Evaluation Of Mixture Toxicity Research Articles

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

In the hazard assessment of mixtures, the mixture predicted no-effect concentration (mPNEC) is always derived by the concentration addition (CA) model (mPNECCA) to assess the risk of mixtures combined with exposure assessment. However, the independent action (IA) model, which is also widely used as the CA model in the prediction and evaluation of mixture toxicity, is always used to calculate the population fraction showing a predefined effect, not mPNEC, and this limits the application of IA model in the mixture risk assessment. In this study, we explored the process of mPNEC derived by the IA method (mPNECIA) based on the species sensitivity distribution (SSD) and compared mPNECIA with mPNECCA. Taking two common pesticides, dimethoate (DIM) and dichlorvos (DIC), exposed in the actual water environment as an example, their SSD models were constructed separately using nine distribution functions after toxicity data screening and quality testing. For both DIC and DIM, all different nine models had passed the Kolmogorov-Smirnov test. Then, the PNECs of two pesticides were derived based on SSD models. Finally, mPNECIA with different concentration ratios was derived and compared to mPNECCA based on 81 combinations of nine SSD models. Most mPNEC values derived by IA model were more conservative than those by CA. It is worth noting that the mPNECIA is more conservative than mPNECCA for the commonly used log-logit distribution (function 7), log-normal distribution (8), and log-Weibull distribution (9). This study provides a new direction for the application of IA in the risk assessment and enriches the framework of mixture risk assessment.

Prediction of the joint action of binary mixtures based on characteristic parameter k∙ECx from concentration-response curves

The evaluation of joint toxicity of mixtures is an important topic in toxicology. Previous studies have found that the parameter k∙ECx of concentration response curves (CRCs) can be used to assess the applicability of concentration addition model (CA). This study further assesses the predictability of k∙ECx on the joint toxicity evaluation. The toxicities of the twelve environmental pollutants, as well as those of binary mixtures with an equivalent-effect concentration ratio, to Vibrio fischeri were determined by using the microplate toxicity analysis. The toxicity evaluation of mixtures was conducted by CA and independent action model (IA). The relationship between the joint toxicity (measured by the relative model deviation ratio (rMDR)) and the k∙ECx was studied. The results shows that the k∙ECx could reflect the shape of CRCs in the whole concentration range. According to the IA and CA, 65% of the mixtures produce strong antagonistic or synergistic effect due to the significant difference of k∙ECx. The percentage of the relative difference of k∙ECx of components and the rMDRx can be fitted by an exponential function. Different types of interactions could be described using this function. It is suggested that the joint toxicity of binary mixtures can be assessed with the parameter k∙ECx, which can quickly get very important data when planning experiments, but also reduce the number of experiments.

The Combined Algae Test for the Evaluation of Mixture Toxicity in Environmental Samples.

The combined algae test is a 96-well plate-based algal toxicity assay with the green algae Raphidocelis subcapitata that combines inhibition of 24-h population growth rate with inhibition of photosynthesis detected after 2 and 24 h with pulse-amplitude modulated (PAM) fluorometry using a Maxi-Imaging PAM. The combined algae test has been in use for more than a decade but has had limitations due to incompatibilities of the measurements of the 2 biological endpoints on the same microtiter plates. These limitations could be overcome by increasing growth rates and doubling times on black, clear-bottom 96-well plates by application of dichromatic red/blue light-emitting diode illumination. Different robotic dosing approaches and additional data evaluation methods helped to further expand the applicability domain of the assay. The combined algae test differentiates between nonspecifically acting compounds and photosynthesis inhibitors, such as photosystem II (PSII) herbicides. The PSII herbicides acted immediately on photosynthesis and showed growth rate inhibition at higher concentrations. If growth was a similar or more sensitive endpoint than photosynthesis inhibition, this was an indication that the tested chemical acted nonspecifically or that a mixture or a water sample was dominated by chemicals other than PSII herbicides acting on algal growth. We fingerprinted the effects of 45 chemicals on photosynthesis inhibition and growth rate and related the effects of the single compounds to designed mixtures of these chemicals detected in water samples and to the effects directly measured in water samples. Most of the observed effects in the water samples could be explained by known photosystem II inhibitors such as triazines and phenylurea herbicides. The improved setup of the combined algae test gave results consistent with those of the previous method but has lower costs, higher throughput, and higher precision. Environ Toxicol Chem 2020;39:2496-2508. © 2020 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Genetically modified Caenorhabditis elegans may lead to inaccurate toxicity evaluation of mixtures

BackgroundOne 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.ResultsAlthough 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.ConclusionsThe 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.

