Systems pharmacology strategies for drug discovery and combination with applications to cardiovascular diseases

Predicting herb-disease associations using network-based measures in human protein interactome

BACKGROUND

Natural herbs are frequently used to treat diseases or to relieve symptoms in many countries. Moreover, as their safety has been proven for a long time, they are considered as main sources of new drug development. However, in many cases, the herbs are still prescribed relying on ancient records and/or traditional practices without scientific evidences. More importantly, the medicinal efficacy of the herbs has to be evaluated in the perspective of MCMT (multi-compound multi-target) effects, but most efforts focus on identifying and analyzing a single compound experimentally. To overcome these hurdles, computational approaches which are based on the scientific evidences and are able to handle the MCMT effects are needed to predict the herb-disease associations.

RESULTS

In this study, we proposed a network-based in silico method to predict the herb-disease associations. To this end, we devised a new network-based measure, WACP (weighted average closest path length), which not only quantifies proximity between herb-related genes and disease-related genes but also considers compound compositions of each herb. As a result, we confirmed that our method successfully predicts the herb-disease associations in the human protein interactome (AUROC = 0.777). In addition, we observed that our method is superior than the other simple network-based proximity measures (e.g. average shortest and closest path length). Additionally, we analyzed the associations between Brassica oleracea var. italica and its known associated diseases more specifically as case studies. Finally, based on the prediction results of the WACP, we suggested novel herb-disease pairs which are expected to have potential relations and their literature evidences.

CONCLUSIONS

This method could be a promising solution to modernize the use of the natural herbs by providing the scientific evidences about the molecular associations between the herb-related genes targeted by multiple compounds and the disease-related genes in the human protein interactome.

Review of Predicting Synergistic Drug Combinations

The prediction of drug combinations is of great clinical significance. In many diseases, such as high blood pressure, diabetes, and stomach ulcers, the simultaneous use of two or more drugs has shown clear efficacy. It has greatly reduced the progression of drug resistance. This review presents the latest applications of methods for predicting the effects of drug combinations and the bioactivity databases commonly used in drug combination prediction. These studies have played a significant role in developing precision therapy. We first describe the concept of synergy. we study various publicly available databases for drug combination prediction tasks. Next, we introduce five algorithms applied to drug combinatorial prediction, which include traditional machine learning methods, deep learning methods, mathematical methods, systems biology methods and search algorithms. In the end, we sum up the difficulties encountered in prediction models.

Discovering Traditional Chinese Medicine (TCM) Formulas for Complex Diseases Based on a Combination of Reverse Systematic Pharmacology and TCM Meridian Tropism Theory: Taking COVID-19 as an Example

OBJECTIVE

Infections and death have been a part of our daily lives since the COVID-2019 pandemic outbreak in 2019, and the societal and economic consequences have lingered for an unanticipated duration. Novel and effective treatments are still desperately needed around the world to combat the infection. Here, we discovered a novel traditional Chinese medicine formula (TCMF) to potentially combat COVID-19 through reverse systematic pharmacology (disease → targets → TCMF → disease).

METHODS

Combining Integrative network pharmacology and the traditional Chinese medicine (TCM) theory, a TCMF for COVID-19 was identified. In silico physiological interactions between herbs and disease hub targets were validated by molecular docking and dynamics simulation.

RESULTS

Based on disease-related gene/pathway targets and a combination of reverse pharmacology and TCM meridian tropism theory, a COVID-19-associated herb database was constructed. A new TCMF, including Gancao, Baitouweng, Congbai, and Diyu (GBCD), was discovered for anti-COVID-19 therapy. The KEGG and GO analyses of 49 intersecting genes suggested that GBCD could combat COVID-19 through antiviral, antiinflammation, immunoregulation, and cytoprotection activities. Moreover, these possible effects were validated through docking and MD simulation.

CONCLUSIONS

To the best of our knowledge, this study is the first to combine reverse pharmacology and meridian tropism theories for TCMF development, and a novel herbal combination, GBCD, was discovered for anti-COVID-19 therapy.

The potential mechanism of qinghua quyu jianpi decoction in the treatment of ulcerative colitis based on network pharmacology and experimental validation

ETHNOPHARMACOLOGICAL RELEVANCE

Ulcerative colitis (UC) is a chronic and recurrent inflammation of the gastrointestinal tract. Following the idea of herbal property and compatibility, a traditional Chinese medicine (TCM) formula consists of a number of TCM herbs. Qinghua Quyu Jianpi Decoction (QQJD) has been clinically proven to be effective in treating UC, however, its therapeutic mechanism has not been fully elucidated.

AIM OF STUDY

Here, we used network pharmacology analysis and ultra-performance liquid chromatography-tandem mass spectrometry to predict the mechanism of action of QQJD, and then validated our predictions through in vivo and in vitro experiments.

MATERIALS AND METHODS

First, based on a number of datasets, relationship network diagrams between QQJD and UC were created. The target network for the QQJD-UC intersection genes was then built, and KEGG analysis was carried out to identify a potential pharmacological mechanism. Finally, the results of the previous prediction were validated in dextran sulfate sodium salt (DSS) induced UC mice and a cellular inflammatory model.

RESULTS

Network pharmacology results suggested that QQJD may play a role in repairing intestinal mucosa by activating Wnt pathway. In vivo experiments have shown that QQJD can significantly reduce weight loss, disease activity index (DAI) score, improve colon length, and effectively repair the tissue morphology of UC mice. In addition, we also found that QQJD can activate the Wnt pathway to promote epithelial cell renewal, reduce apoptosis, and repair the mucosal barrier. To further understand how QQJD promotes cell proliferation in DSS-induced Caco-2 cells, we performed a study in vitro experiment. We were surprised to find that QQJD activated the Wnt pathway by inducing nuclear translocation of β-catenin, accelerating the cell cycle and promoting cell proliferation in vitro.

CONCLUSION

Taken together, network pharmacology and experiments showed that QQJD achieves mucosal healing and restores the colonic epithelium barrier by activating Wnt/β-catenin signaling, regulating cell cycle progression, and promoting the proliferation of epithelial cells.

Farrerol Inhibits Vascular Smooth Muscle Cell Proliferation and Protects Them From Oxidative Injury via Bidirectional Modulation of the PI3K/Akt/mTOR Signaling Pathway

The inhibition of intimal hyperplasia (IH) is an effective strategy to improve the long-term outcome of endovascular therapy and prevent restenosis. Farrerol, a naturally occurring dihydroflavone with a variety of bioactivities, exerts inhibitory effects against balloon injury-induced IH in rats. In the present study, bioinformatics analysis, in combination with in vitro experimental validation, was performed to elucidate the underlying inhibitory mechanisms. The protein–protein interaction (PPI) network was assessed to identify farrerol-related protein targets in the context of IH, based on which biological functions and pathway enrichment were analyzed. The proliferation and cell cycle distribution of vascular smooth muscle cells (VSMCs) were investigated using 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl-2 H-tetrazolium bromide and 5-ethynyl-2-deoxyuridine incorporation assays and flow cytometric analysis, respectively. The level of pro-inflammatory cytokines in the cell culture medium was estimated using an enzyme-linked immunosorbent assay (ELISA). Protein expression in A7r5 cells was determined by western blotting. Forty-six IH-related targets of farrerol were identified, and the PI3K/Akt/mTOR pathway was highly enriched among the 43 predicted pathways ( P < .05). In serum (10% fetal bovine serum)-induced A7r5 cells, farrerol inhibited proliferation through non-cytotoxic effects, induced cell cycle arrest in the G0/G1 phase , and suppressed the activation of the PI3K/Akt/mTOR pathway. In H2O2 (300 µM)-induced A7r5 cells, farrerol reduced the release of IL-1 β and TNF- α and reversed the suppressive effect on the PI3K/Akt/mTOR pathway in response to H2O2 stimulation. In conclusion, farrerol inhibits the proliferation of VSMCs and protects VSMCs from oxidative injury via the bidirectional modulation of the PI3K/Akt/mTOR signaling pathway, which might contribute to the suppression of neointima formation.

