Application of the combination index integrated with confidence intervals to study the toxicological interactions of antibiotics and pesticides in Vibrio qinghaiensis sp.-Q67

Joint action of six-component mixtures based on concentration response curves morphological parameter in acute and long-term toxicity assay

Previous studies found that the multi-component mixtures with hormesis concentration-response curves (CRCs) were divided into three types according to the combined toxicity analysis of the segment-based method and σ2(k∙ECx) (the variance of k∙ECx). In this study, the acute and long-term toxicity of six pollutants and 12 six-component mixtures were assessed using microplate toxicity analyses (MTA). The functional relationship between σ2(k·ECx) and effect ratio (ERx) was determined by means of the independent action (IA) and the ER model to systematically investigate the correlation between mixture types in acute and long-term toxicity. The results indicated that across the entire concentration range, the mixture type of acute toxicity was consistent with short time exposure (0.25 h) measured in the long-term toxicity experiment. In the inhibition effect range, the types of mixtures of acute toxicity remained consistent with the chronic toxicity (exposure for 24 h) in 11 of the 12 mixtures. This study clarified the changes in the joint action of multi-component mixtures on Aliivibrio fischeri in terms of acute and long-term toxicity. The chronic toxicity of the mixtures can be predicted from the acute toxicity results, which provides a theoretical basis for the biological toxicity evaluation of multi-component mixtures.

Prediction of toxicity and identification of key components for complex mixtures containing hormetic components

Mixtures containing hormetic components are likely to induce hormesis. However, due to the presence of stimulatory effects, predicting the toxicity of such mixtures and identifying their key components face challenges. This study investigated the complex relationship between the stimulatory effects of individual components and their mixtures, focusing on predicting mixture toxicity and identifying key components influencing this toxicity. Sixteen chemicals, commonly found in disinfectants and hand sanitizers, were selected to construct a complex mixture system containing hormetic components. Using Vibrio qinghaiensis sp.-Q67 as an indicator organism, the study employed microplate toxicity tests to collect toxicity data for individual chemicals and their mixtures. The independent action (IA) and back-propagation neural network (BPNN) methods were utilized to predict mixture toxicity, while global sensitivity analysis (GSA) identified key components affecting toxicity. Results revealed that six of the sixteen chemicals exhibited time-dependent hormesis. However, when combined into mixtures, the stimulatory effects observed in individual components tended to diminish or disappear, leading to higher overall toxicity, likely due to synergism. Traditional models like the IA significantly underestimated mixture toxicity, whereas the BPNN model demonstrated superior predictive performance. GSA identified five key components, and changes in the levels of some non-toxic components significantly altered the toxicity of the mixtures. Moreover, increasing the levels of certain key components could either increase or decrease the mixture's toxicity, making the strategy of reducing their concentration to control mixture toxicity ineffective. This study revealed the potential of neural networks in predicting the toxicity of mixtures containing hormetic components and the possible characteristics of the effects of key components on mixture toxicity.

Classification and regression machine learning models for predicting the combined toxicity and interactions of antibiotics and fungicides mixtures

Antibiotics and triazole fungicides coexist in varying concentrations in natural aquatic environments, resulting in complex mixtures. These mixtures can potentially affect aquatic ecosystems. Accurately distinguishing synergistic and antagonistic mixtures and predicting mixture toxicity are crucial for effective mixture risk assessment. We tested the toxicities of 75 binary mixtures of antibiotics and fungicides against Auxenochlorella pyrenoidosa. Both regression and classification models for these mixtures were developed using machine learning models: random forest (RF), k-nearest neighbors (KNN), and kernel k-nearest neighbors (KKNN). The KKNN model emerged as the best regression model with high values of determination coefficient (R2 = 0.977), explained variance in prediction leave-one-out (Q2LOO = 0.894), and explained variance in external prediction (Q2F1 = 0.929, Q2F2 = 0.929, and Q2F3 = 0.923). The RF model, the leading classifier, exhibited high accuracy (accuracy = 1 for the training set and 0.905 for the test set) in distinguishing the synergistic and antagonistic mixtures. These results provide crucial value for the risk assessment of mixtures.

