Ecotoxicological read-across models for predicting acute toxicity of freshly dispersed versus medium-aged NMs to Daphnia magna

Dimitra-Danai Varsou; Laura-Jayne A Ellis; Antreas Afantitis; Georgia Melagraki; Iseult Lynch

doi:10.1016/j.chemosphere.2021.131452

Ecotoxicological read-across models for predicting acute toxicity of freshly dispersed versus medium-aged NMs to Daphnia magna

Chemosphere. 2021 Dec:285:131452. doi: 10.1016/j.chemosphere.2021.131452. Epub 2021 Jul 6.

Authors

Dimitra-Danai Varsou¹, Laura-Jayne A Ellis², Antreas Afantitis¹, Georgia Melagraki³, Iseult Lynch⁴

Affiliations

¹ Nanoinformatics Department, NovaMechanics Ltd, Nicosia, Cyprus.
² School of Geography, Earth and Environmental Sciences, University of Birmingham, B15 2TT, Birmingham, UK.
³ Division of Physical Sciences and Applications, Hellenic Military Academy, Vari, Greece. Electronic address: gmelagraki@sse.gr.
⁴ School of Geography, Earth and Environmental Sciences, University of Birmingham, B15 2TT, Birmingham, UK. Electronic address: i.lynch@bham.ac.uk.

PMID: 34265725
DOI: 10.1016/j.chemosphere.2021.131452

Abstract

Nanoinformatics models to predict the toxicity/ecotoxicity of nanomaterials (NMs) are urgently needed to support commercialization of nanotechnologies and allow grouping of NMs based on their physico-chemical and/or (eco)toxicological properties, to facilitate read-across of knowledge from data-rich NMs to data-poor ones. Here we present the first ecotoxicological read-across models for predicting NMs ecotoxicity, which were developed in accordance with ECHA's recommended strategy for grouping of NMs as a means to explore in silico the effects of a panel of freshly dispersed versus environmentally aged (in various media) Ag and TiO₂ NMs on the freshwater zooplankton Daphnia magna, a keystone species used in regulatory testing. The dataset used to develop the models consisted of dose-response data from 11 NMs (5 TiO₂ NMs of identical cores with different coatings, and 6 Ag NMs with different capping agents/coatings) each dispersed in three different media (a high hardness medium (HH Combo) and two representative river waters containing different amounts of natural organic matter (NOM) and having different ionic strengths), generated in accordance with the OECD 202 immobilization test. The experimental hypotheses being tested were (1) that the presence of NOM in the medium would reduce the toxicity of the NMs by forming an ecological corona, and (2) that environmental ageing of NMs reduces their toxicity compared to the freshly dispersed NMs irrespective of the medium composition (salt only or NOM-containing). As per the ECHA guidance, the NMs were grouped into two categories - freshly dispersed and 2-year-aged and explored in silico to identify the most important features driving the toxicity in each group. The final predictive models have been validated according to the OECD criteria and a QSAR model report form (QMRF) report included in the supplementary information to support adoption of the models for regulatory purposes.

Keywords: Ecological corona; Machine learning; Nanoinformatics; Nanomaterials ageing; Nanosafety; Read-across.

MeSH terms

Animals
Daphnia*
Ecotoxicology
Nanostructures*