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

Intermittent multi-generational reproductive toxicities of 1-alkyl-3-methylimidazolium tetrafluoroborate with essential involvement of lipid metabolism

Ionic liquids (ILs) become emerging environmental pollutants. Especially, alkyl imidazolium ILs commonly showed stimulation in toxicological studies and mechanisms remained to be explored. In the present study, alkyl imidazolium tetrafluoroborate ([amim]BF4), with ethyl ([emim]), hexyl ([hmim]) and octyl ([omim]) as side-chains, were chosen as target ILs. Their toxicities on the reproduction and lifespan of Caenorhabditis elegans were explored with two types (A and B) exposure arrangements to mimic realistic intermittent multi-generational exposure scenarios. In type A scenario, there was an exposure every 4 generations with 12 generations in total, and in type B one, there was an exposure every two generations with 12 generations in total. Result showed that [emim]BF4 caused inhibition on the reproduction in 8 generations in type A exposure but 6 ones in type B exposure. Meanwhile, [hmim]BF4 showed inhibition in one generation and stimulation in 3 generations in type A exposure, but stimulation in 6 generations in type B exposure. Also, [omim]BF4 showed stimulation in one generation in type B exposure. Collectively, the results demonstrated less frequencies of inhibition, or more frequencies of stimulation, in the exposure scenario with more frequent exposures. Further mechanism exploration was performed to measure the lipid storage and metabolism in the aspect of energy supply. Results showed that [emim]BF4, [hmim]BF4 and [omim]BF4 commonly stimulated the triglyceride (TG) levels across generations. They also disturbed the activities of glycerol-3-phosphate acyltransferase (GPAT) and acetyl CoA carboxylase (ACC) in lipogenesis, those of adipose triglyceride lipase (ATGL) and carnitine acyl transferase (CPT) in lipolysis, and also the contents of acetyl-CoA (ACA). Further data analysis indicated the energy allocation among life traits including reproduction, antioxidant responses and hormone regulations.

The System of Self-Consistent Models: QSAR Analysis of Drug-Induced Liver Toxicity

Removing a drug-like substance that can cause drug-induced liver injury from the drug discovery process is a significant task for medicinal chemistry. In silico models can facilitate this process. Semi-correlation is an approach to building in silico models representing the prediction in the active (1)-inactive (0) format. The so-called system of self-consistent models has been suggested as an approach for two tasks: (i) building up a model and (ii) estimating its predictive potential. However, this approach has been tested so far for regression models. Here, the approach is applied to building up and estimating a categorical hepatotoxicity model using the CORAL software. This new process yields good results: sensitivity = 0.77, specificity = 0.75, accuracy = 0.76, and Matthew correlation coefficient = 0.51 (all compounds) and sensitivity = 0.83, specificity = 0.81, accuracy = 0.83 and Matthew correlation coefficient = 0.63 (validation set).

Quasi-SMILES: Self-consistent models for toxicity of organic chemicals to tadpoles

Simplified molecular input-line entry systems (SMILES) are the representation of the molecular structure that can be used to establish quantitative structure-property/activity relationships (QSPRs/QSARs) for various endpoints expressed as mathematical functions of the molecular architecture. Quasi-SMILES is extending the traditional SMILES by means of additional symbols that reflect experimental conditions. Using the quasi-SMILES models of toxicity to tadpoles gives the possibility to build up models by taking into account the time of exposure. Toxic effects of experimental situations expressed via 188 quasi-SMILES (the negative logarithm of molar concentrations which lead to lethal 50% tadpoles effected during 12 h, 24 h, 48 h, 72 h, and 96 h) were modelled with good results (the average determination coefficient for the validation sets is about 0.97). In this way, we developed new models for this amphibian endpoint, which is poorly studied.

Quantitative structure-activity relationship modeling for predication of inhibition potencies of imatinib derivatives using SMILES attributes

Chronic myelogenous leukemia (CML) which is resulted from the BCR-ABL tyrosine kinase (TK) chimeric oncoprotein, is a malignant clonal disorder of hematopoietic stem cells. Imatinib is used as an inhibitor of BCR-ABL TK in the treatment of CML patients. The main object of the present manuscript is focused on constructing quantitative activity relationships (QSARs) models for the prediction of inhibition potencies of a large series of imatinib derivatives against BCR-ABL TK. Herren, the inbuilt Monte Carlo algorithm of CORAL software is employed to develop QSAR models. The SMILES notations of chemical structures are used to compute the descriptor of correlation weights (CWs). QSAR models are established using the balance of correlation method with the index of ideality of correlation (IIC). The data set of 306 molecules is randomly divided into three splits. In QSAR modeling, the numerical value of R², Q², and IIC for the validation set of splits 1 to 3 are in the range of 0.7180-0.7755, 0.6891-0.7561, and 0.4431-0.8611 respectively. The numerical result of [Formula: see text] > 0.5 for all three constructed models in the Y-randomization test validate the reliability of established models. The promoters of increase/decrease for pIC₅₀ are recognized and used for the mechanistic interpretation of structural attributes.

Carcinogenicity prediction using the index of ideality of correlation

Carcinogenicity testing is necessary to protect human health and comply with regulations, but testing it with the traditionally used two-year rodent studies is time-consuming and expensive. In certain cases, such as for impurities, alternative methods may be convenient. Thus there is an urgent need for alternative approaches for reliable and robust assessments of carcinogenicity. The Monte Carlo technique with CORAL software is a tool to tackle this task for unknown compounds using available experimental data for a representative set of compounds. The models can be constructed with the simplified molecular input line entry system without additional physicochemical descriptors. We describe here a model based on a data set of 1167 substances. Matthew's correlation coefficient values for calibration and validation sets are 0.747 and 0.577, respectively. Double bonds between carbon atoms and double bonds of oxygen atoms are the molecular features that indicate the carcinogenic potential of a compound.

Monte Carlo optimization method based QSAR modeling of postmortem redistribution of structurally diverse drugs

The Monte Carlo optimization method was employed for the development of the QSAR model for the prediction for postmortem redistribution of structurally diverse drugs.