Drug repositioning based on triangularly balanced structure for tissue-specific diseases in incomplete interactome.

Area of Science:

• Computational Biology
• Pharmacology
• Bioinformatics

Background:

• Drug repurposing is a key strategy for treating diseases, but traditional methods often overlook tissue-specific disease characteristics.
• Existing knowledge of disease genes, drug targets, and protein-protein interaction (PPI) networks is incomplete, posing challenges for accurate drug-disease association prediction.

Purpose of the Study:

• To develop an effective computational approach for predicting new drug indications by integrating tissue specificities and network properties.
• To address the limitations of traditional methods in drug repurposing for complex, tissue-specific diseases.

Main Methods:

• The TTMD (Tissue specificity, Triangle balance theory, and Module Distance) method was developed, combining three drug similarity networks.
• Tissue-specific PPI networks were utilized to calculate drug-disease similarities using module distance and triangularly balanced theory.
• The method was validated using breast cancer and hepatocellular carcinoma (HCC) as case studies.

Main Results:

• TTMD achieved high accuracy in predicting known drug-disease associations, with 96.9% for breast cancer and 90.3% for HCC within the top 5% of predictions.
• Further analyses including clinical verification, literature mining, and KEGG pathway enrichment supported the validity of newly predicted associations.

Conclusions:

• TTMD is a powerful and effective approach for identifying novel drug indications for tissue-specific diseases.
• The method holds significant potential for advancing the treatment of complex diseases through drug repurposing.