网络药理学是指将药物作用网络与生物网络整合在一起，分析药物在此网络中与特定节点或模块的相互作用关系，从而理解药物和机体相互作用的科学。网络药理学突破传统的“一个药物一个靶标，一种疾病”理念，代表了现代生物医药研究的哲学理念与研究模式的转变。以系统生物学和网络生物学基本理论为基础的网络药理学具有整体性、系统性的特点，注重网络平衡（或鲁棒性）和网络扰动，强调理解某个单一生物分子（如基因、mRNA或蛋白等）在生物体系中的生物学地位和动力学过程要比理解其具体生物功能更为重要，揭示药物作用的生物学和动力学谱要比揭示其作用的单个靶标或几个“碎片化”靶标更重要，对认识药物和发现药物的理念产生了深远影响。

The purpose of this study was to investigate the therapeutic mechanism(s) ofClematis chinensisOsbeck/Notopterygium incisumK.C. Ting ex H.T (CN). A network pharmacology approach integrating prediction of ingredients, target exploration, network construction, module partition and pathway analysis was used. This approach successfully helped to identify 12 active ingredients of CN, interacting with 13 key targets (Akt1, STAT3, TNFsf13, TP53, EPHB2, IL-10, IL-6, TNF, MAPK8, IL-8, RELA, ROS1 and STAT4). Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis indicated that CN-regulated pathways were mainly classified into signal transduction and immune system. The present work may help to illustrate the mechanism(s) of action of CN, and it may provide a better understanding of antirheumatic effects.

该文通过网络药理学研究预测复方钩藤降压片潜在的主要活性成分和作用靶点,探讨其多成分-多靶点-多通路的治疗高血压的可能作用机制。采用整合药理学平台(TCMIP)构建复方钩藤降压片成分靶标-疾病靶标网络,通过网络分析方法筛选关键节点,并在此基础上对关键节点进行通路富集分析,探索复方钩藤降压片治疗高血压可能参与的生物过程。靶点网络特征分析显示,所预测复方钩藤降压片中有35个活性成分与前列腺素内源性过氧化物合酶(PTGS1,PTGS2)、ATP合成酶(ATP1A1,ATP5A1,ATP5C1,ATP5B)等29个主要蛋白有很强的相互作用,富集到血压调节、G-蛋白耦合受体激活、心肌细胞肾上腺素能信号转导和血小板激活等15条通路,参与对高血压病理过程不同环节的调控。该研究初步揭示了复方钩藤降压片治疗高血压的潜在活性成分及其可能的作用机制,为进一步的药效物质基础和作用机制实验研究提供了理论依据。

Abstract Traditional Chinese medicine uses ZHENG as the key pathological principle to understand the human homeostasis and guide the applications of Chinese herbs. Here, a systems biology approach with the combination of computational analysis and animal experiment is used to investigate this complex issue, ZHENG, in the context of the neuro-endocrine-immune (NEI) system. By using the methods of literature mining, network analysis and topological comparison, it is found that hormones are predominant in the Cold ZHENG network, immune factors are predominant in the Hot ZHENG network, and these two networks are connected by neuro-transmitters. In addition, genes related to Hot ZHENG-related diseases are mainly present in the cytokine-cytokine receptor interaction pathway, whereas genes related to both the Cold-related and Hot-related diseases are linked to the neuroactive ligand-receptor interaction pathway. These computational findings were subsequently verified by experiments on a rat model of collagen-induced arthritis, which indicate that the Cold ZHENG-oriented herbs tend to affect the hub nodes in the Cold ZHENG network, and the Hot ZHENG-oriented herbs tend to affect the hub nodes in the Hot ZHENG network. These investigations demonstrate that the thousand-year-old concept of ZHENG may have a molecular basis with NEI as background.

