A tissue composition-based algorithm for predicting tissue:air partition coefficients of organic chemicals

Area of Science:

• Toxicology
• Environmental Health
• Computational Chemistry

Background:

• Volatile organic chemicals (VOCs) pose risks to human and animal health.
• Accurate prediction of VOC distribution in tissues is crucial for risk assessment.
• Existing methods for predicting tissue:air partition coefficients (PCs) have limitations.

Purpose of the Study:

• To develop and validate a mechanistic algorithm for predicting rat tissue:air partition coefficients (PCs) of VOCs.
• To apply the algorithm to a diverse set of 45 VOCs.
• To evaluate the algorithm's accuracy against experimental data.

Main Methods:

• Tissue solubility was calculated by summing solubility in neutral lipid, phospholipid, and water fractions.
• Tissue:air PCs were estimated by dividing tissue solubility by saturable vapor concentrations.
• The algorithm was applied to predict liver:air, muscle:air, and adipose tissue:air PCs for 45 VOCs.

Main Results:

• Predicted rat tissue:air PCs showed good agreement with literature values for various chemical classes.
• Average ratios of predicted to experimental values were 0.94 (liver), 0.93 (muscle), and 1.10 (adipose tissue).
• The algorithm demonstrated reliability across different tissue types and chemical structures.

Conclusions:

• The developed algorithm provides a robust method for predicting VOC tissue:air PCs.
• This tool can aid in toxicological assessments and refine exposure models.
• Findings suggest that tissue:air PCs may not be species-invariant, warranting further investigation.