Effects of heat-processed Gynostemma pentaphyllum on high-fat diet-fed mice of obesity and functional analysis on network pharmacology and molecular docking strategy

ETHNOPHARMACOLOGICAL RELEVANCE

Gynostemma pentaphyllum has been used as traditional medicine for many diseases, including metabolic syndrome (Mets), aging, diabetes, neurodegenerative diseases in China, some East Asian and Southeast Asian countries. It was shown that G. pentaphyllum and gypenosides had anti-obesity and cholesterol-lowering effects too. However, its main active ingredients are still unclear.

AIMS

The objective of this study was to compare the effects of gypenosides before and after heat-processing on high fat obese mice, and to analyze the function of G. pentaphyllum saponin via network pharmacology and molecular docking.

METHODS

The leaves of G. pentaphyllum were heat processed at 120 °C for 3 h to obtain heat-processed G. pentaphyllum. Gypenosides (Gyp) and heat-processed gypenosides (HGyp) were prepared by resin HP-20 chromatography and analyzed using LC-MS from the extracts of G. pentaphyllum before and after heat-processing, respectively. Obesity model was made with high fat diet (HFD). Gyp and HGyp were administrated at 100 mg/kg for 12 weeks in HFD obese mice and the body weight, energy intake, and levels of total cholesterol (TC), triglyceride (TG), low-density lipoprotein (LDL), high-density lipoprotein (HDL) were compared. HGyp was administrated at a dose of 50,100,200 mg/kg for 12 weeks in HFD obese mice and the perirenal adipose, epididymal adipose, abdominal adipose, shoulder brown adipose, inguinal adipose were measured. Moreover, the potential targets, hub genes and pathways of damulin A, damulin B, gypenoside L, gypenoside LI for treating Mets were screened out via network pharmacology. According to the results of network pharmacology, core targets of treating Mets were docking with damulin A, gypenoside L, damulin B, gypenoside LI via molecular docking.

RESULTS

HGyp showed stronger effects on body weight loss and lipid-lowering in obese mice than Gyp. The contents of gypenoside L, gypenoside LI, damulin A and damulin B of G. pentaphyllum were increased by heat-processing. HGyp significantly decreased the body weight, calorie intake, and levels of TC, TG, LDL, HDL on the obese mice. It up-regulated PPARα and PPARγ in the liver tissues. HGyp reduced significantly the size of adipocytes in inguinal, abdominal, epididymal adipose and increased the proportion of interscapular brown fat. Network pharmacology results showed that 21 potential targets and 12 related-pathways were screened out. HMGCR, ACE, LIPC, LIPG, PPARα PPARδ, PPARγ were the core targets of HGyp against lipid metabolism by molecular docking. The putative functional targets of HGyp may be modulated by AGE-RAGE, TNF, glycerolipid metabolism, lipid and atherosclerosis, cholesterol metabolism, PPAR, fat digestion and absorption, cell adhesion molecules signaling pathway.

CONCLUSIONS

Gyp and HGyp are valuable for inhibition obesity, lipid-lowering, metabolic regulation. Especially, the effect of HGyp is better than that of Gyp.

In vitro anti-cancer effect of marmesin by suppression of PI3K/Akt pathway in esophagus cancer cells

BACKGROUND

Marmesin, an important coumarin isolated from Broussonetia kazinoki, has been proposed to possess many pharmacological activities including anti-tumor activity. However, the anti-cancer effect of marmesin on esophageal cancer (EC) has not been characterized. The study aimed to explore the anti-cancer role of marmesin using EC cell lines in vitro.

METHODS AND RESULTS

Cell proliferation was evaluated by CCK-8 and Edu cell proliferation assays and apoptosis was detected by TUNEL assay. Western blot analysis was used to determine the expression of Ki67, proliferating cell nuclear antigen (PCNA), Bcl-2, Bax, phosphatidylinositol 3-kinase (PI3K), phosphoryrated-PI3K (p-PI3K), protein kinase B (Akt), and phosphoryrated-Akt (p-Akt). The mechanism of action of marmesin was analyzed using network pharmacology approach. Marmesin exhibited anti-proliferative effect against EC cells, which was further confirmed by the reduced expression of Ki67 and PCNA. Marmesin exerted pro-apoptotic activity on EC cells by downregulating Bcl-2 and upregulating Bax. According to the results from network pharmacology approach, we speculated that PI3K/Akt pathway may participate in the effect of marmesin on EC cells. Additionally, the PI3K/Akt pathway was suppressed by marmesin in EC cells. Moreover, forced expression of Akt reversed the inhibition of cell proliferation and induction of apoptosis induced by marmesin in EC cells.

CONCLUSIONS

Marmesin exerted anti-cancer activity in EC cells by inhibiting the PI3K/Akt pathway.

Identification of Sitogluside as a Potential Skin-Pigmentation-Reducing Agent through Network Pharmacology

Many traditional Chinese medicines (TCMs) with skin-whitening properties have been recorded in the Ben-Cao-Gang-Mu and in folk prescriptions, and some literature confirms that their extracts do have the potential to inhibit pigmentation. However, no systematic studies have identified the specific regulatory mechanisms of the potential active ingredients. The aim of this study was to screen the ingredients in TCMs that inhibit skin pigmentation through a network pharmacology system and to explore underlying mechanisms. We identified 148 potential active ingredients from 14 TCMs, and based on the average “degree” of the topological parameters, the top five TCMs (Fructus Ligustri Lucidi, Hedysarum multijugum Maxim., Ampelopsis japonica, Pseudobulbus Cremastrae Seu Pleiones, and Paeoniae Radix Alba) that were most likely to cause skin-whitening through anti-inflammatory processes were selected. Sitogluside, the most common ingredient in the top five TCMs, inhibits melanogenesis in human melanoma cells (MNT1) and murine melanoma cells (B16F0) and decreases skin pigmentation in zebrafish. Furthermore, mechanistic research revealed that sitogluside is capable of downregulating tyrosinase (TYR) expression by inhibiting the ERK and p38 pathways and inhibiting TYR activity. These results demonstrate that network pharmacology is an effective tool for the discovery of natural compounds with skin-whitening properties and determination of their possible mechanisms. Sitogluside is a novel skin-whitening active ingredient with dual regulatory effects that inhibit TYR expression and activity.

Exploring the Pharmacological Mechanisms of Tripterygium wilfordii Hook F against Cardiovascular Disease Using Network Pharmacology and Molecular Docking

Background

Tripterygium wilfordii Hook F (TwHF) has been used in traditional Chinese medicine (TCM) for treating cardiovascular disease (CVD). However, the underlying pharmacological mechanisms of the effects of TwHF on CVD remain elusive. This study revealed the pharmacological mechanisms of TwHF acting on CVD based on a pharmacology approach.

Materials and Methods

The active compounds were selected from the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database according to the absorption, distribution, metabolism, and excretion (ADME). The potential targets of TwHF were obtained from the SwissTargetPrediction database. The CVD-related therapeutic targets were collected from the DrugBank, the GeneCards database, and the OMIM database. Protein–protein interaction (PPI) network was generated by the STITCH database. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed by R package. The network of drug-targets-diseases-pathways was constructed by the Cytoscape software.

