Halogenated derivatives QSAR model using spectral moments to predict haloacetic acids (HAA) mutagenicity

Comparative cytotoxicity of halogenated aromatic DBPs and implications of the corresponding developed QSAR model to toxicity mechanisms of those DBPs: Binding interactions between aromatic DBPs and catalase play an important role

Halogenated aromatic disinfection byproducts (DBPs) are a new group of emerging DBPs identified recently. They have been detected in disinfected drinking water, wastewater effluents, recreational water and oil/gas produced water, at concentrations of ng/L to μg/L in general. Previously studies have demonstrated that most of them can induce developmental toxicity and growth inhibition in aquatic organisms based on in vivo bioassays. In this study, to further understand the adverse effects of aromatic DBPs to human health, the comparative cytotoxicity of 15 halogenated aromatic DBPs belonging to four subgroups (i.e., halophenols, halonitrophenols, halohydroxybenzaldehydes and halohydroxybenzoic acids) was evaluated with mammalian Chinese Hamster Ovary cells. The results indicated that the selected aromatic DBPs exhibited an in vitro toxicity rank order of halonitrophenols > halophenols > halohydroxybenzaldehydes > halohydroxybenzoic acids. The potential toxicity mechanisms involved with the antioxidant system were investigated by using molecular docking analysis between key antioxidant enzymes (i.e., catalase, superoxide dismutase, and glutathione S-transferase) and aromatic DBPs. Based on the observed cytotoxicity data and screening the candidate descriptors (including binding energies between the aromatic DBPs and key antioxidant enzymes as well as physical-chemical/quantum-chemical/topological descriptors), a QSAR model was developed as log (LC₅₀) ^-1 = - 1.050E_CAT + 0.300E_HOMO - 0.238E_LUMO- 0.164, indicating the importance of the interactions of aromatic DBPs towards catalase and the electrophilic/nucleophilic reactivity of aromatic DBPs in the toxicity mechanisms. In addition, the occurrence of the aromatic DBPs in tap water and finished water was studied in a mega city Shenzhen located in South China. Results showed that halogenated aromatic DBPs commonly existed in Shenzhen drinking water at ng/L levels, and three nitrogenous aromatic DBPs were detected in real drinking water for the first time. The major toxicity drivers among the target aromatic DBPs were identified through the integration of the measured concentrations and observed cytotoxicity; notably, DBPs with the highest concentrations may not contribute the highest proportions of overall toxicity.

Predictive QSAR Models for the Toxicity of Disinfection Byproducts

Several hundred disinfection byproducts (DBPs) in drinking water have been identified, and are known to have potentially adverse health effects. There are toxicological data gaps for most DBPs, and the predictive method may provide an effective way to address this. The development of an in-silico model of toxicology endpoints of DBPs is rarely studied. The main aim of the present study is to develop predictive quantitative structure–activity relationship (QSAR) models for the reactive toxicities of 50 DBPs in the five bioassays of X-Microtox, GSH+, GSH−, DNA+ and DNA−. All-subset regression was used to select the optimal descriptors, and multiple linear-regression models were built. The developed QSAR models for five endpoints satisfied the internal and external validation criteria: coefficient of determination (R²) > 0.7, explained variance in leave-one-out prediction (Q²_LOO) and in leave-many-out prediction (Q²_LMO) > 0.6, variance explained in external prediction (Q²_F1, Q²_F2, and Q²_F3) > 0.7, and concordance correlation coefficient (CCC) > 0.85. The application domains and the meaning of the selective descriptors for the QSAR models were discussed. The obtained QSAR models can be used in predicting the toxicities of the 50 DBPs.

Development of quantitative structure activity relationship (QSAR) model for disinfection byproduct (DBP) research: A review of methods and resources

Quantitative structure-activity relationship (QSAR) models are tools for linking chemical activities with molecular structures and compositions. Due to the concern about the proliferating number of disinfection byproducts (DBPs) in water and the associated financial and technical burden, researchers have recently begun to develop QSAR models to investigate the toxicity, formation, property, and removal of DBPs. However, there are no standard procedures or best practices regarding how to develop QSAR models, which potentially limit their wide acceptance. In order to facilitate more frequent use of QSAR models in future DBP research, this article reviews the processes required for QSAR model development, summarizes recent trends in QSAR-DBP studies, and shares some important resources for QSAR development (e.g., free databases and QSAR programs). The paper follows the four steps of QSAR model development, i.e., data collection, descriptor filtration, algorithm selection, and model validation; and finishes by highlighting several research needs. Because QSAR models may have an important role in progressing our understanding of DBP issues, it is hoped that this paper will encourage their future use for this application.