Key Component Analysis of the Time Toxicity Interaction of Five Antibiotics to Q67

Antibiotics are considered as persistent emerging contaminants. The phenomenon of mixed exposure to the environment is a common occurrence causing serious harm to human health and the environment. Therefore, we employed enrofloxacin (ENR), chlortetracycline (CTC), methotrexate (TMP), chloramphenicol (CMP), and erythromycin (ETM) in this study. Nine treatments were designed using the uniform design concentration ratio (UDCR) method to systematically determine the toxicity of individual contaminants and their mixtures on Vibrio qinghaiensis sp.-Q67 through the time-dependent microplate toxicity assay. The combinatorial index (CI) method and the dose reduction index (DRI) were used to analyze the toxic interactions of the mixtures and the magnitude of the contribution of each component to the toxic interactions. The results showed that the toxicities of ENR, CTC, TMR, CMP, and ETM and their mixtures were time-dependent, with toxic effects being enhanced except when exposure time was prolonged. The types of toxic interactions in the ENR-CTC-TMR-CMP-ETM mixtures were found to be correlated with the proportion of each component's concentration, where the proportion of the components exerted the most significant influence. Through DRI extrapolation, it was determined that the primary components of the mixture exhibited a pronounced dependency on time. Specifically, at the 4 h mark, TMP emerged as the predominant component, gradually giving way to ENR as time advanced. Upon analyzing the frequency of mixture interactions under specified effects, the additive effect appeared most frequently (66.6%), while the antagonist effect appeared the least frequently (15.9%) among the nine rays.

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.

Combined interaction of fungicides binary mixtures: experimental study and machine learning-driven QSAR modeling

Fungicide mixtures are an effective strategy in delaying the development of fungicide resistance. In this research, a fixed ratio ray design method was used to generate fifty binary mixtures of five fungicides with diverse modes of action. The interaction of these mixtures was then analyzed using CA and IA models. QSAR modeling was conducted to assess their fungicidal activity through multiple linear regression (MLR), support vector machine (SVM), and artificial neural network (ANN). Most mixtures exhibited additive interaction, with the CA model proving more accurate than the IA model in predicting fungicidal activity. The MLR model showed a good linear correlation between selected theoretical descriptors by the genetic algorithm and fungicidal activity. However, both ML-based models demonstrated better predictive performance than the MLR model. The ANN model showed slightly better predictability than the SVM model, with R2 and R2cv at 0.91 and 0.81, respectively. For external validation, the R2test value was 0.845. In contrast, the SVM model had values of 0.91, 0.78, and 0.77 for the same metrics. In conclusion, the proposed ML-based model can be a valuable tool for developing potent fungicidal mixtures to delay fungicidal resistance emergence.

A novel mixture sampling strategy combining latin hypercube sampling with optimized one factor at a time method: A case study on mixtures of antibiotics and pesticides

Global sensitivity analysis in conjunction with quantitative high-throughput screening presents a novel technique for identifying the key components that induce the toxicities of mixtures. However, the mixtures currently designed with this method suffer from unequal frequency sampling, repeated mixtures, and only odd factor levels being considered. Accordingly, we use latin hypercube sampling to generate the starting points of the trajectories to achieve equal frequency sampling and non-repeated mixtures, as well as apply different one factor at a time methods for factors with odd and even levels to achieve suitability for factors with both odd and even levels. This method is called LHS-OAT. LHS-OAT was successfully applied to design 110 equal-frequency and non-repeated mixtures consisting of six antibiotics and four pesticides. It was found that four factors, roxithromycin (A5), tetracycline (A6), dichlorvos (P1), and demeton-S (P3), induce the toxicities of mixtures, and A5 and P1 in the Shaying River Basin have risk quotients ≥ 1. Additionally, we developed the toxicity deviation ratio to correct the risk quotients of interacting mixtures for effective risk assessments. This study provides a rational and effective method for mixture design that accurately identifies the important factors that induce the toxicities of mixtures.

Beneficial or harmful: Time-dependent hormesis induced by typical disinfectants and their mixtures with toxicological interaction

Disinfectants and their mixtures can induce hormesis. However, how the mixture hormesis is related to those of components and the interactions in disinfectant mixtures remain unclear. In this paper, the luminescence inhibition toxicities of chlorinated sodium phosphate (CSP), dodecyl dimethyl benzyl ammonium bromide (DOB), dodecyl dimethyl benzyl ammonium chloride (DOC), ethanol (EtOH), glutaraldehyde (GLA), hydrogen peroxide (H2O2), isopropyl alcohol (IPA), n-propanol (NPA), and 20 mixture rays in four mixture systems (EtOH-H2O2, DOB-H2O2, DOC-EtOH, and EtOH-IPA-NPA) containing at least one component showing hormesis to Vibrio qinghaiensis sp.-Q67 (Q67) were determined at 0.25, 3, 6, 9, and 12 h. The synergism-antagonism heatmap based on independent action model (noted as SAHmapIA) was developed to systematically evaluate the interactions in various mixtures. It was shown that five disinfectants (CSP, EtOH, H2O2, NPA, and IPA) and 17 mixture rays exhibited time-dependent hormesis. The hormetic component was responsible for the hormesis of the mixture rays. Most mixture rays showed low- concentration/dose additive action and high-concentration/dose synergism at different time. This study further exemplified the interrelationship between the hormesis in the mixtures and their components and implied the need to pay attention to the time-dependent hormesis and interactions induced by the disinfectants.

