Comparative QSTR studies for predicting mutagenicity of nitro compounds

The occurrence, distribution and removal of adsorbable organic halogens (AOX) in a typical fine chemical industrial park

Coastal water quality in China has been impacted by direct discharge of industrial wastewater, and various kinds of AOX pollutants have been detected in the seawater and sediment. As the dominant pollution source of Hangzhou Bay, a typical fine chemical industry park "HSEDA" was selected as the study area in this research. The AOX in both wastewater and sludge phases from 22 large-scaled enterprises were simultaneously investigated. The results quantitatively illustrated the AOX flows from engineered wastewater and sludge treatment systems to natural environment. It can be seen that industrial enterprises discharged at least 160 t AOX every year, and about 105.4 t/a AOX eventually entered the natural environment. The dye manufacturing industry, which accounted for more than 60% of the total AOX emission load in HSEDA, was identified as the AOX pollution-intensive sector. The occurrence, characteristic pollutants and fate of AOX in dye wastewater were discussed, on the basis of which the improvements of cleaner production and wastewater treatment technologies have been put forward.

In silico prediction of the mutagenicity of nitroaromatic compounds using a novel two-QSAR approach

Certain drugs are nitroaromatic compounds, which are potentially toxic. As such, it is of practical importance to assess and predict their mutagenic potency in the process of drug discovery. A classical quantitative structure-activity relationship (QSAR) model was developed using the linear partial least square (PLS) scheme to understand the underline mutagenic mechanism and a non-classical QSAR model was derived using the machine learning-based hierarchical support vector regression (HSVR) to predict the mutagenicity of nitroaromatic compounds based on a series of mutagenicity data (TA98-S9). It was observed that HSVR performed better than PLS as manifested by the predictions of the samples in the training set, test set, and outlier set as well as various statistical validations. A mock test designated to mimic real challenges also confirmed the better performance of HSVR. Furthermore, HSVR exhibited superiority in predictivity, generalization capabilities, consistent performance, and robustness when compared with various published predictive models. PLS, conversely, revealed some mechanistically interpretable relationships between descriptors and mutagenicity. Thus, this two-QSAR approach using the predictive HSVR and interpretable PLS models in a synergistic fashion can be adopted to facilitate drug discovery and development by designing safer drug candidates with nitroaromatic moiety.

A Fragment-Based Approach for the Computational Prediction of the Nonspecific Binding of Drugs to Hepatic Microsomes

Correction for the nonspecific binding (NSB) of drugs to liver microsomes is essential for the accurate measurement of the kinetic parameters Km and Ki, and hence in vitro-in vivo extrapolation to predict hepatic clearance and drug-drug interaction potential. Although a number of computational approaches for the estimation of drug microsomal NSB have been published, they generally rely on compound lipophilicity and charge state at the expense of other physicochemical and chemical properties. In this work, we report the development of a fragment-based hologram quantitative structure activity relationship (HQSAR) approach for the prediction of NSB using a database of 132 compounds. The model has excellent predictivity, with a noncross-validated r2 of 0.966 and cross-validated r2 of 0.680, with a predictive r2 of 0.748 for an external test set comprising 34 drugs. The HQSAR method reliably predicted the fraction unbound in incubations of 95% of the training and test set drugs, excluding compounds with a steroid or morphinan 4,5-epoxide nucleus. Using the same data set of compounds, performance of the HQSAR method was superior to a model based on logP/D as the sole descriptor (predictive r2 for the test set compounds, 0.534). Thus, the HQSAR method provides an alternative approach to laboratory-based procedures for the prediction of the NSB of drugs to liver microsomes, irrespective of the drug charge state (acid, base, or neutral).

Predicting Carcinogenicity of Anilines by Quantitative Structure-Toxicity Relationship

Carcinogenicity is an important toxicological endpoint which poses a great concern being the major determinants of cancers and tumours. Anilines possess such toxic properties as they can form various electrophilic intermediates and adducts with biological systems. In the present work, the molecular descriptors of anilines have been calculated with semi-empirical AM1 and E-dragon methods, and a quantitative structure–toxicity relationships (QSTR) model for carcinogenic potency (pTD50) model of anilines was developed with multiple linear regression (MLR) analysis. The validation results through the test set indicate that the proposed model is robust and satisfactory. The QSTR study suggests that the molecular structure and the electronegativity of chemicals are closely related to the Carcinogenicity.

Alert-QSAR. Implications for electrophilic theory of chemical carcinogenesis

Given the modeling and predictive abilities of quantitative structure activity relationships (QSARs) for genotoxic carcinogens or mutagens that directly affect DNA, the present research investigates structural alert (SA) intermediate-predicted correlations A(SA) of electrophilic molecular structures with observed carcinogenic potencies in rats (observed activity, A = Log[1/TD(50)], i.e., [Formula: see text]). The present method includes calculation of the recently developed residual correlation of the structural alert models, i.e., [Formula: see text]. We propose a specific electrophilic ligand-receptor mechanism that combines electronegativity with chemical hardness-associated frontier principles, equality of ligand-reagent electronegativities and ligand maximum chemical hardness for highly diverse toxic molecules against specific receptors in rats. The observed carcinogenic activity is influenced by the induced SA-mutagenic intermediate effect, alongside Hansch indices such as hydrophobicity (LogP), polarizability (POL) and total energy (Etot), which account for molecular membrane diffusion, ionic deformation, and stericity, respectively. A possible QSAR mechanistic interpretation of mutagenicity as the first step in genotoxic carcinogenesis development is discussed using the structural alert chemoinformation and in full accordance with the Organization for Economic Co-operation and Development QSAR guidance principles.