Methodology of network pharmacology for research on Chinese herbal medicine against COVID-19: A review

Wogonin inhibits the proliferation of prolactinoma through the PI3K/AKT signaling pathway

Objectives

This investigation sought to explore the inhibitory impact of wogonin on prolactinoma and elucidate its underlying mechanisms through network pharmacology, molecular docking (MD), and molecular biology experiments.

Methods

Target identification for wogonin and prolactinoma was conducted using relevant databases, followed by protein-protein interaction (PPI) analysis of intersecting targets via the STRING database. Functional and pathway enrichment analyses were executed utilizing Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) methodologies. Hub genes were identified from the PPI network, and MD was utilized to assess the binding patterns and interaction strength between wogonin and hub targets. Network pharmacological findings were further validated through in vivo and in vitro experiments.

Results

A sum of 137 drug targets for wogonin and 3,942 disease targets for prolactinoma were identified, with 37 overlapping targets. Nine hub genes were screened, including KDR, EGFR, BCL2, IL6, ESR1, MYC, CCL2, PTGS2, and ESR2. GO and KEGG analyses revealed that wogonin was closely associated with several critical signaling cascades. MD analysis confirmed robust binding interactions between wogonin and the identified hub targets. Cellular experiments suggested that wogonin suppressed cell proliferation and triggered apoptosis in prolactinoma cells in a time- and concentration-dependent manner, primarily via inhibition of the PI3K/AKT signaling cascades. Animal studies further revealed that wogonin markedly suppressed tumor growth and enhanced prolactinoma sensitivity to bromocriptine.

Conclusion

These findings suggest that wogonin exerts its anti-prolactinoma effects via multiple targets and signaling cascades, establishing a robust scientific basis for the development and screening of novel anti-prolactinoma therapeutics.

Revolutionizing Chinese medicine granule placebo with a machine learning four-color model

With the development of new Chinese medicines and the need for clinical double-blind experiments, the use of placebos in Chinese medicine is becoming increasingly important. However, due to the diverse colors and complex color gamut of these particles, existing simulation methods rely on manual comparison and color mixing, leading to high subjectivity and errors. This study addresses this issue by developing a prediction model to accurately simulate the colors of Chinese medicine granules. In this study, 52 commercially available herbal particles were collected. And more than 320 simulated granules were prepared using fillers and four pigments (lemon yellow, carmine, indigo and caramel colors). Their RGB colors were collected using visible light imaging. A granule color prediction model was constructed by machine learning. First, the best clustering model was obtained by optimising the k-value of the Kmeans model. Subsequently, multiple regression models, including Gradient Boosting Regression (GBR), Support Vector Regression (SVR), and Random Forest, were evaluated through network search and cross-validation methods. Among these models, the average R2 of the random forest model reached 0.9249, outperforming other models. The prediction model accurately simulated the color of Chinese medicine granules, with an average color difference (ΔE) of 2.7734 and a high RGB value cosine similarity of 0.9999, alongside a 0.9366 similarity in artificial color scoring. This study introduces an innovative approach for the rapid and accurate prediction of granule colors, facilitating the development of clinically applicable placebos in traditional Chinese medicine.

Effect of icariin on ovarian cancer: a combined network pharmacology and meta-analysis of in vitro studies approach

Introduction

An abundance of experimental evidence indicates that icariin (ICA) could potentially exert an anti-tumor effect on ovarian cancer (OC). Nevertheless, the reliability of this evidence remains ambiguous. This study aimed to explore the impact of ICA on OC and the underlying mechanisms.

Methods

Bioinformatics analysis was employed to pinpoint ICA-targeted genes and signaling pathways implicated in OC, utilizing network pharmacology. Subsequently, PubMed, EMBASE, and Web of Science databases were systematically searched from 2001 through June 2023 for in vitro trials evaluating the anti-tumor efficacy of conventional ICA versus placebo in OC. The pathways and genes identified in the literature were recorded, and the therapeutic targets were statistically analyzed and compared with the predicted targets from network pharmacology to confirm the precision of the targets.

