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Data-mining of potential antitubercular activities from molecular ingredients of traditional Chinese medicines.

ABSTRACT: Background. Traditional Chinese medicine encompasses a well established alternate system of medicine based on a broad range of herbal formulations and is practiced extensively in the region for the treatment of a wide variety of diseases. In recent years, several reports describe in depth studies of the molecular ingredients of traditional Chinese medicines on the biological activities including anti-bacterial activities. The availability of a well-curated dataset of molecular ingredients of traditional Chinese medicines and accurate in-silico cheminformatics models for data mining for antitubercular agents and computational filters to prioritize molecules has prompted us to search for potential hits from these datasets. Results. We used a consensus approach to predict molecules with potential antitubercular activities from a large dataset of molecular ingredients of traditional Chinese medicines available in the public domain. We further prioritized 160 molecules based on five computational filters (SMARTSfilter) so as to avoid potentially undesirable molecules. We further examined the molecules for permeability across Mycobacterial cell wall and for potential activities against non-replicating and drug tolerant Mycobacteria. Additional in-depth literature surveys for the reported antitubercular activities of the molecular ingredients and their sources were considered for drawing support to prioritization. Conclusions. Our analysis suggests that datasets of molecular ingredients of traditional Chinese medicines offer a new opportunity to mine for potential biological activities. In this report, we suggest a proof-of-concept methodology to prioritize molecules for further experimental assays using a variety of computational tools. We also additionally suggest that a subset of prioritized molecules could be used for evaluation for tuberculosis due to their additional effect against non-replicating tuberculosis as well as the additional hepato-protection offered by the source of these ingredients.

SUBMITTER: Jamal S

PROVIDER: S-EPMC4106188 | biostudies-other | 2014

REPOSITORIES: biostudies-other

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Data-mining of potential antitubercular activities from molecular ingredients of traditional Chinese medicines.

Jamal Salma S Scaria Vinod V

PeerJ 20140717

Background. Traditional Chinese medicine encompasses a well established alternate system of medicine based on a broad range of herbal formulations and is practiced extensively in the region for the treatment of a wide variety of diseases. In recent years, several reports describe in depth studies of the molecular ingredients of traditional Chinese medicines on the biological activities including anti-bacterial activities. The availability of a well-curated dataset of molecular ingredients of tra ...[more]

PMID: 25081126

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Project description:Although traditional Chinese medicine is effective and safe for the treatment of angiogenesis, the in vivo intervention mechanism is diverse, complex, and largely unknown. Therefore, we aimed to explore the active ingredients of traditional Chinese medicine and their mechanisms of action against angiogenesis. Data on angiogenesis-related targets were collected from GeneCards, Therapeutic Target Database, Online Mendelian Inheritance in Man, DrugBank, and DisGeNET. These were matched to related molecular compounds and ingredients in the traditional Chinese medicine system pharmacology platform. The data were integrated and based on the condition of degree > 1, and relevant literature, target-compound, compound-medicine, and target-compound-medicine networks were constructed using Cytoscape. Molecular docking was used to predict the predominant binding combination of core targets and components. We obtained 79 targets for angiogenesis; 41 targets were matched to 3839 compounds, of which 110 compounds were selected owing to their high correlation with angiogenesis. Fifty-five combinations in the network were obtained by molecular docking, among which PTGS2-astragalin (-9.18 kcal/mol), KDR-astragalin (-7.94 kcal/mol), PTGS2-quercetin (-7.41 kcal/mol), and PTGS2-myricetin (-7.21 kcal/mol) were top. These results indicated that the selected potential core compounds have good binding activity with the core targets. Eighty new combinations were obtained from the network, and the top combinations based on affinity were KDR-beta-carotene (-10.13 kcal/mol), MMP9-beta-sitosterol (-8.04 kcal/mol), MMP9-astragalin (-7.82 kcal/mol), and MMP9-diosgenin (-7.51 kcal/mol). The core targets included PTGS2, KDR, VEGFA, and MMP9. The essential components identified were astragalin, kaempferol, myricetin, quercetin, and β-sitosterol. The crucial Chinese medicines identified included Polygoni Cuspidati Rhizoma et Radix, Morus alba Root Bark, and Forsythiae Fructus. By systematically analysing the ingredients of traditional Chinese medicine and their targets, it is possible to determine their potential mechanisms of action against pathological angiogenesis. Our study provides a basis for further research and the development of new therapeutics for angiogenesis.

