Abstract

Manufacturers of nanomaterial-enabled products need models of endpoints that are relevant to human safety to support the “safe by design” paradigm and avoid late-stage attrition. Increasingly, embryonic zebrafish (Danio Rerio) are recognised as a key human safety relevant in vivo test system. Hence, machine learning models were developed for identifying metal oxide nanomaterials causing lethality to embryonic zebrafish up to 24 hours post-fertilisation, or excess lethality in the period of 24–120 hours post-fertilisation, at concentrations of 250 ppm or less. Models were developed using data from the Nanomaterial Biological-Interactions Knowledgebase for a dataset of 44 diverse, coated and uncoated metal or, in one case, metalloid oxide nanomaterials. Different modelling approaches were evaluated using nested cross-validation on this dataset. Models were initially developed for both lethality endpoints using multiple descriptors representing the composition of the core, shell and surface functional groups, as well as particle characteristics. However, interestingly, the 24 hours post-fertilisation data were found to be harder to predict, which could reflect different exposure routes. Hence, subsequent analysis focused on the prediction of excess lethality at 120 hours-post fertilisation. The use of two data augmentation approaches, applied for the first time in nano-QSAR research, was explored, yet both failed to boost predictive performance. Interestingly, it was found that comparable results to those originally obtained using multiple descriptors could be obtained using a model based upon a single, simple descriptor: the Pauling electronegativity of the metal atom. Since it is widely recognised that a variety of intrinsic and extrinsic nanomaterial characteristics contribute to their toxicological effects, this is a surprising finding. This may partly reflect the need to investigate more sophisticated descriptors in future studies. Future studies are also required to examine how robust these modelling results are on truly external data, which were not used to select the single descriptor model. This will require further laboratory work to generate comparable data to those studied herein.

Highlights

  • A variety of nanomaterial (NM)-enabled products have already been marketed [1,2] and there is considerable interest in the development of novel engineered nanomaterials (ENMs) for a variety of applications

  • A dataset comprising 44 ENMs was derived from the Nanomaterial Biological-Interactions (NBI) Knowledgebase to support the development of models for classifying coated and uncoated metal oxide ENMs as toxic or nontoxic, according to two distinct categorisations based upon mortality data determined at 24 or 120 hpf for embryonic zebrafish continuously exposed to the ENMs via fish water test medium [31]

  • Strictly speaking, a metalloid oxide, it is considered a metal oxide according to the NBI Knowledgebase terminology and seminal nano-quantitative structure–activity relationships (QSARs) work [18].) Both endpoints were treated as binary classification variables, with ENMs considered toxic if a lowest observed effect levels (LOELs) value of at most 250 ppm could be derived or, otherwise, non-toxic

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Summary

Introduction

A variety of nanomaterial (NM)-enabled products have already been marketed [1,2] and there is considerable interest in the development of novel engineered nanomaterials (ENMs) for a variety of applications. Concerns have been raised regarding the human In the current work, classification models were developed to health relevance of the endpoints modelled in many nano- classify coated or uncoated metal oxide nanomaterials as lethal or non-lethal, based upon whether statistically significant lowest observed effect levels (LOELs) [32] for lethality, or excess lethality, in embryonic zebrafish were detectable at test concentrations up to 250 parts per million (ppm).

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