Probing the Environmental Toxicity of Deep Eutectic Solvents and Their Components: An In Silico Modeling Approach

Abstract

Because of the increasing demand of greener solvents, deep eutectic solvents (DES) have just emerged as low-cost alternative solvents for a broad range of applications. However, recent toxicity assay studies showed a non-negligible toxic behavior for these solvents and their components. Alternative in silico-based approaches such as the one proposed here, multitasking-Quantitative Structure Toxicity Relationships (mtk-QSTR), are increasingly used for risk assessment of chemicals to speed up policy decisions. This work reports a mtk-QSTR modeling of 572 DES and their components under multiple experimental conditions. To set up a reliable model from such data, we examined here the use of 0D–2D descriptors along with classification analysis, and the Box–Jenkins approach. This procedure led to a final mtk-QSTR model with high overall accuracy and predictivity (ca. 90%). The model highlights also the crucial role that polarizability, electronegativity, hydrogen-bond donor (HBD), and topological properties play into the DES toxicity. Furthermore, with the help of the derived mtk-QSTR model, 30 different HBD components were ranked on the basis of their toxic contributions to DES. More importantly, the proposed in silico modeling approach is shown to be a valuable tool to mine relevant STR information, therefore guiding the rational design of potentially safe DES.

Development of Multi-Target Chemometric Models for the Inhibition of Class I PI3K Enzyme Isoforms: A Case Study Using QSAR-Co Tool

“…The global descriptors calculated by AlvaDesc v.1.0.8 only consider the chemical structures of the compounds, and these descriptors are thus incapable of discriminating the influence on the chemical structure when a specific molecule is assayed under more than one experimental condition (i.e., cj ). To solve this problem, we adopted the Box–Jenkins moving average approach [80], the details of which have been discussed previously in detail [10,11,50,60]. Briefly, Box–Jenkins operators are used to calculate successive average values of a defined property of a defined system at different intervals of time.…”

“…Before setting up both these models, the dataset was divided into a modelling set and an external validation set by the k -means cluster analysis ( k -MCA) technique [83] with the help of the STATISTICA software package [84]. The purpose of k-MCA is to ensure that the validation set covers the same chemical-biological space as the training set, on which the model is built with [60]. Constitutional descriptors calculated by AlvaDesc were used along with the IAi ( cj ) values for generating five clusters based on the Euclidian distances from 500 iterations.…”

“…The goodness of prediction for the sub-training, test and external validation sets was evaluated by computing the following statistical measures: sensitivity (correct classification of the active cases), specificity (correct classification of inactive cases), accuracy (overall correct classification), F -measure and Matthews correlation coefficient (MCC) [57,85]. Moreover, a Y -randomization test was used on the sub-training set to judge the uniqueness of the statistical model [59,60]. Therefore, the values of the dependent variable were randomly scrambled 100 times, and the Wilk’s lambda (λ) of the original model was then compared with the average Wilk’s lambda (λ rand ) of the randomized models.…”

Green synthesis of copper nanoparticles using leaf extract of Ageratum houstonianum Mill. and study of their photocatalytic and antibacterial activities

“…. However, use of expensive reagents, lengthy and tedious process, and environmental toxicity are prime concern with these approaches [21]. Conversely, green synthesis of metal NPs follows biological routes involving bacteria, yeasts, moulds, enzymes, agricultural wastes, essential oils and plant extracts [22][23][24].…”

“…Various physicochemical methods are used for the synthesis of NPs, which include thermal decomposition [15], electrochemical reduction [16], vapor deposition [17], microwave irradiation [18], solvothermal [19], laser ablation [20] and chemical reduction of metal salts [15]. However, use of expensive reagents, lengthy and tedious process, and environmental toxicity are prime concern with these approaches [21]. Conversely, green synthesis of metal NPs follows biological routes involving bacteria, yeasts, moulds, enzymes, agricultural wastes, essential oils and plant extracts [22][23][24].…”