Approach for extrapolating in vitro metabolism data to refine bioconcentration factor estimates

Area of Science:

• Environmental Chemistry
• Toxicology
• Computational Modeling

Background:

• Chemical management programs require robust bioaccumulation data for environmental risk assessment.
• Existing data for fish bioaccumulation potential are limited, hindering accurate assessments.
• Metabolic transformation is a key factor in chemical elimination and bioaccumulation in fish.

Purpose of the Study:

• To develop and validate a method for extrapolating in vitro fish metabolic data to whole-body metabolic rates.
• To refine computer-based bioconcentration factor (BCF) models using in vitro metabolic data.
• To assess the utility of in vitro fish metabolism tests for reducing in vivo testing.

Main Methods:

• Utilized in vitro fish liver assays (S9 fraction and hepatocytes) to measure metabolic transformation rates.
• Developed an extrapolation method based on human drug distribution principles to estimate whole-body metabolism.
• Validated the extrapolation approach using chemicals with known in vivo BCF values (logKow 3.5-6.7).

Main Results:

• Demonstrated very good agreement between measured and estimated BCF values when extrapolated whole-body metabolism rates were incorporated.
• Showcased the effectiveness of in vitro biotransformation data in refining BCF model estimates.
• Identified the need for additional fish physiological data for broader parameterization and validation.

Conclusions:

• In vitro fish metabolism assays provide a viable alternative for estimating metabolic potential, reducing reliance on in vivo BCF testing.
• The developed extrapolation method successfully integrates in vitro data into BCF models, improving predictive accuracy.
• Further research is needed to expand the application of this approach across a wider range of chemicals and fish species.