A review of recent advances towards the development of QSAR models for toxicity assessment of ionic liquids

Multi-combined QSAR, molecular docking, molecular dynamics simulation, and ADMET of Flavonoid derivatives as potent cholinesterase inhibitors

In searching for a new and efficient therapeutic agent against Alzheimer's disease, a Quantitative structure-activity relationship (QSAR) was derived for 45 Flavonoid derivatives recently synthesized and evaluated as cholinesterase inhibitors. The multiple linear regression method (MLR) was adopted to develop an adequate mathematical model that describes the relationship between a variety of molecular descriptors of the studied compounds and their biological activities (cholinesterase inhibitors). Golbraikh and Tropsha criteria were applied to verify the validity of the built model. The built MLR model was statistically reliable, robust, and predictive (R2 = 0.801, Q2cv = 0.876, R2test = 0.824). Dreiding energy and Molar Refractivity were the major factors that govern the Anti-cholinesterase activity. These results were further exploited to design a new series of Flavonoid derivatives with higher Anti-cholinesterase activities than the existing ones. Thereafter, molecular docking and molecular dynamic studies were performed to predict the binding types of the designed compounds and to investigate their stability at the active site of the Butyrylcholinestérase BuChE protein. The negative and low binding affinity calculated for all designed compounds shows that designed compound 1 has a favorable affinity for the 4TPK. Moreover, molecular dynamics simulation studies confirmed the stability of designed compound 1 in the active pocket of 4TPK over 100 ns. Finally, the ADMET analysis was incorporated to analyze the pharmacokinetics and toxicity parameters. The designed compounds were found to meet the ADMET descriptor criteria at an acceptable level having respectable intestinal permeability and water solubility and can reach the intended destinations.Communicated by Ramaswamy H. Sarma.

Multi-Endpoint Acute Toxicity Assessment of Organic Compounds Using Large-Scale Machine Learning Modeling

In recent years, alternative animal testing methods such as computational and machine learning approaches have become increasingly crucial for toxicity testing. However, the complexity and scarcity of available biomedical data challenge the development of predictive models. Combining nonlinear machine learning together with multicondition descriptors offers a solution for using data from various assays to create a robust model. This work applies multicondition descriptors (MCDs) to develop a QSTR (Quantitative Structure-Toxicity Relationship) model based on a large toxicity data set comprising more than 80,000 compounds and 59 different end points (122,572 data points). The prediction capabilities of developed single-task multi-end point machine learning models as well as a novel data analysis approach with the use of Convolutional Neural Networks (CNN) are discussed. The results show that using MCDs significantly improves the model and using them with CNN-1D yields the best result (R2train = 0.93, R2ext = 0.70). Several structural features showed a high level of contribution to the toxicity, including van der Waals surface area (VSA), number of nitrogen-containing fragments (nN+), presence of S-P fragments, ionization potential, and presence of C-N fragments. The developed models can be very useful tools to predict the toxicity of various compounds under different conditions, enabling quick toxicity assessment of new compounds.

Acute toxicity of binary mixtures for quorum sensing inhibitors and sulfonamides against Aliivibrio fischeri: QSAR investigations and joint toxic actions

Quorum sensing inhibitors (QSIs), as a kind of ideal antibiotic substitutes, have been recommended to be used in combination with traditional antibiotics in medical and aquaculture fields. Due to the co-existence of QSIs and antibiotics in environmental media, it is necessary to evaluate their joint risk. However, there is little information about the acute toxicity of mixtures for QSIs and antibiotics. In this study, 10 QSIs and 3 sulfonamides (SAs, as the representatives for traditional antibiotics) were selected as the test chemicals, and their acute toxic effects were determined using the bioluminescence of Aliivibrio fischeri (A. fischeri) as the endpoint. The results indicated that SAs and QSIs all induced S-shaped dose-responses in A. fischeri bioluminescence. Furthermore, SAs possessed greater acute toxicity than QSIs, and luciferase (Luc) might be the target protein of test chemicals. Based on the median effective concentration (EC50) for each test chemical, QSI-SA mixtures were designed according to equitoxic (EC50(QSI):EC50(SA) = 1:1) and non-equitoxic ratios (EC50(QSI):EC50(SA) = 1:10, 1:5, 1:0.2, and 1:0.1). It could be observed that with the increase of QSI proportion, the acute toxicity of QSI-SA mixtures enhanced while the corresponding TU values decreased. Furthermore, QSIs contributed more to the acute toxicity of test binary mixtures. The joint toxic actions of QSIs and SAs were synergism for 23 mixtures, antagonism for 12 mixtures, and addition for 1 mixture. Quantitative structure-activity relationship (QSAR) models for the acute toxicity QSIs, SAs, and their binary mixtures were then constructed based on the lowest CDOCKER interaction energy (Ebind-Luc) between Luc and each chemical and the component proportion in the mixture. These models exhibited good robustness and predictive ability in evaluating the toxicity data and joint toxic actions of QSIs and SAs. This study provides reference data and applicable QSAR models for the environmental risk assessment of QSIs, and gives a new perspective for exploring the joint effects of QSI-antibiotic mixtures.

