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The flavor chemicals benzyl alcohol (BEA), phenylethanol (PHA), and cinnamaldehyde (CID) and their binary mixtures have high toxicity sensitivity to the lethal endpoint of

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.

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.

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.

There is currently no generally accepted model to predict the hormesis of mixtures. In order to accurately predict the hormesis of a mixture, we developed a method based on similarity and triangulation, which we named SimTri in this paper. SimTri takes the mixture as scatter points in space, which is constructed by the concentration axes of various components in the mixture system. To test the predictive capability of SimTri, the toxicities of three different types of binary mixtures (no hormetic compound, one hormetic compound, and two hormetic compounds) on Vibrio qinghaiensis sp.-Q67 were determined at 0.25 h and 12 h. For each mixture system, the toxicities of five mixture rays, which were designed by direct equipartition ray design, were used for the internal validation (leave-one-out cross-validation, LOOCV). The toxicities of two mixture rays, which were designed by fixed-ratio ray design on the basis of the NOEC and EC70 ratios, were used for the external validation. The results of LOOCV and external validation indicated that the accuracy of SimTri was greater than 90%, which means that SimTri can accurately predict the toxicity of three different types of binary mixtures and may provide a new way to predict the toxicity of mixtures.

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 pEC 50 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.

Quinolones (QNs), dechloranes (DECs), and chlorinated paraffins (CPs) are three kinds of new halogenated organic pollutants (HOPs), which originate from the use of flame retardants, lubricants and pesticides. Since QNs, DECs, and CPs are frequently detected in waters and sediments, it is necessary to investigate the toxic effects of these HOPs with dwelling phytoplankton, especially for cyanobacteria, to explore their potential hormetic effects and contributions to algal blooms. In the present study, we investigate single and joint toxicity of QNs, DECs and CPs on Microcystis aeruginosa (M. aeruginosa), a cyanobacterium that is frequently implicated with algal blooms. The results indicate single QNs and DECs induce marked hormetic effects on the proliferation of M. aeruginosa but CPs do not. The stimulatory effect of hormesis is linked with accelerated replication of DNA, which is considered to stem from the moderate rise in intracellular reactive oxygen species (ROS). Joint toxicity tests reveal that both QNsCPs mixtures and DECsCPs mixtures show hormetic effects on M. aeruginosa, but QNsDECs mixtures show no hormetic effect. QNsDECs mixtures exhibit synergistic toxic actions, which may be caused by a sharp rise in intracellular ROS simultaneously produced by the agents. Joint toxic actions of both QNsCPs, and DECsCPs shift from addition to antagonism as concentration increases, and this shift may mainly depend on the influence of CPs on cell membrane hydrophobicity of M. aeruginosa. This study provides data and toxic mechanisms for the hormetic phenomenon of single and joint HOPs on M. aeruginosa. The hormetic effects of HOPs may benefit the proliferation of M. aeruginosa in the aquatic environment, aggravating the formation of algal blooms. This study also reflects the important role of hormesis in environmental risk assessment of pollutants.

The widespread use of pesticides results in their frequent detection in water bodies and other environmental media. Pesticide residues may cause certain risks to the environment and human health, and reliable predicted no effect concentrations (PNEC) must be obtained when assessing environmental risks. Species sensitivity distribution (SSD) is an important method for the derivation of chemical PNECs. Construction of the SSD model requires sufficient toxicity data to various species including at least eight families in three phyla, suitable nonlinear fitting functions and assessment factors (AFs) with certain uncertainty. However, most chemicals could not collect sufficient species toxicity data, while some chemicals had sufficient species toxicity data but could not find suitable fitting functions, thus hindering the construction of effective SSD models. To this end, the established QSAR models were applied to predict toxicity of chemicals to specific species to fill in the toxicity data gaps required for SSD and selecting multiple nonlinear functions to optimize the SSD model. Combined with QSAR and SSD methods, a new method of PNEC derivation was developed and successfully applied to the derivation of PNEC for 35 pesticides. Three QSAR models were used to predict the toxicities of six pesticides with few toxicity data. Nine two-parameter nonlinear functions were used to fit the toxicity-cumulative probability data one by one to determine the optimal SSD models. The hazardous concentrations at the cumulative probability of 5% and 10%, i. e, HC

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.

