Abstract
The ability to permeate across the blood brain barrier (BBB) is essential for drugs acting on the central nervous system (CNS). Thus, for speeding up the drug discovery process in the CNS-area, it is of great importance to develop systems that allow rapid and inexpensive screening of the BBB-permeability properties of novel lead compounds or at least small subsets of combinatorial CNS-libraries. In this field, in silico prediction methods gain increasing importance. Starting with simple regression models based on calculation of lipophilicity and polar surface area, the field developed via PLS methods to grid based approaches (e.g. VolSurf). Additionally, the use of artificial neural networks gain increasing importance. However, permeation through the BBB is also influenced by active transport systems. For nutrients and endogenous compounds, such as amino acids, monocarboxylic acids, amines, hexoses, thyroid hormones, purine bases and nucleosides, several transport systems regulating the entry of the respective compound classes into the brain have been identified. The other way round there is striking evidence that expression of active efflux pumps like the multidrug transporter P-glycoprotein (P-gp) on the luminal membrane of the brain capillary endothelial cells accounts for poor BBB permeability of certain drugs. Undoubtedly, P-gp is an important impediment for the entry of hydrophobic drugs into the brain. Thus, proper prediction models should also take into account the active transport phenomena.
Keywords: blood brain barrier permeation, in silico prediction methods, active efflux
Current Medicinal Chemistry
Title: In Silico Prediction Models for Blood-Brain Barrier Permeation
Volume: 11 Issue: 12
Author(s): Gerhard F. Ecker and Christian R. Noe
Affiliation:
Keywords: blood brain barrier permeation, in silico prediction methods, active efflux
Abstract: The ability to permeate across the blood brain barrier (BBB) is essential for drugs acting on the central nervous system (CNS). Thus, for speeding up the drug discovery process in the CNS-area, it is of great importance to develop systems that allow rapid and inexpensive screening of the BBB-permeability properties of novel lead compounds or at least small subsets of combinatorial CNS-libraries. In this field, in silico prediction methods gain increasing importance. Starting with simple regression models based on calculation of lipophilicity and polar surface area, the field developed via PLS methods to grid based approaches (e.g. VolSurf). Additionally, the use of artificial neural networks gain increasing importance. However, permeation through the BBB is also influenced by active transport systems. For nutrients and endogenous compounds, such as amino acids, monocarboxylic acids, amines, hexoses, thyroid hormones, purine bases and nucleosides, several transport systems regulating the entry of the respective compound classes into the brain have been identified. The other way round there is striking evidence that expression of active efflux pumps like the multidrug transporter P-glycoprotein (P-gp) on the luminal membrane of the brain capillary endothelial cells accounts for poor BBB permeability of certain drugs. Undoubtedly, P-gp is an important impediment for the entry of hydrophobic drugs into the brain. Thus, proper prediction models should also take into account the active transport phenomena.
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Cite this article as:
Ecker F. Gerhard and Noe R. Christian, In Silico Prediction Models for Blood-Brain Barrier Permeation, Current Medicinal Chemistry 2004; 11 (12) . https://dx.doi.org/10.2174/0929867043365071
DOI https://dx.doi.org/10.2174/0929867043365071 |
Print ISSN 0929-8673 |
Publisher Name Bentham Science Publisher |
Online ISSN 1875-533X |
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