Advancing the science of a read-across framework
Oct 12
Lizarraga, L. E.; Suter, G. W.; Lambert, J. C.; Patlewicz, G.; Zhao, J. Q.; Dean, J. L.; Kaiser, P.
Advancing the Science of a Read-across Framework for Evaluation of Data-Poor Chemicals Incorporating Systematic and New Approach Methods.
Regulatory Toxicology and Pharmacology 2023, 137, 105293.
https://doi.org/10.1016/j.yrtph.2022.105293.
In the process of updating the
NAMs course, I studied this paper that presents case studies on using metabolic and mechanistic similarity to assess chemicals for screening-level quantitative analysis through read-across. The lessons from these case studies have informed an update to the previous read-across framework. This update includes improvements in problem formulation, systematic review, target chemical profiling, analogue identification, evaluation methods, and the incorporation of evidence from NAMs (New Approach Methodologies). Future case studies will demonstrate how this revised framework helps fill data gaps for environmental chemicals with limited toxicity data in the EPA's efforts.
The first two case studies highlight the use of metabolic relationships, such as precursor or metabolite chemicals, when choosing source analogues for read-across. The third case study demonstrates how mechanistic factors, like chemical reactivity and potential metabolic changes, can guide the selection of source analogues. To assess similarities in toxicokinetic and toxicodynamic properties for read-across, it's ideal to have experimental data on both the target chemical and analogues, especially from in vivo studies. However, when dealing with chemicals lacking comprehensive data, structural inference becomes essential to support the similarity hypothesis for read-across. When test data are limited, predictive software publicly and commercially available may be useful in retrieving information on metabolism to search for analogues.