Ionic liquids as the effective technology for enhancing transdermal drug delivery: Design principles, roles, mechanisms, and future challenges

Ionic liquids (ILs) have been proven to be an effective technology for enhancing drug transdermal absorption. However, due to the unique structural components of ILs, the design of efficient ILs and elucidation of action mechanisms remain to be explored. In this review, basic design principles of ideal ILs for transdermal drug delivery system (TDDS) are discussed considering melting point, skin permeability, and toxicity, which depend on the molar ratios, types, functional groups of ions and inter-ionic interactions. Secondly, the contributions of ILs to the development of TDDS through different roles are described: as novel skin penetration enhancers for enhancing transdermal absorption of drugs; as novel solvents for improving the solubility of drugs in carriers; as novel active pharmaceutical ingredients (API-ILs) for regulating skin permeability, solubility, release, and pharmacokinetic behaviors of drugs; and as novel polymers for the development of smart medical materials. Moreover, diverse action mechanisms, mainly including the interactions among ILs, drugs, polymers, and skin components, are summarized. Finally, future challenges related to ILs are discussed, including underlying quantitative structure-activity relationships, complex interaction forces between anions, drugs, polymers and skin microenvironment, long-term stability, and in vivo safety issues. In summary, this article will promote the development of TDDS based on ILs.

A machine learning-based QSAR model reveals important molecular features for understanding the potential inhibition mechanism of ionic liquids to acetylcholinesterase

The broad application of ionic liquids (ILs) has been hindered by uncertainties surrounding their ecotoxicity. In this work, a Quantitative Structure-Activity Relationship (QSAR) model was devised to predict the inhibition of ILs towards the activity of AChE, employing both Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) machine learning approaches. Fourteen kings of essential molecular feature descriptors were screened from an initial roster of 244 descriptors through the application of a feature importance index and they showed a significant impact on the activity of AChE activity. The two models based solely on the 14 most critical molecular descriptors could maintain model's robustness and reliability. The correlation analysis between these 14 descriptors and the inhibition of AChE activity revealed the potential impact of the molecular characteristics on ILs toxicity. The results underscored the main influence of cations in ILs on the inhibitory activity towards the AChE enzyme. Specifically, cations exhibiting hydrophobicity properties were found to exert more potent inhibitory effects on the AChE enzyme. In addition, some other properties of the cations, such as the degree of branching, atomic weight and partial charge also modulated their inhibition potential. This study enhances the comprehension of the structure-activity relationship between ILs and AChE inhibition, providing a reference for designing safer and greener ILs.

Chronic aquatic toxicity assessment of diverse chemicals on Daphnia magna using QSAR and chemical read-across

We have modeled here chronic Daphnia toxicity taking pNOEC (negative logarithm of no observed effect concentration in mM) and pEC50 (negative logarithm of half-maximal effective concentration in mM) as endpoints using QSAR and chemical read-across approaches. The QSAR models were developed by strictly obeying the OECD guidelines and were found to be reliable, predictive, accurate, and robust. From the selected features in the developed models, we have found that an increase in lipophilicity and saturation, the presence of electrophilic or electronegative or heavy atoms, the presence of sulphur, amine, and their related functionality, an increase in mean atomic polarizability, and higher number of (thio-) carbamates (aromatic) groups are responsible for chronic toxicity. Therefore, this information might be useful for the development of environmentally friendly and safer chemicals and data-gap filling as well as reducing the use of identified toxic chemicals which have chronic toxic effects on aquatic ecosystems. Approved classes of drugs from DrugBank databases and diverse groups of chemicals from the Chemical and Product Categories (CPDat) database were also assessed through the developed models.

Computer-Aided Molecular Design of Ionic Liquids as Advanced Process Media: A Review from Fundamentals to Applications

The unique physicochemical properties, flexible structural tunability, and giant chemical space of ionic liquids (ILs) provide them a great opportunity to match different target properties to work as advanced process media. The crux of the matter is how to efficiently and reliably tailor suitable ILs toward a specific application. In this regard, the computer-aided molecular design (CAMD) approach has been widely adapted to cover this family of high-profile chemicals, that is, to perform computer-aided IL design (CAILD). This review discusses the past developments that have contributed to the state-of-the-art of CAILD and provides a perspective about how future works could pursue the acceleration of the practical application of ILs. In a broad context of CAILD, key aspects related to the forward structure-property modeling and reverse molecular design of ILs are overviewed. For the former forward task, diverse IL molecular representations, modeling algorithms, as well as representative models on physical properties, thermodynamic properties, among others of ILs are introduced. For the latter reverse task, representative works formulating different molecular design scenarios are summarized. Beyond the substantial progress made, some future perspectives to move CAILD a step forward are finally provided.

