Network target for screening synergistic drug combinations with application to traditional Chinese medicine

Integrated bioinformatics and experimental verification to dissect the mechanisms and bioactive ingredients of Radix Rehmanniae in treating multiple sclerosis

Multiple sclerosis (MS), as a primary cause of nontraumatic disability in young adults, has no effective treatment yet. Radix Rehmanniae (RR), a typical Traditional Chinese Medicine (TCM), is commonly used in MS patients as a most frequent herbal item in TCM formulas. Our recent study demonstrated that RR alleviated neurological deficits in an experimental MS model. However, direct evidence regarding the holistic mechanisms and bioactive components of RR for MS remains unclear. In this study, we employed an integrative strategy combining bioinformatics and experimental validation to profile the holistic mechanisms of RR, identify its bioactive components, and investigate their potential targets in MS. First, a network pharmacology approach was used to construct a "compound-target-pathway" network, indicating the action of RR on MS in a multicomponent-multitarget mode, and predicting Echinacoside and Acteoside as the primary bioactive ingredients. Bioinformatics analyses of transcriptomics and single-cell RNA sequencing based on GSE datasets indicated that oxidative stress and inflammatory/immune regulation in microglia might serve as crucial mechanisms of Echinacoside and Acteoside in MS pathology. Then, in vitro assays validated that Echinacoside and Acteoside possessed anti-inflammatory and antioxidant properties by scavenging ONOO- and H2O2 directly, and suppressing microglia-derived ONOO- production through inhibition of NF-κB-mediated iNOS and NADPH oxidase. In addition, molecular docking showed strong affinities between Acteoside and inflammation-related targets TGF-β and SMAD2. These findings provide the scientific evidence for clinical application of RR and bring novel insights into MS drug development.

Vitexin alleviates lipid metabolism disorders and hepatic injury in obese mice through the PI3K/AKT/mTOR/SREBP-1c pathway

Obesity is recognized as a metabolic disorder, and its treatment and management pose ongoing challenges worldwide. Hawthorn, a traditional Chinese herb used to alleviate digestive issues and reduce blood lipid levels, has unclear mechanisms of action regarding its active components in the treatment of obesity. This study investigated the anti-obesity effects of vitexin, a major flavonoid compound found in hawthorn, in high-fat diet (HFD)-induced C57BL/6 mice. The results demonstrated that vitexin significantly reduced body weight, liver weight, blood lipid levels, and inflammatory markers in obese mice, while also inhibiting hepatic lipid accumulation. Mechanistic studies revealed that vitexin likely suppresses adipogenesis by modulating the PI3K-AKT signaling pathway, as evidenced by reduced expression of PI3K, phosphorylated AKT, phosphorylated mTOR, and SREBP-1c in the livers of vitexin-treated obese mice. Additionally, vitexin inhibited NFκB expression by regulating IκBα phosphorylation, thereby alleviating obesity-induced liver injury. These findings suggest that vitexin may be the primary active component in hawthorn responsible for reducing blood lipid levels, highlighting its potential in the treatment of obesity and its associated metabolic disorders.

Metabolomics and network pharmacology approach to identify potential bioactive compounds from Trichoderma sp. against oral squamous cell carcinoma

This study aimed to profile metabolites from five Trichoderma strains and assess their cytotoxic and pharmacological activities, particularly targeting oral squamous cell carcinoma (OSCC). UHPLC-TOF-MS analysis revealed the presence of 25 compounds, including heptelidic acid, viridiol isomers, and sorbicillinol from the different Trichoderma extracts. Pharmacokinetic analysis showed moderate permeability and low interaction with P-glycoprotein, suggesting good drug absorption with minimal interference in cellular uptake. ADME-Tox analysis indicated limited inhibition of cytochrome P450 enzymes, low renal clearance, which are favorable for maintaining therapeutic levels. Toxicity predictions revealed some compounds with potential mutagenicity, but low hepatotoxicity and skin sensitization risks. Network pharmacology identified MAPK1 as a key target for oral cancer, and molecular docking and induced fit docking studies demonstrated strong binding affinities of Trichoderma metabolites, including stachyose and harzianol, to MAPK1. In addition, molecular dynamics (MD) simulations confirmed stable interactions. In vitro studies on NIH3T3 and YD-10B cells showed significant cytotoxicity, particularly with extracts CNU-05-001 (IC50:10.15 µg/mL) and CNU-02-009 (10.00 µg/mL) against YD-10B cells. These findings underscore the potential of Trichoderma metabolites in drug discovery, particularly for cancer therapies.

Elucidating the role of artificial intelligence in drug development from the perspective of drug-target interactions

Drug development remains a critical issue in the field of biomedicine. With the rapid advancement of information technologies such as artificial intelligence (AI) and the advent of the big data era, AI-assisted drug development has become a new trend, particularly in predicting drug-target associations. To address the challenge of drug-target prediction, AI-driven models have emerged as powerful tools, offering innovative solutions by effectively extracting features from complex biological data, accurately modeling molecular interactions, and precisely predicting potential drug-target outcomes. Traditional machine learning (ML), network-based, and advanced deep learning architectures such as convolutional neural networks (CNNs), graph convolutional networks (GCNs), and transformers play a pivotal role. This review systematically compiles and evaluates AI algorithms for drug- and drug combination-target predictions, highlighting their theoretical frameworks, strengths, and limitations. CNNs effectively identify spatial patterns and molecular features critical for drug-target interactions. GCNs provide deep insights into molecular interactions via relational data, whereas transformers increase prediction accuracy by capturing complex dependencies within biological sequences. Network-based models offer a systematic perspective by integrating diverse data sources, and traditional ML efficiently handles large datasets to improve overall predictive accuracy. Collectively, these AI-driven methods are transforming drug-target predictions and advancing the development of personalized therapy. This review summarizes the application of AI in drug development, particularly in drug-target prediction, and offers recommendations on models and algorithms for researchers engaged in biomedical research. It also provides typical cases to better illustrate how AI can further accelerate development in the fields of biomedicine and drug discovery.

Leveraging Network Target Theory for Efficient Prediction of Drug-Disease Interactions: A Transfer Learning Approach

Efficient virtual screening methods can expedite drug discovery and facilitate the development of innovative therapeutics. This study presents a novel transfer learning model based on network target theory, integrating deep learning techniques with diverse biological molecular networks to predict drug-disease interactions. By incorporating network techniques that leverage vast existing knowledge, the approach enables the extraction of more precise and informative drug features, resulting in the identification of 88,161 drug-disease interactions involving 7,940 drugs and 2,986 diseases. Furthermore, this model effectively addresses the challenge of balancing large-scale positive and negative samples, leading to improved performance across various evaluation metrics such as an Area under curve (AUC) of 0.9298 and an F1 score of 0.6316. Moreover, the algorithm accurately predicts drug combinations and achieves an F1 score of 0.7746 after fine-tuning. Additionally, it identifies two previously unexplored synergistic drug combinations for distinct cancer types in disease-specific biological network environments. These findings are further validated through in vitro cytotoxicity assays, demonstrating the potential of the model to enhance drug development and identify effective treatment regimens for specific diseases.

Phytochemical synergies in BK002: advanced molecular docking insights for targeted prostate cancer therapy

Achyranthes japonica (Miq.) Nakai (AJN) and Melandrium firmum (Siebold and Zucc.) Rohrb. (MFR) are medicinal plants recognized for their bioactive phytochemicals, including ecdysteroids, anthraquinones, and flavonoids. This study investigates the anticancer properties of key constituents of these plants, focusing on the BK002 formulation, a novel combination of AJN and MFR. Specifically, the research employs advanced molecular docking and in silico analyses to assess the interactions of bioactive compounds ecdysterone, inokosterone, and 20-hydroxyecdysone (20-HE) with key prostate cancer-related network proteins, including 5α-reductase, CYP17, DNMT1, Dicer, PD-1, and PD-L1. Molecular docking techniques were applied to evaluate the binding affinities contributions of the bioactive compounds in BK002 against prostate cancer-hub network targets. The primary focus was on enzymes like 5α-reductase and CYP17, which are central to androgen biosynthesis, as well as on cancer-related proteins such as DNA methyltransferase 1 (DNMT1), Dicer, programmed death-1 (PD-1), and programmed death ligand-1 (PD-L1). Based on data from prostate cancer patients, key target networks were identified, followed by in silico analysis of the primary bioactive components of BK002.In silico assessments were conducted to evaluate the safety profiles of these compounds, providing insights into their therapeutic potential. The docking studies revealed that ecdysterone, inokosterone, and 20-hydroxyecdysonec demonstrated strong binding affinities to the critical prostate cancer-related enzymes 5α-reductase and CYP17, contributing to a potential reduction in androgenic activity. These compounds also exhibited significant inhibitory interactions with DNMT1, Dicer, PD-1, and PD-L1, suggesting a capacity to interfere with key oncogenic and immune evasion pathways. Ecdysterone, inokosterone, and 20-hydroxyecdysone have demonstrated the ability to target key oncogenic pathways, and their favorable binding affinity profiles further underscore their potential as novel therapeutic agents for prostate cancer. These findings provide a strong rationale for further preclinical and clinical investigations, supporting the integration of BK002 into therapeutic regimens aimed at modulating tumor progression and immune responses.

