Making in silico predictive models for toxicology FAIR

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• dc.contributor.author Cronin, Mark T. D.
• dc.contributor.author Belfield, Samuel J.
• dc.contributor.author Briggs, Katharine
• dc.contributor.author Enoch, Steven J.
• dc.contributor.author Firman, James W.
• dc.contributor.author Frericks, Markus
• dc.contributor.author Garrard, Clare
• dc.contributor.author Maccallum, Peter H.
• dc.contributor.author Madden, Judith C.
• dc.contributor.author Pastor Maeso, Manuel
• dc.contributor.author Sanz, Ferran
• dc.contributor.author Soininen, Inari
• dc.contributor.author Sousoni, Despoina
• dc.date.accessioned 2023-06-06T06:09:44Z
• dc.date.available 2023-06-06T06:09:44Z
• dc.date.issued 2023
• dc.description.abstract 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 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.
• dc.description.sponsorship This research received funding from the Innovative Medicines Initiative 2 Joint Undertaking (IMI2 JU) under grant agreement eTRANSAFE (777365), the European Union’s Horizon 2020 ELIXIR-CONVERGE Project (871075) and research and innovation programme under grant agreement No 964537 (RISK-HUNT3R).
• dc.format.mimetype application/pdf
• dc.identifier.citation Cronin 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.2023.105385
• dc.identifier.doi http://dx.doi.org/10.1016/j.yrtph.2023.105385
• dc.identifier.issn 0273-2300
• dc.identifier.uri http://hdl.handle.net/10230/57049
• dc.language.iso eng
• dc.publisher Elsevier
• dc.relation.ispartof Regul Toxicol Pharmacol. 2023 May;140:105385
• dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/777365
• dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/871075
• dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/964537
• dc.rights © 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/).
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri http://creativecommons.org/licenses/by/4.0/
• dc.subject.keyword FAIR
• dc.subject.keyword In silico model
• dc.subject.keyword New approach methodologies
• dc.subject.keyword Next generation risk assessment
• dc.subject.keyword PBK
• dc.subject.keyword QSAR
• dc.subject.keyword Toxicology
• dc.title Making in silico predictive models for toxicology FAIR
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/publishedVersion