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.

scholarly journals A curated diverse molecular database of blood-brain barrier permeability with chemical descriptors

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Fanwang Meng ◽  
Yang Xi ◽  
Jinfeng Huang ◽  
Paul W. Ayers
Keyword(s):  
Blood Brain Barrier ◽  
Extracellular Fluid ◽  
Chemical Diversity ◽  
Brain Barrier ◽  
Lead Discovery ◽  
Link Type ◽  
Blood Brain ◽  
Close Relationship ◽  
The Central Nervous System ◽  
Bbb Permeability

AbstractThe highly-selective blood-brain barrier (BBB) prevents neurotoxic substances in blood from crossing into the extracellular fluid of the central nervous system (CNS). As such, the BBB has a close relationship with CNS disease development and treatment, so predicting whether a substance crosses the BBB is a key task in lead discovery for CNS drugs. Machine learning (ML) is a promising strategy for predicting the BBB permeability, but existing studies have been limited by small datasets with limited chemical diversity. To mitigate this issue, we present a large benchmark dataset, B3DB, complied from 50 published resources and categorized based on experimental uncertainty. A subset of the molecules in B3DB has numerical log BB values (1058 compounds), while the whole dataset has categorical (BBB+ or BBB−) BBB permeability labels (7807). The dataset is freely available at https://github.com/theochem/B3DB and 10.6084/m9.figshare.15634230.v3 (version 3). We also provide some physicochemical properties of the molecules. By analyzing these properties, we can demonstrate some physiochemical similarities and differences between BBB+ and BBB− compounds.

scholarly journals Miniaturization and Automation of a Human In Vitro Blood–Brain Barrier Model for the High-Throughput Screening of Compounds in the Early Stage of Drug Discovery

Pharmaceutics ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 892
Author(s):  
Elisa L. J. Moya ◽  
Elodie Vandenhaute ◽  
Eleonora Rizzi ◽  
Marie-Christine Boucau ◽  
Johan Hachani ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Blood Brain Barrier ◽  
Early Stage ◽  
Molecular Transport ◽  
Brain Barrier ◽  
Blood Brain ◽  
Cns Drugs ◽  
The Impact ◽  
The Brain

Central nervous system (CNS) diseases are one of the top causes of death worldwide. As there is a difficulty of drug penetration into the brain due to the blood–brain barrier (BBB), many CNS drugs treatments fail in clinical trials. Hence, there is a need to develop effective CNS drugs following strategies for delivery to the brain by better selecting them as early as possible during the drug discovery process. The use of in vitro BBB models has proved useful to evaluate the impact of drugs/compounds toxicity, BBB permeation rates and molecular transport mechanisms within the brain cells in academic research and early-stage drug discovery. However, these studies that require biological material (animal brain or human cells) are time-consuming and involve costly amounts of materials and plastic wastes due to the format of the models. Hence, to adapt to the high yields needed in early-stage drug discoveries for compound screenings, a patented well-established human in vitro BBB model was miniaturized and automated into a 96-well format. This replicate met all the BBB model reliability criteria to get predictive results, allowing a significant reduction in biological materials, waste and a higher screening capacity for being extensively used during early-stage drug discovery studies.

scholarly journals Dual Roles of Astrocyte-Derived Factors in Regulation of Blood-Brain Barrier Function after Brain Damage

2019 ◽  
Vol 20 (3) ◽  
pp. 571 ◽  
Author(s):  
Shotaro Michinaga ◽  
Yutaka Koyama
Keyword(s):  
Brain Damage ◽  
Blood Brain Barrier ◽  
Brain Barrier ◽  
Blood Brain ◽  
Secondary Damage ◽  
The Central Nervous System ◽  
Bbb Permeability ◽  
Novel Therapeutic Strategies ◽  
Glial Derived Neurotrophic Factor ◽  
Endothelial Growth Factors

The blood-brain barrier (BBB) is a major functional barrier in the central nervous system (CNS), and inhibits the extravasation of intravascular contents and transports various essential nutrients between the blood and the brain. After brain damage by traumatic brain injury, cerebral ischemia and several other CNS disorders, the functions of the BBB are disrupted, resulting in severe secondary damage including brain edema and inflammatory injury. Therefore, BBB protection and recovery are considered novel therapeutic strategies for reducing brain damage. Emerging evidence suggests key roles of astrocyte-derived factors in BBB disruption and recovery after brain damage. The astrocyte-derived vascular permeability factors include vascular endothelial growth factors, matrix metalloproteinases, nitric oxide, glutamate and endothelin-1, which enhance BBB permeability leading to BBB disruption. By contrast, the astrocyte-derived protective factors include angiopoietin-1, sonic hedgehog, glial-derived neurotrophic factor, retinoic acid and insulin-like growth factor-1 and apolipoprotein E which attenuate BBB permeability resulting in recovery of BBB function. In this review, the roles of these astrocyte-derived factors in BBB function are summarized, and their significance as therapeutic targets for BBB protection and recovery after brain damage are discussed.