Network Pharmacology Analysis and Biological Validation Systemically Identified the Active Ingredients and Molecular Targets of Kudzu Root on Osteoporosis

As a traditional medicinal food, Kudzu root (KR) has been proven to be an effective medicine for treating osteoporosis (OP). However, its precise targets and underlying integrated pharmacological mechanisms on OP have not yet been systematically investigated. The aim of the present study was to systemically explore the active ingredients, molecular targets, and ingredient-target network of KR against OP by the methods of network pharmacology followed by biological validation in a glucocorticoid-induced bone loss model of zebrafish. Our results identified a total of 15 active compounds with good pharmacokinetic properties in KR and 119 targets related to OP from correspondent databases, forming an ingredient-target network. Additionally, the protein-protein interaction (PPI) network further identified 39 core targets. Enrichment analyses with functional annotation revealed that the TNF signaling pathway and osteoclast differentiation process were significantly enriched by multi-targets including AKT1, P65, MAPK14, JUN, TNF-α, MMP9, IL6, and IL1B, etc., and served as the critical targets for molecular docking, molecular dynamics simulation, and in vivo experiment validation. These critical targets performed effectively in molecular docking and molecular dynamics, with AKT1, MMP9, and TNF-α exhibiting more prominent binding energy with Coumestrol, Genistein, and Genistein 7-glucoside, respectively. Further experimental validation in a zebrafish model indicated that KR could regulate the expressions of critical targets (AKT1, P65, MAPK14, JUN, TNF-α, and MMP9). This study provides a systemic perspective of the relationships between the active ingredients of KR and their multi-targets in OP, thereby constructing a pharmacological network to clarify the mechanisms by which KR ameliorates OP.

Exploring the potential mechanism of Polygonatum sibiricum for Alzheimer's disease based on network pharmacology and molecular docking: An observational study

Alzheimer's disease (AD) is a neurodegenerative disease, and there have been no systematic studies of Polygonatum against Alzheimer's disease. Therefore, our study will elucidate the mechanism of Polygonatum against AD based on network pharmacology and molecular docking. The active ingredients and corresponding targets of Polygonatum were identified using the traditional Chinese medicine systematic pharmacology database and analysis platform. Disease targets of AD were retrieved from the therapeutic target database, Online Mendelian Inheritance in Man, GeneCards, and Disgenet databases. Using the STRING database, we constructed protein interaction networks and performed gene ontology functional enrichment analysis as well as Kyoto encyclopedia of genes and genomes pathway enrichment analysis on common targets. We then drew drug-component-target-pathway-disease network maps using Cytoscape 3.10.1 software and validated the molecular docking using AutoDock4. A total of 10 active ingredients and 108 common targets were screened from Polygonatum, 29 genes (including AKT1 and STAT3) were identified as core genes. According to gene ontology analysis, the core targets were found to be mainly involved in signal transduction, positive regulation of gene expression, negative regulation of the apoptotic process, and so on. The Kyoto encyclopedia of genes and genomes analysis revealed that the signaling pathways comprised pathways in cancer, pathways of neurodegeneration - multiple diseases, and PI3K-Akt signaling pathway. The molecular docking results indicated that 10 of active ingredients from Polygonatum exhibited strong binding affinity with the 6 core targets that were screened before. The activity of Polygonatum against AD could be attributed to the regulation of multiple biological effects via multi-pathways (pathways in cancer, pathways of neurodegeneration - multiple diseases, and PI3K-Akt signaling pathway). The binding activities were estimated as good level by molecular docking. These discoveries disclosed the multi-component, multi-target, and multi-pathway characteristics of Polygonatum against AD, providing a new strategy for such medical problem.

The Role of Flavonoids from Aurantii Fructus Immaturus in the Alleviation of Allergic Asthma: Theoretical and Practical Insights

Flavonoids derived from plants in the citrus family can have an alleviating effect on allergic asthma. The aim of this study was to provide insights into the mechanisms by which these compounds exert their effects on allergic asthma by combining theoretical and practical approaches. Aurantii Fructus Immaturus flavonoids (AFIFs) were obtained by solvent extraction and were determined by high performance liquid chromatography (HPLC). In vivo and in vitro experiments combined with network pharmacology, Mendelian randomization (MR) analysis and the AutoDock method were applied to study the mechanism of their effects. The main AFIFs were found to be hesperidin (13.21 mg/g), neohesperidin (287.26 mg/g), naringin (322.56 mg/g), and narirutin (19.35 mg/g). Based on the network pharmacology and MR analysis results, five targets Caspase 3 (CASP3), CyclinD1 (CCND1), Intercellular adhesion molecule (ICAM), erb-b2 receptor tyrosine kinase 2 (ERBB2), and rubisco accumulation factor 1 (RAF1) were selected, and the interactions between the AFIFs and the targets were studied using AutoDock Vina. The results indicated that glycosidic bonds play an important role in the interactions between AFIFs and both ERBB2 and RAF1.

Therapeutic Targets and Natural Product Screening for Cognitive Impairments Associated with Ferroptosis in Wilson's Disease

Wilson's disease (WD) is a hereditary condition marked by abnormalities in copper metabolism, which precipitate a spectrum of neurological symptoms and cognitive impairments. Emerging research has highlighted ferroptosis (FPT) as a distinct type of programmed cell death, potentially linked to various cognitive dysfunctions. Nevertheless, the connection between FPT and cognitive impairment in Wilson's disease (WDCI) remains largely enigmatic. In our study, we utilized a multifaceted approach, combining reverse network pharmacology, data mining, and molecular docking techniques to explore the potential for treating WDCI via FPT-related pathways. This thorough analysis revealed a series of proteins, including P38[Formula: see text], GSK3[Formula: see text], P53, GPX4, and PTGS2, as pivotal targets for WDCI treatment. Notably, Diosgenin (DG) has been identified as a prospective core component in this therapeutic framework. In the WD copper-loaded rat model, evaluations using the Morris water maze (MWM), Y maze, hematoxylin and eosin staining, transmission electron microscopy (TEM), and immunofluorescence (IF) detection showed that DG significantly enhanced cognitive function recovery, reduced structural damage to hippocampal neurons, and protected mitochondrial integrity. In addition, Western blot (WB) and quantitative reverse transcription PCR (qRT-PCR) analysis showed that DG significantly upregulated the expression levels of proteins and mRNA such as P38[Formula: see text], GSK3[Formula: see text], P53, GPX4, and PTGS2 in animal and cell models. Furthermore, DG effectively reversed the dysregulated expression of oxidative stress markers, including [Formula: see text], malondialdehyde (MDA), superoxide dismutase (SOD), and reactive oxygen species (ROS). This study elucidates the neuroprotective effect of DG on hippocampal neurons by activating the P38[Formula: see text]-mediated FPT pathway, highlighting its efficacy as a potent monomer in traditional Chinese medicine and illuminating its potential role in the clinical treatment of WDCI.

Network-based identification and mechanism exploration of active ingredients against Alzheimer's disease via targeting endoplasmic reticulum stress from traditional chinese medicine

Alzheimer's disease is a neurodegenerative disease that leads to dementia and poses a serious threat to the health of the elderly. Traditional Chinese medicine (TCM) presents as a promising novel therapeutic therapy for preventing and treating dementia. Studies have shown that natural products derived from kidney-tonifying herbs can effectively inhibit AD. Furthermore, endoplasmic reticulum (ER) stress is a critical factor in the pathology of AD. Regulation of ER stress is a crucial approach to prevent and treat AD. Thus, in this study, we first collected kidney-tonifying herbs, integrated chemical ingredients from multiple TCM databases, and constructed a comprehensive drug-target network. Subsequently, we employed the endophenotype network (network proximity) method to identify potential active ingredients in kidney-tonifying herbs that prevented AD via regulating ER stress. By combining the predicted outcomes, we discovered that 32 natural products could ameliorate AD pathology via regulating ER stress. After a comprehensive evaluation of the multi-network model and systematic pharmacological analyses, we further selected several promising compounds for in vitro testing in the APP-SH-SY5Y cell model. Experimental results showed that echinacoside and danthron were able to effectively reduce ER stress-mediated neuronal apoptosis by inhibiting the expression levels of BIP, p-PERK, ATF6, and CHOP in APP-SH-SY5Y cells. Overall, this study utilized the endophenotype network to preliminarily decipher the effective material basis and potential molecular mechanism of kidney-tonifying Chinese medicine for prevention and treatment of AD.

Exploration of compatibility rules and discovery of active ingredients in TCM formulas by network pharmacology

Network pharmacology is an interdisciplinary field that utilizes computer science, technology, and biological networks to investigate the intricate interplay among compounds/ingredients, targets, and diseases. Within the realm of traditional Chinese medicine (TCM), network pharmacology serves as a scientific approach to elucidate the compatibility relationships and underlying mechanisms of action in TCM formulas. It facilitates the identification of potential active ingredients within these formulas, providing a comprehensive understanding of their holistic and systematic nature, which aligns with the holistic principles inherent in TCM theory. TCM formulas exhibit complexity due to their multi-component characteristic, involving diverse targets and pathways. Consequently, investigating their material basis and mechanisms becomes challenging. Network pharmacology has emerged as a valuable approach in TCM formula research, leveraging its holistic and systematic advantages. The manuscript aims to provide an overview of the application of network pharmacology in studying TCM formula compatibility rules and explore future research directions. Specifically, we focus on how network pharmacology aids in interpreting TCM pharmacological theories and understanding formula compositions. Additionally, we elucidate the process of utilizing network pharmacology to identify active ingredients within TCM formulas. These findings not only offer novel research models and perspectives for integrating network pharmacology with TCM theory but also present new methodologies for investigating TCM formula compatibility. All in all, network pharmacology has become an indispensable and crucial tool in advancing TCM formula research.