Topological structural alerts modulations of mammalian cell mutagenicity for halogenated derivatives

Genotoxicity is a key toxicity endpoint for current regulatory requirements regarding new and existing chemicals. However, genotoxicity testing is time-consuming and costly, and involves the use of laboratory animals. This has motivated the development of computational approaches, designed to predict genotoxicity without the need to conduct laboratory tests. Currently, many existing computational methods, like quantitative structure-activity relationship (QSAR) models, provide limited information about the possible mechanisms involved in mutagenicity or predictions based on structural alerts (SAs) do not take statistical models into account. This paper describes an attempt to address this problem by using the TOPological Substructural MOlecular Design (TOPS-MODE) approach to develop and validate improved QSAR models for predicting the mutagenicity of a range of halogenated derivatives. Our most predictive model has an accuracy of 94.12%, exhibits excellent cross-validation and external set statistics. A reasonable interpretation of the model in term of SAs was achieved by means of bond contributions to activity. The results obtained led to the following conclusions: primary halogenated derivatives are more mutagenic than secondary ones; and substitution of chlorine by bromine increases mutagenicity while polyhalogenation decreases activity. The paper demonstrates the potential of the TOPS-MODE approach in developing QSAR models for identifying structural alerts for mutagenicity, combining high predictivity with relevant mechanistic interpretation.

QSAR models to predict mutagenicity of acrylates, methacrylates and alpha,beta-unsaturated carbonyl compounds

OBJECTIVE

The purpose of this study is to develop a quantitative structure-activity relationship (QSAR) model that can distinguish mutagenic from non-mutagenic species with alpha,beta-unsaturated carbonyl moiety using two endpoints for this activity - Ames test and mammalian cell gene mutation test - and also to gather information about the molecular features that most contribute to eliminate the mutagenic effects of these chemicals.

METHODS

Two data sets were used for modeling the two mutagenicity endpoints: (1) Ames test and (2) mammalian cells mutagenesis. The first one comprised 220 molecules, while the second one 48 substances, ranging from acrylates, methacrylates to alpha,beta-unsaturated carbonyl compounds. The QSAR models were developed by applying linear discriminant analysis (LDA) along with different sets of descriptors computed using the DRAGON software.

RESULTS

For both endpoints, there was a concordance of 89% in the prediction and 97% confidentiality by combining the three models for the Ames test mutagenicity. We have also identified several structural alerts to assist the design of new monomers.

SIGNIFICANCE

These individual models and especially their combination are attractive from the point of view of molecular modeling and could be used for the prediction and design of new monomers that do not pose a human health risk.

A topological substructural molecular design approach for predicting mutagenesis end-points of alpha, beta-unsaturated carbonyl compounds

Chemically reactive, alpha, beta-unsaturated carbonyl compounds are common environmental pollutants able to produce a wide range of adverse effects, including, e.g. mutagenicity. This toxic property can often be related to chemical structure, in particular to specific molecular substructures or fragments (alerts), which can then be used in specialized software or expert systems for predictive purposes. In the past, there have been many attempts to predict the mutagenicity of alpha, beta-unsaturated carbonyl compounds through quantitative structure activity relationships (QSAR) but considering only one exclusive endpoint: the Ames test. Besides, even though those studies give a comprehensive understanding of the phenomenon, they do not provide substructural information that could be useful forward improving expert systems based on structural alerts (SAs). This work reports an evaluation of classification models to probe the mutagenic activity of alpha, beta-unsaturated carbonyl compounds over two endpoints--the Ames and mammalian cell gene mutation tests--based on linear discriminant analysis along with the topological Substructure molecular design (TOPS-MODE) approach. The obtained results showed the better ability of the TOPS-MODE approach in flagging structural alerts for the mutagenicity of these compounds compared to the expert system TOXTREE. Thus, the application of the present QSAR models can aid toxicologists in risk assessment and in prioritizing testing, as well as in the improvement of expert systems, such as the TOXTREE software, where SAs are implemented.