Graph Neural Networks and Structural Information on Ionic Liquids: A Cheminformatics Study on Molecular Physicochemical Property Prediction

Ionic liquids (ILs) provide a promising solution in many industrial applications, such as solvents, absorbents, electrolytes, catalysts, lubricants, and many others. However, due to the enormous variety of their structures, uncovering or designing those with optimal attributes requires expensive and exhaustive simulations and experiments. For these reasons, searching for an efficient theoretical tool for finding the relationship between the IL structure and properties has been the subject of many research studies. Recently, special attention has been paid to machine learning tools, especially multilayer perceptron and convolutional neural networks, among many other algorithms in the field of artificial neural networks. For the latter, graph neural networks (GNNs) seem to be a powerful cheminformatic tool yet not well enough studied for dual molecular systems such as ILs. In this work, the usage of GNNs in structure-property studies is critically evaluated for predicting the density, viscosity, and surface tension of ILs. The problem of data availability and integrity is discussed to show how well GNNs deal with mislabeled chemical data. Providing more training data is proven to be more important than ensuring that they are immaculate. Great attention is paid to how GNNs process different ions to give graph transformations and electrostatic information. Clues on how GNNs should be applied to predict the properties of ILs are provided. Differences, especially regarding handling mislabeled data, favoring the use of GNNs over classical quantitative structure-property models are discussed.

In vitro screening of imidazolium and pyrrolidinium based ionic liquids toxicity on subcellular fractions of the Mediterranean mussel Mytilus galloprovincialis

Ionic liquids (ILs) have been considered eco-friendly alternatives to conventional organic solvents. However, several studies have reported that ILs exert toxicity towards aquatic invertebrates. Applying in vitro methodology, the aim of the present study was to evaluate the potential effect of three ILs on the biochemical performance of exposed Mytilus galloprovincialis digestive gland and gills cellular fractions. Carboxylesterase might be involved in the derived toxicity mechanism of ILs as activity levels increased significantly in digestive gland exposed fractions. This group of ILs did not seem to induce genotoxicity, except in gills cellular fractions exposed to 1-butyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide. In the literature, in vitro methodology has been suggested as an important complement to animal testing and in silico studies. The present research underlines its efficacy as a quick pre-screening before in vivo testing, particularly with heterogenic groups of substances with high variability in composition, such as ILs and deep eutectic solvents.

Quantification of pharmaceuticals in hospital effluent: Weighted ranking of environmental risk using a fuzzy hybrid multicriteria method

An analytical method for quantification of seventeen pharmaceuticals and one metabolite was validated and applied in the analysis of hospital effluent samples. Two different sampling strategies were used: seasonal sampling, with 7 samples collected bimonthly; and hourly sampling, with 12 samples collected during 12 h. Thus, the variability was both seasonal and within the same day. High variability was observed in the measured concentrations of the pharmaceuticals and the metabolite. The quantification method, performed using weighted linear regression model, demonstrated results of average concentrations in seasonal samples ranged between 0.19 μgL-1 (carbamazepine) and higher than 61.56 μgL-1 (acetaminophen), while the hourly samples showed average concentrations between 0.07 μgL-1 (diazepam) and higher than 54.91 μgL-1 (acetaminophen). It is described as higher because the maximum concentration of the calibration curve took into account the dilution factor provided by DLLME. The diurnal results showed a trend towards higher concentrations in the first and last hours of sampling. The risk quotient (RQ) was calculated using organisms from three different trophic levels, for all the analytes quantified in the samples. Additionally, in order to understand the level of importance of each RQ, an expert panel was established, with contributions from 23 specialists in the area. The results were analyzed using a hybrid decision-making approach based on a Fuzzy Analytic Hierarchy Process (FAHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method, in order to rank the compounds by environmental risk priority. The compounds of greatest concern were losartan, acetaminophen, 4-aminoantipyrine, sulfamethoxazole, and metoclopramide. Comparison of the environmental risk priority ranking with the potential human health risk was performed by applying the same multicriteria approach, with the prediction of endpoints using in silico (Q)SAR models. The results obtained suggested that sulfamethoxazole and acetaminophen were the most important analytes to be considered for monitoring.

A Benchmark Study of Graph Models for Molecular Acute Toxicity Prediction

With the wide usage of organic compounds, the assessment of their acute toxicity has drawn great attention to reduce animal testing and human labor. The development of graph models provides new opportunities for acute toxicity prediction. In this study, five graph models (message-passing neural network, graph convolution network, graph attention network, path-augmented graph transformer network, and Attentive FP) were applied on four toxicity tasks (fish, Daphnia magna, Tetrahymena pyriformis, and Vibrio fischeri). With the lowest prediction error, Attentive FP was reported to have the best performance in all four tasks. Moreover, the attention weights of the Attentive FP model helped to construct atomic heatmaps and provide good explainability.

A zwitterionic solution for smart ionic liquids to evade cytotoxicity

By linking the cation and anion motifs of ionic liquids (ILs), zwitterionic liquids (ZILs) exhibit at least 146-2740 and 112-1550 folds less cytotoxicity in human gastric and colon cells than those of the structurally related ILs. Computer simulation shows that ZIL molecules hardly penetrate the cell membranes in contrast to ILs. These findings reveal a novel mechanism for ZILs to evade cytotoxicity, establishing a structure-based design principle for the next generation of sustainable ZILs.

