Evidence map›Paper›PMID 37867692›Full record

ArticleACS omega2023

Screening of Multitarget-Directed Natural Compounds as Drug Candidates for Alzheimer's Disease Using

Sukriti Srivastava, Shilpa Sharma, Shashank Deep, Sunil Kumar Khare

Open access · goldAbstract read
In one paragraph

Article in ACS omega, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
3.1field-weighted citation impact, top 8% 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

6 citing papers in PubMed, 13 citations in OpenAlex.

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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

4 authors at 1 institution in 1 country.

Sukriti SrivastavaEnzyme and Microbial Biochemistry Laboratory, Department of Chemistry, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India.
Shilpa SharmaBiophysical Chemistry Laboratory, Department of Chemistry, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India.
Shashank DeepBiophysical Chemistry Laboratory, Department of Chemistry, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India.ORCID https://orcid.org/0000-0002-4908-8205
Sunil Kumar KhareEnzyme and Microbial Biochemistry Laboratory, Department of Chemistry, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India.ORCID https://orcid.org/0000-0002-7339-3058
Indian Institute of Technology Delhi · IN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Alzheimer's disease (AD) is a neurodegenerative disorder that impairs neurocognitive function. Acetylcholinesterase (AChE) and β-site APP cleaving enzyme 1 (BACE1) are the two main proteins implicated in AD. Indeed, the major available commercial drugs (donepezil, rivastigmine, and galantamine) against Alzheimer's are AChE inhibitors. However, none of these drugs are known to reverse or reduce the pathophysiological condition of the disease since there are multiple contributing factors to AD. Therefore, there is a need to develop a multitarget-directed ligand approach for its treatment. In the present study, plant bioactive compounds were screened for their AChE and BACE1 inhibition potential by conducting molecular docking studies. Considering their docking score and pharmacokinetic properties, limonin, peimisine, serratanine B, and withanolide A were selected as the lead compounds. Molecular dynamics simulations of these protein-ligand complexes confirmed the conformational and energetically stabilized enzyme-inhibitor complexes. The inhibition potential of the lead compounds was validated by

Identifiers

PMID37867692
PMCPMC10586450
OpenAlexW4387312592

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.