ABSTRACT
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.
Subject(s)
Air Pollutants/toxicity , Mutagens/toxicity , Polycyclic Aromatic Hydrocarbons/toxicity , Quantitative Structure-Activity Relationship , Cell Line , Cell Proliferation/drug effects , Forecasting , Humans , Reproducibility of ResultsABSTRACT
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.
Subject(s)
Models, Chemical , Mutagens/toxicity , Nitro Compounds/toxicity , Polycyclic Aromatic Hydrocarbons/toxicity , Quantitative Structure-Activity Relationship , Linear Models , Mutagenicity Tests/methods , Mutagens/chemistry , Nitro Compounds/chemistry , Polycyclic Aromatic Hydrocarbons/chemistryABSTRACT
The limited availability and variability of data related to atmospheric degradation reaction is a very relevant issue in studies related to environmental fate and behavior of chemicals. For screening purposes, the experimental data of the oxidation rate constants for the reactions with the radicals OH, NO3 and with ozone of 65 heterogeneous organic compounds were explored by Principal Component Analysis: a ranking of volatile organic chemicals (VOC) according to their relative overall atmospheric degradability and an atmospheric persistence index (ATPIN) is proposed. This index has been modeled by theoretical molecular descriptors to obtain MLR models with high predictive power, both internally and externally validated, and the definition of chemical domain applicability. This procedure allows a fast ranking of VOCs according to their tendency to be degraded by atmospheric oxidants, starting only from the knowledge of their molecular structure.
