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

Research output: Contribution to journalArticlepeer-review

Authors

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

Colleges, School and Institutes

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 TiO 2 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 TiO 2 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.

Bibliographic note

Funding Information: The initial experimental work was funded via a NERC highlight topic grant ( NE/N006569/1 ) while the 2-year aged NM data was generated and read-across model was developed within the H2020 NanoSolveIT project (grant agreement no. 814572 ) with additional support from H2020 Marie-Sklodowska-Curie-Action RISE project NANOGENTOOLS (grant agreement no. 691095 ) and H2020 research infrastructure project NanoCommons (grant agreement no. 731032 ). Access to the University of Birmingham Daphnia Facility and the Electron Microscopy centre at the School of Materials and Metallurgy are acknowledged. All particles used in the work came from the H2020 project NanoFASE (Grant agreement N o. 646002 ) and we are extremely grateful to Promethean Particles Ltd., Applied Nanomaterials and Amepox Microelectronics Ltd for providing them. Publisher Copyright: © 2021 The Author(s)

Details

Original languageEnglish
Article number131452
Number of pages11
JournalChemosphere
Volume285
Early online date6 Jul 2021
Publication statusE-pub ahead of print - 6 Jul 2021

Keywords

  • Ecological corona, Machine learning, Nanoinformatics, Nanomaterials ageing, Nanosafety, Read-across

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