Synergistic Effect of Chlorpyrifos and Mancozeb on The Survival of <i>Poecilia reticulata</i>

A comparison of mixture toxicity assessment: Examining the chronic toxicity of atrazine, permethrin and chlorothalonil in mixtures to Ceriodaphnia cf. dubia

Results of publised pesticide mixure toxicity experiments conducted with aquatic organisms were compiled and evaluated to assess the accuracy of predictive mixture models. Three types of models were evaluated: Concentration addition (CA), independent action (IA), and simple interaction (SI). The CA model was the most often tested (207 experiments), followed by SI (59) and IA (37). The reviewed experiments are listed in the Supplemental material to provide a resource for future investigators. The predictive accuracy of each model was quantified for each experiment by the model deviation ratio (MDR), which was calculated by dividing the predicted toxicity by the observed toxicity. Eighty-eight percent of all experiments that evaluated the CA model had observed effective concentrations within a factor of 2 of predicted values (MDR values from 0.5-2.0). The median MDR was 1, about 5% of MDRs were less than 0.5, and about 5% were greater than 2, indicating unbiased estimates overall. The predictive accuracy of CA and IA models was influenced, however, by the different modes of action (MOA) of the pesticides. For experiments with pesticides with the same MOA, CA more accurately predicted effective concentrations for more experiments compared to IA, which tended to underpredict toxicity. The IA model was somewhat more accurate than the CA model for most mixtures with different MOAs, but in most cases there were relatively small differences between the models. Additionally, 80% of SI experiments had an MDR value below 2.0 despite a bias towards experiments that are likely to have an interaction. Thus, results indicate that the CA model may be used as a slightly conservative, but broadly applicable model with a relatively small likelihood of underestimating effects due to interactions.

Results of published pesticide mixture toxicity experiments conducted with aquatic organisms were compiled and evaluated to assess the accuracy of predictive mixture models. Three types of models were evaluated: Concentration addition (CA), independent action (IA), and simple interaction (SI). The CA model was the most often tested (207 experiments), followed by SI (59) and IA (37). The reviewed experiments are listed in the Supplemental material to provide a resource for future investigators. The predictive accuracy of each model was quantified for each experiment by the model deviation ratio (MDR), which was calculated by dividing the predicted toxicity by the observed toxicity. Eighty-eight percent of all experiments that evaluated the CA model had observed effective concentrations within a factor of 2 of predicted values (MDR values from 0.5–2.0). The median MDR was 1, about 5% of MDRs were less than 0.5, and about 5% were greater than 2, indicating unbiased estimates overall. The predictive accuracy of CA and IA models was influenced, however, by the different modes of action (MOA) of the pesticides. For experiments with pesticides with the same MOA, CA more accurately predicted effective concentrations for more experiments compared to IA, which tended to underpredict toxicity. The IA model was somewhat more accurate than the CA model for most mixtures with different MOAs, but in most cases there were relatively small differences between the models. Additionally, 80% of SI experiments had an MDR value below 2.0 despite a bias towards experiments that are likely to have an interaction. Thus, results indicate that the CA model may be used as a slightly conservative, but broadly applicable model with a relatively small likelihood of underestimating effects due to interactions.

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. 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. 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. 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. 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. 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.

BackgroundZinc oxide nanoparticle (nZnO) and chemicals with different mode of action (MOA, i.e., narcotic and reactive) were frequently detected in the Yangtze River. Organisms are typically exposed to mixtures of nZnO and other chemicals rather than individual nZnO. Toxicity of nZnO is caused by the dissolution of Zn2+, which has been proved in the field of single toxicity. However, it is still unclear whether the released Zn2+ plays a critical role in the nZnO toxicity of nZnO–chemicals mixtures. In the present study, the binary mixture toxicity of nZnO/Zn2+ and chemicals with different MOA was investigated in acute (15 min) and chronic (12 h) toxicity test upon Vibrio fischeri (V. fischeri). The joint effects of nZnO and tested chemicals were explored. Moreover, two classic models, concentration addition (CA) and independent action (IA) were applied to predict the toxicity of mixtures.ResultsThe difference of toxicity unit (TU) values between the mixtures of Zn2+–chemicals with those of nZnO–chemicals was not significant (P > 0.05), not only in acute toxicity test but also in chronic toxicity test. The antagonistic or additive effects for nZnO-chemicals can be observed in most mixtures, with the TU values ranging from 0.75 to 1.77 and 0.47 to 2.45 in acute toxicity test and chronic test, respectively. We also observed that the prediction accuracy of CA and IA models was not very well in the mixtures where the difference between the toxicity ratios of the components was small (less than about 10), with the mean absolute percentage error (MAPE) values ranging from 0.14 to 0.67 for CA model and 0.17–0.51 for IA model, respectively.ConclusionWe found that the dissolved Zn2+ mainly accounted for the nZnO toxicity in the mixtures of nZnO–chemicals, and the joint effects of these mixtures were mostly antagonism and additivity. CA and IA models were unsuitable for predicting the mixture toxicity of nZnO–chemicals at their equitoxic ratios.

