Anusha Sunder¹*, Nikhilesh Anand², Samyuktha Sunkara² and Kirtikaa Chezhian²

¹Doctorate in Life Science/Human Nutrition, Lead Scientist and Nutrigenetic Expert, Xcode Life Sciences, Pvt. Ltd., Chennai, India
²R&D Intern, Xcode Life Sciences, Pvt. Ltd., Chennai, India

*Corresponding Author: Dr. Anusha Sunder, Doctorate in Life Science/Human Nutrition, Lead Scientist and Nutrigenetic Expert, Xcode Life Sciences, Pvt. Ltd. Chennai, India.

Received: May 06, 2026; Published: June 24, 2026

The increasing use of multiple medications in chronic disease management has raised concerns about drug–drug interactions (DDIs) and their impact on patient safety. This challenge is particularly significant in health conditions such as diabetes, which often associates with comorbidities like increased blood pressure, hyperlipidemia, and heart ailments. Such health conditions and their comorbidities require complex multi-drug regimens or polypharmacy. Drug–Drug Interaction (DDIs) occurs when one drug interferes with the pharmacodynamic or pharmacokinetic functions of the other drug, potentially leading to severe toxicity or treatment failure. Such interactions are a major contributor to hospital admissions, and the associated risks escalate as the number of medications increases. This research work, presents a clinical decision-support system which analyzes DDI of multi-drug combinations. It also calculates the overall DDI risk, identifying the risk-contributing drug and suggesting its safer therapeutic alternative. By converting DDI interaction analysis into clear and actionable guidance, this system helps in selecting safer drug combinations and supports better clinical decision-making.

Keywords: Drug–Drug Interaction (DDIs); Graph Neural Network (GNN)

References

1. Alhumaidi R M., et al. “Risk of Polypharmacy and Its Outcome in Terms of Drug Interaction in an Elderly Population: A Retrospective Cross-Sectional Study”. Journal of Clinical Medicine 12.12 (2023): 3960.
2. Mohamed M R., et al. “Association of polypharmacy and potential drug-drug interactions with adverse treatment outcomes in older adults with advanced cancer”. Cancer 129.7 (2023): 1096-1104.
3. Yan Zhao., et al. “Drug–drug interaction prediction: databases, web servers and computational models”. Briefings in Bioinformatics 25.1 (2024).

Citation

Citation: Anusha Sunder., et al. “A User-Interface to Trace, Map and Resolve Drug-Drug Interactions". Acta Scientific Medical Sciences 10.7 (2026): 20-23.

Copyright

Copyright: © 2026 Anusha Sunder., et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.