Modeling Blood-Brain Barrier Partitioning Using the Electrotopological State

Abstract

The challenging problem of modeling blood-brain barrier partitioning is approached through topological representation of molecular structure. A QSAR model is developed for in vivo blood-brain partitioning data treated as the logarithm of the blood-brain concentration ratio. The model consists of three structure descriptors: the hydrogen E-State index for hydrogen bond donors, HS^T(HBd); the hydrogen E-State index for aromatic CHs, HS^T(arom); and the second order difference valence molecular connectivity index, d²χ^v (q² = 0.62.) The model for the set of 106 compounds is validated through use of an external validation test set (20 compounds of the 106, MAE = 0.33, rms = 0.38), 5-fold cross-validation (MAE = 0.38, rms = 0.47), prediction of +/− values for an external test set (27/28 correct), and estimation of logBB values for a large data set of 20 039 drugs and drug-like compounds. Because no 3D structure information is used, computation of logBB by the model is very fast. The quality of the validation statistics supports the claim that the model may be used for estimation of logBB values for drug and drug-like molecules. Detailed structure interpretation is given for the structure indices in the model. The model indicates that molecules that penetrate the blood-brain barrier have large HS^T(arom) values (presence of aromatic groups) but small values of HS^T(HBd) (fewer or weaker H−Bond donors) and smaller d²χ^v values (less branched molecules with fewer electronegative atoms). These three structure descriptors encode influence of molecular context of groups as well as counts of those groups.