A network-based trans-omics approach for predicting synergistic drug combinations

Area of Science:

• Computational biology
• Network pharmacology
• Drug discovery

Background:

• Combination therapy offers enhanced efficacy but discovering synergistic drug combinations is challenging.
• SyndrumNET is a novel computational method for predicting synergistic drug combinations.
• The method utilizes network propagation with trans-omics analyses.

Purpose of the Study:

• To develop and validate a computational method for predicting synergistic drug combinations.
• To identify synergistic drug combinations for complex diseases.
• To improve the efficiency of drug discovery for combination therapies.

Main Methods:

• SyndrumNET employs topological relationships, network-based proximity, and transcriptional correlation between diseases and drugs.
• The method was applied to analyze six diseases: asthma, diabetes, hypertension, colorectal cancer, acute myeloid leukemia (AML), and chronic myeloid leukemia (CML).

Main Results:

• SyndrumNET demonstrates superior accuracy compared to previous prediction methods.
• In vitro assays validated predictions for chronic myeloid leukemia (CML), with 14 out of 17 predicted drug pairs showing synergistic anticancer effects.
• Mode-of-action analysis revealed that the synergy of capsaicin and mitoxantrone involves complementary regulation of 12 pathways, including the Rap1 signaling pathway.

Conclusions:

• The proposed SyndrumNET method is effective for discovering synergistic drug combinations.
• This approach is expected to aid in the development of combination therapies for various complex diseases.
• SyndrumNET offers a promising tool for advancing precision medicine and drug development.