Network analyses in systems pharmacology

Integrating Network Pharmacology and Experimental Validation to Investigate the Effects and Mechanism of Astragalus Flavonoids Against Hepatic Fibrosis

Hepatic fibrosis (HF) represents the excessive wound healing where an excess amount of connective tissues is formed within the liver, finally resulting in cirrhosis or even hepatocellular carcinoma (HCC). Therefore, it is significant to discover the efficient agents and components to treat HF, thus restraining the further progression of hepatopathy. Astragalus membranaceus (Fisch.) Bunge [also called Astragali Radix (AR)] is a famous herb in traditional Chinese medicine (TCM), which possesses a variety of biological activities and exerts good therapeutic effects in the treatment of HF. Flavonoids account for the major active ingredients related to the AR pharmacological effects. Total AR flavonoids have been proved to exert inhibitory effects on hepatic fibrosis. This study aimed to further undertake network pharmacology analysis coupled with experimental validation and molecular docking to investigate the effects and mechanism of multiple flavonoid components from AR against liver fibrosis. The results of the network pharmacology analysis showed that the flavonoids from AR exerted their pharmacological effects against liver fibrosis by modulating multiple targets and pathways. The experimental validation data showed that the flavonoids from AR were able to suppress transforming growth factor beta 1 (TGF-β1)-mediated activation of hepatic stellate cells (HSCs) and reduce extracellular matrix deposition in HSC-T6 cells via regulating the nuclear factor kappa B (NF-κB) signal transduction pathway. The results of the molecular docking study further showed that the flavonoids had a strong binding affinity for IκB kinase (IKKβ) after docking into the crystal structure. The above results indicated that, flavonoids possibly exerted the anti-inflammatory effect on treating HF by mediating inflammatory signaling pathways. The potential mechanism of these flavonoids against liver fibrosis may be related to suppression of the NF-κB pathway through effective inhibition of IKKβ. This study not only provides a scientific basis for clarifying the effects and mechanism of AR flavonoids against liver fibrosis but also suggests a novel promising therapeutic strategy for the treatment of liver fibrosis.

An Integrative Analysis Reveals the Potential Mechanism between Herbal Medicine Yinchen and Immunoregulation in Hepatocellular Carcinoma

Aims. Abundant evidences in traditional Chinese medicine (TCM) supported the therapeutic value of herbal medicine Yinchen in hepatocellular carcinoma (HCC), but the underlying mechanism remains to be investigated. Main Methods. The intersection of immune gene set, module genes, HCC-associated genes, and target genes of Yinchen was employed for further analyses. The module genes were identified by weighted gene coexpression network analysis, and the other three gene sets were obtained from public databases. Subsequently, we further explored the clinical value and immunoregulation of the hub gene of intersection. The relevant pathways related to hub gene expression were investigated by gene set enrichment analysis. Finally, the interaction of active compounds and target genes was validated by molecular docking. Key Findings. Thirteen active compounds and 90 target genes of Yinchen were included. After constructing the network among Yinchen, target genes, and HCC, BIRC5 was identified as the hub gene. Significant difference was found between the high-expressed group and the low-expressed group in survival and stage. Different immune subtypes also presented significant difference in BIRC5 expression. Moreover, NK cell and T cell (CD4+ effector memory and CD4+ memory resting) were negatively correlated with BIRC5 expression, while CTLA4 and LAG3 were positively correlated. The results of molecular docking further validated a good binding activity of quercetin-BIRC5 interaction. Significance. In summary, our research identified for the first time a novel underlying association among herbal medicine Yinchen, BIRC5, immunotherapy, and HCC. We speculated that Yinchen may target the immune checkpoints (CTLA4 and LAG3) and activate the immune cells by suppressing BIRC5.

Systems Pharmacology-Based Identification of Mechanisms of Action of Bolbostemma paniculatum for the Treatment of Hepatocellular Carcinoma

Background

Traditional Chinese medicine has widely used Bolbostemma paniculatum to treat diseases, including cancer, but its underlying mechanisms remain unclear. The present study aimed to elucidate the potential pharmacological mechanisms of “Tu Bei Mu” (TBM), the Chinese name for Bolbostemmatis Rhizoma, the dry tuber of B. paniculatum, for the treatment of hepatocellular carcinoma (HCC).

Material/Methods

The active components and putative therapeutic targets of TBM were explored using SwissTargetPrediction, Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP), and Search Tool for Interactions of Chemicals (STITCH). The HCC-related target database was built using DrugBank, DisGeNet, Online Mendelian Inheritance in Man (OMIM), and Therapeutic Target Database (TTD). A protein–protein interaction network of the common targets was constructed, based on the matches between TBM potential targets and HCC-related targets, using Cytoscape software. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of the cluster networks were used to elucidate the biological functions of TBM.

Results

Pharmacological network diagrams of the TBM compound-target network and HCC-related target network were successfully constructed. A total of 22 active components, 191 predicted biological targets of TBM, and 3775 HCC-related targets were identified. Through construction of an HCC-related target database and a protein–protein interaction network of the common targets, TBM was predicted to be effective in treating HCC mainly through the PI3K-Akt, HIF-1, p53, and PPAR signaling pathways.

Conclusions

The PI3K/Akt, HIF1, p53, and PPAR pathways may play vital roles in TBM treatment of HCC. Also, the potential anti-cancer effect of TBM on HCC appears to stem from the synergetic effect of multiple targets and mechanisms.

Network Pharmacology and Molecular Docking Study on the Potential Mechanism of Yi-Qi-Huo-Xue-Tong-Luo Formula in Treating Diabetic Peripheral Neuropathy

OBJECTIVE

To investigate the potential mechanism of action of Yi-Qi-Huo-Xue-Tong-Luo formula (YQHXTLF) in the treatment of diabetic peripheral neuropathy (DPN).

METHODS

Network pharmacology and molecular docking techniques were used in this study. Firstly, the active ingredients and the corresponding targets of YQHXTLF were retrieved using the Traditional Chinese Medicine Systems Pharmacology (TCMSP) platform; subsequently, the targets related to DPN were retrieved using GeneCards, Online Mendelian Inheritance in Man (OMIM), Pharmgkb, Therapeutic Target Database (TTD) and Drugbank databases; the common targets of YQHXTLF and DPN were obtained by Venn diagram; afterwards, the "YQHXTLF Pharmacodynamic Component-DPN Target" regulatory network was visualized using Cytoscape 3.6.1 software, and Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed on the potential targets using R 3.6.3 software. Finally, molecular docking of the main chemical components in the PPI network with the core targets was verified by Autodock Vina software.

RESULTS

A total of 86 active ingredients and 229 targets in YQHXTLF were screened, and 81 active ingredients and 110 targets were identified to be closely related to diabetic peripheral neuropathy disease. PPI network mapping identified TP53, MAPK1, JUN, and STAT3 as possible core targets. KEGG pathway analysis showed that these targets are mostly involved in AGE-RAGE signaling pathway in diabetic complications, TNF signaling pathway, and MAPK signaling pathway. The molecular docking results showed that the main chemical components of YQHXTLF have a stable binding activity to the core pivotal targets.

CONCLUSION

YQHXTLF may act on TP53, MAPK1, JUN, and STAT3 to regulate inflammatory response, apoptosis, or proliferation as a molecular mechanism for the treatment of diabetic peripheral neuropathy, reflecting its multitarget and multipathway action, and providing new ideas to further uncover its pharmacological basis and mechanism of action.

