Uniform design ray in the assessment of combined toxicities of multi-component mixtures

Screening for frequently detected quaternary ammonium mixture systems in waters based on frequent itemset mining and prediction of their toxicity

Screening and prioritizing research on frequently detected mixture systems in the environment is of great significance, as conducting toxicity testing on all mixtures is impractical. Therefore, the frequent itemset mining (FIM) was introduced and applied in this paper to identify variables that commonly co-occur in a dataset. Based on the dataset of the quaternary ammonium compounds (QACs) in the water environment, the four frequent QAC mixture systems with detection rate ≥ 35 % were found, including [BDMM]+Cl--[BTMM]+Cl- (M1), [BDMM]+Cl--[BHMM]+Cl- (M2), [BTMM]+Cl- -[BHMM]+Cl- (M3), and [BDMM]+Cl--[BTMM]+Cl--[BHMM]+Cl- (M4). [BDMM]+Cl-, [BTMM]+Cl-, and [BHMM]+Cl- are benzyl dodecyl dimethyl ammonium chloride, benzyl tetradecyl dimethyl ammonium chloride, and benzyl hexadecyl dimethyl ammonium chloride, respectively. Then, the toxicity of the representative mixture rays and components for the four frequently detected mixture systems was tested using Vibrio qinghaiensis sp.-Q67 (Q67) as a luminescent indicator organism at 0.25 and 12 h. The toxicity of the mixtures was predicted using concentration addition (CA) and independent action (IA) models. It was shown that both the components and the representative mixture rays for the four frequently detected mixture systems exhibited obvious acute and chronic toxicity to Q67, and their median effective concentrations (EC50) were below 7 mg/L. Both CA and IA models predicted the toxicity of the four mixture systems well. However, the CA model had a better predictive ability for the toxicity of the M3 and M4 mixtures than IA at 12 h.

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.

Algal toxicity and food chain transport characteristics of three common bisphenols and their mixtures

Various bisphenols (BPs) have been frequently detected in the aquatic environment and coexist in the form of mixtures with potential huge risks. As we all know, food chain is a media by which BPs mixtures and their mixtures probably enter the organisms at different trophic levels due to their environmental persistence. As a result, the concentrations of BPs and their mixtures may continuously magnify to varying degrees, which can produce higher risks to different levels of organisms, and even human health. However, the related researches about mixtures are few due to the complexity of mixtures. So, the ternary BP mixtures were designed by the uniform design ray method using bisphenol A (BPA), bisphenol S (BPS) and bisphenol F (BPF) to investigate their food chain effects including bioconcentration and biomagnification. Here, Chlorella pyrenoidosa (C. pyrenoidosa) and Daphnia magna (D. magna) were selected to construct a food chain. The toxic effects of single BPs and their mixtures were also systematically investigated by the time-dependent microplate toxicity analysis (t-MTA) method. Toxicity interaction within the ternary mixture was analyzed by the concentration addition model (CA) and the deviation from the CA model (dCA). The results show that the C. pyrenoidosa and D. magna had obvious bioconcentration and biomagnification effects on BPs and their mixture. The mixture had the potential to enrich at higher nutrient levels. And BPF had the largest bioconcentration effect (BCF1 = 481.86, BCF2 = 772.02) and biomagnification effect (BMF = 1.6). Three BPs were toxic to C. pyrenoidosa by destroying algal cells and decreasing protein and chlorophyll contents, and their toxicity order was BPF > BPA > BPS. Moreover, their ternary mixture exhibits synergism with time/concentration-dependency. The obtained results are of significant reference value for objectively and accurately assessing the ecological and environmental risks of bisphenol pollutants.

Combined toxicity and adverse outcome pathways of common pesticides on Chlorella pyrenoidosa

Pesticides due to their extensive use have entered the soil and water environment through various pathways, causing great harm to the environment. Herbicides and insecticides are common pesticides with long-term biological toxicity and bioaccumulation, which can harm the human body. The concept of the adverse outcome pathway (AOP) involves systematically analyzing the response levels of chemical mixtures to health-related indicators at the molecular and cellular levels. The AOP correlates the structures of chemical pollutants, toxic molecular initiation events and adverse outcomes of biological toxicity, providing a new model for toxicity testing, prediction, and evaluation of pollutants. Therefore, typical pesticides including diquat (DIQ), cyanazine (CYA), dipterex (DIP), propoxur (PRO), and oxamyl (OXA) were selected as research objects to explore the combined toxicity of typical pesticides on Chlorella pyrenoidosa (C. pyrenoidosa) and their adverse outcome pathways (AOPs). The mixture systems of pesticides were designed by the direct equipartition ray (EquRay) method and uniform design ray (UD-Ray) method. The toxic effects of single pesticides and their mixtures were systematically investigated by the time-dependent microplate toxicity analysis (t-MTA) method. The interactions of their mixtures were analyzed by the concentration addition model (CA) and the deviation from the CA model (dCA). The toxicity data showed a good concentration-effect relationship; the toxicities of five pesticides were different and the order was CYA > DIQ > OXA > PRO > DIP. Binary, ternary and quaternary mixture systems exhibited antagonism, while quinary mixture systems exhibited an additive effect. The AOP of pesticides showed that an excessive accumulation of peroxide in green algae cells led to a decline in stress resistance, inhibition of the synthesis of chlorophyll and protein in algal cells, destruction of the cellular structure, and eventually led to algal cell death.

