Network target for screening synergistic drug combinations with application to traditional Chinese medicine

Material basis and molecular mechanisms of Dachengqi decoction in the treatment of acute pancreatitis based on network pharmacology

BACKGROUND

Dachengqi decoction (DCQD) is a classical prescription in traditional Chinese medicine (TCM). It has been used to treat abdominal pain and acute pancreatitis (AP) for thousands of years in China.

OBJECTIVE

To predict the active components and signaling pathway of DCQD and to further explore the potential molecular mechanism of DCQD as a treatment of AP using network pharmacology.

METHODS

Network pharmacology and bioinformatics were used to determine the active components of DCQD and its potential target in the treatment of AP. The AP model was induced by Cerulein (Cer) combined with lipopolysaccharide (LPS). The pharmacodynamic basis of DCQD in the treatment of AP was evaluated in vitro and in vivo and Western blot analysis and immunofluorescence were used to determine the molecular mechanism of DCQD.

RESULTS

Screening using relevant databases and topological analysis revealed 71 active components and 535 potential target proteins in DCQD. In addition, 445 differential genes for AP were also screened. Pathway enrichment analysis, PPI network analysis and transcription factor prediction showed that DCQD played an important role in the PI3K-Akt signal pathway, and 17 DCQD monomers were found in this signal pathway. In the AP model, DCQD promoted pancreatic acinar cell apoptosis, reduction in inflammation, and regulation of the PI3K-AKT signaling pathway. DCQD inhibited the expression of p-AKT and p- NF-kB proteins in pancreatic tissue of the AP model both in vitro and in vivo.

CONCLUSION

This study reveals that 17 active components of DCQD improve AP by regulating the PI3K/AKT signaling pathway and promoting apoptosis and suppressing pathological injury and inflammation.

Systems Pharmacology-Based Method to Assess the Mechanism of Action of Weight-Loss Herbal Intervention Therapy for Obesity

Obesity is a multi-factorial chronic disease that has become a serious, prevalent, and refractory public health challenge globally because of high rates of various complications. Traditional Chinese medicines (TCMs) as a functional food are considered to be a valuable and readily available resource for treating obesity because of their better therapeutic effects and reduced side effects. However, their "multi-compound" and "multi-target" features make it extremely difficult to interpret the potential mechanism underlying the anti-obesity effects of TCMs from a holistic perspective. An innovative systems-pharmacology approach was employed, which combined absorption, distribution, metabolism, and excretion screening and multiple target fishing, gene ontology enrichment analysis, network pharmacology, and pathway analysis to explore the potential therapeutic mechanism of weight-loss herbal intervention therapy in obesity and related diseases. The current study provides a promising approach to facilitate the development and discovery of new botanical drugs.

A Network Pharmacology Study of the Multi-Targeting Profile of an Antiarrhythmic Chinese Medicine Xin Su Ning

Xin Su Ning (XSN) is a China patented and certified traditional Chinese herbal medicine used to treat premature ventricular contractions (PVCs) since 2005. XSN is formulated with 11 herbs, designed to treat arrhythmia with phlegm-heat heart-disturbed syndrome (PHHD) according to Chinese medicine theory. The rational compatibility of the 11 herbs decides the therapeutic outcome of XSN. Due to the multicomponent nature of traditional Chinese medicine, it is difficult to use conventional pharmacology to interpret the therapeutic mechanism of XSN in terms of clear-cut drug molecule and target interactions. Network pharmacology/systematic pharmacology usually consider all the components in a formula with the same weight; therefore, the proportion of the weight of the components has been ignored. In the present study, we introduced a novel coefficient to mimic the relative amount of all the components in relation with the weight of the corresponding herb in the formula. The coefficient is also used to weigh the pharmacological effect of XSN on all relative biological pathways. We also used the cellular electrophysiological data generated in our lab, such as the effect of liensinine and isoliquiritigenin on Na_V1.5 channels; we therefore set sodium channel as one of the targets of these two components, which would support the clinical efficacy of XSN in treating tachyarrhythmia. Combining the collected data and our discovery, a panoramagram of the pharmacological mechanism of XSN was established. Pathway enrichment and analysis showed that XSN treated PHHD arrhythmia through multiple ion channels regulation, protecting the heart from I/R injury, inhibiting the apoptosis of cardiomyocyte, and improving glucose and lipid metabolism.

Drug-Drug Interaction Predicting by Neural Network Using Integrated Similarity

Drug-Drug Interaction (DDI) prediction is one of the most critical issues in drug development and health. Proposing appropriate computational methods for predicting unknown DDI with high precision is challenging. We proposed "NDD: Neural network-based method for drug-drug interaction prediction" for predicting unknown DDIs using various information about drugs. Multiple drug similarities based on drug substructure, target, side effect, off-label side effect, pathway, transporter, and indication data are calculated. At first, NDD uses a heuristic similarity selection process and then integrates the selected similarities with a nonlinear similarity fusion method to achieve high-level features. Afterward, it uses a neural network for interaction prediction. The similarity selection and similarity integration parts of NDD have been proposed in previous studies of other problems. Our novelty is to combine these parts with new neural network architecture and apply these approaches in the context of DDI prediction. We compared NDD with six machine learning classifiers and six state-of-the-art graph-based methods on three benchmark datasets. NDD achieved superior performance in cross-validation with AUPR ranging from 0.830 to 0.947, AUC from 0.954 to 0.994 and F-measure from 0.772 to 0.902. Moreover, cumulative evidence in case studies on numerous drug pairs, further confirm the ability of NDD to predict unknown DDIs. The evaluations corroborate that NDD is an efficient method for predicting unknown DDIs. The data and implementation of NDD are available at https://github.com/nrohani/NDD.

Novel Compound-Target Interactions Prediction for the Herbal Formula Hua-Yu-Qiang-Shen-Tong-Bi-Fang

Herbal formulae have a long history in clinical medicine in Asia. While the complexity of the formulae leads to the complex compound-target interactions and the resultant multi-target therapeutic effects, it is difficult to elucidate the molecular/therapeutic mechanism of action for the many formulae. For example, the Hua-Yu-Qiang-Shen-Tong-Bi-Fang (TBF), an herbal formula of Chinese medicine, has been used for treating rheumatoid arthritis. However, the target information of a great number of compounds from the TBF formula is missing. In this study, we predicted the targets of the compounds from the TBF formula via network analysis and in silico computing. Initially, the information of the phytochemicals contained in the plants of the herbal formula was collected, and subsequently computed to their corresponding fingerprints for the sake of structural similarity calculation. Then a compound structural similarity network infused with available target information was constructed. Five local similarity indices were used and compared for their performance on predicting the potential new targets of the compounds. Finally, the Preferential Attachment Index was selected for it having an area under curve (AUC) of 0.886, which outperforms the other four algorithms in predicting the compound-target interactions. This method could provide a promising direction for identifying the compound-target interactions of herbal formulae in silico.

Efficacy of leflunomide combined with ligustrazine in the treatment of rheumatoid arthritis: prediction with network pharmacology and validation in a clinical trial

BACKGROUND

Leflunomide (LEF) is a first-line disease-modifying antirheumatic drug (DMARD) for rheumatoid arthritis (RA). However, there are still a few nonresponders. It is logical to suggest that employing combinations including LEF that produce synergistic effects in terms of pharmacological activity is a promising strategy to improve clinical outcomes.

METHODS

We propose a novel approach for predicting LEF combinations through investigating the potential effects of drug targets on the disease signaling network. We first constructed an RA signaling network with disease-associated driver genes. Thousands of available FDA-approved and investigational compounds were then selected based on a drug-RA network, which was generated using an algorithm model named synergistic score that combines chemical structure, functional prediction and target pathway. We then validated our predicted combination in a prospective clinical trial.

RESULTS

Ligustrazine (LIG), a key component of the Chinese herb Chuanxiong and an approved drug in China, ranked first according to synergistic score. In the clinical trial, after 48 weeks, the American College of Rheumatology (ACR) 20 response rate was significantly lower (P < 0.05) in the LEF group [58.8% (45.4%, 72.3%)] than in the LEF + LIG group [78.7% (68.5%, 89.0%)]. Consistently, the erosion score was lower in patients treated with LEF + LIG than in those treated with LEF (0.34 ± 0.20 vs 1.12 ± 0.30, P < 0.05).