Data Integration Using Advances in Machine Learning in Drug Discovery and Molecular Biology

While the term artificial intelligence and the concept of deep learning are not new, recent advances in high-performance computing, the availability of large annotated data sets required for training, and novel frameworks for implementing deep neural networks have led to an unprecedented acceleration of the field of molecular (network) biology and pharmacogenomics. The need to align biological data to innovative machine learning has stimulated developments in both data integration (fusion) and knowledge representation, in the form of heterogeneous, multiplex, and biological networks or graphs. In this chapter we briefly introduce several popular neural network architectures used in deep learning, namely, the fully connected deep neural network, recurrent neural network, convolutional neural network, and the autoencoder. Deep learning predictors, classifiers, and generators utilized in modern feature extraction may well assist interpretability and thus imbue AI tools with increased explication, potentially adding insights and advancements in novel chemistry and biology discovery.The capability of learning representations from structures directly without using any predefined structure descriptor is an important feature distinguishing deep learning from other machine learning methods and makes the traditional feature selection and reduction procedures unnecessary. In this chapter we briefly show how these technologies are applied for data integration (fusion) and analysis in drug discovery research covering these areas: (1) application of convolutional neural networks to predict ligand-protein interactions; (2) application of deep learning in compound property and activity prediction; (3) de novo design through deep learning. We also: (1) discuss some aspects of future development of deep learning in drug discovery/chemistry; (2) provide references to published information; (3) provide recently advocated recommendations on using artificial intelligence and deep learning in -omics research and drug discovery.

Network Pharmacology Approach to Explore the Potential Mechanisms of Jieduan-Niwan Formula Treating Acute-on-Chronic Liver Failure

Background

Acute-on-chronic liver failure (ACLF) is a clinical syndrome with acute jaundice and coagulation dysfunction caused by various inducements on the basis of chronic liver disease. Western medical treatment is limited. Previous studies have confirmed that Jieduan-Niwan Formula (JDNW Formula), an empirical prescription for the treatment of ACLF, can inhibit inflammation and resist hepatocyte apoptosis. However, potential targets and mechanisms still need to be explored.

Methods

In this study, network pharmacological analysis was performed to investigate the key components and potential mechanisms of JDNW Formula treating ACLF. Firstly, we predicted the potential active ingredients of JDNW Formula and the corresponding potential targets through TCMSP, BATMAN-TCM platform, and literature supplement. Then, the ACLF targets database was built using OMIM, DisGeNET, and GeneCard database. Based on the matching targets between JDNW Formula and ACLF, the PPI network was constructed for MCODE analysis and common targets were enriched by Metascape. Furthermore, the ACLF rat model was used to verify the potential mechanism of JDNW Formula in treating ACLF.

Results

132 potential bioactive components of JDNW Formula and 168 common targets were obtained in this study. The enrichment analysis shows that the AMPK signaling pathway was associated with the treating effects of JDNW Formula. Quercetin was hypothesized to be the key bioactive ingredient in JDNW Formula and has a good binding affinity to AMPK based on molecular docking verification. JDNW Formula and quercetin were verified to treat ACLF by regulating the AMPK/PGC-1α signaling pathway as a prediction.

Conclusion

The study predicted potential mechanisms of JDNW Formula in the treatment of ACLF, involving downregulation of inflammatory factor expression, antioxidant stress, and inhibition of hepatocyte apoptosis. JDNW Formula may improve mitochondrial quality in ACLF via the AMPK signaling pathway, which serves as a guide for further study.

Single Cell Systems Analysis: Decision Geometry In Outliers

MOTIVATION

Anti-cancer therapeutics of the highest calibre currently focus on combinatorial targeting of specific oncoproteins and tumour suppressors. Clinical relapse depends upon intratumoral heterogeneity which serves as substrate variation during evolution of resistance to therapeutic regimens.

RESULTS

The present review advocates single cell systems biology as the optimal level of analysis for remediation of clinical relapse. Graph theory approaches to understanding decision-making in single cells may be abstracted one level further, to the geometry of decision-making in outlier cells, in order to define evolution-resistant cancer biomarkers. Systems biologists currently working with omics data are invited to consider phase portrait analysis as a mediator between graph theory and deep learning approaches. Perhaps counter-intuitively, the tangible clinical needs of cancer patients may depend upon the adoption of higher level mathematical abstractions of cancer biology.

SUPPLEMENTARY INFORMATION

supplementary data available at Bioinformatics online.

Systems pharmacology unravels the synergic target space and therapeutic potential of Rhodiola rosea L. for non-small cell lung cancer

BACKGROUND

Lung cancer is the most common and mortal cancer worldwide. Rhodiola rosea L. (RR), a well-known traditional Chinese medicine (TCM), has been turned out to be effective in anti-lung cancer therapy, but its molecular mechanism of action has not been clearly understood.

PURPOSE

In this study, we aimed to elucidate the possible molecular mechanism underlying the effect of RR against non-small cell lung cancer (NSCLC) by systems pharmacology.

METHODS

The effects of RR on NSCLC were examined in Lewis lung carcinoma (LLC) tumor-bearing mice models. The possible molecular mechanism was unraveled by systems pharmacology, which includes pharmacokinetics evaluation, active compounds screening, target prediction and network analysis. Cell proliferation was examined by cell counting kit-8 (CCK-8) assay; cell apoptosis was detected by flow cytometry; protein and proinflammatory cytokines expression were evaluated by Western blot and qRT-PCR.

RESULTS

In vivo, RR significantly inhibited the tumor growth and prolonged the survival of the tumor bearing mice. In silico, we identified 19 potential active molecules (e.g., salidroside and rhodiosin), 112 targets (e.g., COX-2 and AKT) and 27 pathways (e.g., PI3K/AKT signaling pathway and NF-κB signaling pathway) for RR. Additionally, targets analysis and networks construction further revealed that RR exerted anti-cancer effects by regulating apoptosis, angiogenesis and inflammation. In vitro, salidroside could significantly decrease expression of pro-angiogenic factors (e.g., VEGF and eNOS) and proinflammatory cytokines (e.g., COX-2, iNOS and TNF-α). Also, Bcl-2, an anti-apoptotic protein was decreased whereas Bax, a pro-apoptotic protein, was increased. Further flow cytometry analysis showed that salidroside could induce apoptosis in H1975 cells.

CONCLUSIONS

Mechanistically, the antitumor effect of RR on NSCLC was responsible for the synergy among anti-inflammatory, anti-angiogenic and pro-apoptotic.

Association between anticholinergic burden and anticholinergic adverse outcomes in the elderly: Pharmacological basis of their predictive value for adverse outcomes

The use of anticholinergic drugs and other drugs with anticholinergic activity is highly prevalent in older people. Cumulative anticholinergic effects, known as anticholinergic burden, are associated with important peripheral and central adverse effects and outcomes. Several methods have been developed to quantify anticholinergic burden and to estimate the risk of adverse anticholinergic effects. Serum anticholinergic activity (SAA) and anticholinergic burden scoring systems are the most commonly used methods to predict the occurrence of important negative outcomes. These tools could guide clinicians in making more rational prescriptions to enhance patient safety, especially in older people. However, the literature has reported conflicting results about the predictive ability of these tools. The majority of these instruments ignore relevant pharmacologic aspects such as the doses used, differential muscarinic receptor subtype affinities, and blood-brain barrier permeability. To increase the clinical relevance of these tools, mechanistic and clinical pharmacology should collaborate. This narrative review describes the rational and pharmacological basis of anticholinergic burden tools and provides insight about their predictive value for adverse outcomes.

Integrated computational approach toward discovery of multi-targeted natural products from Thumbai (Leucas aspera) for attuning NKT cells

A multi-omics-based approach targeting the plant-based natural products from Thumbai (Leucas aspera), an important yet untapped potential source of many therapeutic agents for myriads of immunological conditions and genetic disorders, was conceptualized to reconnoiter its potential biomedical application. A library of 79 compounds from this plant was created, out of which 9 compounds qualified the pharmacokinetics parameters. Reverse pharmacophore technique for target fishing of the screened compounds was executed through which renin receptor (ATP6AP2) and thymidylate kinase (DTYMK) were identified as potential targets. Network biology approaches were used to comprehend and validate the functional, biochemical and clinical relevance of the targets. The target-ligand interaction and subsequent stability parameters at molecular scale were investigated using multiple strategies including molecular modeling, pharmacophore approaches and molecular dynamics simulation. Herein, isololiolide and 4-hydroxy-2-methoxycinnamaldehyde were substantiated as the lead molecules exhibiting comparatively the best binding affinity against the two putative protein targets. These natural lead products from L. aspera and the combinatorial effects may have plausible medical applications in a wide variety of neurodegenerative, genetic and developmental disorders. The lead molecules also exhibit promising alternative in diagnostics and therapeutics through immuno-modulation targeting natural killer T-cell function in transplantation-related pathogenesis, autoimmune and other immunological disorders.Communicated by Ramaswamy H. Sarma.

