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Abstract
A literature curated dataset containing 24 distinct metal oxide (MexOy) nanoparticles (NPs), including 15 physicochemical, structural and assay-related descriptors, was enriched with 62 atomistic computational descriptors and exploited to produce a robust and validated in silico model for prediction of NP cytotoxicity. The model can be used to predict the cytotoxicity (cell viability) of MexOy NPs based on the colorimetric lactate dehydrogenase (LDH) assay and the luminometric adenosine triphosphate (ATP) assay, both of which quantify irreversible cell membrane damage. Out of the 77 total descriptors used, 7 were identified as being significant for induction of cytotoxicity by MexOy NPs. These were NP core size, hydrodynamic size, assay type, exposure dose, the energy of the MexOy conduction band (EC), the coordination number of the metal atoms on the NP surface (Avg. C.N. Me atoms surface) and the average force vector surface normal component of all metal atoms (v⊥ Me atoms surface). The significance and effect of these descriptors is discussed to demonstrate their direct correlation with cytotoxicity. The produced model has been made publicly available by the Horizon 2020 (H2020) NanoSolveIT project and will be added to the project’s Integrated Approach to Testing and Assessment (IATA).
Highlights
Occurring nanoscale particles have existed throughout Earth’s and subsequently human history
We present a meta-analysis of a dataset by Zhang et al (2012) [45] retrieved from the S2NANO database on the cytotoxicity of 24 MexOy NPs to human bronchial epithelial (BEAS-2B) and murine myeloid (RAW 264.7) cell lines using single parameter (% cell viability) adenosine triphosphate (ATP) and lactate dehydrogenase (LDH) assays
The goal of this study is to test whether NP cytotoxicity can be predicted using a combination of physicochemical, molecular and whole NP computational descriptors
Summary
Occurring nanoscale particles (nanoparticles, NPs) have existed throughout Earth’s and subsequently human history. The increased production of engineered NPs, with tuneable physical, chemical and biological properties, has led to them being widely used, among others, in the automotive, electronics, optics, food technology, cosmetics and healthcare industries [1,2]. MexOy NPs are being used in various consumer products, such as food, cosmetics (sunscreen) and electronic and medical devices [10] Due to their small size and high biochemical activity, and despite their useful and beneficial properties, there are concerns that NPs are able to cross biological barriers and access a wide number of organs and tissues in the human body, including (to a limited extent) the blood–brain barrier [11], which can lead to toxic side-effects [11,12,13,14,15,16,17,18,19]. Taking into account the cost and working hours associated with in vivo and in vitro experiments and that traditional “wet-lab” toxicology cannot keep up with diversity and increasing abundance of engineered NPs, computational modelling has the potential to act as a high-throughput alternative [28,29] and is becoming increasingly accepted in regulatory testing as model validation and documentation improves
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