Evidence map›Paper›PMID 29078760›Full record

ArticleBMC neuroscience2017

In silico repurposing of antipsychotic drugs for Alzheimer's disease.

Shivani Kumar, Suman Chowdhury, Suresh Kumar

Open access · goldAbstract read
In one paragraph

Article in BMC neuroscience, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.

0numbers the graph read from it
0cells of the map it votes in
34citing papers in PubMed
6.9field-weighted citation impact, top 3% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

34 citing papers in PubMed, 102 citations in OpenAlex.

  1. Article
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  10. A systematic review of cardiacProgress in biomedical engineering (Bristol, England) · 2023
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  12. Docking-Based Prediction of Peptide Binding to MHC Proteins.Methods in molecular biology (Clifton, N.J.) · 2023
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  13. Review
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  17. Implications ofFrontiers in cellular and infection microbiology · 2022
    Review
  18. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors at 1 institution in 1 country.

Shivani KumarUniversity School of Biotechnology, GGS Indraprastha University, Sector-16C, Dwarka, New Delhi, 110075, India.
Suman ChowdhuryUniversity School of Biotechnology, GGS Indraprastha University, Sector-16C, Dwarka, New Delhi, 110075, India.
Suresh KumarUniversity School of Biotechnology, GGS Indraprastha University, Sector-16C, Dwarka, New Delhi, 110075, India. sk222ind@yahoo.com.ORCID 0000-0002-2637-3927
Guru Gobind Singh Indraprastha University · IN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAlzheimer'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.

resultA 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.

conclusionThe 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.

Indexed as

Drug RepositioningAcetylcholinesteraseAlzheimer DiseaseAmyloid Precursor Protein SecretasesAntipsychotic AgentsAspartic Acid EndopeptidasesButyrophenonesButyrylcholinesteraseGlucosidesHaloperidolHumansMolecular Docking SimulationMolecular StructureMonoamine OxidaseNorisoprenoidsPimozideAcetylcholinesteraseAmyloid Precursor Protein Secretasesanisoposide AAntipsychotic AgentsAspartic Acid EndopeptidasesBACE1 protein, humanbromperidolButyrophenonesButyrylcholinesteraseGlucosidesHaloperidolmetylperonMonoamine OxidaseNorisoprenoidsPimozidePsychotropic DrugsReceptors, N-Methyl-D-AspartateAcetylcholinesteraseAlzheimer’s diseaseAntipsychotic drugsBeta-secretase cleavage enzymeButyrylcholinesteraseDrug repurposingMolecular dockingMonoamine oxidaseN-Methyl-D-aspartateSchrodinger

Identifiers

PMID29078760
PMCPMC5660441
OpenAlexW2765951174

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.