Bioinformatics analysis based on extracted ingredients combined with network pharmacology, molecular docking and molecular dynamics simulation to explore the mechanism of Jinbei oral liquid in the therapy of idiopathic pulmonary fibrosis

Objective

Jinbei oral liquid (JBOL), which is derived from a traditional hospital preparation, is frequently utilized to treat idiopathic pulmonary fibrosis (IPF) and has shown efficacy in clinical therapy. However, there are now several obstacles facing the mechanism inquiry, including target proteins, active components, and the binding affinity between crucial compounds and target proteins. To gain additional insight into the mechanisms underlying JBOL in anti-IPF, this study used bioinformation technologies, including network pharmacology, molecular docking, and molecular dynamic simulation, with a substantial amount of data based on realistic constituents.

Methods

Using network pharmacology, we loaded 118 realistic compounds into the SwissTargetPrediction and SwissADME databases and screened the active compounds and target proteins. IPF-related targets were collected from the OMIM, DisGeNET, and GeneCards databases, and the network of IPF-active constituents was built with Cytoscape 3.10.1. The GO and KEGG pathway enrichment analyses were carried out using Metascape, and the protein-protein interaction (PPI) network was constructed to screen the key targets with the STRING database. Finally, the reciprocal affinity between the active molecules and the crucial targets was assessed through the use of molecular docking and molecular dynamics simulation.

Results

A total of 122 targets and 34 tested active compounds were summarized in this investigation. Among these, kaempferol, apigenin, baicalein were present in high degree. PPI networks topological analysis identified eight key target proteins. AGE-RAGE, EGFR, and PI3K-Akt signaling pathways were found to be regulated during the phases of cell senescence, inflammatory response, autophagy, and immunological response in anti-IPF of JBOL. It was verified by molecular docking and molecular dynamics simulation that the combining way and binding energy between active ingredients and selected targets.

Conclusions

This work forecasts the prospective core ingredients, targets, and signal pathways of JBOL in anti-IPF, which has confirmed the multiple targets and pathways of JBOL in anti-IPF and provided the first comprehensive assessment with bioinformatic approaches. With empirical backing and an innovative approach to the molecular mechanism, JBOL is being considered as a potential new medication.

AI empowering traditional Chinese medicine?

For centuries, Traditional Chinese Medicine (TCM) has been a prominent treatment method in China, incorporating acupuncture, herbal remedies, massage, and dietary therapy to promote holistic health and healing. TCM has played a major role in drug discovery, with over 60% of small-molecule drugs approved by the FDA from 1981 to 2019 being derived from natural products. However, TCM modernization faces challenges such as data standardization and the complexity of TCM formulations. The establishment of comprehensive TCM databases has significantly improved the efficiency and accuracy of TCM research, enabling easier access to information on TCM ingredients and encouraging interdisciplinary collaborations. These databases have revolutionized TCM research, facilitating advancements in TCM modernization and patient care. In addition, advancements in AI algorithms and database data quality have accelerated progress in AI for TCM. The application of AI in TCM encompasses a wide range of areas, including herbal screening and new drug discovery, diagnostic and treatment principles, pharmacological mechanisms, network pharmacology, and the incorporation of innovative AI technologies. AI also has the potential to enable personalized medicine by identifying patterns and correlations in patient data, leading to more accurate diagnoses and tailored treatments. The potential benefits of AI for TCM are vast and diverse, promising continued progress and innovation in the field.

Lipophilic derivatives of EGCG as potent α-amylase and α-glucosidase inhibitors ameliorating oxidative stress and inflammation

Uncontrolled hyperglycemia leads to increased oxidative stress, chronic inflammation, and insulin resistance, rendering diabetes management harder to accomplish. To tackle these myriads of challenges, researchers strive to explore innovative multifaceted treatment strategies, including inhibiting carbohydrate hydrolases. Herein, we report alkyl-ether EGCG derivatives as potent α-amylase and α-glucosidase inhibitors that could simultaneously ameliorate oxidative stress and inflammation. 4″-C18 EGCG, the most promising compound, showed multifold improvement in glycaemic management compared to acarbose, with 230-fold greater inhibition (competitive) of α-glucosidase (IC50 0.81 µM) and 3-fold better inhibition of α-amylase (IC50 3.74 µM). All derivatives showed stronger antioxidant activity (IC50 6.16-15.76 µM) than vitamin C, while acarbose showed none. 4″-C18 EGCG also downregulated pro-inflammatory cytokines and showed no significant cytotoxicity up to 50 µM in primary human peripheral blood mononuclear cells (PBMC), non-cancerous cell line, 3T3-L1 and HEK 293. The in silico binding affinity analysis of 4″-C18 EGCG with α-amylase and α-glucosidase was found to exhibit a good extent of interaction as compared to acarbose. In comparison to EGCG, 4″-Cn EGCG derivatives were found to remain stable in the physiological conditions even after 24 h. Together, the reported molecules demonstrated multifaceted antidiabetic potential inhibiting carbohydrate hydrolases, reducing oxidative stress, and inflammation, which are known to aggravate diabetes.

Shensong yangxin, a multi-functional traditional Chinese medicine for arrhythmia: A review of components, pharmacological mechanisms, and clinical applications

As a common cardiovascular disease (CVD), Arrhythmia refers to any abnormality in the origin, frequency, rhythm, conduction velocity, timing, pathway, sequence, or other aspect of cardiac impulses, and it is one of the common cardiovascular diseases in clinical practice. At present, various ion channel blockers are used for treatment of arrhythmia that include Na+ ion channel blockers, K+ ion channel blockers and Ca2+ ion channel blockers. While these drugs offer benefits, they have led to a gradual increase in drug-related adverse reactions across various systems. As a result, the quest for safe and effective antiarrhythmic drugs is pressing. Recent years have seen some advancements in the treatment of ventricular arrhythmias using traditional Chinese medicine(TCM). The theory of Luobing in TCM has proposed a new drug intervention strategy of "fast and slow treatment, integrated regulation" leading to a shift in mindset from "antiarrhythmic" to "rhythm-regulating". Guided by this theory, the development of Shen Song Yang Xin Capsules (SSYX) has involved various Chinese medicinal ingredients that comprehensively regulate the myocardial electrophysiological mechanism, exerting antiarrhythmic effects on multiple ion channels and non-ion channels. Similarly, in clinical studies, evidence-based research has confirmed that SSYX combined with conventional antiarrhythmic drugs can more effectively reduce the occurrence of arrhythmias. Therefore, this article provides a comprehensive review of the composition and mechanisms of action, pharmacological components, network pharmacology analysis, and clinical applications of SSYX guided by the theory of Luobing, aiming to offer valuable insights for improved clinical management of arrhythmias and related research.

Tongxinluo capsule as a multi-functional traditional Chinese medicine in treating cardiovascular disease: A review of components, pharmacological mechanisms, and clinical applications

Cardiovascular diseases (CVDs) are one of the most significant diseases that pose a threat to human health. The innovative traditional Chinese medicine Tongxinluo Capsule, developed under the guidance of the theory of traditional Chinese medicine, has good clinical efficacy in various cardiovascular diseases, this medicine has effects such as blood protection, vascular protection, myocardial protection, stabilizing vulnerable plaques, and vasodilation. However, CVDs are a multifactorial disease, and their underlying mechanisms are not fully understood. Therefore, exploring the mechanism of action and clinical application of Tongxinluo Capsule in the treatment of various cardiovascular diseases is beneficial for exerting its therapeutic effect from multiple components, targets, and pathways. At the same time, it provides broader treatment ideas for other difficult to treat diseases in the cardiovascular event chain, and has significant theoretical and clinical significance for improving the treatment of cardiovascular diseases with traditional Chinese medicine.

Coumestrol facilitates apoptosis in colorectal cancer cells by interacting with ZIP8 protein via the ferroptosis pathway

Objective: So far, there have been no reports of coumestrol inhibiting colorectal cancer (CRC) through the ferroptosis pathway. This study is to investigate the mechanism of the traditional Chinese medicine monomer coumestrol in the treatment of CRC. Methods: Data on CRC transcriptome sequencing was obtained from the GEO database and TCGA database. Bioinformatics analyses were conducted to screen for CRC prognostic-related key genes and their potential binding monomers in traditional Chinese medicine. The inhibitory effect of coumestrol on CRC cell lines (COLO 205 & HCT 116) was determined using the CCK-8 assay, and cell apoptosis was assessed by flow cytometry. The content of ferrous ions was measured using the Ferrous Ion Content Assay Kit. The expression of ferroptosis pathway-related genes SLC39A8, NCOA4, VDAC2, and NOX2 before and after small interference RNA (siRNA) was examined through real-time PCR and Western blotting. Results: SLC39A8 was found to be associated with CRC clinical progression staging, and its encoded protein ZIP8 may bind to coumestrol. KEGG enrichment analysis suggested that ZIP8 plays a role in iron transmembrane transport and may affect the expression of ferroptosis pathway-related genes NCOA4, VDAC2, and NOX2. Coumestrol was found to induce apoptosis in CRC cell lines by upregulating the expression of ferroptosis pathway-related genes SLC39A8, NCOA4, VDAC2, and NOX2. However, coumestrol was unable to upregulate the expression of ferroptosis pathway-related genes in CRC cell lines after SLC39A8 interference. Conclusion: Coumestrol facilitates apoptosis in CRC cells by interacting with ZIP8 protein via the ferroptosis pathway.