Drugs designed to act against individual molecular targets cannot usually combat multigenic diseases such as cancer, or diseases that affect multiple tissues or cell types such as diabetes and immunoinflammatory disorders. Combination drugs that impact multiple targets simultaneously are better at controlling complex disease systems, are less prone to drug resistance and are the standard of care in many important therapeutic areas. The combination drugs currently employed are primarily of rational design, but the increased efficacy they provide justifies in vitro discovery efforts for identifying novel multi-target mechanisms. In this review, we discuss the biological rationale for combination therapeutics, review some existing combination drugs and present a systematic approach to identify interactions between molecular pathways that could be leveraged for therapeutic benefit.

http://www.nature.com/doifinder/10.1038/nrd1609

Background Deciphering the metabolome is essential for a better understanding of the cellular metabolism as a system. Typical metabolomics data show a few but significant correlations among metabolite levels when data sampling is repeated across individuals grown under strictly controlled conditions. Although several studies have assessed topologies in metabolomic correlation networks, it remains unclear whether highly connected metabolites in these networks have specific functions in known tissue- and/or genotype-dependent biochemical pathways. Results In our study of metabolite profiles we subjected root tissues to gas chromatography-time-of-flight/mass spectrometry (GC-TOF/MS) and used published information on the aerial parts of 3 Arabidopsis genotypes, Col-0 wild-type, methionine over-accumulation 1 (mto1), and transparent testa4 (tt4) to compare systematically the metabolomic correlations in samples of roots and aerial parts. We then applied graph clustering to the constructed correlation networks to extract densely connected metabolites and evaluated the clusters by biochemical-pathway enrichment analysis. We found that the number of significant correlations varied by tissue and genotype and that the obtained clusters were significantly enriched for metabolites included in biochemical pathways. Conclusions We demonstrate that the graph-clustering approach identifies tissue- and/or genotype-dependent metabolomic clusters related to the biochemical pathway. Metabolomic correlations complement information about changes in mean metabolite levels and may help to elucidate the organization of metabolically functional modules.