Results

The 41 effective ingredients of TwHF and the 178 common targets of TwHF and CVD-related were collected. Furthermore, AKT1, amyloid precursor protein (APP), mitogen-activated protein kinase 1 (MAPK), phosphatidylinositol 3-kinase catalytic subunit alpha (PIK3CA), and cellular tumor antigen p53 (TP53) were identified as the core targets involved in the mechanism of TwHF on CVD. Top ten GO (biological processes, cellular components, and molecular functions) and KEGG pathways were screened with a P value ≤0.01. Finally, we constructed the network of TwHF-targets-CVD-GO-KEGG.

Conclusions

These findings demonstrate that the main active compound of TwHF, the core targets, and pathways maybe provide new insights into the development of a natural therapy for the prevention and treatment of CVD.

Deciphering the Therapeutic Mechanisms of Wuzi Ershen Decoction in Treating Oligoasthenozoospermia through the Network Pharmacology Approach

Background

Infertility affects approximately 15% of couples around the world, and male factors are accounted for 40–50%. Oligoasthenozoospermia is the most common reason for male infertility. Unfortunately, effective drug therapy is still lacking except for assisted reproductive technology (ART). Previous researchers found that Wuzi Ershen decoction (WZESD) can increase sperm count, enhance sperm vitality, and improve semen quality. However, the pharmacological mechanisms remain unclear.

Methods

In this study, we screened compounds and predicted the targets of WZESD based on the TCMSP and BATMAN-TCM database combined with literature searching in the PubMed database. We obtained proteins related to oligoasthenozoospermia through GeneCards and submitted them to STRING to obtain the protein-protein interaction (PPI) network. Potential targets of WZESD were mapped to the network, and the hub targets were screened by topology. We used online platform Metascape and Enrichr for GO and KEGG enrichment analyses. AutoDock Vina was utilized for further verification of the binding mode between compounds and targets.

Results

Totally, 276 bioactive compounds were obtained and targeted 681 proteins. 446 oligoasthenozoospermia disease-specific proteins were acquired, and further bioinformatics analysis found that they were mainly involved in the formation of gametes, meiosis, and sperm differentiation. Protein interaction network analysis revealed that target proteins of WZESD were associated with oligoasthenozoospermia disease-specific proteins. The 79 targets of disease-specific proteins, which were anchored by WZESD, mainly participate in the cellular response to the organic cyclic compound, regulation of the apoptotic process, nitricoxide biosynthetic and metabolic process, oxidative stress, and protein phosphorylation regulation, which are the causes for oligoasthenozoospermia. Molecular docking simulation further validated that bioactive compounds originated from WZESD with targeted proteins showed high binding efficiency.

Conclusions

This study uncovers the therapeutic mechanisms of WZESD for oligoasthenozoospermia treatment from the perspective of network pharmacology and may provide a valuable reference for further experimental research studies and clinical applications.

Application of Traditional Chinese Medicine and Systems Pharmacology in Drug Prevention and Treatment against COVID-19

A novel coronavirus named SARS-CoV-2 is causing the severe acute pneumonia (COVID-19) and rapid spread nationally and internationally, resulting in a major global health emergency. Chinese governments and scientists have implemented a series of rigorous measures and scientific research to prevent and control the SARS-CoV-2 infection. However, there is still no specific antiviral drug or vaccine against SARS-CoV-2. It has been proven that traditional Chinese medicine (TCM) exerts an important role in the prevention and treatment of the COVID-19 caused by SARS-CoV-2 during the outbreak. Although the therapeutic effects of these TCM formulas are attractive, the molecular mechanism of action has not been fully elucidated. An emerging strategy of systems pharmacology has been proposed to be a promising method to interpret drug action in complex biological systems and quickly screen out the bioactive compounds from TCM to treat treatment of COVID-19 caused by SARS-CoV-2. Therefore, in this study, the epidemiology, TCM therapy, and the systems pharmacology-based method for TCM are reviewed for COVID-19 to provide a perspective for the prevention and treatment of SARS-CoV-2 infection. Further efforts should be made to reduce disease burden and improve the ability to design antiviral drugs and vaccines, which will benefit the health care system, economic development and even social stability.

Network Pharmacological Analysis and Experimental Validation of the Mechanisms of Action of Si-Ni-San Against Liver Fibrosis

Background: Si-Ni-San (SNS), a commonly used traditional Chinese medicine (TCM) formula, has potency against liver diseases, such as hepatitis and non-alcoholic fatty liver disease (NAFLD). However, the therapeutic efficacy and pharmacological mechanisms of action of SNS against liver fibrosis remain largely unclear. Methods: A carbon tetrachloride (CCl4)-induced liver fibrosis mouse model was adopted for the first time to investigate the beneficial effects of SNS on liver fibrosis. The potential mechanisms of action of SNS were explored using the network pharmacology-based strategy and validated with the aid of diverse assays. Results: SNS treatment reduced collagen and ECM deposition, downregulated fibrosis-related factor (hyaluronic acid and laminin) contents in serum, maintained the morphological structure of liver tissue, and improved liver function in the liver fibrosis model. Based on network pharmacology results, apoptosis, inflammation and angiogenesis, together with the associated pathways (including VEGF, TNF, caspase, PPAR-γ and NF-κB), were identified as the mechanisms underlying the effects of SNS on liver fibrosis. Further in vivo experiments validated the significant mitigatory effects of SNS on inflammatory infiltration and pro-inflammatory cytokine contents (IFNγ, IL-1β and TGF-β1) in liver tissues of mice with liver fibrosis. SNS suppressed pathologic neovascularization as well as levels of VEGFR1, VEGF and VEGFR2 in liver tissues. SNS treatment additionally inhibited hepatic parenchyma cell apoptosis in liver tissues of mice with liver fibrosis and regulated apoptin expression while protecting L02 cells against apoptosis induced by TNF-α and Act D in vitro. Activation of hepatic stellate cells was suppressed and the balance between MMP13 and TIMP1 maintained in vitro by SNS. These activities may be associated with SNS-induced NF-κB suppression and PPAR-γ activation. Conclusion: SNS effectively impedes liver fibrosis progression through alleviating inflammation, ECM accumulation, aberrant angiogenesis and apoptosis of hepatic parenchymal cells along with inhibiting activation of hepatic stellate cells through effects on multiple targets and may thus serve as a novel therapeutic regimen for this condition.

The Acidic Fraction of Isatidis Radix Regulates Inflammatory Response in LPS-Stimulated RAW264.7 Macrophages through MAPKs and NF-κB Pathway

Isatidis Radix, the dried root of Isatidis indigotica Fort, is a traditional heat-clearing and detoxicating herb, which has the antiviral and anti-inflammatory activity and immune regulation. It has been widely used to treat cold, fever, sore throat, mumps, and tonsillitis in clinics. A previous study demonstrated that the acidic fraction of Isatidis Radix (RIAF) had strong anti-inflammatory activity, but the mechanism of action was not well elucidated. Lipopolysaccharide- (LPS-) induced RAW264.7 cells were employed to observe the anti-inflammatory activity of RIAF. The level of interleukin-1β (IL-1β), tumor necrosis factor-α (TNF-α), nitric oxide (NO), prostaglandin E₂ (PGE₂), and interleukin-6 (IL-6) was determined by enzyme-linked immunosorbent assay kits. Western blot was performed to quantify the expression of extracellular signal-regulated kinase (ERK) 1/2, c-jun NH2-termianl kinase (JNK), p38, inducible NO synthetase (iNOS), cyclooxygenase (COX)-2, andnuclear factor-κB (NF-κB). Immunofluorescence assay and electrophoretic mobility shift assay (EMSA) were used to quantify the translocation and the binding-DNA activity of NF-κB. RIAF could inhibit the secretion of inflammatory cytokines (PGE₂, IL-6, IL-1β, and NO, other than TNF-α) in a dose-dependent manner. Further investigation showed that the expression of iNOS and COX-2 induced by LPS were downregulated by treatment with RIAF. Meanwhile, data from the signal pathway exhibited that RIAF significantly suppressed the phosphorylation of ERK1/2, JNK, and p38 and reduced the translocation of NF-κB from the cytoplasm to nucleus, as well as the binding-DNA activity. The anti-inflammatory mechanism of action of RIAF was to reduce inflammation-associated gene expression (iNOS, COX-2, IL-1β, IL-6) by regulating the phosphorylation of the mitogen-activated protein kinases (MAPK) pathway and interventing the activation of the NF-κB pathway, which partly illustrated the basis of treatment of Isatidis Radix on cold, fever, sore throat, mumps, and tonsillitis in clinics.