Exploring active ingredients and function mechanisms of Ephedra-bitter almond for prevention and treatment of Corona virus disease 2019 (COVID-19) based on network pharmacology

BACKGROUND

COVID-19 has caused a global pandemic, and there is no wonder drug for epidemic control at present. However, many clinical practices have shown that traditional Chinese medicine has played an important role in treating the outbreak. Among them, ephedra-bitter almond is a common couplet medicine in anti-COVID-19 prescriptions. This study aims to conduct an exploration of key components and mechanisms of ephedra-bitter almond anti-COVID-19 based on network pharmacology.

MATERIAL AND METHODS

We collected and screened potential active components of ephedra-bitter almond based on the TCMSP Database, and we predicted targets of the components. Meanwhile, we collected relevant targets of COVID-19 through the GeneCards and CTD databases. Then, the potential targets of ephedra-bitter almond against COVID-19 were screened out. The key components, targets, biological processes, and pathways of ephedra-bitter almond anti-COVID-19 were predicted by constructing the relationship network of herb-component-target (H-C-T), protein-protein interaction (PPI), and functional enrichment. Finally, the key components and targets were docked by AutoDock Vina to explore their binding mode.

RESULTS

Ephedra-bitter almond played an overall regulatory role in anti-COVID-19 via the patterns of multi-component-target-pathway. In addition, some key components of ephedra-bitter almond, such as β-sitosterol, estrone, and stigmasterol, had high binding activity to 3CL and ACE2 by molecular docking simulation, which provided new molecular structures for new drug development of COVID-19.

CONCLUSION

Ephedra-bitter almonds were used to prevent and treat COVID-19 through directly inhibiting the virus, regulating immune responses, and promoting body repair. However, this work is a prospective study based on data mining, and the findings need to be interpreted with caution.

Integration of pharmacogenomics and theranostics with nanotechnology as quality by design (QbD) approach for formulation development of novel dosage forms for effective drug therapy

To cater to medication needs in the future healthcare system, we need to shift from the conventional system of drug delivery to modern molecular signature-based drug delivery systems. The current drug therapies are either less effective, ineffective, or produce numerous adverse reactions. One scientific principle or discipline cannot adequately address all the problems, so we need an innovative application of the current scientific principles. Here we are proposing a novel concept of nanoformulation based on pharmacogenomics and theranostics for personalized error-free and targeted therapeutic agent delivery. The addition of more knowledge about the human genome opens the new way to study disease-gene, gene-drug, and drug-effect interactions, which is the basis of future medicines. Pharmacogenomics provides information about the disease etiology, role in genes in disease pathophysiology, disease biomarkers, drug targets, drug effects, and the fate of drugs inside the body. Theranostics approach utilizes the above information in diagnosis, treatment, and monitoring of the disease on a real-time basis. Personalized dosage forms can be formulated into a nanoformulation that provides a better therapeutic effect and minimizes adverse drug reactions. The therapeutic system needs to be shifted from the principle of one drug fits all to one drug unique population. In the present manuscript, we tried to conceptualize a modern therapeutic system by combining the three approaches viz. pharmacogenomics, theranostics, and nanotechnology applied in the area of formulation development to produce a multifunctional single tiny entity.

Revealing the Pharmacological Mechanism of Acorus tatarinowii in the Treatment of Ischemic Stroke Based on Network Pharmacology

Aim

Stroke is the second significant cause for death, with ischemic stroke (IS) being the main type threatening human being's health. Acorus tatarinowii (AT) is widely used in the treatment of Alzheimer disease, epilepsy, depression, and stroke, which leads to disorders of consciousness disease. However, the systemic mechanism of AT treating IS is unexplicit. This article is supposed to explain why AT has an effect on the treatment of IS in a comprehensive and systematic way by network pharmacology.

Methods and Materials

ADME (absorbed, distributed, metabolized, and excreted) is an important property for screening-related compounds in AT, which were screening out of TCMSP, TCMID, Chemistry Database, and literature from CNKI. Then, these targets related to screened compounds were predicted via Swiss Targets, when AT-related targets database was established. The gene targets related to IS were collected from DisGeNET and GeneCards. IS-AT is a common protein interactive network established by STRING Database. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment were analysed by IS-AT common target genes. Cytoscape software was used to establish a visualized network for active compounds-core targets and core target proteins-proteins interactive network. Furthermore, we drew a signal pathway picture about its effect to reveal the basic mechanism of AT against IS systematically.

Results

There were 53 active compounds screened from AT, inferring the main therapeutic substances as follows: bisasaricin, 3-cyclohexene-1-methanol-α,α,4-trimethyl,acetate, cis,cis,cis-7,10,13-hexadecatrienal, hydroxyacoronene, nerolidol, galgravin, veraguensin, 2′-o-methyl isoliquiritigenin, gamma-asarone, and alpha-asarone. We obtained 398 related targets, 63 of which were the same as the IS-related genes from targets prediction. Except for GRM2, remaining 62 target genes have an interactive relation, respectively. The top 10 degree core target genes were IL6, TNF, IL1B, TLR4, NOS3, MAPK1, PTGS2, VEGFA, JUN, and MMP9. There were more than 20 terms of biological process, 7 terms of cellular components, and 14 terms of molecular function through GO enrichment analysis and 13 terms of signal pathway from KEGG enrichment analysis based on P < 0.05.

Conclusion

AT had a therapeutic effect for ischemic via multicomponent, multitarget, and multisignal pathway, which provided a novel research aspect for AT against IS.

Exploration in the Mechanism of Kaempferol for the Treatment of Gastric Cancer Based on Network Pharmacology

Background

Kaempferol is a natural polyphenol in lots of Chinese herbs, which has shown promising treatment for gastric cancer (GC). However, the molecular mechanisms of its action have not been systematically revealed yet. In this work, a network pharmacology approach was used to elucidate the potential mechanisms of kaempferol in the treatment of GC.

Methods

The kaempferol was input into the PharmMapper and SwissTargetPrediction database to get its targets, and the targets of GC were obtained by retrieving the Online Mendelian Inheritance in Man (OMIM) database, MalaCards database, Therapeutic Target Database (TTD), and Coolgen database. The molecular docking was performed to assess the interactions between kaempferol and these targets. Next, the overlap targets of kaempferol and GC were identified for GO and KEGG enrichment analyses. Afterward, a protein-protein interaction (PPI) network was constructed to get the hub targets, and the expression and overall survival analysis of the hub target were investigated. Finally, the overall survival (OS) analysis of hub targets was performed using the Kaplan-Meier Plotter online tool.

Results

A total of 990 genes related to GC and 10 overlapping genes were determined through matching the 24 potential targets of kaempferol with disease-associated genes. The result of molecular docking indicated that kaempferol can bind with these hub targets with good binding scores. These targets were further mapped to 140 GO biological process terms and 11 remarkable pathways. In the PPI network analysis, 3 key targets were identified, including ESR1, EGFR, and SRC. The mRNA and protein expression levels of EGFR and SRC were obviously higher in GC tissues. High expression of these targets was related to poor OS in GC patients.

Conclusions

This study provided a novel approach to reveal the therapeutic mechanisms of kaempferol on GC, which will ease the future clinical application of kaempferol in the treatment of GC.