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.

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

Background The weak magnetic field (WMF) can enhance the ability to remove target pollutants in wastewater, which drives us to consider whether WMF could give rise to the hormesis or not. In our previous study, it was found that polymyxin B sulfate (POL) can induce weak hormesis on Vibrio qinghaiensis sp.-Q67 (Q67). To this end, we set up four different WMF treatments during Q67 culture and POL exposure process, having no WMF in all cases (NW), adding WMF all the time (AW), exerting WMF only during the bacterial culture (BW), and exerting WMF only in POL exposure period (EW). Results It was shown that the concentration–response curves (CRCs) of POL in four WMF treatments at the exposure times of 6, 9, and 12 h are non-monotonic hormetic curves where the maximum stimulative effects (Emin) of POL in BW and EW are obviously larger than those in AW and NW. The maximum Emin is 26.8% occurring in EW and 20.7% in BW at 6 h, while the max Emin is 14.6% in NW at 9 h, it means that stimulations of POL in BW and EW are earlier and stronger than those in NW. These findings first indicated that WMF can enhance the hormesis of POL. Conclusions This study showed that WMF as a key factor may influence the maximum stimulation effect of hormesis. The characteristic of biphasic (hormetic effect) challenges the traditional classical threshold model that is close to chemical risk assessment. But the mechanism of hormesis even now is inconclusive. WMF as a novelty and neglected factor has the potential to support the further development of hormesis mechanism.

This data article presents 1) the acute toxicity (LC50 or EC50 (μg⋅L−1)) values of various chemicals for ten species, which were used to develop ten robust quantitative structure–activity relationship (QSAR) models, 2) the values of the various descriptors in the ten QSAR models, and 3) the acute toxicity values of six pesticides (acetochlor, chlorpyrifos, dimethoate, glyphosate, malathion, and paraquat) for various species, which were applied to establish species sensitivity distribution (SSD) models. The provided LC50 or EC50 (μg⋅L−1) data were collected from the PAN pesticide database and the United States Environmental Protection Agency ecotoxicology database and/or were predicted by the QSAR models. The values of the descriptors in the ten QSAR models were based on the optimal descriptors computed by the DRAGON software (version 7) and subsequently optimized by partial least squares modeling. All the data included in this manuscript are related to the research titled, “Conlecs: A novel procedure for deriving the concentration limits of chemicals outside the criteria of human drinking water using existing criteria and species sensitivity distribution based on quantitative structure–activity relationship prediction” [1]. Keywords: Water quality criteria, Non-linear curve fitting, Toxicity prediction, Variable selection and modeling, Variable importance in projection

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.

Most monotonic S-shaped concentration–response curves (CRCs) can be satisfactorily described by a classical Hill equation. However, the Hill equation cannot effectively describe the non-monotonic J-shaped CRCs that display stimulation at low concentrations and inhibition at high concentrations. On the other hand, the physical meaning of the model parameters in current models describing the J-shaped CRCs is not very clear. It is well known that both toxicity experiments and the fitting process inevitably produce uncertainty. To effectively deal with the J-shaped concentration–response data with uncertainty and make the model parameters meaningful, we developed a method for the fitting of the J-shaped and/or S-shaped concentration–response data (simply called JSFit). The JSFit first uses one Hill equation (S-shaped) or combines with two Hill equations (J-shaped) for fitting, then nonlinear least squares fitting is performed by means of the Levenberg–Marquardt algorithm, and finally the observation-based confidence intervals of the fitting curve are constructed by the delta procedure. For the convenience of application, we wrote a computational program (JSFit) using the MATAB programming language and introduced automation of the initial parameters into the program. The JSFit was then successfully used in the fitting and prediction of the toxic data of pesticides, ionic liquids, antibiotics, and personal skin-care products on Vibrio Qinghaiensis sp.-Q67.

