Global versus local QSAR models for predicting ionic liquids toxicity against IPC-81 leukemia rat cell line: The predictive ability

Sosnowska, Anita; Grzonkowska, Monika; Puzyn, Tomasz

doi:10.1016/j.molliq.2017.02.025

Cited by 30 publications

(15 citation statements)

References 40 publications

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“…The QSPR model has been proposed for the evaluation of the melting temperature of the imidazolium ILs [ 229 ]. The quantitative structural-activity relationship studies (QSAR) have been used for predicting the toxicity against human HeLa and MCF-7 cancer cell lines [ 230 ], the leukemia rat cell line (IPC-81) [ 231 , 232 ]. Zhu et al [ 233 ] elaborated quantitative structure-activity relationships (QSAR) models using an extreme learning machine (ELM) algorithm to evaluate the toxicity of ILs toward the acetylcholinesterase enzyme.…”

Section: Design Of Environmentally Harmless Ilsmentioning

confidence: 99%

Ionic Liquids Toxicity—Benefits and Threats

Flieger

2020

IJMS

244

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Ionic liquids (ILs) are solvents with salt structures. Typically, they contain organic cations (ammonium, imidazolium, pyridinium, piperidinium or pyrrolidinium), and halogen, fluorinated or organic anions. While ILs are considered to be environmentally-friendly compounds, only a few reasons support this claim. This is because of high thermal stability, and negligible pressure at room temperature which makes them non-volatile, therefore preventing the release of ILs into the atmosphere. The expansion of the range of applications of ILs in many chemical industry fields has led to a growing threat of contamination of the aquatic and terrestrial environments by these compounds. As the possibility of the release of ILs into the environment s grow systematically, there is an increasing and urgent obligation to determine their toxic and antimicrobial influence on the environment. Many bioassays were carried out to evaluate the (eco)toxicity and biodegradability of ILs. Most of them have questioned their “green” features as ILs turned out to be toxic towards organisms from varied trophic levels. Therefore, there is a need for a new biodegradable, less toxic “greener” ILs. This review presents the potential risks to the environment linked to the application of ILs. These are the following: cytotoxicity evaluated by the use of human cells, toxicity manifesting in aqueous and terrestrial environments. The studies proving the relation between structures versus toxicity for ILs with special emphasis on directions suitable for designing safer ILs synthesized from renewable sources are also presented. The representants of a new generation of easily biodegradable ILs derivatives of amino acids, sugars, choline, and bicyclic monoterpene moiety are collected. Some benefits of using ILs in medicine, agriculture, and the bio-processing industry are also presented.

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Section: Design Of Environmentally Harmless Ilsmentioning

confidence: 99%

Ionic Liquids Toxicity—Benefits and Threats

Flieger

2020

IJMS

244

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“…Studies have shown that the fewer substituents, the more linear the molecule, the greater the toxicity of ILs. [21] Take the pyridinium chlorides (D1-D2) in Figure 5 as an example. The number of non-hydrogen atoms on the cationic head groups of both D1 and D2 is 6, the number of substituents is 3 and 1, but their toxicities is 3.42 and 2.80, respectively.…”

Section: Resultsmentioning

confidence: 99%

“…[20] Anita Sosnowska et al developed six local models and one global model for predicting the toxicity against the leukemia rat cell line (IPC-81) and verified how the QSAR local/global modeling works for ILs. [21] Similar as most studies, the descriptors they proposed were calculated by Dragon, and then obtained by variable selections. Although these descriptors are excellent for developing quantitative structure-property/activity relationship studies, they are not intuitive and are not easily understood for the researchers not familiar with chemometrics.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Ionic Liquids Toxicity against Leukemia Rat Cell Line IPC‐81 based on the Empirical‐like Models using Intuitive and Explainable Fingerprint Descriptors

Chen

et al. 2020

Molecular Informatics

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Ionic liquids as green solvents have been paid extensive attention in recent years. However, mostly it is cost and time-consuming to measure their properties. Thus, theorical methods, especially ultrafast chemoinformatics methods were introduced into these studies. Instead of abstract and complex models in some QSPR studies, in this study, the 2D structural features related to the toxicity of ionic liquids were discussed at first, and then the corresponding intuitive and meaningful descriptors were suggested to construct quantitative chemoinformatics models, finally a multiple linear regression (MLR) based on the empirical-like models were applied to the estimation of toxicities of 304 ionic liquids. For the test sets, the relationship coefficients reached up to R = 0.90. An external test set of 11 ionic liquids collected from other literatures was submitted to the achieved MLR equations, and the satisfactory result (R = 0.94) was obtained.

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“…However, due to the size of their wide-ranging applicability domains, there can exist issues with model performance. 25 Begging the question, can large robust models with a large applicability domain be developed, allowing for universal models that allow the accurate and sensitive prediction of mutagenicity? Alternatively, does the key lie in the construction of individual small models for each chemical category; although these models may have a limited applicability domain, can their targeted focus ensure they remain highly sensitive and highly predictive?…”

Section: Computational Models For the Prediction Of Mutagenicitymentioning

confidence: 99%

Density Functional Theory in the Prediction of Mutagenicity: A Perspective

Townsend

Grayson

2020

Chem. Res. Toxicol.

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As a field, computational toxicology is concerned with using in silico models to predict and understand the origins of toxicity. It is fast, relatively inexpensive, and avoids the ethical conundrum of using animals in scientific experimentation. In this perspective, we discuss the importance of computational models in toxicology, with a specific focus on the different model types that can be used in predictive toxicological approaches toward mutagenicity (SARs and QSARs). We then focus on how quantum chemical methods, such as density functional theory (DFT), have previously been used in the prediction of mutagenicity. It is then discussed how DFT allows for the development of new chemical descriptors that focus on capturing the steric and energetic effects that influence toxicological reactions. We hope to demonstrate the role that DFT plays in understanding the fundamental, intrinsic chemistry of toxicological reactions in predictive toxicology.

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Global versus local QSAR models for predicting ionic liquids toxicity against IPC-81 leukemia rat cell line: The predictive ability

Cited by 30 publications

References 40 publications

Ionic Liquids Toxicity—Benefits and Threats

Ionic Liquids Toxicity—Benefits and Threats

Estimation of Ionic Liquids Toxicity against Leukemia Rat Cell Line IPC‐81 based on the Empirical‐like Models using Intuitive and Explainable Fingerprint Descriptors

Density Functional Theory in the Prediction of Mutagenicity: A Perspective

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