Free Wanderer Powder regulates AMPA receptor homeostasis in chronic restraint stress-induced rat model of depression with liver-depression and spleen-deficiency syndrome

Free Wanderer Powder (FWP) is a classic formula for depression with digestive dysfunctions, i.e., liver-depression and spleen-deficiency syndrome (LDSDS) in Chinese Medicine. But its protective mechanism has not been fully clarified. Here a chronic restraint stress (CRS) induced rat model showed depression with LDSDS in food intake, metabolism, and behaviour tests. Then 75 rats were randomly divided, and received CRS and different treatment with behaviour tests. Expressions of c-Fos and AMPA-type glutamate receptor subunits GluR1-3 in hippocampus CA1, CA3, DG and amygdala BLA were detected by immunohistochemistry, western blot and RT-PCR, respectively. In CRS rats, FWP alleviated depressive behaviour and c-Fos expression. FWP suppressed the increasement of GluR1 in CA1 and DG, p-GluR1 in CA1, and p-GluR2 and GluR3 in BLA. FWP also blocked the decrease of GluR1 and Glur2/3 in CA3, p-GluR1 in CA3, and p-GluR2 in CA1 and CA3. Furthermore, constituents of FWP and their potential targets were explored using UHPLC-MS and systematic bioinformatics analysis. There were 23 constituents identified in FWP, 9 of which regulated glutamatergic synapse. Together, these results suggest that FWP contains effective constituents and alleviates depression with LDSDS by regulating AMPA-type glutamate receptor homeostasis in amygdala and hippocampus.

Network pharmacology based virtual screening of active constituents of Prunella vulgaris L. and the molecular mechanism against breast cancer

Prunella vulgaris L, a perennial herb widely used in Asia in the treatment of various diseases including cancer. In vitro studies have demonstrated the therapeutic effect of Prunella vulgaris L. against breast cancer through multiple pathways. However, the nature of the biological mechanisms remains unclear. In this study, a Network pharmacology based approach was used to explore active constituents and potential molecular mechanisms of Prunella vulgaris L. for the treatment of breast cancer. The methods adopted included active constituents prescreening, target prediction, GO and KEGG pathway enrichment analysis. Molecular docking experiments were used to further validate network pharmacology results. The predicted results showed that there were 19 active ingredients in Prunella vulgaris L. and 31 potential gene targets including AKT1, EGFR, MYC, and VEGFA. Further, analysis of the potential biological mechanisms of Prunella vulgaris L. against breast cancer was performed by investigating the relationship between the active constituents, target genes and pathways. Network analysis showed that Prunella vulgaris L. exerted a promising preventive effect on breast cancer by acting on tumor-associated signaling pathways. This provides a basis to understand the mechanism of the anti-breast cancer activity of Prunella vulgaris L.

Systematic Elucidation of the Mechanism of Quercetin against Gastric Cancer via Network Pharmacology Approach

This study was aimed at elucidating the potential mechanisms of quercetin in the treatment of gastric cancer (GC). A network pharmacology approach was used to analyze the targets and pathways of quercetin in treating GC. The predicted targets of quercetin against GC were obtained through database mining, and the correlation of these targets with GC was analyzed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. Next, the protein-protein interaction (PPI) network was constructed, and overall survival (OS) analysis of hub targets was performed using the Kaplan–Meier Plotter online tool. Finally, the mechanism was further analyzed via molecular docking of quercetin with the hub targets. Thirty-six quercetin-related genes were identified, 15 of which overlapped with GC-related targets. These targets were further mapped to 319 GO biological process terms and 10 remarkable pathways. In the PPI network analysis, six hub targets were identified, including AKT1, EGFR, SRC, IGF1R, PTK2, and KDR. The high expression of these targets was related to poor OS in GC patients. Molecular docking analysis confirmed that quercetin can bind to these hub targets. In conclusion, this study provided a novel approach to reveal the therapeutic mechanisms of quercetin on GC, which will ease the future clinical application of quercetin in the treatment of GC.

Exploring multiple mechanisms of Qingjie Fanggan prescription for prevention and treatment of influenza based on systems pharmacology

Influenza is a type of acute disease characterized by strong contagiousness and short incubation period, which have posed a large potential threat to public health. Traditional Chinese Medicine (TCM) advocates to the aim of combating complex diseases from a holistic view, which has shown effectiveness in anti-influenza. However, the mechanism of TCM prescription remains puzzling. Here, we applied a system pharmacology approach to reveal the underlying molecular mechanisms of Qingjie Fanggan prescription (QFP) in the prevention and treatment of influenza. In this study, we identified 228 potential active compounds by means of absorption, distribution, metabolism, and excretion (ADME) evaluation system and literature research. Then, the targets of the potential active compounds were predicted by using the WES (Weighted Ensemble Similarity) method, and the influenza-related targets were obtained according to some existing gene databases. Next, an herb-component-target network was constructed to further dissect the multi-directional therapeutic approach for QFP. Meanwhile, we also performed gene ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) annotation analysis on 344 potential targets. Finally, a target-pathway network was constructed to further dissect the core pathways and targets in treatment of influenza for QFP. And the key components and targets were docked by AutoDock Vina to explore their binding mode. All of these demonstrated that QFP had multi-scale curative activity in regulating influenza-related biological processes, which facilitates the application of traditional medicine in modern medicine.

Ziziphora clinopodioides flavonoids based on network pharmacology attenuates atherosclerosis in rats induced by high-fat emulsion combined with vitamin D3 by down-regulating VEGF/AKT/NF-κB signaling pathway

Ziziphora clinopodioides flavonoids (ZCF) is a major bioactive total flavonoids compound isolated from Ziziphora clinopodioides Lam. It has been long used as an anti-atherosclerosis (AS) in clinics. However, anti-AS effects of ZCF have not been fully investigated. The objective of this study is to further investigate the anti-AS activities and mechanisms of ZCF in vivo. The main chemical components, action targets and signal pathways of Ziziphora clinopodioides Lam were predicted and analyzed by network pharmacology technology. The main bioactive components of Ziziphora clinopodioides Lam were identified using high performance liquid chromatography-mass spectrometry (HPLC-MS). In vivo experiments, atherosclerosis in rats induced by high-fat emulsion combined with vitamin D₃ and treated with simvastatin (0.45 mg/kg/d), ZCF (6.25, 12.5, 25 g/kg/d) for 7 weeks. We found that ZCF significantly reduced blood lipid levels (TG, TC, and LDL-C), and decreased lipid deposition in the aorta and atherosclerotic lesion size, inhibited mitochondrial mem- brane potential (MMP2/9/12/13) impairment. Meanwhile, ZCF may down-regulated the levels of VEGF, AKT, NF-κB, ICAM-1 and VCAM-1 proteins, indicating ZCF may play an anti-atherosclerotic role by down-regulating the VEGF/AKT/NF-κB signaling pathway. Results from this study demonstrated that ZCF have an anti-AS ability to lower lipid concentrations and protect endothelial function, antioxidant and anti-inflammatory activity, and suggested that ZCF might be a potential therapeutic drug in the prevention of AS.

The Role of ARID5B in Acute Lymphoblastic Leukemia and Beyond

Acute lymphoblastic leukemia (ALL) is the most common malignancy in children with distinct characteristics among different subtypes. Although the etiology of ALL has not been fully unveiled, initiation of ALL has been demonstrated to partly depend on genetic factors. As indicated by several genome wide association studies (GWASs) and candidate gene analyses, ARID5B, a member of AT-rich interactive domain (ARID) protein family, is associated with the occurrence and prognosis of ALL. However, the mechanisms by which ARID5B genotype impact on the susceptibility and treatment outcome remain vague. In this review, we outline developments in the understanding of ARID5B in the susceptibility of ALL and its therapeutic perspectives, and summarize the underlying mechanisms based on the limited functional studies, hoping to illustrate the possible mechanisms of ARID5B impact and highlight the potential treatment regimens.

