Researchers at the Icahn School of Medicine at Mount Sinai have devised a powerful computational tool named iDOMO, aimed at enhancing the prediction of drug synergy and accelerating the creation of combination therapies for complex diseases. This study, published in Briefings in Bioinformatics on February 20 [10.1093/bib/bbaf054], underscores iDOMO’s proficiency in detecting synergistic drug combinations by utilizing gene expression data, outperforming current methods.
Advancing Drug Discovery Through Computational Approaches
Combination therapies, which employ multiple drugs targeting various pathways involved in disease processes, are increasingly crucial for treating complex conditions such as cancer. However, identifying effective drug pairs through experimental means is both costly and time-consuming. iDOMO addresses this challenge by analyzing gene expression data – a measure of the activity levels of genes within a biological sample – and gene signatures, which represent distinctive patterns of gene activity linked to specific states like disease or drug response.
By comparing these gene signatures for drugs and diseases, iDOMO predicts both beneficial and detrimental effects from combining different medications. Our approach provides an improved method for forecasting combinations that could lead to new therapeutic options for treating human diseases. This could substantially increase the treatment options available to clinicians and enhance outcomes for patients who do not respond well to standard therapies.
Bin Zhang, PhD
Senior Author
Willard T.C. Johnson Research Professor of Neurogenetics
Director of the Mount Sinai Center for Transformative Disease Modeling
“Validation in Triple-Negative Breast Cancer”
The study applied iDOMO to triple-negative breast cancer, an aggressive and challenging form of cancer. The model identified a promising drug combination – trifluridine and monobenzone – which was subsequently tested through in vitro experiments. These findings confirmed that this combination significantly inhibited the growth of triple-negative breast cancer cells more effectively than either medication alone, thus validating iDOMO’s predictions.
“By utilizing computational tools like iDOMO,” Dr. Zhang added, “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.”
Implications for Medicine and Research and Future Directions
iDOMO offers clinicians more therapeutic options, potentially leading to new and more effective treatments for patients who are resistant to conventional therapies. The approach provides a cost-efficient, scalable solution for identifying synergistic drug pairs, paving the way for broader applications across various diseases.
Future work will focus on expanding iDOMO’s application beyond triple-negative breast cancer into other illnesses. Additionally, ongoing efforts aim at refining its predictive capabilities and integrating it into larger drug development pipelines.
