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Making in silico predictive models for toxicology FAIR

Cronin, M, Belfield, SJ, Briggs, K, Enoch, SJ, Firman, JW, Frericks, M, Garrard, C, MacCallum, P, Madden, JC, Pastor, M, Sanz, F, Soininen, I and Sousoni, D (2023) Making in silico predictive models for toxicology FAIR. Regulatory Toxicology and Pharmacology, 140. ISSN 0273-2300

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Open Access URL: https://doi.org/10.1016/j.yrtph.2023.105385 (published)


In silico predictive models for toxicology include quantitative structure-activity relationship (QSAR) and physiologically based kinetic (PBK) approaches to predict physico-chemical and ADME properties, toxicological effects and internal exposure. Such models are used to fill data gaps as part of chemical risk assessment. There is a growing need to ensure in silico predictive models for toxicology are available for use and reproducible. This paper describes how the FAIR (Findable, Accessible, Interoperable, Reusable) principles, developed for data sharing, have been applied to in silico predictive models. In particular, this investigation has focussed on how the FAIR principles could be applied to improved regulatory acceptance of predictions from such models. Eighteen principles have been developed that cover all aspects of FAIR. It is intended that FAIRification of in silico predictive models for toxicology will increase their use and acceptance.

Item Type: Article
Uncontrolled Keywords: 1115 Pharmacology and Pharmaceutical Sciences; Toxicology
Subjects: R Medicine > RS Pharmacy and materia medica
Divisions: Pharmacy & Biomolecular Sciences
Publisher: Elsevier
SWORD Depositor: A Symplectic
Date Deposited: 18 Apr 2023 15:46
Last Modified: 18 Apr 2023 16:00
DOI or ID number: 10.1016/j.yrtph.2023.105385
URI: https://researchonline.ljmu.ac.uk/id/eprint/19242
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