Predicting the occurrence of antagonism within ternary guanidine mixture pollutants based on the concentration ratio of components

The widespread prevalence and coexistence of diverse guanidine compounds pose substantial risks of potential toxicity interactions, synergism or antagonism, to environmental organisms. This complexity presents a formidable challenge in assessing the risks associated with various pollutants. Hence, a method that is both accurate and universally applicable for predicting toxicity interactions within mixtures is crucial, given the unimaginable diversity of potential combinations. A toxicity interaction prediction method (TIPM) developed in our past research was employed to predict the toxicity interaction, within guanidine compound mixtures. Here, antagonism were found in the mixtures of three guanidine compounds including chlorhexidine (CHL), metformin (MET), and chlorhexidine digluconate (CDE) by selecting Escherichia coli (E. coli) as the test organism. The antagonism in the mixture was probably due to the competitive binding of all three guanidine compounds to the anionic phosphates of E. coli cell membranes, which eventually lead to cell membrane rupture. Then, a good correlation between toxicity interactions (antagonisms) and components' concentration ratios (pis) within binary mixtures (CHL-MET, CHL-CDE, MET-CDE) was established. Based on the correlation, the TIPM was constructed and accurately predicted the antagonism in the CHL-MET-CDE ternary mixture, which once again proved the accuracy and applicability of the TIPM method. Therefore, TIPM can be suggested to identify or screen rapidly the toxicity interaction within ternary mixtures exerting potentially adverse effects on the environment.

Differences in the response of Chlorella pyrenoidosa to three antidepressants and their mixtures in different light-dark start cycles

The use of antidepressants is increasing along with the continuing spike in the prevalence of depression worldwide. As a result, more and more antidepressants are entering the water and probably does harm to the aquatic organisms and even human health. Therefore, three antidepressants, including fluoxetine (FLU), citalopram (CIT), and aspirin (APC), were selected to investigate the toxic risks of antidepressants and their mixtures to a freshwater green alga Chlorella pyrenoidosa (C. pyrenoidosa). Due light is critical for the growth of green algae, six different light-dark cycle experiments were constructed to investigate the differences in toxicity and interaction responses of C. pyrenoidosa to antidepressants and their ternary mixture designed by the uniform design ray method. The toxic effects of individual antidepressants and their mixtures on C. pyrenoidosa were systematically investigated by the time-dependent microplate toxicity analysis (t-MTA) method. Toxicity interactions (synergism or antagonism) within mixtures were analyzed by the concentration addition (CA) and the deviation from the CA model (dCA) models. The results showed that the toxicities of the three antidepressants were different, and the order was FLU > APC > CIT. Light-dark cycles obviously affect the toxicity of three antidepressants and their combined toxicity interaction. Toxicity of the three antidepressants increases with the duration of light but decreases with the duration of darkness. The ternary antidepressant mixture exhibits antagonism, and the longer the initial lighting is, the stronger the antagonism. The antagonism of the ternary mixture is also affected by exposure time and mixture components' pi as well as exposure concentration.

A novel method for predicting the emergence of toxicity interaction in ternary mixtures

The environment is teeming with a wide variety of pollutants, but the complexity and diversity of their combinations make it difficult to fully assess their toxicity interaction. A novel toxicity interaction prediction method (TIPM) based on the three-dimensional (3D) surface form of the concentration addition (CA) deviation model (dCA) was proposed to predict the emergence of toxicity interaction in ternary mixtures. Doxycycline hyclate (DH), bromoacetic acid (BAA) and iodoacetic acid (IAA) were used as target pollutants. The toxicity of binary and ternary mixtures designed by the direct equipartition ray design method (EquRay) and the uniform design ray method (UD-Ray) against Escherichia coli (E. coli) was determined by using a time-dependent microplate toxicity analysis (t-MTA) method. The toxicity interaction within mixtures was characterized qualitatively and quantitatively using dCA 3D surface modeling and the emergence of DH-MAA-IAA toxicity interaction was predicted by TIPM. The results showed that the dCA 3D surface model could well characterize the toxicity interactions of the mixtures, and toxicity interaction was closely related to the components' concentration ratio (pi). TIPM could predict the emergence of DH-MAA-IAA toxicity interactions well based on the relationship. Due the model is only related to the toxicity interactions and pi value of a mixture, so it can be suggested to predict toxicity interaction within the more complex multicomponent mixtures, which provides a novel approach for the environmental risk assessment and prediction of hazardous substances.