Chemotherapies, HIV infections, and treatments to block organ transplant rejection are creating a population of immunocompromised individuals at serious risk of systemic fungal infections. Since single-agent therapies are susceptible to failure due to either inherent or acquired resistance, alternative therapeutic approaches such as multi-agent therapies are needed. We have developed a bioinformatics-driven approach that efficiently predicts compound synergy for such combinatorial therapies. The approach uses chemogenomic profiles in order to identify compound profiles that have a statistically significant degree of similarity to a fluconazole profile. The compounds identified were then experimentally verified to be synergistic with fluconazole and with each other, in both Saccharomyces cerevisiae and the fungal pathogen Candida albicans. Our method is therefore capable of accurately predicting compound synergy to aid the development of combinatorial antifungal therapies.SynopsisDrugs that act against individual molecular targets are often insufficient to combat fungal infections, multigenic diseases, and multiple cell or tissue type diseases (White et al, 1998; Sams-Dodd, 2005; Onyewu and Heitman, 2007; Zimmermann et al, 2007). Combinatorial therapies that impact multiple targets simultaneously are less prone to the development of drug resistance, and increase therapeutic efficacy (Groll and Walsh, 2002; Zimmermann et al, 2007). One of the major benefits of combinatorial therapies is the potential for synergistic effects: that is, the overall therapeutic benefit of the drug combination is greater than the sum of the effects of the drugs individually. These advantages have driven drug discovery efforts towards the search for combinatorial therapies (Borisy et al, 2003; Fitzgerald et al, 2006; Onyewu and Heitman, 2007; Zimmermann et al, 2007).Large-scale searches have demonstrated that high-throughput screens of thousands of compounds can be straightforward (Zhang et al, 2007), but it is unlikely that experimental techniques will be sufficient to survey the complete combinatorial chemical space in a cost-effective and timely manner. Nelander et al (2008) attempted to use data from perturbation screens and prior knowledge regarding the targets of compounds to model the effects of these compounds when they are used alone or in combination. This approach is currently limited to compounds with known targets, but such an approach could potentially be extended to predict synergistic compound pairs. However, there remains a clear need for an approach that reduces the vast combinatorial chemical space to a set of combinations that is sufficiently small for experimental testing yet enriched with synergistic combinations.We introduce here a combined experimental and bioinformatics approach to identify synergistic compound pairs for antifungal combinatorial therapies (Figure 1). Each compound is represented in silico by its chemogenomic profile, which we define as the set of genes corresponding to the single gene deletions in Saccharomyces cerevisiae that confer hypersensitivity to the compound. Therefore, we collected from the literature 1300 chemogenomic profiles generated with a broad range of compounds.We then evaluated a measure of chemogenomic profile similarity for its ability to predict antifungal synergy, where a compound pair is predicted to be synergistic if the measured similarity between the corresponding profiles is sufficiently large. A gold standard set of positive and negative examples of antifungal synergy was assembled for this purpose. The similarity measure quantifies the significance of the overlap between two hypersensitive gene sets and the enrichment of its predictions with true synergies (i.e. positive examples in the gold standard set) is significant relative to the expected baseline levels (P=0.0236). These results suggest that chemogenomic profile similarity predicts antifungal synergy.Fluconazole is a fungistatic drug, and it is thus possible for fungal cells to recover from treatment due to acquired drug resistance (Cowen et al, 2002). However, the drug has favourable pharmacokinetic and toxicological properties (Grant and Clissold, 1990), and would thus be an ideal constituent compound of a combinatorial antifungal therapy. Therefore, we applied our method of predicting antifungal synergy by first generating a de novo chemogenomic profile for fluconazole, which we call the FCZ-Fungicidal profile (Figure 1B). The FCZ-Fungicidal profile specifies the set of genes corresponding to deletions that are lethal in the presence of the drug.In the next step of our method, we found that eight compounds have profiles that are sufficiently similar to the FCZ-Fungicidal profile and are thus predicted to be synergistic with fluconazole. We noticed that many of these compounds are also predicted to be synergistic with each other. In the final step of our method, we thus experimentally tested predicted synergistic combinations involving fluconazole and pairings of the predicted fluconazole partners for antifungal synergy.The combinations were tested in both S. cerevisiae and the fungal pathogen Candida albicans using dose-matrix response assays that measure the growth arrest and monitor the death of treated cells. We showed that eight synergistic combinations identified in S. cerevisiae are also synergistic in C. albicans, and we identified three and two additional synergies only in S. cerevisiae and only in C. albicans, respectively. We also tested the novel synergistic combination of fluconazole (FDA-approved) and wortmannin (analogues are in clinical trials; Noble et al, 2004) in two fluconazole-resistant clinical isolates of C. albicans (Morschhauser et al, 2007; Dunkel et al, 2008). The compounds act synergistically to kill the cells of both isolates (Figure 5), suggesting potential clinical relevance. Taken together, the validation success rate for the predictor of antifungal synergy (69%) implies that our method identifies true synergies at a rate that is 鈭20-fold better than the estimated rate for testing randomly selected compound pairs (Borisy et al, 2003).In summary, we have shown that our method is capable of rapidly and accurately predicting compound pairs that exhibit antifungal synergy. Therefore, our method may enable efficient development of combinatorial therapies to attack the persisting problem of drug-resistant C. albicans strains in the clinic.

Given the complex nature of biological systems, pathways often need to function in a coordinated fashion in order to produce appropriate physiological responses to both internal and external stimuli. Therefore, understanding the interaction and crosstalk between pathways is important for understanding the function of both cells and more complex systems.We have developed a computational approach to detect crosstalk among pathways based on protein interactions between the pathway components. We built a global mammalian pathway crosstalk network that includes 580 pathways (covering 4753 genes) with 1815 edges between pathways. This crosstalk network follows a power-law distribution: P(k) approximately k(-)(gamma), gamma = 1.45, where P(k) is the number of pathways with k neighbors, thus pathway interactions may exhibit the same scale-free phenomenon that has been documented for protein interaction networks. We further used this network to understand colorectal cancer progression to metastasis based on transcriptomic data.yong.2.li@gsk.comSupplementary data are available at Bioinformatics online.

