A Predictive Model for Blood-Brain Barrier Penetration

Motivation. It is important to determine whether a candidate molecule is capable of penetrating the blood-brain barrier in drug discovery and development. The aim of this paper is to establish a predictive model for blood-brain barrier penetration only using two simple descriptors, molecular volume and polar surface area. Method. A dataset of 111 compounds is divided into a training set of 86 compounds and a test set of 25 compounds. Molecular volumes and polar surface areas are obtained from the molecular conformations optimized using the semiempirical self-consistent field molecular orbital calculation AM1 method. The model to predict blood-brain barrier penetration from molecular volume and polar surface area is derived on the training set using the stepwise multiple regression analysis and then cross-validated using leave-one-out procedure and tested on the external prediction. Results. The logarithm of the ratio of the steady-state concentration of a compound in the brain to in the blood, logBB, is correlated with its molecular volume parabolically and its polar surface area inversely. Both calculated logBB values for the training set and predicted logBB values for the test set are in good agreement with respective experimental ones. Conclusions. The model derived in this paper appears to be very simple but robust and effective for predictive use, so it is suitable for the rapid prediction of the blood-brain barrier penetration for a wide range of drug candidates.