Citation:

Nicolas, C., M. Linakis, M. Minto, K. Mansouri, R. Clewell, M. Yoon, J. Wambaugh, G. Patlewicz, P. McMullen, M. Andersen, AND H. Clewell. Estimating provisional margins of exposure for data-poor chemicals using high-throughput computational methods. Frontiers in Pharmacology. Frontiers, Lausanne, Switzerland, 13:980747, (2022). https://doi.org/10.3389/fphar.2022.980747

Impact/Purpose:

Over 45,000 environmental chemicals have been used to generate decision-tree-derived thresholds of toxicological concern (TTCs) and predicted exposures for estimating provisional margins of exposure (MoEs). In vitro MoEs for 709 ToxCast compounds were compared with their respective TTC-based MoEs to evaluate high-throughput risk-based prioritization metrics. Published in vivo-derived no observed adverse effect limits (NOAELs) were used to demonstrate the potential to ground-truth the chemical prioritization approaches.  Multiple rapid and large-scale data-driven schemes incorporating exposures in the decision-making context support a more informed tiered safety assessment approach.

Description:

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.

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