Predicting new molecular targets for rhein using network pharmacology

A network pharmacology-based approach to decipher the pharmacological mechanisms of Salvia officinalis in neurodegenerative disorders

The present study aimed to assess the pharmacological mechanism of Salvia officinalis in Neurodegenerative disorders using a network pharmacology approach followed by molecular docking analysis. Phytoconstituents of S.officinalis were obtained from various databases, followed by the screening of active ingredients using the Swiss ADME web tool. Potential targets of active ingredients were identified using PubChem & SwissTargetPrediction. Genes related to Alzheimer's disease (AD), Parkinson's disease (PD), and Huntington's disease (HD) were gathered using online databases. Besides, the correlation between active ingredient targets and disease-associated genes was linked. Networks were constructed, visualized, and analyzed using Cytoscape. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG) pathway enrichment analysis were performed using DAVID database. Decisively, Autodock was used for molecular docking. The results of network analysis identified 9 key active ingredients based on topological analysis of the active ingredient-candidate targets network. Also, the analysis revealed a shared target of 9 key active ingredients of S. officinalis that interacted with 133 AD-related targets whereas only 6 active ingredients interacted with 85 and 58 targets of PD and HD respectively. The core genes from the network were AKT1, BACE1, CASP3, MAPK1, TNF, and IL6. Furthermore, GO and KEGG enrichment analysis showed that FOXO, TNF, MAPK, PI3K-Akt, Rap 1, and neurotrophin signalling pathways as enriched, which were further evaluated by molecular docking suggesting the protective role of S. officinalis in neurodegenerative diseases. Our research reveals the therapeutic benefits of S. officinalis, which might play a crucial role in modulating neurodegenerative diseases.

Identify Diabetes-related Targets based on ForgeNet_GPC

BACKGROUND

Research on potential therapeutic targets and new mechanisms of action can greatly improve the efficiency of new drug development.

AIMS

Polygenic genetic diseases, such as diabetes, are caused by the interaction of multiple gene loci and environmental factors.

OBJECTIVES

In this study, a disease target identification algorithm based on protein recognition is proposed.

MATERIALS AND METHODS

In this method, the related and unrelated targets are collected from literature databases for treating diabetes. The transcribed proteins corresponding to each target are queried in order to construct a protein dataset. Six protein feature extraction algorithms (AAC, CKSAAGP, DDE, DPC, GAAP, and TPC) are utilized to obtain the feature vectors of each protein, which are merged into the full feature vectors.

RESULTS

A novel classifier (forgeNet_GPC) based on forgeNet and Gaussian process classifier (GPC) is proposed to classify the proteins.

CONCLUSION

In forgeNet_GPC, forgeNet is utilized to select the important features, and GPC is utilized to solve the classification problem. The experimental results reveal that forgeNet_GPC performs better than 22 classifiers in terms of ROC-AUC, PR-AUC, MCC, Youden Index, and Kappa.

Computer-aided drug design combined network pharmacology to explore anti-SARS-CoV-2 or anti-inflammatory targets and mechanisms of Qingfei Paidu Decoction for COVID-19

Introduction

Coronavirus Disease-2019 (COVID-19) is an infectious disease caused by SARS-CoV-2. Severe cases of COVID-19 are characterized by an intense inflammatory process that may ultimately lead to organ failure and patient death. Qingfei Paidu Decoction (QFPD), a traditional Chines e medicine (TCM) formula, is widely used in China as anti-SARS-CoV-2 and anti-inflammatory. However, the potential targets and mechanisms for QFPD to exert anti-SARS-CoV-2 or anti-inflammatory effects remain unclear.

Methods

In this study, Computer-Aided Drug Design was performed to identify the antiviral or anti-inflammatory components in QFPD and their targets using Discovery Studio 2020 software. We then investigated the mechanisms associated with QFPD for treating COVID-19 with the help of multiple network pharmacology approaches.

Results and discussion

By overlapping the targets of QFPD and COVID-19, we discovered 8 common targets (RBP4, IL1RN, TTR, FYN, SFTPD, TP53, SRPK1, and AKT1) of 62 active components in QFPD. These may represent potential targets for QFPD to exert anti-SARS-CoV-2 or anti-inflammatory effects. The result showed that QFPD might have therapeutic effects on COVID-19 by regulating viral infection, immune and inflammation-related pathways. Our work will promote the development of new drugs for COVID-19.

