Evidence map›Paper›PMID 34589142›Full record

ArticleTherapeutic advances in musculoskeletal disease2021

Repurposing haloperidol for the treatment of rheumatoid arthritis: an integrative approach using data mining techniques.

Chihiro Nakagawa, Satoshi Yokoyama, Kouichi Hosomi, Mitsutaka Takada

Open access · goldAbstract read
In one paragraph

Article in Therapeutic advances in musculoskeletal disease, 2021. 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
1.4field-weighted citation impact, top 19% 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, 11 citations in OpenAlex.

  1. 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

4 authors at 1 institution in 1 country.

Chihiro NakagawaDivision of Drug Informatics, School of Pharmacy, Kindai University, Higashiosaka City, Japan.ORCID https://orcid.org/0000-0003-2301-1514
Satoshi YokoyamaDivision of Drug Informatics, School of Pharmacy, Kindai University, 3-4-1 Kowakae, Higashiosaka City 577-8502, Osaka, Japan.ORCID https://orcid.org/0000-0002-9488-6097
Kouichi HosomiDivision of Drug Informatics, School of Pharmacy, Kindai University, Higashiosaka City, Japan.
Mitsutaka TakadaDivision of Drug Informatics, School of Pharmacy, Kindai University, Higashiosaka City, Japan.
Kindai University · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionTreatment of rheumatoid arthritis (RA) has advanced with the introduction of biological disease-modifying antirheumatic drugs. However, more than 20% of patients with RA still have moderate or severe disease activity. Hence, novel antirheumatic drugs are required. Recently, drug repurposing, a process of identifying new indications for existing drugs, has received great attention. Furthermore, a few reports have shown that antipsychotics are capable of affecting several cytokines that are also modulated by existing antirheumatic drugs. Therefore, we investigated the association between antipsychotics and RA by data mining using real-world data and bioinformatics databases.

methodsDisproportionality and sequence symmetry analyses were employed to identify the associations between the investigational drugs and RA using the US Food and Drug Administration Adverse Event Reporting System (2004-2016) and JMDC administrative claims database (January 2005-April 2017; JMDC Inc., Tokyo, Japan), respectively. The reporting odds ratio (ROR) and information component (IC) were used in the disproportionality analysis to indicate a signal. The adjusted sequence ratio (SR) was used in the sequence symmetry analysis to indicate a signal. The bioinformatics analysis suite, BaseSpace Correlation Engine (Illumina, CA, USA) was employed to explore the molecular mechanisms associated with the potential candidates identified by the drug-repurposing approach.

resultsA potential inverse association between the antipsychotic haloperidol and RA, which exhibited significant inverse signals with ROR, IC, and adjusted SR, was found. Furthermore, the results suggested that haloperidol may exert antirheumatic effects by modulating various signaling pathways, including cytokine and chemokine signaling, major histocompatibility complex class-II antigen presentation, and Toll-like receptor cascade pathways.

conclusionOur drug-repurposing approach using data mining techniques identified haloperidol as a potential antirheumatic drug candidate.

Indexed as

bioinformatics databasedata miningdrug repurposinghaloperidolreal-world datarheumatoid arthritis

Identifiers

PMID34589142
PMCPMC8474350
OpenAlexW3201827710

What OpenQuestion holds

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Registered trials

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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.