Virtual Screening Technique Used to Estimate the Mechanism of Adhatoda vasica Nees for the Treatment of Rheumatoid Arthritis Based on Network Pharmacology and Molecular Docking

Adhatoda vasica Nees (AVN) is commonly used to treat joint diseases such as rheumatoid arthritis (RA) in ethnic minority areas of China, especially in Tibetan and Dai areas, and its molecular mechanisms on RA still remain unclear. Network pharmacology, a novel strategy, utilizes bioinformatics to predict and evaluate drug targets and interactions in disease. Here, network pharmacology was used to investigate the mechanism by which AVN acts in RA. The chemical compositions and functional targets of AVN were retrieved using the systematic pharmacological analysis platform PharmMapper. The targets of RA were queried through the DrugBank database. The protein-protein interaction network (PPI), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of key targets were constructed in the STRING database, and the network visualization analysis was performed in Cytoscape. Maestro 11.1, a type of professional software, was used for verifying prediction and analysis based on network pharmacology. By comparing the predicted target information with the targets of RA-related drugs, 25 potential targets may be related to the treatment of RA, among which MAPK1, TNF, DHODH, IL2, PTGS2, and JAK2 may be the main potential targets for the treatment of RA. Finally, the chemical components and potential target proteins were scored by molecular docking, and compared with the ligands of the protein, the prediction results of network pharmacology were preliminarily verified. The active ingredients and mechanism of AVN against RA were firstly investigated using network pharmacology. Additionally, this research provided a solid foundation for further experimental studies.

Proanthocyanidins: Components, Pharmacokinetics and Biomedical Properties

Proanthocyanidins (PAs) are a group of polyphenols enriched in plant and human food. In recent decades, epidemiological studies have upheld the direct relationship between PA consumption and health benefits; therefore, studies on PAs have become a research hotspot. Although the oral bioavailability of PAs is quite low, pharmacokinetics data revealed that some small molecules and colonic microbial metabolites of PAs could be absorbed and exert their health beneficial effects. The pharmacological effects of PAs mainly include anti-oxidant, anticancer, anti-inflammation, antimicrobial, cardiovascular protection, neuroprotection, and metabolism-regulation behaviors. Moreover, current toxicological studies show that PAs have no observable toxicity to humans. This review summarizes the resources, extraction, structures, pharmacokinetics, pharmacology, and toxicology of PAs and discusses the limitations of current studies. Areas for further research are also proposed.

Identification of the active compounds and drug targets of Chinese medicine in heart failure based on the PPARs-RXRα pathway

ETHNOPHARMACOLOGICAL RELEVANCE

Danqi Pill (DQP), commonly known as a routinely prescribed traditional Chinese medicine (TCM), is composed of Salviae Miltiorrhizae Radix et Rhizoma and Notoginseng Radix et Rhizoma and effective in treating heart failure (HF) clinically due to their multicompound and multitarget properties. However, the exact active compounds and corresponding targets of DQP are still unknown.

AIM OF THE STUDY

This study aimed to investigate active compounds and drug targets of DQP in heart failure based on the PPARs-RXRα pathway.

MATERIALS AND METHODS

Network pharmacology was used to predict the compound-target interactions of DQP. Left anterior descending (LAD)-induced HF mouse model and oxygen-glucose deprivation/recovery (OGD/R)-induced H9C2 model were constructed to screen the active compounds of DQP.

RESULTS

According to BATMAN-TCM (a bioinformatics analysis tool for molecular mechanism of traditional Chinese medicine we previously developed), 24 compounds in DQP were significantly enriched in the peroxisome proliferator activated receptors-retinoid X receptor α (PPARs-RXRα) pathway. Among them, Ginsenoside Rb3 (G-Rb3) had the best pharmacodynamics against OGD/R-induced loss of cell viability, and it was selected to verify the compound-target interaction. In HF mice, G-Rb3 protected cardiac functions and activated the PPARs-RXRα pathway. In vitro, G-Rb3 protected against OGD/R-induced reactive oxygen species (ROS) production, promoted the expressions of RXRα and sirtuin 3 (SIRT3), thereafter improved the intracellular adenosine triphosphate (ATP) level. Immunofluorescent staining demonstrated that G-Rb3 could activate RXRα, and facilitate RXRα shifting to the nucleus. HX531, the specific inhibitor of RXRα, could abolish the protective effects of G-Rb3 on RXRα translocation. Consistently, the effect was also confirmed on RXRα siRNA cardiomyocytes model. Moreover, surface plasmon resonance (SPR) assays identified that G-Rb3 bound directly to RXRα with the affinity of KD = 10 × 10^-5 M.

CONCLUSION

By integrating network pharmacology and experimental validation, we identified that as the major active compound of DQP, G-Rb3 could ameliorate ROS-induced energetic metabolism dysfunction, maintain mitochondrial function and facilitate energy metabolism via directly targeting on RXRα. This study provides a promising strategy to dissect the effective patterns for TCM and finally promote the modernization of TCM.

Investigation on the Mechanism of Qubi Formula in Treating Psoriasis Based on Network Pharmacology

Objective

To elucidate the pharmacological mechanisms of Qubi Formula (QBF), a traditional Chinese medicine (TCM) formula which has been demonstrated as an effective therapy for psoriasis in China.

Methods

The Traditional Chinese Medicine Systems Pharmacology (TCMSP) database, BATMAN-TCM database, and literature search were used to excavate the pharmacologically active ingredients of QBF and to predict the potential targets. Psoriasis-related targets were obtained from Therapeutic Target Database (TTD), DrugBank database (DBD), MalaCards database, and DisGeNET database. Then, we established the network concerning the interactions of potential targets of QBF with well-known psoriasis-related targets by using protein-protein interaction (PPI) data in String database. Afterwards, topological parameters (including DNMC, Degree, Closeness, and Betweenness) were calculated to excavate the core targets of Qubi Formula in treating psoriasis (main targets in the PPI network). Cytoscape was used to construct the ingredients-targets core network for Qubi Formula in treating psoriasis, and ClueGO was used to perform GO-BP and KEGG pathway enrichment analysis on these core targets.

Results

The ingredient-target-disease core network of QBF in treating psoriasis was screened to contain 175 active ingredients, which corresponded to 27 core targets. Additionally, enrichment analysis suggested that targets of QBF in treating psoriasis were mainly clustered into multiple biological processes (associated with nuclear translocation of proteins, cellular response to multiple stimuli (immunoinflammatory factors, oxidative stress, and nutrient substance), lymphocyte activation, regulation of cyclase activity, cell-cell adhesion, and cell death) and related pathways (VEGF, JAK-STAT, TLRs, NF-κB, and lymphocyte differentiation-related pathways), indicating the underlying mechanisms of QBF on psoriasis.

