Targeted Pathway-based In Vivo Testing Using Thyroperoxidase Inhibition to Evaluate Plasma Thyroxine as a Surrogate Metric of Metamorphic Success in Model Amphibian Xenopus laevis.

Chemical safety evaluation is in the midst of a transition from traditional whole-animal toxicity testing to molecular pathway-based in vitro assays and in silico modeling. However, to facilitate the shift in reliance on apical effects for risk assessment to predictive surrogate metrics having characterized linkages to chemical mechanisms of action, targeted in vivo testing is necessary to establish these predictive relationships. In this study, we demonstrate a means to predict thyroid-related metamorphic success in the model amphibian Xenopus laevis using relevant biochemical measurements during early prometamorphosis. The adverse outcome pathway for thyroperoxidase inhibition leading to altered amphibian metamorphosis was used to inform a pathway-based in vivo study design that generated response-response relationships. These causal relationships were used to develop Bayesian probabilistic network models that mathematically determine conditional dependencies between biochemical nodes and support the predictive capability of the biochemical profiles. Plasma thyroxine concentrations were the most predictive of metamorphic success with improved predictivity when thyroid gland sodium-iodide symporter gene expression levels (a compensatory response) were used in conjunction with plasma thyroxine as an additional regressor. Although thyroid-mediated amphibian metamorphosis has been studied for decades, this is the first time a predictive relationship has been characterized between plasma thyroxine and metamorphic success. Linking these types of biochemical surrogate metrics to apical outcomes is vital to facilitate the transition to the new paradigm of chemical safety assessments.

Editor's Highlight: Sequence Alignment to Predict Across Species Susceptibility (SeqAPASS): A Web-Based Tool for Addressing the Challenges of Cross-Species Extrapolation of Chemical Toxicity.

Conservation of a molecular target across species can be used as a line-of-evidence to predict the likelihood of chemical susceptibility. The web-based Sequence Alignment to Predict Across Species Susceptibility (SeqAPASS; https://seqapass.epa.gov/seqapass/) application was developed to simplify, streamline, and quantitatively assess protein sequence/structural similarity across taxonomic groups as a means to predict relative intrinsic susceptibility. The intent of the tool is to allow for evaluation of any potential protein target while remaining amenable to variable degrees of protein characterization, in the context of available information about the chemical/protein interaction and the molecular target itself. To accommodate this flexibility in the analysis, 3 levels of evaluation were developed. The first level of the SeqAPASS analysis compares primary amino acid sequences to a query sequence, calculating a metric for sequence similarity (including detection of orthologs); the second level evaluates sequence similarity within selected functional domains (eg, ligand-binding domain); and the third level of analysis compares individual amino acid residue positions of importance for protein conformation and/or interaction with the chemical upon binding. Each level of the SeqAPASS analysis provides additional evidence to apply toward rapid, screening-level assessments of probable cross species susceptibility. Such analyses can support prioritization of chemicals for further evaluation, selection of appropriate species for testing, extrapolation of empirical toxicity data, and/or assessment of the cross-species relevance of adverse outcome pathways. Three case studies are described herein to demonstrate application of the SeqAPASS tool: the first 2 focused on predictions of pollinator susceptibility to molt-accelerating compounds and neonicotinoid insecticides, and the third on evaluation of cross-species susceptibility to strobilurin fungicides. These analyses illustrate challenges in species extrapolation and demonstrate the broad utility of SeqAPASS for risk-based decision making and research.