In Silico Prediction of Blood-Brain Barrier Permeability of Compounds by Machine Learning and Resampling Methods

ChemMedChem ◽  
2018 ◽  
Vol 13 (20) ◽  
pp. 2189-2201 ◽  
Author(s):  
Zhuang Wang ◽  
Hongbin Yang ◽  
Zengrui Wu ◽  
Tianduanyi Wang ◽  
Weihua Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Blood Brain Barrier ◽  
In Silico ◽  
Brain Barrier ◽  
In Silico Prediction ◽  
Resampling Methods ◽  
Barrier Permeability ◽  
Blood Brain Barrier Permeability ◽  
Blood Brain ◽  
Brain Barrier Permeability

Ligand and Structure-based Modeling of Passive Diffusion through the Blood-Brain Barrier

2018 ◽  
Vol 25 (9) ◽  
pp. 1073-1089 ◽  
Author(s):  
Santiago Vilar ◽  
Eduardo Sobarzo-Sanchez ◽  
Lourdes Santana ◽  
Eugenio Uriarte
Keyword(s):  
Blood Brain Barrier ◽  
In Silico ◽  
Passive Diffusion ◽  
Brain Barrier ◽  
Barrier Permeability ◽  
Blood Brain Barrier Permeability ◽  
Blood Brain ◽  
Brain Barrier Permeability ◽  
Different Types ◽  
The Brain

Background: Blood-brain barrier transport is an important process to be considered in drug candidates. The blood-brain barrier protects the brain from toxicological agents and, therefore, also establishes a restrictive mechanism for the delivery of drugs into the brain. Although there are different and complex mechanisms implicated in drug transport, in this review we focused on the prediction of passive diffusion through the blood-brain barrier. Methods: We elaborated on ligand-based and structure-based models that have been described to predict the blood-brain barrier permeability. Results: Multiple 2D and 3D QSPR/QSAR models and integrative approaches have been published to establish quantitative and qualitative relationships with the blood-brain barrier permeability. We explained different types of descriptors that correlate with passive diffusion along with data analysis methods. Moreover, we discussed the applicability of other types of molecular structure-based simulations, such as molecular dynamics, and their implications in the prediction of passive diffusion. Challenges and limitations of experimental measurements of permeability and in silico predictive methods were also described. Conclusion: Improvements in the prediction of blood-brain barrier permeability from different types of in silico models are crucial to optimize the process of Central Nervous System drug discovery and development.

scholarly journals Automated Quantitative Analysis of ex vivo Blood-Brain Barrier Permeability Using Intellesis Machine-Learning

2021 ◽  
Vol 15 ◽  
Author(s):  
Michael Nesbit ◽  
John C. Mamo ◽  
Maimuna Majimbi ◽  
Virginie Lam ◽  
Ryusuke Takechi
Keyword(s):  
Machine Learning ◽  
Rat Brain ◽  
Blood Brain Barrier ◽  
Capillary Permeability ◽  
Ex Vivo ◽  
Brain Barrier ◽  
Barrier Permeability ◽  
Blood Brain Barrier Permeability ◽  
Blood Brain ◽  
Brain Barrier Permeability

BackgroundAn increase in blood brain barrier permeability commonly precedes neuro-inflammation and cognitive impairment in models of dementia. Common methods to estimate capillary permeability have potential confounders, or require laborious and subjective semi-manual analysis.New methodHere we used snap frozen mouse and rat brain sections that were double-immunofluorescent labeled for immunoglobulin G (IgG; plasma protein) and laminin-α4 (capillary basement membrane). A Machine Learning Image Analysis program (Zeiss ZEN Intellisis) was trained to recognize and segment laminin-α4 to equivocally identify blood vessels in large sets of images. An IgG subclass based on a threshold intensity was segmented and quantitated only in extravascular regions. The residual parenchymal IgG fluorescence is indicative of blood-to-brain extravasation of IgG and was accurately quantitated.ResultsAutomated machine-learning and threshold based segmentation of only parenchymal IgG extravasation accentuates otherwise indistinct capillary permeability, particularly frequent in minor BBB leakage. Comparison with Existing Methods: Large datasets can be processed and analyzed quickly and robustly to provide an overview of vascular permeability throughout the brain. All human bias or ambiguity involved in classifying and measuring leakage is removed.ConclusionHere we describe a fast and precise method of visualizing and quantitating BBB permeability in mouse and rat brain tissue, while avoiding the confounding influence of unphysiological conditions such as perfusion and eliminating any human related bias from analysis.

Synthesis, in silico and in vivo blood brain barrier permeability of ginkgolide B cinnamate

Fitoterapia ◽  
2015 ◽  
Vol 106 ◽  
pp. 110-114 ◽  
Author(s):  
Yong-Ming Lu ◽  
Jian Pan ◽  
Wen-Na Zhang ◽  
Ai-Ling Hui ◽  
Wen-Qiang Guo ◽  
...  
Keyword(s):  
Blood Brain Barrier ◽  
In Silico ◽  
Brain Barrier ◽  
Ginkgolide B ◽  
Barrier Permeability ◽  
Blood Brain Barrier Permeability ◽  
Blood Brain ◽  
Brain Barrier Permeability
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