Quinazoline sulfonamide derivatives targeting MicroRNA-34a/MDM4/p53 apoptotic axis with radiosensitizing activity

Aim: New quinazoline benzenesulfonamide hybrids 4a-n were synthesized to determine their cytotoxicity and effect on the miR-34a/MDM4/p53 apoptotic pathway. Materials & methods: Cytotoxicity against hepatic, breast, lung and colon cancer cell lines was estimated using the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay. Results: Compound 4d was the most potent against HepG2 and MCF-7 cancer cells, with potential apoptotic activity verified by a significant upregulation of miR-34a and p53 gene expressions. The apoptotic effect of 4d was further investigated and showed downregulation of miR-21, VEGF, STAT3 and MDM4 gene expression. Conclusion: The anticancer and apoptotic activities of 4d were enhanced post irradiation by a single dose of 8 Gy γ-radiation. Docking analysis demonstrated a valuable affinity of 4d toward VEGFR2 and MDM4 active sites.

Toxoplasma gondii infection: novel emerging therapeutic targets

INTRODUCTION

Toxoplasmosis constitutes a challenge for public health, animal production, and welfare. So far, only a limited panel of drugs has been marketed for clinical applications. In addition to classical screening, the investigation of unique targets of the parasite may lead to the identification of novel drugs.

AREAS COVERED

Herein, the authors describe the methodology to identify novel drug targets in Toxoplasma gondii and review the literature with a focus on the last two decades.

EXPERT OPINION

Over the last two decades, the investigation of essential proteins of T. gondii as potential drug targets has fostered the hope of identifying novel compounds for the treatment of toxoplasmosis. Despite good efficacies in vitro, only a few classes of these compounds are effective in suitable rodent models, and none has cleared the hurdle to applications in humans. This shows that target-based drug discovery is in no way better than classical screening approaches. In both cases, off-target effects and adverse side effects in the hosts must be considered. Proteomics-driven analyses of parasite- and host-derived proteins that physically bind drug candidates may constitute a suitable tool to characterize drug targets, irrespectively of the drug discovery methods.

Network pharmacology reveals multitarget mechanism of action of drugs to be repurposed for COVID-19

The coronavirus disease 2019 pandemic accelerated drug/vaccine development processes, integrating scientists all over the globe to create therapeutic alternatives against this virus. In this work, we have collected information regarding proteins from SARS-CoV-2 and humans and how these proteins interact. We have also collected information from public databases on protein–drug interactions. We represent this data as networks that allow us to gain insights into protein–protein interactions between both organisms. With the collected data, we have obtained statistical metrics of the networks. This data analysis has allowed us to find relevant information on which proteins and drugs are the most relevant from the network pharmacology perspective. This method not only allows us to focus on viral proteins as the main targets for COVID-19 but also reveals that some human proteins could be also important in drug repurposing campaigns. As a result of the analysis of the SARS-CoV-2–human interactome, we have identified some old drugs, such as disulfiram, auranofin, gefitinib, suloctidil, and bromhexine as potential therapies for the treatment of COVID-19 deciphering their potential complex mechanism of action.

Effects of Chang-Kang-Fang Formula on the Microbiota-Gut-Brain Axis in Rats With Irritable Bowel Syndrome

Chang-Kang-Fang formula (CKF), a multi-herb traditional Chinese medicine, has been used in clinical settings to treat irritable bowel syndrome (IBS). Recent studies show that 5.0 g/kg/d CKF can alleviate the symptoms of IBS rats by modulating the brain-gut axis through the production of brain-gut peptides (BGPs), thus relieving pain, and reversing the effects of intestinal propulsion disorders. However, the exact mechanisms underlying the therapeutic effects of CKF in IBS remain unclear. The microbiota-gut-brain axis (MGBA) is central to the pathogenesis of IBS, regulating BGPs, depression-like behaviors, and gut microbiota. Given that CKF ameliorates IBS via the MGBA, we performed metabolomic analyses, evaluated the gut microbiota, and system pharmacology to elucidate the mechanisms of action of CKF. The results of intestinal tract motility, abdominal withdrawal reflex (AWR), sucrose preference test (SPT), and the forced swimming test (FST) showed that the male Sprague-Dawley rats subjected to chronic acute combining stress (CACS) for 22 days exhibited altered intestinal motility, visceral hypersensitivity, and depression-like behaviors. Treatment of IBS rats with CKF normalized dysfunctions of CACS-induced central and peripheral nervous system. CKF regulated BDNF and 5-HT levels in the colon and hippocampus as well as the expressions of the related BGP pathway genes. Moreover, the system pharmacology assays were used to assess the physiological targets involved in the action of CKF, with results suggesting that CKF putatively functioned through the 5-HT-PKA-CREB-BDNF pathway. LC-MS-based metabolomics identified the significantly altered 5-HT pathway-related metabolites in the CKF treatment group, and thus, the CKF-related signaling pathways were further examined. After pyrosequencing-based analysis of bacterial 16S rRNA (V3 + V4 region) using rat feces, the Lefse analysis of gut microbiota suggested that CKF treatment could induce structural changes in the gut microbiota, thereby regulating it by decreasing Clostridiales, and the F-B ratio while increasing the levels of Lactobacillus. Furthermore, the integrated analysis showed a correlation of CKF-associated microbes with metabolites. These findings showed that CKF effectively alleviated IBS, which was associated with the altered features of the metabolite profiles and the gut microbiota through a bidirectional communication along the microbiota-gut-brain axis.

A landscape for drug-target interactions based on network analysis

In this work, we performed an analysis of the networks of interactions between drugs and their targets to assess how connected the compounds are. For our purpose, the interactions were downloaded from the DrugBank database, and we considered all drugs approved by the FDA. Based on topological analysis of this interaction network, we obtained information on degree, clustering coefficient, connected components, and centrality of these interactions. We identified that this drug-target interaction network cannot be divided into two disjoint and independent sets, i.e., it is not bipartite. In addition, the connectivity or associations between every pair of nodes identified that the drug-target network is constituted of 165 connected components, where one giant component contains 4376 interactions that represent 89.99% of all the elements. In this regard, the histamine H1 receptor, which belongs to the family of rhodopsin-like G-protein-coupled receptors and is activated by the biogenic amine histamine, was found to be the most important node in the centrality of input-degrees. In the case of centrality of output-degrees, fostamatinib was found to be the most important node, as this drug interacts with 300 different targets, including arachidonate 5-lipoxygenase or ALOX5, expressed on cells primarily involved in regulation of immune responses. The top 10 hubs interacted with 33% of the target genes. Fostamatinib stands out because it is used for the treatment of chronic immune thrombocytopenia in adults. Finally, 187 highly connected sets of nodes, structured in communities, were also identified. Indeed, the largest communities have more than 400 elements and are related to metabolic diseases, psychiatric disorders and cancer. Our results demonstrate the possibilities to explore these compounds and their targets to improve drug repositioning and contend against emergent diseases.