CONCLUSIONS

Our algorithm combines structure and target pathways into one model that predicted that the combination of LEF and LIG can reduce joint inflammation and attenuate bone erosion in RA patients. To our knowledge, this study is the first to apply this paradigm to evaluate drug combination hypotheses.

Precise and systematic survey of the efficacy of multicomponent drugs against functional dyspepsia

Functional dyspepsia (FD) is one of the most prevalent functional gastrointestinal disorders, and more and more multicomponent drugs represented by traditional Chinese medicines have provided a favorable therapeutic effect in its treatment. However, their precise localization in the clinic, as well as corresponding mechanism, is ambiguous, thus hindering their widespread use. To meet this requirement, a precise and systematic approach based on a restriction of special disease-related molecules and the following network pharmacology analysis was developed and applied to a multicomponent conventional drug, XiaoErFuPi (XEFP) granules. Experimental verification of the results indicates that this approach can facilitate the prediction, and the precise and systematic efficacy of XEFP could be easily revealed, which shows that XEFP has an advantage over the positive control drug on lactate, gastrin, interleukin 4 and calcitonin gene-related peptide. Moreover, by the proteomics analysis, its superposition of multi-target effects was revealed and a new candidate target for the treatment of FD, striatin, was obtained and verified. This study provides a practicable precise approach for the investigation of the efficacy of multicomponent drugs against FD and offers a promising alternative for the systematical management of FD.

A Network Pharmacology Approach Used to Estimate the Active Ingredients of Moutan Cortex Charcoal and the Potential Targets in Hemorrhagic Diseases

Moutan Cortex charcoal has been used to ameliorate blood heat symptoms and treat pathologic hemorrhage down the ages. Although well known as an agent with the effect of astringency and hemostasis, its active ingredients and action mechanism remain unclear. In the present study, molecular docking technology was employed to screen the potential hemostatic compounds in Moutan Cortex charcoal and their target proteins. Protein-protein-interaction (PPI) analysis was performed to explain the functions and enrichment pathways of the target proteins. The results showed that a total of 25 compounds were estimated as active constituents targeting multiple proteins related to hemostatic diseases, including 5 proteins (SERPINC1, FVIII, FX, FII and FXII) that were considered as the key targets. Then the drug-target (D-T) network was constructed to analyze the underlying hemostatic mechanism of Moutan Cortex charcoal, followed by a hierarchical cluster analysis (HCA) for compounds clustering, and a coagulation screening test for compound verification on their coagulation activities, with the results indicating that M15 (5-Tetradecenoic acid) and M31 (1-Monolinolein) might be the key compounds contributing to the hemostasis effect of Moutan Cortex charcoal by involving in the pathways related to complement, coagulation cascades and the platelet activation, particularly by activating FVIII, FX, FII and FXII and inhibiting SERPINC1. This study has demonstrated that Moutan Cortex charcoal may work as a hemostatic through the interaction between multiple-compounds and multiple-proteins, which provides the basis for further researches on the hemostasis mechanism of Moutan Cortex charcoal.

Advances in computational approaches in identifying synergistic drug combinations

Accumulated empirical clinical experience, supported by animal or cell line models, has initiated efforts of predicting synergistic combinatorial drugs with more-than-additive effect compared with the sum of the individual agents. Aiming to construct better computational models, this review started from the latest updated data resources of combinatorial drugs, then summarized the reported mechanism of the known synergistic combinations from aspects of drug molecular and pharmacological patterns, target network properties and compound functional annotation. Based on above, we focused on the main in silico strategies recently published, covering methods of molecular modeling, mathematical simulation, optimization of combinatorial targets and pattern-based statistical/learning model. Future thoughts are also discussed related to the role of natural compounds, drug combination with immunotherapy and management of adverse effects. Overall, with particular emphasis on mechanism of action of drug synergy, this review may serve as a rapid reference to design improved models for combinational drugs.

LC-MS/MS analysis and network pharmacology of Trigonella foenum-graecum - A plant from Ayurveda against hyperlipidemia and hyperglycemia with combination synergy

BACKGROUND

The seed of Trigonella foenum-graecum L. (Methika in Sanskrit) is a well known kaphahara (balancing kapha) herb in Ayurveda indicated in Prameha or early diabetes mellitus. It is also useful in obesity and reduces lipid level of blood.

PURPOSE

We aimed to explore the metabolites present in the plant extract and to establish the combination synergy and the network pharmacology along with the underlying the mechanism of action involved.

STUDY DESIGN

LC-MS/MS based metabolite screening followed by ADME screening and finally network pharmacology exploration of the mechanism of action involved against hyperlipidemia and hypolipidemia with neighbourhood based combination synergy approach.

METHODS

Ethanolic extract of Trigonella foenum-graecum L. (TFHE) was subjected to LC-MS/MS analysis to identify the active constituents. Oral bioavailability and drug likeness was screened for all the compounds. Databases- Binding DB, DAVID, KEGG and STRING were used to gather information to develop the networks. The networks were constructed using Cytoscape 3.2.1. Combination synergy analysis was performed with the help of Cytoscape network analyzer tool with neighbourhood approach.

RESULTS

The LC-MS/MS analysis identified 13 compounds which were found to be bio-available and drug like following the QED and Veber drug likeness parameters. The pathway analysis showed enrichment for different pathways like MAPK pathway (p-4.69E-07), JAK-STAT pathway (p-6.30E-05), Adipocytokine (p-0.00179), Type 2 Diabetes mellitus (0.00441), Insulin signalling pathway (p-0.0121), mTOR signalling pathway (p-0.000378), which are all connected to hyperlipidemia and hyperglycemia. The combination synergy network identified 23 targets interacting with 13 compounds based on a network neighbourhood approach.

CONCLUSION

The network pharmacology analysis strongly suggested the multimode evidences that TFHE largely works on the insulin signalling pathway and mainly based on its antioxidant potential due to its interaction with carbonic anhydrase. Various compounds were found to be interacting with key proteins that activates EGFR/AKT/mTOR signalling cascade which has therapeutic implication in hyperglycemia and hyperlipidemia. The combination synergy network analysis based on neighbourhood approach can help us in further understanding mechanism of multi-molecular fixed dose combinations.

Yinchenhao decoction suppresses rat liver fibrosis involved in an apoptosis regulation mechanism based on network pharmacology and transcriptomic analysis

Yinchenhao decoction (YCHD) is a classical Traditional Chinese Medicine (TCM) formula that has been widely used in the treatment of liver fibrosis caused by chronic hepatitis B and jaundice for more than 1800 years. The purpose of this study was to investigate the apoptosis regulation mechanisms of YCHD and its active components suppresses liver fibrosis. The active components and putative targets of YCHD were predicted by network pharmacology approach. Functional and pathway enrichment analysis were presented in the present study by using clusterProfiler. Further, experimental validation was done by using terminal deoxynucleotidyl transferase (TDT) dUTP nick end labelling (TUNEL) assay and western blotting in dimethylnitrosamine (DMN)-induced liver fibrosis rats, and cell proliferation assay, apoptosis assay, and western blotting in human hepatic L02 cells and LX2 cells. 45 active compounds in YCHD formula, 592 potential target proteins and 1191 liver fibrosis-related human genes were identified. Functional and pathway enrichment analysis indicated that YCHD obviously influenced TNF, PI3K-Akt signaling pathways. Further, In vivo experiment indicated that YCHD treatment not only attenuated the symptoms of liver fibrosis, but also decrease the apoptosis of hepatic parenchyma cells. Moreover, in vitro experiments showed that rhein, kaempferol and quercetin treatments remarkably decreased the protein levels of cleaved caspase-3 and increased p-ERK1/2, PI3K and Bcl-XL protein expression in TNF-α-stimulated L02 cells. On the contrary, rhein, kaempferol, aloe-emodin and quercetin inhibited the proliferation of LX2 cells and up-regulated the protein levels of Bax and cleaved caspase-8. In conclusion, 45 active components and 296 potential targets of YCHD against liver fibrosis were identified by the analysis of network pharmacology and transcriptomics combination. The mechanisms of YCHD against liver fibrosis were involved in the regulation of multiple targets, especially affecting the apoptosis-related signaling pathways.

