Making in silico predictive models for toxicology FAIR

Regulatory Toxicology and Pharmacology
Cronin Mark T D, Belfield Samuel J., Briggs Katharine A., Enoch Steven J., Firman James W., Frericks Markus, Garrard Clare, Maccallum Peter H., Madden Judith C., Pastor Manuel, Sanz Ferran, Soininen Inari, Sousoni Despoina
DOI: 10.1016/j.yrtph.2023.105385
PMID: 37037390
Keyword: FAIR · In silico model · new approach methodologies · next generation risk assessment · PBK · QSAR · toxicology


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.