Analysis of functional effects of a mixture of five pesticides using a ray design

Acute toxicity of binary-metal mixtures of copper, zinc, and nickel to Pimephales promelas: Evidence of more-than-additive effect

Metal mixture toxicity has been studied for decades. However, the results are not consistent, and thus ecological risk assessment and regulation of mixtures has been difficult. The objective of the present study was to use a systematic experimental design to characterize the toxicity of binary-metal mixture of Cu, Zn, and Ni to Pimephales promelas, typically to determine whether the effect of these binary-metal mixtures on P. promelas is more-than-additive. Standard 96-h toxicity tests were conducted with larval P. promelas based on US Environmental and Protection Agency methods to determine metal mixture effects. All experiments were conducted in synthetic moderately hard water with no addition of dissolved organic matter. Three different effect analysis approaches, the MixTox model, the Finney model, and the toxic unit method, were used for comparison. The results indicate that the toxicity of Cu+Zn, Cu+Ni, and Zn+Ni mixtures to P. promelas was more-than-additive. Among the 3 mixtures, the effect of the Cu+Ni mixture was the most profound. The results of the present study are useful for applications to models such as the metal mixture biotic ligand model. More research should be conducted to determine the mechanisms of acute and chronic toxicity of metal mixtures.

Prediction of the joint toxicity of five organophosphorus pesticides to Daphnia magna

The individual toxicities of five organophosphorus pesticides (dichlorvos, parathion, methyl parathion, malathion and dimethoate) to Daphnia magna were investigated in 24-h immobilization experiments. Using these toxicity data, their combined toxicities were measured in pesticide mixtures designed using either 'equivalent-effect concentration ratios' or 'uniform-design concentration ratios'. The toxicities of mixtures of similarly or dissimilarly acting toxicants are often predicted from the individual toxicities of the component compounds, using one of two distinct biometric models: concentration addition (CA) or independent action (IA). The relative accuracies of the CA and IA models were assessed using the model deviation rate (MDR), which represents the difference between the effect predicted from the individual pesticide concentrations and the observed effect. The mean MDR value of CA was 0.93 (range 0.75-1.31) and the mean value obtained by IA was 3.13 (range 2.52-4.37). We conclude that the CA model is better able to predict the joint toxicities of mixtures of organophosphorus pesticides to D. magna.

The effect of mixtures of organophosphate and carbamate pesticides on acetylcholinesterase and application of chemometrics to identify pesticides in mixtures

Organophosphate (OP) and carbamate (CP) pesticides act by the inhibition of acetylcholinesterase (AChE). This enables the use of this enzyme for the detection of these pesticides in the environment. While many studies have looked at the effect of single pesticides on AChE, the effect of mixtures of pesticides still requires extensive investigation. This is important to evaluate the cumulative risk in the case of simultaneous exposure to multiple pesticides. Therefore we examined the effect of five different pesticides (carbaryl, carbofuran, parathion, demeton-S-methyl, and aldicarb) on AChE activity to determine whether combinations had an additive, synergistic, or antagonistic inhibitory effect. Results indicated that the mixtures had an additive inhibitory effect on AChE activity. The data from the assays of the mixtures were used to develop and train an artificial neural network (ANN) which was then utilised successfully for the identification of pesticides and their concentrations in mixtures. This study is significant because it evaluated mixtures of OPs and CPs where previous studies focused on either OPs or CPs. Previous studies have only examined up to three pesticides while this study evaluated mixtures of five pesticides simultaneously. This is also the first study where an ANN was able to utilise data from the inhibition of a single enzyme to differentiate five different pesticides and their concentrations from mixtures.

Predicting mixture toxicity of seven phenolic compounds with similar and dissimilar action mechanisms to Vibrio qinghaiensis sp.nov.Q67

The predictions of mixture toxicity for chemicals are commonly based on two models: concentration addition (CA) and independent action (IA). Whether the CA and IA can predict mixture toxicity of phenolic compounds with similar and dissimilar action mechanisms was studied. The mixture toxicity was predicted on the basis of the concentration-response data of individual compounds. Test mixtures at different concentration ratios and concentration levels were designed using two methods. The results showed that the Weibull function fit well with the concentration-response data of all the components and their mixtures, with all relative coefficients (Rs) greater than 0.99 and root mean squared errors (RMSEs) less than 0.04. The predicted values from CA and IA models conformed to observed values of the mixtures. Therefore, it can be concluded that both CA and IA can predict reliable results for the mixture toxicity of the phenolic compounds with similar and dissimilar action mechanisms.