Abstract Kinases and proteases are responsible for two fundamental regulatory mechanisms--phosphorylation and proteolysis--that orchestrate the rhythms of life and death in all organisms. Recent studies have highlighted the elaborate interplay between both post-translational regulatory systems. Many intracellular or pericellular proteases are regulated by phosphorylation, whereas multiple kinases are activated or inactivated by proteolytic cleavage. The functional consequences of this regulatory crosstalk are especially relevant in the different stages of cancer progression. What are the clinical implications derived from the fertile dialogue between kinases and proteases in cancer?

Abstract G protein-coupled receptors (GPCRs) are the largest super family with more than 800 membrane receptors. Currently, over 30% of the approved drugs target human GPCRs. However, only approximately 30 human GPCRs have been resolved three-dimensional crystal structures, which limits traditional structure-based drug discovery. Recent advances in network-based systems pharmacology approaches have demonstrated powerful strategies for identifying new targets of GPCR ligands. In this study, we proposed a network-based systems pharmacology framework for comprehensive identification of new drug-target interactions on GPCRs. Specifically, we reconstructed both global and local drug-target interaction networks for human GPCRs. Network analysis on the known drug-target networks showed rational strategies for designing new GPCR ligands and evaluating side effects of the marketed GPCR drugs. We further built global and local network-based models for predicting new targets of the known GPCR ligands. The area under the receiver operating characteristic curve of more than 0.96 was obtained for the best network-based models in cross validation. In case studies, we identified that several network-predicted GPCR off-targets (e.g. ADRA2A, ADRA2C and CHRM2) were associated with cardiovascular complications (e.g. bradycardia and palpitations) of the marketed GPCR drugs via an integrative analysis of drug-target and off-target-adverse drug event networks. Importantly, we experimentally validated that two newly predicted compounds, AM966 and Ki16425, showed high binding affinities on prostaglandin E2 receptor EP4 subtype with IC 50 =2.67 M and 6.34 M, respectively. In summary, this study offers powerful network-based tools for identifying polypharmacology of GPCR ligands in drug discovery and development. Copyright 2017 Elsevier Ltd. All rights reserved.

Networks are increasingly used to study the impact of drugs at the systems level. From the algorithmic standpoint, a drug can "attack" nodes or edges of a protein-protein interaction network. In this work, we propose a new network strategy, "The Interface Attack", based on protein-protein interfaces. Similar interface architectures can occur between unrelated proteins. Consequently, in principle, a drug that binds to one has a certain probability of binding to others. The interface attack strategy simultaneously removes from the network all interactions that consist of similar interface motifs. This strategy is inspired by network pharmacology and allows inferring potential off-targets. We introduce a network model that we call "Protein Interface and Interaction Network (P2IN)", which is the integration of protein-protein interface structures and protein interaction networks. This interface-based network organization clarifies which protein pairs have structurally similar interfaces and which proteins may compete to bind the same surface region. We built the P2IN with the p53 signaling network and performed network robustness analysis. We show that (1) "hitting" frequent interfaces (a set of edges distributed around the network) might be as destructive as eleminating high degree proteins (hub nodes), (2) frequent interfaces are not always topologically critical elements in the network, and (3) interface attack may reveal functional changes in the system better than the attack of single proteins. In the off-target detection case study, we found that drugs blocking the interface between CDK6 and CDKN2D may also affect the interaction between CDK4 and CDKN2D.

