Predicting blood-brain barrier partitioning of organic molecules using membrane-interaction QSAR analysis

Purpose. Membrane-interaction quantitative structure-activity relationship (QSAR) analysis (MI-QSAR) has been used to develop predictive models of blood-brain barrier partitioning of organic compounds by, in part, simulating the interaction of an organic compound with the phospholipid-rich regions of cellular membranes. Method. A training set of 56 structurally diverse compounds whose blood-brain barrier partition coefficients were measured was used to construct MI-QSAR models. Molecular dynamics simulations were used to determine the explicit interaction of each test compound (solute) with a model DMPC monolayer membrane model. An additional set of intramolecular solute descriptors were computed and considered in the trial pool of descriptors for building MI-QSAR models. The QSAR models were optimized using multidimensional linear regression fitting and a genetic algorithm. A test set of seven compounds was evaluated using the MI-QSAR models as part of a validation process. Results. Significant MI-QSAR models (R-2 = 0.845, Q(2) = 0.795) of the blood-brain partitioning process were constructed. Blood-brain barrier partitioning is found to depend upon the polar surface area, the octanol/water partition coefficient, and the conformational flexibility of the compounds as well as the strength of their "binding" to the model biologic membrane. The blood-brain barrier partitioning measures of the test set compounds were predicted with the same accuracy as the compounds of the training set. Conclusion. The MI-QSAR models indicate that the blood-brain barrier partitioning process can be reliably described for structurally diverse molecules provided interactions of the molecule with the phospholipids-rich regions of cellular membranes are explicitly considered.