Quantitative ion character-activity relationship methods for assessing the ecotoxicity of soil metal(loid)s to lettuce

New pollution elements introduced by the rapid development of modern industry and agriculture may pose a serious threat to the soil ecosystem. To explore the ecotoxicity and risk of these elements, we systematically studied the acute toxicity of 18 metal(loid)s toward lettuce using hydroponic experiments and quantitative relationships between element toxicity and ionic characteristics using ion-grouping and ligand-binding theory methods, thereby establishing a quantitative ion character-activity relationship (QICAR) model for predicting the phytotoxicity threshold of data-poor elements. The toxicity of 18 ions to lettuce differed by more than four orders of magnitude (0.05-804.44 μM). Correlation and linear regression analysis showed that the ionic characteristics significantly associated with this toxicity explained only 23.8-50.3% of the toxicity variation (R2Adj = 0.238-0.503, p < 0.05). Relationships between toxicity and ionic properties significantly improved after separating metal(loid) ions into soft and hard, with R2Adj of 0.793 and 0.784 (p < 0.05), respectively. Three ligand-binding parameters showed different predictive effects on lettuce metal(loid) toxicity. Compared with the binding constant of the biotic ligand model (log K) and the hard ligand scale (HLScale) (p > 0.05), the softness consensus scale (σCon) was significantly correlated with toxicity and provided the best prediction (R2Adj = 0.844, p < 0.001). We selected QICAR equations based on soft-hard ion classification and σCon methods to predict phytotoxicity of metal(loid)s, which can be used to derive ecotoxicity for data-poor metal(loid)s, providing preliminary assessment of their ecological risks.

Qualitative evaluation of pharmaceuticals and metabolites in hospital effluent: Influence of sample preparation technique and outranking by environmental risk using the ELECTRE method

The presence of pharmaceuticals and metabolites in effluents has become a serious environmental problem, so it is essential to be able to monitor these microcontaminants using qualitative approaches, as well as to assess the potential environmental risks that such compounds may present. Therefore, in this study, suspect screening analysis was performed of 2030 pharmaceuticals and metabolites in hospital effluent samples, applying different sample preparation techniques. Additionally, a pioneering association of (Q)SAR assessment of identified contaminants with the ELECTRE multi-criteria decision analysis technique made it possible to prioritize analytes according to their environmental risk, in order to enable their inclusion in environmental monitoring programs. The results showed that the most advantageous alternative sample preparation technique consisted of cleanup (100 mg of silica/alumina sorbent) + dispersive liquid-liquid microextraction (7.5 of aqueous matrix, 325 μL of chloroform (extracting solvent), and 500 μL of acetonitrile as dispersing solvent). This procedure resulted in the identification of 70 pharmaceuticals and metabolites in the composite sample tested. The suspect screening analysis found a total of 105 microcontaminants, 28 of them being "confirmed compounds" and 77 being "suspect compounds". Of the compounds identified, 87% were pharmaceuticals and 13% were metabolites. The compounds identified were subsequently evaluated using different open access software packages, considering eight endpoints: mobility, persistence, estrogen receptor binding, wastewater treatment plant total removal, biodegradability, PBT (persistent, bioaccumulation and toxic), mutagenicity, and carcinogenicity. The (Q)SAR prediction results were used as input data for the ELECTRE outranking method. Categorization of the identified compounds by ELECTRE resulted in the kernel (priority compounds) and a further 19 groups. ELECTRE sensitivity evaluation indicated that for all the cases, the kernel and the following two groups coincided. The categorization provided by the ELECTRE method constitutes a highly intuitive decision and choice tool, which can assist in the selection of compounds if subsequent quantitative analysis is to be carried out.

Deep Probabilistic Learning Model for Prediction of Ionic Liquids Toxicity

Identification of ionic liquids with low toxicity is paramount for applications in various domains. Traditional approaches used for determining the toxicity of ionic liquids are often expensive, and can be labor intensive and time consuming. In order to mitigate these limitations, researchers have resorted to using computational models. This work presents a probabilistic model built from deep kernel learning with the aim of predicting the toxicity of ionic liquids in the leukemia rat cell line (IPC-81). Only open source tools, namely, RDKit and Mol2vec, are required to generate predictors for this model; as such, its predictions are solely based on chemical structure of the ionic liquids and no manual extraction of features is needed. The model recorded an RMSE of 0.228 and R2 of 0.943. These results indicate that the model is both reliable and accurate. Furthermore, this model provides an accompanying uncertainty level for every prediction it makes. This is important because discrepancies in experimental measurements that generated the dataset used herein are inevitable, and ought to be modeled. A user-friendly web server was developed as well, enabling researchers and practitioners ti make predictions using this model.