Natural Product Target Network Reveals Potential for Cancer Combination Therapies

A body of research demonstrates examples of in vitro and in vivo synergy between natural products and anti-neoplastic drugs for some cancers. However, the underlying biological mechanisms are still elusive. To better understand biological entities targeted by natural products and therefore provide rational evidence for future novel combination therapies for cancer treatment, we assess the targetable space of natural products using public domain compound-target information. When considering pathways from the Reactome database targeted by natural products, we found an increase in coverage of 61% (725 pathways), relative to pathways covered by FDA approved cancer drugs collected in the Cancer Targetome, a resource for evidence-based drug-target interactions. Not only is the coverage of pathways targeted by compounds increased when we include natural products, but coverage of targets within those pathways is also increased. Furthermore, we examined the distribution of cancer driver genes across pathways to assess relevance of natural products to critical cancer therapeutic space. We found 24 pathways enriched for cancer drivers that had no available cancer drug interactions at a potentially clinically relevant binding affinity threshold of < 100nM that had at least one natural product interaction at that same binding threshold. Assessment of network context highlighted the fact that natural products show target family groupings both distinct from and in common with cancer drugs, strengthening the complementary potential for natural products in the cancer therapeutic space. In conclusion, our study provides a foundation for developing novel cancer treatment with the combination of drugs and natural products.

A Review of Chinese Herbal Medicine for the Treatment of Chronic Heart Failure

Heart failure is one of the major causes of mortality worldwide and it is the end stage of sev-eral cardiovascular diseases. Traditional Chinese medicine has been used in the management of heart failure for a long time. Only until recently, well-designed clinical trials have been put into practice to study the efficacies of Chinese herbs. Extensive studies have also been carried out to explore the under-lying mechanisms of pharmaceutical actions of Chinese herbs. In this study, we will summarize the frequently used Chinese herbs, formulae and patent Chinese drugs in treating patients with heart failure and review published clinical evaluations of Chinese herbs in treating cardiovascular diseases. The mechanisms by which Chinese herbs exert cardio-protective effects will also be reviewed. In the end, we will point out the limitations of current studies and challenges facing modernization of traditional Chi-nese medicine.

Network Pharmacology in Research of Chinese Medicine Formula: Methodology, Application and Prospective

Chinese medicine (CM) is usually prescribed as CM formula to treat disease. The lack of effective research approach makes it difficult to elucidate the molecular mechanisms of CM formula owing to its complicated chemical compounds. Network pharmacology is increasingly applied in CM formula research in recent years, which is identified suitable for the study of CM formula. In this review, we summarized the methodology of network pharmacology, including network construction, network analysis and network verification. The aim of constructing a network is to achieve the interaction between the bioactive compounds and targets and the interaction between various targets, and then find out and validate the key nodes via network analysis and network verification. Besides, we reviewed the application in CM formula research, mainly including targets discovery, bioactive compounds screening, toxicity evaluation, mechanism research and quality control research. Finally, we proposed prospective in the future and limitations of network pharmacology, expecting to provide new strategy and thinking on study for CM formula.

Network Pharmacology Databases for Traditional Chinese Medicine: Review and Assessment

The research field of systems biology has greatly advanced and, as a result, the concept of network pharmacology has been developed. This advancement, in turn, has shifted the paradigm from a “one-target, one-drug” mode to a “network-target, multiple-component-therapeutics” mode. Network pharmacology is more effective for establishing a “compound-protein/gene-disease” network and revealing the regulation principles of small molecules in a high-throughput manner. This approach makes it very powerful for the analysis of drug combinations, especially Traditional Chinese Medicine (TCM) preparations. In this work, we first summarized the databases and tools currently used for TCM research. Second, we focused on several representative applications of network pharmacology for TCM research, including studies on TCM compatibility, TCM target prediction, and TCM network toxicology research. Third, we compared the general statistics of several current TCM databases and evaluated and compared the search results of these databases based on 10 famous herbs. In summary, network pharmacology is a rational approach for TCM studies, and with the development of TCM research, powerful and comprehensive TCM databases have emerged but need further improvements. Additionally, given that several diseases could be treated by TCMs, with the mediation of gut microbiota, future studies should focus on both the microbiome and TCMs to better understand and treat microbiome-related diseases.

Integrating Network Pharmacology and Metabolomics Study on Anti-rheumatic Mechanisms and Antagonistic Effects Against Methotrexate-Induced Toxicity of Qing-Luo-Yin

Qing-Luo-Yin (QLY) is a traditional Chinese medicine (TCM) formula used to treat Hot Syndrome-related rheumatoid arthritis (RA). Previously, we uncovered partial mechanisms involved in the therapeutic actions of QLY on RA. In this study, we further elucidated its anti-rheumatic mechanisms and investigated its possible interactions with methotrexate (MTX) in vivo using an integrating strategy coupled with network pharmacology and metabolomics techniques. Chemical composition of QLY was characterized by HPLC analysis. Collagen induced arthritis (CIA) was developed in male SD rats. The CIA rats were then assigned into different groups, and received QLY, MTX or QLY+MTX treatments according to the pre-arrangement. Therapeutic effects of QLY and its possible interactions with MTX in vivo were evaluated by clinical parameters, digital radiography assessment, histological/immunohistochemical examination, and serological biomarkers. Mechanisms underlying these actions were deciphered with network pharmacology methods, and further validated by metabolomics clues based on UPLC-Q-TOF/MS analysis of urines. Experimental evidences demonstrated that QLY notably alleviated the severity of CIA and protected joints from destruction. Re-balanced levels of hemoglobin and alanine transaminase in serum indicated reduced MTX-induced hepatic injury and myelosuppression under the co-treatment of QLY. Network-based target prediction found dozens of RA related proteins as potential targets of QLY. Upon the further biological function enrichment analysis, we found that a large amount of them were involved in nucleotide metabolism and immune functions. Metabolomics analysis showed that QLY restored amino acids, fatty acids, and energy metabolisms in CIA rats, which solidly supported its therapeutic effects on CIA. Consistently to findings from network pharmacology analysis, metabolomics study also found altered purine, pyrimidine, and pentose phosphate metabolisms in CIA rats receiving QLY treatment. All these clues suggested that inhibition on nucleic acid synthesis was essential to the immunosuppressive activity of QLY in vivo, and could contribute great importance to its therapeutic effects on CIA. Additionally, QLY induced significant antifolate resistance in rats, which would prevent folate from depletion during long-term MTX treatment, and should account for reduced side effects in combination regimen with MTX and QLY.

Development of Fangjiomics for Systems Elucidation of Synergistic Mechanism Underlying Combination Therapy

The rapid development of omics technology provides an opportunity for fulfilling the understanding of the synergistic mechanism of combination therapy. However, a systems theory to analyze synergy remains an ongoing challenge. Fangjiomics is a novel systems science based on a holistic theory integrated with reductionism which has been utilized to systematically elucidate the synergistic mechanisms underlying combination therapy using multi-target-, pathway- or network-based quantitative methods. Besides, our ability to understand the polyhierarchical structure in synergy is driven based on multi-level omics data fusion in Fangjiomics. According to the basic principle of “Jun-Chen-Zuo-Shi”, further global integration across various omics platforms and phenotype-driven quantitative multi-scale modeling would accelerate development in Fangjiomics-based dissection of synergy in multi-drug combination therapies.

•

Fangjiomics is a novel systems science based on a holistic theory integrated with reductionism.

•

We developed the pathway-based analysis of synergistic mechanisms in Fangjiomics.

•

The theory of network-based synergistic targets is proposed in Fangjiomics.

•

The hierarchical relationship of synergy in multilevel omics is dissected in Fangjiomics.

•

The principle of “Jun-Chen-Zuo-Shi” is proposed to accelerate the development in Fangjiomics-based dissection of synergy.