Disease-related compound identification based on deeping learning method

Acute lung injury (ALI) is a serious respiratory disease, which can lead to acute respiratory failure or death. It is closely related to the pathogenesis of New Coronavirus pneumonia (COVID-19). Many researches showed that traditional Chinese medicine (TCM) had a good effect on its intervention, and network pharmacology could play a very important role. In order to construct "disease-gene-target-drug" interaction network more accurately, deep learning algorithm is utilized in this paper. Two ALI-related target genes (REAL and SATA3) are considered, and the active and inactive compounds of the two corresponding target genes are collected as training data, respectively. Molecular descriptors and molecular fingerprints are utilized to characterize each compound. Forest graph embedded deep feed forward network (forgeNet) is proposed to train. The experimental results show that forgeNet performs better than support vector machines (SVM), random forest (RF), logical regression (LR), Naive Bayes (NB), XGBoost, LightGBM and gcForest. forgeNet could identify 19 compounds in Erhuang decoction (EhD) and Dexamethasone (DXMS) more accurately.

The Ameliorative Effects of Arctiin and Arctigenin on the Oxidative Injury of Lung Induced by Silica via TLR-4/NLRP3/TGF-β Signaling Pathway

Silicosis remains one of the most serious diseases worldwide, with no effective drug for its treatment. Our research results have indicated that arctiin and arctigenin could increase the mitochondrial membrane potential, which in turn reduces the production of reactive oxygen species (ROS), blocks the polarization of macrophages, and inhibits the differentiation of myofibroblasts to reduce oxidative stress, inflammation, and fibrosis. Further, our study revealed that arctiin and arctigenin suppressed the activation of NLRP3 inflammasome through the TLR-4/Myd88/NF-κB pathway and the silica-induced secretion of TNF-α, IL-1β, TGF-β, and α-SMA. Besides, the silica-induced increase in the levels of serum ceruloplasmin and HYP was also inhibited. Results of metabolomics indicated that arctiin and arctigenin could regulate the abnormal metabolic pathways associated with the development of silicosis, which involve pantothenate and CoA biosynthesis, cysteine and methionine metabolism, linoleic acid metabolism, and arginine and proline metabolism successively. Furthermore, the analysis of metabolomics, together with network topological analysis in different phases of silicosis, revealed that urine myristic acid, serum 4-hydroxyproline, and L-arginine could be regarded as diagnosis biomarkers in the early phase and formation of pulmonary fibrosis in the latter phases of silicosis. Arctiin and arctigenin could downregulate the increased levels of myristic acid in the early phase and serum 4-hydroxyproline in the latter phase of silicosis. Interestingly, the integration of TLR-4/NLRP3/TGF-β signaling and metabolomics verified the importance of macrophage polarization in the silicosis fibrosis process. To the best of our knowledge, this is the first study reporting that arctiin and arctigenin both can ameliorate silicosis effectively, and the former is a little stronger than its aglycone arctigenin because of its high oral bioavailability, low toxicity, and multimolecular active metabolites as determined by AdmetSAR and molecular docking analysis.

Mechanisms of Paeonia lactiflora in Treatment of Ulcerative Colitis: A Network Pharmacological Study

BACKGROUND Paeonia lactiflora is the main active ingredient of peony decoction, which is used to treat ulcerative colitis (UC) in traditional Chinese medicine (TCM). Network pharmacology indicates the multiple interactions among genes, proteins, and metabolites associated with diseases and drugs from the network perspective, which shows the multi-component and multi-target attributes of TCM. This study predicted the pharmacological mechanism of Paeonia lactiflora in the treatment of UC by network pharmacological method. MATERIAL AND METHODS Chemical constituents of Paeonia lactiflora were searched from TCMSP data, gene names of target sites were extracted from UniProt database, and disease targets of ulcerative colitis were obtained from the CTD disease database. Use Venny online tools to obtain common targets for drugs and diseases. The DAVID database was used to enrich GO and KEGG for the common target, and the related functions and pathways were obtained. Cytoscape 3.7.1 was used to construct the 'drug-compound-target-disease' network. RESULTS There are 70 common target genes between Paeonia lactiflora and UC. GO analysis showed that the biological functions of the common target genes of Paeonia lactiflora and UC include response to lipopolysaccharide, response to estradiol, response to drug, positive regulation of nitric oxide biosynthetic process, and steroid hormone-mediated signaling pathway. Enrichment of the KEGG signaling pathway mainly involves signaling pathways, including Pathways in cancer, TNF signaling pathway, Tuberculosis, Hepatitis B, and Toxoplasmosis. CONCLUSIONS The network pharmacology intuitively shows the multi-component, multi-target, and multi-channel pharmacological effects of Paeonia lactiflora on UC, and provides a scientific basis for studying the mechanism of the effect of Paeonia lactiflora on UC.

