Thompson, CV, Webb, SD, Leedale, JA, Penson, PE, Paini, A, Ebbrell, DJ and Madden, JC (2023) Using Read-Across to Build Physiologically-Based Kinetic Models: Part 2. Case Studies for atenolol and flumioxazin. Computational Toxicology, 29. p. 100293. ISSN 2468-1113

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Abstract

Read-across, wherein information from a data-rich chemical is used to make a prediction for a similar chemical that lacks the relevant data, is increasingly being accepted as an alternative to animal testing. Identifying chemicals that can be considered as similar (analogues) is crucial to the process. Two resources have been developed previously to address the issue of analogue selection and facilitate physiologically-based kinetic (PBK) model development, using read-across. Chemical-specific PBK models, available in the literature, were collated to form a PBK model dataset (PMD) of over 7,500 models. A KNIME workflow was created to accompany this PMD that can aid the selection of appropriate chemical analogues from chemicals within this dataset (i.e. chemicals that are similar to a target of interest and are known to have an existing PBK model). Information from the PBK model for the source chemical can then be used in a read-across approach to inform the development of a new PBK model for the target. The application of these resources is tested here using two case studies (i) for the drug atenolol and (ii) for the plant protection product, flumioxazin. New PBK models were constructed for these two target chemicals using data obtained from source chemicals, identified by the workflow as being similar (analogues). In each case, the published PBK model for the source chemical was initially reproduced, as accurately as possible, before being adapted and used as a template for the target chemical. The performance of the new PBK models was assessed by comparing simulation outputs to existing data on key kinetic properties for the targets. The results demonstrate that a read-across approach can be successfully applied to develop new PBK models for data-poor chemicals, thus enabling their deployment during early-stage risk assessment. This assists prediction of internal exposure whilst reducing reliance on animal testing.

Item Type:	Article
Subjects:	R Medicine > RS Pharmacy and materia medica
Divisions:	Pharmacy & Biomolecular Sciences
Publisher:	Elsevier
SWORD Depositor:	A Symplectic
Date Deposited:	21 Dec 2023 11:59
Last Modified:	21 Dec 2023 12:00
DOI or ID number:	10.1016/j.comtox.2023.100293
URI:	https://researchonline.ljmu.ac.uk/id/eprint/22142

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