QSAR Analysis of Blood–Brain Distribution: The Influence of Plasma and Brain Tissue Binding

Lanevskij, Kiril; Dapkūnas, Justas; Juska, Liutauras; Japertas, Pranas; Didžiapetris, Remigijus

doi:10.1002/jps.22442

Cited by 57 publications

(36 citation statements)

References 58 publications

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“…log PS), the results may be misleading. A low log BB value may not necessarily mean a low penetration, because the parameter depends on binding to plasma and brain tissue as well as active transport (Alavijeh et al 2005;Lanevskij et al 2010;Liu et al 2004). For this reason, it was suggested to replace log BB by log PS values (Martin 2004;Pardridge 2004).…”

Section: Brain Slice Methodsmentioning

confidence: 97%

Brainpeps: the blood–brain barrier peptide database

et al. 2011

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Peptides are able to cross the blood-brain barrier (BBB) through various mechanisms, opening new diagnostic and therapeutic avenues. However, their BBB transport data are scattered in the literature over different disciplines, using different methodologies reporting different influx or efflux aspects. Therefore, a comprehensive BBB peptide database (Brainpeps) was constructed to collect the BBB data available in the literature. Brainpeps currently contains BBB transport information with positive as well as negative results. The database is a useful tool to prioritize peptide choices for evaluating different BBB responses or studying quantitative structure-property (BBB behaviour) relationships of peptides. Because a multitude of methods have been used to assess the BBB behaviour of compounds, we classified these methods and their responses. Moreover, the relationships between the different BBB transport methods have been clarified and visualized.

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Section: Brain Slice Methodsmentioning

confidence: 97%

Brainpeps: the blood–brain barrier peptide database

et al. 2011

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“…This observation agrees with earlier findings in a smaller dataset [18] and is likely due to the fact that K p,uu,brain is mainly determined by transporter interactions. In contrast, K p,brain is highly correlated to unspecific tissue binding where lipophilicity is a major factor [46].…”

Section: Model Interpretationmentioning

confidence: 96%

In silico prediction of unbound brain-to-plasma concentration ratio using machine learning algorithms

Chen

Winiwarter

Fridén

et al. 2011

Journal of Molecular Graphics and Modelling

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“…The good multivariate relationships obtained by multiple regression analysis involved some parameters connected with the bloodebrain barrier permeability, which play a very important role in the biological activity of each investigated group of benzodiazepines. Such parameters as log P, %PSA, V, HA, log P-N þ O, M w , S, Ar, a, log D, V, HA, DM, and R m , were used with good results in QSAR models for prediction of brain/plasma partitioning and brain tissue binding [49].…”

Section: Multiple Regression Analysis (Mr)mentioning

confidence: 99%