Screening Level Estimation of Chemical Mixtures Toxicity Using In Silico Models: A JP-5 Case Study for Environmental...

Area of Science:

• Environmental Toxicology
• Computational Chemistry
• Risk Assessment

Background:

• Environmental incidents can release complex chemical mixtures, posing risks through contaminated drinking water.
• Assessing mixture toxicity is crucial but often hindered by a lack of experimental data.
• JP-5 fuel leaks exemplify the need for predictive toxicology methods.

Purpose of the Study:

• To evaluate the feasibility of using in silico tools to estimate oral toxicity points of departure (PODs) for JP-5.
• To assess the applicability of Quantitative Structure-Activity Relationship (QSAR) models, Threshold of Toxicological Concern (TTC), and read-across methods.
• To derive in silico-based health guidance values (HGVs) for JP-5 exposure.

Main Methods:

• Utilized publicly available in silico tools (QSAR, TTC, read-across) to estimate PODs for JP-5.
• Performed applicability domain assessments for QSAR models (OPERA, ToxTree, VEGA).
• Applied dosimetric adjustments and uncertainty factors to extrapolate rat PODs to HGVs; used concentration addition for mixture toxicity.

Main Results:

• QSAR models demonstrated broad coverage, with OPERA and ToxTree being fully inclusive.
• VEGA models showed moderate-to-high confidence, with exceptions for certain additives.
• In silico-derived HGVs generally aligned with existing guidance values, and TTC estimates were conservative.

Conclusions:

• In silico models are effective in filling toxicological data gaps for chemical mixtures.
• This approach provides valuable screening-level insights for evaluating exposure risks when empirical data are limited.
• The study successfully demonstrated the utility of computational tools in environmental health risk assessment.