A novel equal frequency sampling of factor levels (EFSFL) method is applied to identify the dominant factor inducing the combined toxicities of 13 factors

The research framework combining global sensitivity analysis (GSA) with quantitative high-throughput screening (qHTS), called GSA-qHTS, provides a potentially feasible way to screen for important factors that induce toxicities of complex mixtures. Despite its value, the mixture samples designed using the GSA-qHTS technique still have a shortage of unequal factor levels, which leads to an asymmetry in the importance of elementary effects (EEs). In this study, we developed a novel method for mixture design that enables equal frequency sampling of factor levels (called EFSFL) by optimizing both the trajectory number and the design and expansion of the starting points for the trajectory. The EFSFL has been successfully employed to design 168 mixtures of 13 factors (12 chemicals and time) that each have three levels. By means of high-throughput microplate toxicity analysis, the toxicity change rules of the mixtures are revealed. Based on EE analysis, the important factors affecting the toxicities of the mixtures are screened. It was found that erythromycin is the dominant factor and time is an important non-chemical factor in mixture toxicities. The mixtures can be classified into types A, B, and C mixtures according to their toxicities at 12 h, and all the types B and C mixtures contain erythromycin at the maximum concentration. The toxicities of the type B mixtures increase firstly over time (0.25 ∼ 9 h) and then decrease (12 h), while those of the type C mixtures consistently increase over time. Some type A mixtures produce stimulation that increases with time. With the present new approach to mixture design, the frequency of factor levels in mixture samples is equal. Consequently, the accuracy of screening important factors is improved based on the EE method, providing a new method for the study of mixture toxicity.

Transfer pattern of hormesis into personal care product mixtures from typical hormesis-inducing compounds

Some personal care products (PCPs) and their chemical components showed a hormetic effect in the freshwater photobacterium Vibrio qinghaiensis sp. -Q67 (Q67) after long-term exposure. However, how hormesis transfers between chemical components and PCP mixture, and which chemical component plays a major role remain unknown. To this end, according to the seven compounds detected in one skin lotion (SK5) and their concentration ratios, many mixture rays were constructed to simulate the SK5. Of these seven compounds, three presented monotonic concentration-response curves (CRC) to Q67 at 0.25 and 12 h (called a S-shaped compound). The other four compounds showed hormetic CRCs after 12 h and monotonic CRCs at 0.25 h (called a J-shaped compound). Based on their mixture ratios, we designed one ternary mixture ray of all S-shaped compounds, one quaternary mixture ray of all J-shaped compounds, and four quaternary mixture rays of one J-shaped and three S-shaped compounds. It was shown that SK5 could be approximately simulated by the mixture ray of the seven compounds detected in SK5 and only the mixture rays containing at least one hormesis-inducing compound produced hormesis to Q67 at 12 h. Based on the concentration ratios of various compounds and comparison of four hormetic characteristic parameters to those of various mixture rays, it was found that the compound betaine (BET) is a key compound affecting the hormesis of mixtures. Additionally, we studied the hormesis mechanism of BET on Q67 via quorum sensing (QS). This preliminarily indicated that the autoinducer-2 triggered the QS pathway. This study elucidated the transfer pattern of hormesis into mixtures, which would be an efficient method to identifying the potential components that affect hormesis transfer in mixtures. We expect that this study will provide new insights into hormesis and its mixtures.

SAHmap: Synergistic-antagonistic heatmap to evaluate the combined synergistic effect of mixtures of three pesticides on multiple endpoints of Caenorhabditis elegans

The environmental pollution caused by toxic chemicals such as pesticides has become a global problem. The mixture of dichlorvos (DIC), dimethoate (DIM), aldicarb (ALD) poses potential risks to the environment and human health. To fully explore the interaction of complex mixtures on Caenorhabditis elegans behavioral toxicity endpoint. This study created a synergistic-antagonistic heatmap (SAHmap) based on the combination index to systematically describe the toxicological interaction prospect of the mixture system. It was shown that the three pesticides and their binary as well as ternary mixture rays have significant concentration-response relationship on three behavioral endpoints of nematodes, From the perspective of synergistic-antagonistic heatmaps, all the mixture rays in the DIC-DIM mixture system showed strong synergism on the three behavioral and lethal endpoints. In the ternary mixture system, the five mixture rays showed different interaction between the behavioral endpoint and the lethal endpoint, and showed slight synergism to two behavioral endpoints as a whole. The emergence of synergism should arouse our attention to these hazardous chemicals. In addition, the use of SAHmap and the significant linear correlation among three behavioral endpoints further improved the efficiency of the study on the behavioral toxicity of pesticide mixtures to Caenorhabditis elegans.

