Making in silico predictive models for toxicology FAIR.
Regul Toxicol Pharmacol
; 140: 105385, 2023 May.
Article
em En
| MEDLINE
| ID: mdl-37037390
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.
Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Toxicologia
/
Relação Quantitativa Estrutura-Atividade
Tipo de estudo:
Etiology_studies
/
Prognostic_studies
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Risk_factors_studies
Idioma:
En
Ano de publicação:
2023
Tipo de documento:
Article