Quantitative modeling of dose-response and drug combination based on pathway network.

Jiangyong Gu,Na Li,Wei Xiao,Xinzhuang Zhang,Gu Yuan,Lirong Chen,Zhenzhong Wang,Xiaojie Xu,Fang Luo,Liang Cao,Yimin Ma

doi:10.1186/s13321-015-0066-6

Abstract

BackgroundQuantitative description of dose–response of a drug for complex systems is essential for treatment of diseases and drug discovery. Given the growth of large-scale biological data obtained by multi-level assays, computational modeling has become an important approach to understand the mechanism of drug action. However, due to complicated interactions between drugs and cellular targets, the prediction of drug efficacy is a challenge, especially for complex systems. And the biological systems can be regarded as networks, where nodes represent molecular entities (DNA, RNA, protein and small compound) and processes, edges represent the relationships between nodes. Thus we combine biological pathway-based network modeling and molecular docking to evaluate drug efficacy.ResultsNetwork efficiency (NE) and network flux (NF) are both global measures of the network connectivity. In this work, we used NE and NF to quantitatively evaluate the inhibitory effects of compounds against the lipopolysaccharide-induced production of prostaglandin E2. The edge values of the pathway network of this biological process were reset according to the Michaelis-Menten equation, which used the binding constant and drug concentration to determine the degree of inhibition of the target protein in the pathway. The combination of NE and NF was adopted to evaluate the inhibitory effects. The dose–response curve was sigmoid and the EC50 values of 5 compounds were in good agreement with experimental results (R2 = 0.93). Moreover, we found that 2 drugs produced maximal synergism when they were combined according to the ratio between each EC50.ConclusionsThis quantitative model has the ability to predict the dose–response relationships of single drug and drug combination in the context of the pathway network of biological process. These findings are valuable for the evaluation of drug efficacy and thus provide an effective approach for pathway network-based drug discovery.

Highlights

Quantitative description of dose–response of a drug for complex systems is essential for treatment of diseases and drug discovery
We demonstrate an advance in the quantitative modeling of dose–response and drug combination based on the pathway network of LPS-induced prostaglandin E2 (PGE2) production
Pathway network of LPS-induced PGE2 production The pathway network of LPS-induced PGE2 production (Fig. 1) comprised 30 nodes and 38 edges, where nodes represented proteins and small molecules involved in the process of LPS-induced PGE2 production, and edges meant that the node in front of the arrow was downstream in the pathway

Summary

Introduction

Quantitative description of dose–response of a drug for complex systems is essential for treatment of diseases and drug discovery. Due to complicated interactions between drugs and cellular targets, the prediction of drug efficacy is a challenge, especially for complex systems. We combine biological pathway-based network modeling and molecular docking to evaluate drug efficacy. How to predict the efficacy of a compound for a system (protein, biological process, cell, tissue, organ and the body) is critical for drug discovery. The prediction of drug efficacy is a challenge, especially for complex systems. The drug interaction would generally produce 1 of 3 different effects: synergism, antagonism and additive effect [13, 14]. Synergism means that drug combination could produce exaggerated effect, and antagonism could reduce the total effect. Synergism is especially important in clinical applications since it allows the use of smaller amounts of drugs and reduces the adverse effect or toxicity [14,15,16,17]

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Discussion

Conclusion