| Improving the prediction of drug disposition in the brain. | |
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MedLine Citation:
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PMID: 23294027 Owner: NLM Status: Publisher |
Abstract/OtherAbstract:
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Introduction: Ability to cross the blood-brain barrier is one of the key ADME characteristics of all drug candidates regardless of their target location in the body. While good brain penetration is essential for CNS drugs, it may lead to serious side effects in case of peripherally-targeted molecules. Despite a high demand of computational methods for estimating brain transport early in drug discovery, achieving good prediction accuracy still remains a challenging task. Areas covered: This article reviews various measures employed to quantify brain delivery and recent advances in QSAR approaches for predicting these properties from the compound's structure. Additionally, the authors discuss the classification models attempting to distinguish between permeable and impermeable chemicals. Expert opinion: Recent research in the field of brain penetration modeling showed an increasing understanding of the processes involved in drug disposition, although most models of brain/plasma partitioning still rely on purely statistical considerations. Preferably, new models should incorporate mechanistic knowledge since it is the prerequisite for guiding drug design efforts in the desired direction. To increase the efficiency of computational tools, a broader view is necessary, involving rate and extent of brain penetration, as well as plasma and brain tissue binding strength, instead of relying on any single property. |
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Authors:
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Kiril Lanevskij; Pranas Japertas; Remigijus Didziapetris |
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Publication Detail:
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Type: JOURNAL ARTICLE Date: 2013-1-8 |
Journal Detail:
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Title: Expert opinion on drug metabolism & toxicology Volume: - ISSN: 1744-7607 ISO Abbreviation: Expert Opin Drug Metab Toxicol Publication Date: 2013 Jan |
Date Detail:
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Created Date: 2013-1-8 Completed Date: - Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 101228422 Medline TA: Expert Opin Drug Metab Toxicol Country: - |
Other Details:
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Languages: ENG Pagination: - Citation Subset: - |
Affiliation:
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VšĮ "Aukštieji algoritmai" , A. Mickeviciaus 29, LT-08117 Vilnius , Lithuania +370 5 262 3408 ; +370 5 262 3728 ; kiril.lanevskij@aukstieji.lt. |
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From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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