RepurNet: A network medicine framework for systematic drug repurposing and disease vulnerability discovery

Area of Science:

• Systems biology
• Network medicine
• Computational drug discovery

Background:

• Drug repurposing is a key strategy for rapid drug candidate identification.
• COVID-19 pandemic highlighted the need for efficient therapeutic discovery pipelines.

Purpose of the Study:

• To develop a high-throughput computational framework for identifying repurposed drugs.
• To propose rational drug combinations for treating diseases like COVID-19 and cancer.
• To demonstrate a generalizable approach for precision medicine.

Main Methods:

• Performed a systems-scale in silico screen to identify compounds reversing disease gene signatures.
• Integrated protein-protein interaction networks to design disease modules.
• Analyzed network similarities between diseases to extend the approach.

Main Results:

• Identified compounds that reverse disease-specific gene regulatory signatures.
• Developed RepurNet, a framework for prioritizing drug candidates in early discovery.
• Demonstrated the applicability of the approach to various diseases, including cancer.

Conclusions:

• RepurNet offers a generalizable integrative systems biology and network medicine approach.
• The framework effectively unveils signaling perturbations and identifies optimized repurposed drug candidates.
• This strategy has broad implications for advancing precision medicine across diverse disease contexts.