Screening and Identification of Cardioprotective Compounds From Wenxin Keli by Activity Index Approach and in vivo Zebrafish Model

Wenxin Keli (WXKL) is a widely used Chinese botanical drug for the treatment of arrhythmia, which is consisted of four herbs and amber. In the present study, we analyzed the chemical composition of WXKL using liquid chromatography coupled with high-resolution mass spectrometry (LC-HRMS) to tentatively identify 71 compounds. Through typical separate procession, the total extract of WXKL was divided into fractions for further bioassays. Cardiomyocytes and zebrafish larvae were applied for assessment. In vivo arrhythmia model in Cmlc2-GFP transgenic zebrafish was induced by terfenadine, which exhibited obvious reduction of heart rate and occurrence of atrioventricular block. Dynamic beating of heart was recorded by fluorescent microscope and sensitive camera to automatically recognize the rhythm of heartbeat in zebrafish larvae. By integrating the chemical information of WXKL and corresponding bioactivities of these fractions, activity index (AI) of each identified compound was calculated to screen potential active compounds. The results showed that dozens of compounds including ginsenoside Rg₁, ginsenoside Re, notoginsenoside R₁, lobetyolin, and lobetyolinin were contributed to cardioprotective effects of WXKL. The anti-arrhythmic activities of five compounds were further validated in larvae model and mature zebrafish by measuring electrocardiogram (ECG). Our findings provide a successful example for rapid discovery of bioactive compounds from traditional Chinese medicine (TCM) by activity index based approach coupled with in vivo zebrafish model.

Discovery of synergistic anti-inflammatory compound combination from herbal formula GuGe FengTong Tablet

Multi-components in herbal formulae exert holistic effects in synergistic or additive manners. However, appropriate strategies and supportive evidences are still lacking to uncover the synergistic or additive combinations. The present investigation aimed at seeking a screening strategy to identify the targeted combinations in GuGe FengTong Tablet (GGFTT), an herbal formula. Two compounds, belonging to different chemical classes, were combined with different concentration ratios and their anti-inflammation effects were investigated. The most significant anti-inflammatory combinations were evaluated by combination index (CI) method (additive effect, CI = 1; synergism, CI < 1; antagonism, CI > 1). The modulating effects of candidate combinations on pro-inflammatory cytokines and MAPKs signaling pathway were also detected. Two combinations, "biochanin A + 6-gingerol" (Bio-6G) and "genistein + 6-gingerol" (Gen-6G), showed synergistic effects (CI < 1), and Bio-6G was selected for further study. Compared with single compound, Bio-6G could synergistically inhibit the production of pro-inflammatory cytokines (TNF-α, IL-1β, and IL-6) and the activation of MAPKs signaling pathway in LPS-stimulated RAW264.7 cells. The combined results showed that Bio-6G was a synergistic anti-inflammatory combination in GGFTT. Our results could provide a useful strategy to screen the synergistic combinations in herbal formulae.

Material basis research for Huangqi Jianzhong Tang against chronic atrophic gastritis rats through integration of urinary metabonomics and SystemsDock

ETHNOPHARMACOLOGICAL RELEVANCE

Huangqi Jianzhong Tang (HQJZ), a celebrated traditional Chinese medicine (TCM), is commonly used for treatment of chronic atrophic gastritis (CAG) in China.

AIM OF THE STUDY

We aimed to screen out the material basis of HQJZ against CAG.

MATERIALS AND METHODS

CAG rat model was constructed by alternant administrations of ammonia solution and sodium deoxycholate, and the hunger disorder method. Body weight, biochemical indexes and histopathological exam were used to evaluate the efficacy of HQJZ. ¹H NMR-based metabonomics was employed to analyze the urine metabolic features of HQJZ deviated from CAG rats. SystemsDock analysis was utilized to explore the active compounds involved into the efficacy of HQJZ against CAG based on the targeted metabolic biomarkers.

RESULTS

The metabonomic results indicated that HQJZ could significantly improve 16 urinary perturbed metabolites in CAG rats, which were involved into the metabolism of energy and amino acids. And then 28 related proteins and genes were selected out to be the potential targets of HQJZ against CAG based on the six key metabolites closely correlating with biochemical indexes (α-ketoglutarate, valine, sarcosine, glycine, malonate and fumarate). 71 previous identified compounds were docked through systemsDock-aided molecular docking experiments. And the constructed herb-component-protein-metabolite interaction network (HCPMN) revealed the associations between the herbal formulae and CAG. At last, 51 compounds of them were screened as promising active constitutes for the inhibition of CAG, which could act on various targeted proteins.

CONCLUSIONS

he results showed that the approach integrating of metabonomics and systemsDock is a powerful tool to obtain the material basis and regulatory mechanism of TCM formula.

Wen-Luo-Tong Decoction Attenuates Paclitaxel-Induced Peripheral Neuropathy by Regulating Linoleic Acid and Glycerophospholipid Metabolism Pathways

Chemotherapy-induced peripheral neuropathy (CIPN) is a serious dose-limiting toxicity of many anti-neoplastic agents, especially paclitaxel, and oxaliplatin. Up to 62% of patients receiving paclitaxel regimens turn out to develop CIPN. Unfortunately, there are so few agents proved effective for prevention or management of CIPN. The reason for the current situation is that the mechanisms of CIPN are still not explicit. Traditional Chinese Medicine (TCM) has unique advantages for dealing with complex diseases. Wen-Luo-Tong (WLT) is a TCM ointment for topical application. It has been applied for prevention and management of CIPN clinically for more than 10 years. Previous animal experiments and clinical studies had manifested the availability of WLT. However, due to the unclear mechanisms of WLT, further transformation has been restricted. To investigate the therapeutic mechanisms of WLT, a metabolomic method on the basis of UPLC- MS was developed in this study. Multivariate analysis techniques, such as principal component analysis (PCA) and partial least squares discriminate analysis (PLS-DA), were applied to observe the disturbance in the metabolic state of the paclitaxel-induced peripheral neuropathy (PIPN) rat model, as well as the recovering tendency of WLT treatment. A total of 19 significant variations associated with PIPN were identified as biomarkers. Results of pathway analysis indicated that the metabolic disturbance of pathways of linoleic acid (LA) metabolism and glycerophospholipid metabolism. WLT attenuated mechanical allodynia and rebalanced the metabolic disturbances of PIPN by primarily regulating LA and glycerophospholipid metabolism pathway. Further molecular docking analysis showed some ingredients of WLT, such as hydroxysafflor yellow A (HSYA), icariin, epimedin B and 4-dihydroxybenzoic acid (DHBA), had high affinity to plenty of proteins within these two pathways.

Phenotype-oriented network analysis for discovering pharmacological effects of natural compounds

Although natural compounds have provided a wealth of leads and clues in drug development, the process of identifying their pharmacological effects is still a challenging task. Over the last decade, many in vitro screening methods have been developed to identify the pharmacological effects of natural compounds, but they are still costly processes with low productivity. Therefore, in silico methods, primarily based on molecular information, have been proposed. However, large-scale analysis is rarely considered, since many natural compounds do not have molecular structure and target protein information. Empirical knowledge of medicinal plants can be used as a key resource to solve the problem, but this information is not fully exploited and is used only as a preliminary tool for selecting plants for specific diseases. Here, we introduce a novel method to identify pharmacological effects of natural compounds from herbal medicine based on phenotype-oriented network analysis. In this study, medicinal plants with similar efficacy were clustered by investigating hierarchical relationships between the known efficacy of plants and 5,021 phenotypes in the phenotypic network. We then discovered significantly enriched natural compounds in each plant cluster and mapped the averaged pharmacological effects of the plant cluster to the natural compounds. This approach allows us to predict unexpected effects of natural compounds that have not been found by molecular analysis. When applied to verified medicinal compounds, our method successfully identified their pharmacological effects with high specificity and sensitivity.

Network pharmacology-based prediction of active compounds and molecular targets in Yijin-Tang acting on hyperlipidaemia and atherosclerosis

ETHNOPHARMACOLOGICAL RELEVANCE

Yijin-Tang (YJT) is a traditional prescription for the treatment of hyperlipidaemia, atherosclerosis and other ailments related to dampness phlegm, a typical pathological symptom of abnormal body fluid metabolism in Traditional Korean Medicine. However, a holistic network pharmacology approach to understanding the therapeutic mechanisms underlying hyperlipidaemia and atherosclerosis has not been pursued.

