Biochemical reaction network topology defines dose-dependent Drug–Drug interactions. (March 2023)

• Record Type: Journal Article
• Authors: Babaei, Mehrad; Evers, Tom M.J.; Shokri, Fereshteh; Altucci, Lucia; de Lange, Elizabeth C.M.; Mashaghi, Alireza
• Is Part Of: Computers in biology and medicine. Volume 155(2023)
• Publication Date: 2023-03
• Subjects: Combination therapy -- Drug -- Enzyme -- Reaction network -- Topology; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2023.106584
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: 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. Highlights: Designing optimal combination therapy needs consideration of network topology, drug type, dosage, and target arrangement. Antagonistic interactions and the dosage of drugs are negatively correlated Negative feedback loops show the highest synergistic interactions and, interestingly, require the highest drug dosages. The network topology analysis tool developed in this study can be adapted to analyze various native or synthetic reaction networks.