Exploration of Computational Approaches to Predict the Toxicity of Chemical Mixtures.

Industrial advances have led to generation of multi-component chemicals, materials and pharmaceuticals which are directly or indirectly affecting the environment. Although toxicity data are available for individual chemicals, generally there is no toxicity data of chemical mixtures. Most importantly, the nature of toxicity of these studied mixtures is completely different to the single components, which makes the toxicity evaluation of mixtures more critical and challenging. Interactions of individual chemicals in a mixture can result in multifaceted and considerable deviations in the apparent properties of its ingredients. It results in synergistic or antagonistic effects as opposed to the ideal case of additive behavior i.e., concentration addition (CA) and independent action (IA). The CA and IA are leading models for the assessment of joint activity supported by pharmacology literature. Animal models for toxicity testing are time- and money-consuming as well as unethical. Thus, computational approaches are already proven efficient alternatives for assessing the toxicity of chemicals by regulatory authorities followed by industries. In silico methods are capable of predicting toxicity, prioritizing chemicals, identifying risk and assessing, followed by managing, the risk. In many cases, the mechanism behind the toxicity from species to species can be understood by in silico methods. Until today most of the computational approaches have been employed for single chemical’s toxicity. Thus, only a handful of works in the literature and methods are available for a mixture’s toxicity prediction employing computational or in silico approaches. Therefore, the present review explains the importance of evaluation of a mixture’s toxicity, the role of computational approaches to assess the toxicity, followed by types of in silico methods. Additionally, successful application of in silico tools in a mixture’s toxicity predictions is explained in detail. Finally, future avenues towards the role and application of computational approaches in a mixture’s toxicity are discussed.

Toxicity of mixtures of perfluorooctane sulphonic acid with chlorinated chemicals and lipid regulators

The toxicological interaction of perfluorooctane sulphonic acid (PFOS) with the chlorinated pollutants triclosan and 2,4,6-trichlorophenol and the lipid regulators gemfibrozil and bezafibrate was evaluated using the combination index-isobologram equation. The endpoint for bioassays was the growth rate inhibition of the green alga Pseudokirchneriella subcapitata. The results showed that most of the binary combinations assayed exhibited antagonism at all effect levels. The addition of a third component induced a less antagonistic or even synergistic behaviour. This was particularly marked for the ternary mixture of triclosan and 2,4,6-trichlorophenol with PFOS, for which synergism was very strong at all effect levels, with a combination index as low as 0.034±0.002 at EC50 for the mixture. The results obtained indicate that the evaluation of mixture toxicity from single component data using the concentration addition approach could severely underestimate combined toxicity.

Suitability of Test Media Containing EDTA for the Evaluation of Acute Metal Toxicity toDaphnia magnaStraus

The acute toxicity of six metals [Hg, Cd, Cu, Zn, Cr(II), and Cr(VI)] toDaphnia magnaneonates was evaluated using three different test media (Elendt M7, a complex medium containing EDTA; ASTM hard water and EEC, two simple media free of chelators). The EC50values, at both 24 and 48 h, obtained for Zn, Cr(II), and Cr(VI) were similar in all of the media tested. Hg was more toxic in ASTM than in M7 and in EEC media. The toxicity of Cd and Cu was similar in ASTM and EEC media and higher when evaluated in M7 medium. Thus, M7 should be used only carefully for the toxicity evaluation of mixtures and effluents containing metals. It is recommended, however, that it be excluded from use in tests evaluating samples of unknown composition, or those known to contain Cu and Cd. For the metals tested in this study, a factor of five applied to each 48-h EC50would be sufficient in order to attain the respective acute NOECs for immobility.

Use of the Lethal Body Burden in the Evaluation of Mixture Toxicity

In many mixtures of xenobiotics in the field situation, narcosis is the predominant mechanism of toxic action. Earlier studies have found that narcotic chemicals are additive based on exposure concentration. In this study it is revealed that the narcotic chemicals 1,2- and 1,4-dichlorobenzene are additive on a lethal body burden level. This applicability of the lethal body burden in risk assessment of mixtures of unknown chemicals is discussed, and it is compared with other methods of managing mixtures of unknown chemical composition.