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Assessment of the predictive capacity of a physiologically based kinetic model using a read-across approach

Paini, A, Worth, A, Kulkarni, S, Ebbrell, DJ and Madden, JC (2021) Assessment of the predictive capacity of a physiologically based kinetic model using a read-across approach. Computational Toxicology, 18. ISSN 2468-1113

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Abstract

With current progress in science, there is growing interest in developing and applying Physiologically Based Kinetic (PBK) models in chemical risk assessment, as knowledge of internal exposure to chemicals is critical to understanding potential effects in vivo. In particular, a new generation of PBK models is being developed in which the model parameters are derived from in silico and in vitro methods. To increase the acceptance and use of these “Next Generation PBK models”, there is a need to demonstrate their validity. However, this is challenging in the case of data-poor chemicals that are lacking in kinetic data and for which predictive capacity cannot, therefore, be assessed. The aim of this work is to lay down the fundamental steps in using a read across framework to inform modellers and risk assessors on how to develop, or evaluate, PBK models for chemicals without in vivo kinetic data. The application of a PBK model that takes into account the absorption, distribution, metabolism and excretion characteristics of the chemical reduces the uncertainties in the biokinetics and biotransformation of the chemical of interest. A strategic flow-charting application, proposed herein, allows users to identify the minimum information to perform a read-across from a data-rich chemical to its data-poor analogue(s). The workflow analysis is illustrated by means of a real case study using the alkenylbenzene class of chemicals, showing the reliability and potential of this approach. It was demonstrated that a consistent quantitative relationship between model simulations could be achieved using models for estragole and safrole (source chemicals) when applied to methyleugenol (target chemical). When the PBK model code for the source chemicals was adapted to utilise input values relevant to the target chemical, simulation was consistent between the models. The resulting PBK model for methyleugenol was further evaluated by comparing the results to an existing, published model for methyleugenol, providing further evidence that the approach was successful. This can be considered as a “read-across” approach, enabling a valid PBK model to be derived to aid the assessment of a data poor chemical.

Item Type: Article
Subjects: R Medicine > RM Therapeutics. Pharmacology
Divisions: Pharmacy & Biomolecular Sciences
Publisher: Elsevier
Date Deposited: 01 Mar 2021 12:21
Last Modified: 01 Mar 2021 12:30
DOI or Identification number: 10.1016/j.comtox.2021.100159
URI: https://researchonline.ljmu.ac.uk/id/eprint/14540

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