Making in silico predictive models for toxicology FAIR.
FAIR
In silico model
New approach methodologies
Next generation risk assessment
PBK
QSAR
Toxicology
Journal
Regulatory toxicology and pharmacology : RTP
ISSN: 1096-0295
Titre abrégé: Regul Toxicol Pharmacol
Pays: Netherlands
ID NLM: 8214983
Informations de publication
Date de publication:
May 2023
May 2023
Historique:
received:
03
01
2023
revised:
18
02
2023
accepted:
07
04
2023
medline:
25
4
2023
pubmed:
11
4
2023
entrez:
10
4
2023
Statut:
ppublish
Résumé
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.
Identifiants
pubmed: 37037390
pii: S0273-2300(23)00053-3
doi: 10.1016/j.yrtph.2023.105385
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
105385Informations de copyright
Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.
Déclaration de conflit d'intérêts
Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.