Controlling astrocyte-mediated synaptic pruning signals for schizophrenia drug repurposing with deep graph networks

Schizophrenia is a debilitating psychiatric disorder, leading to both physical and social morbidity. Worldwide 1% of the population is struggling with the disease, with 100,000 new cases annually only in the United States. Despite its importance, the goal of finding effective treatments for schizophrenia remains a challenging task, and previous work conducted expensive large-scale phenotypic screens. This work investigates the benefits of Machine Learning for graphs to optimize drug phenotypic screens and predict compounds that mitigate abnormal brain reduction induced by excessive glial phagocytic activity in schizophrenia subjects. Given a compound and its concentration as input, we propose a method that predicts a score associated with three possible compound effects, i.e., reduce, increase, or not influence phagocytosis. We leverage a high-throughput screening to prove experimentally that our method achieves good generalization capabilities. The screening involves 2218 compounds at five different concentrations. Then, we analyze the usability of our approach in a practical setting, i.e., prioritizing the selection of compounds in the SWEETLEAD library. We provide a list of 64 compounds from the library that have the most potential clinical utility for glial phagocytosis mitigation. Lastly, we propose a novel approach to computationally validate their utility as possible therapies for schizophrenia.

Mimic Nature Using Chemotaxis of Ionic Liquid Microdroplets for Drug Delivery Purposes

Due to the growing prevalence of incurable diseases, such as cancer, worldwide, nowadays, the development of smart drug delivery systems is an inevitable necessity. Chemotaxis-driven movement of ionic liquid microdroplets containing therapeutic compounds is a well-known example of a smart drug delivery system. This review aims to classify, summarize, and compare ionic liquid-based chemotaxis systems in an easily understandable article. Chemotaxis is the basis of the movement of cells and microorganisms in biological environments, which is the cause of many vital biochemical and biological processes. This review attempts to summarize the available literature on single-component biomimetic and self-propelling microdroplet systems based on ionic liquids, which exhibit chemotaxis and spontaneously move in a determined direction by an external gradient, particularly a chemical change. It also aims to review artificial ionic liquid-based chemotaxis systems that can be used as drug carriers for medical purposes. The various ionic liquids used for this purpose are discussed, and different forms of chemical gradients and mechanisms that cause movement in microfluidic channels will be reviewed.

Perspectives in the Computational Modeling of New Generation, Biocompatible Ionic Liquids

In this Perspective, I review the current state of computational simulations on ionic liquids with an emphasis on the recent biocompatible variants. These materials are used here as an example of relatively complex systems that highlights the limits of some of the approaches commonly used to study their structure and dynamics. The source of these limits consists of the coexistence of nontrivial electrostatic, many-body quantum effects, strong hydrogen bonds, and chemical processes affecting the mutual protonation state of the constituent molecular ions. I also provide examples on how it is possible to overcome these problems using suitable simulation paradigms and recently improved techniques that, I expect, will be gradually introduced in the state-of-the-art of computational simulations of ionic liquids.

Atom surface fragment contribution method for predicting the toxicity of ionic liquids

In this study, a novel method-atom surface fragment contribution (ASFC)-was proposed for assessing the properties of compounds. We developed a predictive model using the ASFC method based on the sigma surface areas (S_σ-surface) of fragments/groups for estimating the toxicity of ILs. A toxicity dataset of 140 ILs towards leukemia rat cell line (ICP-81) was gathered and employed to train and validate models. The S_σ-surface values of atoms in each group were firstly calculated from the COSMO profiles of cations and anions for ILs. Then the S_σ-surface values of 26 groups were obtained and used as input descriptors for modelling. The R² and MSE of the built ASFC model were 0.924 and 0.071, respectively. Results indicate that the ASFC model developed by the new approach possesses great accuracy and reliability. In total, the ASFC method has extensive potential for the application of estimating diverse properties of ILs and other compounds due to its remarkable advantages.

Emerging impacts of ionic liquids on eco-environmental safety and human health

Owing to their unique physicochemical properties, ionic liquids (ILs) have been rapidly applied in diverse areas, such as organic synthesis, electrochemistry, analytical chemistry, functional materials, pharmaceutics, and biomedicine. The increase in the production and application of ILs has resulted in their release into aquatic and terrestrial environments. Because of their low vapor pressure, ILs cause very little pollution in the atmosphere compared to organic solvents. However, ILs are highly persistent in aquatic and terrestrial environments due to their stability, and therefore, potentially threaten the safety of eco-environments and human health. Specifically, the environmental translocation and retention of ILs, or their accumulation in organisms, are all related to their physiochemical properties, such as hydrophobicity. Based on results of ecotoxicity, cytotoxicity, and toxicity in mammalian models, the mechanisms involved in IL-induced toxicity include damage of cell membranes and induction of oxidative stress. Recently, artificial intelligence and machine learning techniques have been used in mining and modeling toxicity data to make meaningful predictions. Major future challenges are also discussed. This review will accelerate our understanding of the safety issues of ILs and serve as a guideline for the design of the next generation of ILs.

