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A systematic review of published physiologically-based kinetic models and an assessment of their chemical space coverage

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Across multiple sectors, including the food, personal care, consumer goods and pharmaceutical industries, there is a need to predict the potential effects of xenobiotics. These effects are determined by both the intrinsic ability of the substance, or its derivatives, to interact with the biological system, and its concentration-time profile within different organs. Physiologically-based kinetic (PBK) models can predict organ-level concentration-time profiles, however, the models are time and resource intensive to generate de novo. Read-across is an approach used in safety assessment to reduce or replace animal testing, wherein information from a data-rich chemical is used to make predictions for a related data-poor chemical. The recent increase in published PBK models presents the opportunity to use a read-across approach for PBK modelling i.e using PBK model information from one chemical to inform the development or evaluation of a PBK model for a similar chemical. Essential to this process, is the identification of the chemicals for which a PBK model already exists. Herein, the results of a systematic review of existing PBK models are presented. Model information including species, sex, life-stage, route of administration, software platform used and availability of model equations has been captured for over 7,500 PBK models. Chemical information (identifiers and physico-chemical properties) has also been recorded for over 1,150 unique chemicals that are associated with these models. This PBK dataset been made readily accessible, as a Microsoft Excel spreadsheet, providing a valuable resource, for those developing, using or evaluating PBK models in industry, academia and the regulatory sectors.
2021-12-15
SAGE PUBLICATIONS LTD
JRC125543
0261-1929 (online),   
https://journals.sagepub.com/doi/10.1177/02611929211060264,    https://publications.jrc.ec.europa.eu/repository/handle/JRC125543,   
10.1177/02611929211060264 (online),   
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