Bridging the gap in chemical risk assessment: Leveraging metabolite similarity for enhanced read-across applications

Read-across is frequently used in chemical risk assessment to predict the toxicological properties of data-poor compounds instead of performing new animal tests. The selection of relevant source com­pounds (SCs) for read-across to data-poor target compounds (TCs) is one of the main challenges, particularly for endpoints such as chronic and developmental toxicity, where the mechanisms leading to the observed apical toxicological findings are often unknown. In this study, the predic­tivities of using chemical, biological and/or metabolite similarity to inform read-across strategies were compared. Existing data from two reference compound groups, i.e., the pesticide classes of 27 triazoles and 8 triazinyl-sulfonylureas, were used to assess the performance of the three approaches and their modular combinations to identify relevant SCs from a large database of 468 pesticide active compounds. Metabolite similarity yielded high positive predictive values (PPV) of 85-100% while sensitivity was relatively low (12-65%). Basing metabolite similarity assessment on observed or predicted metabolites yielded comparable results. Chemical and biological simi­larity alone were less effective in separating SCs from irrelevant compounds. Modular approaches combining, e.g., chemical and metabolic similarity, enhanced SC selection (PPV up to 100%). This research underscores the potential of metabolite data to strengthen read-across justifications, thereby contributing to regulatory compliance and safety evaluations. We propose the integration of metabolite similarity assessment into an existing EU-ToxRisk workflow to enhance the reliability of read-across for chemical risk assessments.