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

Cytotoxicity of acridinium-based ionic liquids: Structure-activity relationship and mechanistic studies

Ionic liquids (ILs) are a class of low melting point salts with physicochemical properties suitable for a range of industrial applications such as chemical processing and battery design. Major challenges to the wide-scale adoption of ILs in industry include their eco- and cytotoxic effects, however, this opens up the possibility of the use of ILs use as novel anticancer agents. Understanding the structural features that promote IL cytotoxicity is therefore important. Key structural features that can impact IL cytotoxicity include size and lipophilicity of the cationic head group. In this study, the cytotoxic effects of acridinium-based ILs containing relatively large tri- and tetracyclic cations were evaluated. It was found that 9-phenylacridinium-based ILs are potent cytotoxic agents that reduce the viability of human MDA-MB-231 breast cancer cells with IC50 concentrations in the nanomolar range. In mechanistic studies, it was found that unlike the pyridinium-based analogue, [C16Py][I], acridinium-based ILs did not inhibit oxidative phosphorylation or induce reactive oxygen species formation, and may instead target other mitochondrial processes or components such as mitochondrial DNA.

Effects of cationic head group structure on cytotoxicity and mitochondrial actions of amphiphilic ionic liquids

Ionic liquids (ILs) are a class of low melting point salts with physicochemical properties that make them suitable for a range of industrial applications. Accumulating evidence suggests that certain ILs are cytotoxic and potential environmental pollutants, thus understanding the structural features that promote IL cytotoxicity is important. Amphiphilic ionic liquids (AmILs), a class of ILs with lipophilic N-alkyl chains, containing aromatic head groups are generally more cytotoxic than their aliphatic counterparts, however the impact of other head group properties are less clear. This study therefore sought to provide new structure activity relationship (SAR) insights regarding the role of the cationic head group on AmIL cytotoxicity. A series of AmILs bearing a range of structurally diverse aromatic cations varying in size, charge, and lipophilicity was synthesised and screened against human MDA-MB-231 breast cancer cells. It was found that larger and more lipophilic head groups increased cytotoxicity, although the magnitude of the changes were modest. The mitochondrial effects of representative ILs were assessed. The AmILs induced mitochondrial dysfunction in MDA-MB-231 cells at cytotoxic concentrations, suggesting that they target mitochondria. The new SAR information from this study may assist in the design of AmILs with controlled cytotoxicity.

In silico study of natural antioxidants

Antioxidants are the body's defense system against the damage of reactive oxygen species, which are usually produced in the body through various physiological processes. There are various sources of these antioxidants such as endogenous antioxidants in the body and exogenous food sources. This chapter provides important information on methods used to investigate antioxidant activity and sources of plant antioxidants. Over the past two decades, numerous studies have demonstrated the importance of in silico research in the development of novel natural and synthesized antioxidants. In silico methods such as quantitative structure-activity relationships (QSAR), pharmacophore, docking, and virtual screenings are play critical roles in designing effective antioxidants that may be synthesized and tested later. This chapter introduces the available in silico approaches for different classes of antioxidants. Many successful applications of in silico methods in the development and design of novel antioxidants are thoroughly discussed. The QSAR, pharmacophore, molecular docking techniques, and virtual screenings process summarized here would help readers to find out the proper mechanism for the interaction between the free radicals and antioxidant compounds. Furthermore, this chapter focuses on introducing new QSAR models in combination with other in silico methods to predict antioxidants activity and design more active antioxidants. In silico studies are essential to explore largely unknown plant tissue, food sources for antioxidant synthesis, as well as saving time and money in such studies.

Cholesterol Protects the Liquid-Ordered Phase of Raft Model Membranes from the Destructive Effect of Ionic Liquids

Ionic liquids (ILs), although being a class of promising green solvents, have received many reports on the toxicity to living organisms. In this work, aiming at elucidating the disruptive effect of ILs to cell membrane lipid rafts, we investigated the effect of three 1-octylimidazolium-based ILs on the properties of the liquid ordered phase (L_o, a commonly used lipid raft model) of egg sphingomyelin (SM)-cholesterol model membrane. We found that, in the absence of cholesterol, a very low IL:SM molar ratio of 0.01:1 could disrupt the integrity of the bilayer structure. In sharp contrast, the presence of cholesterol in lipid bilayers helps the L_o phase resist the damaging effect of the ILs. For the role of the IL headgroup, we found that the mono- and trisubstituted species show a stronger destructive effect on the structures of the model rafts than the commonly used disubstituted counterpart.

