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Biochemical reaction network topology defines dose-dependent Drug–Drug interactions
Drug combination therapy is a promising strategy to enhance the desired therapeutic effect, while reducing side effects. High-throughput pairwise drug combination screening is a commonly used method for discovering favorable drug interactions, but is time-consuming and costly. Here, we investigate t...
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Published in: | Computers in biology and medicine 2023-03, Vol.155, p.106584-106584, Article 106584 |
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Main Authors: | , , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | Drug combination therapy is a promising strategy to enhance the desired therapeutic effect, while reducing side effects. High-throughput pairwise drug combination screening is a commonly used method for discovering favorable drug interactions, but is time-consuming and costly. Here, we investigate the use of reaction network topology-guided design of combination therapy as a predictive in silico drug-drug interaction screening approach. We focused on three-node enzymatic networks, with general Michaelis–Menten kinetics. The results revealed that drug-drug interactions critically depend on the choice of target arrangement in a given topology, the nature of the drug, and the desired level of change in the network output. The results showed a negative correlation between antagonistic interactions and the dosage of drugs. Overall, the negative feedback loops showed the highest synergistic interactions (the lowest average combination index) and, intriguingly, required the highest drug doses compared to other topologies under the same condition.
•Designing optimal combination therapy needs consideration of network topology, drug type, dosage, and target arrangement.•Antagonistic interactions andthedosage of drugsare negatively correlated•Negative feedback loops show the highest synergistic interactions and, interestingly, require the highest drug dosages.•Thenetwork topology analysis tool developed in this study can be adapted to analyze various native or synthetic reaction networks. |
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ISSN: | 0010-4825 1879-0534 |
DOI: | 10.1016/j.compbiomed.2023.106584 |