Nonlinear Method to Predict the Distribution of Structurally Diverse Compounds between Blood and Tissue

Area of Science:

• Pharmacokinetics and Drug Metabolism
• Computational Chemistry
• Physiologically Based Pharmacokinetic Modeling

Background:

• Understanding drug distribution between blood and tissues is crucial for pharmacokinetics and drug development.
• Accurate prediction of tissue/blood partition coefficients aids in optimizing drug efficacy and minimizing toxicity.
• Existing models may lack comprehensive data or predictive power for diverse compounds and tissues.

Purpose of the Study:

• To investigate methods for predicting compound distribution between blood and tissue using nonlinear regression analysis.
• To establish a robust model with good prediction ability for tissue/blood partition coefficients.
• To develop individual models for predicting drug distribution in specific tissues and different molecular states (neutral, cation, anion).

Main Methods:

• Utilized nonlinear regression analysis to investigate prediction methods.
• Selected 282 compounds and 810 activity data points for seven tissues.
• Studied twenty-four parameters for each compound state and established a comprehensive study set.
• Randomly divided 773 data points into training (n=623) and test (n=150) sets.

Main Results:

• Achieved a model with good prediction ability, evidenced by high correlation coefficients (r=0.822 for training, r=0.814 for test) and low standard errors (s=0.438 for training, s=0.334 for test).
• The training set yielded a prediction quality (Q) of 0.814.
• Developed separate models for individual tissue/blood distribution coefficients, enhancing predictive accuracy for specific organs.

Conclusions:

• The developed nonlinear regression models demonstrate strong predictive capability for tissue/blood distribution coefficients.
• These models can predict the distribution of drugs in seven human tissues and organs.
• The models also predict the distribution of drug molecules in different ionization states (neutral, cation, anion) within tissue components.