Conclusion

In this work, we have successfully illuminated that Qubi Formula could relieve a wide variety of pathological factors (such as inflammatory infiltration and abnormal angiogenesis) of psoriasis in a "multicompound, multitarget, and multipathway" manner by using network pharmacology. Moreover, our present outcomes might shed light on the further clinical application of QBF on psoriasis treatment.

Prediction of triptolide targets in rheumatoid arthritis using network pharmacology and molecular docking

Network pharmacology is a novel approach that uses bioinformatics to predict and identify multiple drug targets and interactions in disease. Here, we used network pharmacology to investigate the mechanism by which triptolide acts in rheumatoid arthritis (RA). We first searched public databases for genes and proteins known to be associated with RA, as well as those predicted to be targets of triptolide, and then used Ingenuity Pathway Analysis (IPA) to identify enriched gene pathways and networks. Networks and pathways that overlapped between RA-associated proteins and triptolide target proteins were then used to predict candidate protein targets of triptolide in RA. The following proteins were found to occur in both RA-associated networks and triptolide target networks: CD274, RELA, MCL1, MAPK8, CXCL8, STAT1, STAT3, c-JUN, JNK, c-Fos, NF-κB, and TNF-α. Docking studies suggested that triptolide can fit in the binding pocket of the six top candidate triptolide target proteins (CD274, RELA, MCL1, MAPK8, CXCL8 and STAT1). The overlapping pathways were activation of Th1 and Th2 cells, macrophages, fibroblasts and endothelial cells in RA, while the overlapping networks were involved in cellular movement, hematological system development and function, immune cell trafficking, cell-to-cell signaling and interaction, inflammatory response, cellular function and maintenance, and cell death and survival. These results show that network pharmacology can be used to generate hypotheses about how triptolide exerts therapeutic effects in RA. Network pharmacology may be a useful method for characterizing multi-target drugs in complex diseases.

A Network Pharmacology Approach to Uncover the Molecular Mechanisms of Herbal Formula Kang-Bai-Ling for Treatment of Vitiligo

Background

Kang-bai-ling (KBL), a Chinese patent medicine, has been demonstrated as an effective therapy for vitiligo in China. However, the pharmacological mechanisms of KBL have not been completely elucidated.

Methods

In this study, the potential multicomponent, multitarget, and multipathway mechanism of KBL against vitiligo was clarified by using network pharmacology-based strategy. In brief, potential targets of KBL were collected based on TCMSP databases, followed by network establishment concerning the interactions of potential targets of KBL with well-known therapeutic targets of vitiligo by using protein-protein interaction (PPI) data. As a result, key nodes with higher level of seven topological parameters, including “degree centrality (DC),” “betweenness centrality (BC),” “closeness centrality (CC),” “eigenvector centrality (EC),” “network centrality (NC),” and “local average connectivity (LAC)” were identified as the main targets in the network, followed by subsequent incorporation into the ClueGO for GO and KEGG signaling pathway enrichment analysis.

Results

In accordance with the topological importance, a total of 23 potential targets of KBL on vitiligo were identified as main hubs. Additionally, enrichment analysis suggested that targets of KBL on vitiligo were mainly clustered into multiple biological processes (associated with DNA translation, lymphocyte differentiation and activation, steroid biosynthesis, autoimmune and systemic inflammatory reaction, neuron apoptosis, and vitamin deficiency) and related pathways (TNF, JAK-STAT, ILs, TLRs, prolactin, and NF-κB), indicating the underlying mechanisms of KBL on vitiligo.

Conclusion

In this work, we successfully illuminated the “multicompounds, multitargets” therapeutic action of KBL on vitiligo by using network pharmacology. Moreover, our present outcomes might shed light on the further clinical application of KBL on vitiligo treatment.

Literature data based systems pharmacology uncovers the essence of "body fire" in traditional Chinese medicine: A case by Huang-Lian-Jie-Du-Tang

ETHNOPHARMACOLOGY RELEVANCE

Like other concepts in traditional Chinese medical theory, "body fire", a concept that has already been well-known and widely used in describing the symptoms and the treatment of corresponding diseases, is, however, still under suspicions in the western medicine due to its vague essence and symptoms. Presently, Huang-Lian-Jie-Du-Tang (HLJDT), a typical popular TCM formula in cleansing the "body fire", is studied as a probe by a systems pharmacology method we produced, with purpose to explore the mechanisms of the potion, as well as to interpret the essence of "body fire" disease.

METHODS

The systematic process includes a pharmacokinetics prescreening, pharmacodynamics targets and pathways identification, and candidate-target-pathway network construction.

RESULTS

Through this method, 145 chemicals and 91 proteins are identified as active ingredients and "body fire"-related targets. And we find that the mechanism of HLJDT prescription for cleansing "body fire" lies in three, i.e., anti-OS/NS, anti-inflammation and anti-infection function modules, which are mainly executed through four, i.e., PI3K-AKT, MAPK, VEGF as well as Calcium signaling pathways.

CONCLUSIONS

Accordingly, the essence of "body fire" is a gradual process which is an integration of OS/NS, inflammation and infection. This work, we hope, may not only offer a systemic methodology for exploring and elucidating TCM concepts from a multi-scale perspective, but also provide an efficient way for herbal drug discovery.

Predictive approaches for drug combination discovery in cancer

Drug combinations have been proposed as a promising therapeutic strategy to overcome drug resistance and improve efficacy of monotherapy regimens in cancer. This strategy aims at targeting multiple components of this complex disease. Despite the increasing number of drug combinations in use, many of them were empirically found in the clinic, and the molecular mechanisms underlying these drug combinations are often unclear. These challenges call for rational, systematic approaches for drug combination discovery. Although high-throughput screening of single-agent therapeutics has been successfully implemented, it is not feasible to test all possible drug combinations, even for a reduced subset of anticancer drugs. Hence, in vitro and in vivo screening of a large number of drug combinations are not practical. Therefore, devising computational methods to efficiently explore the space of drug combinations and to discover efficacious combinations has attracted a lot of attention from the scientific community in the past few years. Nevertheless, in the absence of consensus regarding the computational approaches used to predict efficacious drug combinations, a plethora of methods, techniques and hypotheses have been developed to date, while the research field lacks an elaborate categorization of the existing computational methods and the available data sources. In this manuscript, we review and categorize the state-of-the-art computational approaches for drug combination prediction, and elaborate on the limitations of these methods and the existing challenges. We also discuss about the recent pan-cancer drug combination data sets and their importance in revising the available methods or developing more performant approaches.

Predicting combinative drug pairs via multiple classifier system with positive samples only

BACKGROUND AND OBJECTIVE

Due to the synergistic effects of drugs, drug combination is one of the effective approaches for treating complex diseases. However, the identification of drug combinations by dose-response methods is still costly. It is promising to develop supervised learning-based approaches to predict potential drug combinations on a large scale. Nevertheless, these approaches have the inadequate utilization of heterogeneous features, which causes the loss of information useful to classification. Moreover, they have an intrinsic bias, because they assume unknown drug pairs as non-combinations, of which some could be real drug combinations in practice.

METHODS

To address above issues, this work first designs a two-layer multiple classifier system (TLMCS) to effectively integrate heterogeneous features involving anatomical therapeutic chemical codes of drugs, drug-drug interactions, drug-target interactions, gene ontology of drug targets, and side effects. To avoid the bias caused by labelling unknown samples as negative, it then utilizes the one-class support vector machines, (which requires no negative instance and only labels approved drug combinations as positive instances), as the member classifiers in TLMCS. Last, both a 10-fold cross validation (10-CV) and a novel prediction are performed to validate the performance of TLMCS.