Quantitative structure-toxicity relationship models for predication of toxicity of ionic liquids towards Leukemia rat cell line IPC-81 based on index of ideality of correlation

The application of ion liquids (ILs) as green solvents has attracted the attention of the scientific community. However, ILs may play the role of toxins. Even though ionic liquids may assist to minimise air pollution, but their discharge into aquatic ecosystems might result in significant water pollution due to their potential toxicity and inaccessibility to biodegradation. Recently, more attention has been paid to the toxicity of ILs on plants, bacteria, and humans. Here, a quantitative structure-toxicity relationship study (QSTR) based on the Monte Carlo method of CORAL software has been applied to estimate the logarithm of the half-maximal effective concentration of toxicity of ILs against leukemia rat cell line IPC-81 (logEC₅₀). A hybrid optimal descriptor is used to build QSTR models for a large set of 304 diverse ILs including ammonium, imidazolium, morpholinium, phosphonium, piperidinium, pyridinium, pyrrolidinium, quinolinium, sulfonium, and protic ILs. The SMILES notations of Ils are utilized to compute the descriptor correlation weight (DCW). Four splits are made from the whole dataset and each split is randomly divided into four sets (training subsets and validation set). The index of ideality of correlation (IIC) is applied to evaluate the authenticity and robustness of the QSTR models. A QSTR model with statistical parameters R²=0.85, CCC =0.92, Q²=0.84, and MAE =0.25 for the validation set of the best split is considered as a prime model. The outliers and promoters of increase/decrease of logEC₅₀ are extracted and the mechanistic interpretation of effective descriptors for the model is also offered.HighlightsGlobal SMILES-based QSAR model was developed to predict the toxicity of ILs.The CORAL software is used to model the ILs toxicity on IPC-81 leukemia rat cell lineIIC is tested as a criterion of predictive potential.The toxicological effects of ILs are discussed based on the proposed model.

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 as environmental hazards - Crucial data in view of future PBT and PMT assessment

Ionic liquids (ILs) constitute a large group of chemical compounds. They have gained much attention among scientists and industry due to their unique properties. Due to the fact that ILs are purely ionic compounds, there is the possibility to design an enormous number of cation and anion combinations, making them designer solvents. Thus it also creates the possibility of producing more environmentally benign solvents. However, significant drawbacks related mainly to their toxicity and persistence have already been noticed. Furthermore the interest in these compounds is constantly growing and their impact on the environment should be defined. More and more ILs are produced or imported in the amount higher than 10 tonnes per year and the group of ILs registered in REACH is still expanding. Thus for an increasing number of compounds, it will be necessary to perform a PBT and PMT assessment using the criteria described in REACH. Therefore the data collected in this work thoroughly sort out the information on the toxicity, bioconcentration/bioaccumulation, biodegradation and mobility of ILs in the context of PBT and PMT assessment.

Norm index in QSTR work for predicting toxicity of ionic liquids on Vibrio fischeri

Ionic liquids have been becoming new 'green solvent' because of the low saturation vapor pressure, less volatilization and more recycling utilization. Since most ILs are soluble in water, it should be indispensable to evaluate the ecotoxicology effect of ILs on aquatic environment before using them widely. Based on the concept of norm index, a set of norm descriptors were proposed for anions, cations and ILs. The whole IL structure optimization method has been used to build a predictive norm index-based quantitative structure-toxicity relationship model for the toxicity of ILs on Vibrio fischeri. Statistical results indicated that norm descriptors were reliable and robust in expressing the relationship between structural information and toxicity of ILs. Meanwhile, a series of ILs without experimental values were predicted based on this stable QSTR model. The results indicated that for imidazole-based ILs, an increase in the length of substituent in the branch could enhance the toxicity of ILs on Vibrio fischeri, and the branch contains hydroxyl group, double bond or triple bonds might reduce the toxicity of ILs. Results obtained in this present work would be valuable for the molecular design and the toxicity evaluation toward aquatic organism of ILs.

