Quantitative structure–toxicity relationship models for predication of toxicity of ionic liquids toward leukemia rat cell line IPC-81 based on index of ideality of correlation. (4th May 2022)

• Record Type: Journal Article
• Authors: Ahmadi, Shahin; Lotfi, Shahram; Kumar, Parvin
• Is Part Of: Toxicology mechanisms and methods. Volume 32:Number 4(2022)
• Page Start: 302
• Page End: 312
• Publication Date: 2022-05-04
• Subjects: QSTR models -- ionic liquids -- SMILES -- LogEC50 -- leukemia rat cell line IPC-81; Analytical toxicology -- Periodicals; Toxicology -- Periodicals; Toxicology -- Methodology -- Periodicals; 615.907
• DOI: 10.1080/15376516.2021.2000686
• Languages: English
• ISSNs: 1537-6516

Abstract

Abstract: The application of ionic 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 minimize 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 R 2 = 0.85, CCC = 0.92, Q 2 = 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. Highlights: Global 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 line. IIC is tested as a criterion of predictive potential. The toxicological effects of ILs are discussed based on the proposed model.