Topological and system-level protein interaction network (PIN) analyses to deduce molecular mechanism of curcumin

Curcumin is an important bioactive component of turmeric and also one of the important natural products, which has been investigated extensively. The precise mode of action of curcumin and its impact on system level protein networks are still not well studied. To identify the curcumin governed regulatory action on protein interaction network (PIN), an interectome was created based on 788 key proteins, extracted from PubMed literatures, and constructed by using STRING and Cytoscape programs. The PIN rewired by curcumin was a scale-free, extremely linked biological system. MCODE plug-in was used for sub-modulization analysis, wherein we identified 25 modules; ClueGo plug-in was used for the pathway’s enrichment analysis, wherein 37 enriched signalling pathways were obtained. Most of them were associated with human diseases groups, particularly carcinogenesis, inflammation, and infectious diseases. Finally, the analysis of topological characteristic like bottleneck, degree, GO term/pathways analysis, bio-kinetics simulation, molecular docking, and dynamics studies were performed for the selection of key regulatory proteins of curcumin-rewired PIN. The current findings deduce a precise molecular mechanism that curcumin might exert in the system. This comprehensive in-silico study will help to understand how curcumin induces its anti-cancerous, anti-inflammatory, and anti-microbial effects in the human body.

A Novel Network Pharmacology Strategy to Decode Metabolic Biomarkers and Targets Interactions for Depression

Depression is one of the most prevalent and serious mental disorders with a worldwide significant health burden. Metabolic abnormalities and disorders in patients with depression have attracted great research attention. Thirty-six metabolic biomarkers of clinical plasma metabolomics were detected by platform technologies, including gas chromatography-mass spectrometry (GC-MS), liquid chromatography-mass spectrometry (LC-MS) and proton nuclear magnetic resonance (¹H-NMR), combined with multivariate data analysis techniques in previous work. The principal objective of this study was to provide valuable information for the pathogenesis of depression by comprehensive analysis of 36 metabolic biomarkers in the plasma of depressed patients. The relationship between biomarkers and enzymes were collected from the HMDB database. Then the metabolic biomarkers-enzymes interactions (MEI) network was performed and analyzed to identify hub metabolic biomarkers and enzymes. In addition, the docking score-weighted multiple pharmacology index (DSWMP) was used to assess the important pathways of hub metabolic biomarkers involved. Finally, validated these pathways by published literature. The results show that stearic acid, phytosphingosine, glycine, glutamine and phospholipids were important metabolic biomarkers. Hydrolase, transferase and acyltransferase involve the largest number of metabolic biomarkers. Nine metabolite targets (TP53, IL1B, TNF, PTEN, HLA-DRB1, MTOR, HRAS, INS and PIK3CA) of potential drug proteins for treating depression are widely involved in the nervous system, immune system and endocrine system. Seven important pathways, such as PI3K-Akt signaling pathway and mTOR signaling pathway, are closely related to the pathology mechanisms of depression. The application of important biomarkers and pathways in clinical practice may help to improve the diagnosis of depression and the evaluation of antidepressant effect, which provides important clues for the study of metabolic characteristics of depression.

A System-Level Investigation into the Mechanisms of Apigenin Against Inflammation

Apigenin is a natural flavone that possesses excellent biological activities especially against aging and cancer. However, the underlying mode of its action is not yet revealed. The purpose of this study was to examine the pharmacological mechanisms of apigenin using the knowledge of network pharmacology, protein-protein interaction (PPI) databases and biological processes analysis through Cytoscape.

Apigenin targets were retrieved through PASS Prediction and STITCH database and the interactive associations between these targets were studied using STITCH, followed by GO (gene ontology) and pathway enrichment analysis.

As a result of target search, 125 protein targets were retrieved. Moreover, 216 GO terms related to various biological processes, 16 GO terms for various molecular processes, 5 GO terms for the cellular components, and 52 Kyoto Encyclopedia of Genes and Genomes pathway terms were achieved by analyzing gene functional annotation clusters and abundance values of these targets. Most of these terms are strongly associated with inflammation through various pathways, for example, FOXO, mammalian target of rapamycin, tumor necrosis factor, p53, AMP-activated protein kinase, p13K-AKT, and mitogen-activated protein kinase, which play an important role in inflammation, aging and cancer.

Apigenin can be used to treat inflammation, aging, and cancer with an underlying mechanism of inflammation suppression. This study contributed excellent information for a better understanding of the modes of action of apigenin. However, further studies such as docking and MD simulation are required to understand the therapeutic and toxicological roles of these targets of apigenin.

Systems Pharmacology and Microbiome Dissection of Shen Ling Bai Zhu San Reveal Multiscale Treatment Strategy for IBD

Generally, inflammatory bowel disease (IBD) can be caused by psychology, genes, environment, and gut microbiota. Therefore, IBD therapy should be improved to utilize multiple strategies. Shen Ling Bai Zhu San (SLBZS) adheres to the aim of combating complex diseases from an integrative and holistic perspective, which is effective for IBD therapy. Herein, a systems pharmacology and microbiota approach was developed for these molecular mechanisms exemplified by SLBZS. First, by systematic absorption-distribution-metabolism-excretion (ADME) analysis, potential active compounds and their corresponding direct targets were retrieved. Then, the network relationships among the active compounds, targets, and disease were built to deduce the pharmacological actions of the drug. Finally, an “IBD pathway” consisting of several regulatory modules was proposed to dissect the therapeutic effects of SLBZS. In addition, the effects of SLBZS on gut microbiota were evaluated through analysis of the V3-V4 region and multivariate statistical methods. SLBZS significantly shifted the gut microbiota structure in a rat model. Taken together, we found that SLBZS has multidimensionality in the regulation of IBD-related physiological processes, which provides new sights into herbal medicine for the treatment of IBD.

