molecular ligand
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2020 ◽  
Vol 132 (46) ◽  
pp. 20809-20816
Author(s):  
Guanhong Wu ◽  
Tongtao Li ◽  
Zhilei Wang ◽  
Mingzhong Li ◽  
Biwei Wang ◽  
...  

2020 ◽  
Vol 59 (46) ◽  
pp. 20628-20635
Author(s):  
Guanhong Wu ◽  
Tongtao Li ◽  
Zhilei Wang ◽  
Mingzhong Li ◽  
Biwei Wang ◽  
...  

2020 ◽  
Vol 9 (4) ◽  
pp. 469-479
Author(s):  
Heting Dong ◽  
Ting Wang ◽  
Meijuan Wang ◽  
Yongdong Yan ◽  
Xinxing Zhang ◽  
...  
Keyword(s):  

2020 ◽  
Author(s):  
Heting Dong ◽  
Wujun JIANG ◽  
Li HUANG ◽  
Meijuan WANG ◽  
Yongdong YAN ◽  
...  

Abstract Background: It has been shown that certain severe and refractory asthma cases are due to neutrophil and not eosinophil infiltration. ICOSL (Inducible costimulatory molecular ligand) expression is closely associated with tumor and autoimmune diseases, while a limited amount of data has been published regarding the significance of ICOSL in children with neutrophilic asthma. The present study aimed to explore the abnormal expression of ICOSL in peripheral blood and bronchoalveolar lavage fluid (BALF) samples of children with neutrophilic asthma and their clinical significance. Methods: The present clinical study selected children who met the diagnostic criteria of asthma from the children's Hospital of Suchow University and excluded the patients with positive etiology. The children who were admitted to the hospital for foreign body inhalation in the same period were collected as the control group. The children with more than 50% (the percentage of neutrophils in BALF was 95% of the percentile in the control group) of neutrophils in BALF samples were assigned to the neutrophilic asthma group (NA group), whereas the remaining subjects comprised the asthma group (A group). The expression levels of ICOSL, IL-4, IL-17, IFN-, neutrophil elastase (NE) and matrix metalloproteinase (MMP)-9 were detected in plasma and BALF samples by enzyme-linked immunosorbent assays in order to analyze the differences in the levels of cytokines and clinical characteristics between children with neutrophilic asthma and non-neutrophilic asthma. Moreover, the potential mechanism of ICOSL in neutrophilic asthma was explored. Results: In strict accordance with the diagnostic criteria of asthma and following exclusion of pathogenic positive children, 32 children were finally enrolled, including 12 children in the NA group and 20 children in the A group. The mean hospitalization time of the NA group was longer than that of the A group (P<0.05). The concentration levels of ICOSL, IL-17, NE and MMP9 of the NA group in plasma and BALF samples were higher than those in the A group, while the levels of IFN-γ exhibited the opposite trend. A significant correlation was evident between ICOSL and IL-17 levels in plasma(r=0.753,P=0.012) and BALF(r=0.774,P=0.009)samples of the NA group. Conclusion: Children with asthma exhibited an immunity imbalance of Th1/Th2/Th17, whereas neutrophilic asthma children were more severely affected. The clinical treatment was considerably difficult and the hospitalization time was longer. ICOSL may regulate the secretion of IL-17 by Th17 and increase the levels of NE and MMP9, which are involved in the development of immune inflammation in neutrophils.


2019 ◽  
Vol 21 (1) ◽  
pp. 19 ◽  
Author(s):  
Robert Ancuceanu ◽  
Bogdan Tamba ◽  
Cristina Silvia Stoicescu ◽  
Mihaela Dinu

A prototype of a family of at least nine members, cellular Src tyrosine kinase is a therapeutically interesting target because its inhibition might be of interest not only in a number of malignancies, but also in a diverse array of conditions, from neurodegenerative pathologies to certain viral infections. Computational methods in drug discovery are considerably cheaper than conventional methods and offer opportunities of screening very large numbers of compounds in conditions that would be simply impossible within the wet lab experimental settings. We explored the use of global quantitative structure-activity relationship (QSAR) models and molecular ligand docking in the discovery of new c-src tyrosine kinase inhibitors. Using a dataset of 1038 compounds from ChEMBL database, we developed over 350 QSAR classification models. A total of 49 models with reasonably good performance were selected and the models were assembled by stacking with a simple majority vote and used for the virtual screening of over 100,000 compounds. A total of 744 compounds were predicted by at least 50% of the QSAR models as active, 147 compounds were within the applicability domain and predicted by at least 75% of the models to be active. The latter 147 compounds were submitted to molecular ligand docking using AutoDock Vina and LeDock, and 89 were predicted to be active based on the energy of binding.


2019 ◽  
Vol 141 (44) ◽  
pp. 17949-17949
Author(s):  
Jesse R. Allardice ◽  
Arya Thampi ◽  
Simon Dowland ◽  
James Xiao ◽  
Victor Gray ◽  
...  

Author(s):  
Robert Ancuceanu ◽  
Bogdan Tamba ◽  
Cristina Silvia Stoicescu ◽  
Mihaela Dinu

Prototype of a family of at least nine members, c-src tyrosine kinase is a therapeutically interesting target, because its inhibition might be of interest not only in a number of malignancies, but also in a diverse array of conditions, from neurodegenerative pathologies to certain viral infections. Computational methods in drug discovery are considerably cheaper than conventional methods and offer opportunities of screening very large numbers of compounds in conditions that would be simply impossible within the wet lab experimental settings. We have explored the use of global QSAR models and molecular ligand docking in the discovery of new c-src tyrosine kinase inhibitors. Using a data set of 1038 compounds from ChEMBL and 19 blocks of molecular descriptors, we have developed over 200 QSAR classification models, based on six machine learning algorithms and 17 feature selection methods. We have selected 49 with reasonably good performance (positive predictive value and balanced accuracy higher than 70% in nested cross validation) and the models were assembled by stacking with a simple majority vote and used for the virtual screening of over the &ldquo;named&rdquo; ZINC data set (over 100,000 compounds). 744 compounds were predicted by at least 50% of the QSAR models as active, 147 compounds were within the applicability domain and predicted by at least 75% of the models to be active. The latter 147 compounds were submitted to molecular ligand docking using Vina and Ledock, and a number of 90 were predicted to be active based on the binding energy. External data from CHEMBL and PUBCHEM confirmed that at least 7.83% (in the case of QSAR) or 6.67% (in the case of integrated QSAR and molecular docking) of the compounds are active on the c-src target.


2019 ◽  
Vol 141 (32) ◽  
pp. 12907-12915 ◽  
Author(s):  
Jesse R. Allardice ◽  
Arya Thampi ◽  
Simon Dowland ◽  
James Xiao ◽  
Victor Gray ◽  
...  

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