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Predictive in silico modeling of emetic potency of liquid cleaning products using an historical in vivo database.

Li, S; Meli, F; Vinson, P; Broening, HW; Andrews, PLR; Nash, JF (2020) Predictive in silico modeling of emetic potency of liquid cleaning products using an historical in vivo database. Food Chem Toxicol, 146. p. 111833. ISSN 1873-6351 https://doi.org/10.1016/j.fct.2020.111833
SGUL Authors: Andrews, Paul Lyn Rodney

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Abstract

The induction of vomiting by activation of mechanisms protecting the body against ingested toxins is not confined to natural products but can occur in response to manmade medicinal and non-medicinal products such as liquid cleaning products where it is a commonly reported adverse effect of accidental ingestion. The present study examined the utility of an historic database (>30 years old) reporting emetic effects of 98 orally administered liquid cleaning formulations studied in vivo (canine model) to objectively identify the main pro-emetic constituents and to derive a predictive model. Data were analysed by categorizing the formulation constituents into 10 main groups followed by using multivariate correlation, partial least squares and recursive partitioning analysis. Using the ED50 we objectively identified high ionic strength, non-ionic surfactants (alcohol ethoxylate) and alkaline pH as the main pro-emetic factors. Additionally, a mathematical model was developed which allows prediction of the ED50 based on formulation. The limitations of the use of historic data and the model are discussed. The results have practical applications in new product formulation and safety but additionally the principles underpinning this in silico study have wider applicability in demonstrating the potential utility of such archival data in current research contributing to animal replacement.

Item Type: Article
Additional Information: © 2020. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
Keywords: 3Rs, Emesis, In silico, Multivariate analysis, Nausea, Recursive partitioning analysis, Vomiting, Emesis, In silico, Multivariate analysis, Nausea, 3Rs, Recursive partitioning analysis, Vomiting, 3Rs, Emesis, In silico, Multivariate analysis, Nausea, Recursive partitioning analysis, Vomiting, Food Science, 0908 Food Sciences
SGUL Research Institute / Research Centre: Academic Structure > Molecular and Clinical Sciences Research Institute (MCS)
Journal or Publication Title: Food Chem Toxicol
ISSN: 1873-6351
Language: eng
Dates:
DateEvent
December 2020Published
29 October 2020Published Online
24 October 2020Accepted
Publisher License: Creative Commons: Attribution-Noncommercial-No Derivative Works 4.0
PubMed ID: 33129935
Web of Science ID: WOS:000604138100048
Go to PubMed abstract
URI: https://openaccess.sgul.ac.uk/id/eprint/112839
Publisher's version: https://doi.org/10.1016/j.fct.2020.111833

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