A novel direct equipartition ray design (EquRay) procedure for toxicity interaction between ionic liquid and dichlorvos

BACKGROUND, AIM AND SCOPE

Pollutants always co-exist in the environment. Determining and characterizing the interaction among chemicals is an important issue. Experimental designs (ED) play an important role in evaluating the interactions. The main aim of our study is to provide the test and analysis of the toxicity interaction with a novel ED method.

MATERIALS AND METHODS

A novel direct equipartition ray design (EquRay) procedure was proposed to effectively and systematically determine the toxicities of binary mixtures on Vibrio qinghaiensis sp.-Q67. Here, one component is ionic liquid, 1-butyl-2,3-dimethylimidazolium chloride (IL1), 1-butylpyridinium bromide (IL2) or N-hexylpyridinium bis(trifluoromethylsulfonyl)imide (IL3), and another is dichlorvos (DIC). The toxicity interaction was evaluated by comparing experiment and additive model together with three-dimension deviation response surface (DRS) analysis.

RESULT

Selecting CA as a reference model, the binary mixtures exerted less than additive (antagonism). Most of the deviations occurred in the centre portion of the DRS where the dCA (deviation from CA) values are between -15% and -26% for IL1-DIC and IL2-DIC mixtures and -10% and -15% for IL3 and DIC. Selecting IA as a additive model, IL1-DIC and IL2-DIC mixtures exhibited less than additive (antagonism) while IL3-DIC displayed an addition action and the absolute values of dIAs (deviation from IA) were less than 10%.

CONCLUSION

A novel EquRay procedure was developed in this study and the EquRay can provide us with the information about the toxicity interaction between binary mixture components (such as DIC and IL) in different concentration regions across different mixture ratios.

Evaluation of the combined toxicity of 15 pesticides by uniform design

BACKGROUND

Environmental pesticides, including insecticides and herbicides, are frequently encountered as mixtures and threaten non-target organisms in water. Evaluation of the combined toxicity of diverse pesticides with various concentration combinations is important, especially using limited experimental effort. Uniform design (UD) is one optimal experimental technique that can rationally arrange the concentrations of mixture components so that, with a minimum number of experimental runs, the combined toxicity of multiple pesticide mixtures can be evaluated.

RESULTS

The concentration compositions of 18 pesticide mixture points designed by UD covered almost all possible concentration ranges of the mixture components on account of the two merits of 'space filling' and 'multiple levels'. The combined toxicities of 18 mixture rays extended by using the fixed-ratio ray design (FRRD) from 18 UD mixture points were evaluated by concentration addition (CA) and independent action (IA) models. It was found that the concentration-response curves (CRCs) predicted by CA were, on the whole, located between the 95% confidence intervals of the experimental CRCs, which implied that the combined toxicity of the pesticide mixture rays could be evaluated by CA. The CRCs predicted by IA were very similar to those from CA.

CONCLUSION

The model developed from the UD mixture rays can effectively simulate mixtures with arbitrary concentration compositions of 15 pesticides. The CA model can accurately evaluate and predict the combined toxicity of the pesticides, which provides a useful tool for risk assessment of a mixture of multiple pesticides in the aquatic environment.

A new effect residual ratio (ERR) method for the validation of the concentration addition and independent action models

BACKGROUND, AIM, AND SCOPE

Glutaraldehyde (GA) often acts as an effective sterilant, disinfectant, and preservative in chemical products. It was found that GA had clearly acute toxicity to aquatic organisms. Furthermore, GA in natural environment could not exist as single species but as complex mixtures. To explore the toxicity interaction between GA and the other environmental pollutant, it is necessary to determine the mixture toxicities of various binary mixtures including GA. Two reference models, concentration addition (CA) and independent action (IA), are often employed to evaluate the mixture toxicity, which can be finished by comparing the concentration-response curves (CRCs) predicted by the reference models with the experimental CRC of the mixture. However, the CRC-based method cannot effectively denote the degree of the deviations from the reference models, especially at very low effect levels. Though the model deviation ratio (MDR) can be used to quantitatively evaluate the deviation of a mixture at EC50 level from the reference model, it is difficult to evaluate the deviations at the lower effect levels. Therefore, the primary aim of this study was to develop a new effect residual ratio (ERR) method to validate the deviations from the reference models at various effect levels.

