Advances in computational methods along the exposure to toxicological response paradigm

Area of Science:

• Environmental Health Sciences
• Toxicology
• Computational Biology

Background:

• Human health risk assessment integrates chemical toxicity, bioavailability, and exposure, complicated by numerous physiological and lifestyle factors.
• The increasing number of environmental chemicals necessitates efficient prioritization for health risk assessment.
• Existing in vivo experimental data is often cost-prohibitive, creating a need for alternative assessment methods.

Purpose of the Study:

• To review computational methods for environmental chemical risk assessment developed and utilized by the U.S. Environmental Protection Agency (EPA).
• To highlight the potential and challenges associated with these computational approaches in prioritizing chemical risks.

Main Methods:

• Utilizing computational methods, including predictive and investigative approaches, to assist in prioritizing chemicals for risk assessment.
• Employing in vitro high throughput screening (HTS) assays to generate specific toxicity information.
• Converting HTS data to in vivo estimates of chemical levels and doses for comparison with exposure levels.

Main Results:

• Computational methods offer a viable alternative to expensive in vivo testing for toxicity assessment.
• HTS assays provide focused toxicity data that can be extrapolated to in vivo scenarios.
• The EPA has developed and implemented various computational tools to aid in chemical risk prioritization.

Conclusions:

• Computational methods are crucial for efficient and timely assessment of environmental chemical risks.
• These methods, particularly those using HTS data, help bridge the gap between in vitro findings and in vivo dose estimations.
• Continued development and application of computational tools are essential for managing the health risks posed by a growing number of environmental chemicals.