Multi-criteria decision-making techniques associated with (Q)SAR risk assessment for ranking surface water microcontaminants identified using LC-QTOF MS

Contaminants of emerging concern (CECs) have been a focus of study for years, with investigations revealing the contamination of different environmental matrices (surface water, soil, air, and sediment) by diverse classes of microcontaminants. Understanding the contamination profiles requires identification and risk assessment of the microcontaminants. In the present work, analysis was made of the presence of 3250 compounds in 27 samples from the Conceição River (Rio Grande do Sul State, Brazil), using an SPE-LC-QTOF MS method. In total, 150 microcontaminants (confirmed and suspected) of different classes, especially pesticides and pharmaceuticals, were identified by an initial qualitative analysis. Subsequently, in silico predictions of eight endpoints, using quantitative structure-activity relationship ((Q)SAR) models, were employed to determine the risk of each previously screened microcontaminant. This large amount of (Q)SAR data, frequently with conflicting information in relation to the responses of the different endpoints, makes it difficult to define which microcontaminants should be prioritized for analysis. Therefore, in order to rank the identified microcontaminants by risk assessment, two multi-criteria decision-making (MCDM) ranking techniques (ToxPi and TOPSIS), associated with a weighting method, were performed to establish the order of priority for further quantitative analysis of the most hazardous microcontaminants. The two rankings were statistically similar, especially for the 20 highest priority microcontaminants. Nonetheless, sensitivity tests carried out for the ToxPi and TOPSIS outputs showed higher performance robustness of TOPSIS, compared to ToxPi. This is the first time that such an approach (screening/(Q)SAR/MCDM methods) has been performed in the context of microcontaminant environmental risk evaluation and demonstrated to be an available strategy to help rank the most concern microcontaminants identified in aqueous environment samples.

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.

Cumulative impact assessment of hazardous ionic liquids towards aquatic species using risk assessment methods

In the present research work, a comprehensive tool for cumulative ecotoxicological impact assessment of ionic liquids (ILs) to aquatic life has been constructed. Using the probabilistic tool, impact of individual ILs to a group of aquatic species is assessed by chemical toxicity distributions (CTDs). The impact of group of ILs to individual aquatic species is assessed by species sensitivity distributions (SSDs). Acute toxicity data of imidazolium ILs with chloride (Cl^-), bromide (Br^-), tetrafluoroborate (BF4^-), and hexafluorophosphate (PF6^-) anions are used in CTD and SSD. Allowable concentrations for a group of Imidazolium ILs with the same mode of action (SMOA) to five aquatic species; Daphnia magna, Vibrio fischeri, Algae, Zebrafish, and Escherichia coli are estimated by CTDs. It has been concluded that 1-Butyl-3-methylimidazolium chloride (BMIMCl) possess the lowest risk at an acceptable risk value of 750 × 10^-5 mmol/L which is 12% less than that of OMIMCl. Furthermore, the sensitivities towards the aquatic species reveal that from the studied ILs, BMIMBF4 with an acceptable risk value of 3200 × 10^-5 mmol/L is the most suitable IL towards the selected aquatic species. Hence, current work provides cumulative allowable concentrations and acceptable risk values for ILs which release to aquatic compartment of ecosystem.

Evaluating the hazardous impact of ionic liquids - Challenges and opportunities

Ionic liquids (ILs), being related to the design of new environmentally friendly solvents, are widely considered for applications within the "green chemistry" concept. Due to their unique properties and wide diversity, ILs allow tailoring new separation procedures and producing new materials for advanced applications. However, despite the promising technical performance, environmental concerns highlighted in recent studies focused on the toxicity and biodegradability of ILs and their metabolites have revealed that ILs safety labels are not as benign as previously claimed. This review refers to the fundamentals about the properties and applications of ILs also in the context of their potential environmental effect. Toxicological issues and harmful effects related to the use of ILs are discussed, including the evaluation of their biodegradability and ecological impact on diverse organisms and ecosystems, also with respect to bacteria, fungi, and cell cultures. In addition, this review covers the tools used to assess the toxicity of ILs, including the predictive computational models and the results of studies involving cell membrane models and molecular simulations. Summing up the knowledge available so far, there are still no reliable criteria for unequivocal attribution of toxicity and environmental impact credentials for ILs, which is a challenging research task.

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.

Biomass-derived chemical substitutes for bisphenol A: recent advancements in catalytic synthesis

Bisphenol A is an oil-derived, large market volume chemical with a wide spectrum of applications in plastics, adhesives and thermal papers. However, bisphenol A is not considered safe due to its endocrine disrupting properties and reproductive toxicity. Several functional substitutes of bisphenol A have been proposed in the literature, produced from plant biomass. Unless otherwise specified, the present review covers the most significant contributions that appeared in the time span January 2015-August 2019, describing the sustainable catalytic synthesis of rigid diols from biomass derivatives. The focus is thereupon on heterogeneous catalysis, use of green solvents and mild conditions, cascade processes in one-pot, and continuous flow setups. More than 500 up-to-date references describe the various substitutes proposed and the catalytic methods for their manufacture, broken down according to the main biomass types from which they originate.