Time-Dependent Toxicity and Health Effects Mechanism of Cadmium to Three Green Algae

As algae are extremely sensitive to heavy-metal ions and can be critical biological indicators in the heavy-metal toxicity analyses conducted by environmental health researchers, this paper explores the sensitivity to temporal toxicity of three species of green algae: Scenedesmus obliquus, Chlorella pyrenoidosa, and Selenastrum capricornutum. The method of time-dependent microplate toxicity analysis was used to systematically investigate the changes in the toxicities of the three green-algae species induced by different concentrations of cadmium (Cd). The chlorophyll a content, antioxidant enzyme activity, and malondialdehyde (MDA) content in the algae were analyzed to explore the mechanism of Cd toxicity after 96 h of exposure. The results showed that the toxic effects of Cd on the three algae species were time-dependent. By comparing the toxic effect of Cd, indicated by pEC₅₀ (the negative logarithm of EC₅₀), on the algae species at four durations of exposure (24, 48, 72, and 96 h), this study found that the indicator organisms had different sensitivities to Cd. The order of sensitivity was C. pyrenoidosa > S. obliquus > S. capricornutum. Cd exposure had significant effects on the chlorophyll a and MDA content and on the enzyme activity of superoxide dismutase (SOD) and catalase (CAT) in the algae species. The chlorophyll a content in the cells of the algae decreased with increasing Cd concentration. The enzyme activity of CAT and content of MDA increased with increasing Cd concentration, which indicated that Cd had an oxidative stress effect on the three algae species.

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.

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.

Combined lethal toxicities of pesticides with similar structures to Caenorhabditis elegans are not necessarily concentration additives

Studies have shown that the mixture toxicity of compounds with similar modes of action (MOAs) is usually predicted by the concentration addition (CA) model. However, due to the lack of toxicological information on compounds, more evidence is needed to determine whether the above conclusion is generally applicable. In general, the same type of compounds with similar chemical structures have similar MOAs, so whether the toxicities of the mixture of these compounds are additive needs to be further studied. In this paper, three types of pesticides with similar chemical structures (three organophosphoruses, two carbamates and two neonicotinoids) that may have similar MOAs were selected and five binary mixture systems were constructed. For each system, five mixture rays with different concentration ratios were designed by the direct equipartition ray design (EquRay) method. The mortality of Caenorhabditis elegans was regarded as the endpoint for the toxicity exposure to single pesticides and binary mixtures. The combined toxicities were evaluated simultaneously using the CA model, isobologram and combination index. The structural similarity of the same type of pesticides was quantitatively analyzed according to the MACCS molecular fingerprint and the slope of dose-response curve at pEC₅₀. The results show that the toxicities of neonicotinoid mixtures and carbamate mixtures are almost antagonistic. The entire mixture system of dichlorvos and dimethoate produced synergism, and four of the five mixture rays of dimethoate and methamidophos induced antagonism, while among the mixture rays of dichlorvos and methamidophos, different concentrations showed different interaction types. The results of structural similarity analysis show that the size of structural similarity showed a certain quantitative relationship with the toxicity interaction of mixtures, that is, the structural similarity of the same type of pesticides may show an additive action in a certain range.

Protein Model and Function Analysis in Quorum-Sensing Pathway of Vibrio qinghaiensis sp.-Q67

Bioluminescent bacteria are mainly found in marine habitats. Vibrio qinghaiensis sp.-Q67 (Q67), a nonpathogenic freshwater bacterium, has been a focus due to its wide use in the monitoring of environmental pollution and the assessment of toxicity. However, the lack of available crystal structures limits the elucidation of the structures of the functional proteins of the quorum-sensing (QS) system that regulates bacterial luminescence in Q67. In this study, 19 functional proteins were built through monomer and oligomer modeling based on their coding proteins in the QS system of Q67 using MODELLER. Except for the failure to construct LuxM due to the lack of a suitable template, 18 functional proteins were successfully constructed. Furthermore, the relationships between the function and predicted structures of 19 functional proteins were explored one by one according to the three functional classifications: autoinducer synthases and receptors, signal transmission proteins (phosphotransferases, an RNA chaperone, and a transcriptional regulator), and enzymes involved in bacterial bioluminescence reactions. This is the first analysis of the whole process of bioluminescence regulation from the perspective of nonpathogenic freshwater bacteria at the molecular level. It provides a theoretical basis for the explanation of applications of Q67 in which luminescent inhibition is used as the endpoint.