Quercetin and Kaempferol as Multi-Targeting Antidiabetic Agents against Mouse Model of Chemically Induced Type 2 Diabetes

Diabetes, a multifactorial metabolic disorder, demands the discovery of multi-targeting drugs with minimal side effects. This study investigated the multi-targeting antidiabetic potential of quercetin and kaempferol. The druggability and binding affinities of both compounds towards multiple antidiabetic targets were explored using pharmacokinetic and docking software (AutoDock Vina 1.1.2). Our findings showed that quercetin and kaempferol obey Lipinski's rule of five and exhibit desirable ADMET (absorption, distribution, metabolism excretion, and toxicity) profiles. Both compounds showed higher binding affinities towards C-reactive protein (CRP), interleukin-1 (IL-1), dipeptidyl peptidase-4 (DPP-IV), peroxisome proliferator-activated receptor gamma (PPARG), protein tyrosine phosphatase (PTP), and sodium-glucose co-transporter-1 (SGLT-1) compared to metformin (the positive control). Both quercetin and kaempferol inhibited α-amylase activity (in vitro) up to 20.30 ± 0.49 and 37.43 ± 0.42%, respectively. Their oral supplementation significantly reduced blood glucose levels (p < 0.001), improved lipid profile (p < 0.001), and enhanced total antioxidant status (p < 0.01) in streptozotocin-nicotinamide (STZ-NA)-induced diabetic mice. Additionally, both compounds significantly inhibited the proliferation of Huh-7 and HepG2 (cancer cells) (p < 0.0001) with no effect on the viability of Vero cell line (non-cancer). In conclusion, quercetin and kaempferol demonstrated higher binding affinities towards multiple targets than metformin. In vitro and in vivo antidiabetic potential along with the anticancer activities of both compounds suggest promise for further development in diabetes management. The combination of both drugs did not show a synergistic effect, possibly due to their same target on the receptors.

Drug repurposing for cancer

In the dynamic landscape of cancer therapeutics, the innovative strategy of drug repurposing emerges as a transformative paradigm, heralding a new era in the fight against malignancies. This book chapter aims to embark on the comprehension of the strategic deployment of approved drugs for repurposing and the meticulous journey of drug repurposing from earlier times to the current era. Moreover, the chapter underscores the multifaceted and complex nature of cancer biology, and the evolving field of cancer drug therapeutics while emphasizing the mandate of drug repurposing to advance cancer therapeutics. Importantly, the narrative explores the latest tools, technologies, and cutting-edge methodologies including high-throughput screening, omics technologies, and artificial intelligence-driven approaches, for shaping and accelerating the pace of drug repurposing to uncover novel cancer therapeutic avenues. The chapter critically assesses the breakthroughs, expanding the repertoire of repurposing drug candidates in cancer, and their major categories. Another focal point of this book chapter is that it addresses the emergence of combination therapies involving repurposed drugs, reflecting a shift towards personalized and synergistic treatment approaches. The expert analysis delves into the intricacies of combinatorial regimens, elucidating their potential to target heterogeneous cancer populations and overcome resistance mechanisms, thereby enhancing treatment efficacy. Therefore, this chapter provides in-depth insights into the potential of repurposing towards bringing the much-needed big leap in the field of cancer therapeutics.

Wiring Between Close Nodes in Molecular Networks Evolves More Quickly Than Between Distant Nodes

As species diverge, a wide range of evolutionary processes lead to changes in protein-protein interaction (PPI) networks and metabolic networks. The rate at which molecular networks evolve is an important question in evolutionary biology. Previous empirical work has focused on interactomes from model organisms to calculate rewiring rates, but this is limited by the relatively small number of species and sparse nature of network data across species. We present a proxy for variation in network topology: variation in drug-drug interactions (DDIs), obtained by studying drug combinations (DCs) across taxa. Here, we propose the rate at which DDIs change across species as an estimate of the rate at which the underlying molecular network changes as species diverge. We computed the evolutionary rates of DDIs using previously published data from a high-throughput study in gram-negative bacteria. Using phylogenetic comparative methods, we found that DDIs diverge rapidly over short evolutionary time periods, but that divergence saturates over longer time periods. In parallel, we mapped drugs with known targets in PPI and cofunctional networks. We found that the targets of synergistic DDIs are closer in these networks than other types of DCs and that synergistic interactions have a higher evolutionary rate, meaning that nodes that are closer evolve at a faster rate. Future studies of network evolution may use DC data to gain larger-scale perspectives on the details of network evolution within and between species.

Antimalarial Drug Combination Predictions Using the Machine Learning Synergy Predictor (MLSyPred©) tool

PURPOSE

Antimalarial drug resistance is a global public health problem that leads to treatment failure. Synergistic drug combinations can improve treatment outcomes and delay the development of drug resistance. Here, we describe the implementation of a freely available computational tool, Machine Learning Synergy Predictor (MLSyPred©), to predict potential synergy in antimalarial drug combinations.

METHODS

The MLSyPred© synergy prediction method extracts molecular fingerprints from the drugs' biochemical structures to use as features and also cleans and prepares the raw data. Five machine learning algorithms (Logistic Regression, Random Forest, Support vector machine, Ada Boost, and Gradient Boost) were implemented to build prediction models. Implementation and application of the MLSyPred© tool were tested using datasets from 1540 combinations of 79 drugs and compounds biologically evaluated in pairs for three strains of Plasmodium falciparum (3D7, HB3, and Dd2).

RESULTS

The best prediction models were obtained using Logistic Regression for antimalarials with the strains Dd2 and HB3 (0.81 and 0.70 AUC, respectively) and Random Forest for antimalarials with 3D7 (0.69 AUC). The MLSyPred© tool yielded 45% precision for synergistically predicted antimalarial drug combinations that were annotated and biologically validated, thus confirming the functionality and applicability of the tool.

CONCLUSION

 The MLSyPred© tool is freely available and represents a promising strategy for discovering potential synergistic drug combinations for further development as novel antimalarial therapies.

Hypolipidemic and Antithrombotic Effect of 6'-O-Caffeoylarbutin from Vaccinium dunalianum Based on Zebrafish Model, Network Pharmacology, and Molecular Docking

Vaccinium dunalianum leaf buds make one of the most commonly used herbal teas of the Yi people in China, which is used to treat articular rheumatism, relax tendons, and stimulates blood circulation in the body. In addition, 6'-O-caffeoylarbutin (CA) is a standardized extract of V. dunalianum, which has been found in dried leaf buds, reaching levels of up to 31.76%. Because of the uncommon phenomenon, it is suggested that CA may have a potential therapeutic role in hyperlipidemia and thrombosis. This study was designed to study the efficacy of CA on treating hyperlipidemia and thrombosis and the possible mechanisms behind these effects. Hyperlipidemia and thrombosis zebrafish models were treated with CA to observe variations of the integrated optical density within the vessels and the intensity of erythrocyte staining within the hearts. The possible mechanisms were explored using network pharmacology and molecular docking. The results demonstrate that CA exhibits an excellent hypolipidemic effect on zebrafish at concentrations ranging from 3.0 to 30.0 μg/mL and shows thrombosis inhibitory activity in zebrafish at a concentration of 30.0 μg/mL, with an inhibition rate of 44%. Moreover, network pharmacological research shows that MMP9, RELA, MMP2, PRKCA, HSP90AA1, and APP are major targets of CA for therapy of hyperlipidemia and thrombosis, and may relate to pathways in cancer, chemical carcinogenesis-receptor activation, estrogen signaling pathway, and the AGE-RAGE signaling pathway in diabetic complications.

Network pharmacology: towards the artificial intelligence-based precision traditional Chinese medicine

Network pharmacology (NP) provides a new methodological perspective for understanding traditional medicine from a holistic perspective, giving rise to frontiers such as traditional Chinese medicine network pharmacology (TCM-NP). With the development of artificial intelligence (AI) technology, it is key for NP to develop network-based AI methods to reveal the treatment mechanism of complex diseases from massive omics data. In this review, focusing on the TCM-NP, we summarize involved AI methods into three categories: network relationship mining, network target positioning and network target navigating, and present the typical application of TCM-NP in uncovering biological basis and clinical value of Cold/Hot syndromes. Collectively, our review provides researchers with an innovative overview of the methodological progress of NP and its application in TCM from the AI perspective.