A network pharmacology study on the triterpene saponins from Medicago sativa L. for the treatment of Neurodegenerative diseases

Neurodegenerative diseases (NDDs) are characterized by progressive and irreversible, is a kind of complex illnesses, and the long-term therapy which is frequently associated with adverse side effects. Medicago sativa L., widely consumed as a vegetable, has the effects of improving memory and relieving central nervous system diseases. However, there are less studies on its specific mechanism for NDDs. In this investigation, we applied a method of network pharmacology, which combined molecular docking and network analysis to decipher the mechanisms of M. sativa in NDDs. The pharmacological system generated 55 triterpene saponins from M. sativa, and predicted 27 potential targets with 100 pathways in the treatment of NDDs. As a result, 13 compounds, 10 target proteins, and 6 signaling pathways were found to play important roles in the treatment of NDDs. In addition, in vitro experiments of isolates confirmed activities for NDDs, which were consistent with the results of network pharmacology prediction. PRACTICAL APPLICATIONS: Medicago sativa L. has been widely consumed as a vegetable, which possesses many nutritional components. As a functional food stuff, M. sativa can improve human health, such as memory improving activities, relieving central nervous system diseases, immunomodulatory, antioxidant, anticancer, and anti-inflammatory. In this article, the mechanism of triterpene saponins from M. sativa against NDDs was successfully predicted by network pharmacology method. The results will serve as a reference of M. sativa against NDDs.

Rhein, An Anthraquinone Drug, Suppresses the NLRP3 Inflammasome and Macrophage Activation in Urate Crystal-Induced Gouty Inflammation

Rhein, an anthraquinone drug, is a widely used traditional Chinese medicine. Rhein is a major bioactive metabolite of diacerein which has been approved for treating osteoarthritis with a good safety profile in humans. Gouty arthritis is an inflammatory disease characterized by urate crystal-induced NLRP3 inflammasome activation with up-regulated caspase-1 protease and IL-1 β in macrophages. Inhibition of the NLRP3 inflammasome formation has been considered as a potential therapeutic avenue for treating or preventing many inflammatory diseases. This study aimed to evaluate the anti-inflammatory effects of rhein on gouty arthritis. Rhein within the physiological levels of humans showed no toxicity on the cell viability and differentiation, but significantly decreased the production of IL-1 β , TNF- α and caspase-1 protease in urate crystal-activated macrophages. Compared to medium controls, rhein at the therapeutic concentration (2.5 μ g/mL) effectively inhibited IL-1 β production by 47% ( P=0.002 ). Rhein did not affect the mRNA levels of CASP1, NLRP3 and ASC, but suppressed the protein expression and enzyme activity of caspase-1. Immunofluorescence confocal microscopy further revealed that rhein suppressed the aggregation of ASC speck and inhibited the formation of NLRP3 inflammasome. Rhein of 5 μ g/mL significantly decreased the ASC speck to 36% ( P=0.0011 ), and reduced the NLRP3 aggregates to 37.5% ( P=0.014 ). Our data demonstrate that rhein possesses pharmacological activity to suppress caspase-1 protease activity and IL-1 β production by interfering with the formation of NLRP3 multiprotein complex. These results suggest that rhein has therapeutic potential for treating NLRP3 inflammasome-mediated diseases such as gouty arthritis.

Network Pharmacology Studies on the Bioactive Compounds and Action Mechanisms of Natural Products for the Treatment of Diabetes Mellitus: A Review

Diabetes mellitus (DM) is a kind of chronic and metabolic disease, which can cause a number of diseases and severe complications. Network pharmacology approach is introduced to study DM, which can combine the drugs, target proteins and disease and form drug-target-disease networks. Network pharmacology has been widely used in the studies of the bioactive compounds and action mechanisms of natural products for the treatment of DM due to the multi-components, multi-targets, and lower side effects. This review provides a balanced and comprehensive summary on network pharmacology from current studies, highlighting different bioactive constituents, related databases and applications in the investigations on the treatment of DM especially type 2. The mechanisms related to type 2 DM, including α-amylase and α-glucosidase inhibitory, targeting β cell dysfunction, AMPK signal pathway and PI3K/Akt signal pathway are summarized and critiqued. It suggests that the network pharmacology approach cannot only provide a new research paradigm for natural products, but also improve the current antidiabetic drug discovery strategies. Furthermore, we put forward the perspectives on the reasonable applications of network pharmacology for the therapy of DM and related drug discovery.

Network pharmacology-based virtual screening of natural products from Clerodendrum species for identification of novel anti-cancer therapeutics

In the present work, latest network pharmacological approach has been used for the screening of natural anticancer compounds from Clerodendrum species.

Protein localization vector propagation: a method for improving the accuracy of drug repositioning

Identifying alternative indications for known drugs is important for the pharmaceutical industry. Many computational methods have been proposed for predicting unknown associations between drugs and target proteins associated with diseases. To produce better prediction, researchers should not only develop accurate algorithms but identify good features that reflect intracellular systems. In this paper, we proposed a novel method for exploiting protein localization. We generated localization vectors (LVs) from protein localization and propagated LVs through a protein interaction network to increase the coverage of the localization information. The LVs showed distinct patterns among targets of known drugs as well as independent characteristics compared to existing features. Based on the experimental results, we determined that including LVs improves cross-validation accuracy and, produces better novel predictions with real and independent clinical trial data. Moreover, the propagation of LVs showed a positive result that it can help in increasing the coverage of the prediction results.