MATERIALS AND METHODS

Four chemicals having possible dissimilar mode of actions with GA, acetonitrile (ACN), dodine (DOD), simetryn (SIM), and metham sodium (MET), were selected as another component in the binary mixtures including GA, which constructed four binary mixtures, GA-ACN, GA-DOD, GA-SIM, and GA-MET ones. For each binary mixture, two equipotent mixture rays where the concentration ratios of GA to another mixture component are respectively EC50 and EC5 ones were designed and their toxicities (expressed as a percent inhibition to Photobacterium phosphoreum) were determined by microplate toxicity analysis. The observed concentration-response curve (CRC) of a ray was compared with that predicted by CA or IA model to qualitatively assess the toxicity interaction of the mixture ray. To quantitatively and effectively examine the deviations at various effect levels from the reference models, a new concept, ERR at an effect, was defined, and the ERR was employed to evaluate the deviation at various effects with confidence intervals.

RESULTS

For three binary mixtures, GA-ACN, GA-DOD, and GA-SIM, the CRCs predicted by IA models were almost located in the 95% confidence intervals of the experimental CRCs for both equipotent mixture rays, which indicated the independent actions between binary mixture components. However, two rays of GA-MET binary mixture displayed a little synergistic action because both CRCs predicted by CA and IA were lower than the experimental CRC. ERR showed the same results as MDR, but ERR results at low effect area were clearer than MDR ones.

DISCUSSION

In CRC comparison, the deviation of CA (for GA-ACN, GA-DOD, and GA-SIM combinations) or IA (for GA-MET) model from the experimental values could be obviously observed at medium area of the CRC. However, at very low effect levels, both deviations of CA and IA and difference between CA and IA model predictions were not very apparent. Thus, it was difficult to confirm which model, CA or IA, had better predicted power at very low effect levels. MDR in many literatures often refers to a ratio at EC50 level. It was also difficult to reflect not only the deviation fact at the other ECx but also the deviation uncertainty. After we extended the definition of MDR to all ECx and examined the 95% confidence intervals based on observation, the plot of the redefined MDRs at many effect levels could better explain the deviations of CA or IA model from the observation. However, MDRs at very low effect levels did not still reflect the high uncertainty there. The ERRs defined in our paper could explicitly explain the degree of deviation from the reference models and especially reflect the high uncertainty at very low effects. It could be said that the ERR is a better indicator than MDR.

CONCLUSIONS

The new ERR validation method developed in our laboratory could provide us with the information about the toxicity interaction between the mixture components and quantitatively assess the accuracy of the reference models (CA or IA) at whole effect levels. The ERR method conquered the invalidation of the classical CRC comparison method on the deviation decision at low effect levels and also got the advantage over the MDR methods.

RECOMMENDATIONS AND PERSPECTIVES

It holds promise to become an effective method of hazard and risk assessments of chemical mixtures by well characterizing the uncertainty at very low effect levels.

Modeling and prediction for the acute toxicity of pesticide mixtures to the freshwater luminescent bacterium Vibrio qinghaiensis sp.-Q67

