iDOMO: identification of drug combinations via multi-set operations for treating diseases

Area of Science:

• Computational biology
• Pharmacology
• Genomics

Background:

• Combination therapy is crucial for complex diseases targeting multiple pathways.
• Experimental drug combination screening is costly and time-consuming.
• Large-scale transcriptomic data enables computational prediction of drug synergy.

Purpose of the Study:

• To develop a computational approach, iDOMO, for predicting synergistic drug combinations.
• To evaluate iDOMO's performance against existing methods.
• To identify and validate synergistic drug combinations for triple-negative breast cancer (TNBC).

Main Methods:

• Developed iDOMO using multi-set operations on drug and disease gene signatures.
• Quantified drug synergy considering both beneficial and detrimental effects.
• Evaluated iDOMO on DREAM Challenge and large-scale drug combination datasets.

Main Results:

• iDOMO significantly outperformed Therapeutic Score and SynergySeq Orthogonality Score.
• Identified synergistic drug pairs for TNBC, notably trifluridine and monobenzone.
• In vitro experiments confirmed the synergistic effect of the top predicted TNBC combination.

Conclusions:

• iDOMO is an effective computational tool for in silico screening of synergistic drug combinations.
• The method aids in developing novel therapeutics for complex diseases.
• Validated synergistic drug combinations can accelerate drug discovery.