Arsenic (As) and lead (Pb) are environmental pollutants found in common sites and linked to similar adverse health effects. Multiple studies have investigated the toxicity of each metal individually or in complex mixtures. Studies defining the joint interaction of a binary exposure to As and Pb, especially during the earliest stages of development, are limited and lack confirmation of the predicted mixture interaction. We hypothesized that a mixture of As (iAsIII) and Pb will have a concentration addition (CA) interaction informed by common pathways of toxicity of the two metals. To test this hypothesis, developing zebrafish (1–120 h post fertilization; hpf) were first exposed to a wide range of concentrations of As or Pb separately to determine 120 hpf lethal concentrations. These data were then used in the CA and independent action (IA) models to predict the type of mixture interaction from a co-exposure to As and Pb. Three titration mixture experiments were completed to test prediction of observed As and Pb mixture interaction by keeping the Pb concentration constant and varying As concentrations in each experiment. The prediction accuracy of the two models was then calculated using the prediction deviation ratio (PDR) and Chi-square test and regression modeling applied to determine type of interaction. Individual metal exposures determined As and Pb concentrations at which 25% (39.0 ppm Pb, 40.2 ppm As), 50% (73.8 ppm Pb, 55.4 ppm As), 75% (99.9 ppm Pb, 66.6 ppm As), and 100% (121.7 ppm Pb, 77.3 ppm As) lethality was observed at 120 hpf. These data were used to graph the predicted mixture interaction using the CA and IA models. The titration experiments provided experimental observational data to assess the prediction. PDR values showed the CA model approached 1, whereas all PDR values for the IA model had large deviations from predicted data. In addition, the Chi-square test showed most observed results were significantly different from the predictions, except in the first experiment (Pb LC25 held constant) with the CA model. Regression modeling for the IA model showed primarily a synergistic response among all exposure scenarios, whereas the CA model indicated additive response at lower exposure concentrations and synergism at higher exposure concentrations. The CA model was a better predictor of the Pb and As binary mixture interaction compared to the IA model and was able to delineate types of mixture interactions among different binary exposure scenarios.

Concentration addition and independent action model: Which is better in predicting the toxicity for metal mixtures on zebrafish larvae

The combined toxicities of binary mixtures of veterinary pharmaceutical active compounds were examined using the bioluminescent bacterium Aliivibrio fischeri as a test organism (Microtox® test). Mixtures were prepared at an equitoxic ratio that corresponded to the inhibitory concentration, 10% (IC10) of individual pharmaceutical active compounds. In addition, the toxicity was determined of a multicomponent mixture that contained all of the investigated pharmaceutical active compounds mixed at a ratio corresponding to their individual predicted no-effect concentration (PNEC) values. The experimental results were successively compared with those obtained by applying the 2 most widely used models for predicting mixture toxicity, the concentration addition (CA) and independent action (IA) models. Although the toxicity of the multicomponent mixture tested was well predicted by the CA and IA models, deviations from the model predictions were found for almost all of the binary mixtures. The deviations from the CA and IA models were greater at lower concentrations, particularly when diclofenac sodium and amoxicillin were present in the mixture. Based on these results, another hypothesis was tested, that of toxicological interactions occurring in binary mixtures (in the direction of synergistic or antagonistic effects), by applying the combination index method, which allowed for computerized quantification of synergism, the additive effect and antagonism. The application of this method confirmed, for at least half of the binary combinations, the clear presence of synergistic deviations at the lowest tested concentrations, with a tendency toward antagonism at the higher ones. In 1 case, a relevant antagonistic interaction was observed. Environ Toxicol Chem 2017;36:815-822. © 2016 SETAC.

Mixture predicted no-effect concentrations derived by independent action model vs concentration addition model based on different species sensitivity distribution models

Risk assessment of chemicals is still primarily focusing on single compound evaluation, even if environmental contamination consists of a mixture of pollutants. The concentration addition (CA) and independent action (IA) models have been developed to predict mixture toxicity. Both models assume no interaction between the components, resulting in an additive mixture effect. In the present study, the embryo toxicity test (OECD TG no. 236) with zebrafish embryos (Danio rerio) was performed to investigate whether the toxicity caused by binary, ternary, and quaternary mixtures of organic (Benzo[a]pyrene, perfluorooctanesulfonate, and 3,3´,4,4´,5-pentachlorobiphenyl 126) and inorganic (arsenate) pollutants can be predicted by CA and IA. The acute toxicity and sub-lethal alterations such as lack of blood circulation were investigated. The models estimated the mixture toxicity well and most of the mixtures were additive. However, the binary mixture of PFOS and PCB126 caused a synergistic effect, with almost a ten-fold difference between the observed and predicted LC50-value. For most of the mixtures, the CA model was better in predicting the mixture toxicity than the IA model, which was not expected due to the chemicals' different modes of action. In addition, some of the mixtures caused sub-lethal effects not observed in the single compound toxicity tests. The mixture of PFOS and BaP caused a division of the yolk and imbalance was caused by the combination of PFOS and As and the ternary mixture of PFOS, As, and BaP. Interestingly, PFOS was part of all three mixtures causing the mixture specific sub-lethal effects. In conclusion, the present study shows that CA and IA are mostly resulting in good estimations of the risks that mixtures with few components are posing. However, for a more reliable assessment and a better understanding of mixture toxicity, further investigations are required to study the underlying mechanisms.

