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  • Title: Insights for predicting blood-brain barrier penetration of CNS targeted molecules using QSPR approaches.
    Author: Fan Y, Unwalla R, Denny RA, Di L, Kerns EH, Diller DJ, Humblet C.
    Journal: J Chem Inf Model; 2010 Jun 28; 50(6):1123-33. PubMed ID: 20578728.
    Abstract:
    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.
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