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

Area of Science:

• Systems biology
• Pharmacology
• Computational biology

Background:

• Limited understanding and treatment options exist for reversing the fibrotic process in systemic sclerosis (SSc).
• Network-based analysis offers a novel approach to understanding drug effects in disease contexts.

Purpose of the Study:

• To develop and apply a network-based analysis to evaluate drug effects on systemic sclerosis (SSc).
• To identify potential therapeutic strategies, including drug repositioning, for SSc.

Main Methods:

• Utilized human interactome networks, drug target-disease gene proximity measures, and genome-wide gene expression data.
• Analyzed disease modules and drug effects within the SSc context.
• Evaluated the proximity of various drugs, including tyrosine kinase inhibitors (TyKIs), to SSc-associated genes and pathways.

Main Results:

• Drugs exhibited varied proximity to SSc genes and pathways; TyKIs targeted both inflammatory and fibrotic processes.
• Nintedanib, imatinib, dasatinib, and acetylcysteine demonstrated significant activity within the SSc disease module.
• TyKI therapy showed remarkable suppression of SSc pathways and alleviation of skin fibrosis in inflammatory subsets.

Conclusions:

• Network-based drug-disease proximity provides a new perspective on drug efficacy within the SSc disease module.
• This approach can inform drug combinations, drug repositioning, and clinical trial design for SSc.