How well do lipophilicity parameters, MEEKC microemulsion capacity factor, and plasma protein binding predict CNS...

Area of Science:

• Pharmacology
• Drug Discovery
• Analytical Chemistry

Background:

• Brain unbound fraction (Fu) is crucial for understanding central nervous system (CNS) drug pharmacokinetics and dynamics.
• Accurate prediction of brain Fu is essential for drug development and interpretation of in vitro-to-in vivo extrapolations.
• Various surrogate predictors, including lipophilicity parameters and chromatographic factors, have been explored to estimate brain Fu.

Purpose of the Study:

• To compare the predictive performance of microemulsion electrokinetic chromatography capacity factor (MEEKC k"), plasma Fu, and lipophilicity parameters (clogP, clogD(7.4)) for brain Fu.
• To evaluate the quantitative accuracy of MEEKC k' and plasma Fu in predicting brain Fu.
• To determine the utility of these predictors for pharmacokinetic/dynamic (PK/PD) data interpretation.

Main Methods:

• Correlational analysis of brain Fu with MEEKC k', plasma Fu, clogP, and clogD(7.4) for 94 and 587 diverse molecules.
• Log-log correlation analysis to assess prediction strength.
• Analysis of prediction error to estimate prediction intervals for brain Fu.

Main Results:

• MEEKC k' (r² = 0.74) was a better predictor of brain Fu than clogP (r² = 0.51-0.54) and clogD(7.4) (r² = 0.41-0.44).
• Plasma Fu (r² = 0.74-0.85) showed comparable or better prediction of brain Fu than MEEKC k'.
• Prediction intervals indicated a 10-fold (MEEKC k') and 6.9-8.6-fold (plasma Fu) uncertainty, limiting quantitative accuracy.

Conclusions:

• MEEKC k' and plasma Fu can predict the order of CNS tissue binding but lack the quantitative precision for in vitro-to-in vivo extrapolations.
• Lipophilicity parameters are less effective predictors of brain Fu compared to MEEKC k' and plasma Fu.
• Further development is needed for predictors that can provide truly quantitative brain Fu estimations for PK/PD applications.