Interpretation of toxicological activity of ionic liquids to acetylcholinesterase inhibition via in silico modelling

Cho, Chul-Woong; Yun, Yeoung‐Sang

doi:10.1016/j.chemosphere.2016.06.005

Cited by 24 publications

(5 citation statements)

References 46 publications

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“…Yan et al predicted the toxicity of ILs on AChE by the quantitative structure–activity relationship (QSAR) method using topological indexes [23]. Cho et al interpreted the toxicological activity of ILs on AChE via in silico modeling [24]. Zhu et al predicted the toxicity of ILs on AChE using QSAR models of multiple linear regression and extreme learning machine algorithms [25].…”

Section: Introductionmentioning

confidence: 99%

Integrative Assessment of Mixture Toxicity of Three Ionic Liquids on Acetylcholinesterase Using a Progressive Approach from 1D Point, 2D Curve, to 3D Surface

Tao

Zhou

et al. 2019

IJMS

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The joint toxicities of [BMIM]BF4, [BMIM]PF6, and [HMIM]BF4 on acetylcholinesterase (AChE) were systematically investigated by using a progressive approach from 1D single effect point, 2D concentration-response curve (CRC), to 3D equivalent-surface (ES) level. The equipartition equivalent-surface design (EESD) method was used to design 10 ternary mixtures, and the direct equipartition ray (EquRay) design was used to design 15 binary mixtures. The toxicities of ionic liquids (ILs) and their mixtures were determined using the microplate toxicity analysis (MTA) method. The concentration addition (CA), independent action (IA), and co-toxicity coefficient (CTC) were used as the additive reference model to analyze the toxic interaction of these mixtures. The results showed that the Weibull function fitted well the CRCs of the three ILs and their mixtures with the coefficient of determination (R2) greater than 0.99 and root-mean-square error (RMSE) less than 0.04. According to the CTC integrated with confidence interval (CI) method (CTCICI) developed in this study, the 25 mixtures were almost all additive action at 20% and 80% effect point levels. At 50% effect, at least half of the 25 mixtures were slightly synergistic action, and the remaining mixtures were additive action. Furthermore, the ESs and CRCs predicted by CA and IA were all within the CIs of mixture observed ESs and CRCs, respectively. Therefore, the toxic interactions of these 25 mixtures were actually additive action. The joint toxicity of the three ILs can be effectively evaluated by the ES method. We also studied the relationship between the mixture toxicities and component concentration proportions. This study can provide reference data for IL risk assessment of combined pollution.

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Section: Introductionmentioning

confidence: 99%

Integrative Assessment of Mixture Toxicity of Three Ionic Liquids on Acetylcholinesterase Using a Progressive Approach from 1D Point, 2D Curve, to 3D Surface

Tao

Zhou

et al. 2019

IJMS

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“…The enzyme toxicity data set was originally generated from the UFT (center for environmental research and sustainable technology) at the University of Bremen, and the uniform experimental standards in single laboratory tests can ensure the quality of these toxicity data. In addition, the data set has been rigorously checked and widely used for QSAR modeling in previous studies. − Hence, the high-quality data can show promise for the following machine learning and molecular modeling. In the data set, the AChE enzyme inhibition of an IL was experimentally determined by the half maximum effective (EC 50 ) concentration (μM), which were converted into a logarithmic form.…”

Section: Methodsmentioning

confidence: 99%

Comprehensive Interrogation on Acetylcholinesterase Inhibition by Ionic Liquids Using Machine Learning and Molecular Modeling

Yan

et al. 2021

Environ. Sci. Technol.

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Quantitative structure−activity relationship (QSAR) modeling can be used to predict the toxicity of ionic liquids (ILs), but most QSAR models have been constructed by arbitrarily selecting one machine learning method and ignored the overall interactions between ILs and biological systems, such as proteins. In order to obtain more reliable and interpretable QSAR models and reveal the related molecular mechanism, we performed a systematic analysis of acetylcholinesterase (AChE) inhibition by 153 ILs using machine learning and molecular modeling. Our results showed that more reliable and stable QSAR models (R 2 > 0.85 for both cross-validation and external validation) were obtained by combining the results from multiple machine learning approaches. In addition, molecular docking results revealed that the cations and organic anions of ILs bound to specific amino acid residues of AChE through noncovalent interactions such as π interactions and hydrogen bonds. The calculation results of binding free energy showed that an electrostatic interaction (ΔE ele < −285 kJ/ mol) was the main driving force for the binding of ILs to AChE. The overall findings from this investigation demonstrate that a systematic approach is much more convincing. Future research in this direction will help design the next generation of biosafe ILs.

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“…The results show that the ELM model has a superior performance in the estimation of toxicity of ILs. In addition, acetylcholinesterase was used to study her inhibition of IL through well-defined and in silico calculated LFER descriptors [ 177 ]. This study served to understand the chemical interactions between ILs with diverse chemical structures and enzyme activity.…”

Section: Effect Of Ionic Liquids On Animalsmentioning

confidence: 99%

Ionic Liquids—A Review of Their Toxicity to Living Organisms

Gonçalves

Paredes

Cristino

et al. 2021

IJMS

128

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Ionic liquids (ILs) were initially hailed as a green alternative to traditional solvents because of their almost non-existent vapor pressure as ecological replacement of most common volatile solvents in industrial processes for their damaging effects on the environment. It is common knowledge that they are not as green as desired, and more thought must be put into the biological consequences of their industrial use. Still, compared to the amount of research studying their physicochemical properties and potential applications in different areas, there is a scarcity of scientific papers regarding how these substances interact with different organisms. The intent of this review was to compile the information published in this area since 2015 to allow the reader to better understand how, for example, bacteria, plants, fish, etc., react to the presence of this family of liquids. In general, lipophilicity is one of the main drivers of toxicity and thus the type of cation. The anion tends to play a minor (but not negligible) role, but more research is needed since, owing to the very nature of ILs, except for the most common ones (imidazolium and ammonium-based), many of them are subject to only one or two articles.

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Interpretation of toxicological activity of ionic liquids to acetylcholinesterase inhibition via in silico modelling

Cited by 24 publications

References 46 publications

Integrative Assessment of Mixture Toxicity of Three Ionic Liquids on Acetylcholinesterase Using a Progressive Approach from 1D Point, 2D Curve, to 3D Surface

Integrative Assessment of Mixture Toxicity of Three Ionic Liquids on Acetylcholinesterase Using a Progressive Approach from 1D Point, 2D Curve, to 3D Surface

Comprehensive Interrogation on Acetylcholinesterase Inhibition by Ionic Liquids Using Machine Learning and Molecular Modeling

Ionic Liquids—A Review of Their Toxicity to Living Organisms

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