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In silico repurposing of antipsychotic drugs for Alzheimer’s disease

Overview of attention for article published in BMC Neuroscience, October 2017
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Title
In silico repurposing of antipsychotic drugs for Alzheimer’s disease
Published in
BMC Neuroscience, October 2017
DOI 10.1186/s12868-017-0394-8
Pubmed ID
Authors

Shivani Kumar, Suman Chowdhury, Suresh Kumar

Abstract

Alzheimer's disease (AD) is the most prevalent form of dementia and represents one of the highest unmet requirements in medicine today. There is shortage of novel molecules entering into market because of poor pharmacokinetic properties and safety issues. Drug repurposing offers an opportunity to reinvigorate the slowing drug discovery process by finding new uses for existing drugs. The major advantage of the drug repurposing approach is that the safety issues are already investigated in the clinical trials and the drugs are commercially available in the marketplace. As this approach provides an effective solution to hasten the process of providing new alternative drugs for AD, the current study shows the molecular interaction of already known antipsychotic drugs with the different protein targets implicated in AD using in silico studies. A computational method based on ligand-protein interaction was adopted in present study to explore potential antipsychotic drugs for the treatment of AD. The screening of approximately 150 antipsychotic drugs was performed on five major protein targets (AChE, BuChE, BACE 1, MAO and NMDA) by molecular docking. In this study, for each protein target, the best drug was identified on the basis of dock score and glide energy. The top hits were then compared with the already known inhibitor of the respective proteins. Some of the drugs showed relatively better docking score and binding energies as compared to the already known inhibitors of the respective targets. Molecular descriptors like molecular weight, number of hydrogen bond donors, acceptors, predicted octanol/water partition coefficient and percentage human oral absorption were also analysed to determine the in silico ADME properties of these drugs and all were found in the acceptable range and follows Lipinski's rule. The present study have led to unravel the potential of leading antipsychotic drugs such as pimozide, bromperidol, melperone, anisoperidone, benperidol and anisopirol against multiple targets associated with AD. Benperidol was found to be the best candidate drug interacting with different target proteins involved in AD.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 175 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 175 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 25 14%
Student > Master 25 14%
Student > Bachelor 25 14%
Researcher 16 9%
Other 6 3%
Other 19 11%
Unknown 59 34%
Readers by discipline Count As %
Pharmacology, Toxicology and Pharmaceutical Science 29 17%
Biochemistry, Genetics and Molecular Biology 26 15%
Chemistry 16 9%
Medicine and Dentistry 9 5%
Agricultural and Biological Sciences 6 3%
Other 25 14%
Unknown 64 37%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 18 December 2017.
All research outputs
#17,918,662
of 23,006,268 outputs
Outputs from BMC Neuroscience
#820
of 1,250 outputs
Outputs of similar age
#235,018
of 328,360 outputs
Outputs of similar age from BMC Neuroscience
#8
of 11 outputs
Altmetric has tracked 23,006,268 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,250 research outputs from this source. They receive a mean Attention Score of 4.3. This one is in the 29th percentile – i.e., 29% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 328,360 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 23rd percentile – i.e., 23% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 11 others from the same source and published within six weeks on either side of this one. This one is in the 27th percentile – i.e., 27% of its contemporaries scored the same or lower than it.