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

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 ([Formula: see text] were developed with the aim of predicting the re...

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Autores principales: Bunmahotama, Warisa, Vijver, Martina G., Peijnenburg, Willie
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9325417/
https://www.ncbi.nlm.nih.gov/pubmed/35234298
http://dx.doi.org/10.1002/etc.5322
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author Bunmahotama, Warisa
Vijver, Martina G.
Peijnenburg, Willie
author_facet Bunmahotama, Warisa
Vijver, Martina G.
Peijnenburg, Willie
author_sort Bunmahotama, Warisa
collection PubMed
description 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 ([Formula: see text] 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 [Formula: see text]. Monte Carlo optimization was used to model the set of [Formula: see text]  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.
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spelling pubmed-93254172022-07-30 Development of a Quasi–Quantitative Structure–Activity Relationship Model for Prediction of the Immobilization Response of Daphnia magna Exposed to Metal‐Based Nanomaterials Bunmahotama, Warisa Vijver, Martina G. Peijnenburg, Willie Environ Toxicol Chem Environmental Toxicology 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 ([Formula: see text] 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 [Formula: see text]. Monte Carlo optimization was used to model the set of [Formula: see text]  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. John Wiley and Sons Inc. 2022-04-08 2022-06 /pmc/articles/PMC9325417/ /pubmed/35234298 http://dx.doi.org/10.1002/etc.5322 Text en © 2022 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ (https://creativecommons.org/licenses/by-nc-nd/4.0/) License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.
spellingShingle Environmental Toxicology
Bunmahotama, Warisa
Vijver, Martina G.
Peijnenburg, Willie
Development of a Quasi–Quantitative Structure–Activity Relationship Model for Prediction of the Immobilization Response of Daphnia magna Exposed to Metal‐Based Nanomaterials
title Development of a Quasi–Quantitative Structure–Activity Relationship Model for Prediction of the Immobilization Response of Daphnia magna Exposed to Metal‐Based Nanomaterials
title_full Development of a Quasi–Quantitative Structure–Activity Relationship Model for Prediction of the Immobilization Response of Daphnia magna Exposed to Metal‐Based Nanomaterials
title_fullStr Development of a Quasi–Quantitative Structure–Activity Relationship Model for Prediction of the Immobilization Response of Daphnia magna Exposed to Metal‐Based Nanomaterials
title_full_unstemmed Development of a Quasi–Quantitative Structure–Activity Relationship Model for Prediction of the Immobilization Response of Daphnia magna Exposed to Metal‐Based Nanomaterials
title_short Development of a Quasi–Quantitative Structure–Activity Relationship Model for Prediction of the Immobilization Response of Daphnia magna Exposed to Metal‐Based Nanomaterials
title_sort development of a quasi–quantitative structure–activity relationship model for prediction of the immobilization response of daphnia magna exposed to metal‐based nanomaterials
topic Environmental Toxicology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9325417/
https://www.ncbi.nlm.nih.gov/pubmed/35234298
http://dx.doi.org/10.1002/etc.5322
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