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.

The key constituents underlying the combined toxicity of eight cosmetic contaminants towards Vibrio qinghaiensis

Cosmetic additives (ADDs) and packaging plasticizers (PLAs) probably present potential risks and dangers to the environment and human body as emerging pollutants. To investigate their potential risks and dangers, five ADDs including methyl paraben (MET), ethyl paraben (ETH), propyl paraben (PRO), butyl-hydroxy anisole (BHA), and salicylic acid (SAL), as well as three PLAs including bisphenol A (BPA), bisphenol S (BPS) and tris(2-butoxyethyl) phosphate (TBEP) were selected as research objects, and ten mixture rays (R1-R10) composed of the eight components were designed by the uniform design ray (UD-Ray) method. The toxicities of the eight cosmetic pollutants and their eight-component mixture system towards Vibrio qinghaiensis sp.-Q67 (Q67) were systematically determined by the time-dependent microplate toxicity analysis (t-MTA) method. The three-dimensional (3D) surface of deviation from the concentration addition model (dCA) was utilized to qualitatively and quantitatively analyse the toxicity interaction of the mixtures and the correlation between toxicity interaction and the components' concentration ratios. Finally, eight individual pollutants and representative rays with significant inhibitory and interactive effects were selected to analyse DNA and soluble proteolysis as well as the microstructure and morphology of Q67 after treatment with single chemicals and their mixtures. The results showed that the eight cosmetic pollutants had conspicuous concentration-dependent toxicity and acute toxicity, and none of them, except BPS, BPA and ETH, had time-dependent toxicity. All rays had time/concentration-dependent toxicity and acute toxicity. At the same time, the toxicity interaction of these mixture rays was predominantly antagonism and the strongest antagonism appeared at high concentrations at 12 h. Nevertheless, the components' concentration ratio (pi) was the decisive factor for the type of mixture interaction. The correlation analysis revealed a significant positive linear correlation between mixture toxicity and pETH and pBPA, which indicated that ETH and BPA were the key components of the toxic effects. However, there was a significant negative linear correlation between the antagonism intensity and pBPA and pTBEP, which demonstrated that BPA and TBEP were the key components of the antagonism intensity. Pollutants and their mixtures can also damage cellular structures, and mixtures can exacerbate the dissolution of DNA and soluble proteins.

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.

Combined effects of fluoroquinolone antibiotics and organophosphate flame retardants on Microcystis aeruginosa

As freshwater harmful algal blooms continue to rise in frequency and severity, increasing focus is made on the effects of mixed pollutants and the dominant cyanobacterial species Microcystis aeruginosa (M. aeruginosa). However, few studies have investigated whether M. aeruginosa has a synergistic relationship with two common pollutants, namely, organophosphate flame retardants (OPFRs) and fluoroquinolone antibiotics (FQs). In this paper, three FQs and three OPFRs commonly detected in freshwaters were selected to construct a ternary mixture of FQs, a ternary mixture of OPFRs, and a six-component mixture of OPFRs and FQs. The effects of single substance and mixture on the growth of M. aeruginosa were determined at 24, 48, 72, and 96 h, and the toxicities of the mixture were evaluated by concentration addition model and independent action model. The results showed that the mixture of FQs and the mixture of OPFRs do not show toxicological interaction. However, partial mixtures of OPFRs and FQs showed antagonism or synergism at different concentrations and times. This indicated that combined toxicities of OPFRs and FQs on M. aeruginosa were mixture ratio dependent, concentration dependent and time dependent. This study improves our understanding of the role of OPFRs and FQs in cyanobacterial outbreaks of Microcystis.

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.

Introduction of Flavor Chemical Eugenol Attenuating the Synergistic Toxicological Interactions of Flavor Mixtures

