Researchers at the Icahn School of Medicine at Mount Sinai have developed a groundbreaking computational tool named iDOMO, designed to enhance the prediction of drug synergy and expedite the creation of combination therapies for complex illnesses. This study, published in Briefings in Bioinformatics on February 20 [10.1093/bib/bbaf054], emphasizes iDOMO’s capability to discover synergistic drug combinations by utilizing gene expression data, a feat that surpasses current methodologies.
In an era where combination therapies—employing multiple drugs targeting diverse pathways involved in disease—are becoming essential for tackling intricate conditions such as cancer—the identification of effective drug pairs remains both expensive and time-consuming. iDOMO introduces a computational strategy that addresses this challenge by analyzing gene expression data, which quantifies the activity levels of genes within a biological sample, alongside gene signatures—unique patterns of gene activity linked to specific states or responses to diseases.
iDOMO compares these drug and disease-specific gene signatures to predict both beneficial and detrimental effects of combined medications. This innovative approach promises more accurate predictions for new therapeutic combinations that could significantly enhance treatment options for patients not responding well to standard therapies, offering substantial potential improvements in clinical outcomes.
Dr. Bin Zhang, PhD, the senior author of this study, is also known as a Willard T.C. Johnson Research Professor of Neurogenetics and directs the Mount Sinai Center for Transformative Disease Modeling Validation in triple-negative breast cancer. The researchers applied iDOMO to triple-negative breast cancer, an aggressive form particularly resistant to conventional treatments.
The model identified trifluridine and monobenzone as a promising combination, which was then tested through in vitro experiments. These tests confirmed that the pair effectively inhibited triple-negative breast cancer cell growth more successfully than either drug used individually, validating iDOMO’s predictive accuracy.
“With tools like iDOMO at our disposal,” Dr. Zhang noted, “we can prioritize the most promising drug combinations for further testing, potentially speeding up the discovery of new treatments across various diseases.”
iDOMO’s potential impact on medicine and research is profound. It offers clinicians a wider array of therapeutic options, potentially leading to more effective treatments for patients resistant to conventional therapies. Furthermore, by providing a cost-efficient and scalable method for identifying synergistic drug pairs, iDOMO paves the way for broader applications in different types of diseases.
Future research will aim at expanding iDOMO’s scope beyond triple-negative breast cancer into other illnesses. Efforts will also focus on refining its predictive capabilities and incorporating it more deeply into mainstream drug development processes to accelerate therapeutic innovations further.
