In silico ADME modelling: prediction models for blood–brain barrier permeation using a systematic variable selection method

2005 ◽  
Vol 13 (8) ◽  
pp. 3017-3028 ◽  
Author(s):  
Ramamurthi Narayanan ◽  
Sitarama B. Gunturi
Keyword(s):  
Variable Selection ◽  
Blood Brain Barrier ◽  
In Silico ◽  
Prediction Models ◽  
Selection Method ◽  
Brain Barrier ◽  
Variable Selection Method ◽  
Blood Brain

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.

In silico models for nanotoxicity evaluation and prediction at the blood-brain barrier level: A mini-review

2017 ◽  
Vol 2 ◽  
pp. 20-27 ◽  
Author(s):  
Sergey Shityakov ◽  
Norbert Roewer ◽  
Jens-Albert Broscheit ◽  
Carola Förster
Keyword(s):  
Blood Brain Barrier ◽  
In Silico ◽  
Brain Barrier ◽  
Blood Brain ◽  
In Silico Models

scholarly journals COMPARATIVE ANALYSIS OF POTENTIALITY OF ESCULIN AND HINOKITOL (Β-THUJAPLICIN) AS ANTI-PARKINSONISM DRUGS: A PILOT IN SILICO STUDY

2016 ◽  
Vol 9 (1) ◽  
pp. 108
Author(s):  
Krishnapriya Madhu Varier ◽  
Sumathi Thangarajan ◽  
Arulvasu Chinnasamy ◽  
Gopalsamy Balakrishnan ◽  
Renjith Paulose
Keyword(s):  
Side Effects ◽  
Blood Brain Barrier ◽  
In Silico ◽  
Glycogen Synthase ◽  
Alpha Synuclein ◽  
Brain Barrier ◽  
Mao B ◽  
Blood Brain ◽  
In Silico Study ◽  
Dopamine D3

<p><strong>Objective: </strong>Parkinson’s disease (PD) is a leading cause of mental disability and death worldwide. Even though there are many advances in drug development against PD, a potent low dosage drug with fewer side effects are still in its nursery. This is a pioneer <em>in silico</em> attempt to test the anti-PD actions of esculin and hinokitol to act novel drugs.</p><p><strong>Methods: </strong>In this study, using Auto dock tools 4.2, esculin and hinokitol (β-Thujaplicin) were predicted for its inhibitory actions with Alpha-Synuclein (AS) Apo site, Dopamine D3 Receptor (D3R), Glycogen Synthase Kinase-3 Beta (GSK3β), Mono Oxidase B (MAO-B), Parkin and Tyrosine 3-Hydroxylase (TH) with levodopa standard. The reliability of the 3D predicted model of these proteins were analysed using RAMPAGE. Further, the blood-brain barrier (BBB) crossing ability of the natural compounds were analysed using cbligand. The <em>In silico </em>ADME (Absorption, Distribution, Metabolism, Excretion) properties of esculin and hinokitol were compared with that of levodopa using molinspiration and admetSAR @ LMMD software.<strong></strong></p><p><strong>Results: </strong>The predictions were that hinokitol, being blood-brain barrier positive (BBB+) with fewer side effects could be a potent anti-PD drug than esculin as it proved to be blood-brain barrier negative (BBB-). Hinokitol was predicted to be good inhibitors of AS, MAO-B and Parkin.</p><p><strong>Conclusion: </strong>The study revealed that hinokitol could be a potent anti-PD drug, being BBB+. Hinokitol was additionally predicted as a good inhibitor of AS, MAO-B and Parkin than levodopa standard.</p><p> </p>

LightBBB: computational prediction model of blood–brain-barrier penetration based on LightGBM

2020 ◽  
Author(s):  
Bilal Shaker ◽  
Myeong-Sang Yu ◽  
Jin Sook Song ◽  
Sunjoo Ahn ◽  
Jae Yong Ryu ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Prediction Model ◽  
Blood Brain Barrier ◽  
Prediction Models ◽  
Chemical Diversity ◽  
Brain Barrier ◽  
Permeability Prediction ◽  
Light Gradient ◽  
Blood Brain ◽  
Bbb Permeability