The flavor chemicals benzyl alcohol (BEA), phenylethanol (PHA), and cinnamaldehyde (CID) and their binary mixtures have high toxicity sensitivity to the lethal endpoint of Caenorhabditis elegans. Some binary flavor mixtures even have synergistic toxicological interactions. Eugenol (EUG) is closely related to human life and has many special nonlethal effects on organisms. The effect of its introduction on the combined toxicities of flavor mixtures is worth studying. We introduced EUG into three binary (BEA-PHA, BEA-CID, and PHA-CID) and one ternary (BEA-PHA-CID) flavor mixture systems. Five representative mixture rays were selected from each of the four mixture systems using the uniform design ray (UD-Ray) method. The lethal toxicity of each mixture ray to C. elegans was measured at three different exposure volumes (100, 200, and 400 μL), and a dose-effect model was established. The new parameter iSPAN was used to quantitatively characterize the toxicity sensitivity of each chemical and mixture ray. The toxicological interaction of each mixture was evaluated by the toxicological interaction heatmap based on the combination index (CI). It can be seen that all flavor chemicals and their ternary and quaternary mixture rays have high iSPANs, and the highest value is 16.160 (BEA-PHA-CID-EUG-R1 at 400 μL). According to the heatmap and CI, the introduction of EUG attenuates the synergistic toxicological interactions of flavor mixtures, leading to the transformation ofsynergistic interactions in flavor mixtures into additive action and even antagonistic interaction, and the CI value of the antagonistic interaction is up to 1.8494 (BEA-CID-EUG-R4 at 400 μL).

New methods of top-to-down mixture toxicity prediction: A case study of eliminating of the effects of cosolvent from binary mixtures

At present, the toxicity prediction of mixtures mainly focuses on the concentration addition (CA) and independent action (IA) based on individual toxicants to predict the toxicity of multicomponent mixtures. This process of predicting the toxicity of multicomponent mixtures based on single substances or low component mixtures is called down-to-top method in this study. However, due to the particularity of some toxicants, we have to use the top-to-down idea to obtain or eliminate the toxicity of some components from mixtures. For example, the toxicity of toxicants is obtained from the toxicity of a mixture with, especially toxic, cosolvent added. In the study, two top-to-down methods, the inverse CA (ICA) and inverse IA (IIA) models, were proposed to eliminate the effects of a certain component from multicomponent mixtures. Furthermore, taking the eight binary mixtures consisting of different shapes of cosolvents (isopropyl alcohol (IPA) having hormesis and dimethyl sulfoxide (DMSO)) and toxicants (two ionic liquids and two pesticides) as an example, combined with the interaction evaluated by CA and IA model, the influence of different shapes of components on top-to-down toxicity prediction was explored. The results showed that cosolvent IPA having hormesis may cause unpredictable effects, even at low concentrations, and should be used with caution. For DMSO, most of the toxicant's toxicity obtained by ICA and IIA models were almost in accordance with those observed experimentally, which showed that ICA and IIA could effectively eliminate the effects of cosolvent, even if toxic cosolvent, from the mixture. Ultimately, a frame of cosolvent use and toxicity correction for the hydrophobic toxicant were suggested based on the top-to-down toxicity prediction method. The proposed methods improve the existing framework of mixture toxicity prediction and provide a new idea for mixture toxicity evaluation and risk assessment.

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.

Combined toxicity of endocrine-disrupting chemicals: A review

The combined toxicological assessment provides a realistic approach for hazard evaluation of chemical cocktails that co-existed in the environment. This review provides a holistic insight into the studies highlighting the mixture toxicity of the endocrine-disrupting chemicals (EDCs), especially focusing on the screening of biochemical pathways and other toxicogenetic endpoints. Reviewed literature showed that numerous multiplexed toxicogenomic techniques were applied to determine reproductive effects in vertebrates, but limited studies were found in non-mammalian species after mixture chemical exposure. Further, we found that the experimental design and concentration selection are the two important parameters in mixture toxicity studies that should be time- and cost-effective, highly precise, and environmentally relevant. A summary of EDC mixtures affecting the thyroid axis, estrogen axis, androgen axis, growth stress, and immune system via in vivo bioassays was also presented. It is interesting to mention that majority of estrogenic effects of the mixtures were sex-dependent, particularly observed in male fish as compared to female fish. Further, the androgen axis was perturbed with serious malformations in male rat testis (epididymal or gubernacular lesions, and deciduous spermatids). Also, transgenerational epigenetic effects were promoted in the F₃ and F₄ generations in the form of DNA methylation epimutations in sperm, increasing polycystic ovaries and reducing the offspring. Similarly, increased oxidative stress, high antioxidant enzymatic activities, disturbed estrous cycle, and decreased steroidogenesis were the commonly found effects after acute or chronic exposure to EDC mixtures. Importantly, the concentration addition (CA) and independent action (IA) models became more prevalent and suitable predictive models to unveil the prominence of synergistic estrogenic and anti-androgenic effects of chemical mixtures. More importantly, this review encompasses the research challenges and gaps in the existing knowledge and specific future research perspectives on combined toxicity.

