An integrative network-based approach for drug target indication expansion.

Area of Science:

• Computational Biology
• Drug Discovery
• Genomics

Background:

• Drug discovery traditionally begins with identifying a target-indication pair, a process with high investment and attrition rates.
• Many drug targets are pleiotropic, meaning they can be effective for multiple indications.
• Systematic methods are needed to evaluate additional indication opportunities for established drug targets.

Purpose of the Study:

• To introduce and validate a network-based approach for systematically screening and prioritizing disease indications for drug targets.
• To enable the identification of novel indications by leveraging existing scientific knowledge and computational methods.

Main Methods:

• Developed a network-based approach integrating disease genomics data and protein-protein interaction networks.
• Utilized network algorithms to generate and compare target and disease subnetworks for indication identification.

Main Results:

• Validated the method by successfully recovering FDA-approved indications for 15 drug targets, outperforming other state-of-the-art approaches.
• Predicted novel indications with literature support for prominent drug targets, including the IL12/IL23 combination.

Conclusions:

• The proposed methodology offers a global approach for indication expansion strategies in drug discovery.
• Enables rapid and systematic evaluation of single and combined drug targets for novel indications.
• Provides insights into the broader roles of genes and pathways for developing new therapeutic interventions.