Evidence map›Paper›PMID 37616260›Full record

ArticlePloS one2023

Discovery of novel RARα agonists using pharmacophore-based virtual screening, molecular docking, and molecular dynamics simulation studies.

Atefeh Ghorayshian, Mahshid Danesh, Tahereh Mostashari-Rad, Afshin Fassihi

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.8field-weighted citation impact, top 27% 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

4 citing papers in PubMed, 5 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 3 institutions in 1 country.

Atefeh GhorayshianDepartment of Cell and Molecular Biology, Faculty of Biological Science and Technology, University of Isfahan, Isfahan, Iran.ORCID 0000-0001-6376-2991
Mahshid DaneshFunctional Genomics & System Biology Group, Department of Bioinformatics, Biocenter, Am Hubland, University of Wuerzburg, Wuerzburg, Germany.
Tahereh Mostashari-RadDepartment of Artificial Intelligence, Smart University of Medical Sciences, Tehran, Iran.ORCID 0000-0003-0207-5826
Afshin FassihiDepartment of Medicinal Chemistry, School of Pharmacy and Pharmaceutical Sciences, Isfahan University of Medical Sciences, Isfahan, Iran.ORCID 0000-0002-3897-9723
Isfahan University of Medical Sciences · IRTehran University of Medical Sciences · IRUniversity of Isfahan · IR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Nuclear retinoic acid receptors (RARs) are ligand-dependent transcription factors involved in various biological processes, such as embryogenesis, cell proliferation, differentiation, reproduction, and apoptosis. These receptors are regulated by retinoids, i.e., retinoic acid (RA) and its analogs, as receptor agonists. RAR agonists are promising therapeutic agents for the treatment of serious dermatological disorders, including some malignant conditions. By inducing apoptosis, they are able to inhibit the proliferation of diverse cancer cell lines. Also, RAR agonists have recently been identified as therapeutic options for some neurodegenerative diseases. These features make retinoids very attractive molecules for medical purposes. Synthetic selective RAR agonists have several advantages over endogenous ones, but they suffer poor pharmacokinetic properties. These compounds are normally lipophilic acids with unfavorable drug-like features such as poor oral bioavailability. Recently, highly selective, potent, and less toxic RAR agonists with proper lipophilicity, thus, good oral bioavailability have been developed for some therapeutic applications. In the present study, ligand and structure-based virtual screening technique was exploited to introduce some novel RARα agonists. Pharmacokinetic assessment was also performed in silico to suggest those compounds which have optimized drug-like features. Finally, two compounds with the best in silico pharmacological features are proposed as lead molecules for future development of RARα agonists.

Indexed as

Molecular Dynamics SimulationPharmacophoreReceptors, Retinoic AcidLigandsMolecular Docking SimulationLigandsReceptors, Retinoic Acid

Identifiers

PMID37616260
PMCPMC10449137
OpenAlexW4386127668

What OpenQuestion holds

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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.