Hormesis is a biphasic concentration–response relationship. During the luminescence inhibition test of Vibrio qinghaiensis sp.-Q67 (Q67), some organic solvents display the hormesis phenomenon. However, the mechanism of hormesis with respect to organic solvents remains unclear. This study focuses on luciferase, which is the key factor in luminescent reactions, and explores its role in the mechanism of hormesis. Q67 luciferase has two subunits, α and β. Molecular docking and molecular dynamics simulations were carried out by taking organic solvents as ligand and the two subunits as receptor. In addition, the binding free energies of the complexes formed by the ligand and Q67 luciferase were calculated. The results showed that the organic solvent ligands exhibiting hormesis bind to the β subunit first, while those that do not exhibit hormesis bind more easily to the α subunit. The hormetic organic solvents bind to β subunit first at low concentration, and change the flexibility of residues Ser145–Arg165 located on the α subunit; this enables flavin mononucleotide (FMN) to bind to the α subunit, exhibiting the hormesis phenomenon. With the increasing concentration, redundant molecules start to bind to the α subunit and compete with/block FMN binding to the α subunit, resulting in inhibition.

In the real environment, organisms are simultaneously exposed to different types of chemicals. Different types of chemicals usually have different time-dependent toxicity characteristics, however, people know little about the time-dependent toxicities of mixtures. Hence, the time-dependent toxicity of one pesticide, two ionic liquids (ILs), two antibiotics 20 ternary mixture rays and ten quaternary mixture rays to Vibrio qinghaiensis sp.-Q67 (V. qinghaiensis) were determined by the time-dependent microplate toxicity analysis. The results showed that the toxicity of pesticide (imidacloprid, IMI) to V. qinghaiensis does not change with time, the toxicities of two ILs (1-hexyl-3-methyl-imidazolium bromide, [hmim]Br, and 1-hexyl-3-methylimidazolium chloride, [hmim]Cl) decrease with time, and those of two antibiotics (chloramphenicol, CHL, and polymyxin B sulfate, POL) increase with time. The mixture toxicity almost doesn't change with time. Taking the concentration addition model as an additive reference, the combined toxicities of chemicals are assessed. It is shown that the mixture toxicities of most rays in two ternary mixture systems, [hmim]Br–IMI–CHL and [hmim]Cl–IMI–CHL, are additive at any time, while those in [hmim]Br–IMI–POL and [hmim]Cl–IMI–POL systems are antagonistic at some time, which implies that the POL may be a key component inducing the antagonism. To further validate the antagonism induced by POL, the POL is respectively merged into two ternary [hmim]Br–IMI–CHL and [hmim]Cl–IMI–CHL systems having no antagonism to form two quaternary mixture systems. It is found that many rays in the two new quaternary mixture systems have antagonistic action at some times, which validates that POL is indeed a key component. These findings indicate that the mixture toxicity of pesticide, ILs and antibiotics is complicated and prone to generate interaction, and doing risk assessment should take this type of mixtures into consideration.

Current Chinese surface water environmental quality standard GB3838-2002 for ammonia fails to take water quality factors and native organism distributions in different basins into consideration. In this study, ammonia toxicity tests were performed using three aquatic organisms native to the Shaying River Basin (China). Published ammonia toxicity data with pH and temperature, and toxicity data acquired in this study were used to establish water quality criteria. The final criterion maximum concentration (CMC) and criterion continuous concentration (CCC) for the Shaying River Basin were 5.09 and 1.36 (mg total ammonia nitrogen (TAN))/L (pH 7 and 20 °C), respectively. In addition, based on the corresponding relationship between ammonia toxicity and temperature and pH, the ecological risk assessment of ammonia was conducted in different seasons for the Shaying River using a tiered approach of both hazard quotient (HQ) and the joint probability (JPC) methods. Two methods gave consistent results: the ecological risks of ammonia to aquatic species in the Shaying River Basin were severe and the risk could be ranked as wet season > flat season > dry season. It is therefore indicating that monitoring, evaluation, and early warning of ammonia pollution need to be taken to prevent and control the risks posed by ammonia pollution, especially for wet season (because of high temperatures and pH) or flat season (because of high pH values). We hope the present work could provide valuable information to manage and control ammonia pollution in the Shaying River Basin.

