In silico predictive model to determine vector-mediated transport properties for the blood-brain barrier choline transporter.

Abstract

The blood-brain barrier choline transporter (BBB-ChT) may have utility as a drug delivery vector to the central nervous system (CNS). We therefore initiated molecular docking studies with the AutoDock and AutoDock Vina (ADVina) algorithms to develop predictive models for compound screening and to identify structural features important for binding to this transporter. The binding energy predictions were highly correlated with r(2) =0.88, F=692.4, standard error of estimate =0.775, and P-value<0.0001 for selected BBB-ChT-active/inactive compounds (n=93). Both programs were able to cluster active (Gibbs free energy of binding <-6.0 kcal*mol(-1)) and inactive (Gibbs free energy of binding >-6.0 kcal*mol(-1)) molecules and dock them significantly better than at random with an area under the curve value of 0.86 and 0.84, respectively. In ranking smaller molecules with few torsional bonds, a size-related bias in scoring producing false-negative outcomes was detected. Finally, important blood-brain barrier parameters, such as the logBBpassive and logBBactive values, were assessed to predict compound transport to the CNS accurately. Knowledge gained from this study is useful to better understand the binding requirements in BBB-ChT, and until such time as its crystal structure becomes available, it may have significant utility in developing a highly predictive model for the rational design of drug-like compounds targeted to the brain.