Identification of Active Compounds of Mahuang Fuzi Xixin Decoction and Their Mechanisms of Action by LC-MS/MS and Network Pharmacology

The decoction is an important dosage form of traditional Chinese medicine (TCM) administration. The Mahuang Fuzi Xixin decoction (MFXD) is widely used to treat allergic rhinitis (AR) in China. However, its active compounds and therapeutic mechanisms are unclear. The aim of this study was to establish an integrative method to identify the bioactive compounds and reveal the mechanisms of action of MFXD. LC-MS/MS was used to identify the compounds in MFXD, followed by screening for oral bioavailability. TCMSP, BindingDB, STRING, DAVID, and KEGG databases and algorithms were used to gather information. Cytoscape was used to visualize the networks. Twenty-four bioactive compounds were identified, and thirty-seven predicted targets of these compounds were associated with AR. DAVID analysis suggested that these compounds exert their therapeutic effects by modulating the Fc epsilon RI, B-cell receptor, Toll-like receptor, TNF, NF-κB, and T-cell receptor signaling pathways. The PI3K/AKT and cAMP signaling pathways were also implicated. Ten of the identified compounds, quercetin, pseudoephedrine, ephedrine, β-asarone, methylephedrine, α-linolenic acid, cathine, ferulic acid, nardosinone, and higenamine, seemed to account for most of the beneficial effects of MFXD in AR. This study showed that LC-MS/MS followed by network pharmacology analysis is useful to elucidate the complex mechanisms of action of TCM formulas.

Active constituents and mechanisms of Respiratory Detox Shot, a traditional Chinese medicine prescription, for COVID-19 control and prevention: Network-molecular docking-LC-MSE analysis

OBJECTIVE

Lung-toxin Dispelling Formula No. 1, referred to as Respiratory Detox Shot (RDS), was developed based on a classical prescription of traditional Chinese medicine (TCM) and the theoretical understanding of herbal properties within TCM. Therapeutic benefits of using RDS for both disease control and prevention, in the effort to contain the coronavirus disease 2019 (COVID-19), have been shown. However, the biochemically active constituents of RDS and their mechanisms of action are still unclear. The goal of the present study is to clarify the material foundation and action mechanism of RDS.

METHODS

To conduct an analysis of RDS, an integrative analytical platform was constructed, including target prediction, protein-protein interaction (PPI) network, and cluster analysis; further, the hub genes involved in the disease-related pathways were identified, and the their corresponding compounds were used for in vitro validation of molecular docking predictions. The presence of these validated compounds was also measured in samples of the RDS formula to quantify the abundance of the biochemically active constituents. In our network pharmacological study, a total of 26 bioinformatic programs and databases were used, and six networks, covering the entire Zang-fu viscera, were constructed to comprehensively analyze the intricate connections among the compounds-targets-disease pathways-meridians of RDS.

RESULTS

For all 1071 known chemical constituents of the nine ingredients in RDS, identified from established TCM databases, 157 passed drug-likeness screening and led to 339 predicted targets in the constituent-target network. Forty-two hub genes with core regulatory effects were extracted from the PPI network, and 134 compounds and 29 crucial disease pathways were implicated in the target-constituent-disease network. Twelve disease pathways attributed to the Lung-Large Intestine meridians, with six and five attributed to the Kidney-Urinary Bladder and Stomach-Spleen meridians, respectively. One-hundred and eighteen candidate constituents showed a high binding affinity with SARS-coronavirus-2 3-chymotrypsin-like protease (3CLpro), as indicated by molecular docking using computational pattern recognition. The in vitro activity of 22 chemical constituents of RDS was validated using the 3CLpro inhibition assay. Finally, using liquid chromatography mass spectrometry in data-independent analysis mode, the presence of seven out of these 22 constituents was confirmed and validated in an aqueous decoction of RDS, using reference standards in both non-targeted and targeted approaches.

CONCLUSION

RDS acts primarily in the Lung-Large Intestine, Kidney-Urinary Bladder and Stomach-Spleen meridians, with other Zang-fu viscera strategically covered by all nine ingredients. In the context of TCM meridian theory, the multiple components and targets of RDS contribute to RDS's dual effects of health-strengthening and pathogen-eliminating. This results in general therapeutic effects for early COVID-19 control and prevention.

An Integrated Pharmacology-Based Analysis for Antidepressant Mechanism of Chinese Herbal Formula Xiao-Yao-San

Clinical studies and basic science experiments have widely demonstrated the antidepressant and anxiolytic effects of the herbal formula Xiao-Yao-San (XYS). However, the system mechanism of these effects has not been fully characterized. The present study conducted a comprehensive network pharmacological analysis of XYS and sorted all pharmacologically active components (149) through the TCMSP webserver. Then, all potential molecular targets (449) were predicted, of which there were 99 genes clearly related to depression. To further investigate the mechanism of antidepressant effects of XYS, a compound-depression targets (C-DTs) network was constructed, and Gene Ontology (GO) functional and KEGG pathway enrichment analyses were performed for the 99 targets. Enrichment results revealed that XYS could regulate multiple aspects of depression through these targets, related to metabolism, neuroendocrine function, and neuroimmunity. Prediction and analysis of protein–protein interactions resulted in selection of three hub genes (AKT1, TP53, and VEGFA). In addition, a total of seven ingredients from XYS could act on these hub genes and they were identified through ultra-high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-Q/TOF-MS), including paeoniflorin, quercetin, luteolin, acacetin, aloe-emodin, Glyasperin C, kaempferol. Hereafter, we investigated the effects of paeoniflorin and its predicted target, the results suggest that it can reverse the neurotoxicity produced by CORT and could be a neuroprotective effect by promoting the phosphorylation of Akt. Overall, our research revealed the complicated antidepressant mechanism of XYS, and also provided a rational strategy for revealing the complex composition and function of Chinese herbal formula.

System Prediction and Validation of TCM for Chronic Myeloid Leukemia Treatment from the Perspective of Low-Toxicity Chemotherapy: A Stilbene α-Viniferin Has a Proapoptotic Effect on K562 Cells via the Mitochondrial Pathway

Objective

In traditional Chinese medicine (TCM), chronic myeloid leukemia (CML) has been attributed to “poisoned bone marrow,” which is viewed as a loss of Qi or blood, a deficiency in Yin or Yang that causes a complex imbalance between cell growth and death. Malignant myeloid progenitor cells display excessive growth that is difficult to control without toxicity. More than 60 herbs in TCM have shown efficacy against CML. However, the key molecules and mechanisms involved in the holistic-level characterization, as well as the effective target associations, are still unknown.

Methods

The present study employed a computational approach with filtering potential compounds via admetSAR, systems biology-based functional data prediction, and biochemical and molecular biological validation.

Results

We generated 118 bioactive compounds from 11 herbs within four dialectical therapy groups that are most commonly used to treat CML and predicted 141 potential targets. The stilbene resveratrol and its derivatives were found to be highly related to these targets. Among them, α-viniferin was predicted to target Bcl-2, caspase-3, 8, and 9, MAPK14, CDK2, HSP90AA1, and others, reflecting CML therapeutic strategies. In vitro, experimental data showed a nonnecrotic growth limitation of K562 cells caused by α-viniferin was predicted to target Bcl-2, caspase-3, 8, and 9, MAPK14, CDK2, HSP90AA1, and others, reflecting CML therapeutic strategies. μg·mL⁻¹ at 24 h. Finally, we validated the chemotherapeutic effect of α-viniferin was predicted to target Bcl-2, caspase-3, 8, and 9, MAPK14, CDK2, HSP90AA1, and others, reflecting CML therapeutic strategies.

Conclusions

Our work sheds light on the mechanism of the efficacy of the stilbene α-viniferin in TCM for the prevention of CML. This work also predicts and validates targets in the mitochondrial signaling pathway, providing a novel strategy for CML treatment.α-viniferin was predicted to target Bcl-2, caspase-3, 8, and 9, MAPK14, CDK2, HSP90AA1, and others, reflecting CML therapeutic strategies.

Targeting tRNA-synthetase interactions towards novel therapeutic discovery against eukaryotic pathogens

The development of chemotherapies against eukaryotic pathogens is especially challenging because of both the evolutionary conservation of drug targets between host and parasite, and the evolution of strain-dependent drug resistance. There is a strong need for new nontoxic drugs with broad-spectrum activity against trypanosome parasites such as Leishmania and Trypanosoma. A relatively untested approach is to target macromolecular interactions in parasites rather than small molecular interactions, under the hypothesis that the features specifying macromolecular interactions diverge more rapidly through coevolution. We computed tRNA Class-Informative Features in humans and independently in eight distinct clades of trypanosomes, identifying parasite-specific informative features, including base pairs and base mis-pairs, that are broadly conserved over approximately 250 million years of trypanosome evolution. Validating these observations, we demonstrated biochemically that tRNA:aminoacyl-tRNA synthetase (aaRS) interactions are a promising target for anti-trypanosomal drug discovery. From a marine natural products extract library, we identified several fractions with inhibitory activity toward Leishmania major alanyl-tRNA synthetase (AlaRS) but no activity against the human homolog. These marine natural products extracts showed cross-reactivity towards Trypanosoma cruzi AlaRS indicating the broad-spectrum potential of our network predictions. We also identified Leishmania major threonyl-tRNA synthetase (ThrRS) inhibitors from the same library. We discuss why chemotherapies targeting multiple aaRSs should be less prone to the evolution of resistance than monotherapeutic or synergistic combination chemotherapies targeting only one aaRS.