Quantitative characterization of toxicity interaction within antibiotic-heavy metal mixtures on Chlorella pyrenoidosa by a novel area-concentration ratio method

Toxicity interaction, synergism and antagonism, may occur when multiple pollutants are exposed to the environment simultaneously, which limits the utility of some standard models to assess toxicity hazards and risks. The development and application of models which can provide an insight into the combined toxicity of pollutants becomes necessary. Therefore, a novel model, area-concentration ratio (ACR) method, was developed to characterize the toxicity interaction within mixtures of three aminoglycoside antibiotics (AGs), kanamycin sulfate (KAN), paromomycin sulfate (PAR), tobramycin (TOB) and one heavy metal copper (Cu) in this study. The inhibition toxicity of single contaminants and mixtures designed by direct equilibration ray method and uniform design ray method to Chlorella pyrenoidosa (C. pyrenoidosa) was determined by the microplate toxicity analysis (MTA). The results showed that the novel method ACR could be used for quantitative characterization of combined toxicity. According to the ACR, all the binary AG antibiotic mixture systems display obvious synergism and weak antagonism. The addition of the heavy metal Cu into binary AG antibiotic mixtures can obviously change toxicity interaction, but toxicity interaction changing trend varies greatly in different ternary mixture systems. Toxicity interaction in the six mixture systems has component concentration-ratio dependence. ACR can be suggested as an effective novel method to quantitatively characterize toxicity interaction when assessing the hazards and risks of the combined pollution.

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.

Quantitative evaluation and the toxicity mechanism of synergism within three organophosphorus pesticide mixtures to Chlorella pyrenoidosa

Organophosphorus pesticide (OPP) pollutants in the environment pose toxicity risks to living organisms, and the possible toxicity mechanism needs to be further clarified. Therefore, the individual and combined toxicity of three OPPs namely acephate (ACE), trichlorfon (TRI) and glyphosate (GIY) towards a freshwater green alga Chlorella pyrenoidosa (C. pyrenoidosa) was investigated by the time-dependent microplate toxicity analysis method. Here, a ternary mixture system of the three OPPs including five rays with different concentration ratios was designed by the uniform design ray method. The standard additive reference model, concentration addition (CA), was used to analyse toxicity interaction within ternary mixtures and the toxicity interaction intensity was characterized using a deviation from CA model (dCA). Besides, the effects of the three OPPs and their mixtures on the chlorophyll (CHL) content, superoxide dismutase (SOD) activity and malondialdehyde (MDA) content of C. pyrenoidosa were also investigated to explore the possible mechanisms. The results show that toxicity of the three pesticides and their ternary mixture rays is time-dependent and the combined toxicity correlates well with the components, ACE and GLY. It is likely that there is a significant time-dependent synergism in ternary mixtures induced by ACE and GLY. The synergism intensity of the ternary mixtures is not more than 30% at the whole experimental concentration level. The CHL reduction rate and MDA content of C. pyrenoidosa increase, while the SOD activity of C. pyrenoidosa decreases with the lengthening of exposure time under the action of the three pesticides and their ternary mixtures. So, the possible mechanism of the three pesticides and their mixtures may be by affecting the photosynthesis, and then causing oxidative damage to C. pyrenoidosa cells. The results can provide reference for the combined toxicity assessment of OPPs to living organisms.

Time-dependent synergism of five-component mixture systems of aminoglycoside antibiotics to Vibrio qinghaiensis sp.-Q67 induced by a key component

A large number of antibiotics are entering the aquatic environment accompanying human and animal excreta, which will threaten the survival of aquatic organisms and even human health. It has been found that binary mixtures of aminoglycoside (AG) exhibit additive action and can be evaluated well by a classical model, concentration addition (CA) in our past study. Therefore, to investigate the toxicity interaction within multi-component mixtures of AG antibiotics, five antibiotics, kanamycin sulfate (KAN), neomycin sulfate (NEO), tobramycin (TOB), streptomycin sulfate (STS), and gentamicin sulfate (GEN), were selected to construct five-component mixture systems by a uniform design ray method. The toxic effects (luminescence inhibition) of single antibiotic and five-antibiotic mixture systems towards a photobacterium Vibrio qinghaiensis sp.-Q67 (V. qinghaiensis) in different exposure time (0.25, 2, 4, 8, and 12 h) were determined by the time-dependent microplate toxicity analysis method. The concentration-effect data were fitted by a nonlinear least square method, toxicity interaction within mixture systems was analyzed by a CA model, and the interaction intensity was characterized by deviation from the CA model (dCA). Besides, the toxicity mechanism of five antibiotics and their five-component mixtures to V. qinghaiensis was analyzed by electron microscopy. The results show that toxicity of five antibiotics and their five-component mixture systems to V. qinghaiensis is time-dependent and has strong long-term toxicity. Different from binary AG antibiotic mixture systems, five-antibiotic mixture systems exhibit obviously time-dependent synergism. In addition, toxicity of the five-antibiotic mixtures can be 1.4 times higher than that of the mixtures without synergisms at the same concentration level. According to dCA, synergism intensity (dCA) curves of rays move slowly from the high concentration region to the medium or lower one and the maximum dCA values also increase, decrease, or first increase, then decrease with the lengthening of exposure time. The inhibition activity and synergism intensity of mixture rays have good correlation with the concentration ratios of STS, the key component for synergism. The cell morphology of V. qinghaiensis indicates the strong toxicity of five antibiotics and their mixture rays is not due to the destruction of cell structure, but the inhibition of the light-emitting activity of the photobacterium.

