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

Area of Science:

• Environmental chemistry and toxicology
• Computational toxicology and risk assessment
• High-throughput screening and predictive modeling

Background:

• Thousands of chemicals in commerce require prioritization for toxicological testing.
• Existing computational tools can aid in this prioritization process.
• Margins of Exposure (MoEs) provide a risk-based metric using hazard and exposure data.

Purpose of the Study:

• To demonstrate comparative risk-prioritization approaches using surrogate hazard and exposure data.
• To evaluate the utility of Thresholds of Toxicological Concern (TTCs) as a surrogate for bioactivity.
• To apply a TTC-based MoE approach for high-throughput prioritization of environmental chemicals.

Main Methods:

• Estimated chemical exposures using the U.S. EPA's ExpoCast (SEEM3) model.
• Predicted bioactivity using Oral Equivalent Doses (OEDs) from ToxCast and TTCs from Toxtree.
• Calculated MoEs using predicted values and compared them to MoEs derived from traditional NOAELs.

Main Results:

• TTC-based MoEs were significantly lower than NOAEL-based MoEs for most compounds, indicating a conservative estimate.
• TTC-based MoEs showed good correlation with NOAEL-based MoEs (r=0.59) and were lower than OED-based MoEs for a majority of chemicals.
• A TTC-based MoE approach successfully prioritized over 45,000 environmental chemical structures.

Conclusions:

• TTCs serve as a conservative and effective surrogate for chemical-specific hazard data in MoE calculations.
• Computational methods, particularly TTC-based MoEs, are valuable for pre-assessment and high-throughput prioritization.
• This approach enables rapid and conservative prioritization of untested chemicals for further toxicological study.