A method combining the uniform design-based ray procedure (UD-Ray) and combination index (CI) integrated with observation-based confidence intervals (OCIs) was proposed to assess the combined toxicities of ternary mixtures of Cu, ionic liquids (1-hexyl-3-methylimidazolium bromide ([hmim]Br) or 3-methyl-1-octylimidazolium chloride ([omim]Cl)) and pesticides (dichlorvos (DIC) or glyphosate (GLY)) to Caenorhabditis elegans (C. elegans). A 96-well microplate was selected as an exposure experiment carrier where six blanks and 12 concentration treatments of a chemical or a mixture ray in four replicates were set. Lethality to C. elegans after 24 h of exposure to the chemical was taken as the toxicity endpoint. A data normalization method was introduced to calibrate the number of worms in various wells. UD-Ray was used to design the basic concentration compositions of five mixture rays (R1–R5) with different mixture ratios, because the UD-Ray can effectively simulate various concentration compositions in the mixture concentration space. Combination index (CI) independent of the action modes of the components was used to evaluate the combined toxicities of various mixtures. It was shown that 11 out of 20 mixture rays in four ternary mixture systems (Cu–DIC–[hmim]Br, Cu–DIC–[omim]Cl, Cu–GLY–[hmim]Br and Cu–GLY–[omim]Cl) display obvious synergistic interactions. The interactions of another nine rays changed from antagonism through additive action to synergism with increasing concentration. Moreover, one mixture ray was designed by fixed no observed effect concentration (NOEC) ratio to validate the predictability of the UD-Ray mixtures. The similarity of interactions in the mixture rays designed by the UD-Ray and fixed NOEC ratio demonstrated that the UD-Ray mixtures can simulate or predict the fixed NOEC ratio mixtures.

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.

Hormesis describes a specific phenomenon in a biphasic concentration-response curve: low concentrations stimulate a response, while high concentrations suppress it. Hormesis could be influenced by several environmental factors, e.g. pH. In this study, the concentration-response/bioluminescence inhibition profiles (CRPs) of six components in personal care products to Vibrio qinghaiensis sp.-Q67 were measured at five different pH levels. When the exposure lasted for 0.25 h, CRPs of the six components at various pH levels were S-shaped, except ascorbic acid 2-glucoside (AA2G) at pH 10.5. When it lasted for 12 h, the CRPs were J-shaped, except AA2G at pH 6.5, 7.5, and 9.5. To rationally explain these changes in hormesis expressed by J-shaped CRP, four characteristic parameters, the minimum effect (Emin) and its corresponding concentration (ECmin), the median effective concentration (EC50), and the zero effect concentration point (ZEP, where the effect is 0 and the concentration is ZEP), were used to quantify the J-shaped CRP. The results indicated that these parameters vary with pH. Additionally, ZEP showed an excellent linear relationship with EC10 (R2 = 0.9994) at all pH levels, indicating that EC10 could replace the no-observed effective concentration (NOEC) in ecological risk assessment. Furthermore, to elucidate the possible mechanism of hormesis, the binding of the components to the luciferase receptors was analyzed using molecular docking technology. The results showed that the components displaying hormesis bind more easily to the α subunit of luciferase than to the β subunit.

Water quality criteria (WQC) for an increasing number of emerging chemicals need to be developed to protect human health and biological safety. Existing species sensitivity distribution (SSD) methods can only be used to help establish WQC for ecological protection, and cannot be extended to the protection of human beings from various hazards. In this study, a novel procedure called Conlecs is proposed to derive the concentration limits (ConLs) of pesticides outside the criteria for human drinking water (CHDW) using the existing criteria of pesticides and SSD integrated with the toxicity prediction achieved through robust QSAR models. Optimal SSD models of four pesticides (within the CHDW) and two pesticides (outside the CHDW) on 12 species were first constructed, and the existing ConLs of four pesticides within the CHDW were then utilized to select the most suitable species for the optimal proportions to avoid human hazards (PHH), allowing the ConLs of two pesticides outside the CHDW to be derived.