Drug-target networks for Tanshinone IIA identified by data mining

Tanshinone IIA is a pharmacologically active compound isolated from Danshen (Salvia miltiorrhiza), a traditional Chinese herbal medicine for the management of cardiac diseases and other disorders. But its underlying molecular mechanisms of action are still unclear. The present investigation utilized a data mining approach based on network pharmacology to uncover the potential protein targets of Tanshinone IIA. Network pharmacology, an integrated multidisciplinary study, incorporates systems biology, network analysis, connectivity, redundancy, and pleiotropy, providing powerful new tools and insights into elucidating the fine details of drug-target interactions. In the present study, two separate drug-target networks for Tanshinone IIA were constructed using the Agilent Literature Search (ALS) and STITCH (search tool for interactions of chemicals) methods. Analysis of the ALS-constructed network revealed a target network with a scale-free topology and five top nodes (protein targets) corresponding to Fos, Jun, Src, phosphatidylinositol-4, 5-bisphosphate 3-kinase, catalytic subunit alpha (PIK3CA), and mitogen-activated protein kinase kinase 1 (MAP2K1), whereas analysis of the STITCH-constructed network revealed three top nodes corresponding to cytochrome P450 3A4 (CYP3A4), cytochrome P450 A1 (CYP1A1), and nuclear factor kappa B1 (NFκB1). The discrepancies were probably due to the differences in the divergent computer mining tools and databases employed by the two methods. However, it is conceivable that all eight proteins mediate important biological functions of Tanshinone IIA, contributing to its overall drug-target network. In conclusion, the current results may assist in developing a comprehensive understanding of the molecular mechanisms and signaling pathways of in a simple, compact, and visual manner.

Systems Biology - A Pivotal Research Methodology for Understanding the Mechanisms of Traditional Medicine

Objectives:

Systems biology is a novel subject in the field of life science that aims at a systems’ level understanding of biological systems. Because of the significant progress in high-throughput technologies and molecular biology, systems biology occupies an important place in research during the post-genome era.

Methods:

The characteristics of systems biology and its applicability to traditional medicine research have been discussed from three points of view: data and databases, network analysis and inference, and modeling and systems prediction.

Results:

The existing databases are mostly associated with medicinal herbs and their activities, but new databases reflecting clinical situations and platforms to extract, visualize and analyze data easily need to be constructed. Network pharmacology is a key element of systems biology, so addressing the multi-component, multi-target aspect of pharmacology is important. Studies of network pharmacology highlight the drug target network and network target. Mathematical modeling and simulation are just in their infancy, but mathematical modeling of dynamic biological processes is a central aspect of systems biology. Computational simulations allow structured systems and their functional properties to be understood and the effects of herbal medicines in clinical situations to be predicted.

Conclusion:

Systems biology based on a holistic approach is a pivotal research methodology for understanding the mechanisms of traditional medicine. If systems biology is to be incorporated into traditional medicine, computational technologies and holistic insights need to be integrated.

Rhein prevents endotoxin-induced acute kidney injury by inhibiting NF-κB activities

This study aimed to explore the effect and mechanisms of rhein on sepsis-induced acute kidney injury by injecting lipopolysaccharide (LPS) and cecal ligation and puncture (CLP) in vivo, and on LPS-induced HK-2 cells in vitro. For histopathological analysis, rhein effectively attenuated the severity of renal injury. Rhein could significantly decrease concentration of BUN and SCr and level of TNF-α and IL-1β in two different mouse models of experimental sepsis. Moreover, rhein could markedly attenuate circulating leukocyte infiltration and enhance phagocytic activity of macrophages partly impaired at 12 h after CLP. Rhein could enhance cell viability and suppresse the release of MCP-1 and IL-8 in LPS-stimulated HK-2 cells Furthermore, rhein down regulated the expression of phosphorylated NF-κB p65, IκBα and IKKβ stimulated by LPS both in vivo and in vitro. All these results suggest that rhein has protective effects on endotoxin-induced kidney injury. The underlying mechanism of rhein on anti-endotoxin kidney injury may be closely related with its anti-inflammatory and immunomodulatory properties by decreasing NF-κB activation through restraining the expression and phosphorylation of the relevant proteins in NF-κB signal pathway, hindering transcription of NF-κB p65.These evidence suggest that rhein has a potential application to treat endotoxemia-associated acute kidney injury.

