Cyto- and enzyme toxicities of ionic liquids modelled on the basis of VolSurf+ descriptors and their principal properties

Review of the toxic effects of ionic liquids

Interest in ionic liquids (ILs), called green or designer solvents, has been increasing because of their excellent properties such as thermal stability and low vapor pressure; thus, they can replace harmful organic chemicals and help several industrial fields e.g., energy-storage materials production and biomaterial pretreatment. However, the claim that ILs are green solvents should be carefully considered from an environmental perspective. ILs, given their minimal vapor pressure, may not directly cause atmospheric pollution. However, they have the potential to cause adverse effects if leaked into the environment, for instance if they are spilled due to human mistakes or technical errors. To estimate the risks of ILs, numerous ILs have had their toxicity assessed toward several micro- and macro-organisms over the past few decades. Since the toxic effects of ILs depend on the method of estimating toxicity, it is necessary to briefly summarize and comprehensively discuss the biological effects of ILs according to their structure and toxicity testing levels. This can help simplify our understanding of the toxicity of ILs. Therefore, in this review, we discuss the key findings of toxicological information of ILs, collect some toxicity data of ILs to different species, and explain the influence of IL structure on their toxic properties. In the discussion, we estimated two different sensitivity values of toxicity testing levels depending on the experiment condition, which are theoretical magnitudes of the inherent sensitivity of toxicity testing levels in various conditions and their changes in biological response according to the change in IL structure. Finally, some perspectives, future research directions, and limitations to toxicological research of ILs, presented so far, are discussed.

Application of general toxic effects of ionic liquids to predict toxicities of ionic liquids to Spodoptera frugiperda 9, Eisenia fetida, Caenorhabditis elegans, and Danio rerio

Modeling for the toxicity of ionic liquids (ILs) is necessary to fill data gaps for untested chemicals and to understand the relevant mechanisms at the molecular level. In order for many researchers to easily predict toxicity and/or develop some prediction model, simple method(s) based on a single parameter should be proposed. Therefore, previously our group developed a comprehensive toxicity prediction model with unified linear free-energy relationship descriptors to address the single parameter for predicting the toxicities, as follows (Cho et al., 2016b). Log 1/toxicity in the unit of mM= (2.254 E_c - 2.545 S_c + 0.646 A_c - 1.471 B_c + 1.650 V_c + 2.917 J⁺ - 0.201 E_a + 0.418 V_a + 0.131 J^-) - 0.709. It is considered that the model can calculate the general toxicological effect of ILs in parenthesis, as it was developed on the basis of numerous toxic effects i.e., 58 toxicity testing methods and about 1600 data points. In order to check the hypothesis, the values calculated by the model were correlated with four different datasets from insect cell line (Spodoptera frugiperda 9), earthworm (Eisenia fetida), nematode (Caenorhabditis elegans), and fish (Danio rerio). The results clearly showed that the calculated values are in good agreement with each dataset. In the case of S. frugiperda 9 cells, the calculated parameters were correlated with log1/LC₅₀ values, measured after 24 h and 48 h incubation, in R² of 0.67 and 0.88, respectively. The R² values for the earthworm, nematode, and fish were 0.88, 0.96, and 0.94-0.95, respectively. This study confirmed that the comprehensive model can be simply and accurately used to predict toxicity of ILs.

A detailed structural study of cytotoxicity effect of ionic liquids on the leukemia rat cell line IPC-81 by three dimensional quantitative structure toxicity relationship

In the present study, a very thorough and in-depth three-dimensional quantitative structure-toxicity relationship (3D-QSTR) analysis has been implemented to make a correlation between the structural information of the ionic liquids (ILs) and their cytotoxicity towards Leukemia rat cell line IPC-81, as one of the ILs' toxicological consequences. To do this, alignment free GRid-INdependent Descriptors (GRINDs), which were derived from molecular interaction fields (MIFs), were correlated to the cytotoxicity values by partial least squares (PLS) and support vector regression (SVR). Genetic algorithm (GA), as a powerful linear tool, was used to select the best and interpretative subset of variables for the predictive model building. The selected variables with the capability to screen the effective structural features, showed direct and inverse contribution to the cytotoxicity. In silico modeling can reduce the amount of cellular testing necessary by predicting the toxicological functions of the chemical structures. Acceptable predictions of both internal and external validation sets made it possible to develop the predictive models for a large set of 269 diverse ILs containing 9 cationic cores and 44 types of anions. The constructed 3D-QSTR models use simple and interpretable descriptors to provide an in-depth and mechanistic interpretation of structural characteristics. This helps provide a clear understanding of the cytotoxicity effects of the understudy ILs. The effects of the nature of the cations, anions, and substituents on the cytotoxicities were evaluated and discussed.

Modeling from Theory and Modeling from Data: Complementary or Alternative Approaches? The Case of Ionic Liquids

In the field of ionic liquids (ILs), theory-driven modeling approaches aimed at the best fit for all available data by using a unique, and often nonlinear, model have been widely adopted to develop quantitative structure-property relationship (QSPR) models. In this context, we propose chemoinformatic and chemometric data-driven procedures that lead to QSPR soft models with local validity that are able to predict relevant physicochemical properties of ILs, such as viscosity, density, decomposition temperature, and conductivity. These models, which use readily available and easily interpretable VolSurf+ descriptors, represent an unexploited opportunity for experimentalists to model and predict the physicochemical properties of ILs in industrial R&D design.

Comprehensive approach for predicting toxicological effects of ionic liquids on several biological systems using unified descriptors

The challenge and opportunity for design of environmentally-benign ionic liquids (ILs) would start from prediction of their toxicological effects on several endpoints solely based on the structural formulas. Especially, a comprehensive yet simple equation able to predict several biological responses to IL toxicity is of much advantage. Therefore, based on 50 toxicity testing systems on ILs a comprehensively approachable prediction method was developed. For the modelling, approximately 1600 toxicity values measured by several biological systems and an amended linear free energy relationship (LFER) model were used. Since the toxicological activities of an IL could be differently described according to sensitivity of toxicity testing systems, the sensitivity of each of toxicity testing systems was also estimated in the modelling. By statistical analysis with the calculated descriptors, a LFER model was built. Also the sensitivity value of each system on the basis of the comprehensively approachable model was numerically estimated. In results, it was observed that the combination of single model and sensitivity terms was able to predict each of 50 toxicological effects of ILs with R(2) of 0.593~0.978, and SE of 0.098~0.699 log unit, and the total data set with R(2) of 0.901 and SE of 0.426 log unit.

A QSPR approach to the ecotoxicity of ionic liquids (Vibrio fischeri) using VolSurf principal properties

Recently derived in silico structural descriptors for both IL cations and anions allowed the development of a QSPR model correlating ionic liquid structures to Vibrio fischeri toxicity using the partial least squares (PLS) approach. Interpretation of the PLS model confirmed the effect of IL cationic structural features such as the influence of cation side chain length, presence of heteroatoms, and non-aromaticity of the heterocyclic scaffold on toxicity. The PLS model also provided a quantitative evaluation of anion effects, previously not evidenced due to the structural similarity of the anions considered. A simple equation in which three descriptors (two for the cations and one for the anions) allow the prediction of Vibrio fischeri toxicity for over 8000 ILs is reported.

Prediction of ionic liquid's heat capacity by means of their in silico principal properties

VolSurf+ in silico principal properties of ionic liquids were used to develop a QSPR model providing affordable heat capacity predictions which were experimentally validated.