P1-399: BACE1 gene regulation: A novel drug target in Alzheimer's disease

Mempasin 2, a ϐ-secretase, is the membrane-anchored aspartic protease that initiates the cleavage of amyloid precursor protein leading to the production of ϐ-amyloid and the onset of Alzheimer's disease. Thus memapsin 2 is a major therapeutic target for the development of inhibitor drugs for the disease. Many biochemical tools, such as the specificity and crystal structure, have been established and have led to the design of potent and relatively small transition-state inhibitors. Although developing a clinically viable mempasin 2 inhibitor remains challenging, progress to date renders hope that memapsin 2 inhibitors may ultimately be useful for therapeutic reduction of ϐ-amyloid.

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Molecular Gender Differences In Alzheimer’s Disease May Point To A New Drug Target

10.31988/scitrends.10676 ◽

2018 ◽

Author(s):

Enrico Glaab

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Gender Differences ◽

Drug Target ◽

New Drug

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Current Strategies and Novel Drug Approaches for Alzheimer Disease

CNS & Neurological Disorders - Drug Targets ◽

10.2174/1871527319666200717091513 ◽

2020 ◽

Vol 19 (9) ◽

pp. 676-690 ◽

Cited By ~ 2

Author(s):

Roma Ghai ◽

Kandasamy Nagarajan ◽

Meenakshi Arora ◽

Parul Grover ◽

Nazakat Ali ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Cholinergic Neurons ◽

Amyloid Plaque ◽

Neural Pathways ◽

Current Trends ◽

Personality Changes ◽

Traditional Therapies ◽

Novel Drug ◽

The Brain

Alzheimer’s Disease (AD) is a chronic, devastating dysfunction of neurons in the brain leading to dementia. It mainly arises due to neuronal injury in the cerebral cortex and hippocampus area of the brain and is clinically manifested as a progressive mental failure, disordered cognitive functions, personality changes, reduced verbal fluency and impairment of speech. The pathology behind AD is the formation of intraneuronal fibrillary tangles, deposition of amyloid plaque and decline in choline acetyltransferase and loss of cholinergic neurons. Tragically, the disease cannot be cured, but its progression can be halted. Various cholinesterase inhibitors available in the market like Tacrine, Donepezil, Galantamine, Rivastigmine, etc. are being used to manage the symptoms of Alzheimer’s disease. The paper’s objective is to throw light not only on the cellular/genetic basis of the disease, but also on the current trends and various strategies of treatment including the use of phytopharmaceuticals and nutraceuticals. Enormous literature survey was conducted and published articles of PubMed, Scifinder, Google Scholar, Clinical Trials.org and Alzheimer Association reports were studied intensively to consolidate the information on the strategies available to combat Alzheimer’s disease. Currently, several strategies are being investigated for the treatment of Alzheimer’s disease. Immunotherapies targeting amyloid-beta plaques, tau protein and neural pathways are undergoing clinical trials. Moreover, antisense oligonucleotide methodologies are being approached as therapies for its management. Phytopharmaceuticals and nutraceuticals are also gaining attention in overcoming the symptoms related to AD. The present review article concludes that novel and traditional therapies simultaneously promise future hope for AD treatment.

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Artificial intelligence-based computational framework for drug-target prioritization and inference of novel repositionable drugs for Alzheimer’s disease

Alzheimer s Research & Therapy ◽

10.1186/s13195-021-00826-3 ◽

2021 ◽

Vol 13 (1) ◽

Author(s):

Shingo Tsuji ◽

Takeshi Hase ◽

Ayako Yachie-Kinoshita ◽

Taiko Nishino ◽

Samik Ghosh ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Deep Learning ◽

Drug Target ◽

Target Genes ◽

Drug Repositioning ◽

Therapeutic Targets ◽

Machine Learning Techniques ◽

Computational Framework ◽

Novel Therapeutic

Abstract Background Identifying novel therapeutic targets is crucial for the successful development of drugs. However, the cost to experimentally identify therapeutic targets is huge and only approximately 400 genes are targets for FDA-approved drugs. As a result, it is inevitable to develop powerful computational tools that can identify potential novel therapeutic targets. Fortunately, the human protein-protein interaction network (PIN) could be a useful resource to achieve this objective. Methods In this study, we developed a deep learning-based computational framework that extracts low-dimensional representations of high-dimensional PIN data. Our computational framework uses latent features and state-of-the-art machine learning techniques to infer potential drug target genes. Results We applied our computational framework to prioritize novel putative target genes for Alzheimer’s disease and successfully identified key genes that may serve as novel therapeutic targets (e.g., DLG4, EGFR, RAC1, SYK, PTK2B, SOCS1). Furthermore, based on these putative targets, we could infer repositionable candidate-compounds for the disease (e.g., tamoxifen, bosutinib, and dasatinib). Conclusions Our deep learning-based computational framework could be a powerful tool to efficiently prioritize new therapeutic targets and enhance the drug repositioning strategy.

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