AIM OF THE STUDY

To examine the network pharmacological potential effects of YJT on hyperlipidaemia and atherosclerosis, we analysed components, performed target prediction and network analysis, and investigated interacting pathways using a network pharmacology approach.

MATERIALS AND METHODS

Information on compounds in herbal medicines was obtained from public databases, and oral bioavailability and drug-likeness was screened using absorption, distribution, metabolism, and excretion (ADME) criteria. Correlations between compounds and genes were linked using the STITCH database, and genes related to hyperlipidaemia and atherosclerosis were gathered using the GeneCards database. Human genes were identified and subjected to Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis.

RESULTS

Network analysis identified 447 compounds in five herbal medicines that were subjected to ADME screening, and 21 compounds and 57 genes formed the main pathways linked to hyperlipidaemia and atherosclerosis. Among them, 10 compounds (naringenin, nobiletin, hesperidin, galangin, glycyrrhizin, homogentisic acid, stigmasterol, 6-gingerol, quercetin and glabridin) were linked to more than four genes, and are bioactive compounds and key chemicals. Core genes in this network were CASP3, CYP1A1, CYP1A2, MMP2 and MMP9. The compound-target gene network revealed close interactions between multiple components and multiple targets, and facilitates a better understanding of the potential therapeutic effects of YJT.

CONCLUSIONS

Pharmacological network analysis can help to explain the potential effects of YJT for treating dampness phlegm-related diseases such as hyperlipidaemia and atherosclerosis.

Ensemble Prediction of Synergistic Drug Combinations Incorporating Biological, Chemical, Pharmacological, and Network Knowledge

Combinatorial therapy may reduce drug side effects and improve drug efficacy, making combination therapy a promising strategy to treat complex diseases. However, in the existing computational methods, the natural properties and network knowledge of drugs have not been adequately and simultaneously considered, making it difficult to identify effective drug combinations. Computational methods that incorporate multiple sources of information (biological, chemical, pharmacological, and network knowledge) offer more opportunities to screen synergistic drug combinations. Therefore, we developed a novel Ensemble Prediction framework of Synergistic Drug Combinations (EPSDC) to accurately and efficiently predict drug combinations by integrating information from multiple-sources. EPSDC constructs feature vector of drug pair by concatenating different types of drug similarities, and then uses these groups in a feature-based base predictor. Next, transductive learning is applied on heterogeneous drug-target networks to achieve a network-based score for the drug pair. Finally, two types of ensemble rules are introduced to combine the feature-based score and the network-based score, and then potential drug combinations are prioritized. To demonstrate the effect of the ensemble rule, comprehensive experiments were conducted to compare single models and ensemble models. The experimental results indicated that our method outperformed the state-of-the-art method in five-fold cross validation and de novo prediction tests on the two benchmark datasets. We further analyzed the effect of maximum length of the meta-path and the impacts of different types of features. Moreover, the practical usefulness of our method was confirmed in the predicted novel drug combinations. The source code of EPSDC is available at https://github.com/KDDing/EPSDC.

An integrated framework for identification of effective and synergistic anti-cancer drug combinations

Combination drug therapy is considered a better treatment option for various diseases, such as cancer, HIV, hypertension, and infections as compared to targeted drug therapies. Combination or synergism helps to overcome drug resistance, reduction in drug toxicity and dosage. Considering the complexity and heterogeneity among cancer types, drug combination provides promising treatment strategy. Increase in drug combination data raises a challenge for developing a computational approach that can effectively predict drugs synergism. There is a need to model the combination drug screening data to predict new synergistic drug combinations for successful cancer treatment. In such a scenario, machine learning approaches can be used to alleviate the process of drugs synergy prediction. Experimental data from a single-agent or multi-agent drug screens provides feature data for model training. On the contrary, identification of effective drug combination using clinical trials is a time consuming and resource intensive task. This paper attempts to address the aforementioned challenges by developing a computational approach to effectively predict drug synergy. Single-drug efficacy is used for predicting drug synergism. Our approach obviates the need to understand the underlying drug mechanism to predict drug combination synergy. For this purpose, nine machine learning algorithms are trained. It is observed that the Random forest models, in comparison to other models, have shown significant performance. The <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>K</mml:mi></mml:math> -fold cross-validation is performed to evaluate the robustness of the best predictive model. The proposed approach is applied to mutant-BRAF melanoma and further validated using melanoma cell-lines from AstraZeneca-Sanger Drug Combination Prediction DREAM Challenge dataset.

An integrated evidence-based targeting strategy for determining combinatorial bioactive ingredients of a compound herbal medicine Qishen Yiqi dripping pills

ETHNOPHARMACOLOGICAL RELEVANCE

Qishen Yiqi is a widely used Chinese herbal medicine formula with "qi invigorating and blood activating" property. Its dripping pill preparation (QSYQ) is a commercial herbal medicine approved by the China Food and Drug Administration (CFDA) in 2003 and is extensively used clinically to treat cardiovascular diseases, such as ischemic heart failure and angina pectoris, as well as for the secondary prevention of myocardial infarction. However, the bioactive ingredients of QSYQ remain unclear. As QSYQ is a compound herbal formula, it is of great importance to elucidate its pharmacologically active ingredients and underlying synergetic effects.

AIM OF THE STUDY

This experimental study was conducted to comprehensively determine the combinatorial bioactive ingredients (CBIs) in QSYQ and to elucidate their potential synergetic effects. The established strategy may shed new light on how to rapidly determine CBIs in complex herbal formulas with holistic properties.

MATERIALS AND METHODS

An integrated evidence-based targeting strategy was introduced and validated to determine CBIs in QSYQ. The strategy included the following steps: (1) Chemical ingredients in QSYQ were analyzed via UPLC-Q-TOF/MS in the negative and positive modes and were identified by comparison with standard compounds and previously reported data. Their potential therapeutic activities were predicted based on the ChEMBL database to preliminarily search for candidate bioactive ingredients, and their combination was defined as the CBIs. (2) The CBIs were directly trapped and prepared from QSYQ with a two-dimensional chromatographic separation system, and the remaining part was defined as the rest ingredients (RIs). (3) As animal and cell models, left anterior descending coronary artery ligation (LAD)-induced heart failure in rats and hypoxia-induced cardiac myocyte injury in H9c2 cells were applied to compare the potency of QSYQ, CBIs and RIs. (4) The synergetic effects on cardiac myocyte protection of multiple ingredients in CBIs were examined in this cell model.

RESULTS

(1) Forty-three ingredients in QSYQ were identified via UPLC-Q-TOF/MS. Based on evidence-based screening using the ChEMBL database, 24 ingredients were predicted to be bioactive ingredients, and their combination was considered the CBIs. (2) The CBIs and RIs were successfully prepared according to a two-dimensional chromatographic system. The CBIs were directly trapped and knocked out from QSYQ by hydrophilic interaction liquid chromatography coupled with reverse-phase liquid chromatography. The remaining part was used as RIs. (3) The results from pharmacological evaluation revealed that CBIs and QSYQ, but not RIs, significantly prevented myocardium injury; improved the ejection fraction (EF) and fractional shortening (FS); decreased the release of cardiac enzymes, including CK, CK-MB, and LDH; alleviated mitochondrial dysfunction; and protected the cell nucleus number and mitochondrial mass. Furthermore, QSYQ and CBIs possessed similar potency. (4) In hypoxia-stimulated H9c2 cells, CBIs showed far greater potency regarding the protection of cardiac myocyte injury than the individual ingredients in QSYQ, exhibiting obvious synergetic effects.

CONCLUSIONS

An integrated evidence-based targeting strategy was successfully established and validated to determine CBIs from QSYQ with excellent efficiency. Importantly, the holistic property of QSYQ was retained in the CBIs. Hence, this study may shed new light on how to rapidly reveal combinatorial bioactive ingredients from complex prescriptions and will be greatly helpful in the establishment of an appropriate approach to quality control for herbal medicines.

The Active Components of Fuzheng Huayu Formula and Their Potential Mechanism of Action in Inhibiting the Hepatic Stellate Cells Viability - A Network Pharmacology and Transcriptomics Approach

Purpose: This study aimed to identify the active components of Fuzheng Huayu (FZHY) formula and the mechanism by which they inhibit the viability of hepatic stellate cells (HSCs) by a combination of network pharmacology and transcriptomics.