Assessing the combined toxicity of carbamate mixtures as well as organophosphorus mixtures to Caenorhabditis elegans using the locomotion behaviors as endpoints

Carbamate pesticides (CMs) and organophosphorus pesticides (OPs) have been widely used in agriculture and toxicologically affect non-target organisms. Although there are many reports about their toxicities, the combined behavioral toxicities of CM/OP mixtures on Caenorhabditis elegans have rarely been studied. In this study, body bend inhibition (BBI), head thrash inhibition (HTI), and swimming speed inhibition (SSI) by CMs and OPs were chosen as the toxicity endpoints. The locomotion behavioral toxicities of individual pesticides (carbofuran (CAR), methomyl (MET), chlorpyrifos (CPF), and triazophos (TAP)) and their binary mixtures on C. elegans were determined systematically and the toxicological interaction profiles of various CM/OP mixture rays constructed using the combination index. It was shown that four pesticides and their binary mixture rays have significant inhibitory effects on the locomotion behavior of C. elegans; that is, they produce locomotion behavioral toxicities and the toxicity of two OPs is higher than those of two CMs. The toxicological interactions in the binary CM and OP mixtures are different from each other. For example, one mixture ray (CAR-MET-R1) in the CM system on the SSI endpoint exhibits synergism at all concentration levels, another ray (CAR-MET-R3) displays low-dose synergism and high-dose additive action on BBI and HTI endpoints, and weak synergism at high-dose on SSI, and other rays perform additive action. Two rays (CPF-TAP-R1 and CPF-TAP-R2) in the OP mixture system display low-dose additive action and high-dose antagonism on the three endpoints. Another ray (CPF-TAP-R3) shows the additive action at all concentration levels. It can be concluded that it is not sufficient to evaluate the combined toxicity of binary CM/OP mixtures using only one concentration ratio ray and that it is necessary to examine multiple concentration ratios.

BNNmix: A new approach for predicting the mixture toxicity of multiple components based on the back-propagation neural network

The chemical mixtures in various environmental media not only have concentration diversity but also mixture-ratio diversity. It is impossible to experimentally determine the toxicities of all mixtures; therefore, it is necessary to develop effective methods based on models to predict mixture toxicity. In this study, a new approach (BNNmix) based on the back-propagation neural network (BPNN) was developed and used to predict the toxicities of seven-component mixtures (consisting of two substituted phenols, two pesticides, two ionic liquids, and one heavy metal) on Caenorhabditis elegans. We found that the combined toxicities of various mixtures used in the experiments were neither global concentration-additive nor global response-additive, which implied that it was impossible to accurately predict the toxicities of such mixtures by using common models such as concentration addition (CA) and response addition (independent action, IA). Using the BNNmix approach to estimate or predict the toxicities of the mixtures under test, it was found that the predictive toxicities of various mixtures with different mixture ratios and concentrations were almost in accordance with those observed experimentally. Unlike the CA and IA models, the BNNmix approach can predict not only the toxicities of mixtures having toxicological interactions but also those with global concentration or response additivities.

Comparative examination on synergistic toxicities of chlorpyrifos, acephate, or tetraconazole mixed with pyrethroid insecticides to honey bees (Apis mellifera L.)

Potential synergistic toxicity of pesticide mixtures has increasingly become a concern to the health of crop pollinators. The toxicities of individual and mixture of chlorpyrifos (CHL), acephate (ACE), or tetraconazole (TET) with nine pyrethroid insecticides to honey bees (Apis mellifera L.) were evaluated to reveal any aggregated interaction between pesticides. Results from feeding toxicity tests of individual pesticides indicated that organophosphate insecticides CHL and ACE had higher toxicities to honey bees compared to nine pyrethroids. Moreover, different pyrethroids exhibited considerable variation in toxicity with LC₅₀ values ranging from 10.05 (8.60-11.69) to 1125 (922.4-1442) mg a.i. L^-1 after exposure for 7 days. Among the 12 examined pesticides, a relatively low toxicity to A. mellifera was detected from the fungicide TET. All the binary mixtures of ACE or TET in combination with pyrethroids exhibited synergistic effects. However, TET in combination with pyrethroids showed greater synergistic toxicity to A. mellifera than ACE in combination with pyrethroids. Approximately 50% binary mixtures of CHL in combination with pyrethroids also showed synergistic responses in honey bees. In particular, CHL, ACE, or TET in combination with either lambda-cyhalothrin (LCY) or bifenthrin (BIF) showed the strongest synergy in A. mellifera, followed by CHL, ACE, or TET in combination with either zeta-cypermethrin (ZCY) or cypermethrin (CYP). The findings indicated that the co-exposure of various pesticides in natural settings might lead to severe injury to crop pollinators. Therefore, pesticide mixtures should be applied carefully in order to minimize negative effects on honey bees while maintaining effective management against crop pests.

