[P2-060]: CHARACTERIZATION OF A SMALL-MOLECULE APP TRANSLATION INHIBITOR FOR ALZHEIMER's DISEASE PREVENTION AND THERAPY

Abstract:: Recent studies have proven that the purinergic signaling pathway plays a key role in neurotransmission and neuromodulation, and is involved in various neurodegenerative diseases and psychiatric disorders. With the characterization of the subtypes of receptors in purinergic signaling, i.e. the P1 (adenosine), P2X (ion channel) and P2Y (G protein-coupled), more attentions were paid to the pathophysiology and therapeutic potential of purinergic signaling in central nervous system disorders. Alzheimer’s disease (AD) is a progressive and deadly neurodegenerative disease that is characterized by memory loss, cognitive impairment and dementia. However, as drug development aimed to prevent or control AD follows a series of failures in recent years, more researchers focused on the neuroprotection-related mechanisms such as purinergic signaling in AD patients to find a potential cure. This article reviews the recent discoveries of purinergic signaling in AD, summaries the potential agents as modulators for the receptors of purinergic signaling in AD related research and treatments. Thus, our paper provided an insight for purinergic signaling in the development of anti-AD therapies.

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

Current Alzheimer Research ◽

10.2174/1567205016666190103154855 ◽

2019 ◽

Vol 16 (3) ◽

pp. 193-208 ◽

Cited By ~ 2

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.

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