Project description:Ethnopharmacological relevanceInhibition of soluble epoxide hydrolase (sEH) has been extensively reported to be anti-inflammatory in multiple animal models. Some anti-inflammatory traditional Chinese medicines (TCMs) and a few natural compounds were also found to be inhibitory to sEH in vitro.Aim of the studyTo determine whether the active intergradient (AI) against sEH of anti-inflammatory TCMs in vitro is anti-inflammatory in vivo and the sEH inhibitory action of the AI contributes to its anti-inflammatory effect in vivo.Materials and methodsIn vitro inhibition assay of the sEH was conducted for the methanol and ethanol extracts of 27 anti-inflammatory TCMs. Two potent extracts were subject to further separation guided by bioassay to afford promising AI against sEH in vitro [Fr.5 of the crude ethanol extract of Rhizoma coptidis (FFCERC)]. Finally, the in vivo anti-inflammatory effect and sEH inhibitory potency of FFCERC was evaluated in a lipopolysacchride (LPS)-challenged murine model of acute systemic inflammation. The inflammatory status was characterized by the inflammatory cytokines TNF-α and interleukin-6 (IL-6) and sEH inhibitory function was evaluated by the plasma levels of epoxyeicosantrienoic acids (EETs) and dihydroxyeicosatrienoic acids (DHETs), which are the sEH mediated substrates and products, respectively.ResultsAt the concentration of 25µg/mL, the crude ethanol extracts of 6 TCMs including Herba Asari, Radix Polygalae, Fructus Amomi, Radix Astragali, Radix Scutellariae, and Rhizoma Coptidis were potent against sEH. The crude extracts of Herba Asari and Rhizoma Coptidis were selected for further separation to afford FFCERC as the most promising AI for in vivo evaluation. Oral administration of FFCERC attenuated the significant increase in TNF-α and IL-6 caused by LPS challenge in a dose-dependent manner. In parallel, oral administration of FFCERC shifted the changes in plasma levels of EETs and DHETs caused by LPS-challenge like a synthetic sEH inhibitor.ConclusionsA sEH inhibitory AI from Rhizoma Coptidis is anti-inflammatory and the inhibition of sEH contributes to this biological effect, indicating that sEH may be at least one of multiple therapeutic targets for relevant TCMs.

Project description:In recent years, liver injury induced by Traditional Chinese Medicines (TCMs) has gained increasing attention worldwide. Assessing the hepatotoxicity of compounds in TCMs is essential and inevitable for both doctors and regulatory agencies. However, there has been no effective method to screen the hepatotoxic ingredients in TCMs available until now. In the present study, we initially built a large scale dataset of drug-induced liver injuries (DILIs). Then, 13 types of molecular fingerprints/descriptors and eight machine learning algorithms were utilized to develop single classifiers for DILI, which resulted in 5416 single classifiers. Next, the NaiveBayes algorithm was adopted to integrate the best single classifier of each machine learning algorithm, by which we attempted to build a combined classifier. The accuracy, sensitivity, specificity, and area under the curve of the combined classifier were 72.798, 0.732, 0.724, and 0.793, respectively. Compared to several prior studies, the combined classifier provided better performance both in cross validation and external validation. In our prior study, we developed a herb-hepatotoxic ingredient network and a herb-induced liver injury (HILI) dataset based on pre-clinical evidence published in the scientific literature. Herein, by combining that and the combined classifier developed in this work, we proposed the first instance of a computational toxicology to screen the hepatotoxic ingredients in TCMs. Then Polygonum multiflorum Thunb (PmT) was used as a case to investigate the reliability of the approach proposed. Consequently, a total of 25 ingredients in PmT were identified as hepatotoxicants. The results were highly consistent with records in the literature, indicating that our computational toxicology approach is reliable and effective for the screening of hepatotoxic ingredients in Pmt. The combined classifier developed in this work can be used to assess the hepatotoxic risk of both natural compounds and synthetic drugs. The computational toxicology approach presented in this work will assist with screening the hepatotoxic ingredients in TCMs, which will further lay the foundation for exploring the hepatotoxic mechanisms of TCMs. In addition, the method proposed in this work can be applied to research focused on other adverse effects of TCMs/synthetic drugs.

Project description:ObjectiveThis study aims to investigate the functional gene network in gastric carcinogenesis by using bioinformatics; besides, the diagnostic utility of key genes and potential active ingredients of traditional Chinese medicine (TCM) for treatment in gastric cancer have been explored.MethodsThe Cancer Genome Atlas and Gene Expression Omnibus databases have been applied to analyze the differentially expressed genes (DEGs) between gastric cancer and normal gastric tissues. Then, the DEGs underwent Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses using the Metascape database. The STRING database and the Cytoscape software were utilized for the protein-protein interaction network of DEGs and hub genes screening. Furthermore, survival and expression analyses of hub genes were conducted using Gene Expression Profiling Interactive Analysis and Human Protein Atlas databases. By using the Comparative Toxicogenomics Database, the hub genes interconnected with active ingredients of TCM were analyzed to provide potential information for the treatment of gastric cancer. After the molecular docking of the active ingredients of TCM to specific hub gene receptor proteins, the molecular dynamics simulation GROMACS was applied to validate the conformation of the strongest binding ability in the molecular docking.ResultsA total of 291 significant DEGs were found, from which 12 hub genes were screened out. Among these hub genes, the expressions of five hub genes including COL1A1, COL5A2, MMP12, SERPINE1, and VCAN were significantly correlated with the overall survival. Furthermore, four potential therapeutic active ingredients of TCM were acquired, including quercetin, resveratrol, emodin, and schizandrin B. In addition, the molecular docking results exhibited that the active ingredients of TCM formed stable binding with the hub gene targets. SERPINE1 (3UT3)-Emodin and COL1A1 (7DV6)-Quercetin were subjected to molecular dynamics simulations as conformations of continuing research significance, and both were found to be stably bound as a result of the interaction of van der Waals potentials, electrostatic, and hydrogen bonding.ConclusionOur findings may provide novel insights and references for the screening of biomarkers, the prognostic evaluation, and the identification of potential active ingredients of TCM for gastric cancer treatment.

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Data-mining of potential antitubercular activities from molecular ingredients of traditional Chinese medicines.

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Data-mining of potential antitubercular activities from molecular ingredients of traditional Chinese medicines.

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