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Modelling the effects of variability in feeding rate on growth – a vital step for DEB-TKTD modelling

A major limitation of dietary toxicity studies on rodents is that food consumption often differs between treatments. The control treatment serves as a reference of how animals would have grown if not for the toxicant in their diet, but this comparison unavoidably conflates the effects of toxicity an...

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Autores principales: Martin, Thomas, Hodson, Mark E., Ashauer, Roman
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8873987/
https://www.ncbi.nlm.nih.gov/pubmed/35104776
http://dx.doi.org/10.1016/j.ecoenv.2022.113231
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author Martin, Thomas
Hodson, Mark E.
Ashauer, Roman
author_facet Martin, Thomas
Hodson, Mark E.
Ashauer, Roman
author_sort Martin, Thomas
collection PubMed
description A major limitation of dietary toxicity studies on rodents is that food consumption often differs between treatments. The control treatment serves as a reference of how animals would have grown if not for the toxicant in their diet, but this comparison unavoidably conflates the effects of toxicity and feeding rate on body weight over time. A key advantage of toxicity models based on dynamic energy budget theory (DEB) is that chemical stress and food consumption are separate model inputs, so their effects on growth rate can be separated. To reduce data requirements, DEB convention is to derive a simplified feeding input, f, from food availability; its value ranges from zero (starvation) to one (food available ad libitum). Observed food consumption in dietary toxicity studies shows that, even in the control treatment, rats limit their food consumption, contradicting DEB assumptions regarding feeding rate. Relatively little work has focused on addressing this mismatch, but accurately modelling the effects of food intake on growth rate is essential for the effects of toxicity to be isolated. This can provide greater insight into the results of chronic toxicity studies and allows accurate extrapolation of toxic effects from laboratory data. Here we trial a new method for calculating f, based on the observed relationships between food consumption and body size in laboratory rats. We compare model results with those of the conventional DEB method and a previous effort to calculate f using observed food consumption data. Our results showed that the new method improved model accuracy while modelled reserve dynamics closely followed observed body fat percentage over time. The new method assumes that digestive efficiency increases with body size(.) Verifying this relationship through data collection would strengthen the basis of DEB theory and support the case for its use in ecological risk assessment.
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spelling pubmed-88739872022-03-01 Modelling the effects of variability in feeding rate on growth – a vital step for DEB-TKTD modelling Martin, Thomas Hodson, Mark E. Ashauer, Roman Ecotoxicol Environ Saf Article A major limitation of dietary toxicity studies on rodents is that food consumption often differs between treatments. The control treatment serves as a reference of how animals would have grown if not for the toxicant in their diet, but this comparison unavoidably conflates the effects of toxicity and feeding rate on body weight over time. A key advantage of toxicity models based on dynamic energy budget theory (DEB) is that chemical stress and food consumption are separate model inputs, so their effects on growth rate can be separated. To reduce data requirements, DEB convention is to derive a simplified feeding input, f, from food availability; its value ranges from zero (starvation) to one (food available ad libitum). Observed food consumption in dietary toxicity studies shows that, even in the control treatment, rats limit their food consumption, contradicting DEB assumptions regarding feeding rate. Relatively little work has focused on addressing this mismatch, but accurately modelling the effects of food intake on growth rate is essential for the effects of toxicity to be isolated. This can provide greater insight into the results of chronic toxicity studies and allows accurate extrapolation of toxic effects from laboratory data. Here we trial a new method for calculating f, based on the observed relationships between food consumption and body size in laboratory rats. We compare model results with those of the conventional DEB method and a previous effort to calculate f using observed food consumption data. Our results showed that the new method improved model accuracy while modelled reserve dynamics closely followed observed body fat percentage over time. The new method assumes that digestive efficiency increases with body size(.) Verifying this relationship through data collection would strengthen the basis of DEB theory and support the case for its use in ecological risk assessment. Elsevier 2022-03-01 /pmc/articles/PMC8873987/ /pubmed/35104776 http://dx.doi.org/10.1016/j.ecoenv.2022.113231 Text en © 2022 The Authors https://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
Martin, Thomas
Hodson, Mark E.
Ashauer, Roman
Modelling the effects of variability in feeding rate on growth – a vital step for DEB-TKTD modelling
title Modelling the effects of variability in feeding rate on growth – a vital step for DEB-TKTD modelling
title_full Modelling the effects of variability in feeding rate on growth – a vital step for DEB-TKTD modelling
title_fullStr Modelling the effects of variability in feeding rate on growth – a vital step for DEB-TKTD modelling
title_full_unstemmed Modelling the effects of variability in feeding rate on growth – a vital step for DEB-TKTD modelling
title_short Modelling the effects of variability in feeding rate on growth – a vital step for DEB-TKTD modelling
title_sort modelling the effects of variability in feeding rate on growth – a vital step for deb-tktd modelling
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8873987/
https://www.ncbi.nlm.nih.gov/pubmed/35104776
http://dx.doi.org/10.1016/j.ecoenv.2022.113231
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