Search

QSAR models are proposed for predicting the toxicity of 33 piperidine derivatives againstAedes aegypti. From 2D topological descriptors, calculated with the PaDEL software, ordinary least squares multilinear regression (OLS-MLR) treatment from the QSARINS software and machine learning and related approaches including linear and radial support vector machine (SVM), projection pursuit regression (PPR), radial basis function neural network (RBFNN), general regression neural network (GRNN) andk-nearest neighbours (k-NN), led to four-variable models. Their robustness and predictive ability were evaluated through both internal and external validation. Determination coefficients (r2) greater than 0.85 on the training sets and 0.8 on the test sets were obtained with OLS-MLR and linear SVM. They slightly outperform PPR, radial SVM and RBFNN, whereas GRNN andk-NN showed lower performance. The easy availability of the involved structural descriptors and the simplicity of the MLR model make the corresponding model attractive at an exploratory level for proposing, from this limited dataset, guidelines in the design of new potentially active molecules. [ABSTRACT FROM PUBLISHER]

The determination of the potential endocrine disruption (ED) activity of chemicals such as poly/perfluorinated compounds (PFCs) and brominated flame retardants (BFRs) is still hindered by a limited availability of experimental data. Quantitative structure–activity relationship (QSAR) strategies can be applied to fill this data gap, help in the characterization of the ED potential, and screen PFCs and BFRs with a hazardous toxicological profile. This paper proposes the modelling of T4-TTR (thyroxin-transthyretin) competing potency and relative binding potency toward T4 (logT4-REP) of PFCs and BFRs by regression and classification QSAR models. This study is a follow up of a former work, which analysed separately the interaction of BFRs and PFCs with the carrier TTR. The new results demonstrate the possibility of developing robust and predictive QSARs, which include both BFRs and PFCs in the training set, obtaining larger applicability domains than the existing models developed separately for BFRs and PFCs. The selection of modelling molecular descriptors confirms the importance of structural features, such as the aromatic OH or the molecular length, to increase the binding of the studied chemicals to TTR. Additionally, the need of experimental tests for some chemicals, and in particular for some of the BFRs, is highlighted. [ABSTRACT FROM AUTHOR]

Perfluorinated compounds (PFCs) are a class of emerging pollutants still widely used in different materials as non-adhesives, waterproof fabrics, fire-fighting foams, etc. Their toxic effects include potential for endocrine-disrupting activity, but the amount of experimental data available for these pollutants is limited. The use of predictive strategies such as quantitative structure–activity relationships (QSARs) is recommended under the REACH regulation, to fill data gaps and to screen and prioritize chemicals for further experimentation, with a consequent reduction of costs and number of tested animals. In this study, local classification models for PFCs were developed to predict their T4-TTR (thyroxin-transthyretin) competing potency. The best models were selected by maximizing the sensitivity and external predictive ability. These models, characterized by robustness, good predictive power and a defined applicability domain, were applied to predict the activity of 33 other PFCs of environmental concern. Finally, classification models recently published by our research group for T4-TTR binding of brominated flame retardants and for estrogenic and anti-androgenic activity were applied to the studied perfluorinated chemicals to compare results and to further evaluate the potential for these PFCs to cause endocrine disruption. [ABSTRACT FROM AUTHOR]

Fragrance materials are used as ingredients in many consumer and personal care products. The wide and daily use of these substances, as well as their mainly uncontrolled discharge through domestic sewage, make fragrance materials both potential indoor and outdoor air pollutants which are also connected to possible toxic effects on humans (asthma, allergies, headaches). Unfortunately, little is known about the environmental fate and toxicity of these substances. However, the use of alternative, predictive approaches, such as quantitative structure-activity relationships (QSARs), can help in filling the data gap and in the characterization of the environmental and toxicological profile of these substances. In the proposed study, ordinary least squares regression-based QSAR models were developed for three toxicological endpoints: mouse oral LD50, inhibition of NADH-oxidase (EC50 NADH-Ox) and the effect on mitochondrial membrane potential (EC50 ΔΨm). Theoretical molecular descriptors were calculated by using DRAGON software, and the best QSAR models were developed according to the principles defined by the Organization for Economic Co-operation and Development. [ABSTRACT FROM AUTHOR]

The troposphere is the principal recipient of volatile organic chemicals (VOCs) of both anthropogenic and biogenic origin. The persistence of these compounds in the troposphere is an important factor for the evaluation of their fate, and the possible negative effects to the environment and human health. In this study, the tropospheric lifetime of 166 VOCs, in terms of night-time degradation rates with nitrate radical (NO3), was modelled by the quantitative structure-property relationships (QSPR) approach. The multiple linear regression method was applied, in combination with the genetic algorithm-variable subset selection procedure, to a variety of theoretical molecular descriptors, calculated by the DRAGON software. The models were developed according to the OECD principles for regulatory acceptance of QSARs, with particular attention to external validation and applicability domain (AD). The external validation was performed on an unbiased external test set or by splitting the available data using self-organized maps or the random by response approach. The best QSPR models presented in this study showed good internal (range of [image omitted]: 89-92%) as well as external predictivity (range of Qext²: 75-89%). The AD of the models was analysed by the leverage approach, and was represented graphically in the Williams graph. [ABSTRACT FROM AUTHOR]