New Insight Regarding the Relationship Between Enantioselective Toxicity Difference and Enantiomeric Toxicity Interaction from Chiral Ionic Liquids

Chirality is an important property of molecules. The study of biological activity and toxicity of chiral molecules has important theoretical and practical significance for toxicology, pharmacology, and environmental science. The toxicological significance of chiral ionic liquids (ILs) has not been well revealed. In the present study, the enantiomeric joint toxicities of four pairs of chiral ILs 1-alkyl-3-methylimidazolium lactate to Allivibrio fischeri were systematically investigated by using a comprehensive approach including the co-toxicity coefficient (CTC) integrated with confidence interval (CI) method (CTCICI), concentration-response curve (CRC), and isobole analysis. The direct equipartition ray (EquRay) design was used to design five binary mixtures of enantiomers according to molar ratios of 1:5, 2:4, 3:3, 4:2, and 5:1. The toxicities of chiral ILs and their mixtures were determined using the microplate toxicity analysis (MTA) method. Concentration addition (CA) and independent action (IA) were used as the additive reference models to construct the predicted CRC and isobole of mixtures. On the whole, there was an enantioselective toxicity difference between [BMIM]D-Lac and [BMIM]L-Lac, and [HMIM]D-Lac and [HMIM]L-Lac, while no enantioselective toxicity difference was observed for [EMIM]D-Lac and [EMIM]L-Lac, and [OMIM]D-Lac and [OMIM]L-Lac. Thereinto, the enantiomer mixtures of [BMIM]D-Lac and [BMIM]L-Lac, and [HMIM]D-Lac and [HMIM]L-Lac presented antagonistic action, and the enantiomer mixtures of [EMIM]D-Lac and [EMIM]L-Lac, and [OMIM]D-Lac and [OMIM]L-Lac overall presented additive action. Moreover, the greatest antagonistic toxicity interaction occurred at the equimolar ratio of enantiomers. Based on these results, we proposed two hypotheses, (1) chiral molecules with enantioselective toxicity difference tended to produce toxicity interactions, (2) the highest or lowest toxicity was usually at the equimolar ratio and its adjacent ratio for the enantiomer mixture. These hypotheses will need to be further validated by other enantiomer mixtures.

Integrative Assessment of Mixture Toxicity of Three Ionic Liquids on Acetylcholinesterase Using a Progressive Approach from 1D Point, 2D Curve, to 3D Surface

The joint toxicities of [BMIM]BF₄, [BMIM]PF₆, and [HMIM]BF₄ on acetylcholinesterase (AChE) were systematically investigated by using a progressive approach from 1D single effect point, 2D concentration-response curve (CRC), to 3D equivalent-surface (ES) level. The equipartition equivalent-surface design (EESD) method was used to design 10 ternary mixtures, and the direct equipartition ray (EquRay) design was used to design 15 binary mixtures. The toxicities of ionic liquids (ILs) and their mixtures were determined using the microplate toxicity analysis (MTA) method. The concentration addition (CA), independent action (IA), and co-toxicity coefficient (CTC) were used as the additive reference model to analyze the toxic interaction of these mixtures. The results showed that the Weibull function fitted well the CRCs of the three ILs and their mixtures with the coefficient of determination (R²) greater than 0.99 and root-mean-square error (RMSE) less than 0.04. According to the CTC integrated with confidence interval (CI) method (CTCICI) developed in this study, the 25 mixtures were almost all additive action at 20% and 80% effect point levels. At 50% effect, at least half of the 25 mixtures were slightly synergistic action, and the remaining mixtures were additive action. Furthermore, the ESs and CRCs predicted by CA and IA were all within the CIs of mixture observed ESs and CRCs, respectively. Therefore, the toxic interactions of these 25 mixtures were actually additive action. The joint toxicity of the three ILs can be effectively evaluated by the ES method. We also studied the relationship between the mixture toxicities and component concentration proportions. This study can provide reference data for IL risk assessment of combined pollution.

Using an interpolation-based method (IDVequ) to predict the combined toxicities of hormetic ionic liquids

In the field of computational toxicology, predicting toxicological interaction or hormesis effect of a mixture from individuals is still a challenge. The two most frequently used model concentration addition (CA) and independent action (IA) also cannot solve these challenges perfectly. In this paper, we used IDV_equ (an interpolation method based on the Delaunay triangulation and Voronoi tessellation as well as the training set of direct equipartition ray design (EquRay) mixtures) to predict the toxicities of binary mixtures composed of hormetic ionic liquids (ILs). One of the purposes is to verify the predictive ability of IDV_equ. The other one is to improve the risk assessment of ILs mixtures especial hormetic ILs, because the toxicity reports of ILs mixtures are rarely reported in particular the toxicity of the hormetic ILs mixtures. Hence, we determined time-dependent toxicities of four ILs and their binary mixtures (designed by EquRay) to Vibrio qinghaiensis sp.-Q67 at first. Then, mixture toxicities were predicted and compared using the IDV_equ and CA. The results show that, the accuracy of IDV_equ is higher than the accuracy of CA. And, more important, to some mixtures out of the CA application, IDV_equ also can predict the mixture effects accurately. It showed that IDV_equ can be applied to predict the toxicity of any binary mixture regardless of the type of concentration-response curve of the components. These toxicity data provided useful information for researching the prediction of hormesis or toxicological interaction of the mixture and toxicities of ILs mixtures.

