Insights for Predicting Blood-Brain Barrier Penetration of CNS Targeted Molecules Using QSPR Approaches

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• 作者：Yi Fan ; Rayomand Unwalla ; Rajiah A. Denny ; Li Di ; Edward H. Kerns ; David J. Diller ; Christine Humblet
• 刊名：Journal of Chemical Information and Modeling
• 出版年：2010
• 出版时间：June 28, 2010
• 年：2010
• 卷：50
• 期：6
• 页码：1123-1133
• 全文大小：302K
• 年卷期：v.50,no.6(June 28, 2010)
• ISSN：1549-960X

文摘

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 r² = 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.