iDOMO: Revolutionizing Drug Combination Predictions for Complex Diseases

Researchers at the Icahn School of Medicine at Mount Sinai have unveiled a revolutionary computational tool called **iDOMO**, designed to enhance the prediction of drug synergy, thus facilitating the development of combination therapies for complex diseases, particularly **cancer**. Published in *Briefings in Bioinformatics*, iDOMO leverages gene expression data to identify promising drug combinations, outperforming existing methodologies. Combination therapies, crucial for diseases like cancer, involve using multiple drugs to target various disease pathways. Traditional methods of discovering effective drug pairs are often time-consuming and costly. **iDOMO** offers an ingenious solution by analyzing gene expression data, which denotes the activity levels of genes in biological samples, and **gene signatures**, identifying distinct gene activity patterns related to specific conditions like disease states or drug responses. This comparison allows iDOMO to predict both beneficial and harmful effects of drug combinations. The tool's efficacy was demonstrated in ***triple-negative breast cancer***, where it successfully identified a drug combination, trifluridine and monobenzone, which was validated through *in vitro* experiments. These experiments resulted in significant inhibition of cancer cell growth, affirming iDOMO's predictive power. Dr. Bin Zhang emphasized that iDOMO could potentially revolutionize treatment options, especially for patients unresponsive to conventional therapies. iDOMO is not only cost-effective but also scalable, offering broader applications across various diseases. Future research will aim to expand iDOMO's applicability beyond breast cancer and refine its predictive capabilities further, possibly integrating it into wider drug development processes.