Abstract: Systems as diverse as genetic networks or the world wide web are best described as networks with complex topology. A common property of many large networks is that the vertex connectivities follow a scale-free power-law distribution. This feature is found to be a consequence of the two generic mechanisms that networks expand continuously by the addition of new vertices, and new vertices attach preferentially to already well connected sites. A model based on these two ingredients reproduces the observed stationary scale-free distributions, indicating that the development of large networks is governed by robust self-organizing phenomena that go beyond the particulars of the individual systems.

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To illuminate the molecular targets for schisandrin against cerebrovascular disease based on the combined methods of network pharmacology prediction and experimental verification. A protein database was established through constructing the drug-protein network from literature mining data. The protein-protein network was built through an in-depth exploration of the relationships between the proteins. The computational platform was implemented to predict and extract the sensitive sub-network with significant P-values from the protein-protein network. Then the key targets and pathways were identified from the sensitive sub-network. The most related targets and pathways were also confirmed in hydrogen peroxide (H2O2)-induced PC12 cells by Western blotting. Twelve differentially expressed proteins (gene names: NFKB1, RELA, TNFSF10, MAPK1, CHUK, CASP8, PIGS2, MAPK14, CREB1, IFNG, APP, and BCL2) were confirmed as the central nodes of the interaction network (45 nodes, 93 edges). The NF-B signaling pathway was suggested as the most related pathway of schisandrin for cerebrovascular disease. Furthermore, schisandrin was found to suppress the expression and phosphorylation of IKK, as well as p50 and p65 induced by H2O2 in PC12 cells by Western blotting. The computational platform that integrates literature mining data, protein-protein interactions, sensitive sub-network, and pathway results in identification of the NF-B signaling pathway as the key targets and pathways for schisandrin.

This study aimed to evaluate the clinical analgesic efficacy and identify the molecular targets of XGDP for treating primary dysmenorrhea (PD) by a network pharmacology approach. Analysis of pain disappearance rate of XGDP in PD treatment was conducted based on data from phase II and III randomized, double-blind, double-simulation, and positive parallel controlled clinical trials. The bioactive compounds were obtained by the absorption, distribution, metabolism, and excretion processes with oral bioavailability (OB) and drug-likeness (DL) evaluation. Subsequently, target prediction, pathway identification, and network construction were employed to clarify the mechanisms of the analgesic effect of XGDP on PD. The pain disappearance rates in phase II and III clinical trials of XGDP in PD treatment were 62.5% and 55.8%, respectively, yielding a significant difference (P<0.05) when compared with the control group using Tongjingbao granules (TJBG). Among 331 compounds, 53 compounds in XGDP were identified as the active compounds related to PD through OB, DL, and target prediction. The active compounds and molecular targets of XGDP were identified, and our study showed that XGDP may exert its therapeutic effects on PD through the regulation of the targets related to anti-inflammation analgesia and central analgesia and relieving smooth muscle contraction.

Despite massive investments in drug research and development, the significant decline in the number of new drugs approved or translated to clinical use raises the question, whether single targeted drug discovery is the right approach. To combat complex systemic diseases that harbour robust biological networks such as cancer, single target intervention is proved to be ineffective. In such cases, network pharmacology approaches are highly useful, because they differ from conventional drug discovery by addressing the ability of drugs to target numerous proteins or networks involved in a disease. Pleiotropic natural products are one of the promising strategies due to their multi-targeting and due to lower side effects. In this review, we discuss the application of network pharmacology for cancer drug discovery. We provide an overview of the current state of knowledge on network pharmacology, focus on different technical approaches and implications for cancer therapy (e.g.polypharmacology and synthetic lethality), and illustrate the therapeutic potential with selected examples green tea polyphenolics,Eleutherococcus senticosus, Rhodiola rosea,andSchisandra chinensis). Finally, we present future perspectives on their plausible applications for diagnosis and therapy of cancer.

