Computational toxicology: From cheminformatics to nanoinformatics

Zeta-Potential Read-Across Model Utilizing Nanodescriptors Extracted via the NanoXtract Image Analysis Tool Available on the Enalos Nanoinformatics Cloud Platform

Zeta potential is one of the most critical properties of nanomaterials (NMs) which provides an estimation of the surface charge, and therefore electrostatic stability in medium and, in practical terms, influences the NM's tendency to form agglomerates and to interact with cellular membranes. This paper describes a robust and accurate read-across model to predict NM zeta potential utilizing as the input data a set of image descriptors derived from transmission electron microscopy (TEM) images of the NMs. The image descriptors are calculated using NanoXtract (http://enaloscloud.novamechanics.com/EnalosWebApps/NanoXtract/), a unique online tool that generates 18 image descriptors from the TEM images, which can then be explored by modeling to identify those most predictive of NM behavior and biological effects. NM TEM images are used to develop a model for prediction of zeta potential based on grouping of the NMs according to their nearest neighbors. The model provides interesting insights regarding the most important similarity features between NMs-in addition to core composition the main elongation emerged, which links to key drivers of NM toxicity such as aspect ratio. Both the NanoXtract image analysis tool and the validated model for zeta potential (http://enaloscloud.novamechanics.com/EnalosWebApps/ZetaPotential/) are freely available online through the Enalos Nanoinformatics platform.

Use of the index of ideality of correlation to improve predictive potential for biochemical endpoints

The CORAL software is a tool to build up quantitative structure-property/activity relationships (QSPRs/QSARs). The project of updated version of the CORAL software is discussed in terms of practical applications for building up various models. The updating is the possibility to improve the predictive potential of models using the so-called Index of Ideality of Correlation (IIC) as a criterion of the predictive potential for QSPR/QSAR models. Efficacy of the IIC is examined with three examples of building up QSARs: (i) models for anticancer activity; (ii) models for mutagenicity; and (iii) models for toxicity of psychotropic drugs. The validation of these models has been carried out with several splits into the training, invisible training, calibration, and validation sets. The ability of IIC to be an indicator of predictive potential of QSAR models is confirmed. The updated version of the CORAL software (CORALSEA-2017, http://www.insilico.eu/coral ) is available on the Internet.