RESULTS

The comparison with three state-of-the-art approaches under 10-CV exhibits the superiority of TLMCS, which achieves the area under the receiver operating characteristic curve = 0.824 and the area under the precision-recall curve = 0.372. Moreover, the experiment under the novel prediction demonstrates its ability, where 9 out of the top-20 predicted combinative drug pairs are validated by checking the published literature. Furthermore, for each of the newly-validated drug combinations, this work analyses the combining mode of the member drugs and investigates their relationship in terms of drug targeting pathways.

CONCLUSIONS

The proposed TLMCS provides an effective framework to integrate those heterogeneous features and is trained by only positive samples such that the bias of taking unknown drug pairs as negative samples can be avoided. Furthermore, its results in the novel prediction reveal five types of drug combinations and three types of drug relationships in terms of pathways.

In Silico Target Prediction for Small Molecules

Drugs modulate disease states through their actions on targets in the body. Determining these targets aids the focused development of new treatments, and helps to better characterize those already employed. One means of accomplishing this is through the deployment of in silico methodologies, harnessing computational analytical and predictive power to produce educated hypotheses for experimental verification. Here, we provide an overview of the current state of the art, describe some of the well-established methods in detail, and reflect on how they, and emerging technologies promoting the incorporation of complex and heterogeneous data-sets, can be employed to improve our understanding of (poly)pharmacology.

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.

Discovery of synergistic anti-inflammatory compound combination from herbal formula GuGe FengTong Tablet

Multi-components in herbal formulae exert holistic effects in synergistic or additive manners. However, appropriate strategies and supportive evidences are still lacking to uncover the synergistic or additive combinations. The present investigation aimed at seeking a screening strategy to identify the targeted combinations in GuGe FengTong Tablet (GGFTT), an herbal formula. Two compounds, belonging to different chemical classes, were combined with different concentration ratios and their anti-inflammation effects were investigated. The most significant anti-inflammatory combinations were evaluated by combination index (CI) method (additive effect, CI = 1; synergism, CI < 1; antagonism, CI > 1). The modulating effects of candidate combinations on pro-inflammatory cytokines and MAPKs signaling pathway were also detected. Two combinations, "biochanin A + 6-gingerol" (Bio-6G) and "genistein + 6-gingerol" (Gen-6G), showed synergistic effects (CI < 1), and Bio-6G was selected for further study. Compared with single compound, Bio-6G could synergistically inhibit the production of pro-inflammatory cytokines (TNF-α, IL-1β, and IL-6) and the activation of MAPKs signaling pathway in LPS-stimulated RAW264.7 cells. The combined results showed that Bio-6G was a synergistic anti-inflammatory combination in GGFTT. Our results could provide a useful strategy to screen the synergistic combinations in herbal formulae.

Predicting combinative drug pairs towards realistic screening via integrating heterogeneous features

Background

Drug Combination is one of the effective approaches for treating complex diseases. However, determining combinative drug pairs in clinical trials is still costly. Thus, computational approaches are used to identify potential drug pairs in advance. Existing computational approaches have the following shortcomings: (i) the lack of an effective integration of heterogeneous features leads to a time-consuming training and even results in an over-fitted classifier; and (ii) the narrow consideration of predicting potential drug combinations only among known drugs having known combinations cannot meet the demand of realistic screenings, which pay more attention to potential combinative pairs among newly-coming drugs that have no approved combination with other drugs at all.

Results

In this paper, to tackle the above two problems, we propose a novel drug-driven approach for predicting potential combinative pairs on a large scale. We define four new features based on heterogeneous data and design an efficient fusion scheme to integrate these feature. Moreover importantly, we elaborate appropriate cross-validations towards realistic screening scenarios of drug combinations involving both known drugs and new drugs. In addition, we perform an extra investigation to show how each kind of heterogeneous features is related to combinative drug pairs. The investigation inspires the design of our approach. Experiments on real data demonstrate the effectiveness of our fusion scheme for integrating heterogeneous features and its predicting power in three scenarios of realistic screening. In terms of both AUC and AUPR, the prediction among known drugs achieves 0.954 and 0.821, that between known drugs and new drugs achieves 0.909 and 0.635, and that among new drugs achieves 0.809 and 0.592 respectively.

Conclusions

Our approach provides not only an effective tool to integrate heterogeneous features but also the first tool to predict potential combinative pairs among new drugs.

Rosmarinic Acid Protects against Inflammation and Cardiomyocyte Apoptosis during Myocardial Ischemia/Reperfusion Injury by Activating Peroxisome Proliferator-Activated Receptor Gamma

The cardiac ischemia-reperfusion (I/R) injury greatly influences the therapeutic effect and remains an urgent challenge in clinical therapy. Polypharmacology opens a new therapeutic opportunity to design drugs with a specific target for improving the efficacy. In this study, we first forecasted that Rosmarinic acid (RosA) could be used for the treatment of cardiovascular disease using text mining, chemometric and chemogenomic methods. Consistent with the effect of the positive drug (pioglitazone, PIO), we subsequently validated that RosA pretreatment could restore the decreased cardiac hemodynamic parameters (LVDP, ± dp/dt_min, ± dp/dt_max and CF), decreased the infarct size and the cardiomyocyte apoptosis in a rat model of cardiac I/R injury. Furthermore, RosA pre-treatment inhibited the levels of inflammatory cytokines (IL-6, TNF-α and CRP), up-regulated PPARγ expression and down-regulated NF-κB expression in myocardial tissue isolated from the rat model of I/R-induced myocardial injury. In addition, the effects of RosA were reversed by co-treatment with PPAR-γ inhibitor GW9662 and T0070907, respectively. These data suggest that RosA attenuates cardiac injury through activating PPARγ and down-regulating NF-κB-mediated signaling pathway, which inhibiting inflammation and cardiomyocyte apoptosis in a rat model of cardiac I/R injury.

Multistage analysis method for detection of effective herb prescription from clinical data

Determining effective traditional Chinese medicine (TCM) treatments for specific disease conditions or particular patient groups is a difficult issue that necessitates investigation because of the complicated personalized manifestations in real-world patients and the individualized combination therapies prescribed in clinical settings. In this study, a multistage analysis method that integrates propensity case matching, complex network analysis, and herb set enrichment analysis was proposed to identify effective herb prescriptions for particular diseases (e.g., insomnia). First, propensity case matching was applied to match clinical cases. Then, core network extraction and herb set enrichment were combined to detect core effective herb prescriptions. Effectiveness-based mutual information was used to detect strong herb-symptom relationships. This method was applied on a TCM clinical data set with 955 patients collected from well-designed observational studies. Results revealed that groups of herb prescriptions with higher effectiveness rates (76.9% vs. 42.8% for matched samples; 94.2% vs. 84.9% for all samples) compared with the original prescriptions were found. Particular patient groups with symptom manifestations were also identified to help investigate the indications of the effective herb prescriptions.

A New Strategy for Deleting Animal drugs from Traditional Chinese Medicines based on Modified Yimusake Formula

Traditional Chinese medicine (TCM), such as Uyghur Medicine (UM) has been used in clinical treatment for many years. TCM is featured as multiple targets and complex mechanisms of action, which is normally a combination of medicinal herbs and sometimes even contains certain rare animal medicinal ingredients. A question arises as to whether these animal materials can be removed replaced from TCM applications due to their valuable rare resources or animal ethics. Here, we select a classical UM Yimusake formula, which contains 3 animal drugs and other 8 herbs, and has got wealthy experience and remarkable achievements in treating erectile dysfunction (ED) in China. The active components, drug targets and therapeutic mechanisms have been comprehensively analyzed by systems-pharmacology methods. Additionally, to validate the inhibitory effects of all candidate compounds on their related targets, in vitro experiments, computational analysis and molecular dynamics simulations were performed. The results show that the modified, original and three animal materials display very similar mechanisms for an effective treatment of ED, indicating that it is quite possible to remove these three animal drugs from the original formula while still keep its efficiency. This work provides a new attempt for deleting animal materials from TCM, which should be important for optimization of traditional medicines.