Methods: The active components of FZHY formula were screened out by text mining. Similarity match and molecular docking were used to predict the target proteins of these compounds. We then searched the STRING database to analyze the key enriched processes, pathways and related diseases of these target proteins. The relevant networks were constructed by Cytoscape. A network analysis method was established by integrating data from above network pharmacology with known transcriptomics analysis of quiescent HSCs-activated HSCs to identify the most possible targets of the active components in FZHY formula. A cell-based assay (LX-2 and T6 cells) and surface plasmon resonance (SPR) analysis were used to validate the most possible active component-target protein interactions (CTPIs).

Results: 40 active ingredients in FZHY formula and their 79 potential target proteins were identified by network pharmacology approach. Further network analysis reduced the 79 potential target proteins to 31, which were considered more likely to be the target proteins of the active components in FZHY formula. In addition, further enrichment analysis of 31 target proteins indicated that the HIF-1, PI3K-Akt, FoxO, and chemokine signaling pathways may be the primary pathways regulated by FZHY formula in inhibiting the HSCs viability for the treatment of liver fibrosis. Of the 31 target proteins, peroxisome proliferator activator receptor gamma (PPARG) was selected for validation by experiments at the cellular and molecular level. The results demonstrated that schisandrin B, salvianolic acid A and kaempferol could directly bind to PPARG, decreasing the viability of HSCs (T6 cells and LX-2 cells) and exerting anti-fibrosis effects.

Conclusion: The active ingredients of FZHY formula were successfully identified and the mechanisms by which they inhibit HSC viability determined, using network pharmacology and transcriptomics. This work is expected to benefit the clinical application of this formula.

Five traditional Nigerian Polyherbal remedies protect against high fructose fed, Streptozotocin-induced type 2 diabetes in male Wistar rats

BACKGROUND

This present study sought to assess the modulatory effects of five Nigerian traditional polyherbal in high fructose-fed, streptozotocin-induced (HF-STZ) Type 2 diabetes (T2D) in rats. T2D was achieved via fructose feeding (20%^W/V) ad libitum for 2 weeks and streptozotocin (STZ, 40 mg/kg) (15th Day) intraperitoneally.

METHODS

Seventy-two hours after STZ injection, fourty-eight diabetic rats were divided into eight of 6 rats/group: Diabetic normal untreated, glibenclamide (GBLI, 0.07 mL/kg) or yoyo (YB, 0.43), ruzu (RB, 0.08), fajik (FJB, 0.20), oroki (OB, 0.16), and fidson (FB, 0.43)/ mL/kg bitters respectively. Controls normal and diabetic untreated groups received intragastric carboxylmethylcellulose (CMC, 1 mL/kg) for eleven days.

RESULTS

T2D was characterized in rats by an increased (p < 0.001-0.05) blood glucose levels (BGL), total cholesterol, triglycerides, low-density lipoprotein and alanine aminotransferase compared with control CMC group. Similarly, hepatic and pancreatic malondialdehyde (MDA) were increased by 180 and 97% respectively. Polyherbal treatments demonstrated efficacies on BGL as follow: YB (55.6%, 160.7 mg/dL); RB (59.7%, 145.2 mg/dL); FJB (59.8%, 243.4 mg/dL); OB (60.8%, 194.5 mg/dL) and FB (61.3%, 203.3 mg/dL) respectively by day 11 (versus GBLI, 65.1%) compared with control untreated diabetic rats. Also, elevated TC, LDL cholesterol, ALT were lowered (p < 0.05) by YB, FJB, and FB respectively in rats. YB, FJB, and OB lowered MDA levels in treated rats. Further, YB, RB, FJB and FB restored changes in liver, and pancreas histopathology. Predominant non-polar bioactive include oleic, hexadecanoic, octadecanoic among others following gas chromatography-mass spectrophotometry analyses.

CONCLUSION

Overall, these present results demonstrate anti-hyperglycemic potentials, although with cautions, of some polyherbal in T2D rats, which may, in part, be antioxidants mediated.

Molecular targets of Chinese herbs: a clinical study of metastatic colorectal cancer based on network pharmacology

Increasing evidence has shown that Chinese herbal medicine (CHM) has promising therapeutic effects in colorectal cancer (CRC); however, the active ingredients and potential targets remain unclear. In this study, we aimed to investigate the relative molecular targets of the Chinese herbs that have been found effective in treating metastatic CRC (mCRC) based on clinical data and network pharmacology. In multivariate analysis CHM resulted an independent prognostic factor. The hazard ratio was 0.103 (95% confidence interval = 0.064-0.164; P < 0.001). Compared with the non-CHM group, the median survival time of the CHM group was also improved (40 versus 12 months; P < 0.001). Eighteen out of 295 herbs showed significant correlation with survival results (P < 0.05). Bioinformatics analysis indicated that the 18 herbs realize anti-CRC activity mainly through suppressing the proliferative activity of ERBB2, peroxisome proliferator-activated receptor gamma, and retinoid X receptor, suppressing angiogenesis via inhibition of VEGFR and VEGFA expression, inhibiting the phosphatidylinositol-3-kinase/AKT1 signaling pathway directly through SRC and AKT1, and reducing tumor necrosis factor-induced inflammation.

DRUG-NEM: Optimizing drug combinations using single-cell perturbation response to account for intratumoral heterogeneity

Single-cell high-throughput technologies enable the ability to identify combination cancer therapies that account for intratumoral heterogeneity, a phenomenon that has been shown to influence the effectiveness of cancer treatment. We developed and applied an approach that identifies top-ranking drug combinations based on the single-cell perturbation response when an individual tumor sample is screened against a panel of single drugs. This approach optimizes drug combinations by choosing the minimum number of drugs that produce the maximal intracellular desired effects for an individual sample.

An individual malignant tumor is composed of a heterogeneous collection of single cells with distinct molecular and phenotypic features, a phenomenon termed intratumoral heterogeneity. Intratumoral heterogeneity poses challenges for cancer treatment, motivating the need for combination therapies. Single-cell technologies are now available to guide effective drug combinations by accounting for intratumoral heterogeneity through the analysis of the signaling perturbations of an individual tumor sample screened by a drug panel. In particular, Mass Cytometry Time-of-Flight (CyTOF) is a high-throughput single-cell technology that enables the simultaneous measurements of multiple (>40) intracellular and surface markers at the level of single cells for hundreds of thousands of cells in a sample. We developed a computational framework, entitled Drug Nested Effects Models (DRUG-NEM), to analyze CyTOF single-drug perturbation data for the purpose of individualizing drug combinations. DRUG-NEM optimizes drug combinations by choosing the minimum number of drugs that produce the maximal desired intracellular effects based on nested effects modeling. We demonstrate the performance of DRUG-NEM using single-cell drug perturbation data from tumor cell lines and primary leukemia samples.

TCMAnalyzer: A Chemo- and Bioinformatics Web Service for Analyzing Traditional Chinese Medicine

Traditional Chinese medicine (TCM) has been widely used and proven effective in long term clinical practice. However, the molecular mechanism of action for many TCMs remains unclear due to the complexity of many ingredients and their interactions with biological receptors. This is one of the major roadblocks in TCM modernization. In order to solve this problem, we have developed TCMAnalyzer, which is a free web-based toolkit allowing a user to (1) identify the potential compounds that are responsible for the bioactivities for a TCM herb through scaffold-activity relation searches using structural search techniques, (2) investigate the molecular mechanism of action for a TCM herb at the systemic level, and (3) explore the potentially targeted bioactive herbs. The toolkit can result in TCM networks that demonstrate the relations among natural product molecules (small molecular ligands), putative protein targets, pathways, and diseases. These networks are graphically depicted to reveal the mechanism of actions for a TCM herb or to identify new molecular scaffolds for new chemotherapies. TCMAnalyzer is freely available at http://www.rcdd.org.cn/tcmanalyzer .