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.

Joint toxicity of six common heavy metals to Chlorella pyrenoidosa

Six common heavy metals (Ni, Fe, Zn, Pb, Cd, and Cr) in the water environment were selected to present five groups of binary mixture systems (Ni-Fe, Ni-Zn, Ni-Pb, Ni-Cd, and Ni-Cr) through a direct equipartition ray design. Microplate toxicity analysis based on Chlorella pyrenoidosa measured the 96-h joint toxicities of the binary mixtures. Toxicity interaction of the binary mixture was analyzed by comparing the observed toxicity data with the reference model (concentration addition). The results indicated that Ni-Fe, Ni-Pb, and Ni-Cr mixtures showed additive effects at concentration tested. It was indicated that Ni-Zn and Ni-Cd mixtures presented additive effects at low concentrations whereas synergistic effects were seen at high concentrations.

Predicting the hormesis and toxicological interaction of mixtures by an improved inverse distance weighted interpolation

The prediction of toxicological interactions and hormesis of chemical mixtures is important because organisms are mostly exposed to numerous contaminants and typically to low dose of these mixtures, and it is still a challenge. Although many models have been developed to predict the mixture toxicities such as concentration addition (CA) and independent action (IA), they cannot solve these challenges perfectly. This study has developed an improved inverse distance weighted (IDW) interpolation for prediction of the mixture toxicities. IDW uses the mixture and the single compound as scatter points in space, and the space can be constructed by the concentration axes of various components in the mixture system. Some known mixtures (or the single compound) closest to the unknown mixture are selected as interpolation nodes. To be more accurate in calculation, a new normalization method for concentration has been proposed through dividing the concentration of the mixture and the single compound by the respective EC₅₀ values. Sixteen binary mixture systems are selected for leave-one-out cross-validation and three binary mixture systems are selected for external validation. The results show that the accuracy of IDW is ≥95% for three types of mixtures including no hormetic component, one hormetic component (show no toxicological interaction), and two hormetic components. The IDW also show higher prediction accuracy than that of CA and IA. The IDW developed in this study can be used to predict the toxicity of various mixture systems, thus providing predictive information for chemical mixtures risk assessment.

Polyethylene glycol 400 significantly enhances the stimulation of 2-phenoxyethanol on Vibrio qinghaiensis sp.-Q67 bioluminescence

Previous studies demonstrated long-term stimulation of some commercial personal care products (PCPs) on freshwater luminescent bacteria Vibrio qinghaiensis sp.-Q67 (Q67). However, whether a certain component can affect mixture's hormetic effect is still unknown. In this paper, two of ingredients in PCPs, 2-phenoxyethanol (PhE) and polyethylene glycol 400 (PEG400), were selected as object compounds to explore the relationship between concentration-response (CR) of mixtures and that of a single component. It was found that PEG400 has monotonic CR (MCR) on Q67 both at the short-term (0.25 h) and long-term (12 h) exposures while PhE has MCR at 0.25 h and hormetic CR (HCR) at 12 h. Here, the concentration-response curves (CRCs) of PEG400 at 0.25 and 12 h are overlapped each other and the CRCs of PEG400 are on the right of PhE. If the pEC₅₀ is taken as a toxic index, the toxicities of PEG400 at two times are basically the same, and those of PhE are the same, too, but PhE is twice as toxic as PEG400. For the mixtures of PEG400 and PhE, all rays except R1 have MCRs at 0.25 h while all rays have HCRs at 12 h where the higher the mixture ratio of PhE is, the more negative the maximum stimulation effect is. More importantly, the E_min values of all rays are more negative (1.79-3.17-fold) than that of PhE worked alone, which implies that the introduction of PEG400 significantly enhances stimulative effect of PhE. At 0.25 h, all binary mixture rays but R1 produce a low-concentration additive action and high-concentration synergism. At 12 h, all rays display additive action, antagonism, additive action, and synergism in turn when the concentration changes from low to high. The overall findings suggested toxicological interactions should be considered in the risk assessment of PCPs and their potential impacts on ecological balances.

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

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

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.