The purpose of this study is to evaluate effect of heavy metals (i.e., Cu2+, Zn2+, Cr6+, Cd2+, Hg2+, and Pb2+) to toxicity of Triclosan as binary mixture. The individual toxicity and combined toxic effects of Triclosan with heavy metals were evaluated by Vibrio fischeri assay. In individual toxicity, the Hg2+ was found to be most toxic followed by Triclosan, Pb2+, Cr6+, Cu2+, Zn2+, and Cd2+, respectively. To evaluate combined toxic effect, correlation analysis of ‘predicted value’ calculated by Concentration addition (CA) model and Independent action (IA) model with ‘experimental value’ were performed based on the toxicity of individual compound. As a result, all of the combinations showed that IA model were more correlated with experimental value than CA model. On the basis of the median effect concentration of combination (EC50mix) predicted by IA model, experimental EC50mix of Triclosan + Cu, Triclosan + Zn, Triclosan + Pb, Triclosan + Hg, Triclosan + Cd, and Triclosan + Cr were 191%, 226%, 138%, 137%, 209%, and 138% of EC50mix predicted by IA model, respectively, indicating that all of the combinations produced antagonistic effect.

The assessment of the combined effects of substances is usually based on concentration addition (CA), independent action (IA) or effect summation (ES) models. Both concepts are founded on different modes of actions of substances, but the knowledge about their relationship is rare. In this paper, we derived a series of inequalities for CA, IA and ES models, and proposed two novel models i.e., ES with the exponent e (ESE) model and ES with the power of the number of components n (ESN) model to evaluate the mixture effect. Our results may have certain significance in mixture risk assessment practices.

As the knowledge on the joint effects of pharmaceuticals towards different non-target organisms is still limited, the aim of our study was to evaluate the toxicity of mixtures of pharmaceuticals, as well as their baseline toxicity towards three selected organisms, namely the bioluminescent bacteria Aliivibrio fischeri, the crustacean Daphnia magna, and the duckweed Lemna minor. Different mixtures composed of three up to five pharmaceuticals having the same or different mechanisms of action in terms of their therapeutic activity (non-steroidal anti-inflammatory drugs, opioid analgesic, antibacterial and anti-epileptic drugs) were investigated. The observed EC50s were compared with those predicted using the concentration addition (CA) and independent action (IA) models. In general, the EC50 values for mixtures predicted with the CA model were lower than those obtained with the IA model, although, in some cases, test predictions of these two models were almost identical. Most of the experimentally determined EC50 values for the specific mixtures were slightly higher than those predicted with the CA model; hence, a less than additive effect was noted. Based on the obtained results, it might be concluded that the CA model assumes the worst-case scenario and gives overall closer predictions; therefore, it should be recommended also for modeling the mixture toxicity of pharmaceuticals with different modes of action.

To study 17β-estradiol (E2), ethinylestradiol (EE2), estriol (E3), estrone (E1) on MCF-7 proliferation effects, and compare the effects of independent action (IA) model with concentration addition (CA) model in assessing the combined effects of estrogen. The combinations of E2 + EE2, E2 + E3 and E2 + E1 were chosen and the cellular proliferation effects were examined by MTT assay. The maximum proliferation effects at dose of 10⁻⁹ mol/L was 325.48% for E2, 330.34% for EE2, 255.22% for E3, and 199.61% for E1. In the E2 + EE2, E2 + E3, E2 + E1 groups, the results of IA model analysis were very close to the experimental results. The IA model tend to overestimated the experimental results, while the CA model often underestimated the experimental results. In the EC (E2, 30) + C (EE2, 70) group, the results exceed the maximum estrogen effects of E2, while in other groups, the results were lower. The estrogenic effects of the four tested substances from high to low efficiency were that: EE2 > E2 > E3 > E1. The effect of IA model in predicting the combined effects of binary mixture was better than CA model. A small proportion of binary mixture showed synergy.

Short-term influence of polytetrafluoroethylene micro/nano-plastics on the inhibition of copper and/or ciprofloxacin on the nitrifying sludge activities based on concentration addition and independent action models