Network pharmacology study on the mechanism of traditional Chinese medicine for upper respiratory tract infection

Traditional Chinese medicine (TCM) is a multi-component and multi-target agent and could treat complex diseases in a holistic way, especially infection diseases. However, the underlying pharmacology remains unclear. Fortunately, network pharmacology by integrating system biology and polypharmacology provides a strategy to address this issue. In this work, Reduning Injection (RDN), a well-used TCM treatment in the clinic for upper respiratory tract infections (URTIs), was investigated to interpret the molecular mechanism and predict new clinical directions by integrating molecular docking, network analysis and cell-based assays. 32 active ingredients and 38 potential targets were identified. In vitro experiments confirmed the bioactivities of the compounds against lipopolysaccharide (LPS)-stimulated PGE2 and NO production in RAW264.7 cells. Moreover, network analysis showed that RDN could not only inhibit viral replication but also alleviate the sickness symptoms of URTIs through directly targeting the key proteins in the respiratory viral life cycle and indirectly regulating host immune systems. In addition, other clinical applications of RDN such as neoplasms, cardiovascular diseases and immune system diseases were predicted on the basis of the relationships between targets and diseases.

An analysis of chemical ingredients network of Chinese herbal formulae for the treatment of coronary heart disease

As a complex system, the complicated interactions between chemical ingredients, as well as the potential rules of interactive associations among chemical ingredients of traditional Chinese herbal formulae are not yet fully understood by modern science. On the other hand, network analysis is emerging as a powerful approach focusing on processing complex interactive data. By employing network approach in selected Chinese herbal formulae for the treatment of coronary heart disease (CHD), this article aims to construct and analyze chemical ingredients network of herbal formulae, and provide candidate herbs, chemical constituents, and ingredient groups for further investigation. As a result, chemical ingredients network composed of 1588 ingredients from 36 herbs used in 8 core formulae for the treatment of CHD was produced based on combination associations in herbal formulae. In this network, 9 communities with relative dense internal connections are significantly associated with 14 kinds of chemical structures with P<0.001. Moreover, chemical structural fingerprints of network communities were detected, while specific centralities of chemical ingredients indicating different levels of importance in the network were also measured. Finally, several distinct herbs, chemical ingredients, and ingredient groups with essential position in the network or high centrality value are recommended for further pharmacology study in the context of new drug development.

Drug target mining and analysis of the Chinese tree shrew for pharmacological testing

The discovery of new drugs requires the development of improved animal models for drug testing. The Chinese tree shrew is considered to be a realistic candidate model. To assess the potential of the Chinese tree shrew for pharmacological testing, we performed drug target prediction and analysis on genomic and transcriptomic scales. Using our pipeline, 3,482 proteins were predicted to be drug targets. Of these predicted targets, 446 and 1,049 proteins with the highest rank and total scores, respectively, included homologs of targets for cancer chemotherapy, depression, age-related decline and cardiovascular disease. Based on comparative analyses, more than half of drug target proteins identified from the tree shrew genome were shown to be higher similarity to human targets than in the mouse. Target validation also demonstrated that the constitutive expression of the proteinase-activated receptors of tree shrew platelets is similar to that of human platelets but differs from that of mouse platelets. We developed an effective pipeline and search strategy for drug target prediction and the evaluation of model-based target identification for drug testing. This work provides useful information for future studies of the Chinese tree shrew as a source of novel targets for drug discovery research.

Rhein exerts pro- and anti-inflammatory actions by targeting IKKβ inhibition in LPS-activated macrophages

Because steroids and cyclooxygenase inhibitors may cause serious side effects, the IκB kinase (IKK) β/nuclear factor-κB (NF-κB) system has become an intriguing candidate anti-inflammatory target. Rhein, the active metabolite of diacerein, possesses anti-inflammatory ability with a gastrointestinal protective effect. However, in a preliminary study, we accidentally found that rhein showed both anti- and proinflammatory activities in lipopolysaccharide (LPS)-activated macrophages. Thus, in this study, we explored the underlying molecular mechanisms of the dual effects of rhein. In LPS-activated macrophages, rhein inhibits NF-κB activation and sequentially suppresses its downstream inducible nitric oxide synthase, interleukin-6 (IL-6), tumor necrosis factor-α (TNF-α), and interleukin-1β (IL-1β) transcription and supernatant nitric oxide and IL-6 levels by inhibiting IKKβ (IC50 ≈ 11.79μM). But in the meantime, rhein enhances the activity of caspase-1 by inhibiting intracellular (in situ) IKKβ, in turn increasing the IL-1β and high-mobility-group box 1 release, which can be amplified by rhein׳s reductive effect on intracellular superoxide anion. Unexpectedly, it is because of IKKβ inhibition that rhein significantly enhances TNF-α secretion and phagocytosis in macrophages with or without LPS. These results indicate that rhein exerts anti- and proinflammatory activities by targeting IKKβ inhibition, providing a molecular mechanism for the unanticipated role of rhein in macrophages. Furthermore, our study also highlights the potential complications of IKKβ inhibitor (e.g., rhein, diacerein, etc.) application in inflammation disorders, for the overall effects of IKKβ inhibition in various organ systems and disease processes are not easily predictable under all circumstances.