Qili Qiangxin (QLQX) capsule as a multi-functional traditional Chinese medicine in treating chronic heart failure (CHF): A review of ingredients, molecular, cellular, and pharmacological mechanisms

Chronic heart failure (CHF) is a key part of cardiovascular continuum. Under the guidance of the theory of vessel-collateral doctrine, the present study proposes therapeutic benefits of Qili Qiangxin (QLQX) capsules, an innovative Chinese medicine, on chronic heart failure. The studies show that multiple targets of the drug on CHF, including enhancing myocardial systole, promoting urine excretion, inhibiting excessive activation of the neuroendocrine system, preventing ventricular remodeling by inhibiting inflammatory response, myocardial fibrosis, apoptosis and autophagy, enhancing myocardial energy metabolism, promoting angiogenesis, and improving endothelial function. Investigation on the effects and mechanism of the drug is beneficial to the treatment of chronic heart failure (CHF) through multiple targets and/or signaling pathways. Meanwhile, it provides new insights to further understand other refractory diseases in the cardiovascular continuum, and it also has an important theoretical and practical significance in enhancing prevention and therapeutic effect of traditional Chinese medicine for these diseases.

Integrating Chemical Profiling, In Vivo Study, and Network Pharmacology to Explore the Anti-inflammatory Effect of Pterocarpus dalbergioides Fruits and Its Correlation with the Major Phytoconstituents

The purpose of this study is to explore the anti-inflammatory activity of Pterocarpus dalbergioides fruit extract (PFE) and the underlying mechanism. Chemical profiling using ultraperformance liquid chromatography/mass spectrometry identified 28 compounds in PFE (12 flavonoids, 5 fatty acids, 4 phenolic compounds, 3 alkaloids, 2 sesquiterpenes, and 2 xanthophylls). PFE (2 g/kg) significantly inhibited carrageenan-induced rat paw edema after 4 h of administration (42% inhibition). A network-based strategy and molecular docking studies were utilized to uncover the anti-inflammatory mechanism. Out of the identified compounds, 16 compounds with DL ≥ 0.18 and F ≥ 30% were selected using bioavailability (F) and drug-likeness (DL) metrics. The network analysis revealed that 90 genes are considered key targets for the selected compounds and linked to the anti-inflammatory effect. Among all compounds, linoleic acid was found to be the top-most active constituent as it targets maximum genes. Four targets (TNF, IL6, AKT1, and CCL2) among the top 10 genes were found to be the main target genes that may contribute to the anti-inflammatory potential of PFE. Furthermore, KEGG (Kyoto encyclopedia of genes and genomes) pathway analysis revealed that PFE might regulate inflammation through five pathways: neuroactive ligand-receptor interaction, lipid and atherosclerosis, fluid shear stress and atherosclerosis, TNF signaling pathway, and rheumatoid arthritis. The docking study predicted the significant binding affinity between the top four active constituents (linoleic acid, 9-octadecenoic acid, 11,12,13-trihydroxy-9-octadecenoic acid, and rhamnetin-3-O-rhamnoside) and the selected target proteins (TNF and AKT1). The findings highlight PFE as a promising drug lead for controlling inflammation.

Repositioning of clinically approved drug Bazi Bushen capsule for treatment of Aizheimer's disease using network pharmacology approach and in vitro experimental validation

Aims

To explore the new indications and key mechanism of Bazi Bushen capsule (BZBS) by network pharmacology and in vitro experiment.

Methods

The ingredients library of BZBS was constructed by retrieving multiple TCM databases. The potential target profiles of the components were predicted by target prediction algorithms based on different principles, and validated by using known activity data. The target spectrum of BZBS with high reliability was screened by considering the source of the targets and the node degree in compound-target (C-T) network. Subsequently, new indications for BZBS were predicted by disease ontology (DO) enrichment analysis and initially validated by GO and KEGG pathway enrichment analysis. Furthermore, the target sets of BZBS acting on AD signaling pathway were identified by intersection analysis. Based on STRING database, the PPI network of target was constructed and their node degree was calculated. Two Alzheimer's disease (AD) cell models, BV-2 and SH-SY5Y, were used to preliminarily verify the anti-AD efficacy and mechanism of BZBS in vitro.

Results

In total, 1499 non-repeated ingredients were obtained from 16 herbs in BZBS formula, and 1320 BZBS targets with high confidence were predicted. Disease enrichment results strongly suggested that BZBS formula has the potential to be used in the treatment of AD. GO and KEGG enrichment results provide a preliminary verification of this point. Among them, 113 functional targets of BZBS belong to AD pathway. A PPI network containing 113 functional targets and 1051 edges for the treatment of AD was constructed. In vitro experiments showed that BZBS could significantly reduce the release of TNF-α and IL-6 and the expression of COX-2 and PSEN1 in Aβ_25-35-induced BV-2 cells, which may be related to the regulation of ERK_1/2/NF-κB signaling pathway. BZBS reduced the apoptosis rate of Aβ_25-35 induced SH-SY5Y cells, significantly increased mitochondrial membrane potential, reduced the expression of Caspase3 active fragment and PSEN1, and increased the expression of IDE. This may be related to the regulation of GSK-3β/β-catenin signaling pathway.

Conclusions

BZBS formula has a potential use in the treatment of AD, which is achieved through regulation of ERK_1/2, NF-κB signaling pathways, and GSK-3β/β-catenin signaling pathway. Furthermore, the network pharmacology technology is a feasible drug repurposing strategy to reposition new clinical use of approved TCM and explore the mechanism of action. The study lays a foundation for the subsequent in-depth study of BZBS in the treatment of AD and provides a basis for its application in the clinical treatment of AD.

Calycosin inhibits gemcitabine-resistant lung cancer cells proliferation through modulation of the LDOC1/GNL3L/NFκB

Lung cancer is the most common malignant cancer worldwide. Combination therapies are urgently needed to increase patient survival. Calycosin is a phytoestrogen isoflavone that has been reported previously to inhibit tumor cell growth, although its effects on lung cancer remain unclear. The aim of this study was to investigate the effects of calycosin on cell proliferation and apoptosis of gemcitabine-resistant lung cancer cells. Using calycosin to treat human lung cancer cells (CL1-0) and gemcitabine-resistant lung cancer cells (CL1-0 GEMR) and examine the effects on the cells. Cultured human lung cancer cells (CL1-0) and gemcitabine-resistant lung cancer cells (CL1-0 GEMR) were treated with increasing concentrations of calycosin. Cell viability and apoptosis were studied by the 3-(4,5-dimethylthiazol-2-yl)-2,5 diphenyltetrazolium bromide, flow cytometry, and TUNEL assays. Western blots were used to measure the expression levels of proliferation-related proteins and cancer stem cell proteins in CL1-0 GEMR cells. The results showed that calycosin treatment inhibited cell proliferation, decreased cell migration ability, and suppressed cancer stem cell properties in CL1-0 GEMR cells. Interestingly, in CL1-0 GEMR cells, calycosin treatment not only increased LDOC1 but also decreased GNL3L/NFκB protein levels and mRNA levels, in concentration-dependent manners. We speculate that calycosin inhibited cell proliferation of the gemcitabine-resistant cell line through regulating the LDOC1/GNL3L/NFκB pathway.

Network pharmacology, a promising approach to reveal the pharmacology mechanism of Chinese medicine formula

ETHNOPHARMACOLOGICAL RELEVANCE

Network pharmacology is a new discipline based on systems biology theory, biological system network analysis, and multi-target drug molecule design specific signal node selection. The mechanism of action of TCM formula has the characteristics of multiple targets and levels. The mechanism is similar to the integrity, systematization and comprehensiveness of network pharmacology, so network pharmacology is suitable for the study of the pharmacological mechanism of Chinese medicine compounds.

AIM OF THE STUDY

The paper summarizes the present application status and existing problems of network pharmacology in the field of Chinese medicine formula, and formulates the research ideas, up-to-date key technology and application method and strategy of network pharmacology. Its purpose is to provide guidance and reference for using network pharmacology to reveal the modern scientific connotation of Chinese medicine.

MATERIALS AND METHODS

Literatures in this review were searched in PubMed, China National Knowledge Infrastructure (CNKI), Web of Science, ScienceDirect and Google Scholar using the keywords "traditional Chinese medicine", "Chinese herb medicine" and "network pharmacology". The literature cited in this review dates from 2002 to 2022.

RESULTS

Using network pharmacology methods to predict the basis and mechanism of pharmacodynamic substances of traditional Chinese medicines has become a trend.

CONCLUSION

Network pharmacology is a promising approach to reveal the pharmacology mechanism of Chinese medicine formula.

Mechanisms of Ganweikang Tablets against Chronic Hepatitis B: A Comprehensive Study of Network Analysis, Molecular Docking, and Chemical Profiling

The hepatitis B virus (HBV) is one of the major viral infection problems worldwide in public health. The exclusive proprietary Chinese medicine Ganweikang (GWK) tablet has been marketed for years in the treatment of chronic hepatitis B (CHB). However, the pharmacodynamic material basis and underlying mechanism of GWK are not completely clear. This study is aimed at investigating the pharmacological mechanism of the GWK tablet in the treatment of CHB. The chemical ingredient information was obtained from the Traditional Chinese Medicine Database and Analysis Platform (TCMSP), Traditional Chinese Medicines Integrated Database (TCMID), and Shanghai Institute of Organic Chemistry of CAS. Ingredients and disease-related targets were defined by a combination of differentially expressed genes from CHB transcriptome data and open-source databases. Target-pathway-target (TPT) network analysis, molecular docking, and chemical composition analysis were adopted to further verify the key targets and corresponding active ingredients of GWK. Eight herbs of GWK were correlated to 330 compounds with positive oral bioavailability, and 199 correlated targets were identified. The TPT network was constructed based on the 146 enriched targets by KEGG pathway analysis, significantly associated with 95 pathways. Twenty-five nonvolatile components and 25 volatile components in GWK were identified in UPLC-QTOF/MS and GC-MS chromatograms. The key active ingredients of GWK include ferulic acid, oleanolic acid, ursolic acid, tormentic acid, 11-deoxyglycyrrhetic acid, dibenzoyl methane, anisaldehyde, wogonin, protocatechuic acid, psoralen, caffeate, dimethylcaffeic acid, vanillin, β-amyrenyl acetate, formonentin, aristololactam IIIa, and 7-methoxy-2-methyl isoflavone, associated with targets CA2, NFKB1, RELA, AKT1, JUN, CA1, CA6, IKBKG, FOS, EP300, CREB1, STAT1, MMP9, CDK2, ABCB1, and ABCG2.

