Estimating provisional margins of exposure for data-poor chemicals using high-throughput computational methods

Chantel I Nicolas; Matthew W Linakis; Melyssa S Minto; Kamel Mansouri; Rebecca A Clewell; Miyoung Yoon; John F Wambaugh; Grace Patlewicz; Patrick D McMullen; Melvin E Andersen; Harvey J Clewell Iii

doi:10.3389/fphar.2022.980747

Estimating provisional margins of exposure for data-poor chemicals using high-throughput computational methods

Front Pharmacol. 2022 Oct 7:13:980747. doi: 10.3389/fphar.2022.980747. eCollection 2022.

Affiliations

¹ Office of Chemical Safety and Pollution Prevention, US EPA, Washington, DC, United States.
² Ramboll, Raleigh, NC, United States.
³ ScitoVation, Raleigh, NC, United States.
⁴ National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods, Research Triangle Park, NC, United States.
⁵ 21st Century Tox Consulting Chapel Hill, Washington, NC, United States.
⁶ Center for Computational Toxicology and Exposure Office of Research and Development, US EPA, Research Triangle Park, NC, United States.

Abstract

Current computational technologies hold promise for prioritizing the testing of the thousands of chemicals in commerce. Here, a case study is presented demonstrating comparative risk-prioritization approaches based on the ratio of surrogate hazard and exposure data, called margins of exposure (MoEs). Exposures were estimated using a U.S. EPA's ExpoCast predictive model (SEEM3) results and estimates of bioactivity were predicted using: 1) Oral equivalent doses (OEDs) derived from U.S. EPA's ToxCast high-throughput screening program, together with in vitro to in vivo extrapolation and 2) thresholds of toxicological concern (TTCs) determined using a structure-based decision-tree using the Toxtree open source software. To ground-truth these computational approaches, we compared the MoEs based on predicted noncancer TTC and OED values to those derived using the traditional method of deriving points of departure from no-observed adverse effect levels (NOAELs) from in vivo oral exposures in rodents. TTC-based MoEs were lower than NOAEL-based MoEs for 520 out of 522 (99.6%) compounds in this smaller overlapping dataset, but were relatively well correlated with the same (r ² = 0.59). TTC-based MoEs were also lower than OED-based MoEs for 590 (83.2%) of the 709 evaluated chemicals, indicating that TTCs may serve as a conservative surrogate in the absence of chemical-specific experimental data. The TTC-based MoE prioritization process was then applied to over 45,000 curated environmental chemical structures as a proof-of-concept for high-throughput prioritization using TTC-based MoEs. This study demonstrates the utility of exploiting existing computational methods at the pre-assessment phase of a tiered risk-based approach to quickly, and conservatively, prioritize thousands of untested chemicals for further study.

Keywords: Threshold of toxicological concern (TTC); computational toxicology; high-throughput risk prioritization; in silico; margin of exposure (MoE).