A Methodology for Cancer Therapeutics by Systems Pharmacology-Based Analysis: A Case Study on Breast Cancer-Related Traditional Chinese Medicines

Breast cancer is the most common carcinoma in women. Comprehensive therapy on breast cancer including surgical operation, chemotherapy, radiotherapy, endocrinotherapy, etc. could help, but still has serious side effect and resistance against anticancer drugs. Complementary and alternative medicine (CAM) may avoid these problems, in which traditional Chinese medicine (TCM) has been highlighted. In this section, to analyze the mechanism through which TCM act on breast cancer, we have built a virtual model consisting of the construction of database, oral bioavailability prediction, drug-likeness evaluation, target prediction, network construction. The 20 commonly employed herbs for the treatment of breast cancer were used as a database to carry out research. As a result, 150 ingredient compounds were screened out as active molecules for the herbs, with 33 target proteins predicted. Our analysis indicates that these herbs 1) takes a 'Jun-Chen-Zuo-Shi" as rule of prescription, 2) which function mainly through perturbing three pathways involving the epidermal growth factor receptor, estrogen receptor, and inflammatory pathways, to 3) display the breast cancer-related anti-estrogen, anti-inflammatory, regulation of cell metabolism and proliferation activities. To sum it up, by providing a novel in silico strategy for investigation of the botanical drugs, this work may be of some help for understanding the action mechanisms of herbal medicines and for discovery of new drugs from plants.

The pharmacokinetic screening of multiple components of the Nao Mai Tong formula in rat plasma by liquid chromatography tandem mass spectrometry combined with pattern recognition method and its application to comparative pharmacokinetics

The Nao Mai Tong formula (NMT) is composed of Rhubarb, Ginseng, Ligusticum wallichii and Pueraria in a ratio of 3:3:2:2 (w/w) and is a well-known traditional Chinese prescription that has been clinically employed for treating ischemia cerebrovascular disease. The goal of this study was to investigate the pharmacokinetics of multiple components (chryohol-8-O-β-D-glucoyroide, physcion-8-O-β-D-glucopyranoside, aloe-emodin, rhein, emodin, chrysophanol, ginsenoside Rg₁, ginsenoside Rb₁, ginsenoside Rb_3, ginsenoside Rc, senkyunolide I, ligustilide puerarin, daidzein, 3'-methoxy puerarin) after the oral administration of the NMT formula in rats. A rapid and sensitive UHPLC-Quadrupole-Orbitrap-MS with a sequential positive and negative ionization mode was developed to determine the 15 absorbed ingredients. After extraction from blood, the analytes and internal standards were subjected to ultra-high performance liquid chromatography with Agela Venusil MPC₁₈ (2.1mm×100mm, 3μm, Agela, USA). The mobile phase consisted of methanol and ammonium acetate (3mmolL^-1) under gradient elution conditions. This validated method was successfully applied to a comparative pharmacokinetic study of fifteen components in rat plasma after oral administration of the NMT formula or single herb extracts to normal and stroke-afflicted rats. A principal component analysis (PCA) was utilized to evaluate the differences in the pharmacokinetic behavior (time-course) of the absorbed components of NMT, and the absorbed components were assigned to 3 separate clusters. A comparison of the body dynamics of each group indicated that cluster B (ginsenoside Rg₁, ginsenoside Rb₁, ginsenoside Rb_3, ginsenoside Rc) might be the most important constituents controlling the pharmacological effects of NMT. The comparative pharmacokinetic study showed that the different groups had different pharmacokinetic characteristics. The pharmacokinetics-based UHPLC Quadrupole-Orbitrap-MS using a full-scan mode combined with a pattern recognition approach can provide a reliable and suitable means of screening and identifying potentially bioactive components that contribute to the pharmacological effects of Traditional Chinese Medicine (TCM).

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

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

Computational Analysis of Structure-Based Interactions for Novel H₁-Antihistamines

As a chronic disorder, insomnia affects approximately 10% of the population at some time during their lives, and its treatment is often challenging. Since the antagonists of the H₁ receptor, a protein prevalent in human central nervous system, have been proven as effective therapeutic agents for treating insomnia, the H₁ receptor is quite possibly a promising target for developing potent anti-insomnia drugs. For the purpose of understanding the structural actors affecting the antagonism potency, presently a theoretical research of molecular interactions between 129 molecules and the H₁ receptor is performed through three-dimensional quantitative structure-activity relationship (3D-QSAR) techniques. The ligand-based comparative molecular similarity indices analysis (CoMSIA) model (Q² = 0.525, R²ncv = 0.891, R²pred = 0.807) has good quality for predicting the bioactivities of new chemicals. The cross-validated result suggests that the developed models have excellent internal and external predictability and consistency. The obtained contour maps were appraised for affinity trends for the investigated compounds, which provides significantly useful information in the rational drug design of novel anti-insomnia agents. Molecular docking was also performed to investigate the mode of interaction between the ligand and the active site of the receptor. Furthermore, as a supplementary tool to study the docking conformation of the antagonists in the H₁ receptor binding pocket, molecular dynamics simulation was also applied, providing insights into the changes in the structure. All of the models and the derived information would, we hope, be of help for developing novel potent histamine H₁ receptor antagonists, as well as exploring the H₁-antihistamines interaction mechanism.

Identification of key active constituents of Buchang Naoxintong capsules with therapeutic effects against ischemic stroke by using an integrative pharmacology-based approach

Integrative pharmacology has been used to identify the key active constituents (KACs) of Buchang Naoxintong capsules (BNCs), a traditional Chinese medical preparation.

Using quantitative systems pharmacology for novel drug discovery

INTRODUCTION

Over the past three decades, the predominant paradigm in drug discovery was designing selective ligands for a specific target to avoid unwanted side effects. However, in the last 5 years, the aim has shifted to take into account the biological network in which they interact. Quantitative and Systems Pharmacology (QSP) is a new paradigm that aims to understand how drugs modulate cellular networks in space and time, in order to predict drug targets and their role in human pathophysiology.

AREAS COVERED

This review discusses existing computational and experimental QSP approaches such as polypharmacology techniques combined with systems biology information and considers the use of new tools and ideas in a wider 'systems-level' context in order to design new drugs with improved efficacy and fewer unwanted off-target effects.

EXPERT OPINION

The use of network biology produces valuable information such as new indications for approved drugs, drug-drug interactions, proteins-drug side effects and pathways-gene associations. However, we are still far from the aim of QSP, both because of the huge effort needed to model precisely biological network models and the limited accuracy that we are able to reach with those. Hence, moving from 'one molecule for one target to give one therapeutic effect' to the 'big systems-based picture' seems obvious moving forward although whether our current tools are sufficient for such a step is still under debate.

Alpha-adrenoceptor antagonism by Crassostrea gigas oyster extract inhibits noradrenaline-induced vascular contraction in Wistar rats

OBJECTIVE

Crassostrea gigas oyster extract has been reported to have antioxidant, antihypertensive and lipid-lowering properties that may be useful for treating cardiovascular diseases. This study aimed to evaluate the effect of C. gigas oyster extract on cardiovascular function in tissues from healthy rats.

