A network-driven computational framework for identifying FDA-approved drug repurposing across heterogeneous brain...

Area of Science:

• Oncology
• Computational Biology
• Pharmacology

Background:

• Brain cancers exhibit significant heterogeneity, complicating treatment strategies due to dysregulated signaling pathways.
• Key pathway components like EGFR, BRAF, TP53, and VEGFR2 are crucial targets for rational drug selection and repurposing.
• Existing protocols for drug repurposing often utilize Food and Drug Administration (FDA)-approved drugs.

Purpose of the Study:

• To develop a network-driven computational framework for brain cancer drug repurposing.
• To identify novel therapeutic strategies by analyzing molecular signatures of FDA-approved drugs.
• To address the complexity of brain cancer treatment through rational drug selection.

Main Methods:

• Designed a protocol utilizing identified pathway components to define molecular signatures.
• Developed two applications: 'in-mac' for molecular profile generation and 'ReBrain' for network-based drug repurposing analytics.
• Validated the framework using machine learning models and analyzed 2,809 FDA-approved drug molecules.

Main Results:

• The 'in-mac' and 'ReBrain' platform successfully profiled 2,809 FDA-approved drugs, generating fifteen-dimensional activity signatures.
• A network was constructed using molecular profiles, enabling in silico analysis and refinement.
• The profile-network-based method achieved 70%-95% accuracy in drug repurposing for brain-related diseases.

Conclusions:

• Identified three priority repurposed drugs for brain cancer: mefloquine, clofibric acid, and armillarisin A.
• The study suggests potential synergistic drug combinations for various brain tumors.
• The developed applications ('in-mac' and 'ReBrain') are publicly accessible for broader research use.