A nanoinformatics decision support tool for the virtual screening of gold nanoparticle cellular association using protein corona fingerprints

Abstract

The increasing use of nanoparticles (NPs) in a wide range of consumer and industrial applications has necessitated significant effort to address the challenge of characterizing and quantifying the underlying nanostructure - biological response relationships to ensure that these novel materials can be exploited responsibly and safely. Such efforts demand reliable experimental data not only in terms of the biological dose-response, but also regarding the physicochemical properties of the NPs and their interaction with the biological environment. The latter has not been extensively studied, as a large surface to bind biological macromolecules is a unique feature of NPs that is not relevant for chemicals or pharmaceuticals, and thus only limited data have been reported in the literature quantifying the protein corona formed when NPs interact with a biological medium and linking this with NP cellular association/uptake. In this work we report the development of a predictive model for the assessment of the biological response (cellular association, which can include both internalized NPs and those attached to the cell surface) of surface-modified gold NPs, based on their physicochemical properties and protein corona fingerprints, utilizing a dataset of 105 unique NPs. Cellular association was chosen as the end-point for the original experimental study due to its relevance to inflammatory responses, biodistribution, and toxicity in vivo. The validated predictive model is freely available online through the Enalos Cloud Platform ( http://enalos.insilicotox.com/NanoProteinCorona/ ) to be used as part of a regulatory or NP safe-by-design decision support system. This online tool will allow the virtual screening of NPs, based on a list of the significant NP descriptors, identifying those NPs that would warrant further toxicity testing on the basis of predicted NP cellular association.

Nanomaterials in the Environment Acquire an “Eco‐Corona” Impacting their Toxicity to Daphnia Magna—a Call for Updating Toxicity Testing Policies

“…The effect of this agglomeration of the smaller spherical NMs, due to the destabilization resulting from the acquisition of the corona, caused an increase in D. magna mortality upon exposure. By contrast, Au NMs were found to be stabilized by D. magna both during direct exposure and following incubation in D. magna conditioned medium, indicating that the consequences of the NM corona need to be determined for each type of NM for the moment, at least until sufficient data has been collected to allow development of predictive models for eco‐corona formation, as is now being achieved for human serum/plasma coronas …”

Development of Deep Learning Models for Predicting the Effects of Exposure to Engineered Nanomaterials on Daphnia magna

“…Nanoinformatics approaches have gained popularity over the last few years as novel tools to address several challenges in nanotechnology [ 2 ] including design of safer ENMs, based on computational and data analysis methodologies, with the goal of reducing to the greatest possible extent the need for traditional hazard and risk assessment methodologies that are based on animal testing. [ 3 ] Machine learning has been used extensively in nanoinformatics to develop predictive models for toxicity‐ and ecotoxicity‐related endpoints, employing various approaches such as read‐across methods, [ 4–6 ] nano‐quantitative structure–activity relationships (nanoQSAR [ 7–10 ] ), QSAR‐perturbation models, [ 11–13 ] and workflows predicting molecular initiating events and key events in adverse outcome pathways (AOPs). [ 14 ] Among the different types of descriptors used in predictive modeling approaches, image descriptors resulting from the analysis of electronic images of ENMs have been employed successfully.…”

Transcriptomics in Toxicogenomics, Part III: Data Modelling for Risk Assessment

“…Key aspects are robust and accurate predictions, rigorous model validation, well defined AD, and when possible an easy interpretation of model results. Predictive models that satisfy these requirements might assist the risk assessment and decision-making procedure [81].…”

“…ML methods have significantly advanced in recent years and are proven to be important alternatives to experimental testing for chemicals and nanomaterials [80][81][82]. The value of TGx-derived biomarkers of toxicity lays in the fact that they can be detected earlier than histopathological or clinical phenotypes [83].…”