Alzheimer's disease: An overview of amyloid beta dependent pathogenesis and its therapeutic implications along with in silico approaches emphasizing the role of natural products
Abstract Alzheimer's disease (AD) is a progressive and irreversible neurodegenerative disorder characterized by amyloid beta (Aβ) deposition in brain with subsequent formation of neuritic plaques leading to dementia. A number of therapeutic strategies targeted against Aβ depositions have been r...
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Veröffentlicht in: | Journal of the neurological sciences 2016-02, Vol.361, p.256-271 |
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Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | Abstract Alzheimer's disease (AD) is a progressive and irreversible neurodegenerative disorder characterized by amyloid beta (Aβ) deposition in brain with subsequent formation of neuritic plaques leading to dementia. A number of therapeutic strategies targeted against Aβ depositions have been rigorously explored which provided successful results corresponding to the existing symptomatic treatments. However, at the same time, several failures corresponding to the disease altering therapies and drugs have also been observed due to potential drawbacks in understanding of the pathogenesis of the disease, development of drug candidates and subsequent designing of clinical trials. Preclinical research, along with experimental and clinical studies, is continuously providing novel information which may reveal multi-target directed ligands and combination therapies for targeting Aβ. Thus, in view of the estimated increase in the number of AD patients globally, the present review attempts to summarize the available evidence dealing with various therapeutic approaches targeting Aβ, focusing specifically on pharmaceutical compounds under various stages of clinical trials. Furthermore, in view of a number of computational advances having significant impact in the field of computer aided drug design, we have also presented results of analysis of natural compounds as potential therapeutic molecules in preventing Aβ plaque formation using in silico approaches. |
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ISSN: | 0022-510X 1878-5883 |
DOI: | 10.1016/j.jns.2016.01.008 |