In China, water pollution by pesticide mixtures has constituted a serious environmental problem due to potential toxicity and bioaccumulation. But few pesticide combinations have exactly similar and dissimilar mechanisms of action. For this purpose, in tests with the freshwater luminescent bacterium (Vibrio qinghaiensis sp.-Q67), ten pesticides, including three herbicides and seven insecticides, were selected as test substances. Concentration response analysis was performed for ten individual substances, and for mixtures containing all ten substances in twelve different concentration ratios (based on UDCR and EECR methods). The observed mixture toxicity was compared with predictions by the two models, concentration addition (CA) and independent action (IA). The toxicity of the tested mixtures showed a good agreement with those predicted by the concept of CA except four UDCR mixtures: UD10-2, UD10-4, UD10-8 and UD10-10. To examine the influence of imidacloprid in the four UDCR mixtures (UD10-2, UD10-4, UD10-8, UD10-10), it was removed from the ten-pesticide mixtures and the remaining nine chemicals were combined at the same relative proportions based on the UDCR method (UD9-2, UD9-4, UD9-8, UD9-10). There was not significant departure from CA for the scattered points with the nine remaining pesticides omitting imidacloprid. Thus, imidacloprid may significantly influence the other pesticides due to its properties.

Prediction for the mixture toxicity of six organophosphorus pesticides to the luminescent bacterium Q67

Organophosphorus (OP) pesticides are ubiquitous in the surface water as mixtures. To examine the mixture toxicity in the multi-component space, the uniform design (UD) which can explore the concentration changes with few experimental efforts was employed to design the mixtures. On the other hand, the fixed concentration ratio ray was applied into six UD mixtures and two equivalent-effect concentration mixtures to build the whole concentration-response curves to overcome the demerit of the classical "point-to-point" method. The experimental toxicities of six pesticides and their mixtures to the luminescent bacterium Q67 were determined. The mixture toxicities were predicted by two models, concentration addition (CA) and independent action (IA). The results showed that all the mixture toxicities observed had no significant differences from the ones predicted by CA. However, the mixture toxicities were also well predicted by IA especially at the low-concentration section.

Chemical mixture toxicity testing with Vibrio fischeri: combined effects of binary mixtures for ten soft electrophiles

The toxicity of 30 binary combinations of 10 soft electrophiles was examined in Microtox using dose-response curve (DRC) analysis. Chemicals from three groups of soft electrophiles-vinyl Michael acceptors (I--react with a thiol group), dicarbonyl reactive agents (II--react with a primary amine), and alpha-haloactivation compounds (III--react with a thiol group)--were selected for testing to evaluate the relationship between molecular site of chemical action and combined toxic effect. For each combination tested, each single agent was tested alone at six duplicated concentrations and three 1:1 mixtures of the agents were also tested, each at six duplicated concentrations. Exposure duration was 15 min for each single agent and mixture test. Sigmoid DRCs for each single chemical and mixture were constructed and the single chemical curves were used to develop a theoretical dose-addition DRC for the combination. Additivity quotient (AQ) values for slope and EC50 were calculated by dividing the actual mixture slope or EC50 for a given combination by the predicted slope or EC50, respectively, from the theoretical dose-addition DRC. Three criteria were selected for value in determining the combined effect obtained for each combination: (1) slope AQ 95% confidence interval (CI) overlap with 1.0 (1.0=dose addition), (2) EC50 AQ 95% CI overlap with 1.0, and (3) mean mixture data point 95% and 99% CI overlap with the theoretical dose-addition DRC. Each of three sham combinations showed combined effects consistent with dose addition for each criterion. Dose addition was expected for 15 nonsham combinations (nine within-group combinations and six group I:III combinations) and a nondose-additive effect was expected for 12 combinations (all I:II and II:III combinations). Actual combined effects obtained by incorporating all three criteria (noted above) showed only six instances of dose addition. Therefore, time-dependent toxicity (TDT) tests of each soft electrophile alone and for three nonpolar narcotic chemicals alone were conducted, using 15-, 30-, and 45-min exposure durations, to assess the time-dependent nature of the toxicity. Results of the TDT tests suggested that five had fully (or nearly fully) TDT (interpreted as an irreversible effect representing one molecular site of action), five of the soft electrophiles had partially TDT (i.e., representing two or more molecular sites of action for the agents, one irreversible and one reversible), and the three nonpolar narcotics had no TDT (i.e., a fully reversible toxic effect). With this TDT information, the combined effects for 25 of the 27 mixtures, although rather complex, could be explained. It is noteworthy that all combined effects obtained, whether concluded to be dose-additive or not, were close to dose-additive for hazard assessment purposes.