Toward more realistic drug-target interaction predictions

A number of supervised machine learning models have recently been introduced for the prediction of drug-target interactions based on chemical structure and genomic sequence information. Although these models could offer improved means for many network pharmacology applications, such as repositioning of drugs for new therapeutic uses, the prediction models are often being constructed and evaluated under overly simplified settings that do not reflect the real-life problem in practical applications. Using quantitative drug-target bioactivity assays for kinase inhibitors, as well as a popular benchmarking data set of binary drug-target interactions for enzyme, ion channel, nuclear receptor and G protein-coupled receptor targets, we illustrate here the effects of four factors that may lead to dramatic differences in the prediction results: (i) problem formulation (standard binary classification or more realistic regression formulation), (ii) evaluation data set (drug and target families in the application use case), (iii) evaluation procedure (simple or nested cross-validation) and (iv) experimental setting (whether training and test sets share common drugs and targets, only drugs or targets or neither). Each of these factors should be taken into consideration to avoid reporting overoptimistic drug-target interaction prediction results. We also suggest guidelines on how to make the supervised drug-target interaction prediction studies more realistic in terms of such model formulations and evaluation setups that better address the inherent complexity of the prediction task in the practical applications, as well as novel benchmarking data sets that capture the continuous nature of the drug-target interactions for kinase inhibitors.

Traditional Chinese medicine network pharmacology: theory, methodology and application

Traditional Chinese medicine (TCM) has a long history of viewing an individual or patient as a system with different statuses, and has accumulated numerous herbal formulae. The holistic philosophy of TCM shares much with the key ideas of emerging network pharmacology and network biology, and meets the requirements of overcoming complex diseases, such as cancer, in a systematic manner. To discover TCM from a systems perspective and at the molecular level, a novel TCM network pharmacology approach was established by updating the research paradigm from the current "one target, one drug" mode to a new "network target, multi-components" mode. Subsequently, a set of TCM network pharmacology methods were created to prioritize disease-associated genes, to predict the target profiles and pharmacological actions of herbal compounds, to reveal drug-gene-disease co-module associations, to screen synergistic multi-compounds from herbal formulae in a high-throughput manner, and to interpret the combinatorial rules and network regulation effects of herbal formulae. The effectiveness of the network-based methods was demonstrated for the discovery of bioactive compounds and for the elucidation of the mechanisms of action of herbal formulae, such as Qing-Luo-Yin and the Liu-Wei-Di-Huang pill. The studies suggest that the TCM network pharmacology approach provides a new research paradigm for translating TCM from an experience-based medicine to an evidence-based medicine system, which will accelerate TCM drug discovery, and also improve current drug discovery strategies.

CVDHD: a cardiovascular disease herbal database for drug discovery and network pharmacology

Background

Cardiovascular disease (CVD) is the leading cause of death and associates with multiple risk factors. Herb medicines have been used to treat CVD long ago in china and several natural products or derivatives (e.g., aspirin and reserpine) are most common drugs all over the world. The objective of this work was to construct a systematic database for drug discovery based on natural products separated from CVD-related medicinal herbs and to research on action mechanism of herb medicines.

Description

The cardiovascular disease herbal database (CVDHD) was designed to be a comprehensive resource for virtual screening and drug discovery from natural products isolated from medicinal herbs for cardiovascular-related diseases. CVDHD comprises 35230 distinct molecules and their identification information (chemical name, CAS registry number, molecular formula, molecular weight, international chemical identifier (InChI) and SMILES), calculated molecular properties (AlogP, number of hydrogen bond acceptor and donors, etc.), docking results between all molecules and 2395 target proteins, cardiovascular-related diseases, pathways and clinical biomarkers. All 3D structures were optimized in the MMFF94 force field and can be freely accessed.

Conclusions

CVDHD integrated medicinal herbs, natural products, CVD-related target proteins, docking results, diseases and clinical biomarkers. By using the methods of virtual screening and network pharmacology, CVDHD will provide a platform to streamline drug/lead discovery from natural products and explore the action mechanism of medicinal herbs. CVDHD is freely available at http://pkuxxj.pku.edu.cn/CVDHD.

A Drug-Target Network-Based Approach to Evaluate the Efficacy of Medicinal Plants for Type II Diabetes Mellitus

The use of plants as natural medicines in the treatment of type II diabetes mellitus (T2DM) has long been of special interest. In this work, we developed a docking score-weighted prediction model based on drug-target network to evaluate the efficacy of medicinal plants for T2DM. High throughput virtual screening from chemical library of natural products was adopted to calculate the binding affinity between natural products contained in medicinal plants and 33 T2DM-related proteins. The drug-target network was constructed according to the strength of the binding affinity if the molecular docking score satisfied the threshold. By linking the medicinal plant with T2DM through drug-target network, the model can predict the efficacy of natural products and medicinal plant for T2DM. Eighteen thousand nine hundred ninety-nine natural products and 1669 medicinal plants were predicted to be potentially bioactive.

