Insights for predicting blood-brain barrier penetration of CNS targeted molecules using QSPR approaches.
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.
Fan
Yi
Y
Chemical and Screening Sciences, Wyeth Research, Princeton, CN8000, New Jersey 08543-8000, USA. kristi_yifan@yahoo.com
Unwalla
Rayomand
R
Denny
Rajiah A
RA
Di
Li
L
Kerns
Edward H
EH
Diller
David J
DJ
Humblet
Christine
C
D000465:Algorithms; D053829:Amyloid Precursor Protein Secretases / Q000037:antagonists & inhibitors; D001812:Blood-Brain Barrier / Q000187:drug effects / Q000378:metabolism*; D019295:Computational Biology; D004058:Diffusion; D004791:Enzyme Inhibitors / Q000378:metabolism / Q000494:pharmacology; D008954:Models, Biological; D004364:Pharmaceutical Preparations / Q000378:metabolism; D021281:Quantitative Structure-Activity Relationship; D012044:Regression Analysis
D004791:Enzyme Inhibitors; D004364:Pharmaceutical Preparations; D053829:Amyloid Precursor Protein Secretases