Leveraging ToxCast data and protein sequence conservation to complement aquatic life criteria derivation

Abstract

The USEPA's 1985 guidelines for the derivation of aquatic life criteria (ALC) are robust but data-intensive. For many chemicals, the extensive in vivo data sets required for ALC derivation are not available. Thus, alternative analyses and processes that can provide provisional values to guide states, tribes, and other stakeholders while data accumulate and more rigorous criteria are derived would be beneficial. The overarching purpose of this study was to assess the feasibility of using data from new approach methodologies (NAMs) like ToxCast to derive first-pass, provisional values to guide chemical prioritization and resource management as a complement to traditional ALC derivation. To address this goal, the study objectives were to (1) estimate chemical potency using data from NAMs for nine compounds with available aquatic benchmarks, (2) evaluate the utility of using NAM data to elucidate potential mechanisms of toxicity to guide problem formulation, and (3) determine the species relevance of toxicity pathways for compounds with clearly defined mechanisms of action as a means to evaluate whether minimum data requirements could potentially be waived when deriving a more formal ALC. Points of departure were derived from ToxCast data based on the fifth percentile of the distribution of activity concentration above cutoff values falling below the cytotoxic burst. Mechanistic inferences were made based on active target hits in ToxCast and, where applicable, assessed for taxonomic conservation using SeqAPASS. ToxCast-based point-of-departure aligned relatively closely (six of nine test chemicals within a factor of 10; eight of nine within a factor of 100) with aquatic benchmarks from the USEPA and US Department of Energy (DOE). Moreover, pathways of toxicity gleaned from NAM data were reflective of in vivo-based findings from the literature. These results, while preliminary, and based on a limited number of substances, support the potential application of NAM data to complement traditional ALC derivation approaches and prioritization.