Researchers develop novel computational approach for identifying synergistic drug combinations

 Researchers at the Icahn School of Medicine at Mount Sinai have developed a powerful computational tool, named iDOMO, to improve the prediction of drug synergy and accelerate the development of combination therapies for complex diseases. The study, published in Briefings in Bioinformatics on February 20, highlights iDOMO’s ability to identify synergistic drug combinations using gene expression data, outperforming existing methods.

Advancing Drug Discovery Through Computational Approaches

Combination therapies, which use multiple drugs to target different pathways involved in disease, are increasingly critical for treating complex conditions such as cancer. However, the process of experimentally identifying effective drug pairs is costly and time-consuming. iDOMO provides a computational solution by analyzing gene expression data—which measures the activity levels of genes in a given biological sample—and gene signatures, which are distinct patterns of gene activity associated with a specific condition, such as a disease state or drug response. By comparing gene signatures of drugs and diseases, iDOMO predicts the beneficial and detrimental effects of drug combinations.

"Our approach offers a more effective way to predict drug combinations that could serve as novel therapeutic options for treating human diseases," said senior author Bin Zhang, PhD, Willard T.C. Johnson Research Professor of Neurogenetics and Director of the Mount Sinai Center for Transformative Disease Modeling. "This could significantly expand treatment options for clinicians and improve outcomes for patients who do not respond to standard therapies."

Validation in Triple-Negative Breast Cancer

The study applied iDOMO to triple-negative breast cancer, a particularly aggressive and difficult-to-treat form of cancer. The model identified a promising drug combination—trifluridine and monobenzone—which was subsequently tested in in vitro experiments. The findings confirmed that this combination inhibited triple-negative breast cancer cell growth more effectively than either drug alone, validating iDOMO’s prediction.

"By leveraging computational approaches like iDOMO, we can prioritize the most promising drug combinations for further experimental validation, potentially accelerating the discovery of new treatments for a wide range of diseases," Dr. Zhang added.

Implications for Medicine and Research and Future Directions

iDOMO offers clinicians more therapeutic options, potentially leading to new and more effective treatments for patients resistant to conventional therapies. The approach provides a cost-efficient, scalable solution for identifying synergistic drug pairs, paving the way for broader applications across a variety of diseases.

Future work will focus on expanding iDOMO’s application to other diseases beyond triple-negative breast cancer, further refining its predictive capabilities, and integrating it into broader drug development pipelines.