Abstract Motivation Identification of blood–brain barrier (BBB) permeability of a compound is a major challenge in neurotherapeutic drug discovery. Conventional approaches for BBB permeability measurement are expensive, time-consuming and labor-intensive. BBB permeability is associated with diverse chemical properties of compounds. However, BBB permeability prediction models have been developed using small datasets and limited features, which are usually not practical due to their low coverage of chemical diversity of compounds. Aim of this study is to develop a BBB permeability prediction model using a large dataset for practical applications. This model can be used for facilitated compound screening in the early stage of brain drug discovery. Results A dataset of 7162 compounds with BBB permeability (5453 BBB+ and 1709 BBB-) was compiled from the literature, where BBB+ and BBB- denote BBB-permeable and non-permeable compounds, respectively. We trained a machine learning model based on Light Gradient Boosting Machine (LightGBM) algorithm and achieved an overall accuracy of 89%, an area under the curve (AUC) of 0.93, specificity of 0.77 and sensitivity of 0.93, when 10-fold cross-validation was performed. The model was further evaluated using 74 central nerve system compounds (39 BBB+ and 35 BBB-) obtained from the literature and showed an accuracy of 90%, sensitivity of 0.85 and specificity of 0.94. Our model outperforms over existing BBB permeability prediction models. Availabilityand implementation The prediction server is available at http://ssbio.cau.ac.kr/software/bbb.

Blood Brain Barrier Permeability Prediction Using Machine Learning Techniques: An Update

2019 ◽  
Vol 20 (14) ◽  
pp. 1163-1171 ◽  
Author(s):  
Deeksha Saxena ◽  
Anju Sharma ◽  
Mohammed H. Siddiqui ◽  
Rajnish Kumar
Keyword(s):  
Machine Learning ◽  
Blood Brain Barrier ◽  
Prediction Models ◽  
Limited Range ◽  
Vital Role ◽  
Attrition Rate ◽  
Brain Barrier ◽  
Machine Learning Techniques ◽  
Blood Brain ◽  
Bbb Permeability

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.

A method to predict different mechanisms for blood–brain barrier permeability of CNS activity compounds in Chinese herbs using support vector machine

2016 ◽  
Vol 14 (01) ◽  
pp. 1650005 ◽  
Author(s):  
Ludi Jiang ◽  
Jiahua Chen ◽  
Yusu He ◽  
Yanling Zhang ◽  
Gongyu Li
Keyword(s):  
Support Vector Machine ◽  
Nervous System ◽  
Blood Brain Barrier ◽  
Prediction Models ◽  
Chinese Herbs ◽  
Brain Barrier ◽  
Support Vector ◽  
Blood Brain ◽  
Cns Activity ◽  
Bbb Permeability

The blood–brain barrier (BBB), a highly selective barrier between central nervous system (CNS) and the blood stream, restricts and regulates the penetration of compounds from the blood into the brain. Drugs that affect the CNS interact with the BBB prior to their target site, so the prediction research on BBB permeability is a fundamental and significant research direction in neuropharmacology. In this study, we combed through the available data and then with the help of support vector machine (SVM), we established an experiment process for discovering potential CNS compounds and investigating the mechanisms of BBB permeability of them to advance the research in this field four types of prediction models, referring to CNS activity, BBB permeability, passive diffusion and efflux transport, were obtained in the experiment process. The first two models were used to discover compounds which may have CNS activity and also cross the BBB at the same time; the latter two were used to elucidate the mechanism of BBB permeability of those compounds. Three optimization parameter methods, Grid Search, Genetic Algorithm (GA), and Particle Swarm Optimization (PSO), were used to optimize the SVM models. Then, four optimal models were selected with excellent evaluation indexes (the accuracy, sensitivity and specificity of each model were all above 85%). Furthermore, discrimination models were utilized to study the BBB properties of the known CNS activity compounds in Chinese herbs and this may guide the CNS drug development. With the relatively systematic and quick approach, the application rationality of traditional Chinese medicines for treating nervous system disease in the clinical practice will be improved.

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