Lipophilicity Descriptors Correlate Uniquely with Pharmacokinetic and Blood-Brain Barrier Penetration Parameters for Selected Antipsychotic Drugs

Abstract

Lipophilicity is an important physicochemical parameter of biological relevance; although its in- vivo predictive capability is 
dependent on accuracy and reliability of platforms used for its determination. This work examines biomimetic attribute of 
isocratic chromatographic hydrophobicity index (ICHI), experimental logarithm of octanol – water partition coefficient (LogP) 
and some computed lipophilicity indices for eight (8) selected antipsychotic agents and their predictive capability in drug 
discovery. The retention behavior of 5 first-generation and 3 second-generation antipsychotics was determined on reversed-phase 
chromatographic platform using methanol-phosphate buffer (pH 6.8) mobile phase. The retardation factor obtained was 
transformed to Rm, and plotted against volume fraction of organic modifier in the mobile phase to generate linear graph whose 
x- intercept is ICHI. Experimental LogP values were curled from literature while computed LogP were obtained using respective 
software. The experimentally determined LogPoctanol/water and ICHI were first correlated with index of brain permeability (BBB); 
before all lipophilicity indices were comparatively evaluated and correlated with in-vivo-normalized pharmacokinetic parameters 
curled from literature. ICHI gave better correlation with BBB index (r = 0.976) compared to Log Poctanol/water (r = 0.557). 
Comparative lipophilicity evaluation shows clustered pattern for second generation antipsychotics compared to first generation. 
In vivo correlation was poorer for the 8 drugs (r < 0.7), better with subset of phenothiazine homologues (r = 0.51 to 0.97). The 
ALogP, LogPoctanol/water, cLogP and ICHI gave highest correlation with the pharmacokinetic parameters. The biomimetic attributes 
of ICHI is better than for LogPoctanol/water in predicting brain permeability, but lower for in-vivo pharmacokinetic prediction.