Trypanosome parasites pose a significant health risk worldwide. Conventional drug development strategies have proven challenging given the high conservation between humans and pathogens, with off-target toxicity being a common problem. Protein synthesis inhibitors have historically been an attractive target for antimicrobial discovery against bacteria, and more recently for eukaryotic pathogens. Here we propose that exploiting pathogen-specific tRNA-synthetase interactions offers the potential for highly targeted drug discovery. To this end, we improved tRNA gene annotations in trypanosome genomes, identified functionally informative trypanosome-specific tRNA features, and showed that these features are highly conserved over approximately 250 million years of trypanosome evolution. Highlighting the species-specific and broad-spectrum potential of our approach, we identified natural product inhibitors against the parasite translational machinery that have no effect on the homologous human enzyme.

Identification of the Active Constituents and Significant Pathways of Cangfu Daotan Decoction for the Treatment of PCOS Based on Network Pharmacology

Background

Polycystic ovary syndrome (PCOS) is the most common female endocrine disease. Cangfu Daotan Decoction (CDD) can effectively relieve the clinical symptoms of PCOS patients.

Methods

To explore the active ingredients and related pathways of CDD for treating PCOS, a network pharmacology-based analysis was carried out. The active ingredients of CDD and their potential targets were obtained from the TCM system pharmacology analysis platform. The obtained PCOS-related genes from OMIM and GeneCards were imported to establish protein-protein interaction networks in STRING. Finally, GO analysis and significant pathway analysis were conducted with the RStudio (Bioconductor) database.

Results

A total of 111 active compounds were obtained from 1433 ingredients present in the CDD, related to 118 protein targets. In addition, 736 genes were found to be closely related to PCOS, of which 44 overlapped with CDD and were thus considered therapeutically relevant. Pathway enrichment analysis identified the AGE-RAGE signalling pathway in diabetic complications, endocrine resistance, the IL-17 signalling pathway, the prolactin signalling pathway, and the HIF-1 signalling pathway. Moreover, PI3K-Akt, insulin resistance, Toll-like receptor, MAPK, and AGE-RAGE were related to PCOS and treatment.

Conclusions

CDD can effectively improve the symptoms of PCOS, and our network pharmacological analysis lays the foundation for future clinical research.

Data mining and systematic pharmacology to reveal the mechanisms of traditional Chinese medicine in Mycoplasma pneumoniae pneumonia treatment

Traditional Chinese Medicine (TCM) is widely used in the treatment of Mycoplasma pneumoniae Pneumonia (MPP) in East Asia. However, our current understanding of the underlying molecular mechanism remains dispersive and promiscuous. In this study, a systematic pharmacological approach combined with literature data mining was applied for drug similarity evaluation, drug half-life evaluation, oral bioavailability prediction, drug target exploration, Gene Ontology (GO) analysis, KEGG pathway enrichment and network construction, thus providing the rationale for its clinical performance. Five mostly studied herbs, including Ephedra Herba, Amygdalus communis Vas, Platycodon grandiforus, Licorice and Scutellariae Radix, were selected from the literature. Total ninety-three active ingredients, which are expected to be the effective components for MPP treatment, were screened out. Interrelationship between active compounds, drug targets and signaling pathways were analyzed to reveal the therapeutic effect of TCM in detail. Of importance, we found that TNF, β2AR and PTGS2 play pivotal role in TCM mediated MPP inhibition. And mechanistically, epithelial apoptosis (defensive barrier function), GPCR signaling (symptom amelioration) and immune pathways (innate signaling and adaptive Th17 response) are critically involved. Our work, achieved through systematic pharmacology and data mining, enlarges the knowledge of TCM in MPP therapy, and could provide valuable insights for further drug discovery studies.

Turmeric Is Therapeutic in Vivo on Patient-Derived Colorectal Cancer Xenografts: Inhibition of Growth, Metastasis, and Tumor Recurrence

Colorectal cancer is the third most frequently diagnosed cancer worldwide. Clinically, chemotherapeutic agents such as FOLFOX are the mainstay of colorectal cancer treatment. However, the side effects including toxicity of FOLFOX stimulated the enthusiasm for developing adjuvants, which exhibit better safety profile. Turmeric extract (TE), which has been previously shown to suppress the growth of human and murine colon xenografts, was further demonstrated here for its inhibitory effects on colon cancer patient-derived xenografts (PDX). PDX models were successfully established from tissues of colon cancer patients and the PDX preserved the heterogeneous architecture through passages. NOD/SCID mice bearing PDX were treated either with TE or FOLFOX and differential responses toward these treatments were observed. The growth of PDX, metastasis and tumor recurrence in PDX-bearing mice were suppressed after TE treatments with 60% anti-tumor response rate and 83.3% anti-metastasis rate. Mechanistic studies showed that TE reduced tumor cell proliferation, induced cell apoptosis, inhibited metastasis via modulating multiple targets, such as molecules involved in Wnt and Src pathways, EMT and EGFR-related pathways. Nevertheless, FOLFOX treatments inhibited the PDX growth with sharp decreases of mice body weight and only mild anti-metastasis activities were observed. Furthermore, in order to have a better understanding of the underlying mechanisms, network pharmacology was utilized to predict potential targets and mechanism. In conclusion, the present study demonstrated for the first time that oral TE treatment was effective to suppress the growth of colon PDX and the recurrence of colon tumors in mice. The findings obtained from this clinically relevant PDX model would certainly provide valuable information for the potential clinical use of TE in colorectal cancer patients. The application of PDX model was well illustrated here as a good platform to verify the efficacy of multi-targeted herbal extracts.

Cheminformatics in the Identification of Drug Classes for the Treatment of Type 2 Diabetes

Computer-Aided Drug Design has developed into a powerful suite of methods that complement experimental approaches to the identification of new pharmacologically active compounds. In particular, virtual screening has become a standard tool for lead identification. Diverse examples of the application of virtual screening applied to T2DM target proteins have been reported. While several of these indicate successful identification of new lead compounds from synthetic chemical and natural product databases, many of them have been performed on a small scale and with limited validation. Careful study design and collaboration with cheminformaticians and computational chemists will enable these approaches to fulfil their potential for T2DM.

Network Pharmacology-Based Investigation into the Mechanisms of Quyushengxin Formula for the Treatment of Ulcerative Colitis

Objective

Ulcerative colitis (UC) is a chronic idiopathic inflammatory bowel disease whose treatment strategies remain unsatisfactory. This study aims to investigate the mechanisms of Quyushengxin formula acting on UC based on network pharmacology.

Methods

Ingredients of the main herbs in Quyushengxin formula were retrieved from the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database. Absorption, distribution, metabolism, and excretion properties of all ingredients were evaluated for screening out candidate bioactive compounds in Quyushengxin formula. Weighted ensemble similarity algorithm was applied for predicting direct targets of bioactive ingredients. Functional enrichment analyses were performed for the targets. In addition, compound-target network, target-disease network, and target-pathway network were established via Cytoscape 3.6.0 software.

Results

A total of 41 bioactive compounds in Quyushengxin formula were selected out from the TCMSP database. These bioactive compounds were predicted to target 94 potential proteins by weighted ensemble similarity algorithm. Functional analysis suggested these targets were closely related with inflammatory- and immune-related biological progresses. Furthermore, the results of compound-target network, target-disease network, and target-pathway network indicated that the therapeutic effects of Quyushengxin on UC may be achieved through the synergistic and additive effects.