Predicting drug-target interactions using Lasso with random forest based on evolutionary information and chemical structure

The identification of drug-target interactions has great significance for pharmaceutical scientific research. Since traditional experimental methods identifying drug-target interactions is costly and time-consuming, the use of machine learning methods to predict potential drug-target interactions has attracted widespread attention. This paper presents a novel drug-target interactions prediction method called LRF-DTIs. Firstly, the pseudo-position specific scoring matrix (PsePSSM) and FP2 molecular fingerprinting were used to extract the features of drug-target. Secondly, using Lasso to reduce the dimension of the extracted feature information and then the Synthetic Minority Oversampling Technique (SMOTE) method was used to deal with unbalanced data. Finally, the processed feature vectors were input into a random forest (RF) classifier to predict drug-target interactions. Through 10 trials of 5-fold cross-validation, the overall prediction accuracies on the enzyme, ion channel (IC), G-protein-coupled receptor (GPCR) and nuclear receptor (NR) datasets reached 98.09%, 97.32%, 95.69%, and 94.88%, respectively, and compared with other prediction methods. In addition, we have tested and verified that our method not only could be applied to predict the new interactions but also could obtain a satisfactory result on the new dataset. All the experimental results indicate that our method can significantly improve the prediction accuracy of drug-target interactions and play a vital role in the new drug research and target protein development. The source code and all datasets are available at https://github.com/QUST-AIBBDRC/LRF-DTIs/ for academic use.

Screening Strategies and Methods for Better Off-Target Liability Prediction and Identification of Small-Molecule Pharmaceuticals

Pharmaceutical discovery and development is a long and expensive process that, unfortunately, still results in a low success rate, with drug safety continuing to be a major impedance. Improved safety screening strategies and methods are needed to more effectively fill this critical gap. Recent advances in informatics are now making it possible to manage bigger data sets and integrate multiple sources of screening data in a manner that can potentially improve the selection of higher-quality drug candidates. Integrated screening paradigms have become the norm in Pharma, both in discovery screening and in the identification of off-target toxicity mechanisms during later-stage development. Furthermore, advances in computational methods are making in silico screens more relevant and suggest that they may represent a feasible option for augmenting the current screening paradigm. This paper outlines several fundamental methods of the current drug screening processes across Pharma and emerging techniques/technologies that promise to improve molecule selection. In addition, the authors discuss integrated screening strategies and provide examples of advanced screening paradigms.

Deciphering metabonomics biomarkers-targets interactions for psoriasis vulgaris by network pharmacology

OBJECTIVES

Psoriasis vulgaris is a chronic inflammatory and immune-mediated skin disease. 44 metabonomics biomarkers were identified by high-throughput liquid chromatography coupled to mass spectrometry in our previous work, but the roles of metabonomics biomarkers in the pathogenesis of psoriasis is unclear.

METHODS

The metabonomics biomarker-enzyme network was constructed. The key metabonomics biomarkers and enzymes were screened out by network analysis. The binding affinity between each metabonomics biomarker and target was calculated by molecular docking. A binding energy-weighted polypharmacological index was introduced to evaluate the importance of target-related pathways.

RESULTS

Long-chain fatty acids, phospholipids, Estradiol and NADH were the most important metabonomics biomarkers. Most key enzymes belonged hydrolase, thioesterase and acyltransferase. Six proteins (TNF-alpha, MAPK3, iNOS, eNOS, COX2 and mTOR) were extensively involved in inflammatory reaction, immune response and cell proliferation, and might be drug targets for psoriasis. PI3K-Akt signaling pathway and five other pathways had close correlation with the pathogenesis of psoriasis and could deserve further research.

CONCLUSIONS

The inflammatory reaction, immune response and cell proliferation are mainly involved in psoriasis. Network pharmacology provide a new insight into the relationships between metabonomics biomarkers and the pathogenesis of psoriasis. KEY MESSAGES   • Network pharmacology was adopted to identify key metabonomics biomarkers and enzymes.   • Six proteins were screened out as important drug targets for psoriasis.   • A binding energy-weighted polypharmacological index was introduced to evaluate the importance of target-related pathways.

Clarifying of the potential mechanism of Sinisan formula for treatment of chronic hepatitis by systems pharmacology method

Chronic hepatitis is a general designation class of diseases, which results in different degrees of liver necrosis and inflammatory reaction, followed by liver fibrosis, may eventually develop into cirrhosis. However, the molecular pathogenesis of chronic hepatitis is too complex to elucidate. Herbal medicines, featured with multiple targets and compounds, have long displayed therapeutic effect in treating chronic hepatitis, though their molecular mechanisms of contribution remain indistinct. This research utilized the network pharmacology to confirm the molecular pathogenesis of chronic hepatitis through providing a comprehensive analysis of active chemicals, drug targets and pathways' interaction of Sinisan formula for treating chronic hepatitis. The outcomes showed that 80 active ingredients of Sinisan formula interacting with 91 therapeutic proteins were authenticated. Sinisan formula potentially participates in immune modulation, anti-inflammatory and antiviral activities, even has regulating effects on lipid metabolism. These mechanisms directly or indirectly are involved in curing chronic hepatitis by an interaction way. The network pharmacology based analysis demonstrated that Sinisan has multi-scale curative activity in regulating chronic hepatitis related biological processes, which provides a new potential way for modern medicine in the treatment of chronic diseases.

Effects of Qijian mixture on type 2 diabetes assessed by metabonomics, gut microbiota and network pharmacology

Qijian mixture, a new traditional Chinese medicine (TCM) formula comprising of Astragalus membranaceus, Ramulus euonymi, Coptis chinensis and Pueraria lobata, was designed to ameliorate the type 2 diabetes (T2D), and its safety and efficacy were evaluated in the research by metabonomics, gut microbiota and system pharmacology. To study the hypoglycemic effect of Qijian mixture, male KKay mice (28-30 g, 8-9 week) and C57/BL6 mice (18-19 g, 8-9 week) were used. Thirty KKay diabetic mice were randomly distributed into 5 groups, abbreviated as Model group (Model), Low Qijian Mixture group (QJM(L)), High Qijian Mixture group (QJM(H)), Chinese Medicine (Gegen Qinlian Decoction) Positive group (GGQL), and Western Medicine (Metformin hydrochloride) Positive group (Metformin). C57/BL6 was considered as the healthy control group (Control). Moreover, a system pharmacology approach was utilized to assess the physiological targets involved in the action of Qijian mixture. There was no adverse drug reaction of Qijian mixture in the acute toxicity study and HE result, and, compared with Model group, Qijian mixture could modulate blood glycemic level safely and effectively. Qijian Mixture was lesser effective than metformin hydrochloride; however, both showed similar hypoglycemic trend. Based on ¹H NMR based metabonomics study, the profoundly altered metabolites in Qijian mixture treatment group were identified. Qijian mixture-related 55 proteins and 4 signaling pathways, including galactose metabolism, valine, leucine and isoleucine degradation metabolism, aminoacyl-tRNA biosynthesis metabolism and alanine, aspartate and glutamate metabolism pathways, were explored. The PCoA analysis of gut microbiota revealed that Qijian mixture treatment profoundly enriched bacteroidetes. In addition, the system pharmacology paradigm revealed that Qijian mixture acted through TP53, AKT1 and PPARA proteins. It was concluded that Qijian mixture effectively alleviated T2D, and this effect was linked with the altered features of the metabolite profiles and the gut microbiota.

Exploring the biomarkers and therapeutic mechanism of kidney-yang deficiency syndrome treated by You-gui pill using systems pharmacology and serum metabonomics

In this study, systems pharmacology was used to predict the molecular targets of You-gui pill (YGP) and explore the therapeutic mechanism of Kidney-Yang Deficiency Syndrome (KYDS) treated with YGP. On the basis of this, serum samples from control group, KYDS model group and YGP group rats were studied using ¹H NMR to verify the results of systems pharmacology from the level of metabonomics. Simultaneously, ¹H NMR spectra of serum samples were obtained and statistically assessed using pattern recognition analysis. Biochemical analyses of serums were performed via radioimmunoassays. Furthermore, histopathological studies were conducted on the pituitary, adrenal, and thyroid glands, and testicles of the control, KYDS and YGP rats. Using systems pharmacology to analyze the active components of YGP, 61 active compounds were finally found. These compounds were likely to have an effect on 3177 target proteins and involve 234 pathways. Using metabonomics to analyze serum from KYDS rats treated with YGP, 22 endogenous biomarkers were found. These biomarkers were mainly involved in 10 metabolic pathways. Combining systems pharmacology and metabonomics, we found that the regulation of KYDS was primarily associated with 19 active compounds of 5 Chinese herbal medicines in YGP. These active compounds mainly had an effect on 8 target proteins, including phosphoenolpyruvate carboxykinase, betaine-homocysteine s-methyltransferase 1, alcohol dehydrogenase 1C, etc. These target proteins were primarily involved in 6 overlapping pathways, namely aminoacyl-tRNA biosynthesis, glycolysis/gluconeogenesis, glycine, serine and threonine metabolism, valine, leucine and isoleucine biosynthesis, arginine and proline metabolism, and pyruvate metabolism. In addition, there were 4 non-overlapping pathways, respectively alanine, aspartate and glutamate metabolism, d-glutamine and d-glutamate metabolism, ubiquinone and other terpenoid-quinone biosynthesis, and galactose metabolism. In summary, the therapeutic effects of YGP on KYDS were mainly associated with neuroendocrine regulation, energy metabolism, amino acid metabolism, inflammatory responses, apoptosis, oxidative stress and intestinal flora metabolism. What's more, we also found that YGP possessed the potential to protect liver and kidney function. Our study demonstrated that systems pharmacology and metabonomics methods were novel strategies for the exploration of the mechanisms of KYDS and TCM formulas.

In this study, systems pharmacology was used to predict the molecular targets of You-gui pills (YGP) and explore the therapeutic mechanism of Kidney-Yang Deficiency Syndrome (KYDS) treated with YGP.

Enriching the biological space of natural products and charting drug metabolites, through real time biotransformation monitoring: The NMR tube bioreactor

BACKGROUND

Natural products offer a wide range of biological activities, but they are not easily integrated in the drug discovery pipeline, because of their inherent scaffold intricacy and the associated complexity in their synthetic chemistry. Enzymes may be used to perform regioselective and stereoselective incorporation of functional groups in the natural product core, avoiding harsh reaction conditions, several protection/deprotection and purification steps.

METHODS

Herein, we developed a three step protocol carried out inside an NMR-tube. 1st-step: STD-NMR was used to predict the: i) capacity of natural products as enzyme substrates and ii) possible regioselectivity of the biotransformations. 2nd-step: The real-time formation of multiple-biotransformation products in the NMR-tube bioreactor was monitored in-situ. 3rd-step: STD-NMR was applied in the mixture of the biotransformed products to screen ligands for protein targets.

RESULTS

Herein, we developed a simple and time-effective process, the "NMR-tube bioreactor", that is able to: (i) predict which component of a mixture of natural products can be enzymatically transformed, (ii) monitor in situ the transformation efficacy and regioselectivity in crude extracts and multiple substrate biotransformations without fractionation and (iii) simultaneously screen for interactions of the biotransformation products with pharmaceutical protein targets.

CONCLUSIONS

We have developed a green, time-, and cost-effective process that provide a simple route from natural products to lead compounds for drug discovery.

GENERAL SIGNIFICANSE

This process can speed up the most crucial steps in the early drug discovery process, and reduce the chemical manipulations usually involved in the pipeline, improving the environmental compatibility.

In Silico Prediction of the Anti-Depression Mechanism of a Herbal Formula (Tiansi Liquid) Containing Morinda officinalis and Cuscuta chinensis

Purpose: Depression is a sickening psychiatric condition that is prevalent worldwide. To manage depression, the underlying modes of antidepressant effect of herbals are important to be explored for the development of natural drugs. Tiansi Liquid is a traditional Chinese medicine (TCM) that is prescribed for the management of depression, however its underlying mechanism of action is still uncertain. The purpose of this study was to systematically investigate the pharmacological mode of action of a herbal formula used in TCM for the treatment of depression. Methods: Based on literature search, an ingredients-targets database was developed for Tiansi Liquid, followed by the identification of targets related to depression. The interaction between these targets was evaluated on the basis of protein-protein interaction network constructed by STITCH and gene ontology (GO) enrichment analysis using ClueGO plugin. Results: As a result of literature search, 57 components in Tiansi Liquid formula and 106 potential targets of these ingredients were retrieved. A careful screening of these targets led to the identification of 42 potential targets associated with depression. Ultimately, 327 GO terms were found by analysis of gene functional annotation clusters and abundance value of these targets. Most of these terms were found to be closely related to depression. A significant number of protein targets such as IL10, MAPK1, PTGS2, AKT1, APOE, PPARA, MAPK1, MIF, NOS3 and TNF-α were found to be involved in the functioning of Tiansi Liquid against depression. Conclusions: The findings elaborate that Tiansi Liquid can be utilized to manage depression, however, multiple molecular mechanisms of action could be proposed for this effect. The observed core mechanisms could be the sensory perception of pain, regulation of lipid transport and lipopolysaccharide-mediated signaling pathway.

Network pharmacology-based study on the mechanism of action for herbal medicines in Alzheimer treatment

ETHNOPHARMACOLOGICAL RELEVANCE

Alzheimer's disease (AD), as the most common type of dementia, has brought a heavy economic burden to healthcare system around the world. However, currently there is still lack of effective treatment for AD patients. Herbal medicines, featured as multiple herbs, ingredients and targets, have accumulated a great deal of valuable experience in treating AD although the exact molecular mechanisms are still unclear.

MATERIALS AND METHODS

In this investigation, we proposed a network pharmacology-based method, which combined large-scale text-mining, drug-likeness filtering, target prediction and network analysis to decipher the mechanisms of action for the most widely studied medicinal herbs in AD treatment.

RESULTS

The text mining of PubMed resulted in 10 herbs exhibiting significant correlations with AD. Subsequently, after drug-likeness filtering, 1016 compounds were remaining for 10 herbs, followed by structure clustering to sum up chemical scaffolds of herb ingredients. Based on target prediction results performed by our in-house protocol named AlzhCPI, compound-target (C-T) and target-pathway (T-P) networks were constructed to decipher the mechanism of action for anti-AD herbs.

CONCLUSIONS

Overall, this approach provided a novel strategy to explore the mechanisms of herbal medicine from a holistic perspective.

Systems Pharmacology Dissection of the Integrated Treatment for Cardiovascular and Gastrointestinal Disorders by Traditional Chinese Medicine

Though cardiovascular diseases (CVDs) and gastrointestinal disorders (GIDs) are different diseases associated with different organs, they are highly correlated clinically. Importantly, in Traditional Chinese Medicine (TCM), similar treatment strategies have been applied in both diseases. However, the etiological mechanisms underlying them remain unclear. Here, an integrated systems pharmacology approach is presented for illustrating the molecular correlations between CVDs and GIDs. Firstly, we identified pairs of genes that are associated with CVDs and GIDs and found that these genes are functionally related. Then, the association between 115 heart meridian (HM) herbs and 163 stomach meridian (SM) herbs and their combination application in Chinese patent medicine was investigated, implying that both CVDs and GIDs can be treated by the same strategy. Exemplified by a classical formula Sanhe Decoration (SHD) treating chronic gastritis, we applied systems-based analysis to introduce a drug-target-pathway-organ network that clarifies mechanisms of different diseases being treated by the same strategy. The results indicate that SHD regulated several pathological processes involved in both CVDs and GIDs. We experimentally confirmed the predictions implied by the effect of SHD for myocardial ischemia. The systems pharmacology suggests a novel integrated strategy for rational drug development for complex associated diseases.

Inflammatory pathway network-based drug repositioning and molecular phenomics

Inflammation is a protective biological response to body/tissue damage that involves immune cells, blood vessels and molecular mediators. In this work, we constructed the pathway network of inflammation, including 11 sub-pathways of inflammatory factors. Pathway-based network efficiency and network flux were adopted to evaluate drug efficacy. By using approved and experimentally validated anti-inflammatory drugs as training sets, a predictive model was built to screen potential anti-inflammatory drugs from approved drugs in DrugBank. This drug repositioning approach would bring a fast and cheap way to find new indications for approved drugs. Moreover, molecular phenomics profiles of the expression of inflammatory factors will provide new insight into the drug mechanism of action.

Clinical Perspectives on Targeting Therapies for Personalized Medicine

Expected benefits from new technology include more efficient patient selection for clinical trials, more cost-effective treatment pathways for patients and health services and a more profitable accelerated approach for drug developers. Regulatory authorities expect the pharmaceutical and biotechnology industries to accelerate their development of companion diagnostics and companion therapeutics toward the goal of safer and more effective personalized medicine, and expect health services to fund and prescribers to adopt these new therapeutic technologies.

This review discusses the importance of a range of new approaches to developing new and reprofiled medicines to treat common and serious diseases, and rare diseases: new network pharmacology approaches, adaptive trial designs with enriched populations more likely to respond safely to treatment, as assessed by companion diagnostics for response and toxicity risk and use of “real world” data.

Case studies are described of single and multiple protein drug targets in several important therapeutic areas. These case studies also illustrate the value and complexity of use of selective biomarkers of clinical response and risk of adverse drug effects, either singly or in combination.

Systems pharmacology-based approach for dissecting the active ingredients and potential targets of the Chinese herbal Bufei Jianpi formula for the treatment of COPD

Background

The Chinese herbal Bufei Jianpi formula (BJF) provides an effective treatment option for chronic obstructive pulmonary disease (COPD). However, the systems-level mechanism underlying the clinical effects of BJF on COPD remains unknown.

Methods

In this study, a systems pharmacology model based on absorption filtering, network targeting, and systems analyses was applied specifically to clarify the active compounds and therapeutic mechanisms of BJF. Then, a rat model of cigarette smoke- and bacterial infection-induced COPD was used to investigate the therapeutic mechanisms of BJF on COPD and its comorbidity.

Results

The pharmacological system successfully identified 145 bioactive ingredients from BJF and revealed 175 potential targets. There was a significant target overlap between the herbal constituents of BJF. These results suggested that each herb of BJF connected with similar multitargets, indicating potential synergistic effects among them. The integrated target–disease network showed that BJF probably was efficient for the treatment of not only respiratory tract diseases but also other diseases, such as nervous system and cardiovascular diseases. The possible mechanisms of action of BJF were related to activation of inflammatory response, immune responses, and matrix metalloproteinases, among others. Furthermore, we demonstrated that BJF treatment could effectively prevent COPD and its comorbidities, such as ventricular hypertrophy, by inhibition of inflammatory cytokine production, matrix metalloproteinases expression, and other cytokine production in vivo.

Conclusion

This study using the systems pharmacology method, in combination with in vivo experiments, helped us successfully dissect the molecular mechanism of BJF for the treatment of COPD and predict the potential targets of the multicomponent BJF, which provides a new approach to illustrate the synergetic mechanism of the complex prescription and discover more effective drugs against COPD.

Prediction of the anti-inflammatory mechanisms of curcumin by module-based protein interaction network analysis

Curcumin, the medically active component from Curcuma longa (Turmeric), is widely used to treat inflammatory diseases. Protein interaction network (PIN) analysis was used to predict its mechanisms of molecular action. Targets of curcumin were obtained based on ChEMBL and STITCH databases. Protein–protein interactions (PPIs) were extracted from the String database. The PIN of curcumin was constructed by Cytoscape and the function modules identified by gene ontology (GO) enrichment analysis based on molecular complex detection (MCODE). A PIN of curcumin with 482 nodes and 1688 interactions was constructed, which has scale-free, small world and modular properties. Based on analysis of these function modules, the mechanism of curcumin is proposed. Two modules were found to be intimately associated with inflammation. With function modules analysis, the anti-inflammatory effects of curcumin were related to SMAD, ERG and mediation by the TLR family. TLR9 may be a potential target of curcumin to treat inflammation.

Updates on drug-target network; facilitating polypharmacology and data integration by growth of DrugBank database

Network pharmacology elucidates the relationship between drugs and targets. As the identified targets for each drug increases, the corresponding drug-target network (DTN) evolves from solely reflection of the pharmaceutical industry trend to a portrait of polypharmacology. The aim of this study was to evaluate the potentials of DrugBank database in advancing systems pharmacology. We constructed and analyzed DTN from drugs and targets associations in the DrugBank 4.0 database. Our results showed that in bipartite DTN, increased ratio of identified targets for drugs augmented density and connectivity of drugs and targets and decreased modular structure. To clear up the details in the network structure, the DTNs were projected into two networks namely, drug similarity network (DSN) and target similarity network (TSN). In DSN, various classes of Food and Drug Administration-approved drugs with distinct therapeutic categories were linked together based on shared targets. Projected TSN also showed complexity because of promiscuity of the drugs. By including investigational drugs that are currently being tested in clinical trials, the networks manifested more connectivity and pictured the upcoming pharmacological space in the future years. Diverse biological processes and protein-protein interactions were manipulated by new drugs, which can extend possible target combinations. We conclude that network-based organization of DrugBank 4.0 data not only reveals the potential for repurposing of existing drugs, also allows generating novel predictions about drugs off-targets, drug-drug interactions and their side effects. Our results also encourage further effort for high-throughput identification of targets to build networks that can be integrated into disease networks.

Systems pharmacology-based dissection of mechanisms of Chinese medicinal formula Bufei Yishen as an effective treatment for chronic obstructive pulmonary disease

The present work adopted a systems pharmacology-based approach to provide new insights into the active compounds and therapeutic targets of Bufei Yishen formula (BYF) for the treatment of chronic obstructive pulmonary disease (COPD). In addition, we established a rat model of cigarette smoke- and bacterial infection-induced COPD to validate the mechanisms of BYF action that were predicted in systems pharmacology study. The systems pharmacology model derived 216 active compounds from BYF and 195 potential targets related to various diseases. The compound-target network showed that each herbal drug in the BYF formula acted on similar targets, suggesting potential synergistic effects among these herbal drugs. The ClueGo assay, a Cytoscape plugin, revealed that most targets were related to activation of MAP kinase and matrix metalloproteinases. By using target-diseases network analysis, we found that BYF had great potential to treatment of multiple diseases, such as respiratory tract diseases, immune system, and cardiovascular diseases. Furthermore, we found that BYF had the ability to prevent COPD and its comorbidities, such as ventricular hypertrophy, in vivo. Moreover, BYF inhibited the inflammatory cytokine, and hypertrophic factors expression, protease-antiprotease imbalance and the collagen deposition, which may be the underlying mechanisms of action of BYF.

The Mechanism Research of Qishen Yiqi Formula by Module-Network Analysis

Qishen Yiqi formula (QSYQ) has the effect of tonifying Qi and promoting blood circulation, which is widely used to treat the cardiovascular diseases with Qi deficiency and blood stasis syndrome. However, the mechanism of QSYQ to tonify Qi and promote blood circulation is rarely reported at molecular or systems level. This study aimed to elucidate the mechanism of QSYQ based on the protein interaction network (PIN) analysis. The targets' information of the active components was obtained from ChEMBL and STITCH databases and was further used to search against protein-protein interactions by String database. Next, the PINs of QSYQ were constructed by Cytoscape and were analyzed by gene ontology enrichment analysis based on Markov Cluster algorithm. Finally, based on the topological parameters, the properties of scale-free, small world, and modularity of the QSYQ's PINs were analyzed. And based on function modules, the mechanism of QSYQ was elucidated. The results indicated that Qi-tonifying efficacy of QSYQ may be partly attributed to the regulation of amino acid metabolism, carbohydrate metabolism, lipid metabolism, and cAMP metabolism, while QSYQ improves the blood stasis through the regulation of blood coagulation and cardiac muscle contraction. Meanwhile, the “synergy” of formula compatibility was also illuminated.

Quantitative modeling of dose-response and drug combination based on pathway network

Background

Quantitative description of dose–response of a drug for complex systems is essential for treatment of diseases and drug discovery. Given the growth of large-scale biological data obtained by multi-level assays, computational modeling has become an important approach to understand the mechanism of drug action. However, due to complicated interactions between drugs and cellular targets, the prediction of drug efficacy is a challenge, especially for complex systems. And the biological systems can be regarded as networks, where nodes represent molecular entities (DNA, RNA, protein and small compound) and processes, edges represent the relationships between nodes. Thus we combine biological pathway-based network modeling and molecular docking to evaluate drug efficacy.

Results

Network efficiency (NE) and network flux (NF) are both global measures of the network connectivity. In this work, we used NE and NF to quantitatively evaluate the inhibitory effects of compounds against the lipopolysaccharide-induced production of prostaglandin E2. The edge values of the pathway network of this biological process were reset according to the Michaelis-Menten equation, which used the binding constant and drug concentration to determine the degree of inhibition of the target protein in the pathway. The combination of NE and NF was adopted to evaluate the inhibitory effects. The dose–response curve was sigmoid and the EC50 values of 5 compounds were in good agreement with experimental results (R² = 0.93). Moreover, we found that 2 drugs produced maximal synergism when they were combined according to the ratio between each EC50.

Conclusions

This quantitative model has the ability to predict the dose–response relationships of single drug and drug combination in the context of the pathway network of biological process. These findings are valuable for the evaluation of drug efficacy and thus provide an effective approach for pathway network-based drug discovery.

Rosemary tea consumption results to anxiolytic- and anti-depressant-like behavior of adult male mice and inhibits all cerebral area and liver cholinesterase activity; phytochemical investigation and in silico studies

Our aim was to investigate the possible effects of regular drinking of Rosmarinus officinalis L. leaf infusion on behavior and on AChE activity of mice. Rosemary tea (2% w/w) phytochemical profile was investigated through LC/DAD/ESI-MS(n). Adult male mice were randomly divided into two groups: "Rosemary-treated" that received orally the rosemary tea for 4weeks and "control" that received drinking water. The effects of regular drinking of rosemary tea on behavioral parameters were assessed by passive avoidance, elevated plus maze and forced swimming tests. Moreover, its effects on cerebral and liver cholinesterase (ChE) isoforms activity were examined colorimetricaly. Phytochemical analysis revealed the presence of diterpenes, flavonoids and hydroxycinnamic derivatives in rosemary tea; the major compounds were quantitatively determined. Its consumption rigorously affected anxiety/fear and depression-like behavior of mice, though memory/learning was unaffected. ChE isoforms activity was significantly decreased in brain and liver of "rosemary treated" mice. In order to explain the tissue ChE inhibition, principal component analysis, pharmacophore alignment and molecular docking were used to explore a possible relationship between main identified compounds of rosemary tea, i.e. rosmarinic acid, luteolin-7-O-glucuronide, caffeic acid and known AChE inhibitors. Results revealed potential common pharmacophores of the phenolic components with the inhibitors. Our findings suggest that rosemary tea administration exerts anxiolytic and antidepressant effects on mice and inhibits ChE activity; its main phytochemicals may function in a similar way as inhibitors.

Rational drug design and synthesis of molecules targeting the angiotensin II type 1 and type 2 receptors

The angiotensin II (Ang II) type 1 and type 2 receptors (AT1R and AT2R) orchestrate an array of biological processes that regulate human health. Aberrant function of these receptors triggers pathophysiological responses that can ultimately lead to death. Therefore, it is important to design and synthesize compounds that affect beneficially these two receptors. Cardiovascular disease, which is attributed to the overactivation of the vasoactive peptide hormone Αng II, can now be treated with commercial AT1R antagonists. Herein, recent achievements in rational drug design and synthesis of molecules acting on the two AT receptors are reviewed. Quantitative structure activity relationships (QSAR) and molecular modeling on the two receptors aim to assist the search for new active compounds. As AT1R and AT2R are GPCRs and drug action is localized in the transmembrane region the role of membrane bilayers is exploited. The future perspectives in this field are outlined. Tremendous progress in the field is expected if the two receptors are crystallized, as this will assist the structure based screening of the chemical space and lead to new potent therapeutic agents in cardiovascular and other 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.

Direct binding of Bcl-2 family proteins by quercetin triggers its pro-apoptotic activity

Bcl-2 family proteins are important regulators of apoptosis and its antiapoptotic members, which are overexpressed in many types of cancer, are of high prognostic significance, establishing them as attractive therapeutic targets. Quercetin, a natural flavonoid, has drawn much attention because it exerts anticancer effects, while sparing normal cells. A multidisciplinary approach has been employed herein, in an effort to reveal its mode of action including dose-response antiproliferative activity and induced apoptosis effect, biochemical and physicochemical assays, and computational calculations. It may be concluded that, quercetin binds directly to the BH3 domain of Bcl-2 and Bcl-xL proteins, thereby inhibiting their activity and promoting cancer cell apoptosis.

Metabolomics and systems pharmacology: why and how to model the human metabolic network for drug discovery

Metabolism represents the 'sharp end' of systems biology, because changes in metabolite concentrations are necessarily amplified relative to changes in the transcriptome, proteome and enzyme activities, which can be modulated by drugs. To understand such behaviour, we therefore need (and increasingly have) reliable consensus (community) models of the human metabolic network that include the important transporters. Small molecule 'drug' transporters are in fact metabolite transporters, because drugs bear structural similarities to metabolites known from the network reconstructions and from measurements of the metabolome. Recon2 represents the present state-of-the-art human metabolic network reconstruction; it can predict inter alia: (i) the effects of inborn errors of metabolism; (ii) which metabolites are exometabolites, and (iii) how metabolism varies between tissues and cellular compartments. However, even these qualitative network models are not yet complete. As our understanding improves so do we recognise more clearly the need for a systems (poly)pharmacology.

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.

Drug-target and disease networks: polypharmacology in the post-genomic era

With the growing understanding of complex diseases, the focus of drug discovery has shifted away from the well-accepted “one target, one drug” model, to a new “multi-target, multi-drug” model, aimed at systemically modulating multiple targets. Identification of the interaction between drugs and target proteins plays an important role in genomic drug discovery, in order to discover new drugs or novel targets for existing drugs. Due to the laborious and costly experimental process of drug-target interaction prediction, in silico prediction could be an efficient way of providing useful information in supporting experimental interaction data. An important notion that has emerged in post-genomic drug discovery is that the large-scale integration of genomic, proteomic, signaling and metabolomic data can allow us to construct complex networks of the cell that would provide us with a new framework for understanding the molecular basis of physiological or pathophysiological states. An emerging paradigm of polypharmacology in the post-genomic era is that drug, target and disease spaces can be correlated to study the effect of drugs on different spaces and their interrelationships can be exploited for designing drugs or cocktails which can effectively target one or more disease states. The future goal, therefore, is to create a computational platform that integrates genome-scale metabolic pathway, protein–protein interaction networks, gene transcriptional analysis in order to build a comprehensive network for multi-target multi-drug discovery.

A network-based analysis of the types of coronary artery disease from traditional Chinese medicine perspective: potential for therapeutics and drug discovery

ETHNOPHARMACOLOGICAL RELEVANCE

Coronary heart disease (CAD) is one of the most dangerous threats to human health due to its high incidence and high mortality. CAD has several major types, such as blood stasis and qi deficiency according to the syndromes of diagnosis in traditional Chinese medicine (TCM), which are treated with different herbs or compound prescriptions. However, up to now a deep analysis of the relationship between CAD and its types both at molecular or systems levels is still unavailable, which greatly limits the combination of TCMs with Western drugs to form an integrative/alternative medicine for treatment of the complex disease.

MATERIALS AND METHODS

In this review, we attempt to decipher the underlying mechanisms of major types of CAD by connecting the drugs, targets and diseases to obtain the compound-target-disease associations for reconstructing the biologically-meaningful networks based on systems pharmacology method.

RESULTS

The results indicate that the herbs for eliminating blood stasis have pharmacological activity of dilating blood vessel, improving the microcirculation, reducing blood viscosity and regulating blood lipid, while qi-enhancing herbs have the potential for enhancing energy metabolism and anti-inflammation.

CONCLUSIONS

A systematic exploration of types of CAD may bring out the best between research on drug molecules and TCM phenotypic information, so as to accelerate development of network-based drug discovery as well as to facilitate the therapy of this disease.

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.

