Dataset Information

Understanding the polypharmacological anticancer effects of Xiao Chai Hu Tang via a computational pharmacological model.

ABSTRACT: Xiao Chai Hu Tang (XCHT), a traditional herbal formula, is widely administered as a cancer treatment. However, the underlying molecular mechanisms of its anticancer effects are not fully understood. In the present study, a computational pharmacological model that combined chemical space mapping, molecular docking and network analysis was employed to predict which chemical compounds in XCHT are potential inhibitors of cancer-associated targets, and to establish a compound-target (C-T) network and compound-compound (C-C) association network. The identified compounds from XCHT demonstrated diversity in chemical space. Furthermore, they occupied regions of chemical space that were the same, or close to, those occupied by drug or drug-like compounds that are associated with cancer, according to the Therapeutic Targets Database. The analysis of the molecular docking and the C-T network demonstrated that the potential inhibitors possessed the properties of promiscuous drugs and combination therapies. The C-C network was classified into four clusters and the different clusters contained various multi-compound combinations that acted on different targets. The study indicated that XCHT has a polypharmacological role in treating cancer and the potential inhibitory components of XCHT require further investigation as potential therapeutic strategies for cancer patients.

SUBMITTER: Zheng CS

PROVIDER: S-EPMC4043560 | biostudies-literature | 2014 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Understanding the polypharmacological anticancer effects of Xiao Chai Hu Tang via a computational pharmacological model.

Zheng Chun-Song CS Wu Yin-Sheng YS Bao Hong-Juan HJ Xu Xiao-Jie XJ Chen Xing-Qiang XQ Ye Hong-Zhi HZ Wu Guang-Wen GW Xu Hui-Feng HF Li Xi-Hai XH Chen Jia-Shou JS Liu Xian-Xiang XX

Experimental and therapeutic medicine 20140402 6

Xiao Chai Hu Tang (XCHT), a traditional herbal formula, is widely administered as a cancer treatment. However, the underlying molecular mechanisms of its anticancer effects are not fully understood. In the present study, a computational pharmacological model that combined chemical space mapping, molecular docking and network analysis was employed to predict which chemical compounds in XCHT are potential inhibitors of cancer-associated targets, and to establish a compound-target (C-T) network and ...[more]

PMID: 24926384

Similar Datasets

Project description:Background and objectiveWe aimed to predict the targets and signal pathways of Xiao-Chai-Hu-Tang (XCHT) in the treatment of colorectal cancer (CRC) based on network pharmacology, just as well as to further analyze its anti-CRC material basis and mechanism of action.MethodsWe adopted Traditional Chinese Medicine Systems Pharmacology Database (TCMSP) and Traditional Chinese Medicine Integrated Database (TCMID) databases to screen the active ingredients and potential targets of XCHT. CRC-related targets were retrieved by analyzing published microarray data (accession number GSE110224) from the Gene Expression Omnibus (GEO) database. The common targets were used to construct the "herb-active ingredient-target" network using the Cytoscape 3.8.0 software. Next, we constructed and analyzed protein-to-protein interaction (PPI) using BisoGenet and CytoNCA plug-in in Cytoscape. We then performed Gene Ontology (GO) functional and the Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway enrichment analyses of target genes using the R package of clusterProfiler. Furthermore, we used the AutoDock Tools software to conduct molecular docking studies on the active ingredients and key targets to verify the network pharmacological analysis results.ResultsWe identified a total of 71 active XCHT ingredients and 20 potential anti-CRC targets. The network analysis revealed quercetin, stigmasterol, kaempferol, baicalein, and acacetin as potential key compounds, and PTGS2, NR3C2, CA2, and MMP1 as potential key targets. The active ingredients of XCHT interacted with most CRC disease targets. We showed that XCHT's therapeutic effect was attributed to its synergistic action (multi-compound, multi-target, and multi-pathway). Our GO enrichment analysis showed 46 GO entries, including 20 biological processes, 6 cellular components, and 20 molecular functions. We identified 11 KEGG signaling pathways, including the IL-17, TNF, Toll-like receptor, and NF-kappa B signaling pathways. Our results showed that XCHT could play a role in CRC treatment by regulating different signaling pathways. The molecular docking experiment confirmed the correlation between five core compounds (quercetin, stigmasterol, kaempferol, baicalein, and acacetin) just as well as PTGS2, NR3C2, CA2, and MMP1.ConclusionIn this study, we described the potential active ingredients, possible targets, and key biological pathways responsible for the efficacy of XCHT in CRC treatment, providing a theoretical basis for further research.

Project description:IntroductionFor situations in which effective and safe natural-derived products to treat hypertension are needed, recent studies suggest that an herbal medicine, Sihogayonggolmoryeo-tang (SYM), can improve both hypertension and concurrent mood symptoms. We aimed to evaluate the effectiveness and safety of SYM in treating hypertension.MethodsThirteen English, Korean, and Chinese databases were comprehensively searched from their inception to May 2020. Randomized controlled trials (RCTs) using SYM as a monotherapy or adjunctive therapy for hypertension were evaluated. The primary outcome was the systolic and diastolic blood pressure (BP). Descriptive analyses of the relevant data were conducted, and where appropriate data were available, a meta-analysis was performed, and the results were presented as a risk ratio or mean difference with 95% confidence intervals. The risk of bias was assessed using the Cochrane risk of bias tool, and the quality of evidence was evaluated using the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) approach.ResultsSeven RCTs with 711 participants were included. Compared with placebo, SYM significantly lowered systolic and diastolic BP and concurrent depression. SYM significantly lowered systolic and diastolic BP compared with active controls; however, subgroup analysis revealed no differences between SYM and antihypertensives. In addition, SYM significantly decreased the level of concurrent depression compared with antidepressants. There was no consistent difference in BP reduction between SYM combined with antihypertensives and antihypertensives alone. No serious adverse events were reported following SYM administration. Most of the included studies had an unclear risk of bias, and the quality of evidence was generally rated "low."ConclusionCurrent evidence suggests that SYM may have the potential to lower hypertension and concurrent depressive symptoms without serious adverse events. Additional high-quality, placebo-controlled RCTs should be conducted to confirm the efficacy of SYM.