目的:通过网络药理学和计算机辅助药物设计,找出补中益气汤的潜能成分,并探讨其成为治疗子宫内膜癌的药物的可能性.方法:从DisGeNET和BisoGenet应用程序中收集与子宫内膜癌相关的基因和蛋白质以及从中医药数据库中收集复方中药补中益气汤的成分,运用Cytoscape建立网络关系图,并用CytoNCA筛选出能影响子宫内膜癌的蛋白质.再运用Surflex-Dock对补中益气汤的10种中草药的534个成分与子宫内膜癌相关的蛋白质进行柔性分子对接及分析其在活性位点的结合模式,并使用FAF-Drugs3和OSIRIS进行分子结构的筛选以预测其吸收、分布、代谢、排泄和毒性(ADMET).结果:补中益气汤的六氢西红柿红素、八氢西红柿红素、新橙皮苷和橙皮苷这4个化合物与子宫内膜癌相关的蛋白质有好的结合亲和力与良好的药物特性.结论:从研究和文献中发现这4个化合物与癌症相关,有望进一步研究成为治疗子宫内膜癌的药物.

We collected the data on the Sendeng-4 chemical composition corresponding targets through the literature and from DrugBank, SuperTarget, TTD (Therapeutic Targets Database) and other databases and the relevant signaling pathways from the KEGG (Kyoto Encyclopedia of Genes and Genomes) database and established models of the chemical composition-target network and chemical composition-target-disease network using Cytoscape software, the analysis indicated that the chemical composition had at least nine different types of targets that acted together to exert effects on the diseases, suggesting a ulti-component, multi-target feature of the traditional Mongolian medicine. We also employed the rat model of rheumatoid arthritis induced by Collgen Type II to validate the key targets of the chemical components of Sendeng-4, and three of the key targets were validated through laboratory experiments, further confirming the anti-inflammatory effects of Sendeng-4. In all, this study predicted the active ingredients and targets of Sendeng-4, and explored its mechanism of action, which provided new strategies and methods for further research and development of Sendeng-4 and other traditional Mongolian medicines as well.

中国科学院机构知识库(CAS IR GRID)以发展机构知识能力和知识管理能力为目标，快速实现对本机构知识资产的收集、长期保存、合理传播利用，积极建设对知识内容进行捕获、转化、传播、利用和审计的能力，逐步建设包括知识内容分析、关系分析和能力审计在内的知识服务能力，开展综合知识管理。

Neurodegenerative diseases, referring to as the progressive loss of structure and function of neurons, constitute one of the major challenges of modern medicine. Traditional Chinese herbs have been used as a major preventive and therapeutic strategy against disease for thousands years. The numerous species of medicinal herbs and Traditional Chinese Medicine (TCM) compound formulas in nervous system disease therapy make it a large chemical resource library for drug discovery. In this work, we collected 7362 kinds of herbs and 58,147 Traditional Chinese medicinal compounds (Tcmcs). The predicted active compounds in herbs have good oral bioavailability and central nervous system (CNS) permeability. The molecular docking and network analysis were employed to analyze the effects of herbs on neurodegenerative diseases. In order to evaluate the predicted efficacy of herbs, automated text mining was utilized to exhaustively search in PubMed by some related keywords. After that, receiver operator characteristic (ROC) curves was used to estimate the accuracy of predictions. Our study suggested that most herbs were distributed in family of Asteraceae, Fabaceae, Lamiaceae and Apocynaceae. The predictive model yielded good sensitivity and specificity with the AUC values above 0.800. At last, 504 kinds of herbs were obtained by using the optimal cutoff values in ROC curves. These 504 herbs would be the most potential herb resources for neurodegenerative diseases treatment. This study would give us an opportunity to use these herbs as a chemical resource library for drug discovery of anti-neurodegenerative disease.

