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Network-based modeling of drug effects on disease module in systemic sclerosis

Authors
  • Kim, Ki-Jo1, 2
  • Moon, Su-Jin1
  • Park, Kyung-Su1
  • 1 The Catholic University of Korea, Seoul, Republic of Korea , Seoul (South Korea)
  • 2 St. Vincent’s Hospital, 93 Jungbu-daero, Paldal-gu, Suwon, Gyeonggi-do, 16247, Republic of Korea , Suwon (South Korea)
Type
Published Article
Journal
Scientific Reports
Publisher
Springer Nature
Publication Date
Aug 07, 2020
Volume
10
Issue
1
Identifiers
DOI: 10.1038/s41598-020-70280-y
Source
Springer Nature
License
Green

Abstract

The network-based proximity between drug targets and disease genes can provide novel insights regarding the repercussions, interplay, and repositioning of drugs in the context of disease. Current understanding and treatment for reversing of the fibrotic process is limited in systemic sclerosis (SSc). We have developed a network-based analysis for drug effects that takes into account the human interactome network, proximity measures between drug targets and disease-associated genes, genome-wide gene expression and disease modules that emerge through pertinent analysis. Currently used and potential drugs showed a wide variation in proximity to SSc-associated genes and distinctive proximity to the SSc-relevant pathways, depending on their class and targets. Tyrosine kinase inhibitors (TyKIs) approach disease gene through multiple pathways, including both inflammatory and fibrosing processes. The SSc disease module includes the emerging molecular targets and is in better accord with the current knowledge of the pathophysiology of the disease. In the disease-module network, the greatest perturbing activity was shown by nintedanib, followed by imatinib, dasatinib, and acetylcysteine. Suppression of the SSc-relevant pathways and alleviation of the skin fibrosis was remarkable in the inflammatory subsets of the SSc patients receiving TyKI therapy. Our results show that network-based drug-disease proximity offers a novel perspective into a drug’s therapeutic effect in the SSc disease module. This could be applied to drug combinations or drug repositioning, and be helpful guiding clinical trial design and subgroup analysis.

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