AI Article Synopsis
- - The study introduces SyndrumNET, a new computational method designed to predict synergistic drug combinations by analyzing the interactions between drugs and diseases through network propagation and trans-omics analyses.
- - After applying SyndrumNET to six diseases, it showed higher accuracy than previous methods, with 14 out of 17 predicted drug pairs demonstrating effective synergy in cancer treatment during validation, particularly in chronic myeloid leukemia (CML).
- - The findings suggest that SyndrumNET could significantly aid in identifying effective drug combinations for managing complex diseases, highlighting the importance of pathway regulation in drug synergy.
Article Abstract
Background: Combination therapy can offer greater efficacy on medical treatments. However, the discovery of synergistic drug combinations is challenging. We propose a novel computational method, SyndrumNET, to predict synergistic drug combinations by network propagation with trans-omics analyses.
Methods: The prediction is based on the topological relationship, network-based proximity, and transcriptional correlation between diseases and drugs. SyndrumNET was applied to analyzing six diseases including asthma, diabetes, hypertension, colorectal cancer, acute myeloid leukemia (AML), and chronic myeloid leukemia (CML).
Results: Here we show that SyndrumNET outperforms the previous methods in terms of high accuracy. We perform in vitro cell survival assays to validate our prediction for CML. Of the top 17 predicted drug pairs, 14 drug pairs successfully exhibits synergistic anticancer effects. Our mode-of-action analysis also reveals that the drug synergy of the top predicted combination of capsaicin and mitoxantrone is due to the complementary regulation of 12 pathways, including the Rap1 signaling pathway.
Conclusions: The proposed method is expected to be useful for discovering synergistic drug combinations for various complex diseases.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11286857 | PMC |
| http://dx.doi.org/10.1038/s43856-024-00571-2 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!