Combined Toxicity of 2,4-Dichlorophenoxyacetic Acid and Its Metabolites 2,4-Dichlorophenol (2,4-DCP) on Two Nontarget Organisms

2,4-Dichlorophenoxyacetic acid (2,4-D), a phenoxyalkanoic acid herbicide, is among the most widely distributed pollutants in the environment. 2,4-Dichlorophenol (2,4-DCP), as the main metabolite of 2,4-D, always accompanies 2,4-D. In this paper, we did research on the combined toxicities of 2,4-D and 2,4-DCP to Vibrio qinghaiensis sp.-Q67 (Q67) and Caenorhabditis elegans. It was found that the toxicity of 2,4-DCP is more severe than that of its parent 2,4-D at any concentration levels whether to Q67 or to C. elegans. Furthermore, 2,4-DCP to Q67 has the time-dependent toxicity. The toxicity of the mixture of 2,4-D and 2,4-DCP to Q67 is increasing with the exposure time, but that to C. elegans does not change over time. There is a good linear relationship between the pEC₅₀/pLC₅₀ value of binary mixture ray of 2,4-D and 2,4-DCP and the mixture ratio of 2,4-DCP, which implies the predictability of mixture toxicity of 2,4-D and 2,4-DCP. The toxicological interactions of the binary mixtures to Q67 are basically additive actions whether at 0.25 or at 12 h. However, most mixtures have antagonistic interactions against C. elegans.

Assessing the influence of the genetically modified factor on mixture toxicological interactions in Caenorhabditis elegans: Comparison between wild type and a SOD type

How to evaluate the ecological risk of transgenic technology is a focus of scientists because of the safety concerns raised by genetically modified (GM) organisms. Nevertheless, most studies are based on individual chemicals and always analyze the GM organism as a type of toxicant. In this study, we changed the approach and used GM organisms as the test objects with normal chemical exposure. Three types of chemicals (two substituted phenols, 4-chlorophenol and 4-nitrophenol; two ionic liquids, 1-butylpyridinium chloride and 1-butylpyridinium bromide; two pesticides, dichlorvos and glyphosate) were used to construct a six-component mixture system. The lethality to wild-type (N2) and sod-3::GFP (SOD-3) Caenorhabditis elegans was determined when they were exposed to the same mixture system after 12 and 24 h. The results showed that the pEC50 values of all of the single chemicals on SOD-3 were greater than those on N2 at 24 h. The toxicities of the single chemicals and nine mixture rays on the two strains increased with time. Notably, we discovered a significant difference between the two strains; time-dependent synergism occurred in mixtures on N2, but time-dependent antagonism occurred in mixtures on SOD-3. Finally, the strength of the synergism or antagonism turned to additive action on the two strains as the exposure time increased. These findings illustrated that the GM factor of the nematode influenced the mixture toxicological interaction at some exposure times. Compared with N2, SOD-3 were more sensitive to stress or toxic reactions. Therefore, the influence of the GM factor on mixture toxicological interactions in environmental risk assessment must be considered.

Commercial personal care product mixtures exhibit hormetic concentration-responses to Vibrio qinghaiensis sp.-Q67

The biological effects related to personal care products (PCPs) are almost induced by some active ingredients in the PCPs rather than the PCP itself. In this study, 23 common and widely used toner, skin water, and make-up water (TSM) commodities were directly taken as mixture samples, and Vibrio qinghaiensis sp.-Q67 (Q67) was used as the test organism. The toxicities of the TSMs to Q67 were determined via microplate toxicity analysis at 0.25 h and 12 h. Each TSM commodity can be regarded as a complicated mixture (relative concentration is 1). It was shown that the concentration-response curves (CRCs) of 23 TSMs are monotonic sigmoid-shaped (S-shaped) at 0.25 h, the CRCs of six TSMs are also S-shaped but the other 17 TSMs are non-monotonic hormetic or J-shaped at 12 h. In addition, to effectively characterize the nature of the stimulation and inhibition phases, it is suggested that five parameters such as the EC_L (the median stimulation effective concentration (left)), E_min (the maximum stimulation effect), EC_min (the maximum stimulation effective concentration), ZEP (zero effect point where the effect is 0 and the concentration is ZEP), and EC₅₀ can depict the non-monotonic CRC. To the best of our knowledge, this is the first study about the hormetic CRCs of commercial PCP mixtures.

