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Fan Y.,Wyeth Research CN8000 | Unwalla R.,Wyeth Research | Denny R.A.,Wyeth Research | Di L.,Wyeth Research CN8000 | And 3 more authors.
Journal of Chemical Information and Modeling | Year: 2010

Due to the high attrition rate of central nervous system drug candidates during clinical trials, the assessment of blood-brain barrier (BBB) penetration in early research is particularly important. A genetic approximation (GA)-based regression model was developed for predicting in vivo blood-brain partitioning data, expressed as logBB (log[brain]/[blood]). The model was built using an in-house data set of 193 compounds assembled from 22 different therapeutic projects. The final model (cross-validated r 2 = 0.72) with five molecular descriptors was selected based on validation using several large internal and external test sets. We demonstrate the potential utility of the model by applying it to a set of literature reported secretase inhibitors. In addition, we describe a rule-based approach for rapid assessment of brain penetration with several simple molecular descriptors. © 2010 American Chemical Society. Source

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