Gosling, JP orcid.org/0000-0002-4072-3022 (2019) The importance of mathematical modelling in chemical risk assessment and the associated quantification of uncertainty. Computational Toxicology, 10. pp. 44-50. ISSN 2468-1113
Abstract
Computational models pervade modern toxicology and are becoming an accepted part of chemical risk assessments. Mathematical and statistical tools are versatile enough to capture information from wide arrays of existing data and from our mechanistic understanding of human biology and chemical reactions. They are more accessible than ever given the number of readily available guidance documents and software packages. In the present article, we will highlight the utility of modelling for next generation risk assessments whilst emphasising the importance of characterising and reporting uncertainty. The concepts herein are the foundations for a paradigm shift in toxicology where transparency about scientific understanding replaces faith in animal models.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2018, Elsevier B.V. This is an author produced version of an article published in Computational Toxicology. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | In silico predictions; Mathematical modelling; Next generation risk assessment; Uncertainty |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mathematics (Leeds) > Statistics (Leeds) |
Funding Information: | Funder Grant number NC3Rs NC/K001280/1 |
Depositing User: | Symplectic Publications |
Date Deposited: | 18 Dec 2018 12:35 |
Last Modified: | 22 Dec 2019 01:39 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.comtox.2018.12.004 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:140094 |