Polycyclic aromatic hydrocarbons (PAHs) are ubiquitous pollutants of high environmental concern. The experimental data of a mutagenicity test on human B-lymphoblastoid cells (alternative to the Ames bacterial test) for a set of 70 oxo-, nitro- and unsubstituted PAHs, detected in particulate matter (PM), were modelled by Quantitative Structure-Activity Relationships (QSAR) classification methods (k-NN, k-Nearest Neighbour, and CART, Classification and Regression Tree) based on different theoretical molecular descriptors selected by Genetic Algorithms. The best models were validated for predictivity both externally and internally. For external validation, Self Organizing Maps (SOM) were applied to split the original data set. The best models, developed on the training set alone, show good predictive performance also on the prediction set chemicals (sensitivity 69.2-87.1%, specificity 62.5-87.5%). The classification of PAHs according to their mutagenicity, based only on a few theoretical molecular descriptors, allows a preliminary assessment of the human health risk, and the prioritisation of these compounds. [ABSTRACT FROM AUTHOR]

The development of QSAR models useful for the prediction of fish bioconcentration factor (BCF) for a wide range of different chemical classes is crucial for the assessment and prioritisation of potentially persistent bioaccumulative and toxic substances. In this study we present QSAR models for BCF developed on a wide range of chemical structural classes of environmental and toxicological interest (such as dyes and various chlorinated and brominated compounds). The aim is to provide valid QSAR models, statistically validated for predictivity, for the prediction of BCF in general, but also for problematical chemical classes such as highly hydrophobic chemicals. Several descriptors, calculated by different commercially available software packages, have been employed in order to take into account relevant information provided by physicochemical properties (octanol/water partition coefficient and water solubility) and molecular features (structural and quantum-chemical molecular descriptors). The best descriptor subsets for the models were selected using the Genetic Algorithm-Variable Subset Selection strategy (GA-VSS) and calculations were performed by ordinary least squares regression. Starting from a data set of 640 compounds (log K ow range from −2.34 to 12.66), we developed linear QSARs, firstly for a data set of 620 compounds (log K ow range from −2.34 to 10.35) and secondly specifically for 87 highly hydrophobic chemicals (log K ow range from 6.00 to 10.35). All these models have been statistically validated (both internally by cross-validation and bootstrap and externally, by “a priori” splitting of available data by Kohonen Map-ANN in training and prediction sets) and their structural chemical domain has been verified by the leverage approach. [Copyright &y& Elsevier]

Nitrated Polycyclic Aromatic Hydrocarbons (nitro-PAHs), ubiquitous environmental pollutants, are recognized mutagens and carcinogens. A set of mutagenicity data (TA100) for 48 nitro-PAHs was modeled by the Quantitative Structure-Activity Relationships (QSAR) regression method, and OECD principles for QSAR model validation were applied. The proposed Multiple Linear Regression (MLR) models are based on two topological molecular descriptors. The models were validated for predictivity by both internal and external validation. For the external validation, three different splitting approaches, D-optimal Experimental Design, Self Organizing Maps (SOM) and Random Selection by activity sampling, were applied to the original data set in order to compare these methodologies and to select the best descriptors able to model each prediction set chemicals independently of the splitting method applied. The applicability domain was verified by the leverage approach. [ABSTRACT FROM AUTHOR]

The tropospheric lifetime of most organic chemicals deriving from terrestrial emissions is mainly controlled by their degradation reaction with OH radicals and ozone during the daytime, and NO3 radicals at night. The studied data set, composed of 399 structurally heterogeneous volatile organic compounds (VOCs), consists of data for the three studied oxidation reactions, available either experimentally or from our herein reported quantitative structure–activity relationships (QSAR) models. A principal component analysis (PCA) model based on these principal oxidative degradations has been proposed to evaluate the overall tropospheric degradability of chemicals. The score of the first principal component can be proposed as an ATmospheric Degradability INdex (ATDIN) and is modelled by theoretical molecular descriptors to obtain a multiple linear regression (MLR) QSAR model with high predictive power, both internally and externally validated. This modelling approach allows a fast and preliminary VOC ranking according to their tendency to be degraded by atmospheric oxidants, starting only from the knowledge of their molecular structure. [Copyright &y& Elsevier]