| Literature DB >> 28190938 |
Xia Shen1, Zhenyu Zhao1, Hao Wang1, Zihu Guo2, Benxiang Hu1, Gang Zhang1.
Abstract
Objective. This study was aimed at elucidating the molecular mechanisms underlying the anti-inflammatory effect of the combined application of Bupleuri Radix and Scutellariae Radix and explored the potential therapeutic efficacy of these two drugs on inflammation-related diseases. Methods. After searching the databases, we collected the active ingredients of Bupleuri Radix and Scutellariae Radix and calculated their oral bioavailability (OB) and drug-likeness (DL) based on the absorption-distribution-metabolism-elimination (ADME) model. In addition, we predicted the drug targets of the selected active components based on weighted ensemble similarity (WES) and used them to construct a drug-target network. Gene ontology (GO) analysis and KEGG mapper tools were performed on these predicted target genes. Results. We obtained 30 compounds from Bupleuri Radix and Scutellariae Radix of good quality as indicated by ADME assays, which possess potential pharmacological activity. These 30 ingredients have a total of 121 potential target genes, which are involved in 24 biological processes related to inflammation. Conclusions. Combined application of Bupleuri Radix and Scutellariae Radix was found not only to directly inhibit the synthesis and release of inflammatory cytokines, but also to have potential therapeutic effects against inflammation-induced pain. In addition, a combination therapy of these two drugs exhibited systemic treatment efficacy and provided a theoretical basis for the development of drugs against inflammatory diseases.Entities:
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Year: 2017 PMID: 28190938 PMCID: PMC5278517 DOI: 10.1155/2017/3709874
Source DB: PubMed Journal: Mediators Inflamm ISSN: 0962-9351 Impact factor: 4.711
Figure 1The brief workflow of system pharmacological analyses in searching Bupleuri-Scutellariae Radix anti-inflammation mechanism.
Potential active constituents in Bupleuri Radix and Scutellariae Radix.
| ID | Ingredient name | OB | DL |
|---|---|---|---|
| CH08 | Stigmasterol | 43.82985 | 0.75664 |
| CH22 | Areapillin | 55.14803 | 0.41394 |
| CH26 | Octalupine | 47.82225 | 0.27864 |
| CH29 | Saikogenin G | 51.83940 | 0.63197 |
| CH39 | Sainfuran | 81.60749 | 0.23333 |
| CH40 | Thymonin | 43.16284 | 0.40714 |
| CH54 | Saikosaponin c_qt | 30.51828 | 0.63193 |
| CH57 |
| 42.97937 | 0.75693 |
| CH60 | Cubebin | 57.12813 | 0.63980 |
| HQ01 | Campesterol | 35.02838 | 0.71579 |
| HQ02 | Norwogonin | 40.44827 | 0.20723 |
| HQ03 | 5,2′-Dihydroxy-6,7,8-trimethoxyflavone | 30.07322 | 0.35463 |
| HQ04 | Coptisine | 30.40885 | 0.85647 |
| HQ05 | Supraene | 33.54594 | 0.42162 |
| HQ13 | Carthamidin | 40.28190 | 0.24188 |
| HQ14 | Dihydrobaicalin | 41.53938 | 0.20722 |
| HQ15 | Salvigenin | 53.87782 | 0.33279 |
| HQ16 | Ganhuangenin | 93.43294 | 0.37375 |
| HQ19 | 5,7,2′,6′-Tetrahydroxyflavone | 35.42827 | 0.24383 |
| HQ24 | 5,7,4′-Trihydroxy-8-methoxyflavone | 34.76242 | 0.26666 |
| HQ29 | 11,13-Eicosadienoic acid | 39.27534 | 0.22891 |
| HQ31 | 5,7,4′-Trihydroxy-6-methoxyflavanone | 37.00241 | 0.26833 |
| HQ32 | 5,2′-Dihydroxy-7,8,6′-trimethoxyflavone | 38.39282 | 0.36629 |
| HQ36 | Chrysin | 48.03082 | 0.18140 |
| HQ39 | Dihydrooroxylin A | 46.37778 | 0.23057 |
| HQ43 | Oroxylin A | 45.40775 | 0.23231 |
| HQ44 | Rivularin | 43.74214 | 0.36628 |
| HQ46 | Skullcapflavone I | 51.70113 | 0.29148 |
| HQ47 | Skullcapflavone II | 43.90662 | 0.43793 |
| HQ48 | Tenaxin I | 32.77480 | 0.35463 |
Figure 2Bupleuri Radix-Scutellariae Radix active ingredients and potential drug targets. Blue: target gene ID; green: active ingredient in Bupleuri Radix; red: active ingredient in Scutellariae Radix. Node size indicates the degree in the network—bigger nodes represent more target genes and smaller nodes indicated fewer targets.
Figure 3The gene count of inflammation-related gene ontology (GO) term classification.
Figure 4KEGG pathways of inflammation-related bioactive targets. Blue block: the target can be affected by the ingredients from two herbal medicines; red block: cancer-related targets in the KEGG database; white block: key target in the related pathway, but having no binding effect with our herbal ingredients; small circle: chemical metabolites in pathway.
Figure 5Target-disease-MeSH network. Blue node: the potential bioactive targets. Red node: potentially relevant diseases from CTD, TTD, and PharmGKB databases. Green node: MeSH classifications of potentially relevant disease.