Chinese herbal medicine for the treatment of cardiovascular diseases ─ targeting cardiac ion channels

Cardiovascular disease (CVD) remains the leading cause of morbidity and mortality, imposing an increasing global health burden. Cardiac ion channels (voltage-gated Na_V, Ca_V, K_Vs, and others) synergistically shape the cardiac action potential (AP) and control the heartbeat. Dysfunction of these channels, due to genetic mutations, transcriptional or post-translational modifications, may disturb the AP and lead to arrhythmia, a major risk for CVD patients. Although there are five classes of anti-arrhythmic drugs available, they can have varying levels of efficacies and side effects on patients, possibly due to the complex pathogenesis of arrhythmias. As an alternative treatment option, Chinese herbal remedies have shown promise in regulating cardiac ion channels and providing anti-arrhythmic effects. In this review, we first discuss the role of cardiac ion channels in maintaining normal heart function and the pathogenesis of CVD, then summarize the classification of Chinese herbal compounds, and elaborate detailed mechanisms of their efficacy in regulating cardiac ion channels and in alleviating arrhythmia and CVD. We also address current limitations and opportunities for developing new anti-CVD drugs based on Chinese herbal medicines.

Calycosin attenuates Angiostrongylus cantonensis-induced parasitic meningitis through modulation of HO-1 and NF-κB activation

Angiostrongylus cantonensis causes a form of parasitic meningitis in humans. Albendazole (ABZ) kills nematode larvae in the brain. However, dead larvae can trigger a severe inflammatory response, resulting in brain damage. Accumulating evidence suggests that calycosin represents a potential anti-inflammatory therapeutic candidate. In this study, we investigated the combined effects of ABZ and calycosin in angiostrongyliasis caused by A. cantonensis in BALB/c mice. Inflammatory mediators (such as phospho-nuclear factor-κB, cyclooxygenase-2, matrix metalloproteinase-9, tumour necrosis factor-α and interleukin-1β) are associated with the development of meningitis and immune inflammatory reactions. We found that A. cantonensis significantly induces inflammatory mediator production and increases the blood–brain barrier (BBB) permeability. However, co-administration of both ABZ and calycosin markedly suppressed meningitis and inflammatory mediator production and decreased the BBB permeability compared to treatment with a single drug. Furthermore, calycosin and ABZ plus calycosin treatment facilitated production of the antioxidant haem oxygenase-1 (HO-1). Moreover, co-therapy with ABZ and calycosin failed to mitigate angiostrongyliasis in the presence of tin-protoporphyrin IX, an HO-1-specific inhibitor. This finding suggests that the beneficial effects of ABZ plus calycosin treatment on the regulation of inflammation are mediated by the modulation of HO-1 activation. The present results provide new insights into the treatment of human angiostrongyliasis using co-therapy with ABZ and calycosin.

Mechanism Repositioning Based on Integrative Pharmacology: Anti-Inflammatory Effect of Safflower in Myocardial Ischemia–Reperfusion Injury

Safflower (Carthamus tinctorius. L) possesses anti-tumor, anti-thrombotic, anti-oxidative, immunoregulatory, and cardio-cerebral protective effects. It is used clinically for the treatment of cardio-cerebrovascular disease in China. This study aimed to investigate the effects and mechanisms of action of safflower extract on myocardial ischemia–reperfusion (MIR) injury in a left anterior descending (LAD)-ligated model based on integrative pharmacology study and ultra-performance liquid chromatography–quadrupole time-of-flight-tandem mass spectrometer (UPLC-QTOF-MS/MS). Safflower (62.5, 125, 250 mg/kg) was administered immediately before reperfusion. Triphenyl tetrazolium chloride (TTC)/Evans blue, echocardiography, terminal deoxynucleotidyl transferase-mediated dUTP nick-end labeling (TUNEL) assay, lactate dehydrogenase (LDH) ability, and superoxide dismutase (SOD) levels were determined after 24 h of reperfusion. Chemical components were obtained using UPLC-QTOF-MS/MS. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed. Quantitative real-time polymerase chain reaction (qRT-PCR) and Western blotting were used to analyze mRNA and protein levels, respectively. Safflower dose-dependently reduced myocardial infarct size, improved cardiac function, decreased LDH levels, and increased SOD levels in C57/BL6 mice. A total of 11 key components and 31 hub targets were filtered based on the network analysis. Comprehensive analysis indicated that safflower alleviated inflammatory effects by downregulating the expression of NFκB1, IL-6, IL-1β, IL-18, TNFα, and MCP-1 and upregulating NFκBia, and markedly increased the expression of phosphorylated PI3K, AKT, PKC, and ERK/2, HIF1α, VEGFA, and BCL2, and decreased the level of BAX and phosphorylated p65. Safflower shows a significant cardioprotective effect by activating multiple inflammation-related signaling pathways, including the NFκB, HIF-1α, MAPK, TNF, and PI3K/AKT signaling pathways. These findings provide valuable insights into the clinical applications of safflower.

Prospects for the application of traditional Chinese medicine network pharmacology in food science research

There has always been a particular difficulty with in-depth research on the mechanisms of food nutrition and bioactivity. The main function of food is to meet the nutritional needs of the human body, rather than to exert a therapeutic effect. Its relatively modest biological activity makes it difficult to study from the perspective of general pharmacological models. With the popularity of functional foods and the concept of dietary therapy, and the development of information and multi-omics technology in food research, research into these mechanisms is moving towards a more microscopic future. Network pharmacology has accumulated nearly 20 years of research experience in traditional Chinese medicine (TCM), and there has been no shortage of work from this perspective on the medicinal functions of food. Given the similarity between the concept of 'multi-component-multi-target' properties of food and TCM, we think that network pharmacology is applicable to the study of the complex mechanisms of food. Here we review the development of network pharmacology, summarize its application to 'medicine and food homology', and propose a methodology based on food characteristics for the first time, demonstrating its feasibility for food research. © 2023 Society of Chemical Industry.

Insights into the mechanisms of triptolide nephrotoxicity through network pharmacology-based analysis and RNA-seq

INTRODUCTION

Triptolide (TPL) is a promising plant-derived compound for clinical therapy of multiple human diseases; however, its application was limited considering its toxicity.

METHODS

To explore the underlying molecular mechanism of TPL nephrotoxicity, a network pharmacology based approach was utilized to predict candidate targets related with TPL toxicity, followed by deep RNA-seq analysis to characterize the features of three transcriptional elements include protein coding genes (PCGs), long noncoding RNAs (lncRNAs) and circular RNAs (circRNAs) as well as their associations with nephrotoxicity in rats with TPL treatment.

RESULTS & DISCUSSION

Although the deeper mechanisms of TPL nephrotoxcity remain further exploration, our results suggested that c-Jun is a potential target of TPL and Per1 related circadian rhythm signaling is involved in TPL induced renal toxicity.

Network-based drug repurposing for the treatment of COVID-19 patients in different clinical stages

In the severe acute respiratory coronavirus disease 2019 (COVID-19) pandemic, there is an urgent need to develop effective treatments. Through a network-based drug repurposing approach, several effective drug candidates are identified for treating COVID-19 patients in different clinical stages. The proposed approach takes advantage of computational prediction methods by integrating publicly available clinical transcriptome and experimental data. We identify 51 drugs that regulate proteins interacted with SARS-CoV-2 protein through biological pathways against COVID-19, some of which have been experimented in clinical trials. Among the repurposed drug candidates, lovastatin leads to differential gene expression in clinical transcriptome for mild COVID-19 patients, and estradiol cypionate mainly regulates hormone-related biological functions to treat severe COVID-19 patients. Multi-target mechanisms of drug candidates are also explored. Erlotinib targets the viral protein interacted with cytokine and cytokine receptors to affect SARS-CoV-2 attachment and invasion. Lovastatin and testosterone block the angiotensin system to suppress the SARS-CoV-2 infection. In summary, our study has identified effective drug candidates against COVID-19 for patients in different clinical stages and provides comprehensive understanding of potential drug mechanisms.

Biochemical reaction network topology defines dose-dependent Drug-Drug interactions

Drug combination therapy is a promising strategy to enhance the desired therapeutic effect, while reducing side effects. High-throughput pairwise drug combination screening is a commonly used method for discovering favorable drug interactions, but is time-consuming and costly. Here, we investigate the use of reaction network topology-guided design of combination therapy as a predictive in silico drug-drug interaction screening approach. We focused on three-node enzymatic networks, with general Michaelis-Menten kinetics. The results revealed that drug-drug interactions critically depend on the choice of target arrangement in a given topology, the nature of the drug, and the desired level of change in the network output. The results showed a negative correlation between antagonistic interactions and the dosage of drugs. Overall, the negative feedback loops showed the highest synergistic interactions (the lowest average combination index) and, intriguingly, required the highest drug doses compared to other topologies under the same condition.