Ecotoxicological and regulatory aspects of environmental sustainability of nanopesticides

Recent years have seen the development of various colloidal formulations of pesticides and other agrochemicals aimed at use in sustainable agriculture. These formulations include inorganic, organic or hybrid particulates, or nanocarriers composed of biodegradable polymers, that can provide a better control of the release of active ingredients. The very small particle sizes and high surface areas of nanopesticides may however also lead to some unintended (eco)toxicological effects due to the way in which they interact with the target and non-target species and the environment. The current level of knowledge on ecotoxicological effects of nanopesticides is scarce, especially in regard to the fate and behaviour of such formulations in the environment. Nanopesticides will however have to cross a stringent regulatory scrutiny before marketing in most countries for health and environmental risks under a range of regulatory frameworks that require pre-market notification, risk assessment and approval, followed by labelling, post-market monitoring and surveillance. This review provides an overview of the key regulatory and ecotoxicological aspects relating to nanopesticides that will need to be considered for environmentally-sustainable use in agriculture.

Solar photo-Fenton treatment of the anti-cancer drug anastrozole in different aqueous matrices at near-neutral pH: Transformation products identification, pathways proposal, and in silico (Q)SAR risk assessment

Anastrozole (ANZ) is a breast cancer drug that was introduced onto the pharmaceutical market in the 1990s and is still one of the most widely consumed cytotoxic compounds. Due to the persistence of the drug, its continued presence after passing through wastewater treatment plants can lead to harm to aquatic environments. The present study investigates use of the solar photo-Fenton (SPF) process applied for ANZ degradation, considering the fate of ANZ and its transformation products (TPs). The SPF process was performed using different concentrations of ferrous iron (Fe²⁺) and H₂O₂ in solutions produced with deionized water (DW) and hospital wastewater (HWW), at pH close to neutrality. When solar irradiation in the SPF process was carried out the best ANZ removal rates were found under the following conditions: (i) for the DW matrix, [ANZ]₀ = 50 μg L^-1, [Fe²⁺] = 5 mg L^-1, and [H₂O₂]₀ = 25 mg L^-1, achieving 95% primary ANZ elimination; (ii) for the HWW matrix, [ANZ]₀ = 50 μg L^-1, [Fe²⁺] = 10 mg L^-1(multiple additions), and [H₂O₂]₀ = 25 mg L^-1, achieving 51% primary ANZ elimination. LC-QTOF MS analysis allowed to identify tentatively five transformation products (TPs) formed during the ANZ degradation process in DW, and two TPs when HWW was used. The main proposed degradation pathways were demethylation and hydroxylation. Different in silico models free available (quantitative) structure-activity relationship ((Q)SAR) software were used to predict the ecotoxicities and environmental fates of ANZ and the TPs. The in silico (Q)SAR predictions indicated that ANZ and the TPs were non-biodegradable compounds. In silico (Q)SAR predictions for mutagenicity and carcinogenicity end-points identified some TPs that require further study. Attention is drawn to the formation of several TPs for which statistical and rule-based positive alerts for mutagenic activities were found, requiring further confirmatory in vitro validation tests.

Bioprocessing of Chaetoceros calcitrans for the recovery of fucoxanthin using CO2-based alkyl carbamate ionic liquids

Ionic liquids (ILs) have emerged as an alternative solvent used in the bioprocessing of microalgae for recovery of valuable biomolecules. The aim of this work is to extract fucoxanthin from Chaetoceros calcitrants (C. calcitrans) by using the readily distillable CO₂-based alkyl carbamate ILs. The degree of cell permeabilization was analysed by the quantification of extracted fucoxanthin and the analyses of cell surface morphology. Among the tested CO₂-based alkyl carbamate ILs, diallylammonium diallylcarbamate (DACARB) extraction system gave the maximal yield of fucoxanthin at 17.51 mg/g under the optimal extraction conditions [90% (v/v), 3 min and 25 °C]. Moreover, the extracted fucoxanthin fraction exhibited the satisfactory antioxidant activities. The recyclability of DACARB was demonstrated in the multiple batches of fucoxanthin extraction. Hence, CO₂-based alkyl carbamate ILs can prospectively substitute conventional organic solvents in the downstream processing of bioactive compounds from microalgae.

Effects of Ionic Liquids on Metalloproteins

In the past decade, innovative protein therapies and bio-similar industries have grown rapidly. Additionally, ionic liquids (ILs) have been an area of great interest and rapid development in industrial processes over a similar timeline. Therefore, there is a pressing need to understand the structure and function of proteins in novel environments with ILs. Understanding the short-term and long-term stability of protein molecules in IL formulations will be key to using ILs for protein technologies. Similarly, ILs have been investigated as part of therapeutic delivery systems and implicated in numerous studies in which ILs impact the activity and/or stability of protein molecules. Notably, many of the proteins used in industrial applications are involved in redox chemistry, and thus often contain metal ions or metal-associated cofactors. In this review article, we focus on the current understanding of protein structure-function relationship in the presence of ILs, specifically focusing on the effect of ILs on metal containing proteins.