Matrine Is Identified as a Novel Macropinocytosis Inducer by a Network Target Approach

Comprehensively understanding pharmacological functions of natural products is a key issue to be addressed for the discovery of new drugs. Unlike some single-target drugs, natural products always exert diverse therapeutic effects through acting on a "network" that consists of multiple targets, making it necessary to develop a systematic approach, e.g., network pharmacology, to reveal pharmacological functions of natural products and infer their mechanisms of action. In this work, to identify the "network target" of a natural product, we perform a functional analysis of matrine, a marketed drug in China extracted from a medical herb Ku-Shen (Radix Sophorae Flavescentis). Here, the network target of matrine was firstly predicted by drugCIPHER, a genome-wide target prediction method. Based on the network target of matrine, we performed a functional gene set enrichment analysis to computationally identify the potential pharmacological functions of matrine, most of which are supported by the literature evidence, including neurotoxicity and neuropharmacological activities of matrine. Furthermore, computational results demonstrated that matrine has the potential for the induction of macropinocytosis and the regulation of ATP metabolism. Our experimental data revealed that the large vesicles induced by matrine are consistent with the typical characteristics of macropinosome. Our verification results also suggested that matrine could decrease cellular ATP level. These findings demonstrated the availability and effectiveness of the network target strategy for identifying the comprehensive pharmacological functions of natural products.

Network pharmacology analysis of the anti-cancer pharmacological mechanisms of Ganoderma lucidum extract with experimental support using Hepa1-6-bearing C57 BL/6 mice

ETHNOPHARMACOLOGICAL RELEVANCE

Ganoderma lucidum (GL) is an oriental medical fungus, which was used to prevent and treat many diseases. Previously, the effective compounds of Ganoderma lucidum extract (GLE) were extracted from two kinds of GL, [Ganoderma lucidum (Leyss. Ex Fr.) Karst.] and [Ganoderma sinense Zhao, Xu et Zhang], which have been used for adjuvant anti-cancer clinical therapy for more than 20 years. However, its concrete active compounds and its regulation mechanisms on tumor are unclear.

AIM OF THE STUDY

In this study, we aimed to identify the main active compounds from GLE and to investigate its anti-cancer mechanisms via drug-target biological network construction and prediction.

MATERIALS AND METHODS

The main active compounds of GLE were identified by HPLC, EI-MS and NMR, and the compounds related targets were predicted using docking program. To investigate the functions of GL holistically, the active compounds of GL and related targets were predicted based on four public databases. Subsequently, the Identified-Compound-Target network and Predicted-Compound-Target network were constructed respectively, and they were overlapped to detect the hub potential targets in both networks. Furthermore, the qRT-PCR and western-blot assays were used to validate the expression levels of target genes in GLE treated Hepa1-6-bearing C57 BL/6 mice.

RESULTS

In our work, 12 active compounds of GLE were identified, including Ganoderic acid A, Ganoderenic acid A, Ganoderic acid B, Ganoderic acid H, Ganoderic acid C2, Ganoderenic acid D, Ganoderic acid D, Ganoderenic acid G, Ganoderic acid Y, Kaemferol, Genistein and Ergosterol. Using the docking program, 20 targets were mapped to 12 compounds of GLE. Furthermore, 122 effective active compounds of GL and 116 targets were holistically predicted using public databases. Compare with the Identified-Compound-Target network and Predicted-Compound-Target network, 6 hub targets were screened, including AR, CHRM2, ESR1, NR3C1, NR3C2 and PGR, which was considered as potential markers and might play important roles in the process of GLE treatment. GLE effectively inhibited tumor growth in Hepa1-6-bearing C57 BL/6 mice. Finally, consistent with the results of qRT-PCR data, the results of western-blot assay demonstrated the expression levels of PGR and ESR1 were up-regulated, as well as the expression levels of NR3C2 and AR were down-regulated, while the change of NR3C1 and CHRM2 had no statistical significance.

CONCLUSIONS

The results indicated that these 4 hub target genes, including NR3C2, AR, ESR1 and PGR, might act as potential markers to evaluate the curative effect of GLE treatment in tumor. And, the combined data provide preliminary study of the pharmacological mechanisms of GLE, which may be a promising potential therapeutic and chemopreventative candidate for anti-cancer.

Text Mining of Rheumatoid Arthritis and Diabetes Mellitus to Understand the Mechanisms of Chinese Medicine in Different Diseases with Same Treatment

OBJECTIVE

To identify the commonalities between rheumatoid arthritis (RA) and diabetes mellitus (DM) to understand the mechanisms of Chinese medicine (CM) in different diseases with the same treatment.

METHODS

A text mining approach was adopted to analyze the commonalities between RA and DM according to CM and biological elements. The major commonalities were subsequently verified in RA and DM rat models, in which herbal formula for the treatment of both RA and DM identified via text mining was used as the intervention.

RESULTS

Similarities were identified between RA and DM regarding the CM approach used for diagnosis and treatment, as well as the networks of biological activities affected by each disease, including the involvement of adhesion molecules, oxidative stress, cytokines, T-lymphocytes, apoptosis, and inflammation. The Ramulus Cinnamomi-Radix Paeoniae Alba-Rhizoma Anemarrhenae is an herbal combination used to treat RA and DM. This formula demonstrated similar effects on oxidative stress and inflammation in rats with collagen-induced arthritis, which supports the text mining results regarding the commonalities between RA and DM.

CONCLUSION

Commonalities between the biological activities involved in RA and DM were identified through text mining, and both RA and DM might be responsive to the same intervention at a specific stage.

Prediction of the targets of the main components in blood after oral administration ofXanthii Fructus: a network pharmacology study

Xanthii Fructus (XF), a famous traditional Chinese medicine (TCM), has been widely used in the treatment of rhinitis and other diseases. However, the targets of the main XF components found in the blood after oral administration of XF extract are still unclear. In the current study, a feasible systems pharmacology method was developed to predict these targets. In accordance with our previous research, XF components were selected including cleomiscosin A, myristic acid, succinic acid, xanthosine, sitostenone, emodin, apigenin, and chrysophanol. Three components, namely emodin, apigenin, and chrysophanol, failed to be detected with target proteins, thus the other five components, namely cleomiscosin A, myristic acid, succinic acid, xanthosine and sitostenone, were eventually chosen for further systematic analysis. Ninety-nine target proteins and fifty-two pathways were found after a series of analyses. The frequency of some target proteins was much higher than that of others; high frequencies were obtained for P15086, P07360, P07195, MAOM_HUMAN (P23368), P35558, P35520, ACE_HUMAN (P12821), C1S_HUMAN (P09871), PH4H_HUMAN (P00439), FPPS_HUMAN (P14324), P50613, P12724, IMPA1_HUMAN (P29218), HXK1_HUMAN (P19367), P14061, and MCR_HUMAN (P08235). The frequency of eight pathways was also high, including Generic Transcription Pathway, RNA Polymerase II Transcription, Metabolism, Metabolism of steroids, Gene expression (Transcription), Cellular responses to stress, Platelet activation, signaling and aggregation, Signaling by Receptor Tyrosine Kinases, and Cellular Senescence. This study identified a common pathway – the Metabolism pathway – for all five XF components. We successfully developed a network pharmacology method to predict the potential targets of the main XF components absorbed in serum after oral administration of XF extract.

This paper developed a network pharmacology method to predict the potential pathways targeted by oral administration of XF extract.

PDTCM: a systems pharmacology platform of traditional Chinese medicine for psoriasis

INTRODUCTION

Psoriasis is a refractory skin disorder, and usually requires a lifetime control. Traditional Chinese medicine (TCM) is effective and safe for this disease. However, the cellular and molecular mechanisms of TCM remedies for psoriasis are still not fully understood. TCM contains numerous natural products. Natural products have historically been invaluable as a resource of therapeutic agents. Yet, there is no integrated information about active compounds of TCM for psoriasis.

METHOD

We use systems pharmacology methods to develop the Psoriasis Database of Traditional Chinese Medicine (PDTCM). The database covered a number of psoriasis-related information (formulas, TCM, compounds, target proteins, diseases and biomarkers). With these data information, an online platform was constructed Results: PDTCM comprises 38 empirical therapeutic formulas, 34373 compounds from 1424 medicinal plants, 44 psoriasis-related proteins and 76 biomarkers from 111 related diseases. On this platform, users can screen active compounds for a psoriasis-related target and explore molecular mechanisms of TCM. Accordingly, users can also download the retrieved structures and data information with a defined value set. In addition, it helps to get a better understanding of Chinese prescriptions in disease treatment.