Prediction of toxicity of Ionic Liquids based on GC-COSMO method

In order to evaluate the toxicity of several different ionic liquids (ILs) towards the leukemia rat cell line (ICP-81), an efficient and reliable quantitative structure-activity relationships (QSAR) model is developed based on descriptors from COSMO-SAC (conductor-like screening model for segment activity coefficient) model. The distribution of screen charge density (σ-profile) of 127 ILs is calculated by GC-COSMO (group contribution based COSMO) method. Two segmentation methods toward σ-profile are used to find out the appropriate descriptors for the QSAR model. The optimal subset of descriptors is obtained by enhanced replacement method (ERM). A multiple linear regression (MLR) and multilayer perceptron technique (MLP) are used to build the linear and nonlinear models, respectively, and the applicability domain of the models is assessed by the Williams plot. It turns out that the nonlinear model based the second segmentation method (MLP-2) is the best QSAR model with an R²=0.975, MSE=0.019 for the training set and R²=0.938, MSE=0.037 for the test set. The reliability and robustness of the presented QSAR models are confirmed by Leave-One-Out (LOO) cross and external validations.

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.

Identification of Essential 2D and 3D Chemical Features for Discovery of the Novel Tubulin Polymerization Inhibitors

Background:

Tubulin polymerization inhibitors interfere with microtubule assembly and their functions lead to mitotic arrest, therefore they are attractive target for design and development of novel anticancer compounds.

Objective:

The proposed novel and effective structures following the use of three-dimensionalquantitative structure activity relationship (3D-QSAR) pharmacophore based virtual screening clearly demonstrate the high efficiency of this method in modern drug discovery.

Method:

Combined computational approach was applied to extract the essential 2D and 3D features requirements for higher activity as well as identify new anti-tubulin agents.

Results:

The best quantitative pharmacophore model, Hypo1, exhibited good correlation of 0.943 (RMSD=1.019) and excellent predictive power in the training set compounds. Generated model AHHHR, was well mapped to colchicine site and three-dimensional spatial arrangement of their features were in good agreement with the vital interactions in the active site. Total prediction accuracy (0.92 for training set and 0.86 for test set), enrichment factor (4.2 for training set and 4.5 for test set) and the area under the ROC curve (0.86 for training set and 0.94 for the test set), the developed model using Extended Class FingerPrints of maximum diameter 4 (ECFP_4) was chosen as the best model.

Conclusion:

Developed computational platform provided a better understanding of requirement features for colchicine site inhibitors and we believe the results of this study might be useful for the rational design and optimization of new inhibitors.

Chemometric modeling of aquatic toxicity of contaminants of emerging concern (CECs) in Dugesia japonica and its interspecies correlation with daphnia and fish: QSTR and QSTTR approaches

The contaminants of emerging concern (CEC) are universally detected in surface water and soil. They can affect the wild life, and their subsequent translocation through the food chain can affect human health, which is an issue of serious concern. Very few amounts of ecotoxicological data are available on the environmental behavior and ecotoxicity of CEC, thus modeling approaches are essential to bridge the existing gap in experimental data. In this present study, we have developed quantitative structure-toxicity relationship (QSTR) models using a data set of 75 compounds for the prediction of aquatic ecotoxicity of CECs on fresh water planarian (Dugesia japonica) by partial least squares (PLS) regression algorithm using simple molecular descriptors selected by genetic algorithm approach. We also explore the correlations between toxicity against D. japonica and those against daphnia (D. magna) and fish (P. promelas), and these were improved on addition of a few molecular descriptors (B08[C-O] and B09[N-O] in case of daphnia and C-006 and H-052 in case of fish) which allowed us to develop predictive interspecies quantitative structure toxicity-toxicity relationship (QSTTR) models, allowing to extrapolate data from one endpoint to another endpoint. The QSTR (Q²_LOO ranging from 0.630 to 0.720 and R²_pred ranging from 0.723 to 0.798) and QSTTR (Q²_LOO = 0.60 and 0.67, R²_pred = 0.88 and 0.84) models have desirable statistical qualities and acceptable internal and external validation measures, meeting rigorous criteria of different validation metrics and showing acceptability for regulatory purposes as proposed by Organization for Economic Cooperation and Development (OECD). Consensus predictions were also performed based on multiple models generated in this study by using the "Intelligent Consensus Predictor" (ICP) tool to enhance the prediction quality for external set compounds.