A Review of Process Systems Engineering (PSE) Tools for the Design of Ionic Liquids and Integrated Biorefineries

In this review paper, a brief overview of the increasing applicability of Process Systems Engineering (PSE) tools in two research areas, which are the design of ionic liquids and the design of integrated biorefineries, is presented. The development and advances of novel computational tools and optimization approaches in recent years have enabled these applications with practical results. A general introduction to ionic liquids and their various applications is presented followed by the major challenges in the design of optimal ionic liquids. Significant improvements in computational efficiency have made it possible to provide more reliable data for optimal system design, minimize the production cost of ionic liquids, and reduce the environmental impact caused by such solvents. Hence, the development of novel computational tools and optimization tools that contribute to the design of ionic liquids have been reviewed in detail. A detailed review of the recent developments in PSE applications in the field of integrated biorefineries is then presented. Various value-added products could be processed by the integrated biorefinery aided with applications of PSE tools with the aim of enhancing the sustainability performance in terms of economic, environmental, and social impacts. The application of molecular design tools in the design of integrated biorefineries is also highlighted. Major developments in the application of ionic liquids in integrated biorefineries have been emphasized. This paper is concluded by highlighting the major opportunities for further research in these two research areas and the areas for possible integration of these research fields.

Industrial Applications of Ionic Liquids

Since their conception, ionic liquids (ILs) have been investigated for an extensive range of applications including in solvent chemistry, catalysis, and electrochemistry. This is due to their designation as designer solvents, whereby the physiochemical properties of an IL can be tuned for specific applications. This has led to significant research activity both by academia and industry from the 1990s, accelerating research in many fields and leading to the filing of numerous patents. However, while ILs have received great interest in the patent literature, only a limited number of processes are known to have been commercialised. This review aims to provide a perspective on the successful commercialisation of IL-based processes, to date, and the advantages and disadvantages associated with the use of ILs in industry.

Sulfur dioxide removal: An overview of regenerative flue gas desulfurization and factors affecting desulfurization capacity and sorbent regeneration

Numerous mitigation techniques have been incorporated to capture or remove SO₂ with flue gas desulfurization (FGD) being the most common method. Regenerative FGD method is advantageous over other methods due to high desulfurization efficiency, sorbent regenerability, and reduction in waste handling. The capital costs of regenerative methods are higher than those of commonly used once-through methods simply due to the inclusion of sorbent regeneration while operational and management costs depend on the operating hours and fuel composition. Regenerable sorbents like ionic liquids, deep eutectic solvents, ammonium halide solutions, alkyl-aniline solutions, amino acid solutions, activated carbons, mesoporous silica, zeolite, and metal-organic frameworks have been reported to successfully achieve high SO₂ removal. The presence of other gases in flue gas, e.g., O₂, CO₂, NOx, and water vapor, and the reaction temperature critically affect the sorption capacity and sorbent regenerability. To obtain optimal SO₂ removal performance, other parameters such as pH, inlet SO₂ concentration, and additives need to be adequately governed. Due to its high removal capacity, easy preparation, non-toxicity, and low regeneration temperature, the use of deep eutectic solvents is highly feasible for upscale utilization. Metal-organic frameworks demonstrated highest reported SO₂ removal capacity; however, it is not yet applicable at industrial level due to its high price, weak stability, and robust formulation.

Representation of the Structure-A Key Point of Building QSAR/QSPR Models for Ionic Liquids

The process of encoding the structure of chemicals by molecular descriptors is a crucial step in quantitative structure-activity/property relationships (QSAR/QSPR) modeling. Since ionic liquids (ILs) are disconnected structures, various ways of representing their structure are used in the QSAR studies: the models can be based on descriptors either derived for particular ions or for the whole ionic pair. We have examined the influence of the type of IL representation (separate ions vs. ionic pairs) on the model's quality, the process of the automated descriptors selection and reliability of the applicability domain (AD) assessment. The result of the benchmark study showed that a less precise description of ionic liquid, based on the 2D descriptors calculated for ionic pairs, is sufficient to develop a reliable QSAR/QSPR model with the highest accuracy in terms of calibration as well as validation. Moreover, the process of a descriptors' selection is more effective when the possible number of variables can be decreased at the beginning of model development. Additionally, 2D descriptors usually demand less effort in mechanistic interpretation and are more convenient for virtual screening studies.

A study into the species sensitivity of green algae towards imidazolium-based ionic liquids using flow cytometry

The sensitivity of individual organisms towards toxic agents is an important indicator of environmental pollution. However, organism-specific quantification of sensitivity towards pollutants remains a challenge. In this study, we determined the sensitivity of Chlorella vulgaris (C. vulgaris) and Scenedesmus quadricauda (S. quadricauda) towards three ionic liquids (ILs), 1-alkyl-3-methyl-imidazolium chlorides [C_nmim][Cl] (n = 4,6,8). We kept all external parameters constant to identify the biotic parameters responsible for discrepancies in species sensitivity, and used flow cytometry to determine four conventional endpoints to characterise cell viability and cell vitality. Our results demonstrate that after exposure to the ILs, cell proliferation was inhibited in both species. At the same time, the cell size, complexity and membrane permeability of both algae also increased. However, while Chl a synthesis by S. quadricauda was inhibited, that of C. vulgaris was enhanced. S. quadricauda has evolved a metabolic defense that can counteract the decreased esterase activity that has been shown to occur in the presence of ILs. While it is likely that S. quadricauda was less sensitive than C. vulgaris to the ILs because of this metabolic defense, this alga may also exhibit better membrane resistance towards ILs.