DEVELOPMENT OF A COMPUTATIONAL APPROACH TO PREDICT BLOOD-BRAIN BARRIER PERMEABILITY

Abstract

The objectives of this study were to generate a data set of blood-brain barrier (BBB) permeability values for drug-like compounds and to develop a computational model to predict BBB permeability from structure. The BBB permeability, expressed as permeability-surface area product (PS, quantified as logPS), was determined for 28 structurally diverse drug-like compounds using the in situ rat brain perfusion technique. A linear model containing three descriptors, logD, van der Waals surface area of basic atoms, and polar surface area, was developed based on 23 compounds in our data set, where the penetration across the BBB was assumed to occur primarily by passive diffusion. The correlation coefficient (R²) and standard deviation (S.D.) of the model-predicted logPS against the observed are 0.74 and 0.50, respectively. If an outlier was removed from the training data set, the R² and S.D. were 0.80 and 0.44, respectively. This new model was tested in two literature data sets, resulting in an R² of 0.77 to 0.94 and a S.D. of 0.38 to 0.51. For comparison, four literature models, logP, logD, log(D · MW^–0.5), and linear free energy relationship, were tested using the set of 23 compounds primarily crossing the BBB by passive diffusion, resulting in an R² of 0.33 to 0.61 and a S.D. of 0.59 to 0.76. In summary, we have generated the largest PS data set and developed a robust three-descriptor model that can quantitatively predict BBB permeability. This model may be used in a drug discovery setting to predict the BBB permeability of new chemical entities.