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.

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

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.

The hormesis characterized by low-concentration stimulation and high-concentration inhibition has gained significant interest over the past decades. Some organic solvents and ionic liquids (ILs) have hormetic concentration responses (HCR) to bioluminescence such as firefly luciferase and Vibrio qinghaiensis sp.-Q67. In this study, we determine the effects of 1-alkyl-3-methylimidazolium chlorine ILs ([Cnmim]Cl, n = 2, 4, 6, 8, 10 and 12) to firefly luciferase in order to verify the mechanism of hormesis. The luminescence inhibition toxicity tests show that the stimulation effects of [C8mim]Cl and [C10mim]Cl are obvious, [C6mim]Cl and [C12mim]Cl are minor, and [C2mim]Cl and [C4mim]Cl are rare. The enzyme kinetics show that [C8mim]Cl and [C10mim]Cl are the competitive inhibitors with ATP while [C2mim]Cl and [C4mim]Cl are the noncompetitive ones. Molecular dynamics simulation results reveal that imidazolium rings of [C8mim] and [C10mim] locate at the entrance of luciferin pocket which is adjacent to AMP pocket, while alkyl-chains insert into the bottom of the luciferin pocket. Combining the results from inhibition test, kinetics assay and molecular simulation, we can deduce that occupying AMP pocket by imidazolium ring is responsible for hormetic stimulation.

The concentration addition (CA) model is usually used to assess toxicological interaction and predict mixture toxicity. However, when the maximum effect of a mixture is different from that of a single component, it is impossible to directly use CA to predict the mixture concentration (cmix) at some effect zones that are called predictive blind zones (PBZones). A mixture containing no effect chemicals (NECs) is a special case of the PBZone. Aiming at this special PBZone, we assumed the ECx of NECs to be infinity at all effects and thus readily computed various cmixs in PBZones. At the same time, the combination index (CIx) combined with its 95% observation-based confidence intervals (OCIx) was employed to characterize the toxicological interaction. The approach was successfully used to predict the combined toxicity and assess the toxicological interactions of five chemicals, imidacloprid and metalaxyl (pesticides), 1-butylpyridinium bromide (ionic liquid), and apramycin sulfate and neomycin sulphate (antibiotics, being NECs). The results showed that except for the toxicological interactions of five mixture rays at higher effects than 25% being antagonistic, all the other mixtures are due to additive action.

It is necessary to explore the effect of confidence intervals on the combination index (CI) so that rationally evaluate the toxicological interaction (synergism or antagonism) which is dependent on the concentration ratio, the mixture concentration and the exposure time. To effectively detect the toxicological interaction taking place in mixtures, we combined the CI with the observation-based confidence intervals (OCI) which can characterize the uncertainty in toxicity test and in data fitting. In time scale, the short-term (15 min) and long-term (12 h) toxicities of three chemicals (imidacloprid (IMI), pirimicarb (PIR) and streptomycin sulfate (STR)) and their binary mixtures on Vibrio qinghaiensis sp.-Q67 were determined by the microplate toxicity analysis (MTA). The mixtures of IMI, PIR and STR have additive actions all but four IMI–PIR rays (R2–R5) at the effect levels above about 30–40% whose long-term toxicological interaction are synergism.

Whether the toxicities of aminoglycoside (AG) antibiotics in aquatic environments and their mixtures change with time and whether AG antibiotic mixtures exhibit a toxicological interaction are seldom reported. In this paper, four AG antibiotics, apramycin sulfate, dihydrostreptomycin, kanamycin sulfate, and neomycin sulfate, were selected as mixture components to construct six binary mixture systems. Five rays with different concentration ratios were designed by using the direct equipartition ray design procedure for each system. The toxicities of single antibiotics and their binary mixtures to Vibrio qinghaiensis sp.-Q67 (V. qinghaiensis) at five time points, 0.25, 2, 4, 8 and 12 h, were determined by the time-dependent microplate toxicity analysis method. It was found that the AG antibiotics and their binary mixtures have time-dependent toxicity to V. qinghaiensis, i.e., their toxicities increase with time. Taking the concentration addition as an additive reference, we found that the toxicities of all mixtures are concentration additive, in other words, the additivity does not change with time, concentration, and concentration ratio.

