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ORIGINAL RESEARCH article

Front. Pharmacol.
Sec. Experimental Pharmacology and Drug Discovery
Volume 15 - 2024 | doi: 10.3389/fphar.2024.1442752

Integrating Text-Mining and Network Models for Successful Target Identification: In vitro validation in MASH-induced liver fibrosis

Provisionally accepted
Jennifer Venhorst Jennifer Venhorst 1*Roeland Hanemaaijer Roeland Hanemaaijer 2Remon Dulos Remon Dulos 3Karin Toet Karin Toet 2Joline Attema Joline Attema 2Christa De Ruiter Christa De Ruiter 2Gino Kalkman Gino Kalkman 1Tanja Rouhani-Rankhoui Tanja Rouhani-Rankhoui 1Lars Verschuren Lars Verschuren 3
  • 1 Biomedical and Digital Health, Netherlands Organisation for Applied Scientific Research, Utrecht, Netherlands
  • 2 Metabolic Health Research, Netherlands Organisation for Applied Scientific Research (TNO), Leiden, Netherlands
  • 3 Microbiology and System Biology, Netherlands Organisation for Applied Scientific Research, Zeist, Netherlands

The final, formatted version of the article will be published soon.

    An in silico target discovery pipeline was developed that includes a directional and weighted molecular disease network for metabolic dysfunction-associated steatohepatitis (MASH)-induced liver fibrosis. The applied methodology integrates text-mining, network biology, and artificial intelligence/machine learning approaches with clinical transcriptome data for optimal translational power. On the mechanistic level, critical players in the progression of the disease were identified from the constructed disease network using in silico knockouts. Top-ranked genes were subjected to a target efficacy analysis; the top 5 candidate targets were validated in vitro. For three targets, including EP300, their role in liver fibrosis was confirmed. EP300 gene-silencing reduced collagen by 37%; compound intervention studies performed in human primary hepatic stellate cells and the hepatic stellate cell line LX-2 resulted in significant inhibition of collagen of 81% compared to the TGF-stimulated control (1M inobrodib in LX-2 cells). The validated in silico pipeline presents a unique approach for the identification of human disease mechanism-relevant drug targets. The directionality of the network ensures adherence to physiologically relevant signaling cascades, whereas inclusion of clinical data boosts translational power and ensures that the most relevant disease pathways are identified. In silico knockouts provide molecular insights crucial for successful target identification.

    Keywords: Disease network, target discovery, Target validation, MASH & liver fibrosis, textmining, Drug Discovery, system biology. (Min.5-Max. 8)

    Received: 02 Jun 2024; Accepted: 28 Aug 2024.

    Copyright: © 2024 Venhorst, Hanemaaijer, Dulos, Toet, Attema, De Ruiter, Kalkman, Rouhani-Rankhoui and Verschuren. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Jennifer Venhorst, Biomedical and Digital Health, Netherlands Organisation for Applied Scientific Research, Utrecht, Netherlands

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.