Theoretical Calculation and Prediction of Brain–Blood Partitioning of Organic Solutes Using MolSurf Parametrization and PLS Statistics

Abstract

Sixty-three structurally diverse compounds were investigated to statistically model the brain-blood partitioning of organic solutes using theoretically computed molecular descriptors and multivariate statistics. The program MolSurf was used to compute theoretical molecular descriptors related to physicochemical properties such as lipophilicity, polarity, polarizability, and hydrogen bonding. The multivariate Partial Least Squares Projections to Latent Structures (PLS) method was used to delineate the relationship between the brain-blood partitioning of organic solutes and the theoretically computed molecular descriptors. Good statistical models were derived. Properties associated with polarity and Lewis base strength had the largest impact on the blood-brain partitioning and should be kept to a minimum to promote high partitioning. The absence of atoms capable of hydrogen bonding interactions as well as high lipophilicity and the presence of polarizable surface electrons, i.e., valence electrons, were also found to promote high brain-blood partitioning. The results indicate that theoretically computed molecular MolSurf descriptors in conjunction with multivariate statistics of PLS type can be used to successfully model the brain-blood partitioning of organic solutes and hence differentiate drugs with poor partitioning from those with acceptable partitioning at an early stage of the preclinical drug-discovery process.