Monitoring Aquaculture Water Quality: Design of an Early Warning Sensor with Aliivibrio fischeri and Predictive Models

A novel toxicity-warning sensor for water quality monitoring in recirculating aquaculture systems (RAS) is presented. The design of the sensor system mainly comprises a whole-cell biosensor. Aliivibrio fischeri, a luminescent bacterium widely used in toxicity analysis, was tested for a mixture of known fish-health stressors, namely nitrite, un-ionized ammonia, copper, aluminum and zinc. Two toxicity predictive models were constructed. Correlation, root mean squared error, relative error and toxic behavior were analyzed. The linear concentration addition (LCA) model was found suitable to ally with a machine learning algorithm for prediction of toxic events, thanks to additive behavior near the limit concentrations for these stressors, with a root-mean-squared error (RMSE) of 0.0623, and a mean absolute error of 4%. The model was proved to have a smaller relative deviation than other methods described in the literature. Moreover, the design of a novel microfluidic chip for toxicity testing is also proposed, which is to be integrated in a fluidic system that functions as a bypass of the RAS tank to enable near-real time monitoring. This chip was tested with simulated samples of RAS water spiked with zinc, with an EC50 of 6,46E-7 M. Future work will be extended to the analysis of other stressors with the novel chip.

Combined acute ecotoxicity of malathion and deltamethrin to Daphnia magna (Crustacea, Cladocera): comparison of different data analysis approaches

We studied the combined acute effect (i.e., after 48 h) of deltamethrin (a pyrethroid insecticide) and malathion (an organophosphate insecticide) on Daphnia magna. Two approaches were used to examine the potential interaction effects of eight mixtures of deltamethrin and malathion: (i) calculation of mixture toxicity index (MTI) and safety factor index (SFI) and (ii) response surface methodology coupled with isobole-based statistical model (using generalized linear model). According to the calculation of MTI and SFI, one tested mixture was found additive while the two other tested mixtures were found no additive (MTI) or antagonistic (SFI), but these differences between index responses are only due to differences in terminology related to these two indexes. Through the surface response approach and isobologram analysis, we concluded that there was a significant antagonistic effect of the binary mixtures of deltamethrin and malathion that occurs on D. magna immobilization, after 48 h of exposure. Index approaches and surface response approach with isobologram analysis are complementary. Calculation of mixture toxicity index and safety factor index allows identifying punctually the type of interaction for several tested mixtures, while the surface response approach with isobologram analysis integrates all the data providing a global outcome about the type of interactive effect. Only the surface response approach and isobologram analysis allowed the statistical assessment of the ecotoxicological interaction. Nevertheless, we recommend the use of both approaches (i) to identify the combined effects of contaminants and (ii) to improve risk assessment and environmental management.

QSAR prediction of additive and non-additive mixture toxicities of antibiotics and pesticide

Antibiotics and pesticides may exist as a mixture in real environment. The combined effect of mixture can either be additive or non-additive (synergism and antagonism). However, no effective predictive approach exists on predicting the synergistic and antagonistic toxicities of mixtures. In this study, we developed a quantitative structure-activity relationship (QSAR) model for the toxicities (half effect concentration, EC₅₀) of 45 binary and multi-component mixtures composed of two antibiotics and four pesticides. The acute toxicities of single compound and mixtures toward Aliivibrio fischeri were tested. A genetic algorithm was used to obtain the optimized model with three theoretical descriptors. Various internal and external validation techniques indicated that the coefficient of determination of 0.9366 and root mean square error of 0.1345 for the QSAR model predicted that 45 mixture toxicities presented additive, synergistic, and antagonistic effects. Compared with the traditional concentration additive and independent action models, the QSAR model exhibited an advantage in predicting mixture toxicity. Thus, the presented approach may be able to fill the gaps in predicting non-additive toxicities of binary and multi-component mixtures.

Time-dependent and Pb-dependent antagonism and synergism towards Vibrio qinghaiensis sp.-Q67 within heavy metal mixtures

Toxicity interaction has aroused many researchers' interest in the combined toxicity of pollutants. Recently, some published studies have shown that the toxicity of some mixture pollutants is time dependent and well correlated with certain components in the mixture. Therefore, to investigate whether toxicity interaction is affected by the exposure time or certain components, synergism and antagonism within typical environmental contaminants of heavy metal mixtures were analyzed in different exposure times. Herein, three binary and one ternary mixture systems were designed by using three heavy metals: cadmium chloride, lead chloride (Pb) and manganese(ii) chloride tetrahydrate (Mn). For each mixture system, five mixture rays with different concentration ratios were arranged by direct equipartition ray design and uniform design ray methods. The toxicities of the three heavy metals and 20 mixture rays towards photobacteria Vibrio qinghaiensis sp.-Q67 (Q67) were determined by the established time-dependent microplate toxicity analysis (t-MTA) in different exposure durations of 0.25, 2, 4, 8 and 12 h. It was shown that the toxicities of three heavy metals (Cd, Pb and Mn) as well as their binary and ternary mixture rays to Q67 were also time dependent, but different metals or mixture rays showed different time characteristics. Surprisingly, some mixture rays exhibited antagonism or synergism with time dependency and the time characteristics varied in different mixture systems. Furthermore, the binary or ternary mixture systems with Pb displayed antagonism, while the Cd–Mn mixture system without Pb exhibited additive action or synergism, which indicated that Pb was probably the causative agent of antagonism produced by mixtures.

