Computational approaches for drug repositioning and combination therapy design

Area of Science:

• Computational biology
• Bioinformatics
• Genomics

Background:

• High-throughput biological data for diseases like glioblastoma are abundant but challenging to integrate manually for therapy design.
• Glioblastoma remains a disease with limited effective treatment options, necessitating novel therapeutic strategies.

Purpose of the Study:

• To present a novel computational workflow for designing glioblastoma therapy using pathway analysis and gene expression data.
• To identify potentially effective chemical compounds for glioblastoma treatment.
• To explore optimal drug combinations for enhanced glioblastoma therapy.

Main Methods:

• Utilized Ariadne Genomics Pathway Studio software for a computational workflow.
• Integrated public microarray data for glioblastoma with ResNet and ChemEffect databases.
• Employed Sub-Network Enrichment Analysis (SNEA) to analyze differential gene expression in glioblastoma patients.

Main Results:

• Constructed a glioblastoma signaling pathway and identified compounds affecting it.
• SNEA pinpointed angiogenesis-related protein Cyr61 as a key regulator in glioblastoma.
• Identified Fulvestrant as a potential inhibitor of the glioblastoma pathway and Cyr61.

Conclusions:

• The developed computational workflow can identify potential therapeutic agents for glioblastoma.
• Fulvestrant is proposed as a promising candidate for glioblastoma treatment.
• Optimal drug combinations involving Fulvestrant may enhance therapeutic efficacy for glioblastoma.