Navigating traditional chinese medicine network pharmacology and computational tools

The concept of "network target" has ushered in a new era in the field of traditional Chinese medicine (TCM). As a new research approach, network pharmacology is based on the analysis of network models and systems biology. Taking advantage of advancements in systems biology, a high degree of integration data analysis strategy and interpretable visualization provides deeper insights into the underlying mechanisms of TCM theories, including the principles of herb combination, biological foundations of herb or herbal formulae action, and molecular basis of TCM syndromes. In this study, we review several recent developments in TCM network pharmacology research and discuss their potential for bridging the gap between traditional and modern medicine. We briefly summarize the two main functional applications of TCM network models: understanding/uncovering and predicting/discovering. In particular, we focus on how TCM network pharmacology research is conducted and highlight different computational tools, such as network-based and machine learning algorithms, and sources that have been proposed and applied to the different steps involved in the research process. To make network pharmacology research commonplace, some basic network definitions and analysis methods are presented.

Network-based target ranking for polypharmacological therapies

With the growing understanding of complex diseases, the focus of drug discovery has shifted from the well-accepted "one target, one drug" model, to a new "multi-target, multi-drug" model, aimed at systemically modulating multiple targets. In this context, polypharmacology has emerged as a new paradigm to overcome the recent decline in productivity of pharmaceutical research. However, finding methods to evaluate multicomponent therapeutics and ranking synergistic agent combinations is still a demanding task. At the same time, the data gathered on complex diseases has been progressively collected in public data and knowledge repositories, such as protein-protein interaction (PPI) databases. The PPI networks are increasingly used as universal platforms for data integration and analysis. A novel computational network-based approach for feasible and efficient identification of multicomponent synergistic agents is proposed in this paper. Given a complex disease, the method exploits the topological features of the related PPI network to identify possible combinations of hit targets. The best ranked combinations are subsequently computed on the basis of a synergistic score. We illustrate the potential of the method through a study on Type 2 Diabetes Mellitus. The results highlight its ability to retrieve novel target candidates, which role is also confirmed by the analysis of the related literature.

Network pharmacology: a new approach for chinese herbal medicine research

The dominant paradigm of "one gene, one target, one disease" has influenced many aspects of drug discovery strategy. However, in recent years, it has been appreciated that many effective drugs act on multiple targets rather than a single one. As an integrated multidisciplinary concept, network pharmacology, which is based on system biology and polypharmacology, affords a novel network mode of "multiple targets, multiple effects, complex diseases" and replaces the "magic bullets" by "magic shotguns." Chinese herbal medicine (CHM) has been recognized as one of the most important strategies in complementary and alternative medicine. Though CHM has been practiced for a very long time, its effectiveness and beneficial contribution to public health has not been fully recognized. Also, the knowledge on the mechanisms of CHM formulas is scarce. In the present review, the concept and significance of network pharmacology is briefly introduced. The application and potential role of network pharmacology in the CHM fields is also discussed, such as data collection, target prediction, network visualization, multicomponent interaction, and network toxicology. Furthermore, the developing tendency of network pharmacology is also summarized, and its role in CHM research is discussed.

Polypharmacology: drug discovery for the future

In recent years, even with remarkable scientific advancements and a significant increase of global research and development spending, drugs are frequently withdrawn from markets. This is primarily due to their side effects or toxicities. Drug molecules often interact with multiple targets, coined as polypharmacology, and the unintended drug-target interactions could cause side effects. Polypharmacology remains one of the major challenges in drug development, and it opens novel avenues to rationally design the next generation of more effective, but less toxic, therapeutic agents. This review outlines the latest progress and challenges in polypharmacology studies.

Material basis of Chinese herbal formulas explored by combining pharmacokinetics with network pharmacology

The clinical application of Traditional Chinese medicine (TCM), using several herbs in combination (called formulas), has a history of more than one thousand years. However, the bioactive compounds that account for their therapeutic effects remain unclear. We hypothesized that the material basis of a formula are those compounds with a high content in the decoction that are maintained at a certain level in the system circulation. Network pharmacology provides new methodological insights for complicated system studies. In this study, we propose combining pharmacokinetic (PK) analysis with network pharmacology to explore the material basis of TCM formulas as exemplified by the Bushen Zhuanggu formula (BZ) composed of Psoralea corylifolia L., Aconitum carmichaeli Debx., and Cnidium monnieri (L.) Cuss. A sensitive and credible liquid chromatography tandem mass spectrometry (LC-MS/MS) method was established for the simultaneous determination of 15 compounds present in the three herbs. The concentrations of these compounds in the BZ decoction and in rat plasma after oral BZ administration were determined. Up to 12 compounds were detected in the BZ decoction, but only 5 could be analyzed using PK parameters. Combined PK results, network pharmacology analysis revealed that 4 compounds might serve as the material basis for BZ. We concluded that a sensitive, reliable, and suitable LC-MS/MS method for both the composition and pharmacokinetic study of BZ has been established. The combination of PK with network pharmacology might be a potent method for exploring the material basis of TCM formulas.

