Cronin, Mark T. D.Belfield, Samuel J.Briggs, KatharineEnoch, Steven J.Firman, James W.Frericks, MarkusGarrard, ClareMaccallum, Peter H.Madden, Judith C.Pastor Maeso, ManuelSanz, FerranSoininen, InariSousoni, Despoina2023-06-062023-06-062023Cronin MTD, Belfield SJ, Briggs KA, Enoch SJ, Firman JW, Frericks M, Garrard C, Maccallum PH, Madden JC, Pastor M, Sanz F, Soininen I, Sousoni D. Making in silico predictive models for toxicology FAIR. Regul Toxicol Pharmacol. 2023 May;140:105385. DOI: 10.1016/j.yrtph.2023.1053850273-2300http://hdl.handle.net/10230/57049In 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 that they are 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.application/pdfeng© 2023 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).info:eu-repo/semantics/articlehttp://dx.doi.org/10.1016/j.yrtph.2023.105385FAIRIn silico modelNew approach methodologiesNext generation risk assessmentPBKQSARToxicologyinfo:eu-repo/semantics/openAccess