Background: The antineoplastic activity of Chelidonium majus has been reported, but its mechanism of action (MoA) is unsuspected. The emerging theory of systems pharmacology may be a useful approach to analyze the complicated MoA of this multi-ingredient traditional Chinese medicine (TCM).

Methods: We collected the ingredients and related compound-target interactions of C. majus from several databases. The bSDTNBI (balanced substructure-drug-target network-based inference) method was applied to predict each ingredient’s targets. Pathway enrichment analysis was subsequently conducted to illustrate the potential MoA, and prognostic genes were identified to predict the certain types of cancers that C. majus might be beneficial in treatment. Bioassays and literature survey were used to validate the in silico results.

Results: Systems pharmacology analysis demonstrated that C. majus exerted experimental or putative interactions with 18 cancer-associated pathways, and might specifically act on 13 types of cancers. Chelidonine, sanguinarine, chelerythrine, berberine, and coptisine, which are the predominant components of C. majus, may suppress the cancer genes by regulating cell cycle, inducing cell apoptosis and inhibiting proliferation.

Conclusions: The antineoplastic MoA of C. majus was investigated by systems pharmacology approach. C. majus exhibited promising pharmacological effect against cancer, and may consequently be useful material in further drug development. The alkaloids are the key components in C. majus that exhibit anticancer activity.

Background: Arachidonic acid (AA) metabolic network is activated in the most inflammatory related diseases, and small-molecular drugs targeting AA network are increasingly available. However, side effects of above mentioned drugs have always been the biggest obstacle. (+)-2-(1-hydroxyl-4-oxocyclohexyl) ethyl caffeate (HOEC), a natural product acted as an inhibitor of 5-lipoxygenase (5-LOX) and 15-LOX in vitro, exhibited weaker therapeutic effect in high dose than that in low dose to collagen induced arthritis (CIA) rats. In this study, we tried to elucidate the potential regulatory mechanism by using quantitative pharmacology.

Methods: First, we generated an experimental data set by monitoring the dynamics of AA metabolites’ concentration in A23187 stimulated and different doses of HOEC co-incubated RAW264.7. Then we constructed a dynamic model of A23187-stimulated AA metabolic model to evaluate how a model-based simulation of AA metabolic data assists to find the most suitable treatment dose by predicting the pharmacodynamics of HOEC.

Results: Compared to the experimental data, the model could simulate the inhibitory effect of HOEC on 5-LOX and 15-LOX, and reproduced the increase of the metabolic flux in the cyclooxygenase (COX) pathway. However, a concomitant, early-stage of stimulation-related decrease of prostaglandins (PGs) production in HOEC incubated RAW264.7 cells was not simulated in the model.

Conclusion: Using the model, we predict that higher dose of HOEC disrupts the flux balance in COX and LOX of the AA network, and increased COX flux can interfere the curative effects of LOX inhibitor on resolution of inflammation which is crucial for the efficient and safe drug design.

Background: Traditional Chinese medicine (TCM) treats diseases in a holistic manner, while TCM formulae are multi-component, multi-target agents at the molecular level. Thus there are many parallels between the key ideas of TCM pharmacology and network pharmacology. These years, TCM network pharmacology has developed as an interdisciplinary of TCM science and network pharmacology, which studies the mechanism of TCM at the molecular level and in the context of biological networks. It provides a new research paradigm that can use modern biomedical science to interpret the mechanism of TCM, which is promising to accelerate the modernization and internationalization of TCM.

Results: In this paper we introduce state-of-the-art free data sources, web servers and softwares that can be used in the TCM network pharmacology, including databases of TCM, drug targets and diseases, web servers for the prediction of drug targets, and tools for network and functional analysis.

Conclusions: This review could help experimental pharmacologists make better use of the existing data and methods in their study of TCM.

Background: Quantitative systems pharmacology (QSP) is an emerging discipline that integrates diverse data to quantitatively explore the interactions between drugs and multi-scale systems including small compounds, nucleic acids, proteins, pathways, cells, organs and disease processes.

Results: Various computational methods such as ADME/T evaluation, molecular modeling, logical modeling, network modeling, pathway analysis, multi-scale systems pharmacology platforms and virtual patient for QSP have been developed. We reviewed the major progresses and broad applications in medical guidance, drug discovery and exploration of pharmacodynamic material basis and mechanism of traditional Chinese medicine.

Conclusion: QSP has significant achievements in recent years and is a promising approach for quantitative evaluation of drug efficacy and systematic exploration of mechanisms of action of drugs.