Neurotoxicological interactions of a five-pesticide mixture in preweanling rats

The estimation of risk following exposure to mixtures is an important feature of pesticide risk assessment. Also of concern is the potential for increased sensitivity of the young to pesticide toxicity. We have conducted interaction studies using a mixture of five organophosphorus (OP) pesticides (chlorpyrifos, diazinon, dimethoate, acephate, and malathion) in both adult (published previously) and preweanling rats using a fixed-ratio ray design. In the present study, cholinesterase inhibition and behavioral changes (motor activity, gait, and tail-pinch response) were measured in 17-day-old Long-Evans male rats following acute exposure to the OPs. The ratio of pesticides in the mixture reflected the relative dietary exposure estimates projected by the U.S. Environmental Protection Agency Dietary Exposure Evaluation Model. Dose-response data were collected for each OP alone, which were used (alone or in conjunction with the mixture data) to build an additivity model to predict the effects of the pesticide mixture along a ray of increasing total doses, using the same fixed ratio of components. The mixture data (full ray) were similarly modeled and statistically compared to the additivity model along the ray. Since malathion has been shown to produce synergistic interactions with certain OPs, it was of interest to evaluate the influence of malathion in this study. A second pesticide mixture, without malathion (reduced ray), was tested using the same dose levels of the remaining four OPs. Analysis of the full ray revealed significant greater-than-additive responses for all endpoints. The magnitude of this shift ranged from two- to threefold for estimates of the ED(20) and ED(50). The deviation from additivity was also detected in the reduced ray for all but two endpoints (motor activity and tail-pinch response); however, for all endpoints, the reduced ray was significantly different from the full ray. Thus, greater-than-additive responses were detected in preweanling rats with this OP mixture, and this effect can only partially be attributed to the malathion in the mixture.

Neurotoxicological and Statistical Analyses of a Mixture of Five Organophosphorus Pesticides Using a Ray Design

Environmental exposures generally involve chemical mixtures instead of single chemicals. Statistical models such as the fixed-ratio ray design, wherein the mixing ratio (proportions) of the chemicals is fixed across increasing mixture doses, allows for the detection and characterization of interactions among the chemicals. In this study, we tested for interaction(s) in a mixture of five organophosphorus (OP) pesticides (chlorpyrifos, diazinon, dimethoate, acephate, and malathion). The ratio of the five pesticides (full ray) reflected the relative dietary exposure estimates of the general population as projected by the US EPA Dietary Exposure Evaluation Model (DEEM). A second mixture was tested using the same dose levels of all pesticides, but excluding malathion (reduced ray). The experimental approach first required characterization of dose-response curves for the individual OPs to build a dose-additivity model. A series of behavioral measures were evaluated in adult male Long-Evans rats at the time of peak effect following a single oral dose, and then tissues were collected for measurement of cholinesterase (ChE) activity. Neurochemical (blood and brain cholinesterase [ChE] activity) and behavioral (motor activity, gait score, tail-pinch response score) endpoints were evaluated statistically for evidence of additivity. The additivity model constructed from the single chemical data was used to predict the effects of the pesticide mixture along the full ray (10-450 mg/kg) and the reduced ray (1.75-78.8 mg/kg). The experimental mixture data were also modeled and statistically compared to the additivity models. Analysis of the 5-OP mixture (the full ray) revealed significant deviation from additivity for all endpoints except tail-pinch response. Greater-than-additive responses (synergism) were observed at the lower doses of the 5-OP mixture, which contained non-effective dose levels of each of the components. The predicted effective doses (ED20, ED50) were about half that predicted by additivity, and for brain ChE and motor activity, there was a threshold shift in the dose-response curves. For the brain ChE and motor activity, there was no difference between the full (5-OP mixture) and reduced (4-OP mixture) rays, indicating that malathion did not influence the non-additivity. While the reduced ray for blood ChE showed greater deviation from additivity without malathion in the mixture, the non-additivity observed for the gait score was reversed when malathion was removed. Thus, greater-than-additive interactions were detected for both the full and reduced ray mixtures, and the role of malathion in the interactions varied depending on the endpoint. In all cases, the deviations from additivity occurred at the lower end of the dose-response curves.