Development of a Quasi–Quantitative Structure–Activity Relationship Model for Prediction of the Immobilization Response of Daphnia magna Exposed to Metal-Based Nanomaterials

Environmental Toxicology and Chemistry, vol. 41, pp. 1439–1450

Abstract

Abstract The conventional Hill equation model is suitable to fit dose–response data obtained from performing (eco)toxicity assays. Models based on quasi–quantitative structure–activity relationships (QSARs) to estimate the Hill coefficient (nH) were developed with the aim of predicting the response of the invertebrate species Daphnia magna to exposure to metal-based nanomaterials. Descriptors representing the pristine properties of nanoparticles and media conditions were coded to a quasi–simplified molecular input line entry system and correlated to experimentally derived values of nH. Monte Carlo optimization was used to model the set of nH values, and the model was trained on the basis of reported dose–response relationships of 60 data sets (n = 367 individual response observations) of 11 metal-based nanomaterials as obtained from 20 literature reports. The model simulates the training data well, with only 2.3% deviation between experimental and modeled response data. The technique was employed to predict the dose–response relationships of 15 additional data sets (n = 72 individual observations) not included in model development of seven metal-based nanomaterials from 10 literature reports, with an average error of 3.5%. Combining the model output with either the median effective concentration value or any other known effect level as obtained from experimental data allows the prediction of full dose–response curves of D. magna immobilization. This model is an accurate screening tool that allows the determination of the shape and slope of dose–response curves, thereby greatly reducing experimental effort in case of novel advanced metal-based nanomaterials or the prediction of responses in altered exposure media. This screening model is compliant with the 3Rs (replacement, reduction, and refinement) principle, which is embraced by the scientific and regulatory communities dealing with nano-safety. Environ Toxicol Chem 2022;41:1439–1450. © 2022 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Authors 3

1. Warisa Bunmahotama Aachen Institute of Environmental Sciences (CML) Leiden University Leiden The Netherlands Institute of Environmental Sciences (CML)

   Leiden University

   Affiliation as printed

   Institute of Environmental Sciences Leiden University Leiden The Netherlands

   Institute of Environmental Sciences, Leiden University, Leiden, The Netherlands

2. Martina G. Vijver Aachen Institute of Environmental Sciences (CML) Leiden University Leiden The Netherlands Institute of Environmental Sciences (CML)

   Leiden University

   Affiliation as printed

   Institute of Environmental Sciences Leiden University Leiden The Netherlands

   Institute of Environmental Sciences, Leiden University, Leiden, The Netherlands

3. Willie J.G.M. Peijnenburg corresponding Aachen Institute of Environmental Sciences (CML) Leiden University Leiden The Netherlands Institute of Environmental Sciences (CML)

   Leiden University · National Institute for Public Health and the Environment

   Affiliation as printed

   Center for Safety of Substances and Products National Institute of Public Health and the Environment Bilthoven The Netherlands

   Institute of Environmental Sciences Leiden University Leiden The Netherlands

   Center for Safety of Substances and Products, National Institute of Public Health and the Environment, Bilthoven, The Netherlands

   Institute of Environmental Sciences, Leiden University, Leiden, The Netherlands