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Computational models for the assessment of manufactured nanomaterials: Development of model reporting standards and mapping of the model landscape

Different types of computational models have been developed for predicting the biokinetics, environmental fate, exposure levels and toxicological effects of chemicals and manufactured nanomaterials (MNs). However, these models are not described in a consistent manner in the scientific literature, wh...

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Autores principales: Lamon, L., Asturiol, D., Vilchez, A., Ruperez-Illescas, R., Cabellos, J., Richarz, A., Worth, A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier B.V 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6472618/
https://www.ncbi.nlm.nih.gov/pubmed/31008416
http://dx.doi.org/10.1016/j.comtox.2018.12.002
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author Lamon, L.
Asturiol, D.
Vilchez, A.
Ruperez-Illescas, R.
Cabellos, J.
Richarz, A.
Worth, A.
author_facet Lamon, L.
Asturiol, D.
Vilchez, A.
Ruperez-Illescas, R.
Cabellos, J.
Richarz, A.
Worth, A.
author_sort Lamon, L.
collection PubMed
description Different types of computational models have been developed for predicting the biokinetics, environmental fate, exposure levels and toxicological effects of chemicals and manufactured nanomaterials (MNs). However, these models are not described in a consistent manner in the scientific literature, which is one of the barriers to their broader use and acceptance, especially for regulatory purposes. Quantitative structure-activity relationships (QSARs) are in silico models based on the assumption that the activity of a substance is related to its chemical structure. These models can be used to provide information on (eco)toxicological effects in hazard assessment. In an environmental risk assessment, environmental exposure models can be used to estimate the predicted environmental concentration (PEC). In addition, physiologically based kinetic (PBK) models can be used in various ways to support a human health risk assessment. In this paper, we first propose model reporting templates for systematically and transparently describing models that could potentially be used to support regulatory risk assessments of MNs, for example under the REACH regulation. The model reporting templates include (a) the adaptation of the QSAR Model Reporting Format (QMRF) to report models for MNs, and (b) the development of a model reporting template for PBK and environmental exposure models applicable to MNs. Second, we show the usefulness of these templates to report different models, resulting in an overview of the landscape of available computational models for MNs.
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spelling pubmed-64726182019-04-19 Computational models for the assessment of manufactured nanomaterials: Development of model reporting standards and mapping of the model landscape Lamon, L. Asturiol, D. Vilchez, A. Ruperez-Illescas, R. Cabellos, J. Richarz, A. Worth, A. Comput Toxicol Article Different types of computational models have been developed for predicting the biokinetics, environmental fate, exposure levels and toxicological effects of chemicals and manufactured nanomaterials (MNs). However, these models are not described in a consistent manner in the scientific literature, which is one of the barriers to their broader use and acceptance, especially for regulatory purposes. Quantitative structure-activity relationships (QSARs) are in silico models based on the assumption that the activity of a substance is related to its chemical structure. These models can be used to provide information on (eco)toxicological effects in hazard assessment. In an environmental risk assessment, environmental exposure models can be used to estimate the predicted environmental concentration (PEC). In addition, physiologically based kinetic (PBK) models can be used in various ways to support a human health risk assessment. In this paper, we first propose model reporting templates for systematically and transparently describing models that could potentially be used to support regulatory risk assessments of MNs, for example under the REACH regulation. The model reporting templates include (a) the adaptation of the QSAR Model Reporting Format (QMRF) to report models for MNs, and (b) the development of a model reporting template for PBK and environmental exposure models applicable to MNs. Second, we show the usefulness of these templates to report different models, resulting in an overview of the landscape of available computational models for MNs. Elsevier B.V 2019-02 /pmc/articles/PMC6472618/ /pubmed/31008416 http://dx.doi.org/10.1016/j.comtox.2018.12.002 Text en © 2018 The Authors http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Lamon, L.
Asturiol, D.
Vilchez, A.
Ruperez-Illescas, R.
Cabellos, J.
Richarz, A.
Worth, A.
Computational models for the assessment of manufactured nanomaterials: Development of model reporting standards and mapping of the model landscape
title Computational models for the assessment of manufactured nanomaterials: Development of model reporting standards and mapping of the model landscape
title_full Computational models for the assessment of manufactured nanomaterials: Development of model reporting standards and mapping of the model landscape
title_fullStr Computational models for the assessment of manufactured nanomaterials: Development of model reporting standards and mapping of the model landscape
title_full_unstemmed Computational models for the assessment of manufactured nanomaterials: Development of model reporting standards and mapping of the model landscape
title_short Computational models for the assessment of manufactured nanomaterials: Development of model reporting standards and mapping of the model landscape
title_sort computational models for the assessment of manufactured nanomaterials: development of model reporting standards and mapping of the model landscape
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6472618/
https://www.ncbi.nlm.nih.gov/pubmed/31008416
http://dx.doi.org/10.1016/j.comtox.2018.12.002
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