Correlating toxicological effects of ionic liquids on Daphnia magna with in silico calculated linear free energy relationship descriptors

Toward the Proactive Design of Sustainable Chemicals: Ionic Liquids as a Prime Example

The tailorable and often unique properties of ionic liquids (ILs) drive their implementation into a broad variety of seminal technologies. The modular design of ILs allows in this context a proactive selection of structures that favor environmental sustainability─ideally without compromising their technological performance. To achieve this objective, the whole life cycle must be taken into account and various aspects considered simultaneously. In this review, we discuss how the structural design of ILs affects their environmental impacts throughout all stages of their life cycles and scrutinize the available data in order to point out knowledge gaps that need further research activities. The design of more sustainable ILs starts with the selection of the most beneficial precursors and synthesis routes, takes their technical properties and application specific performance into due account, and considers its environmental fate particularly in terms of their (eco)toxicity, biotic and abiotic degradability, mobility, and bioaccumulation potential. Special emphasis is placed on reported structure-activity relationships and suggested mechanisms on a molecular level that might rationalize the empirically found design criteria.

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

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 (R² > 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.

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.

Ionic Liquids-A Review of Their Toxicity to Living Organisms

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.

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.

Expert judgment based multicriteria decision models to assess the risk of pesticides on reproduction failures of grey partridge

A suite of models is proposed for estimating the risk of pesticides against the grey partridge (Perdix perdix) and their clutches. Radio-tracked data of females, description and location of the clutches, and data on the pesticide treatments during the laying periods of the partridges were used as basic information. Quantitative structure-activity relationship (QSAR) and quantitative structure-property relationship (QSPR) modelling allowed us to characterize the pesticides by their 1-octanol/water partition coefficient (log P), vapour pressure, primary and ultimate biodegradation potential, acute toxicity (LD₅₀) on P. perdix, and endocrine disruption potential. From these physicochemical and toxicological data, the system of integration of risk with interaction of scores (SIRIS) method was used to design scores of risk for pesticides, alone or in mixture. A program, written in R (version 3.1.1), called Simulation of Toxicity in Perdix perdix (SimToxPP), was designed for estimating the risk of substances, considered alone or in mixture, against the grey partridge during breeding. The software tool is flexible enough to simulate realistic in situ scenarios. Different examples of applications are shown. The advantages and limitations of the approach are briefly discussed.

Environmental Impact of Ionic Liquids: Recent Advances in (Eco)toxicology and (Bio)degradability

This Review aims to integrate the most recent and pertinent data available on the (bio)degradability and toxicity of ionic liquids for global and critical analysis and on the conscious use of these compounds on a large scale thereafter. The integrated data will enable focus on the recognition of toxicophores and on the way the community has been dealing with them, with the aim to obtain greener and safer ionic liquids. Also, an update of the most recent biotic and abiotic methods developed to overcome some of these challenging issues will be presented. The review structure aims to present a potential sequence of events that can occur upon discharging ionic liquids into the environment and the potential long-term consequences.

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.

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

For designing environmentally friendly ionic liquids (ILs), their structural effects on the toxicity should be interpreted via modelling based on the quantitative-structure-activity-relationship (QSAR) concept. For the purpose, QSAR models for predicting IL toxicity in acetylcholinesterase activity were developed by using linear free-energy relationship (LFER) descriptors, whose chemical meanings are well defined. These are excess molar refraction (Ec or a), dipolarity/polarizability (Sc or a), H-bonding acidity (Ac or a), H-bonding basicity (Bc or a), McGowan volume (Vc or a), and ionic interactions of cation (J(+)) and anion (J(-)). Since the experimentally determined LFER descriptors are not available, we calculated them based on density functional theory, conductor-like screening model and the open-source software, obprop. The toxicity values of imidazolium- and pyridinium-based ILs could be predicted by a combination of four descriptors (Ac, Bc, Vc and Sa) with an R(2) of 0.828, and (Ec, Ac, Ea and Sa) with an R(2) of 0.879, respectively. In prediction study using the overall dataset containing various IL structures, the six calculated terms (Ec, Sc, Ac, J(+), Ea, and Sa) were selected and correlated with the observed toxicity values in R(2) of 0.748 for the training set, R(2) of 0.711 for the test set and R(2) of 0.655 for external validation set. And this study explains how the selected terms are contributing to the prediction models, and their chemical meanings were understood.