Conclusion

Quyushengxin may act on immune and inflammation-related targets to suppress UC progression in a synergistic and additive manner.

Detection of Vasodilators From Herbal Components by a Transcriptome-Based Functional Gene Module Reference Approach

Vasodilatation is one of the key therapeutic strategies for the treatment of various cardiovascular diseases with high blood pressure. Therefore, development of drugs assisting blood vessel dilation is promising. It has been proven that many drugs display definite vasorelaxant effects. However, there are very few studies that systemically explore the effective vasodilators. In this work, we build a transcriptome-based functional gene module reference approach for systematic pursuit of agents with vasorelaxant effects. We firstly curate two functional gene modules that are specifically involved in positive and negative regulation of vascular diameter based on the known gene functional interaction knowledge. Secondly, a collection of gene expression profiles following herbal component treatment are collected from a public gene expression database. Then, the correlation of the gene modules is evaluated in each herbal component–induced gene expression profile by gene set enrichment analysis. The vasorelaxant effects of the candidate compounds can be predicted and ordered by the values of a defined index. Finally, the top 10 candidate compounds are experimentally tested for their vasorelaxant effects on vessel contraction induced by Phe in aortic rings. This strategy integrating different types of technologies is expected to help to create new opportunities for the development of novel vasodilators.

Network Pharmacology Identifies the Mechanisms of Action of Shaoyao Gancao Decoction in the Treatment of Osteoarthritis

BACKGROUND Osteoarthritis (OA) affects the health and wellbeing of the elderly. Shaoyao Gancao decoction (SGD) is used in traditional Chinese medicine (TCM) for the treatment of OA and has two active components, shaoyao (SY) and gancao (GC). This study aimed to undertake a network pharmacology analysis of the mechanism of the effects of SGD in OA. MATERIAL AND METHODS The active compounds and candidates of SGD were obtained from the Traditional Chinese Medicine (TCM) Databases@Taiwan, the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database, the STITCH database, the ChEMBL database, and PubChem. The network pharmacology approach involved network construction, target prediction, and module analysis. Significant signaling pathways of the cluster networks for SGD and OA were identified using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. RESULTS Twenty-three bioactive compounds were identified, corresponding to 226 targets for SGD. Also, 187 genes were closely associated with OA, of which 161 overlapped with the targets of SGD and were considered to be therapeutically relevant. Functional enrichment analysis suggested that SGD exerted its pharmacological effects in OA by modulating multiple pathways, including cell cycle, cell apoptosis, drug metabolism, inflammation, and immune modulation. CONCLUSIONS A novel approach was developed to systematically identify the mechanisms of the TCM, SGD in OA using network pharmacology analysis.

Systems Pharmacology Uncovers Multiple Mechanisms of Erxian Decoction () for Treatment of Premature Ovarian Failure

OBJECTIVE

To predict the chemical compositions and drug targets and to systematically dissect the pharmacological mechanism of Erxian Decoction (, EXD) as a treatment for premature ovarian failure (POF) using a systems pharmacology approach.

METHODS

The compounds present in EXD were obtained from three databases. The active ingredient was identified by analyzing the values of oral bioavailability (OB), drug-likeness (DL), and Lipinski's rule (LR). The active ingredients were further searched in research articles, drug targets in the DrugBank database, and the C-T and T-P networks, as well as by pathway analysis using the Cytoscape platform.

RESULTS

A total of 728 compounds were identified in EXD. Of these, 59 were identified as active compounds that conformed to the criteria with OB ⩾30% and DL ⩾0.18. By further searches in the literature, 126 related targets were identified that could interact with the active compounds. Additionally, it was found that the beneficial effects of EXD in POF are probably exerted via regulation of the immune system, modulation of estrogen levels, and anti-oxidative activities, and that it may act in a synergistic or cooperative manner with other therapeutic agents.

CONCLUSIONS

The systems pharmacology approach is a comprehensive system that was used to elucidate the pharmacological mechanism of EXD as a treatment for POF. The results of this study will also facilitate the application of traditional medicine in modern treatment strategies.

A Systems Pharmacology-Based Study of the Molecular Mechanisms of San Cao Decoction for Treating Hypertension

Traditional Chinese medicine (TCM) has a longstanding history and has gained widespread clinical applications. San Cao Decoction (SCD) is an experience prescription first formulated by Prof. Duzhou Liu. We previously demonstrated its antihypertensive effects; however, to systematically explain the underlying mechanisms of action, we employed a systems pharmacology approach for pharmacokinetic screening and target prediction by constructing protein-protein interaction networks of hypertension-related and putative SCD-related targets, and Database for Annotation, Visualization, and Integrated Discovery enrichment analysis. We identified 123 active compounds in SCD and 116 hypertension-related targets. Furthermore, the enrichment analysis of the drug-target network showed that SCD acts in a multidimensional manner to regulate PI3K-Akt-endothelial nitric oxide synthase signaling to maintain blood pressure. Our results highlighted the molecular mechanisms of antihypertensive actions of medicinal herbs at a systematic level.

To Embed or Not: Network Embedding as a Paradigm in Computational Biology

Current technology is producing high throughput biomedical data at an ever-growing rate. A common approach to interpreting such data is through network-based analyses. Since biological networks are notoriously complex and hard to decipher, a growing body of work applies graph embedding techniques to simplify, visualize, and facilitate the analysis of the resulting networks. In this review, we survey traditional and new approaches for graph embedding and compare their application to fundamental problems in network biology with using the networks directly. We consider a broad variety of applications including protein network alignment, community detection, and protein function prediction. We find that in all of these domains both types of approaches are of value and their performance depends on the evaluation measures being used and the goal of the project. In particular, network embedding methods outshine direct methods according to some of those measures and are, thus, an essential tool in bioinformatics research.

A Comparative Study of Cluster Detection Algorithms in Protein-Protein Interaction for Drug Target Discovery and Drug Repurposing

The interactions between drugs and their target proteins induce altered expression of genes involved in complex intracellular networks. The properties of these functional network modules are critical for the identification of drug targets, for drug repurposing, and for understanding the underlying mode of action of the drug. The topological modules generated by a computational approach are defined as functional clusters. However, the functions inferred for these topological modules extracted from a large-scale molecular interaction network, such as a protein–protein interaction (PPI) network, could differ depending on different cluster detection algorithms. Moreover, the dynamic gene expression profiles among tissues or cell types causes differential functional interaction patterns between the molecular components. Thus, the connections in the PPI network should be modified by the transcriptomic landscape of specific cell lines before producing topological clusters. Here, we systematically investigated the clusters of a cell-based PPI network by using four cluster detection algorithms. We subsequently compared the performance of these algorithms for target gene prediction, which integrates gene perturbation data with the cell-based PPI network using two drug target prioritization methods, shortest path and diffusion correlation. In addition, we validated the proportion of perturbed genes in clusters by finding candidate anti-breast cancer drugs and confirming our predictions using literature evidence and cases in the ClinicalTrials.gov. Our results indicate that the Walktrap (CW) clustering algorithm achieved the best performance overall in our comparative study.

Inferring Drug-Protein⁻Side Effect Relationships from Biomedical Text

Background: Although there are many studies of drugs and their side effects, the underlying mechanisms of these side effects are not well understood. It is also difficult to understand the specific pathways between drugs and side effects. Objective: The present study seeks to construct putative paths between drugs and their side effects by applying text-mining techniques to free text of biomedical studies, and to develop ranking metrics that could identify the most-likely paths. Materials and Methods: We extracted three types of relationships—drug-protein, protein-protein, and protein–side effect—from biomedical texts by using text mining and predefined relation-extraction rules. Based on the extracted relationships, we constructed whole drug-protein–side effect paths. For each path, we calculated its ranking score by a new ranking function that combines corpus- and ontology-based semantic similarity as well as co-occurrence frequency. Results: We extracted 13 plausible biomedical paths connecting drugs and their side effects from cancer-related abstracts in the PubMed database. The top 20 paths were examined, and the proposed ranking function outperformed the other methods tested, including co-occurrence, COALS, and UMLS by P@5-P@20. In addition, we confirmed that the paths are novel hypotheses that are worth investigating further. Discussion: The risk of side effects has been an important issue for the US Food and Drug Administration (FDA). However, the causes and mechanisms of such side effects have not been fully elucidated. This study extends previous research on understanding drug side effects by using various techniques such as Named Entity Recognition (NER), Relation Extraction (RE), and semantic similarity. Conclusion: It is not easy to reveal the biomedical mechanisms of side effects due to a huge number of possible paths. However, we automatically generated predictable paths using the proposed approach, which could provide meaningful information to biomedical researchers to generate plausible hypotheses for the understanding of such mechanisms.

