Evidence map›Paper›PMID 31937840›Full record

ArticleScientific reports2020

Combined network pharmacology and virtual reverse pharmacology approaches for identification of potential targets to treat vascular dementia.

Alexey A Lagunin, Sergey M Ivanov, Tatyana A Gloriozova, Pavel V Pogodin, Dmitry A Filimonov, Sandeep Kumar, Rajesh K Goel

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 1 pooled it
2.6field-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

13 citing papers in PubMed, 1 synthesis or guideline pooled it, 33 citations in OpenAlex.

  1. Pooled it
  2. Review
  3. Article
  4. Article
  5. Review
  6. Article
  7. Article
  8. Drug Discovery and Development Targeting Dementia.Pharmaceuticals (Basel, Switzerland) · 2023
    Review
  9. Review
  10. In silico Methods for Identification of Potential Therapeutic Targets.Interdisciplinary sciences, computational life sciences · 2022
    Review
  11. Article
  12. Article
  13. Article
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

7 authors at 3 institutions in 2 countries.

Alexey A LaguninPirogov Russian National Research Medical University, Department of Bioinformatics, Moscow, 117997, Russia. alexey.lagunin@ibmc.msk.ru.
Sergey M IvanovPirogov Russian National Research Medical University, Department of Bioinformatics, Moscow, 117997, Russia.
Tatyana A GloriozovaInstitute of Biomedical Chemistry, Department of Bioinformatics, Moscow, 119121, Russia.
Pavel V PogodinInstitute of Biomedical Chemistry, Department of Bioinformatics, Moscow, 119121, Russia.
Dmitry A FilimonovInstitute of Biomedical Chemistry, Department of Bioinformatics, Moscow, 119121, Russia.
Sandeep KumarPunjabi University, Department of Pharmaceutical Sciences and Drug Research, Patiala, 147002, India.
Rajesh K GoelPunjabi University, Department of Pharmaceutical Sciences and Drug Research, Patiala, 147002, India. goelrkpup@gmail.com.
Institute of Biomedical Chemistry · RUPirogov Russian National Research Medical University · RUPunjabi University · IN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Dementia is a major cause of disability and dependency among older people. If the lives of people with dementia are to be improved, research and its translation into druggable target are crucial. Ancient systems of healthcare (Ayurveda, Siddha, Unani and Sowa-Rigpa) have been used from centuries for the treatment vascular diseases and dementia. This traditional knowledge can be transformed into novel targets through robust interplay of network pharmacology (NetP) with reverse pharmacology (RevP), without ignoring cutting edge biomedical data. This work demonstrates interaction between recent and traditional data, and aimed at selection of most promising targets for guiding wet lab validations. PROTEOME, DisGeNE, DISEASES and DrugBank databases were used for selection of genes associated with pathogenesis and treatment of vascular dementia (VaD). The selection of new potential drug targets was made by methods of NetP (DIAMOnD algorithm, enrichment analysis of KEGG pathways and biological processes of Gene Ontology) and manual expert analysis. The structures of 1976 phytomolecules from the 573 Indian medicinal plants traditionally used for the treatment of dementia and vascular diseases were used for computational estimation of their interactions with new predicted VaD-related drug targets by RevP approach based on PASS (Prediction of Activity Spectra for Substances) software. We found 147 known genes associated with vascular dementia based on the analysis of the databases with gene-disease associations. Six hundred novel targets were selected by NetP methods based on 147 gene associations. The analysis of the predicted interactions between 1976 phytomolecules and 600 NetP predicted targets leaded to the selection of 10 potential drug targets for the treatment of VaD. The translational value of these targets is discussed herewith. Twenty four drugs interacting with 10 selected targets were identified from DrugBank. These drugs have not been yet studied for the treatment of VaD and may be investigated in this field for their repositioning. The relation between inhibition of two selected targets (GSK-3, PTP1B) and the treatment of VaD was confirmed by the experimental studies on animals and reported separately in our recent publications.

Indexed as

Molecular Targeted TherapyDatabases, FactualDementia, VascularDrug Evaluation, PreclinicalPharmacologyUser-Computer Interface

Identifiers

PMID31937840
PMCPMC6959222
OpenAlexW3000356820

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.