Blood Brain Barrier Permeability Prediction Using Machine Learning Techniques: An Update.
Blood brain barrier
central nervous system
machine learning
model
permeability
prediction.
Journal
Current pharmaceutical biotechnology
ISSN: 1873-4316
Titre abrégé: Curr Pharm Biotechnol
Pays: Netherlands
ID NLM: 100960530
Informations de publication
Date de publication:
2019
2019
Historique:
received:
23
01
2019
revised:
01
05
2019
accepted:
16
07
2019
pubmed:
23
8
2019
medline:
6
2
2020
entrez:
22
8
2019
Statut:
ppublish
Résumé
Blood Brain Barrier (BBB) is the collection of vessels of blood with special properties of permeability that allow a limited range of drug and compounds to pass through it. The BBB plays a vital role in maintaining balance between intracellular and extracellular environment for brain. Brain Capillary Endothelial Cells (BECs) act as vehicle for transport and the transport mechanisms across BBB involve active and passive diffusion of compounds. Efficient prediction models of BBB permeability can be vital at the preliminary stages of drug development. There have been persistent efforts in identifying the prediction of BBB permeability of compounds employing multiple machine learning methods in an attempt to minimize the attrition rate of drug candidates taking up preclinical and clinical trials. However, there is an urgent need to review the progress of such machine learning derived prediction models in the prediction of BBB permeability. In the current article, we have analyzed the recently developed prediction model for BBB permeability using machine learning.
Identifiants
pubmed: 31433750
pii: CPB-EPUB-100382
doi: 10.2174/1389201020666190821145346
doi:
Substances chimiques
Pharmaceutical Preparations
0
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
1163-1171Informations de copyright
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