The dominant paradigm in drug discovery is the concept of designing maximally selective ligands to act on individual drug targets. However, many effective drugs act via modulation of multiple proteins rather than single targets. Advances in systems biology are revealing a phenotypic robustness and a network structure that strongly suggests that exquisitely selective compounds, compared with multitarget drugs, may exhibit lower than desired clinical efficacy. This new appreciation of the role of polypharmacology has significant implications for tackling the two major sources of attrition in drug development09”efficacy and toxicity. Integrating network biology and polypharmacology holds the promise of expanding the current opportunity space for druggable targets. However, the rational design of polypharmacology faces considerable challenges in the need for new methods to validate target combinations and optimize multiple structure-activity relationships while maintaining drug-like properties. Advances in these areas are creating the foundation of the next paradigm in drug discovery: network pharmacology.

Abstract Top of page Abstract Introduction Efficacy Safety The future for complementary medicines References This is the second of two papers which review issues concerning complementary medicines. The first reviewed the extent of use of complementary medicines, and issues related to the regulation and pharmaceutical quality of these products; the second considers evidence for the efficacy of several well-known complementary medicines, and discusses complementary-medicines pharmacovigilance. The term complementary medicines describes a range of pharmaceutical-type preparations, including herbal medicines, homoeopathic remedies, essential oils and dietary supplements, which mainly sit outside conventional medicine. The use of complementary medicines is a popular healthcare approach in the UK, and there are signs that the use of such products is continuing to increase. Patients and the public use complementary medicines for health maintenance, for the treatment or prevention of minor ailments, and also for serious, chronic illnesses. There is a growing body of evidence from randomized controlled trials and systematic reviews to support the efficacy of certain herbal extracts and dietary supplements in particular conditions. However, many other preparations remain untested. Strictly speaking, evidence of efficacy (and safety) for herbal medicines should be considered to be extract specific. Pharmacovigilance for complementary medicines is in its infancy. Data are lacking in several areas relevant to safety. Standard pharmacovigilance tools have additional limitations when applied to investigating safety concerns with complementary medicines.

Research on herbal medicinal products is increasingly published in estern scientific journals dedicated primarily to conventional medicines. Publications are concerned mainly not only on the issues of safety and interactions, but also on efficacy. In reviews, a recurring complaint has been a lack of quality studies. In this opinion article, we present the case of Chinese herbal medicines as an example, as they have been extensively used in the global market and increasingly studied worldwide. We analyze the potential reasons for problems and propose some ways forward. As in the case of any drug, clinical trials for safety, efficacy, and/or effectiveness are the ultimate demonstration of therapeutic usefulness of herbal products. These will only make scientific sense when the tested herbal products are authentic, standardized, and quality controlled, if good practice guidelines of evidence-based medicine are followed, and if relevant controls and outcome measures are scientifically defined. Herbal products are complex mixtures, and for such complexity, an obvious approach for mechanistic studies is network pharmacology based on omic tools and approaches, which has already begun to revolutionize the study of conventional drugs, emphasizing networks, interactions, and polypharmacological features behind the action of many drugs.

Multi-target therapeutics is a promising paradigm for drug discovery which is expected to produce greater levels of efficacy with fewer adverse effects and toxicity than monotherapies. Medical herbs featuring multi-components and multi-targets may serve as valuable resources for network-based multi-target drug discovery.In this study, we report an integrated systems pharmacology platform for drug discovery and combination, with a typical example applied to herbal medicines in the treatment of cardiovascular diseases.First, a disease-specific drug-target network was constructed and examined at systems level to capture the key disease-relevant biology for discovery of multi-targeted agents. Second, considering an integration of disease complexity and multilevel connectivity, a comprehensive database of literature-reported associations, chemicals and pharmacology for herbal medicines was designed. Third, a large-scale systematic analysis combining pharmacokinetics, chemogenomics, pharmacology and systems biology data through computational methods was performed and validated experimentally, which results in a superior output of information for systematic drug design strategies for complex diseases.This strategy integrating different types of technologies is expected to help create new opportunities for drug discovery and combination.