Evidence map›Paper›PMID 28691737›Full record

ArticleBritish journal of pharmacology2017

Evaluating the antifibrotic potency of galunisertib in a human ex vivo model of liver fibrosis.

Theerut Luangmonkong, Su Suriguga, Emilia Bigaeva, Miriam Boersema, Dorenda Oosterhuis, Koert P de Jong, Detlef Schuppan, Henricus A M Mutsaers, Peter Olinga

Open access · bronzeAbstract read
In one paragraph

Article in British journal of pharmacology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 papers.

0numbers the graph read from it
0cells of the map it votes in
54citing papers in PubMed
4.5field-weighted citation impact, top 4% 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

54 citing papers in PubMed, 87 citations in OpenAlex.

  1. Article
  2. Precision-Cut Bladder Slices: A Novel Model for the Study of Bladder Fibrosis and Potential Anti-Fibrotic Agents.International journal of urology : official journal of the Japanese Urological Association · 2026
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  13. TGF-β signaling: critical nexus of fibrogenesis and cancer.Journal of translational medicine · 2024
    Review
  14. Logic-based mechanistic machine learning on high-content images reveals how drugs differentially regulate cardiac fibroblasts.Proceedings of the National Academy of Sciences of the United States of America · 2024
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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

9 authors at 3 institutions in 4 countries.

Theerut LuangmonkongDepartment of Pharmaceutical Technology and Biopharmacy, University of Groningen, The Netherlands.ORCID http://orcid.org/0000-0003-3616-3787
Su SurigugaDepartment of Pharmaceutical Technology and Biopharmacy, University of Groningen, The Netherlands.
Emilia BigaevaDepartment of Pharmaceutical Technology and Biopharmacy, University of Groningen, The Netherlands.
Miriam BoersemaDepartment of Pharmaceutical Technology and Biopharmacy, University of Groningen, The Netherlands.
Dorenda OosterhuisDepartment of Pharmaceutical Technology and Biopharmacy, University of Groningen, The Netherlands.
Koert P de JongDepartment of Hepato-Pancreato-Biliary Surgery and Liver Transplantation, University Medical Center Groningen, University of Groningen, The Netherlands.
Detlef SchuppanInstitute of Translational Immunology and Research Center for Immunotherapy, University of Mainz Medical Center, Mainz, Germany.
Henricus A M MutsaersDepartment of Pharmaceutical Technology and Biopharmacy, University of Groningen, The Netherlands.
Peter OlingaDepartment of Pharmaceutical Technology and Biopharmacy, University of Groningen, The Netherlands.ORCID http://orcid.org/0000-0003-4855-8452
University of Groningen · NLBeth Israel Deaconess Medical Center · USUniversity Medical Center Groningen · NL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

background and purposeLiver fibrosis is a major cause of liver-related mortality and, so far, no effective antifibrotic drug is available. Galunisertib, a TGF-β receptor type I kinase inhibitor, is a potential candidate for the treatment of liver fibrosis. Here, we evaluated the potency of galunisertib in a human ex vivo model of liver fibrosis. EXPERIMENTAL APPROACH: Antifibrotic potency and associated mechanisms were studied ex vivo, using both healthy and cirrhotic human precision-cut liver slices. Fibrosis-related parameters, both transcriptional and translational level, were assessed after treatment with galunisertib. KEY

resultsGalunisertib showed a prominent antifibrotic potency. Phosphorylation of SMAD2 was inhibited, while that of SMAD1 remained unchanged. In healthy and cirrhotic human livers, spontaneous transcription of numerous genes encoding collagens, including collagen type I, α 1, collagen maturation, non-collageneous extracellular matrix (ECM) components, ECM remodelling and selected ECM receptors was significantly decreased. The reduction of fibrosis-related transcription was paralleled by a significant inhibition of procollagen I C-peptide released by both healthy and cirrhotic human liver slices. Moreover, galunisertib showed similar antifibrotic potency in human and rat lives. CONCLUSIONS AND IMPLICATIONS: Galunisertib is a drug that deserves to be further investigated for the treatment of liver fibrosis. Inhibition of SMAD2 phosphorylation is probably a central mechanism of action. In addition, blocking the production and maturation of collagens and promoting their degradation are related to the antifibrotic action of galunisertib.

Indexed as

AnimalsDose-Response Relationship, DrugHumansLiver CirrhosisMalePhosphorylationPyrazolesQuinolinesRatsRats, WistarSmad2 ProteinStructure-Activity RelationshipLY-2157299PyrazolesQuinolinesSmad2 ProteinSMAD2 protein, human

Identifiers

PMID28691737
PMCPMC5573419
OpenAlexW2735395814

What OpenQuestion holds

Textmetadata
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.