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J Chem Inf Model. 2010 Jun 28;50(6):1123-33. doi: 10.1021/ci900384c.

Insights for predicting blood-brain barrier penetration of CNS targeted molecules using QSPR approaches.

Author information

1
Chemical and Screening Sciences, Wyeth Research, Princeton, CN8000, New Jersey 08543-8000, USA. kristi_yifan@yahoo.com

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.

PMID:
20578728
DOI:
10.1021/ci900384c
[Indexed for MEDLINE]

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