Toxicological interaction of multi-component mixtures to Vibrio qinghaiensis sp.-Q67 induced by at least three components

It has been stated by researchers that the antibiotic polymyxin B sulfate (POL) is a key component inducing time-dependent antagonism in freshwater luminescent bacteria, Vibrio qinghaiensis sp.-Q67, exposed in the ternary mixture system of the ionic liquids, pesticide and antibiotics. However, the previous statement is limited to ternary and quaternary mixtures without considering situations such as the binary system. In order to prove the direct inducing of antagonism by POL in a more complete and systematic way, two categories of experiments (adding POL in non-antagonistic ternary system and decomposing antagonistic ternary system with POL into the binary system) have been conducted in this paper. The results showed that quaternary mixture systems (adding POL to non-antagonism ternary mixture, up verification) exhibit antagonistic action in a majority of rays, at some point in the experiment. However, by decomposing the antagonistic ternary mixtures with POL into binary mixtures (down verification), the combined toxicities of binary mixtures at all time points in the experiment are additive. Obviously, the POL has a significant contribution to antagonism only in the ternary and quaternary mixtures, but not in the binary mixtures. We can draw a new conclusion that the antagonism of the multi-component mixtures is induced by at least three components (including POL), with complex chemical interactions. Therefore, considering POL's influence of antagonism as an example, future environmental protection practitioners and academic researchers should construct more scenarios of mixtures when assessing the influences and reactions of certain chemicals causing pollutions.

The time-dependent synergism of the six-component mixtures of substituted phenols, pesticides and ionic liquids to Caenorhabditis elegans

Traditional environmental risk assessment rarely focused on exposures to multi-component mixtures which may cause toxicological interactions and usually ignored that toxicity is a process in time, which may underestimate the environment risk of mixtures. In this paper, six chemicals belonging to three categories, two substituted phenols, two pesticides and two Ionic liquids, were picked to construct a six-component mixture system. To systematically examine the effects of various concentration compositions, the uniform design ray method was employed to design nine mixture rays with nine mixture ratios and for every mixture ray 12 concentration levels were specified by the fixed ratio ray design. The improved combination index was used to evaluate the combined toxicities of the mixtures to Caenorhabditis elegans (C. elegans) in the exposure times of 6, 12 and 24h. It was shown that the mixture rays display time-dependent synergism, i.e. the range of synergistic effect narrows and the strength of synergism runs down with exposure time, which illustrates that the mixture toxicity of some chemicals is not a sum of individual toxicities at some exposure times and it is necessary to consider the toxicological interaction in mixtures.

Using Delaunay triangulation and Voronoi tessellation to predict the toxicities of binary mixtures containing hormetic compound

Concentration addition (CA) was proposed as a reasonable default approach for the ecological risk assessment of chemical mixtures. However, CA cannot predict the toxicity of mixture at some effect zones if not all components have definite effective concentrations at the given effect, such as some compounds induce hormesis. In this paper, we developed a new method for the toxicity prediction of various types of binary mixtures, an interpolation method based on the Delaunay triangulation (DT) and Voronoi tessellation (VT) as well as the training set of direct equipartition ray design (EquRay) mixtures, simply IDV_equ. At first, the EquRay was employed to design the basic concentration compositions of five binary mixture rays. The toxic effects of single components and mixture rays at different times and various concentrations were determined by the time-dependent microplate toxicity analysis. Secondly, the concentration-toxicity data of the pure components and various mixture rays were acted as a training set. The DT triangles and VT polygons were constructed by various vertices of concentrations in the training set. The toxicities of unknown mixtures were predicted by the linear interpolation and natural neighbor interpolation of vertices. The IDV_equ successfully predicted the toxicities of various types of binary mixtures.

Polymyxin B sulfate inducing time-dependent antagonism of the mixtures of pesticide, ionic liquids, and antibiotics to Vibrio qinghaiensis sp.-Q67

In the real environment, organisms are simultaneously exposed to different types of chemicals.

Complex toxicological interaction between ionic liquids and pesticides to Vibrio qinghaiensis sp.-Q67

Ionic liquids (ILs) and pesticides may coexist in ecosystems, because more and more people try to extract pesticides from various samples using ILs.

Predictability of the time-dependent toxicities of aminoglycoside antibiotic mixtures to Vibrio qinghaiensis sp.-Q67

The combined toxicities of all binary mixtures constructed by four aminoglycoside (AG) antibiotics are concentration additive, which has nothing to do with exposure time, mixture ratio, and concentration level.