A Systems Pharmacology Approach for Identifying the Multiple Mechanisms of Action of the Wei Pi Xiao Decoction for the Treatment of Gastric Precancerous Lesions

The Wei Pi Xiao (WPX) decoction, based on the theory of traditional Chinese medicine, has been widely used for the treatment of gastric precancerous lesions (GPL). Although WPX is known to be effective for the treatment of GPL, its active ingredients, cellular targets, and the precise molecular mechanism of action are not known. This study aimed to identify the multiple mechanisms of action of the WPX decoction in the treatment of GPL. The active compounds, drug targets, and the key pathways involved in the therapeutic effect of WPX in the treatment of GPL were analyzed by an integrative analysis pipeline. The information pertaining to the compounds present in WPX and their disease targets was obtained from TCMSP and GeneCards, respectively. The mechanisms underlying the therapeutic effect of WPX were investigated with gene ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. A total of 82 bioactive compounds and 146 related targets were identified in this study. Following target analyses, the targets were further mapped to 26 key biological processes and 21 related pathways to construct a target-pathway network and an integrated GPL pathway. The study demonstrated that the WPX formula primarily treats the dysfunctions of GPL arising from cell proliferation, apoptosis, and mucosal inflammation, which offered a novel insight into the pathogenesis of GPL and revealed the molecular mechanism underlying the therapeutic effects of the WPX formula in GPL. This study offers a novel approach for the systematic investigation of the mechanisms of action of herbal medicines, which will provide an impetus to the GPL drug development pipeline.

Bioinformatics Based Therapeutic Effects of Sinomenium Acutum

OBJECTIVE

To decipher the possible mechanisms of Sinomenium Acutum (SA) in treating diseases by a bioinformatics method.

METHODS

SA ingredients were searched according to Chinese Pharmacopoeia, Chinese Medicine Dictionary and Traditional Chinese Medicines Database (TCMD). Active compounds and target proteins of SA were acquired through the Pubchem platform. Pathway, network and function analyses of SA were performed with ingenuity pathway analysis (IPA), a bioinformatics analysis platform. Disease, biofunction-target networks were established with Cytoscape.

RESULTS

Eighteen ingredients from SA were obtained. Seven active ingredients with 31 active target proteins were acquired according to PubChem Bioassay test. By IPA analysis, 277 canonical pathways belonging to 17 function categories were collected, 23 kinds of diseases, 21 categories bio-functions were obtained. Based on P value, calculated by IPA, the top 5 significant pathway of SA targets include phosphatidylinositol 3 kinase/Akt (PI3K/Akt) signaling, prostate cancer signaling, macrophage migration inhibitory factor (MIF) regulation of innate immunity, Guanosine-binding protein coupled receptor (GPCR) signaling, and ataxia telangiectasia mutated protein (ATM) signaling. Disease and bio-function network analysis indicated that mitogen activated protein kinase 1 (MAPK1), MAPK3, p65 nuclear factor κB (RELA), nuclear factor of κB inhibitor alpha (NFκBIA), interleukin 1β(IL-1β), prostaglandin G/H synthase 2 (PTGS2) and tumor protein 53 (TP53) were the critical targets in various diseases treated by SA.

CONCLUSION

In the different view of target, pathway, disease and bio-function, inflammation was found to be a central theme in many chronic conditions. SA could be used not only as an anti-inflammatory agent, but also for the treatment of cancers, neurological diseases, psychological disorders and metabolic diseases.

A Novel Systems Pharmacology Method to Investigate Molecular Mechanisms of Scutellaria barbata D. Don for Non-small Cell Lung Cancer

Non-small cell lung cancer (NSCLC) is the most ordinary type of lung cancer which leads to 1/3 of all cancer deaths. At present, cytotoxic chemotherapy, surgical resection, radiation, and photodynamic therapy are the main strategies for NSCLC treatment. However, NSCLC is relatively resistant to the above therapeutic strategies, resulting in a rather low (20%) 5-year survival rate. Therefore, there is imperative to identify or develop efficient lead compounds for the treatment of NSCLC. Here, we report that the herb Scutellaria barbata D. Don (SBD) can effectively treat NSCLC by anti-inflammatory, promoting apoptosis, cell cycle arrest, and angiogenesis. In this work, we analyze the molecular mechanism of SBD for NSCLC treatment by applying the systems pharmacology strategy. This method combines pharmacokinetics analysis with pharmacodynamics evaluation to screen out the active compounds, predict the targets and assess the networks and pathways. Results show that 33 compounds were identified with potential anti-cancer effects. Utilizing these active compounds as probes, we predicted that 145 NSCLC related targets mainly involved four aspects: apoptosis, inflammation, cell cycle, and angiogenesis. And in vitro experiments were managed to evaluate the reliability of some vital active compounds and targets. Overall, a complete overview of the integrated systems pharmacology method provides a precise probe to elucidate the molecular mechanisms of SBD for NSCLC. Moreover, baicalein from SBD effectively inhibited tumor growth in an LLC tumor-bearing mice models, demonstrating the anti-tumor effects of SBD. Our findings further provided experimental evidence for the application in the treatment of NSCLC.

Uncovering the mechanism of Maxing Ganshi Decoction on asthma from a systematic perspective: A network pharmacology study

Maxing Ganshi Decoction (MXGSD) is used widely for asthma over thousands of years, but its underlying pharmacological mechanisms remain unclear. In this study, systematic and comprehensive network pharmacology was utilized for the first time to reveal the potential pharmacological mechanisms of MXGSD on asthma. Specifically, we collected 141 bioactive components from the 600 components in MXGSD, which shared 52 targets common to asthma-related ones. In-depth network analysis of these 52 common targets indicated that asthma might be a manifestation of systemic neuro-immuno-inflammatory dysfunction in the respiratory system, and MXGSD could treat asthma through relieving airway inflammation, improving airway remodeling, and increasing drug responsiveness. After further cluster and enrichment analysis of the protein-protein interaction network of MXGSD bioactive component targets and asthma-related targets, we found that the neurotrophin signaling pathway, estrogen signaling pathway, PI3K-Akt signaling pathway, and ErbB signaling pathway might serve as the key points and principal pathways of MXGSD gene therapy for asthma from a systemic and holistic perspective, and also provides a novel idea for the development of new drugs for asthma.

Self-adaptive multiscaling algorithm for efficient simulations of many-protein systems in crowded conditions

A method is described for the efficient simulation of multiprotein systems in crowded environments. It is based on an adaptive, reversible structural coarsening algorithm that preserves relevant physical features of the proteins across scales. Water is treated implicitly whereas all the other components of the aqueous solution, such as ions, cosolutes, or osmolytes, are treated in atomic detail. The focus is on the analytical adaptation of the solvent model to different levels of molecular resolutions, which allows continuous, on-the-fly transitions between scales. This permits the analytical calculation of forces during dynamics and preserves detailed balance in Monte Carlo simulations. A major computational speedup can be achieved in systems containing hundreds of proteins without cutting off the long-range interactions. The method can be combined with a self-adaptive configurational-bias sampling technique described previously, designed to detect strong, weak, or ultra-weak protein associations and shown to improve sampling efficiency and convergence. The implementation aims to simulate early stages of multimeric complexation, aggregation, or self-assembly. The method can be adopted as the basis for a more general algorithm to identify vertices, edges, and hubs in protein interaction networks or to predict critical steps in signal transduction pathways.