/kg body weight, respectively. At BMDL10 doses based on the CA concept and the factorial analysis, the mode of combined effects of the three EDCs were dose addition. Mixtures in LOAEL doses, NP and DES combined effects on rat uterine/body weight ratio indicates antagonistic based on the CA concept but additive based on the factorial analysis. Combined effects of other mixtures are all additive by using the two models.

CONCLUSION

Our results showed that CA model provide more accurate results than the factorial analysis, the mode of combined effects of the three EDCs were dose addition, except mixtures in LOAEL doses, NP and DES combined effects indicates antagonistic effects based on the CA model but additive based on the factorial analysis. In particular, BPA and NP produced combination effects that are larger than the effect of each mixture component applied separately at BMDL doses, which show that additivity is important in the assessment of chemicals with estrogenic effects. The use of BMDL as point of departure in risk assessment may lead to underestimation of risk, and a more balanced approach should be considered in risk assessment.

Two-Stage Prediction on Effects of Mixtures Containing Phenolic Compounds and Heavy Metals on Vibrio qinghaiensis sp. Q67

Two-stage prediction (TSP) model had been developed to predict toxicities of mixtures containing complex components, but its prediction power need to be further validated. Six phenolic compounds and six heavy metals were selected as mixture components. One mixture (M1) was built with equivalent-effect concentration ratio and four mixtures (M2-M5) were designed with fixed concentration ratio. In M1-M5, the toxicities were well predicted by TSP model, while CA overestimated and IA underestimated the toxicities. In M1-M5, compared with the actual mixture EC50 value, the prediction errors of TSP model (13.9%, 17.9%, 19.2%, and 17.3% and 15.8%, respectively) were significantly lower than those in the CA (higher than 30%) and IA models (20.9%, 33.0%, 20.6%, 21.8% and 12.5%, respectively). Thus, the TSP model performed better than the CA and IA model.

Generalized concentration addition approach for predicting mixture toxicity

A new mathematical model for analyzing data and predicting the effect of mixtures of toxic substances is presented as a generalized form of the concentration addition model. The proposed method, the generalized concentration addition (GCA) model, can be applied to mixtures with arbitrary strengths of interactions (synergistic or antagonistic). It requires mixture effect data for least 1 exposure concentration of the mixture in which fractions of all components and concentration-response functions for each component are known. The GCA model evaluates the interaction between components by introducing a novel response function, which is independent of the response functions for each individual components, to describe the effect of addition between different components. The GCA method was applied to published mixture toxicity data, and it was found to fit the mixture effect better than both the concentration addition model and the independent action model, the implication being that the proposed approach is widely applicable. Environ Toxicol Chem 2017;36:265-275. © 2016 SETAC.

Single and mixture effects of aquatic micropollutants studied in precision-cut liver slices of Atlantic cod (Gadus morhua)

The low concentrations of most contaminants in the aquatic environment individually may not affect the normal function of the organisms on their own. However, when combined, complex mixtures may provoke unexpected effects even at low amounts. Selected aquatic micropollutants such as chlorpyrifos, bis-(2-ethylhexyl)-phthalate (DEHP), perfluorooctanoic acid (PFOA) and 17α-ethinylestradiol (EE2) were tested singly and in mixtures at nM to μM concentrations using precision-cut liver slices (PCLS) of Atlantic cod (Gadus morhua). Fish liver is a target organ for contaminants due to its crucial role in detoxification processes. In order to understand the effects on distinct key liver metabolic pathways, transcription levels of various genes were measured, including cyp1a1 and cyp3a, involved in the metabolism of organic compounds, including toxic ones, and the catabolism of bile acids and steroid hormones; cyp7a1, fabp and hmg-CoA, involved in lipid and cholesterol homeostasis; cyp24a1, involved in vitamin D metabolism; and vtg, a key gene in xenoestrogenic response. Only EE2 had significant effects on gene expression in cod liver slices when exposed singly at the concentrations tested. However, when exposed in combinations, effects not detected in single exposure conditions arose, suggesting complex interactions between studied pollutants that could not be predicted from the results of individual exposure scenarios. Thus, the present work highlights the importance of assessing mixtures when describing the toxic effects of micropollutants to fish liver metabolism.