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 IDVequ. 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 IDVequ successfully predicted the toxicities of various types of binary mixtures.

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.

Various chemicals in the environment always exist as mixtures. Toxicity interaction within mixtures may pose potential hazards and risks to the environmental safety and human health. Recent studies showed that toxicity interaction by ionic liquid (IL) mixtures can be related to a certain component. To identify the component, we developed a novel procedure integrating an up-to-down process with the uniform design-based ray method (UDUD) and applied it into an IL mixture system of four 1-butyl-3-methylimidazolium ILs (simply [bmim]X) where X=Cl−, Br−, CH3OSO3− and CH3(CH2)7OSO3−. It was shown that two mixture rays in the quaternary system exhibited significant antagonistic interaction. In this paper, the UDUD was first employed to design four ternary mixture systems. The microplate toxicity analysis was used to determine the toxicities of various mixtures to a freshwater photobacterium Vibrio qinghaiensis sp.-Q67. The concentration addition was taken as an additive reference to assess the toxicity interactions taking place in mixtures. The results revealed that some ternary mixture rays including [bmim]CH3(CH2)7OSO3 display antagonism while the ternary rays without [bmim]CH3(CH2)7OSO3 exhibit additivity. On these grounds, we again designed all binary mixtures containing [bmim]CH3(CH2)7OSO3, determined their toxicities and assessed toxicity interaction. The results showed that three binary mixture systems produce antagonism. Thus, it may be concluded that [bmim]CH3(CH2)7OSO3 is indeed a key component inducing mixture antagonism.

In the environment, chemical pollutants always exist as mixtures. To evaluate mixture toxicity, we should examine the concentration ratios and the concentration levels of mixture components as well as the interactive time between the components and testing organism. Selecting three pesticides, metribuzin (MET), dodine (DOD) and ridomil (RID), as mixture components, Vibrio qinghaiensis sp.-Q67 as testing organism, and 96-well microplate as exposure experiment carrier, the luminescence inhibition toxicities of the pesticides and their binary mixtures at six exposure time points (0.25, 2, 4, 8, 12, and 16 h) were determined. The results showed that the three pesticides have different time-toxicity profiles. The toxicity of MET increases over time, and those of DOD and RID show a slight decrease or increase in the first 4 h, respectively, and then remain unchanged to 16 h. The toxicities of the binary mixtures consisting of the three pesticides depend on not only the mixture composition but also the exposure time. The time-toxicity profiles of all rays in MET-DOD mixture system are U-shaped curves, those in MET-RID system are basically monotonically increased and those in DOD-RID system are basically monotonically decreased. However, our findings indicate that for the test substances, mixture toxicity can be predicted by the extended concentration addition model.

It is a tricky but fundamental problem in risk assessment to predict the combined toxicity of several chemicals by methods other than by experiment, especially for binary mixtures, in order to save time and experimental costs. Though several models have been developed, making a choice among them is difficult owing to the variation in mode of inhibition (MOI). To choose a reference model appropriately, we propose an in silico procedure which employs molecular simulation techniques to identify MOI, and which highlights the binding pattern of a small molecule to a biomacromolecule. Specifically, the method is verified by experimental study and shows that 15 binary mixtures of three pesticides, baygon, metacrate, and velpar, inhibit firefly luciferase bioluminescence. The results reveal that the pesticides share the same binding site at the bottom of the luciferin pocket, and combined toxicities could be predicted by the concentration addition model, which enables us to identify the MOI using molecular simulation techniques. In addition, there is a linear relationship between the binding free energy of the mixture (ΔGmix) calculated from the ΔGs of the components and the median effective concentrations (EC50) of the mixture.