Discovery of small molecule cholinesterase inhibitors as potential treatments for Alzheimer’s disease using virtual screening

2021 ◽  
Author(s):  
◽  
Jared Adam Miles
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Virtual Screening ◽  
Small Molecule ◽  
Cholinesterase Inhibitors

scholarly journals Discovery of new acetylcholinesterase and butyrylcholinesterase inhibitors through structure-based virtual screening

RSC Advances ◽  
2017 ◽  
Vol 7 (6) ◽  
pp. 3429-3438 ◽  
Author(s):  
Yao Chen ◽  
Hongzhi Lin ◽  
Hongyu Yang ◽  
Renxiang Tan ◽  
Yaoyao Bian ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Virtual Screening ◽  
Small Molecule ◽  
Therapeutic Strategies ◽  
Che Inhibitors

Small molecule cholinesterase (ChE) inhibitors represent one of the most effective therapeutic strategies for the treatment of Alzheimer's disease (AD).

Drug Design of Inhibitors of Alzheimer’s Disease (AD): POM and DFT Analyses of Cholinesterase Inhibitory Activity of β-amino di-Carbonyl Derivatives

2019 ◽  
Vol 19 (8) ◽  
pp. 688-705
Author(s):  
Taibi Ben Hadda ◽  
Abdur Rauf ◽  
Hsaine Zgou ◽  
Fatma Sezer Senol ◽  
Ilkay Erdogan Orhan ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cholinesterase Inhibitors ◽  
Metal Accumulation ◽  
Microtiter Plate ◽  
Metal Chelation ◽  
Carbonyl Derivatives ◽  
Cholinesterase Inhibitory Activity ◽  
Inhibitory Potential

Background:Since deficit of acetylcholine has been evidenced in the Alzheimer’s disease (AD) patients, cholinesterase inhibitors are currently the most specified drug category for the remediation of AD.Method:In the present study, 16 compounds (1-16) with dicarbonyl skeletons have been synthesized and tested for their inhibitory potential in vitro against AChE and BChE using ELISA microtiter plate assays at 100 μg/mL. Since metal accumulation is related to AD, the compounds were also tested for their metal-chelation capacity.Results and Conclusion:All the investigated dicarbonyl compounds exerted none or lower than 30% inhibition against both cholinesterases, whereas compounds 2, 8 and 11 showed 37, 42, 41% of inhibition towards BChE, being the most active. The highest metal-chelation capacity was observed with compound 8 (53.58 ± 2.06%). POM and DFT analyses are in good harmonization with experimental data.

Identify Compounds' Target Against Alzheimer's Disease Based on In-Silico Approach

2019 ◽  
Vol 16 (3) ◽  
pp. 193-208 ◽  
Author(s):  
Yan Hu ◽  
Guangya Zhou ◽  
Chi Zhang ◽  
Mengying Zhang ◽  
Qin Chen ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Small Molecule ◽  
Characteristic Curve ◽  
Machine Learning Algorithms ◽  
Learner Model ◽  
Mao B ◽  
Number Of Patients ◽  
Sar Model ◽  
Set Up

Background: Alzheimer's disease swept every corner of the globe and the number of patients worldwide has been rising. At present, there are as many as 30 million people with Alzheimer's disease in the world, and it is expected to exceed 80 million people by 2050. Consequently, the study of Alzheimer’s drugs has become one of the most popular medical topics. Methods: In this study, in order to build a predicting model for Alzheimer’s drugs and targets, the attribute discriminators CfsSubsetEval, ConsistencySubsetEval and FilteredSubsetEval are combined with search methods such as BestFirst, GeneticSearch and Greedystepwise to filter the molecular descriptors. Then the machine learning algorithms such as BayesNet, SVM, KNN and C4.5 are used to construct the 2D-Structure Activity Relationship(2D-SAR) model. Its modeling results are utilized for Receiver Operating Characteristic curve(ROC) analysis. Results: The prediction rates of correctness using Randomforest for AChE, BChE, MAO-B, BACE1, Tau protein and Non-inhibitor are 77.0%, 79.1%, 100.0%, 94.2%, 93.2% and 94.9%, respectively, which are overwhelming as compared to those of BayesNet, BP, SVM, KNN, AdaBoost and C4.5. Conclusion: In this paper, we conclude that Random Forest is the best learner model for the prediction of Alzheimer’s drugs and targets. Besides, we set up an online server to predict whether a small molecule is the inhibitor of Alzheimer's target at http://47.106.158.30:8080/AD/. Furthermore, it can distinguish the target protein of a small molecule.

scholarly journals Potential Cancer- and Alzheimer’s Disease-Targeting Phosphodiesterase Inhibitors from Uvaria alba: Insights from In Vitro and Consensus Virtual Screening

ACS Omega ◽  
2021 ◽  
Vol 6 (12) ◽  
pp. 8403-8417
Author(s):  
Mark Tristan Quimque ◽  
Kin Israel Notarte ◽  
Arianne Letada ◽  
Rey Arturo Fernandez ◽  
Delfin Yñigo Pilapil ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Virtual Screening ◽  
Phosphodiesterase Inhibitors ◽  
Potential Cancer

scholarly journals Identification of Novel Cathepsin B Inhibitors with Implications in Alzheimer’s Disease: Computational Refining and Biochemical Evaluation

Cells ◽  
2021 ◽  
Vol 10 (8) ◽  
pp. 1946
Author(s):  
Nitin Chitranshi ◽  
Ashutosh Kumar ◽  
Samran Sheriff ◽  
Veer Gupta ◽  
Angela Godinez ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Hydrogen Bond ◽  
Virtual Screening ◽  
Cathepsin B ◽  
Amyloid Β ◽  
Proteolytic Degradation ◽  
Hydrogen Bond Acceptor ◽  
Starting Point ◽  
The Brain

Amyloid precursor protein (APP), upon proteolytic degradation, forms aggregates of amyloid β (Aβ) and plaques in the brain, which are pathological hallmarks of Alzheimer’s disease (AD). Cathepsin B is a cysteine protease enzyme that catalyzes the proteolytic degradation of APP in the brain. Thus, cathepsin B inhibition is a crucial therapeutic aspect for the discovery of new anti-Alzheimer’s drugs. In this study, we have employed mixed-feature ligand-based virtual screening (LBVS) by integrating pharmacophore mapping, docking, and molecular dynamics to detect small, potent molecules that act as cathepsin B inhibitors. The LBVS model was generated by using hydrophobic (HY), hydrogen bond acceptor (HBA), and hydrogen bond donor (HBD) features, using a dataset of 24 known cathepsin B inhibitors of both natural and synthetic origins. A validated eight-feature pharmacophore hypothesis (Hypo III) was utilized to screen the Maybridge chemical database. The docking score, MM-PBSA, and MM-GBSA methodology was applied to prioritize the lead compounds as virtual screening hits. These compounds share a common amide scaffold, and showed important interactions with Gln23, Cys29, His110, His111, Glu122, His199, and Trp221. The identified inhibitors were further evaluated for cathepsin-B-inhibitory activity. Our study suggests that pyridine, acetamide, and benzohydrazide compounds could be used as a starting point for the development of novel therapeutics.

Pharmacotherapies for Alzheimer's disease: Beyond cholinesterase inhibitors

2012 ◽  
Vol 134 (1) ◽  
pp. 8-25 ◽  
Author(s):  
Haythum O. Tayeb ◽  
Hyun Duk Yang ◽  
Bruce H. Price ◽  
Frank I. Tarazi
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cholinesterase Inhibitors
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