No abstract

We present toxFlow, a web application developed for enrichment analysis of omics data coupled with read-across toxicity prediction. A sequential analysis workflow is suggested where users can filter omics data using enrichment scores and incorporate their findings into a correlation-based read-across technique for predicting the toxicity of a substance based on its analogs. Either embedded or in-house gene signature libraries can be used for enrichment analysis. The suggested approach can be used for toxicity prediction of diverse chemical entities; however, this article focuses on the multiperspective characterization of nanoparticles and selects their neighbors based on both physicochemical and biological similarity criteria. In addition, visualization options are offered to interactively explore correlation patterns in the data, whereas results can be exported for further analysis. toxFlow is accessible at http://147.102.86.129:3838/toxflow .

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 (https://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 (https://enaloscloud.novamechanics.com/EnalosWebApps/ZetaPotential/) are freely available online through the Enalos Nanoinformatics platform.

This study presents the results of applying deep learning methodologies within the ecotoxicology field, with the objective of training predictive models that can support hazard assessment and eventually the design of safer engineered nanomaterials (ENMs). A workflow applying two different deep learning architectures on microscopic images of Daphnia magna is proposed that can automatically detect possible malformations, such as effects on the length of the tail, and the overall size, and uncommon lipid concentrations and lipid deposit shapes, which are due to direct or parental exposure to ENMs. Next, classification models assign specific objects (heart, abdomen/claw) to classes that depend on lipid densities and compare the results with controls. The models are statistically validated in terms of their prediction accuracy on external D. magna images and illustrate that deep learning technologies can be useful in the nanoinformatics field, because they can automate time‐consuming manual procedures, accelerate the investigation of adverse effects of ENMs, and facilitate the process of designing safer nanostructures. It may even be possible in the future to predict impacts on subsequent generations from images of parental exposure, reducing the time and cost involved in long‐term reproductive toxicity assays over multiple generations.