Improvement of obesity by Liupao tea is through the IRS-1/PI3K/AKT/GLUT4 signaling pathway according to network pharmacology and experimental verification

BACKGROUND

Obesity is a state of accumulating excessive body fat, charactering by a high blood lipid and associating with various metabolic diseases. As a kind of dark tea, many studies revealed that long-term drinking Liupao tea (LT) can reduce weight (Liu et al., 2014). However, the anti-obesity mechanism and active ingredients of LT are not known.

METHODS

Liquid chromatography-mass spectrometry (LC-MS) combined with network pharmacology was used to screen the active components and related targets of Liupao tea water extract (LTWE). The key anti-obesity targets and pathways of LTWE were predicted by protein-protein interaction (PPI) networks, and enrichment analyses using Kyoto Encyclopedia of Genes and Genomes and Gene Ontology databases. Then, the active components selected by high-performance liquid chromatography (HPLC) fingerprinting were used together with LTWE in an adipogenic model and insulin resistance (IR) model in vitro.

RESULTS

Most of the compounds identified from LTWE were flavonofids, esters, and amides. Key targets such as RAC-alpha serine/threonine-protein kinase, insulin, and tumor necrosis factor (TNF) were involved in the phosphatidylinositol-3-kinase-protein kinase B (PI3K-AKT) signaling pathway, pathways in cancer, and other pathways. Four active components were screened by network pharmacology combined with HPLC fingerprinting. The in vitro experiment of LTWE and its four active components showed that in insulin-resistant 3T3-L1 cells, LTWE, (-)-epigallocatechin gallate (EGCG) and gallic acid (GA) inhibited adipocyte differentiation. Three factors could inhibit the differentiation of 3T3-L1 cells by decreasing gene expression of peroxisome proliferators-activated receptor γ (PPARγ), fatty acid synthase (FAS), CCAAT/enhancer binding proteins-α (C/EBPα) and interleukin-6 (IL-6). Caffeine and ellagic acid (EA) showed opposite results, but their effects on promoting adipose differentiation diminished with increasing concentrations of drug. In dexamethasone-induced insulin-resistant 3T3-L1 cells, the fluorescence intensity of 2-Deoxy-2-[(7-nitro-2,1,3-Benzoxadiazol-4-yl)amino]-d-glucose revealed that LTWE, GA, EGCG, caffeine, and EA significantly promoted glucose consumption. LTWE, GA, and EA improved insulin resistance in adipocytes by upregulating gene expression of insulin receptor substrate-1 (IRS-1), PI3K, AKT, and glucose transporter 4 (GLUT4).

CONCLUSION

LC-MS combined with network pharmacology preliminarianized that LTWE acts mainly on the PI3K-AKT signaling pathway. Cell experiments revealed that the anti-obesity effect of LTWE is the result of multi-component action, which inhibits the proliferation and differentiation of preadipocytes by regulating gene expression of adipogenic transcription factors and proinflammatory factors, and improves IR by activating the IRS-1/PI3K/AKT/GLUT4 pathway.

Exploration of the Potential Mechanisms of Lingqihuangban Granule for Treating Diabetic Retinopathy Based on Network Pharmacology

BACKGROUND

The Lingqihuangban Granule (LQHBG), a remarkable Chinese herbal compound, has been used for decades to treat diabetic retinopathy (DR) in the Department of Ophthalmology, Shanghai General Hospital (National Clinical Research Center for Eye Diseases) with obvious effects. Through the method of network pharmacology, the present study constructed bioactive component-relative targets and protein-protein interaction network of the LQHBG and implemented gene function analysis and pathway enrichment of targets, discussing the mechanisms of traditional Chinese medicine LQHBG in treating DR.

MATERIALS AND METHODS

The bioactive ingredients of LQHBG were screened and obtained using TCMSP and ETCM databases, while the potential targets of bioactive ingredients were predicted by SwissTargetPrediction and ETCM databases. Compared with the disease target databases of TTD, Drugbank, OMIM and DisGeNET, the therapeutic targets of LQHBG for DR were extracted. Based on the DAVID platform, GO annotation and KEGG pathway analyses of key targets were explored, combined with the screening of core pathways on the Omicshare database and pathway annotation on the Reactome database.

RESULTS

A total of 357 bioactive components were screened from LQHBG, involving 86 possible targets of LQHBG treating DR. In the PPI network, INS and ALB were identified as key genes. The effective targets were enriched in multiple signaling pathways, such as PI3K/Akt and MAPK pathways.

CONCLUSION

This study revealed the possible targets and pathways of LQHBG treating DR, reflecting the characteristics of multicomponent, multitarget and multipathway treatment of a Chinese herbal compound, and provided new ideas for further discussion.

Network pharmacology predicts combinational effect of novel herbal pair consist of Ephedrae herba and Coicis semen on adipogenesis in 3T3-L1 cells

BACKGROUND

Herbal combinations are regarded as basic strategy in oriental medicine with various purposes. Ephedrae herba (EH) and Coicis semen (CS) are two herbal medicines used to treat obesity in many herbal prescriptions, yet the effect and significance of this herbal pair have not been evaluated.

PURPOSE

This study is to elucidate the effect of a novel herbal pair, EH-CS, on obesity and identify the key synergistic mechanism underlying it.

METHODS

We investigated the network of herbs comprising the anti-obesity herbal prescriptions. Using the tools of network pharmacology, we investigated the compound-target interactions of EH and CS in combination to predict their effects in combination. Five EH-CS samples with different EH to CS ratios were prepared to investigate their efficacies in adipocytes.

RESULTS

1-mode network analysis of herbs in prescriptions based on literature review revealed the importance of EH-CS in anti-obesity prescriptions. The herbal combination comprised of equivalent weights (1:1) of EH and CS most potently reduced mature adipocyte adiposity, although several markers of adipogenesis and lipid synthesis were more suppressed by pure EH. PTGS2 (COX-2 gene) expression, a common target of EH and CS as deduced by compound-target network analysis, was affected by EH-CS extract treatments. However, EH at high concentration (25 μg/ml) notably increased PTGS2 expression without adversely affecting cell viability. However, EH-CS combination of the same concentration markedly decreased PTGS2 gene expression.

CONCLUSION

These results show that the compounds in CS and EH act in concert to enhance the pharmacological effect of EH, but control unexpected effects of EH treatment.

Gou Qi Zi inhibits proliferation and induces apoptosis through the PI3K/AKT1 signaling pathway in non-small cell lung cancer

Background

Gou Qi Zi (Lycium barbarum) is a traditional herbal medicine with antioxidative effects. Although Gou Qi Zi has been used to prevent premature aging and in the treatment of non-small cell lung cancer (NSCLC), its mechanism of action in NSCLC remains unclear. The present study utilized network pharmacology to assess the potential mechanism of action of Gou Qi Zi in the treatment of NSCLC.

Methods

The TCMSP, TCMID, SwissTargetPrediction, DrugBank, DisGeNET, GeneCards, OMIM and TTD databases were searched for the active components of Gou Qi Zi and their potential therapeutic targets in NSCLC. Protein-protein interaction networks were identified and the interactions of target proteins were analyzed. Involved pathways were determined by GO enrichment and KEGG pathway analyses using the Metascape database, and molecular docking technology was used to study the interactions between active compounds and potential targets. These results were verified by cell counting kit-8 assays, BrdU labeling, flow cytometry, immunohistochemistry, western blotting, and qRT-PCR.

Results

Database searches identified 33 active components in Gou Qi Zi, 199 predicted biological targets and 113 NSCLC-related targets. A network of targets of traditional Chinese medicine compounds and potential targets of Gou Qi Zi in NSCLC was constructed. GO enrichment analysis showed that Gou Qi Zi targeting of NSCLC was mainly due to the effect of its associated lipopolysaccharide. KEGG pathway analysis showed that Gou Qi Zi acted mainly through the PI3K/AKT1 signaling pathway in the treatment of NSCLC. Molecular docking experiments showed that the bioactive compounds of Gou Qi Zi could bind to AKT1, C-MYC and TP53. These results were verified by experimental assays.

Conclusion

Gou Qi Zi induces apoptosis and inhibits proliferation of NSCLC in vitro and in vivo by inhibiting the PI3K/AKT1 signaling pathway.

Potential bioactive compounds and mechanisms of Fibraurea recisa Pierre for the treatment of Alzheimer's disease analyzed by network pharmacology and molecular docking prediction

Introduction

Heat-clearing and detoxifying Chinese medicines have been documented to have anti-Alzheimer's disease (AD) activities according to the accumulated clinical experience and pharmacological research results in recent decades. In this study, Fibraurea recisa Pierre (FRP), the classic type of Heat-clearing and detoxifying Chinese medicine, was selected as the object of research.

Methods

12 components with anti-AD activities were identified in FRP by a variety of methods, including silica gel column chromatography, multiple databases, and literature searches. Then, network pharmacology and molecular docking were adopted to systematically study the potential anti-AD mechanism of these compounds. Consequently, it was found that these 12 compounds could act on 235 anti-AD targets, of which AKT and other targets were the core targets. Meanwhile, among these 235 targets, 71 targets were identified to be significantly correlated with the pathology of amyloid beta (Aβ) and Tau.

