Predicting passive and active tissue:plasma partition coefficients: interindividual and interspecies variability

Area of Science:

• Pharmacokinetics and Toxicokinetics
• Physiologically Based Pharmacokinetic (PBPK) Modeling
• Interspecies Extrapolation

Background:

• Predicting chemical distribution in tissues is crucial for risk assessment and drug development.
• Understanding interspecies and interindividual variability in tissue:plasma partition coefficients (K(t:pl)) is essential for accurate extrapolation.
• Existing models often lack comprehensive incorporation of active transport mechanisms and species-specific biological data.

Purpose of the Study:

• To develop and validate a mechanistic tissue composition model for predicting K(t:pl) across multiple mammalian species.
• To assess interindividual and interspecies variability in K(t:pl) using a Monte Carlo analysis.
• To evaluate the role of active transport in determining chemical partitioning and its concentration-dependent effects.

Main Methods:

• Developed a mechanistic model integrating chemical properties (lipophilicity, pKa, binding) and tissue composition (lipids, proteins, water, pH).
• Incorporated passive and active transport mechanisms, with active transport quantified using Michaelis-Menten parameters.
• Compiled species-specific biological data from 126 articles for eight species (mouse, rat, guinea pig, rabbit, dog, pig, monkey, human) and performed Monte Carlo simulations.

Main Results:

• The model successfully predicted K(t:pl) for organic chemicals across 10 tissues and eight species.
• Significant interspecies variability in K(t:pl) was observed for several tissues, though some differences were obscured by uncertainty.
• Chemicals undergoing active transport exhibited concentration-dependent K(t:pl), highlighting the importance of this mechanism.

Conclusions:

• The tissue composition-based mechanistic model provides a robust framework for predicting K(t:pl) across species.
• This model is valuable for drug development, particularly for scaling K(t:pl) from animal models to humans.
• The model's ability to incorporate active transport improves the accuracy of chemical distribution predictions and risk assessments.