Toxicity interaction has gained much interest in the research of toxicity of mixture pollutants.

Global concentration additivity and prediction of mixture toxicities, taking nitrobenzene derivatives as an example

The toxicity of a mixture depends not only on the mixture concentration level but also on the mixture ratio. For a multiple-component mixture (MCM) system with a definite chemical composition, the mixture toxicity can be predicted only if the global concentration additivity (GCA) is validated. The so-called GCA means that the toxicity of any mixture in the MCM system is the concentration additive, regardless of what its mixture ratio and concentration level. However, many mixture toxicity reports have usually employed one mixture ratio (such as the EC₅₀ ratio), the equivalent effect concentration ratio (EECR) design, to specify several mixtures. EECR mixtures cannot simulate the concentration diversity and mixture ratio diversity of mixtures in the real environment, and it is impossible to validate the GCA. Therefore, in this paper, the uniform design ray (UD-Ray) was used to select nine mixture ratios (rays) in the mixture system of five nitrobenzene derivatives (NBDs). The representative UD-Ray mixtures can effectively and rationally describe the diversity in the NBD mixture system. The toxicities of the mixtures to Vibrio qinghaiensis sp.-Q67 were determined by the microplate toxicity analysis (MTA). For each UD-Ray mixture, the concentration addition (CA) model was used to validate whether the mixture toxicity is additive. All of the UD-Ray mixtures of five NBDs are global concentration additive. Afterwards, the CA is employed to predict the toxicities of the external mixtures from three EECR mixture rays with the NOEC, EC₃₀, and EC₇₀ ratios. The predictive toxicities are in good agreement with the experimental toxicities, which testifies to the predictability of the mixture toxicity of the NBDs.

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.

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.

Mixture cytotoxicity assessment of ionic liquids and heavy metals in MCF-7 cells using mixtox

Ionic liquids (ILs) are widely used as extractants for heavy metals. However, the effect of mixtures of ILs and heavy metals is rarely understood. In this study, we tested the cytotoxicity of four ILs, four heavy metals and their mixtures on human MCF-7 cells in 96-well microplates. The toxicity of single compounds in MCF-7 cells ranges from 3.07 × 10(-6) M for Cu(II) to 2.20 × 10(-3) M for 1-ethyl-3-methylimidazolium tetrafluoroborate. The toxicity of heavy metals in MCF-7 is generally higher than the toxicity of ILs. A uniform experimental design was used to simulate environmentally realistic mixtures. Two classical reference models (concentration addition and independent action) were used to predict their mixture. The experiments to evaluate the toxicity of the mixture revealed antagonism among four ILs and four heavy metals in MCF-7 cells. Pearson correlation analysis showed that Ni(II) and 1-dodecyl-3-methylimidazolium chloride are positively correlated with the extent of antagonism, while 1-hexyl-3-methylimidazolium tetrafluoroborate showed a negative correlation. Data analysis was conducted in the R package mixtox, which integrates features such as curve fitting, experimental design, and mixture toxicity prediction. The international community of toxicologists is welcome to use this package and provide feedback as suggestions and comments.

Chemical and in vitro biological information to predict mouse liver toxicity using recursive random forests

In this study, recursive random forests were used to build classification models for mouse liver toxicity. The mouse liver toxicity endpoint (67 toxic and 166 non-toxic) was a composition of four in vivo chronic systemic and carcinogenic toxicity endpoints (non-proliferative, neoplastic, proliferative and gross pathology). A multiple under-sampling approach and a shifted classification threshold of 0.288 (non-toxic < 0.288 and toxic ≥ 0.288) were used to cope with the unbalanced data. Our study showed that recursive random forests are very efficient in variable selection and for the development of predictive in silico models. Generally, over 95% redundant descriptors could be reduced from modelling for all the chemical, biological and hybrid models in this study. The predictive performance of chemical models (CCR of 0.73) is comparable with hybrid model performance (CCR of 0.74). Descriptors related to the octanol-water partition coefficient are vital for model performance. The in vitro endpoint of CYP2A2 played a key role in the development and interpretation of hybrid models. Identifying high-throughput screening assays relevant to liver toxicity would be key for improving in silico models of liver toxicity.

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.