Drug repurposing: translational pharmacology, chemistry, computers and the clinic

The process of discovering a pharmacological compound that elicits a desired clinical effect with minimal side effects is a challenge. Prior to the advent of high-performance computing and large-scale screening technologies, drug discovery was largely a serendipitous endeavor, as in the case of thalidomide for erythema nodosum leprosum or cancer drugs in general derived from flora located in far-reaching geographic locations. More recently, de novo drug discovery has become a more rationalized process where drug-target-effect hypotheses are formulated on the basis of already known compounds/protein targets and their structures. Although this approach is hypothesis-driven, the actual success has been very low, contributing to the soaring costs of research and development as well as the diminished pharmaceutical pipeline in the United States. In this review, we discuss the evolution in computational pharmacology as the next generation of successful drug discovery and implementation in the clinic where high-performance computing (HPC) is used to generate and validate drug-target-effect hypotheses completely in silico. The use of HPC would decrease development time and errors while increasing productivity prior to in vitro, animal and human testing. We highlight approaches in chemoinformatics, bioinformatics as well as network biopharmacology to illustrate potential avenues from which to design clinically efficacious drugs. We further discuss the implications of combining these approaches into an integrative methodology for high-accuracy computational predictions within the context of drug repositioning for the efficient streamlining of currently approved drugs back into clinical trials for possible new indications.

Proteomics study on the hepatoprotective effects of traditional Chinese medicine formulae Yin-Chen-Hao-Tang by a combination of two-dimensional polyacrylamide gel electrophoresis and matrix-assisted laser desorption/ionization-time of flight mass spectrometry

Proteomics can bring breakthroughs in the study of traditional Chinese medicine (TCM). Yin-Chen-Hao-Tang (YCHT), a famous TCM formulae, has been used to alleviate various types of liver injury. However, the underlying mechanisms and drug targets of YCHT associated with the hepatic injury are largely unknown. To identify the possible target proteins of YCHT, two-dimensional gel electrophoresis (2-DE)-based proteomics was performed and proteins altered after YCHT treatment were identified by MALDI-TOF/TOF-MS. Interestingly, 15 modulated proteins were identified, out of which 7 were found to be significantly altered by YCHT. YCHT treatment caused a statistically significant down-regulation of zinc finger protein 407, haptoglobin, macroglobulin, alpha-1-antitrypsin; significant up-regulation of transthyretin, vitamin D-binding protein, and prothrombin, appear to be involved in metabolism, energy generation, chaperone, antioxidation, signal transduction, protein folding and apoptosis. Finally, interaction network from 7 differentially expressed protein to the signal-related proteins was established using bioinformatic analysis. Of note, these signal-related proteins could be included in a network together with 7 proteins through direct interaction or only one intermediate partner. Functional pathway analysis suggested that these proteins were closely related in the protein-protein interaction network and the modulation of multiple vital physiological pathways. Thus, our data will help to understand the molecular mechanisms of hepatoprotective effects of YCHT.

The application of Chinese medicine to novel drug discovery

INTRODUCTION

The apparent productivity crisis in the pharmaceutical industry and the economic and political rise of China have contributed to renewed interest in the application of Chinese medicine for drug discovery.

AREAS COVERED

The author presents an overview of the historical development and basic principles of theory and practice of Chinese herbal medicine, its materia medica and prescription formulas, and discusses the motivation for and rationale of its application to drug discovery. Furthermore, the author distinguishes the five main approaches to drug discovery from Chinese herbal medicine, based on the decreasing amount and detail of historical and clinical Chinese medicine knowledge that informed the research effort.

EXPERT OPINION

Many compounds that have been isolated from the Chinese materia medica exhibit pharmacological activities comparable to pharmaceutical drugs. With the exception of the antimalarial drug artemisinin, however, this knowledge has not led to the successful development of new drugs outside of China. The chance of success in a Chinese medicine-based drug discovery effort will be increased by consideration of the empirical knowledge that has been documented over many centuries in the historical materia medica and prescription literature. Most Chinese medicine-derived compounds affect more than one target and do not correspond to the one compound/one-target drug discovery paradigm. A new frontier is opening up with the development of drugs consisting of combinations of multiple compounds acting on multiple targets under the paradigm of network pharmacology. The ancient practice of combining multiple drugs in prescription formulas can serve as inspirational analogy and a practical guide.