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Article in Chinese | WPRIM | ID: wpr-878772


This paper aimed to investigate the active components and mechanism of Taohong Siwu Decoction in the treatment of primary dysmenorrhea(PD) based on network pharmacology and molecular docking technology. Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform(TCMSP) was used to search the chemical compositions and targets of six herbs in Taohong Siwu Decoction. The targets for PD treatment were selected through the databases of DrugBank, OMIM, TTD and CTD, and gene annotation of the targets was conducted with UniProt database. Cytoscape 3.7.2 was then used to construct the drug-compound-target network. The protein-protein interaction(PPI) network was constructed based on STRING, and the core targets of Taohong Siwu Decoction in the treatment of PD were selected according to the topological parameters. David database was used for GO enrichment analysis and KOBAS 3.0 was used for KEGG enrichment analysis. The molecular docking technology was used to connect the components with higher medium values in the network with core targets. The results showed that the network contained 36 compounds such as quercetin, kaempferol, luteolin, myricanone and ferulic acid, and 99 targets such as PTGS2, PTGS2, PGR and PPARG. Totally 102 GO terms were obtained by GO functional enrichment analysis(P<0.01), and 228 signal pathways were obtained by KEGG pathway enrichment(P<0.05), mainly involving inflammatory factors, hormone regulation, central analgesia, amino acid metabolism and spasmolysis. The results of molecular docking showed that the main active components can spontaneously bind to the targets. This study preliminarily revealed the mechanism of Taohong Siwu Decoction for treatment of primary dysmenorrheal through multi-components, multi-targets and multi-pathways, providing theoretical references for further researches on mechanism of Taohong Siwu Decoction.

Drugs, Chinese Herbal , Dysmenorrhea/drug therapy , Female , Humans , Molecular Docking Simulation , Technology
Article in Chinese | WPRIM | ID: wpr-828063


The aim of this paper was to study the prescription compatibility connotation in the treatment of primary dysmenorrhea(PD) and verify the mechanism as predicted by network pharmacology of Siwu Decoction(SWD). Mice PD model was constructed by using estradiol benzoate-oxytocin. PD mice were randomly divided into 8 groups, namely normal group, model group, positive group, complete formula group, Rehmanniae Radix Praeparata-free group, Paeoniae Radix Alba-free group, volatile oil-free group, Chuan-xiong Rhizoma and Angelicae Sinensis Radix-free group. Latent time, writhing times, inhibition rate, prostaglandin F_2_α(PGF_2_α) and prostaglandin E_2(PGE_2) levels in serum, endothelin-1, Ca~(2+), expression levels of prostaglandin synthase 2 G/H(PTGS2), estrogen receptor(ESR1), glucocorticoid receptor gene(NR3 C1) mRNA and protein expression levels in the uterus homogenate and pathological changes of uterine tissue were index to explore the prescription compatibility connotation and verify the mechanism of SWD in the treatment of PD. Compared with the extraction liquid of the whole recipe, the effect of Rehmanniae Radix Praeparata-free group and Paeoniae Radix Alba-free group with volatile oil were slightly lower, the effect of essential oil-free group was significantly lower, and the effect of Chuanxiong Rhizoma and Angelicae Sinensis Radix-free group was worse than that of the whole recipe. The relative expression levels of PTGS2 protein and mRNA were significantly reduced by the SWD. The relative expressions of protein and mRNA of ESR1, NR3 C1 were significantly increased. SWD treats PD by regulating the expression of key proteins PTGS2, ESR1 and NR3 C1.Its main medicinal herbs were Angelicae Sinensis Radix and Chuanxiong Rhizoma. Active components were mainly in volatile oil, but Paeo-niae Radix Alba and Rehmanniae Radix Praeparata also had some contributions.

Animals , Drugs, Chinese Herbal , Dysmenorrhea , Female , Humans , Mice , Paeonia , Plant Roots , Rhizome
Article in Chinese | WPRIM | ID: wpr-777464


This paper aimed to predict and explore the mechanism of multiple components, targets and pathways of Siwu decoction for treatment of primary dysmenorrhea, and to establish a network pharmacological model of "compound-target-pathway-disease". According to the active ingredients in Siwu Decoction, Swiss Target Prediction server was used to predict the active component targets based on the reverse pharmacodynamic group matching method, and the primary dysmenorrhea targets approved by FDA were selected by database including DrugBank, OMIM and TTD. According to the enrichment analysis of the target pathways by using KEGG, the Cytoscape software was used to construct the network of "compound-target-pathway-disease" of Siwu Decoction. Network analysis showed that there were 20 active components involved in 114 pathways. And 16 components, 16 target proteins and 24 pathways were related to primary dysmenorrhea. Siwu Decoction may play a role in treating primary dysmenorrhea by acting on protein targets and pathways related to hormone regulation, central analgesia, spasmolysis,inflammation and immunity. This study revealed the potential active compounds and possible mechanism of Siwu Decoction for treatment of primary dysmenorrhea, providing theoretical references for further systematic laboratory experiments on effective compounds and action mechanism of Siwu Decoction.

Drugs, Chinese Herbal , Therapeutic Uses , Dysmenorrhea , Drug Therapy , Female , Humans , Software
Article in Chinese | WPRIM | ID: wpr-850840


Objective: To optimize the water extraction process of Siwu Decoction by BP neural network combined with orthogonal experiment. Methods: The water amount, the extraction time, and the extraction times were taken as factors. Entropy weight method was used to calculate the comprehensive scores of the multi-indicators of eight active components of 5-hydroxymethylfurfural, chlorogenic acid, caffeic acid, paeoniflorin, ferulic acid, verbascoside, senkyunolide A, and ligustilide in R language environment. Using comprehensive score as an evaluation indicator, the BP neural network model was established by orthogonal experiment design, and the optimal water extraction process of Siwu Decoction was predicted through network training. Results: The optimized extraction process of Siwu Decoction was carried out by adding 8 times of water and extracting 3 times for 1 h. The relative error between the network predicted value and the actual measured value of the test sample was less than 1%. Conclusion: The established mathematical model can analyze and predict the water extraction process of Siwu Decoction. The obtained process is stable and feasible, and can effectively extract the active ingredients in Siwu Decoction.