Results and discussion

In view of the analysis results of the network of active ingredients and targets, it was observed that palmatine, berberine, and other alkaloids in FRP were the key active ingredients for the treatment of AD. Further, Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analysis revealed that the neuroactive ligand-receptor interaction pathway and PI3K-Akt signaling pathway were the most significant signaling pathways for FRP to play an anti-AD role. Findings in our study suggest that multiple primary active ingredients in FRP can play a multitarget anti-AD effect by regulating key physiological processes such as neurotransmitter transmission and anti-inflammation. Besides, key ingredients such as palmatine and berberine in FRP are expected to be excellent leading compounds of multitarget anti-AD drugs.

Multiple-Gene Regulation for Enhanced Antitumor Efficacy with Branch-PCR-Assembled TP53 and MYC Gene Nanovector

Multiple proteins are involved in network regulation through the crosstalk of different signaling pathways in cancers. Here, we propose a novel strategy of genome therapy with branch-PCR-assembled gene nanovectors to perform network-based gene regulation at multiple levels for cancer therapy. To validate network-based multiplex-gene regulation for genome therapy, we chose to simultaneously target one tumor suppressor gene (TP53) and one oncogene (MYC) in two different signaling pathways. The results showed that, compared to gene nanovectors targeting single genes (NP-TP53 and NP-shMYC), branch-PCR-assembled gene nanovectors simultaneously expressing p53 proteins and MYC shRNA arrays (NP-TP53-shMYC) showed enhanced antitumor efficacy in both MDA-MB-231 cancer cells and an MDA-MB-231-tumor-bearing mouse model. These findings indicate the feasibility and effectiveness of genome therapy in cancer therapy.

Identification of the Mechanism of Matrine Combined with Glycyrrhizin for Hepatocellular Carcinoma Treatment through Network Pharmacology and Bioinformatics Analysis

Matrine and glycyrrhizin are representative active ingredients of traditional Chinese medicine (TCM) used in clinical practice. Studies have demonstrated that matrine has antitumor pharmacological effects and that glycyrrhizin protects liver function. However, the potential bioactive compounds and mechanisms remain unknown, as well as whether they have synergistic effects in killing cancer cells and protecting liver cells. To investigate the synergistic effects and mechanism of matrine combined with glycyrrhizin in hepatocellular carcinoma (HCC) treatment, we used both network pharmacology and bioinformatics analyses. First, the chemical gene interaction information of matrine and glycyrrhizin was obtained from the PubChem database. The pathogenic genes of HCC were accessed from five public databases. The RNA sequencing data and clinical information of HCC patients were downloaded from The Cancer Genome Atlas (TCGA). Next, the overlapping genes among the potential targets of matrine and glycyrrhizin and HCC-related targets were determined using bioinformatics analysis. We constructed the drug-target interaction network. Prognosis-associated genes were acquired through the univariate Cox regression model and Lasso-Cox regression model. The results were verified by the International Cancer Genome Consortium (ICGC) database. Finally, we predicted the immune function of the samples. The drug-target interaction network consisted of 10 matrine and glycyrrhizin targets. We selected a Lasso-Cox regression model consisting of 3 differentially expressed genes (DEGs) to predict the efficacy of the combination in HCC. Subsequently, we successfully predicted the overall survival of HCC patients using the constructed prognostic model and investigated the correlation of the immune response. Matrine and glycyrrhizin have synergistic effects on HCC. The model we obtained consisted of three drug-target genes by Lasso-Cox regression analysis. The model independently predicted the combined effect of matrine and glycyrrhizin in HCC treatment and OS, which will be helpful for guiding clinical treatment. The prognostic model was correlated with the immune cells and immune checkpoints of patients, which had an adjuvant effect on HCC immunotherapy. Matrine and glycyrrhizin can have therapeutic effects on HCC by promoting the production or enhancing the core gene activity in the drug network and improving the immune system function of patients.

Systems pharmacology approaches in herbal medicine research: a brief review

Herbal medicine, a multi-component treatment, has been extensively practiced for treating various symptoms and diseases. However, its molecular mechanism of action on the human body is unknown, which impedes the development and application of herbal medicine. To address this, recent studies are increasingly adopting systems pharmacology, which interprets pharmacological effects of drugs from consequences of the interaction networks that drugs might have. Most conventional network- based approaches collect associations of herb-compound, compound-target, and target-disease from individual databases, respectively, and construct an integrated network of herb-compound- target-disease to study the complex mechanisms underlying herbal treatment. More recently, rapid advances in highthroughput omics technology have led numerous studies to exploring gene expression profiles induced by herbal treatments to elicit information on direct associations between herbs and genes at the genome-wide scale. In this review, we summarize key databases and computational methods utilized in systems pharmacology for studying herbal medicine. We also highlight recent studies that identify modes of action or novel indications of herbal medicine by harnessing drug-induced transcriptome data. [BMB Reports 2022; 55(9): 417-428].

Yuan-Zhi decoction in the treatment of Alzheimer’s disease: An integrated approach based on chemical profiling, network pharmacology, molecular docking and experimental evaluation

Yuan-Zhi Decoction (YZD) is a traditional Chinese medical formulation with demonstrated clinical benefits in Alzheimer’s disease (AD). We used liquid chromatography coupled with mass spectrometry to identify 27 unique chemical components of YZD. Analyzing these using network pharmacology and molecular docking models identified 34 potential interacting molecular targets involved in 26 biochemical pathways. When tested in an animal model of AD, the APP/PS1 transgenic mice showed measurable improvements in spatial orientation and memory after the administration of YZD. These improvements coincided with significantly reduced deposition of Aβ plaques and tau protein in the hippocampi in the treated animals. In addition, a decreased BACE1 and beta-amyloid levels, a downregulation of the p-GSK-3β/GSK-3β, and an upregulation of the PI3K and p-AKT/AKT pathway was seen in YZD treated animals. These in vivo changes validated the involvement of molecular targets and pathways predicted in silico analysis of the chemical components of YZD. This study provides scientific support for the clinical use of YZD and justifies further investigations into its effects in AD. Furthermore, it demonstrates the utility of network pharmacology in elucidating the biochemical mechanisms underlying the beneficial effects of traditional Chinese medicines (TCM).

MatchMaker: A Deep Learning Framework for Drug Synergy Prediction

Drug combination therapies have been a viable strategy for the treatment of complex diseases such as cancer due to increased efficacy and reduced side effects. However, experimentally validating all possible combinations for synergistic interaction even with high-throughout screens is intractable due to vast combinatorial search space. Computational techniques can reduce the number of combinations to be evaluated experimentally by prioritizing promising candidates. We present MatchMaker that predicts drug synergy scores using drug chemical structure information and gene expression profiles of cell lines in a deep learning framework. For the first time, our model utilizes the largest known drug combination dataset to date, DrugComb. We compare the performance of MatchMaker with the state-of-the-art models and observe up to  ∼ 15% correlation and  ∼ 33% mean squared error (MSE) improvements over the next best method. We investigate the cell types and drug pairs that are relatively harder to predict and present novel candidate pairs. MatchMaker is built and available at https://github.com/tastanlab/matchmaker.

Metabolomics and integrated network pharmacology analysis reveal SNKAF decoction suppresses cell proliferation and induced cell apoptisis in hepatocellular carcinoma via PI3K/Akt/P53/FoxO signaling axis

BACKGROUND

There is no comprehensive treatment method for hepatocellular carcinoma (HCC); hence, research and development are still focused on systemic therapies, including drugs. Sinikangai fang (SNKAF) decoction, a classic Chinese herbal prescription, has been widely used to treat liver cancer. However, there is no research on its core active component and target.

METHODS

Mouse models were established to measure the anticancer effect of SNKAF decoction on HCC. Further, we investigated the effect of SNKAF decoction on inhibition of hepatoma cells proliferation using cell viability, cloning and invasion assays in vitro. The components of SNKAF were collected from the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database and TCM@Taiwan database. Metabolomic analysis was used to identify the potential genes and pathways in HCC treated with SNKAF decoction. Then, the expression of phosphoinositide 3-kinase (PI3K), Akt, P53, FoxO proteins of the potential signal pathways were detected using Western blot.

RESULTS

The animal experiments showed that SNKAF decoction inhibited tumor growth (P < 0.05) and induced no weight loss in the mice. In vitro data showed that HCCLM3 and MHCC97H cell proliferation was inhibited by SNKAF serum in a time- and concentration dependent manner. Further combined analysis network pharmacology with metabonomics showed that 217 target genes overlapped. The core target genes included BCL2, MCL1, Myc, PTEN, gsk3b, CASP9, CREB1, MDM2, pt53 and CCND1. Cancer-associated pathways were largely involved in SNKAF mechanisms, including P53, FoxO, and PI3K/Akt signaling pathways, which are closely related to induced-tumor cell apoptosis. In addition, Western bolt verified that 10% SNKAF serum significantly affected the main proteins of PI3K/Akt/P53/FoxO signaling pathway in both cell lines.

CONCLUSION

SNKAF decoction-containing serum inhibited HCCLM3 and MHCC97H cell proliferation, migration, invasion, and induced-tumor cell apoptosis in-vivo. We confirmed that SNKAF decoction is a promising alternative treatments for HCC patients.