Project description:Viral pneumonia is widespread, progresses rapidly, and has a high mortality rate. Developing safe and effective therapies to treat viral pneumonia can minimize risks to public health and alleviate pressures on the associated health systems. Xiao-Chai-Hu (XCH) decoction can be used in the treatment of viral pneumonia. However, the mechanisms of XCH on viral pneumonia remain unclear. In this study, poly (I:C) was used to establish a mouse model of viral pneumonia, and the therapeutic effects of XCH on viral pneumonia were assessed. Furthermore, we evaluated the effects of XCH on inflammatory response. Lastly, untargeted metabolomics were used to study the metabolic regulatory mechanisms of XCH on viral pneumonia model mice. Our results showed that XCH treatment decreased the wet/dry ratio in lung tissue, total protein concentration, and total cell count in bronchoalveolar lavage fluid (BALF). H&E staining indicated that XCH treatment alleviated the pathological changes in lung. Moreover, XCH treatment decreased the levels of proinflammatory cytokines (IL-1β, IL-6, and TNF-α) and lowered the ratio of CD86+/CD206+ macrophages and CD11b+LY6G+ neutrophils in BALF. XCH treatment also decreased the myeloperoxidase (MPO) and reduced the phosphorylations of PI3K, AKT, and NF-κB p65 in lung. Serum untargeted metabolomics analysis showed that XCH treatment could affect 18 metabolites in serum such as creatine, hydroxyproline, cortisone, hydrocortisone, corticosterone, hypotaurine, and taurine. These metabolites were associated with arginine and proline metabolism, steroid hormone biosynthesis, and taurine and hypotaurine metabolism processes. In conclusion, our study demonstrated that treatment with XCH can ameliorate viral pneumonia and reduce inflammatory response in viral pneumonia. The mechanism of action of XCH in the treatment of viral pneumonia may be associated with inhibiting the activation of PI3K/AKT/NF-κB signaling pathway in lung and regulating arginine and proline metabolism, steroid hormone biosynthesis, and taurine and hypotaurine metabolism in serum.

Project description:This systematic review and meta-analysis aimed to analyze the efficacy and safety of Sihogayonggolmoryeo-tang (SGYMT), a classical herbal medicine consisting of 11 herbs, for treatment of post-stroke depression (PSD). Thirteen databases were comprehensively searched from their inception dates until July 2019. Only randomized controlled trials (RCTs) using SGYMT as a monotherapy or adjunctive therapy for PSD patients were included. Where appropriate data were available, meta-analysis was performed and presented as risk ratio (RR) or mean difference (MD) with 95% confidence intervals (CIs). We assessed the quality of RCTs using the Cochrane risk of bias tool and the Jadad scale. The quality of evidence for each main outcome was evaluated using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach. Twenty-one RCTs with 1,644 participants were included. In the comparison between the SGYMT and antidepressants groups, the SGYMT group scored significantly lower on both the Hamilton Depression Scale (HAMD) (8 studies; MD -2.08, 95% CI -2.62 to -1.53, I2?=?34%) and the National Institutes of Health Stroke Scale (NIHSS) (2 studies; MD -0.84, 95% CI -1.40 to -0.29, I2?=?19%), and significantly higher on the Barthel index (3 studies; MD 4.30, 95% CI 2.04 to 6.57, I2?=?66%). Moreover, the SGYMT group was associated with significantly fewer adverse events (6 studies; RR 0.13, 95% CI 0.05 to 0.37, I2?=?0%) than the antidepressants group. In the subgroup analysis, SGYMT treatment consistently reduced HAMD scores within the first 8 weeks of treatment, but thereafter this difference between groups disappeared. Comparisons between SGYMT combined with antidepressants, and antidepressants alone, showed significantly lower scores in the combination group for both HAMD (7 studies; MD?=?-6.72, 95% CI?=?-11.42 to -2.01, I2?=?98%) and NIHSS scores (4 studies; MD -3.03, 95% CI -3.60 to -2.45, I2?=?87%). In the subgroup analysis, the reductions of HAMD scores in the SGYMT combined with antidepressants group were consistent within 4 weeks of treatment, but disappeared thereafter. The quality of RCTs was generally low and the quality of evidence evaluated by the GRADE approach was rated mostly "Very low" to "Moderate." The main causes of low quality ratings were the high risk of bias and imprecision of results. Current evidence suggests that SGYMT, used either as a monotherapy or an adjuvant therapy to antidepressants, might have potential benefits for the treatment of PSD, including short-term reduction of depressive symptoms, improvement of neurological symptoms, and few adverse events. However, since the methodological quality of the included studies was generally low and there were no large placebo trials to ensure reliability, it remains difficult to draw definitive conclusions on this topic. Further well-designed RCTs addressing these shortcomings are needed to confirm our results.

Dataset Information

Understanding the polypharmacological anticancer effects of Xiao Chai Hu Tang via a computational pharmacological model.

Publications

Understanding the polypharmacological anticancer effects of Xiao Chai Hu Tang via a computational pharmacological model.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets