Quantitative and Systems Pharmacology 3. Network-Based Identification of New Targets for Natural Products Enables Potential Uses in Aging-Associated Disorders

Study on the mechanism of vitamin E alleviating non-alcoholic fatty liver function based on non-targeted metabolomics analysis in rats

Non-alcoholic fatty liver disease (NAFLD) is a hepatic manifestation of metabolic syndrome. Vitamin E (VE) has antioxidant properties and can mediate lipid metabolism. Non-targeted metabolomics technology was employed to uncover comprehensively the metabolome of VE in NAFLD rats. NAFLD model was created with a high-fat and high-cholesterol diet (HFD) in rats. NAFLD rats in the VE group were given 75 mg/(kg day) VE. The metabolites in the serum of rats were identified via UPLC and Q-TOF/MS analysis. KEGG was applied for the pathway enrichment. VE improved the liver function, lipid metabolism, and oxidative stress in NAFLD rats induced by HFD. Based on the metabolite profile data, 132 differential metabolites were identified between VE group and the HFD group, mainly including pyridoxamine, betaine, and bretylium. According to the KEGG results, biosynthesis of cofactors was a key metabolic pathway of VE in NAFLD rats. VE can alleviate NAFLD induced by HFD, and the underlying mechanism is associated with the biosynthesis of cofactors, mainly including pyridoxine and betaine.

Network-Based Methods and Their Applications in Drug Discovery

Drug discovery is time-consuming, expensive, and predominantly follows the "one drug → one target → one disease" paradigm. With the rapid development of systems biology and network pharmacology, a novel drug discovery paradigm, "multidrug → multitarget → multidisease", has emerged. This new holistic paradigm of drug discovery aligns well with the essence of networks, leading to the emergence of network-based methods in the field of drug discovery. In this Perspective, we initially introduce the concept and data sources of networks and highlight classical methodologies employed in network-based methods. Subsequently, we focus on the practical applications of network-based methods across various areas of drug discovery, such as target prediction, virtual screening, prediction of drug therapeutic effects or adverse drug events, and elucidation of molecular mechanisms. In addition, we provide representative web servers for researchers to use network-based methods in specific applications. Finally, we discuss several challenges of network-based methods and the directions for future development. In a word, network-based methods could serve as powerful tools to accelerate drug discovery.

Deciphering the role of lipoproteins and lipid metabolic alterations in ageing and ageing-associated renal fibrosis

Fibrosis is the ultimate pathological feature of many chronic diseases, and ageing a major risk factor for fibrotic diseases. Current therapies are limited to those that reduce the rate of functional decline in patients with mild to moderate disease, but few interventions are available to specifically target the pathogenesis of fibrosis. In this context, new treatments that can significantly improve survival time and quality of life for these patients are urgently needed. In this review, we outline both the synthesis and metabolism of lipids and lipoproteins associated with ageing-associated renal fibrosis and the prominent contribution of lipids and lipidomics in the discovery of biomarkers that can be used for the prevention, diagnosis, and treatment of renal ageing and fibrosis. Next, we describe the effect of dyslipidaemia on ageing-related renal fibrosis and the pathophysiological changes in the kidney caused by dyslipidaemia. We then summarize the enzymes, transporters, transcription factors, and RNAs that contribute to dysregulated lipid metabolism in renal fibrosis and discuss their role in renal fibrosis in detail. We conclude by discussing the progress in research on small molecule therapeutic agents that prevent and treat ageing and ageing-associated renal fibrosis by modulating lipid metabolism. A growing number of studies suggest that restoring aberrant lipid metabolism may be a novel and promising therapeutic strategy to combat ageing and ageing-associated renal fibrosis.

Research trends and hotspots of neurodegenerative diseases employing network pharmacology: A bibliometric analysis

Background: Employing network pharmacology in neurodegenerative diseases (NDs) has been extensively studied recently. However, no comprehensive study has conducted on this subject employing bibliometrics so far. The purpose of this study was to find out the developmental trends and hotspots, and to predict potential research directions in this filed. Methods: Relevant research were collected from the Web of Science Core Collection Bibliometrics and visual analysis were executed using CiteSpace, VOSviewer, Histcite and R-bibliometrix. Results: A total of 420 English articles on network pharmacology in NDs published in 2008-2022 were obtained from the WOSCC database. From 2008 to 2022, annual publications showed a steady growing trend, especially in 2014-2022. China, Beijing Univ Chinese Med, Frontiers in Pharmacology, and Geerts H are the most prolific country, institution, journal, and author, respectively. China, Nucleic Acids Research, and Hopkins AL are the most highly cited country, journal, and author, respectively. Moreover, network pharmacology and Alzheimer's disease are the focal areas of current researches according to analysis of co-cited references and keywords. Finally, in the detection of burst keywords, systems pharmacology and database are new approaches to disease and drug research, while traditional Chinese medicine (TCM) and Alzheimer's disease are hot research directions. The above keywords are speculated to be the research frontiers. Conclusion: Network pharmacology and Alzheimers' disease are the main topics of researches on network pharmacology in NDs. Network pharmacology and the TCM treatment of Alzheimer's disease have been the recent research hotspots. To sum up, the potential for exploring TCM treatment of AD with network pharmacology is huge.

Aryl hydrocarbon receptor: From pathogenesis to therapeutic targets in aging-related tissue fibrosis

Aging promotes chronic inflammation, which contributes to fibrosis and decreases organ function. Fibrosis, the excessive synthesis and deposition of extracellular matrix components, is the main cause of most chronic diseases including aging-related organ failure. Organ fibrosis in the heart, liver, and kidneys is the final manifestation of many chronic diseases. The aryl hydrocarbon receptor (AHR) is a cytoplasmic receptor and highly conserved transcription factor that is activated by a variety of small-molecule ligands to affect a wide array of tissue homeostasis functions. In recent years, mounting evidence has revealed that AHR plays an important role in multi-organ fibrosis initiation, progression, and therapy. In this review, we summarise the relationship between AHR and the pathogenesis of aging-related tissue fibrosis, and further discuss how AHR modulates tissue fibrosis by regulating transforming growth factor-β signalling, immune response, and mitochondrial function, which may offer novel targets for the prevention and treatment of this condition.

Resveratrol-Based MTDLs to Stimulate Defensive and Regenerative Pathways and Block Early Events in Neurodegenerative Cascades

By replacing a phenolic ring of (E)-resveratrol with an 1,3,4-oxadiazol-2(3H)-one heterocycle, new resveratrol-based multitarget-directed ligands (MTDLs) were obtained. They were evaluated in several assays related to oxidative stress and inflammation (monoamine oxidases, nuclear erythroid 2-related factor, quinone reductase-2, and oxygen radical trapping) and then in experiments of increasing complexity (neurogenic properties and neuroprotection vs okadaic acid). 5-[(E)-2-(4-Methoxyphenyl)ethenyl]-3-(prop-2-yn-1-yl)-1,3,4-oxadiazol-2(3H)-one (4e) showed a well-balanced MTDL profile: cellular activation of the NRF2-ARE pathway (CD = 9.83 μM), selective inhibition of both hMAO-B and QR2 (IC₅₀s = 8.05 and 0.57 μM), and the best ability to promote hippocampal neurogenesis. It showed a good drug-like profile (positive in vitro central nervous system permeability, good physiological solubility, no glutathione conjugation, and lack of PAINS or Lipinski alerts) and exerted neuroprotective and antioxidant actions in both acute and chronic Alzheimer models using hippocampal tissues. Thus, 4e is an interesting MTDL that could stimulate defensive and regenerative pathways and block early events in neurodegenerative cascades.

Chemical Distance Measurement and System Pharmacology Approach Uncover the Novel Protective Effects of Biotransformed Ginsenoside C-Mc against UVB-Irradiated Photoaging

Long-term exposure to ultraviolet light induces photoaging and may eventually increase the risk of skin carcinogenesis. Rare minor ginsenosides isolating from traditional medicine Panax (ginseng) have shown biomedical efficacy as antioxidation and antiphotodamage agents. However, due to the difficulty of component extraction and wide variety of ginsenoside, the identification of active antiphotoaging ginsenoside remains a huge challenge. In this study, we proposed a novel in silico approach to identify potential compound against photoaging from 82 ginsenosides. Specifically, we calculated the shortest distance between unknown and known antiphotoaging ginsenoside set in the chemical space and applied chemical structure similarity assessment, drug-likeness screening, and ADMET evaluation for the candidates. We highlighted three rare minor ginsenosides (C-Mc, Mx, and F2) that possess high potential as antiphotoaging agents. Among them, C-Mc deriving from American ginseng (Panax quinquefolius L.) was validated by wet-lab experimental assays and showed significant antioxidant and cytoprotective activity against UVB-induced photodamage in human dermal fibroblasts. Furthermore, system pharmacology analysis was conducted to explore the therapeutic targets and molecular mechanisms through integrating global drug-target network, high quality photoaging-related gene profile from multiomics data, and skin tissue-specific expression protein network. In combination with in vitro assays, we found that C-Mc suppressed MMP production through regulating the MAPK/AP-1/NF-κB pathway and expedited collagen synthesis via the TGF-β/Smad pathway, as well as enhanced the expression of Nrf2/ARE to hold a balance of endogenous oxidation. Overall, this study offers an effective drug discovery framework combining in silico prediction and in vitro validation, uncovering that ginsenoside C-Mc has potential antiphotoaging properties and might be a novel natural agent for use in oral drug, skincare products, or functional food.

In Silico Prediction and Bioactivity Evaluation of Chemical Ingredients Against Influenza A Virus From Isatis tinctoria L

Influenza A virus (IAV) is one of the major causes of seasonal endemic diseases and unpredictable periodic pandemics. Due to the high mutation rate and drug resistance, it poses a persistent threat and challenge to public health. Isatis tinctoria L. (Banlangen, BLG), a traditional herbal medicine widely used in Asian countries, has been reported to possess strong efficacy on respiratory viruses, including IAV. However, its effective anti-IAV components and the mechanism of actions (MOAs) are not yet fully elucidated. In this study, we first summarized the chemical components and corresponding contents in BLG according to current available chemical analysis literature. We then presented a network-based in silico framework for identifying potential drug candidates against IAV from BLG. A total of 269 components in BLG were initially screened by drug-likeness and ADME (absorption, distribution, metabolism, and excretion) evaluation. Thereafter, network predictive models were built via the integration of compound–target networks and influenza virus–host proteins. We highlighted 23 compounds that possessed high potential as anti-influenza virus agents. Through experimental evaluation, six compounds, namely, eupatorin, dinatin, linarin, tryptanthrin, indirubin, and acacetin, exhibited good inhibitory activity against wild-type H1N1 and H3N2. Particularly, they also exerted significant effects on drug-resistant strains. Finally, we explored the anti-IAV MOAs of BLG and showcased the potential biological pathways by systems pharmacology analysis. In conclusion, this work provides important information on BLG regarding its use in the development of anti-IAV drugs, and the network-based prediction framework proposed here also offers a powerfulful strategy for the in silico identification of novel drug candidates from complex components of herbal medicine.

Antioxidant Effects of Caffeic Acid Lead to Protection of Drosophila Intestinal Stem Cell Aging

The dysfunction or exhaustion of adult stem cells during aging is closely linked to tissue aging and age-related diseases. Circumventing this aging-related exhaustion of adult stem cells could significantly alleviate the functional decline of organs. Therefore, identifying small molecular compounds that could prevent the age-related decline of stem cell function is a primary goal in anti-aging research. Caffeic acid (CA), a phenolic compound synthesized in plants, offers substantial health benefits for multiple age-related diseases and aging. However, the effects of CA on adult stem cells remain largely unknown. Using the Drosophila midgut as a model, this study showed that oral administration with CA significantly delayed age-associated Drosophila gut dysplasia caused by the dysregulation of intestinal stem cells (ISCs) upon aging. Moreover, administering CA retarded the decline of intestinal functions in aged Drosophila and prevented hyperproliferation of age-associated ISC by suppressing oxidative stress-associated JNK signaling. On the other hand, CA supplementation significantly ameliorated the gut hyperplasia defect and reduced environmentally induced mortality, revealing the positive effects of CA on tolerance to stress responses. Taken together, our findings report a crucial role of CA in delaying age-related changes in ISCs of Drosophila.

Network-Based Identification and Experimental Validation of Drug Candidates Toward SARS-CoV-2 via Targeting Virus-Host Interactome

Despite that several therapeutic agents have exhibited promising prevention or treatment on Coronavirus disease-2019 (COVID-19), there is no specific drug discovered for this pandemic. Targeting virus-host interactome provides a more effective strategy for antivirus drug discovery compared with targeting virus proteins. In this study, we developed a network-based infrastructure to prioritize promising drug candidates from natural products and approved drugs via targeting host proteins of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). We firstly measured the network distances between drug targets and COVID-19 disease module utilizing the network proximity approach, and identified 229 approved drugs as well as 432 natural products had significant associations with SARS-CoV-2. After searching for previous literature evidence, we found that 60.7% (139/229) of approved drugs and 39.6% (171/432) of natural products were confirmed with antivirus or anti-inflammation. We further integrated our network-based predictions and validated anti-SARS-CoV-2 activities of some compounds. Four drug candidates, including hesperidin, isorhapontigenin, salmeterol, and gallocatechin-7-gallate, have exhibited activity on SARS-COV-2 virus-infected Vero cells. Finally, we showcased the mechanism of actions of isorhapontigenin and salmeterol via network analysis. Overall, this study offers forceful approaches for in silico identification of drug candidates on COVID-19, which may facilitate the discovery of antiviral drug therapies.

Metabolomics profiling reveals the mechanism of caffeic acid in extending lifespan in Drosophila melanogaster

Caffeic acid is a phenolic compound widely synthesized by plants, which has shown health benefits for multiple aging-related diseases. The aim of this study was to investigate the life-extending effect of caffeic acid and its underlying mechanisms. The effects of caffeic acid on lifespan, climbing behavior, starvation resistance, and heat sensitivity of Drosophila melanogaster (D. melanogaster) were evaluated. 1H-NMR-based metabolomics and biochemical detection were performed to explore the potential mechanisms. The results demonstrated that supplementation with caffeic acid extended the lifespan, and improved climbing behavior and stress resistance in D. melanogaster. Additionally, continuous supplementation with caffeic acid caused the metabolic profile of 30-day D. melanogaster closer to that of 3-day D. melanogaster, among which 17 differential metabolites were significantly regulated by caffeic acid, involved in amino acid metabolism and mitochondrial metabolism. Furthermore, caffeic acid significantly prevented oxidative damage and improved mitochondrial function. Correlation analysis indicated that the differential metabolites regulated by caffeic acid were correlated with its antioxidant effect and mitochondrial improvement function. In conclusion, our data support that caffeic acid could extend lifespan in D. melanogaster through regulation of metabolic abnormality and improvement of mitochondrial function.

Network Pharmacology-Based Strategy for Elucidating the Molecular Basis Forthe Pharmacologic Effects of Licorice (Glycyrrhiza spp.)

Licorice (Glycyrrhiza spp.) is used widely in traditional Chinese medicine (TCM) due to its numerous pharmacologic effects. However, the mechanisms of action of the chemical constituents of licorice and their structure–function relationships are not fully understood. To address these points, we analyzed the chemical compounds in licorice listed in the TCM Systems Pharmacology database and TCM Integrated database. Target proteins of the compounds were predicted using Integrative Pharmacology-based Research Platform of TCM v2.0. Information on the pharmacologic effects of licorice was obtained from the 2020 Chinese Pharmacopoeia, and disease-related genes that have been linked to these effects were identified from the Encyclopedia of TCM database. Pathway analyses using the Kyoto Encyclopedia of Genes and Genomes database were carried out for target proteins, and pharmacologic networks were constructed based on drug target–disease-related gene and protein–protein interactions. A total of 451 compounds were analyzed, of which 211 were from the medicinal parts of the licorice plant. The 241 putative targets of 106 bioactive compounds in licorice comprised 52 flavonoids, 47 triterpenoids, and seven coumarins. Four distinct pharmacologic effects of licorice were defined: 61 major hubs were the putative targets of 23 compounds in heat-clearing and detoxifying effects; 68 were targets of six compounds in spleen-invigorating and qi-replenishing effects; 28 were targets of six compounds in phlegm-expulsion and cough-suppressant effects; 25 compounds were targets of six compounds in spasm-relieving and analgesic effects. The major bioactive compounds of licorice were identified by ultra-high-performance liquid chromatography–quadrupole time-of-flight–tandem mass spectrometry. The anti-inflammatory properties of liquiritin apioside, liquiritigenin, glycyrrhizic acid and isoliquiritin apioside were demonstrated by enzyme-linked immunosorbent assay (ELISA) and Western blot analysis. Liquiritin apioside, liquiritigenin, isoliquiritin, isoliquiritin apioside, kaempferol, and kumatakenin were the main active flavonoids, and 18α- and 18β-glycyrrhetinic acid were the main active triterpenoids of licorice. The former were associated with heat-clearing and detoxifying effects, whereas the latter were implicated in the other three pharmacologic effects. Thus, the compounds in licorice have distinct pharmacologic effects according to their chemical structure. These results provide a reference for investigating the potential of licorice in treatment of various diseases.

In silico identification of natural products from Traditional Chinese Medicine for cancer immunotherapy

Advances in immunotherapy have revolutionized treatments in many types of cancer. Traditional Chinese Medicine (TCM), which has a long history of clinical adjuvant application against cancer, is emerging as an important medical resource for developing innovative cancer treatments, including immunotherapy. In this study, we developed a quantitative and systems pharmacology-based framework to identify TCM-derived natural products for cancer immunotherapy. Specifically, we integrated 381 cancer immune response-related genes and a compound-target interaction network connecting 3273 proteins and 766 natural products from 66 cancer-related herbs based on literature-mining. Via systems pharmacology-based prediction, we uncovered 182 TCM-derived natural products having potential anti-tumor immune responses effect. Importantly, 32 of the 49 most promising natural products (success rate = 65.31%) are validated by multiple evidence, including published experimental data from clinical studies, in vitro and in vivo assays. We further identified the mechanism-of-action of TCM in cancer immunotherapy using network-based functional enrichment analysis. We showcased that three typical natural products (baicalin, wogonin, and oroxylin A) in Huangqin (Scutellaria baicalensis Georgi) potentially overcome resistance of known oncology agents by regulating tumor immunosuppressive microenvironments. In summary, this study offers a novel and effective systems pharmacology infrastructure for potential cancer immunotherapeutic development by exploiting the medical wealth of natural products in TCM.

Systems pharmacology approach uncovers Ligustilide attenuates experimental colitis in mice by inhibiting PPARγ-mediated inflammation pathways

Inflammatory bowel disease (IBD) is a chronic idiopathic disorder causing inflammation in the gastro-intestinal tract, which is lack of effective drug targets and medications. To identify novel therapeutic agents against consistent targets, we exploited a systems pharmacology-driven framework that incorporates drug-target networks of natural product and IBD disease genes. Our in silico approach found that Ligustilide (LIG), one of the major active components of Angelica acutiloba and Cnidium Officinale, potently attenuated IBD. The following in vivo and in vitro results demonstrated that LIG prevented experimental mice colitis induced by dextran sulfate sodium (DSS) via suppressing inflammatory cell infiltration, the activity of MPO and iNOS, and the expression and production of IL-1β, IL-6, and TNF-α. Subsequently, the network analysis helped to validate that LIG alleviated colitis by inhibiting NF-κB and MAPK/AP-1 pathway through activating PPARγ, which were further confirmed in RAW 264.7 cells and bone marrow-derived macrophages in vitro. In summary, this study reveals that LIG activated PPARγ to inhibit the activation of NF-κB and AP-1 signaling thus eventually alleviated DSS-induced colitis, which has promising activities and may serve as a candidate for the treatment of IBD.Graphical abstract This study suggested novel computational and experimental pharmacology approaches to identify potential IBD therapeutic agents by exploiting polypharmacology of natural products. We demonstrated that LIG could attenuate inflammation in IBD by inhibiting NF-κB and AP-1 pathways via PPARγ activation to reduce the expression of pro-inflammatory cytokines in macrophages. These findings offer comprehensive pre-clinical evidence that LIG may serve as a promising candidate for IBD therapy in the future. Graphical headlights: 1. Systems pharmacology uncovered Ligustilide attenuates experimental colitis in mice. 2. Network-based analysis predicted the mechanism of Ligustilide against IBD, which was validated by inhibiting PPARγ-mediated inflammation pathways. 3. Ligustilide activated PPARγ to inhibit NF-κB and AP-1 activation thus eventually alleviated DSS-induced colitis.4. Ligustilide has promising activities and may serve as a candidate for the treatment of IBD.

Network pharmacology and pharmacokinetics integrated strategy to investigate the pharmacological mechanism of Xianglian pill on ulcerative colitis

BACKGROUND

Ulcerative colitis (UC) is a chronic inflammatory bowel disease with high morbidity, which leads to poor quality of life. The Xianglian pill (XLP) is a classical Chinese patent medicine and has been clinically proven to be an effective treatment for UC.

PURPOSE

The pharmacological mechanism of the key bioactive ingredients of XLP for the treatment of UC was investigated by a network pharmacology and pharmacokinetics integrated strategy.

STUDY DESIGN AND METHODS

Network pharmacology was used to analyze the treatment effect of nine quantified XLP ingredients on UC. Key pathways were enriched and analyzed by protein-protein interaction and Kyoto Encyclopedia of Genes and Genomes analyses. The effect of XLP on Th17 cell differentiation was validated using a mouse model of UC. The binding of nine compounds with JAk2, STAT3, HIF-1α, and HSP90AB1 was assessed using molecular docking. A simple and reliable ultra-high-performance liquid chromatography-tandem mass spectrometry method was developed for the simultaneous quantification of nine ingredients from XLP in plasma and applied to a pharmacokinetic study following oral administration.

RESULTS

Nine compounds of XLP, including coptisine, berberine, magnoflorine,berberrubine, jatrorrhizine, palmatine, evodiamine, rutaecarpine, and dehydrocostus lactone, were detected. Network pharmacology revealed 50 crossover genes between the nine compoundsand UC. XLP treats UC mainly by regulating key pathways of the immune system, including Th17 cell differentiation, Jak-Stat, and PI3K-Akt signaling pathways. An in vivo validation in mice found that XLP inhibits Th17 cell differentiation by suppressing the Jak2-Stat3 pathway, which alleviates mucosal inflammation in UC. Molecular docking confirmed that eight compounds are capable of binding with JAk2, HIF-1α, and HSP90AB1, further confirming the inhibitory effect of XLP on the Jak2-Stat3 pathway. Moreover, apharmacokinetic study revealed that the nine ingredients of XLP are exposed in the plasma and colon tissue, which demonstrates its pharmacological effect on UC.

CONCLUSION

This study evaluates the clinical treatment efficacy of XLP for UC. The network pharmacology and pharmacokinetics integrated strategy evaluation paradigm is efficient in discovering the key pharmacological mechanism of herbal formulae.

Cheminformatics in Natural Product-based Drug Discovery

This review seeks to provide a timely survey of the scope and limitations of cheminformatics methods in natural product-based drug discovery. Following an overview of data resources of chemical, biological and structural information on natural products, we discuss, among other aspects, in silico methods for (i) data curation and natural products dereplication, (ii) analysis, visualization, navigation and comparison of the chemical space, (iii) quantification of natural product-likeness, (iv) prediction of the bioactivities (virtual screening, target prediction), ADME and safety profiles (toxicity) of natural products, (v) natural products-inspired de novo design and (vi) prediction of natural products prone to cause interference with biological assays. Among the many methods discussed are rule-based, similarity-based, shape-based, pharmacophore-based and network-based approaches, docking and machine learning methods.

Onion Bulb Extract Downregulates EGFR/ERK1/2/AKT Signaling Pathway and Synergizes With Steroids to Inhibit Allergic Inflammation

The treatment of allergic diseases, such as asthma, with both conventional and novel therapies presents a challenge both in terms of optimal effect and cost. On the other hand, traditional therapies utilizing natural products such as onion have been in use for centuries with demonstrated efficacy and safety but without much knowledge of their mechanims of action. In this study, we investigated if the anti-inflammatory effects of onion bulb extract (OBE) are mediated via the modulation of the EGFR/ERK1/2/AKT signaling pathway, and whether OBE can synergise with steroids to produce greater anti-inflammatory actions. Treatment with OBE inhibited the house dust mite (HDM)-induced increased phosphorylation of EGFR, ERK1/2 and AKT which resulted in the inhibition of HDM-induced increase in airway cellular influx, perivascular and peribronchial inflammation, goblet cell hyper/metaplasia, and also inhibited ex vivo eosinophil chemotaxis. Moreover, treatment with a combination of a low dose OBE and low dose dexamethasone resulted in a significant inhibition of the HDM-induced cellular influx, perivascular and peribronchial inflammation, goblet cell hyper/metaplasia, and increased the pERK1/2 levels, whereas neither treatment, when given alone, had any discernible effects. This study therefore shows that inhibition of the EGFR/ERK1/2/AKT-dependent signaling pathway is one of the key mechanisms by which OBE can mediate its anti-inflammatory effects in diseases such as asthma. Importantly, this study also demonstrates that combining OBE with steroids results in significantly enhanced anti-inflammatory effects. This action may have important potential implications for future asthma therapy.

Computational Drug Repositioning: Current Progress and Challenges

Novel drug discovery is time-consuming, costly, and a high-investment process due to the high attrition rate. Therefore, many trials are conducted to reuse existing drugs to treat pressing conditions and diseases, since their safety profiles and pharmacokinetics are already available. Drug repositioning is a strategy to identify a new indication of existing or already approved drugs, beyond the scope of their original use. Various computational and experimental approaches to incorporate available resources have been suggested for gaining a better understanding of disease mechanisms and the identification of repurposed drug candidates for personalized pharmacotherapy. In this review, we introduce publicly available databases for drug repositioning and summarize the approaches taken for drug repositioning. We also highlight and compare their characteristics and challenges, which should be addressed for the future realization of drug repositioning.

The dynamic nature of ageing: novel findings, therapeutic avenues and medical interventions

Ageing is one of the most complex and difficult problems for humans to face and for science to solve. Although human senescence was viewed as a passive and uncontrollable process of deteriora­tion over time with little or no genetic regulation, the concept that ageing is caused by both genetic and environmental factors is now generally accepted, even though it remains difficult to distinguish between ageing sensu stricto and the effects of age-related diseases. Empirical data show that mechanisms of ageing are highly conserved during evolution. Moreover, it has been established that there are specific molecular ‘instructions’ for ageing, which suggests that a better understanding of the molecular biology of ageing will open new possibilities regarding future interventions. The complexity of ageing diminishes the possibility that any general theory will completely explain this metaphenomenon. Likewise, it is highly unlikely that any medication can stop or reverse human senescence. Nevertheless, ageing as a dynamic and malleable metaphenomenon can be modulated by a variety of influences. The concept of the shrinkage of the homeo­dynamic space with age, i.e. homeostenosis, is especially interesting and intriguing as it shows that novel therapeutic approaches and rational strategies can help delay the onset of the ageing-associated pathologies by enhancing the homeodynamic capabilities of the body. The aim of this article is to present current data from evolutionary and molecular gerontology and discuss them within the ambit of this review which is devoted to the dynamic, emergent and plastic nature of human ageing and implications for future inter­ventions.

Aging and drug discovery

Multiple interventions in the aging process have been discovered to extend the healthspan of model organisms. Both industry and academia are therefore exploring possible transformative molecules that target aging and age-associated diseases. In this overview, we summarize the presented talks and discussion points of the 5th Annual Aging and Drug Discovery Forum 2018 in Basel, Switzerland. Here academia and industry came together, to discuss the latest progress and issues in aging research. The meeting covered talks about the mechanistic cause of aging, how longevity signatures may be highly conserved, emerging biomarkers of aging, possible interventions in the aging process and the use of artificial intelligence for aging research and drug discovery. Importantly, a consensus is emerging both in industry and academia, that molecules able to intervene in the aging process may contain the potential to transform both societies and healthcare.

A Systems Pharmacology Approach Uncovers Wogonoside as an Angiogenesis Inhibitor of Triple-Negative Breast Cancer by Targeting Hedgehog Signaling

Triple-negative breast cancer (TNBC) is an aggressive and heterogeneous disease that lacks clinically actionable genetic alterations that limit targeted therapies. Here we explore a systems pharmacology approach that integrates drug-target networks and large-scale genomic profiles of TNBC and identify wogonoside, one of the major active flavonoids, as a potent angiogenesis inhibitor. We validate that wogonoside attenuates cell migration, tube formation, and rat aorta microvessel outgrowth, and reduces formation of blood vessels in chicken chorioallantoic membrane and TNBC cell-induced Matrigel plugs. In addition, wogonoside inhibits growth and angiogenesis in TNBC cell xenograft models. This network-based approach predicts, and we empirically validate, wogonoside's antiangiogenic effects resulting from vascular endothelial growth factor secretion. Mechanistically, wogonoside inhibits Gli1 nuclear translocation and transcriptional activities associated with Hedgehog signaling, by promoting Smoothened degradation in a proteasome-dependent mechanism. This study offers a powerful, integrated, systems pharmacology-based strategy for oncological drug discovery and identifies wogonoside as a potential TNBC angiogenesis inhibitor.

An Insight Into the Molecular Mechanism of Berberine Towards Multiple Cancer Types Through Systems Pharmacology

Over the past several decades, natural products with poly-pharmacological profiles have demonstrated promise as novel therapeutics for various complex diseases, including cancer. Berberine (PubChem CID: 2353), a soliloquies quaternary alkaloid, has been validated to exert powerful effects in many cancers. However, the underlying molecular mechanism is not yet fully elucidated. In this study, we summarized the molecular effects of berberine against multiple cancers based on current available literatures. Furthermore, a systems pharmacology infrastructure was developed to discover new cancer indications of berberine and explore their molecular mechanisms. Specifically, we incorporated 289 high-quality protein targets of berberine by integrating experimental drug-target interactions (DTIs) extracted from literatures and computationally predicted DTIs inferred by network-based inference approach. Statistical network models were developed for identification of new cancer indications of berberine through integration of DTIs and curated cancer significantly mutated genes (SMGs). High accuracy was yielded for our statistical models. We further discussed three typical cancer indications (hepatocarcinoma, lung adenocarcinoma, and bladder carcinoma) of berberine with new mechanisms of actions (MOAs) based on our systems pharmacology framework. In summary, this study systematically provides a powerful strategy to identify potential anti-cancer effects of berberine with novel mechanisms from a systems pharmacology perspective.

Top-100 Most Cited Publications Concerning Network Pharmacology: A Bibliometric Analysis

Background

Network pharmacology (NP) has become an increasingly important focus in the drug research field over the past decade. However, no study to date has mapped the current status of NP. Therefore, we performed a bibliometric study to evaluate the top 100 cited papers on NP.

Methods

We searched the Web of Science Core Collection from its inception to February 25, 2019, using the terms “network pharmacology” and “systems pharmacology.” Key data, including title, publication year, number of citations, authors, countries/regions, organizations, and journals, were retrieved and analyzed using Excel 2016 and VOSviewer 1.6.10.

Results

The total number of citations for the 100 cited papers ranged from 21 to 1,238, published in 53 journals, from 2005 to 2017. The top three journals with the most publications on NP were Clinical Pharmacology & Therapeutics (n = 8, IF2017 = 6.544), Journal of Ethnopharmacology (n = 8, IF2017 = 3.115), and PLoS One (n = 7, IF2017 = 2.766). Most published articles were from the USA (n = 41) and China (n = 35). The most active author was Wang Yonghua from the Northwest A&F University, and of the 100 publications, 14 listing his name. The most frequently used substantive terms included “drug discovery,” “traditional Chinese medicine (TCM),” “in-vitro,” “cancer,” and “cardiovascular disease.” Conclusions. The USA and China made the greatest contribution to NP research. The current NP research mainly focused on NP methods (including experimental validation) and using them to explore the molecular mechanisms of TCM for some critical diseases such as cardiovascular diseases and cancers. Furthermore, we believe some guidelines should be developed to regulate NP studies.

Identification of novel analgesics through a drug repurposing strategy

The identification of new indications for approved or failed drugs is a process called drug repositioning or drug repurposing. The motivation includes overcoming the productivity gap that exists in drug development, which is a high-cost–high-risk process. Repositioning also includes rescuing drugs that have safely entered the market but have failed to demonstrate sufficient efficiency for the initial clinical indication. Considering the high prevalence of chronic pain, the lack of sufficient efficacy and the safety issues of current analgesics, repositioning seems to be an attractive approach. This review presents example of drugs that already have been repositioned and highlights new technologies that are available for the identification of additional compounds to stimulate the curiosity of readers for further exploration.

In Silico Oncology Drug Repositioning and Polypharmacology

Network-aided in silico approaches have been widely used for prediction of drug-target interactions and evaluation of drug safety to increase the clinical efficiency and productivity during drug discovery and development. Here we review the advances and new progress in this field and summarize the translational applications of several new network-aided in silico approaches we developed recently. In addition, we describe the detailed protocols for a network-aided drug repositioning infrastructure for identification of new targets for old drugs, failed drugs in clinical trials, and new chemical entities. These state-of-the-art network-aided in silico approaches have been used for the discovery and development of broad-acting and targeted clinical therapies for various complex diseases, in particular for oncology drug repositioning. In this chapter, the described network-aided in silico protocols are appropriate for target-centric drug repositioning to various complex diseases, but expertise is still necessary to perform the specific oncology projects based on the cancer targets of interest.

Systems pharmacology approach to investigate the molecular mechanisms of herb Rhodiola rosea L. radix

Rhodiola rosea L. radix (RRL) is one of the most popular medical herb which has been widely used for the treatment of different diseases effectively, including cardiovascular diseases and nerve system diseases. However, due to the multiple compounds in RRL, the underlying molecular mechanisms of RRL are remained unclear. To decipher the action mechanisms of RRL from a systematic perspective, a systems pharmacology approach integrated absorption, distribution, metabolism, and excretion (ADME) system, drug targeting, and network analysis was introduced. First, by the ADME screening system and the target fishing process, 56 potential active compounds and 62 targets were obtained, respectively. In addition, compound-target network demonstrated that most compounds interacted with multiple targets, indicating that RRL may enhance its therapeutic effects probably through hitting on multiple targets in a holistic level. Moreover, target-pathway network and gene ontology analysis showed that multiple targets of RRL were involved in several biological pathways, i.e. Neuroactive ligand-receptor interaction, calcium signaling pathway, adrenergic signaling in cardiomyocytes, and VEGF signaling pathway, which dissecting the therapeutic effects of RRL on various diseases, such as cardiovascular diseases, depression, adaptation diseases, etc. In summary, this work successfully explains the potential active compounds and the multi-scale curative action mechanisms of RRL for treating various diseases; meanwhile, it implies that RRL could be applied as a novel therapeutic agent in arthritic diseases. Most importantly, this work provides an in silico strategy to understand the action mechanisms of herbal medicines from molecular/system levels, which will promote the new drug development of traditional Chinese medicine.

Quantitative and systems pharmacology 4. Network-based analysis of drug pleiotropy on coronary artery disease

Despite recent advance of therapeutic development, coronary artery disease (CAD) remains one of the major issues to public health. The use of genomics and systems biology approaches to inform drug discovery and development have offered the possibilities for new target identification and in silico drug repurposing. In this study, we propose a network-based, systems pharmacology framework for target identification and drug repurposing in pharmacologic treatment and chemoprevention of CAD. Specifically, we build in silico models by integrating known drug-target interactions, CAD genes derived from the genetic and genomic studies, and the human protein-protein interactome. We demonstrate that the proposed in silico models can successfully uncover approved drugs and novel natural products in potentially treating and preventing CAD. In case studies, we highlight several approved drugs (e.g., fasudil, parecoxib, and dexamethasone) or natural products (e.g., resveratrol, luteolin, daidzein and caffeic acid) with new mechanism-of-action in chemical intervention of CAD by network analysis. In summary, this study offers a powerful systems pharmacology approach for target identification and in silico drug repurposing on CAD.

Network-Based Methods for Prediction of Drug-Target Interactions

Drug-target interaction (DTI) is the basis of drug discovery. However, it is time-consuming and costly to determine DTIs experimentally. Over the past decade, various computational methods were proposed to predict potential DTIs with high efficiency and low costs. These methods can be roughly divided into several categories, such as molecular docking-based, pharmacophore-based, similarity-based, machine learning-based, and network-based methods. Among them, network-based methods, which do not rely on three-dimensional structures of targets and negative samples, have shown great advantages over the others. In this article, we focused on network-based methods for DTI prediction, in particular our network-based inference (NBI) methods that were derived from recommendation algorithms. We first introduced the methodologies and evaluation of network-based methods, and then the emphasis was put on their applications in a wide range of fields, including target prediction and elucidation of molecular mechanisms of therapeutic effects or safety problems. Finally, limitations and perspectives of network-based methods were discussed. In a word, network-based methods provide alternative tools for studies in drug repurposing, new drug discovery, systems pharmacology and systems toxicology.

A Computational Systems Pharmacology Approach to Investigate Molecular Mechanisms of Herbal Formula Tian-Ma-Gou-Teng-Yin for Treatment of Alzheimer's Disease

Traditional Chinese medicine (TCM) is typically prescribed as formula to treat certain symptoms. A TCM formula contains hundreds of chemical components, which makes it complicated to elucidate the molecular mechanisms of TCM. Here, we proposed a computational systems pharmacology approach consisting of network link prediction, statistical analysis, and bioinformatics tools to investigate the molecular mechanisms of TCM formulae. Taking formula Tian-Ma-Gou-Teng-Yin as an example, which shows pharmacological effects on Alzheimer’s disease (AD) and its mechanism is unclear, we first identified 494 formula components together with corresponding 178 known targets, and then predicted 364 potential targets for these components with our balanced substructure-drug–target network-based inference method. With Fisher’s exact test and statistical analysis we identified 12 compounds to be most significantly related to AD. The target genes of these compounds were further enriched onto pathways involved in AD, such as neuroactive ligand–receptor interaction, serotonergic synapse, inflammatory mediator regulation of transient receptor potential channel and calcium signaling pathway. By regulating key target genes, such as ACHE, HTR2A, NOS2, and TRPA1, the formula could have neuroprotective and anti-neuroinflammatory effects against the progression of AD. Our approach provided a holistic perspective to study the relevance between TCM formulae and diseases, and implied possible pharmacological effects of TCM components.