CONCLUSION

With the systems pharmacology-based data, PDTCM would become a valuable resource for TCM in psoriasis-related research. Key messages PDTCM platform comprises a great deal of data on TCM and psoriasis. On this platform, users can retrieve and get needed information with systems pharmacology methods, such as active compounds screening, target prediction and molecular mechanisms exploration. It is a tool for psoriasis-related research on natural drugs systematically.

In silico-based screen synergistic drug combinations from herb medicines: a case using Cistanche tubulosa

Neuroinflammation is characterized by the elaborated inflammatory response repertoire of central nervous system tissue. The limitations of the current treatments for neuroinflammation are well-known side effects in the clinical trials of monotherapy. Drug combination therapies are promising strategies to overcome the compensatory mechanisms and off-target effects. However, discovery of synergistic drug combinations from herb medicines is rare. Encouraged by the successfully applied cases we move on to investigate the effective drug combinations based on system pharmacology among compounds from Cistanche tubulosa (SCHENK) R. WIGHT. Firstly, 63 potential bioactive compounds, the related 133 direct and indirect targets are screened out by Drug-likeness evaluation combined with drug targeting process. Secondly, Compound-Target network is built to acquire the data set for predicting drug combinations. We list the top 10 drug combinations which are employed by the algorithm Probability Ensemble Approach (PEA), and Compound-Target-Pathway network is then constructed by the 12 compounds of the combinations, targets, and pathways to unearth the corresponding pharmacological actions. Finally, an integrating pathway approach is developed to elucidate the therapeutic effects of the herb in different pathological features-relevant biological processes. Overall, the method may provide a productive avenue for developing drug combination therapeutics.

Predicting new indications of compounds with a network pharmacology approach: Liuwei Dihuang Wan as a case study

With the ever increasing cost and time required for drug development, new strategies for drug development are highly demanded, whereas repurposing old drugs has attracted much attention in drug discovery. In this paper, we introduce a new network pharmacology approach, namely PINA, to predict potential novel indications of old drugs based on the molecular networks affected by drugs and associated with diseases. Benchmark results on FDA approved drugs have shown the superiority of PINA over traditional computational approaches in identifying new indications of old drugs. We further extend PINA to predict the novel indications of Traditional Chinese Medicines (TCMs) with Liuwei Dihuang Wan (LDW) as a case study. The predicted indications, including immune system disorders and tumor, are validated by expert knowledge and evidences from literature, demonstrating the effectiveness of our proposed computational approach.

DESIGNER Extracts as Tools to Balance Estrogenic and Chemopreventive Activities of Botanicals for Women's Health

Botanical dietary supplements contain multiple bioactive compounds that target numerous biological pathways. The lack of uniform standardization requirements is one reason that inconsistent clinical effects are reported frequently. The multifaceted biological interactions of active principles can be disentangled by a coupled pharmacological/phytochemical approach using specialized ("knock-out") extracts. This is demonstrated for hops, a botanical for menopausal symptom management. Employing targeted, adsorbent-free countercurrent separation, Humulus lupulus extracts were designed for pre- and postmenopausal women by containing various amounts of the phytoestrogen 8-prenylnaringenin (8-PN) and the chemopreventive constituent xanthohumol (XH). Analysis of their estrogenic (alkaline phosphatase), chemopreventive (NAD(P)H-quinone oxidoreductase 1 [NQO1]), and cytotoxic bioactivities revealed that the estrogenicity of hops is a function of 8-PN, whereas their NQO1 induction and cytotoxic properties depend on XH levels. Antagonization of the estrogenicity of 8-PN by elevated XH concentrations provided evidence for the interdependence of the biological effects. A designed postmenopausal hop extract was prepared to balance 8-PN and XH levels for both estrogenic and chemopreventive properties. An extract designed for premenopausal women contains reduced 8-PN levels and high XH concentrations to minimize estrogenic while retaining chemopreventive properties. This study demonstrates the feasibility of modulating the concentrations of bioactive compounds in botanical extracts for potentially improved efficacy and safety.

How Can Synergism of Traditional Medicines Benefit from Network Pharmacology?

Many prescriptions of traditional medicines (TMs), whose efficacy has been tested in clinical practice, have great therapeutic value and represent an excellent resource for drug discovery. Research into single compounds of TMs, such as artemisinin from Artemisia annua L., has achieved great success; however, it has become evident that a TM prescription (which frequently contains various herbs or other components) has a synergistic effect in effecting a cure or reducing toxicity. Network pharmacology targets biological networks and analyzes the links among drugs, targets, and diseases in those networks. Comprehensive, systematic research into network pharmacology is consistent with the perspective of holisticity, which is a main characteristic of many TMs. By means of network pharmacology, research has demonstrated that many a TM show a synergistic effect by acting at different levels on multiple targets and pathways. This approach effectively bridges the gap between modern medicine and TM, and it greatly facilitates studies into the synergistic actions of TMs. There are different kinds of synergistic effects with TMs, such as synergy among herbs, effective parts, and pure compounds; however, for various reasons, new drug discovery should at present focus on synergy among pure compounds.

Innovating Chinese Herbal Medicine: From Traditional Health Practice to Scientific Drug Discovery

As one of the major contemporary alternative medicines, traditional Chinese medicine (TCM) continues its influence in Chinese communities and has begun to attract the academic attention in the world of western medicine. This paper aims to examine Chinese herbal medicine (CHM), the essential branch of TCM, from both narrative and scientific perspectives. CHM is a traditional health practice originated from Chinese philosophy and religion, holding the belief of holism and balance in the body. With the development of orthodox medicine and science during the last centuries, CHM also seized the opportunity to change from traditional health practice to scientific drug discovery illustrated in the famous story of the herb-derived drug artemisinin. However, hindered by its culture and founding principles, CHM faces the questions of the research paradigm posed by the convention of science. To address these questions, we discussed two essential questions concerning the relationship of CHM and science, and then upheld the paradigm of methodological reductionism in scientific research. Finally, the contemporary narrative of CHM in the 21st century was discussed in the hope to preserve this medical tradition in tandem with scientific research.

Chinese Herbal Medicine Meets Biological Networks of Complex Diseases: A Computational Perspective

With the rapid development of cheminformatics, computational biology, and systems biology, great progress has been made recently in the computational research of Chinese herbal medicine with in-depth understanding towards pharmacognosy. This paper summarized these studies in the aspects of computational methods, traditional Chinese medicine (TCM) compound databases, and TCM network pharmacology. Furthermore, we chose arachidonic acid metabolic network as a case study to demonstrate the regulatory function of herbal medicine in the treatment of inflammation at network level. Finally, a computational workflow for the network-based TCM study, derived from our previous successful applications, was proposed.

TCM-Mesh: The database and analytical system for network pharmacology analysis for TCM preparations

With the advancement of systems biology research, we have already seen great progress in pharmacology studies, especially in network pharmacology. Network pharmacology has been proven to be effective for establishing the "compounds-proteins/genes-diseases" network, and revealing the regulation principles of small molecules in a high-throughput manner, thus would be very effective for the analysis of drug combinations, especially for TCM preparations. In this work, we have proposed the TCM-Mesh system, which records TCM-related information collected from various resources and could serve for network pharmacology analysis for TCM preparations in a high-throughput manner (http://mesh.tcm.microbioinformatics.org/). Currently, the database contains 6,235 herbs, 383,840 compounds, 14,298 genes, 6,204 diseases, 144,723 gene-disease associations, 3,440,231 pairs of gene interactions, 163,221 side effect records and 71 toxic records, and web-based software construct a network between herbs and treated diseases, which will help to understand the underlying mechanisms for TCM preparations at molecular levels. We have used 1,293 FDA-approved drugs, as well as compounds from an herbal material Panax ginseng and a patented drug Liuwei Dihuang Wan (LDW) for evaluating our database. By comparison of different databases, as well as checking against literature, we have demonstrated the completeness, effectiveness, and accuracy of our database.