P4-348: Current drug target sites in clinical practice or clinical trail: Extent of disease modification for 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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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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Measuring Disease Modification in Alzheimer's Disease

CNS Spectrums ◽

10.1017/s109285290002589x ◽

2007 ◽

Vol 12 (S1) ◽

pp. 11-14

Author(s):

Jeffrey L. Cummings

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Immunoglobulin A ◽

Disease Process ◽

Underlying Disease ◽

Therapeutic Interventions ◽

Disease Modification ◽

Amyloid Disease ◽

Therapeutic Modalities ◽

Disease Modifying

AbstractWe appear to be on the brink of a new epoch of treatment for Alzheimer's disease. Compelling evidence suggests that Aβ42 secretion is the triggering event in the pathogenesis of Alzheimer's disease, and that tau aggregation may be an important secondary event linked to neurodegeneration. Prophylactic administration of anti-amyloid agents designed to prevent Aβ accumulation in persons with subclinical disease is likely to be more effective than therapeutic interventions in established Alzheimer's disease. Drug development programs in Alzheimer's disease focus primarily on agents with anti-amyloid disease-modifying properties, and many different pharmacologic approaches to reducing amyloid pathology and tauopathy are being studied. Classes of therapeutic modalities currently in advanced-stage clinical trial testing include forms of immunotherapy (active β -amyloid immunoconjugate and human intravenous immunoglobulin), a γ-secretase inhibitor, the selective Aβ42-lowering agent R-flurbiprofen, and the anti-aggregation agent tramiprosate. Non-traditional dementia therapies such as the HMG-CoA reductase inhibitors (statins), valproate, and lithium are now being assessed for clinical benefit as anti-amyloid disease-modifying treatments. Positive findings of efficacy and safety from clinical studies are necessary but not sufficient to demonstrate that a drug has disease-modifying properties. Definitive proof of disease-modification requires evidence from validated animal models of Alzheimer's disease; rigorously controlled clinical trials showing a significantly improved, stabilized, or slowed rate of decline in cognitive and global function compared to placebo; and prospectively obtained evidence from surrogate biomarkers that the treatment resulted in measurable biological changes associated with the underlying disease process.

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