Purpose: To develop a computational model for predicting CNS drug exposure using a novel in vivo dataset.
Methods: The brain-to-plasma (B:P) ratio of 43 diverse compounds was assessed following intravenous administration to Swiss Outbred mice. B:P ratios were subjected to PLS modeling using calculated molecular descriptors. The obtained results were transferred to a qualitative setting in which compounds predicted to have a B:P ratio > 0.3 were sorted as high CNS exposure compounds and those below this value were sorted as low CNS exposure compounds. The model was challenged with an external test set consisting of 251 compounds for which semi-quantitative values of CNS exposure were available in the literature.
Results: The dataset ranged more than 1700-fold in B:P ratio, with 16 and 27 compounds being sorted as low and high CNS exposure drugs, respectively. The model was a one principal component model based on five descriptors reflecting molecular shape, electronegativity, polarisability and charge transfer, and allowed 74% of the compounds in the training set and 76% of the test set to be predicted correctly.
Conclusion: A qualitative computational model has been developed which accurately classifies compounds as being high or low CNS exposure drugs based on rapidly calculated molecular descriptors.