THEORETICAL CALCULATION AND PREDICTION OF BRAIN-BLOOD PARTITIONING OFORGANIC SOLUTES USING MOLSURF PARAMETRIZATION AND PLS STATISTICS

Sixty-three structurally diverse compounds were investigated to statis tically model the brain-blood partitioning of organic solutes using th eoretically computed molecular descriptors and multivariate statistics . The program MolSurf was used to compute theoretical molecular descri ptors related to physicochemical properties such as lipophilicity, pol arity, polarizability, and hydrogen bonding. The multivariate Partial Least Squares Projections to Latent Structures (PLS) method was used t o delineate the relationship between the brain-blood partitioning of o rganic solutes and the theoretically computed molecular descriptors. G ood statistical models were derived. Properties associated with polari ty and Lewis base strength had the largest impact on the blood-brain p artitioning and should be kept to a minimum to promote high partitioni ng. The absence of atoms capable of hydrogen bonding interactions as w ell as high lipophilicity and the presence of polarizable surface elec trons, i.e., valence electrons, were also found to promote high brain- blood partitioning. The results indicate that theoretically computed m olecular MolSurf descriptors in conjunction with multivariate statisti cs of PLS type can be used to successfully model the brain-blood parti tioning of organic solutes and hence differentiate drugs with poor par titioning from those with acceptable partitioning at an early stage of the preclinical drug-discovery process.