scholarly journals Pseudogene ACTBP2 increases blood–brain barrier permeability by promoting KHDRBS2 transcription through recruitment of KMT2D/WDR5 in Aβ1–42 microenvironment

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Qianshuo Liu ◽  
Xiaobai Liu ◽  
Defeng Zhao ◽  
Xuelei Ruan ◽  
Rui Su ◽  
...  
Keyword(s):  
Blood Brain Barrier ◽  
Molecular Mechanisms ◽  
Rna Binding ◽  
Vital Role ◽  
Brain Barrier ◽  
Blood Brain ◽  
Bbb Permeability ◽  
Novel Target ◽  
And Function

AbstractThe blood–brain barrier (BBB) has a vital role in maintaining the homeostasis of the central nervous system (CNS). Changes in the structure and function of BBB can accelerate Alzheimer’s disease (AD) development. β-Amyloid (Aβ) deposition is the major pathological event of AD. We elucidated the function and possible molecular mechanisms of the effect of pseudogene ACTBP2 on the permeability of BBB in Aβ1–42 microenvironment. BBB model treated with Aβ1–42 for 48 h were used to simulate Aβ-mediated BBB dysfunction in AD. We proved that pseudogene ACTBP2, RNA-binding protein KHDRBS2, and transcription factor HEY2 are highly expressed in ECs that were obtained in a BBB model in vitro in Aβ1–42 microenvironment. In Aβ1–42-incubated ECs, ACTBP2 recruits methyltransferases KMT2D and WDR5, binds to KHDRBS2 promoter, and promotes KHDRBS2 transcription. The interaction of KHDRBS2 with the 3′UTR of HEY2 mRNA increases the stability of HEY2 and promotes its expression. HEY2 increases BBB permeability in Aβ1–42 microenvironment by transcriptionally inhibiting the expression of ZO-1, occludin, and claudin-5. We confirmed that knocking down of Khdrbs2 or Hey2 increased the expression levels of ZO-1, occludin, and claudin-5 in APP/PS1 mice brain microvessels. ACTBP2/KHDRBS2/HEY2 axis has a crucial role in the regulation of BBB permeability in Aβ1–42 microenvironment, which may provide a novel target for the therapy of AD.

scholarly journals Early Appearance of Activated Matrix Metalloproteinase-9 after Focal Cerebral Ischemia in Mice: A Possible Role in Blood—Brain Barrier Dysfunction

1999 ◽  
Vol 19 (9) ◽  
pp. 1020-1028 ◽  
Author(s):  
Yvan Gasche ◽  
Miki Fujimura ◽  
Yuiko Morita-Fujimura ◽  
Jean-Christophe Copin ◽  
Makoto Kawase ◽  
...  
Keyword(s):  
Cerebral Ischemia ◽  
Blood Brain Barrier ◽  
Focal Cerebral Ischemia ◽  
Vasogenic Edema ◽  
Tissue Inhibitor Of Metalloproteinase ◽  
Brain Barrier ◽  
Critical Event ◽  
Secondary Brain Injury ◽  
Blood Brain ◽  
Bbb Permeability

During cerebral ischemia blood–brain barrier (BBB) disruption is a critical event leading to vasogenic edema and secondary brain injury. Gelatinases A and B are matrix metalloproteinases (MMP) able to open the BBB. The current study analyzes by zymography the early gelatinases expression and activation during permanent ischemia in mice (n = 15). ProMMP-9 expression was significantly ( P < 0.001) increased in ischemic regions compared with corresponding contralateral regions after 2 hours of ischemia (mean 694.7 arbitrary units [AU], SD ± 238.4 versus mean 107.6 AU, SD ± 15.6) and remained elevated until 24 hours (mean 745,7 AU, SD ± 157.4). Moreover, activated MMP-9 was observed 4 hours after the initiation of ischemia. At the same time as the appearance of activated MMP-9, we detected by the Evan's blue extravasation method a clear increase of BBB permeability, Tissue inhibitor of metalloproteinase-1 was not modified during permanent ischemia at any time. The ProMMP-2 was significantly ( P < 0.05) increased only after 24 hours of permanent ischemia (mean 213.2 AU, SD ± 60.6 versus mean 94.6 AU, SD ± 13.3), and no activated form was observed. The appearance of activated MMP-9 after 4 hours of ischemia in correlation with BBB permeability alterations suggests that MMP-9 may play an active role in early vasogenic edema development after stroke.

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