Prediction and Evaluation of the Mixture Toxicity of Twelve Phenols and Ten Anilines to the Freshwater Photobacterium<i>Vibrio qinghaiensis</i>sp.-Q67

Mo, Ling-Yun; Zhu, Zongqiang; Zhu, Yinian; Zeng, Honghu; Li, Yanhong

doi:10.1155/2014/728254

Cited by 5 publications

(4 citation statements)

References 25 publications

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“…In the present study, the toxicity of a petroleum hydrocarbon mixture of F2 (C10-C16) and F3 (>C16-C34) petroleum hydrocarbons to soil invertebrates fit models of similar or dissimilar mechanisms of action equally well, despite the consensus that petroleum hydrocarbons have similar modes of action. Other studies on toxicants with similar and dissimilar mechanisms of action found comparable results, that is, independent action and concentration addition fit observed mixture toxicity data similarly (Faust et al 1994;Backhaus et al 2004;Cedergreen et al 2008;Mo et al 2014;Schnug et al 2014;Qiu et al 2016). Of the limited studies on binary petroleum hydrocarbon mixture toxicity to soil invertebrates, Cermak et al (2013) found that mixtures of F2 and F3 petroleum hydrocarbon followed concentration addition based on earthworm organism tissue concentration.…”

Section: Mixture Toxicitymentioning

confidence: 86%

Petroleum hydrocarbon mixture toxicity and a trait‐based approach to soil invertebrate species for site‐specific risk assessments

Gainer

Cousins

Hogan

et al. 2018

Enviro Toxic and Chemistry

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Although petroleum hydrocarbons released to the environment typically occur as mixtures, petroleum hydrocarbon remediation guidelines often reflect individual substance toxicity. It is well documented that groups of aliphatic petroleum hydrocarbons act via the same mechanism of action (nonpolar narcosis) and, theoretically, concentration addition mixture toxicity principles apply. To assess this theory, 10 standardized acute and chronic soil invertebrate toxicity tests on a range of organisms (Eisenia fetida, Lumbricus terrestris, Enchytraeus crypticus, Folsomia candida, Oppia nitens, and Hypoaspis aculeifer) were conducted with a refined petroleum hydrocarbon binary mixture. Reference models for concentration addition and independent action were applied to the mixture toxicity data with consideration of synergism, antagonism, and dose level toxicity. Both concentration addition and independent action, without further interactions, provided the best fit with observed response to the mixture. Individual fraction effective concentration values were predicted from optimized, fitted reference models. Concentration addition provided a better estimate than independent action of individual fraction effective concentrations based on comparison with available literature and species trends observed in toxic responses to the mixture. Interspecies differences in standardized laboratory soil invertebrate species responses to petroleum hydrocarbon-contaminated soil was reflected in unique traits. Diets that included soil, large body size, permeable cuticle, low lipid content, lack of ability to molt, and no maternal transfer were traits linked to a sensitive survival response to petroleum hydrocarbon-contaminated soil in laboratory tests. Traits linked to sensitive reproduction response in organisms tested were long life span and small clutch size. By deriving single-fraction toxicity endpoints considerate of mixtures, we can reduce the resources and time required to conduct site-specific risk assessments for the protection of a soil organism's exposure pathway. Environ Toxicol Chem 2018;37:2222-2234. © 2018 SETAC.

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Section: Mixture Toxicitymentioning

confidence: 86%

Petroleum hydrocarbon mixture toxicity and a trait‐based approach to soil invertebrate species for site‐specific risk assessments

Gainer

Cousins

Hogan

et al. 2018

Enviro Toxic and Chemistry

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“…Considering the simplicity ease to utilization, TU based on the CA model has been used in the context of unknown toxic effects of a single component. There are partly researches were implemented to investigate the feasibility of CA and IA (Zhang et al, 2008;Liu et al, 2009;Zhou et al, 2010;Mo et al, 2014;Villa et al, 2014;Tong et al, 2015). It is not to say, CA (or IA) is only propitious to the similar (dissimilar) toxicology modes of action.…”

Section: Independent Action Methodsmentioning

confidence: 99%

“…There are partly researches were implemented to investigate the feasibility of CA and IA (Zhang et al, 2008 ; Liu et al, 2009 ; Zhou et al, 2010 ; Mo et al, 2014 ; Villa et al, 2014 ; Tong et al, 2015 ). It is not to say, CA (or IA) is only propitious to the similar (dissimilar) toxicology modes of action.…”

Section: Methods Of Concentration Addition and Independence Actionmentioning

confidence: 99%

Mathematical Modeling Approaches for Assessing the Joint Toxicity of Chemical Mixtures Based on Luminescent Bacteria: A Systematic Review

Wang

Bai

et al. 2020

Front. Microbiol.

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Developments in industrial applications inevitably accelerate the discharge of enormous substances into the environment, whereas multi-component mixtures commonly cause joint toxicity which is distinct from the simple sum of independent effect. Thus, ecotoxicological assessment, by luminescent bioassays has recently brought increasing attention to overcome the environmental risks. Based on the above viewpoint, this review included a brief introduction to the occurrence and characteristics of toxic bioassay based on the luminescent bacteria. In order to assess the environmental risk of mixtures, a series of models for the prediction of the joint effect of multi-component mixtures have been summarized and discussed in-depth. Among them, Quantitative Structure-Activity Relationship (QSAR) method which was widely applied in silico has been described in detail. Furthermore, the reported potential mechanisms of joint toxicity on the luminescent bacteria were also overviewed, including the Trojan-horse type mechanism, funnel hypothesis, and fishing hypothesis. The future perspectives toward the development and application of toxicity assessment based on luminescent bacteria were proposed.

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“…Mo et al [39] employed mixtures of 22 phenolic and aniline derivatives (PADs) to investigate if the dose addition and independent action models can be used to evaluate their toxicity. A mixture’s photobacterium toxicity to the Vibrio qinghaiensis sp.…”

Section: Successful Application Of Computational Modeling For Predmentioning

confidence: 99%

Exploration of Computational Approaches to Predict the Toxicity of Chemical Mixtures

Kar

Leszczyński

2019

Toxics

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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.

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Prediction and Evaluation of the Mixture Toxicity of Twelve Phenols and Ten Anilines to the Freshwater PhotobacteriumVibrio qinghaiensissp.-Q67

Cited by 5 publications

References 25 publications

Petroleum hydrocarbon mixture toxicity and a trait‐based approach to soil invertebrate species for site‐specific risk assessments

Petroleum hydrocarbon mixture toxicity and a trait‐based approach to soil invertebrate species for site‐specific risk assessments

Mathematical Modeling Approaches for Assessing the Joint Toxicity of Chemical Mixtures Based on Luminescent Bacteria: A Systematic Review

Exploration of Computational Approaches to Predict the Toxicity of Chemical Mixtures

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