Modelling the toxicity of a large set of metal and metal oxide nanoparticles using the OCHEM platform.

Inorganic nanomaterials have become one of the new areas of modern knowledge and technology and have already found an increasing number of applications. However, some nanoparticles show toxicity to living organisms, and can potentially have a negative influence on environmental ecosystems. While toxicity can be determined experimentally, such studies are time consuming and costly. Computational toxicology can provide an alternative approach and there is a need to develop methods to reliably assess Quantitative Structure-Property Relationships for nanomaterials (nano-QSPRs). Importantly, development of such models requires careful collection and curation of data. This article overviews freely available nano-QSPR models, which were developed using the Online Chemical Modeling Environment (OCHEM). Multiple data on toxicity of nanoparticles to different living organisms were collected from the literature and uploaded in the OCHEM database. The main characteristics of nanoparticles such as chemical composition of nanoparticles, average particle size, shape, surface charge and information about the biological test species were used as descriptors for developing QSPR models. QSPR methodologies used Random Forests (WEKA-RF), k-Nearest Neighbors and Associative Neural Networks. The predictive ability of the models was tested through cross-validation, giving cross-validated coefficients q2 = 0.58-0.80 for regression models and balanced accuracies of 65-88% for classification models. These results matched the predictions for the test sets used to develop the models. The proposed nano-QSPR models and uploaded data are freely available online at http://ochem.eu/article/103451 and can be used for estimation of toxicity of new and emerging nanoparticles at the early stages of nanomaterial development.

Prioritisation of polybrominated diphenyl ethers (PBDEs) by using the QSPR-THESAURUS web tool.

The prioritisation of chemical compounds is important for the identification of those chemicals that represent the highest threat to the environment. As part of the CADASTER project (http: / /www.cadaster.eu), we developed an online web tool that allows the calculation of the environmental risk of chemical compounds from a web interface. The environmental fate of compounds in the aquatic compartment is assessed by using the SimpleBox model, while adverse effects on the aquatic compartment are assessed by the Species Sensitivity Distribution approach. The main purpose of this web tool is to exemplify the use of quantitative structure-activity relationships (QSARs) to support risk assessment. A case study of QSAR integrated risk assessment of 209 polybrominated diphenyl ethers (PBDEs) demonstrates the treatment and influence of uncertainty in the predicted physicochemical and toxicity parameters in probabilistic risk assessment.