QSAR prediction of additive and non-additive mixture toxicities of antibiotics and pesticide

Qin, Li-Tang; Chen, Yuhan; Zhang, Xin; Mo, Ling-Yun; Zeng, Honghu; Liang, Yanpeng

doi:10.1016/j.chemosphere.2018.01.142

Cited by 56 publications

(21 citation statements)

References 51 publications

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“…The population size was set as 50, the generation per size was 1000, and the mutation size was 20. Mixture descriptors were calculated from the descriptors of individual and binary disinfection by-product mixtures using Equations 9 to 11 that were successfully utilized for the mixture QSAR (Gaudin et al 2015;Qin et al 2018):…”

Section: Resultsmentioning

confidence: 99%

“…Mixture descriptors were calculated from the descriptors of individual and binary disinfection by‐product mixtures using Equations 9 to 11 that were successfully utilized for the mixture QSAR (Gaudin et al 2015; Qin et al 2018):

{italicD}_{mix, 1} = {(x_{1} p_{1} + x_{2} p_{2})}^{2}

D_{italicmix, 2} = x_{1}^{2} p_{1} + x_{2}^{2} p_{2}

{italicD}_{mix, 3} = {false(1 − | {italicp}_{1} − {italicp}_{2} | false) ({italicx}_{1} − {italicx}_{2})}^{2}

…”

Section: Methodsmentioning

confidence: 99%

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Concentration Addition, Independent Action, and Quantitative Structure–Activity Relationships for Chemical Mixture Toxicities of the Disinfection By products of Haloacetic Acids on the Green Alga Raphidocelis subcapitata

Qin

Liu

Zhang

et al. 2021

Enviro Toxic and Chemistry

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The potential toxicity of haloacetic acids (HAAs), common disinfection by products (DBPs), has been widely studied; but their combined effects on freshwater green algae remain poorly understood. The present study was conducted to investigate the toxicological interactions of HAA mixtures in the green alga Raphidocelis subcapitata and predict the DBP mixture toxicities based on concentration addition, independent action, and quantitative structure–activity relationship (QSAR) models. The acute toxicities of 6 HAAs (iodoacetic acid [IAA], bromoacetic acid [BAA], chloroacetic acid [CAA], dichloroacetic acid [DCAA], trichloroacetic acid [TCAA], and tribromoacetic acid [TBAA]) and their 68 binary mixtures to the green algae were analyzed in 96‐well microplates. Results reveal that the rank order of the toxicity of individual HAAs is CAA > IAA ≈ BAA > TCAA > DCAA > TBAA. With concentration addition as the reference additive model, the mixture effects are synergetic in 47.1% and antagonistic in 25%, whereas the additive effects are only observed in 27.9% of the experiments. The main components that induce synergism are DCAA, IAA, and BAA; and CAA is the main component that causes antagonism. Prediction by concentration addition and independent action indicates that the 2 models fail to accurately predict 72% mixture toxicity at an effective concentration level of 50%. Modeling the mixtures by QSAR was established by statistically analyzing descriptors for the determination of the relationship between their chemical structures and the negative logarithm of the 50% effective concentration. The additive mixture toxicities are accurately predicted by the QSAR model based on 2 parameters, the octanol–water partition coefficient and the acid dissociation constant (pKa). The toxicities of synergetic mixtures can be interpreted with the total energy (ET) and pKa of the mixtures. Dipole moment and ET are the quantum descriptors that influence the antagonistic mixture toxicity. Therefore, in silico modeling may be a useful tool in predicting disinfection by‐product mixture toxicities. Environ Toxicol Chem 2021;40:1431–1442. © 2021 SETAC

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Section: Resultsmentioning

confidence: 99%

{italicD}_{mix, 1} = {(x_{1} p_{1} + x_{2} p_{2})}^{2}

D_{italicmix, 2} = x_{1}^{2} p_{1} + x_{2}^{2} p_{2}

{italicD}_{mix, 3} = {false(1 − | {italicp}_{1} − {italicp}_{2} | false) ({italicx}_{1} − {italicx}_{2})}^{2}

…”

Section: Methodsmentioning

confidence: 99%

Concentration Addition, Independent Action, and Quantitative Structure–Activity Relationships for Chemical Mixture Toxicities of the Disinfection By products of Haloacetic Acids on the Green Alga Raphidocelis subcapitata

Qin

Liu

Zhang

et al. 2021

Enviro Toxic and Chemistry

Self Cite

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“…Qin et al developed a generalized QSAR model for predicting the additive and non-additive toxicities of multi-component mixtures, the experiment tested the joint toxicity of 45 multi-component mixtures composed of two antibiotics (Tetracycline hydrochloric and Chloramphenicol) and four pesticides (Metribuzine, Trichlorfon, dichlorvos, Linuron) to A. fisheri . Compared with classical CA and IA models, the result demonstrated that the QSAR model exhibited high predictive capability for predicting joint toxicity (Qin et al, 2018 ).…”

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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“…Quin et al [42] had developed a QSAR model for the acute toxicities toward Aliivibrio fischeri of 45 binary and multi-component mixtures consisting of four pesticides and two antibiotics. A genetic algorithm (GA) tool was employed to attain the three theoretical descriptors’ model with acceptable internal ( R 2 = 0.94, Q 2 LOO = 0.91) and external ( R 2 pred = 0.78) validation parameters.…”

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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QSAR prediction of additive and non-additive mixture toxicities of antibiotics and pesticide

Cited by 56 publications

References 51 publications

Concentration Addition, Independent Action, and Quantitative Structure–Activity Relationships for Chemical Mixture Toxicities of the Disinfection By products of Haloacetic Acids on the Green Alga Raphidocelis subcapitata

Concentration Addition, Independent Action, and Quantitative Structure–Activity Relationships for Chemical Mixture Toxicities of the Disinfection By products of Haloacetic Acids on the Green Alga Raphidocelis subcapitata

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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