METHODS

Single-cell microelectrode and isolated thoracic aortic organ bath studies were performed on tissues from 8-week-old healthy Wistar rats, using varying concentrations of C. gigas oyster extract. To elucidate a mechanism of action for the oyster's vasoactive properties, concentration response curves were carried out in the presence of a calcium channel inhibitior (verapamil), a nitric oxide synthase inhibitor (N(G)-nitro-L-arginine methyl ester), a potassium channel inhibitor (4-aminopyridine), in addition to the α-adrenoceptor inhibitor prazosin.

RESULTS

Oyster solution at 7 500 mg/mL inhibited noradrenaline-induced contraction in isolated aortic rings. Cardiac electrophysiology results showed that neither concentration of oyster solution was able to significantly reduce action potential duration at all phases of repolarisation in left ventricular papillary muscles from healthy animals.

CONCLUSION

When administered to healthy vascular tissue, C. gigas oyster extract inhibits contraction induced by noradrenaline. This effect is likely to be mediated through α-adrenoceptor inhibition, and to a lesser extent, calcium modulating activity.

Deciphering the underlying mechanisms of Diesun Miaofang in traumatic injury from a systems pharmacology perspective

Diesun Miaofang (DSMF) is a traditional herbal formula, which has been reported to activate blood, remove stasis, promote qi circulation and relieve pain. DSMF holds a great promise for the treatment of traumatic injury in an integrative and holistic manner. However, its underlying mechanisms remain to be elucidated. In the present study, a systems pharmacology model, which integrated cluster ligands, human intestinal absorption and aqueous solution prediction, chemical space mapping, molecular docking and network pharmacology techniques were used. The compounds from DSMF were diverse in the clusters and chemical space. The majority of the compounds exhibited drug-like properties. A total of 59 compounds were identified to interact with 16 potential targets. In the herb-compound-target network, the majority of compounds acted on only one target; however, a small number of compounds acted on a large number of targets, up to a maximum of 12. The comparison of key topological properties in compound-target networks associated with the above efficacy intuitively demonstrated that potential active compounds possessed diverse functions. These results successfully explained the polypharmcological mechanism underlying the efficiency of DSMF for the treatment of traumatic injury as well as provided insight into potential novel therapeutic strategies for traumatic injury from herbal medicine.

Pathway as a pharmacological target for herbal medicines: an investigation from reduning injection

As a rich natural resource for drug discovery, Traditional Chinese Medicine (TCM) plays an important role in complementary and alternative medical systems. TCM shows a daunting complexity of compounds featuring multi-components and multi-targets to cure diseases, which thus always makes it extremely difficult to systematically explain the molecular mechanisms adequately using routine methods. In the present work, to reveal the systematic mechanism of herbal formulae, we developed a pathway-based strategy by combining the pathways integrating, target selection, reverse drug targeting and network analysis together, and then exemplified it by Reduning injection (RDN), a clinically widely used herbal medicine injection, in combating inflammation. The anti-inflammatory effects exerted by the major ingredients of RDN at signaling pathways level were systematically investigated. More importantly, our predicted results were also experimentally validated. Our strategy provides a deep understanding of the pharmacological functions of herbal formulae from molecular to systematic level, which may lead to more successful applications of systems pharmacology for drug discovery and development.

Large-scale exploration and analysis of drug combinations

MOTIVATION

Drug combinations are a promising strategy for combating complex diseases by improving the efficacy and reducing corresponding side effects. Currently, a widely studied problem in pharmacology is to predict effective drug combinations, either through empirically screening in clinic or pure experimental trials. However, the large-scale prediction of drug combination by a systems method is rarely considered.

RESULTS

We report a systems pharmacology framework to predict drug combinations (PreDCs) on a computational model, termed probability ensemble approach (PEA), for analysis of both the efficacy and adverse effects of drug combinations. First, a Bayesian network integrating with a similarity algorithm is developed to model the combinations from drug molecular and pharmacological phenotypes, and the predictions are then assessed with both clinical efficacy and adverse effects. It is illustrated that PEA can predict the combination efficacy of drugs spanning different therapeutic classes with high specificity and sensitivity (AUC = 0.90), which was further validated by independent data or new experimental assays. PEA also evaluates the adverse effects (AUC = 0.95) quantitatively and detects the therapeutic indications for drug combinations. Finally, the PreDC database includes 1571 known and 3269 predicted optimal combinations as well as their potential side effects and therapeutic indications.

AVAILABILITY AND IMPLEMENTATION

The PreDC database is available at http://sm.nwsuaf.edu.cn/lsp/predc.php.

Profiling the interaction mechanism of indole-based derivatives targeting the HIV-1 gp120 receptor

A glycoprotein exposed on a viral surface, human immunodeficiency virus type 1 (HIV-1) gp120 is essential for virus entry into cells as it plays a vital role in seeking out specific cell surface receptors for entry.

In silico research to assist the investigation of carboxamide derivatives as potent TRPV1 antagonists

The transient receptor potential vanilloid type 1 (TRPV1), a non-selective cation channel, is known for its essential role in the pathogenesis of various pain conditions such as nerve damage induced hyperalgesia, diabetic neuropathy and cancer pain.

A novel systems pharmacology model for herbal medicine injection: a case using Reduning injection

BACKGROUND

Compared with the traditional oral administration form, injection administration is basically superior in terms of both biological availability and therapeutic effects. However, few researches have focused on the traditional Chinese medicinal injection due to the complicated constituents and the intricate mechanism of action.

METHODS

In the present work, a novel systems pharmacology model, integrating ADME (absorption, distribution, metabolism, and excretion) filtering such as half-life evaluation, network targeting, pathway and systems analyses, is specifically developed for the identification of active compounds and the study of the mechanism of action of TCM injection, which is exemplified by Reduning injection confronting the influenza.

RESULTS

The ADME filter successfully identifies 35 bioactive compounds (31 molecules and 4 metabolites) from the Reduning injection. The systems analysis and experimental validation further reveal a new way of confronting influenza disease of this injection: 1) stimulating the immunomodulatory agents for immune response activation, and 2) regulating the inflammatory agents for anti-inflammation.

CONCLUSIONS

The novel systems pharmacology method used in this study has the potential to advance the understanding of the molecular mechanisms of action of multicomponent herbal injections, and provide clues to discovering more effective drugs against complex diseases.

TCMSP: a database of systems pharmacology for drug discovery from herbal medicines

Background

Modern medicine often clashes with traditional medicine such as Chinese herbal medicine because of the little understanding of the underlying mechanisms of action of the herbs. In an effort to promote integration of both sides and to accelerate the drug discovery from herbal medicines, an efficient systems pharmacology platform that represents ideal information convergence of pharmacochemistry, ADME properties, drug-likeness, drug targets, associated diseases and interaction networks, are urgently needed.

Description

The traditional Chinese medicine systems pharmacology database and analysis platform (TCMSP) was built based on the framework of systems pharmacology for herbal medicines. It consists of all the 499 Chinese herbs registered in the Chinese pharmacopoeia with 29,384 ingredients, 3,311 targets and 837 associated diseases. Twelve important ADME-related properties like human oral bioavailability, half-life, drug-likeness, Caco-2 permeability, blood-brain barrier and Lipinski’s rule of five are provided for drug screening and evaluation. TCMSP also provides drug targets and diseases of each active compound, which can automatically establish the compound-target and target-disease networks that let users view and analyze the drug action mechanisms. It is designed to fuel the development of herbal medicines and to promote integration of modern medicine and traditional medicine for drug discovery and development.

Conclusions

The particular strengths of TCMSP are the composition of the large number of herbal entries, and the ability to identify drug-target networks and drug-disease networks, which will help revealing the mechanisms of action of Chinese herbs, uncovering the nature of TCM theory and developing new herb-oriented drugs. TCMSP is freely available at http://sm.nwsuaf.edu.cn/lsp/tcmsp.php.