Isoform level expression profiles provide better cancer signatures than gene level expression profiles

BACKGROUND

The majority of mammalian genes generate multiple transcript variants and protein isoforms through alternative transcription and/or alternative splicing, and the dynamic changes at the transcript/isoform level between non-oncogenic and cancer cells remain largely unexplored. We hypothesized that isoform level expression profiles would be better than gene level expression profiles at discriminating between non-oncogenic and cancer cellsgene level.

METHODS

We analyzed 160 Affymetrix exon-array datasets, comprising cell lines of non-oncogenic or oncogenic tissue origins. We obtained the transcript-level and gene level expression estimates, and used unsupervised and supervised clustering algorithms to study the profile similarity between the samples at both gene and isoform levels.

RESULTS

Hierarchical clustering, based on isoform level expressions, effectively grouped the non-oncogenic and oncogenic cell lines with a virtually perfect homogeneity-grouping rate (97.5%), regardless of the tissue origin of the cell lines. However, gene levelthis rate was much lower, being 75% at best based on the gene level expressions. Statistical analyses of the difference between cancer and non-oncogenic samples identified the existence of numerous genes with differentially expressed isoforms, which otherwise were not significant at the gene level. We also found that canonical pathways of protein ubiquitination, purine metabolism, and breast-cancer regulation by stathmin1 were significantly enriched among genes thatshow differential expression at isoform level but not at gene level.

CONCLUSIONS

In summary, cancer cell lines, regardless of their tissue of origin, can be effectively discriminated from non-cancer cell lines at isoform level, but not at gene level. This study suggests the existence of an isoform signature, rather than a gene signature, which could be used to distinguish cancer cells from normal cells.

Platelet aggregation pathway network-based approach for evaluating compounds efficacy

Traditional Chinese medicines (TCMs) contain a large quantity of compounds with multiple biological activities. By using multitargets docking and network analysis in the context of pathway network of platelet aggregation, we proposed network efficiency and network flux model to screen molecules which can be used as drugs for antiplatelet aggregation. Compared with traditional single-target screening methods, network efficiency and network flux take into account the influences which compounds exert on the whole pathway network. The activities of antiplatelet aggregation of 19 active ingredients separated from TCM and 14 nonglycoside compounds predicated from network efficiency and network flux model show good agreement with experimental results (correlation coefficient = 0.73 and 0.90, resp.). This model can be used to evaluate the potential bioactive compounds and thus bridges the gap between computation and clinical indicator.

Computational approaches for exploring the potential synergy and polypharmacology of Duhuo Jisheng Decoction in the therapy of osteoarthritis

The Duhuo Jisheng Decoction (DHJSD), a traditional Chinese medicine (TCM), has been widely used to treat osteoarthritis (OA). However, the mechanisms of action of the DHJSD have not been clearly elucidated. In the present study, the compounds in the DHJSD were characterized by three computational methods; the ligand clustering, chemical space distribution and network construction and analysis methods. The compounds that formed the medical composition of the DHJSD were divided into 10 clusters and possessed a broad diversity in chemical space distribution. The compounds also had the same coverage of chemical space as the OA drug/drug‑like compounds from DrugBank. In addition, multiple active compounds were identified as able to target multiple proteins in the drug‑target association networks (D‑T networks). A certain number of key compounds in the D‑T networks have been previously reported in the literature. The present study also constructed drug‑drug association networks (D‑D networks) and classified the DHJSD compounds into five clusters. The clusters represented multiple diverse combinations binding to the OA targets. These results suggested that the DHJSD had drug‑ and lead‑like compounds with potential synergy and polypharmacology against OA.

Construction of functional linkage gene networks by data integration

Networks of functional associations between genes have recently been successfully used for gene function and disease-related research. A typical approach for constructing such functional linkage gene networks (FLNs) is based on the integration of diverse high-throughput functional genomics datasets. Data integration is a nontrivial task due to the heterogeneous nature of the different data sources and their variable accuracy and completeness. The presence of correlations between data sources also adds another layer of complexity to the integration process. In this chapter we discuss an approach for constructing a human FLN from data integration and a subsequent application of the FLN to novel disease gene discovery. Similar approaches can be applied to nonhuman species and other discovery tasks.

The promiscuous binding of pharmaceutical drugs and their transporter-mediated uptake into cells: what we (need to) know and how we can do so

A recent paper in this journal sought to counter evidence for the role of transport proteins in effecting drug uptake into cells, and questions that transporters can recognize drug molecules in addition to their endogenous substrates. However, there is abundant evidence that both drugs and proteins are highly promiscuous. Most proteins bind to many drugs and most drugs bind to multiple proteins (on average more than six), including transporters (mutations in these can determine resistance); most drugs are known to recognise at least one transporter. In this response, we alert readers to the relevant evidence that exists or is required. This needs to be acquired in cells that contain the relevant proteins, and we highlight an experimental system for simultaneous genome-wide assessment of carrier-mediated uptake in a eukaryotic cell (yeast).

A system-level investigation into the mechanisms of Chinese Traditional Medicine: Compound Danshen Formula for cardiovascular disease treatment

Compound Danshen Formula (CDF) is a widely used Traditional Chinese Medicine (TCM) which has been extensively applied in clinical treatment of cardiovascular diseases (CVDs). However, the underlying mechanism of clinical administrating CDF on CVDs is not clear. In this study, the pharmacological effect of CDF on CVDs was analyzed at a systemic point of view. A systems-pharmacological model based on chemical, chemogenomics and pharmacological data is developed via network reconstruction approach. By using this model, we performed a high-throughput in silico screen and obtained a group of compounds from CDF which possess desirable pharmacodynamical and pharmacological characteristics. These compounds and the corresponding protein targets are further used to search against biological databases, such as the compound-target associations, compound-pathway connections and disease-target interactions for reconstructing the biologically meaningful networks for a TCM formula. This study not only made a contribution to a better understanding of the mechanisms of CDF, but also proposed a strategy to develop novel TCM candidates at a network pharmacology level.

Personalized medicine: the impact on chemistry

An effective strategy for personalized medicine requires a major conceptual change in the development and application of therapeutics. In this article, we argue that further advances in this field should be made with reference to another conceptual shift, that of network pharmacology. We examine the intersection of personalized medicine and network pharmacology to identify strategies for the development of personalized therapies that are fully informed by network pharmacology concepts. This provides a framework for discussion of the impact personalized medicine will have on chemistry in terms of drug discovery, formulation and delivery, the adaptations and changes in ideology required and the contribution chemistry is already making. New ways of conceptualizing chemistry's relationship with medicine will lead to new approaches to drug discovery and hold promise of delivering safer and more effective therapies.