Bayesian based similarity assessment of nanomaterials to inform grouping

Tsiliki, Georgia; Ag Seleci, Didem; Zabeo, Alex; Basei, Gianpietro; Hristozov, Danail; Jeliazkova, Nina; Boyles, Matthew; Murphy, Fiona; Peijnenburg, Willie; Wohlleben, Wendel; Stone, Vicki

Abstract

Nanoforms can be manufactured in plenty of variants by differing their physicochemical properties and toxicokinetic behaviour which can affect their hazard potential. To avoid testing of each single nanomaterial and nanoform variation and subsequently save resources, grouping and read-across strategies are used to estimate groups of substances, based on carefully selected evidence, that could potentially have similar human health and environmental hazard impact. A novel computational similarity method is presented aiming to compare dose-response curves and identify sets of similar nanoforms. The suggested method estimates the statistical model that best fits the data by leveraging pairwise Bayes Factor analysis to compare pairs of curves and evaluate whether each of the nanoforms is sufficiently similar to all other nanoforms. Pairwise comparisons to benchmark materials are used to define threshold similarity values and set the criteria for identifying groups of nanoforms with comparatively similar toxicity. Applications to use case data are shown to demonstrate that the method can support grouping hypotheses linked to a certain hazard endpoint and route of exposure.

• Journal Article Type: Article
• Acceptance Date: Feb 2, 2022
• Online Publication Date: Feb 5, 2022
• Publication Date: 2022-01
• Deposit Date: Oct 13, 2023
• Print ISSN: 2452-0748
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 25
• Article Number: 100389
• DOI: https://doi.org/10.1016/j.impact.2022.100389
• Keywords: Similarity, Grouping, Dose-response data, Pairwise comparisons, Biological relevance