The use of Bayesian methodology in the development and validation of a tiered assessment approach towards prediction of rat acute oral toxicity

• Published in: Archives of toxicology Vol. 96; no. 3; pp. 817 - 830
• Main Authors: Firman, James W., Cronin, Mark T. D., Rowe, Philip H., Semenova, Elizaveta, Doe, John E.
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2022; Springer Nature B.V
• Subjects: Animals; Archives & records; Bayes Theorem; Bayesian analysis; Biocompatibility; Bioinformatics and Statistics; Biomedical and Life Sciences; Biomedical materials; Biomedicine; Chemical Safety - methods; Computer Simulation; Environmental Health; In vivo methods and tests; Laboratory animals; Lethal Dose 50; Lethality; Machine learning; Methods; Occupational Medicine/Industrial Medicine; Pharmacology/Toxicology; Rats; Risk Assessment - methods; Safety; Statistical inference; Toxicity; Toxicity Tests, Acute - methods; Toxicology; Toxicology - methods; In silico toxicology; Tiered assessment; Bayesian inference; Regulatory toxicology; New approach methodologies; Acute toxicity
• ISSN: 0340-5761; 1432-0738; 1432-0738
• DOI: 10.1007/s00204-021-03205-x

There exists consensus that the traditional means by which safety of chemicals is assessed—namely through reliance upon apical outcomes obtained following in vivo testing—is increasingly unfit for purpose. Whilst efforts in development of suitable alternatives continue, few have achieved levels of robustness required for regulatory acceptance. An array of “new approach methodologies” (NAM) for determining toxic effect, spanning in vitro and in silico spheres, have by now emerged. It has been suggested, intuitively, that combining data obtained from across these sources might serve to enhance overall confidence in derived judgment. This concept may be formalised in the “tiered assessment” approach, whereby evidence gathered through a sequential NAM testing strategy is exploited so to infer the properties of a compound of interest. Our intention has been to provide an illustration of how such a scheme might be developed and applied within a practical setting—adopting for this purpose the endpoint of rat acute oral lethality. Bayesian statistical inference is drawn upon to enable quantification of degree of confidence that a substance might ultimately belong to one of five LD50-associated toxicity categories. Informing this is evidence acquired both from existing in silico and in vitro resources, alongside a purposely-constructed random forest model and structural alert set. Results indicate that the combination of in silico methodologies provides moderately conservative estimations of hazard, conducive for application in safety assessment, and for which levels of certainty are defined. Accordingly, scope for potential extension of approach to further toxicological endpoints is demonstrated.