Results and Discussion

Fourteen target genes were validated with success. The pathways corresponding to the remaining genes-excluding these 14-were analyzed and found to be primarily associated with cell apoptosis, anti-tumor, and other related pathways. Out of the 76 studies retrieved, eight fulfilled the inclusion criteria. The subsequent meta-analysis suggested that ICA treatment was significantly correlated with reduced cell growth and induced apoptosis. This study demonstrated a certain efficacy of ICA compared to placebo in enhancing anti-tumor outcomes, characterized by increased abilities in reducing cell growth and inducing apoptosis. The pathways involved in the therapeutic effect may be linked to cell apoptosis and anti-tumor mechanisms.

Efficacy of traditional Chinese medicines in the treatment of solar lentigo based on network pharmacology and experimental validation

BACKGROUND AND AIMS

Solar lentigo is a prevalent skin condition that affects a significant number of individuals. Fortunately, certain traditional Chinese medicines and monomers (TCMM) have proven effective in addressing these concerns. In this study, we evaluated the efficacy and underlying mechanism of TCMM, a combination of TCM and monomers in repairing solar lentigo.

METHODS

We detected and identified the main compounds of TCM using liquid chromatography-mass spectrometry (LC-MS) and through the approach of network pharmacology, we screened drug and disease targets, visualized networks with Cytoscape software, analyzed targets via Gene ontology and KEGG, clinically validated predictions. In a mouse model, UVB-induced pigmentation was assessed, and the effects of TCMM were evaluated. A clinical trial on 30 patients validated the depigmenting agent.

RESULTS

Active ingredients such as MSH, Butylated hydroxytoluen, Valerophenone, and Geranylacetone aid pigmentation treatment. One hundred and forty-three crore targets impact PI3K-Akt, MAPK signaling pathway, ect. pathways. TCMM reduced UVB-induced pigmentation, water loss, epidermal thickness, and melanin. It inhibited TYR, MITF, AKT1, VEGFA, PTGS2, TNF-α, IL-6, IL-1β. Clinical and microscopic analysis showed significant pigmentation reduction.

CONCLUSIONS

The treatment of solar lentigo can benefit from the TCMM. By targeting multiple factors and pathways, this approach offers a comprehensive and effective treatment strategy.

Dendrobium huoshanense in the treatment of ulcerative colitis: Network pharmacology and experimental validation

ETHNOPHARMACOLOGICAL RELEVANCE

Dendrobium huoshanense C. Z. Tang et S. J. Cheng (DH) is a traditional medicinal herb with a long history of medicinal use. DH has been recorded as protecting the gastrointestinal function. Modern pharmacology research shows that DH regulates intestinal flora, intestinal mucosal immunity, gastrointestinal peristalsis and secretion of digestive juices. At the same time, some studies have shown that DH has a good therapeutic effect on ulcerative colitis, but its mechanism of action has not been fully elucidated.

AIMS OF THIS STUDY

To investigate the mechanism and effect of Dendrobium huoshanense C. Z. Tang et S. J. Cheng (DH) in the treatment of ulcerative colitis (UC) by combining network pharmacology and in vivo experimental validation.

METHODS

A network pharmacology approach was used to perform component screening, target prediction, PPI network interaction analysis, GO and KEGG enrichment analysis to initially predict the mechanism of DH treatment for UC. Then, the mechanism was validated with the UC mouse model induced by 3% DSS.

RESULTS

Based on the network pharmacological analysis, a comprehensive of 101 active components were identified, with 19 of them potentially serving as the crucial elements in DH's effectiveness against UC treatment. Additionally, the study revealed 314 potential core therapeutic targets along with the top 5 key targets: SRC, STAT3, AKT1, HSP90AA1, and PIK3CA. In experiments conducted on live mice with UC, DH was found to decrease the levels of IL-6 and TNF-α in the blood, while increasing the levels of IL-10 and TGF-β. This led to notable improvements in colon length, injury severity, and an up-regulation of SRC, STAT3, HSP90AA1, PIK3CA, p-AKT1 and PI3K/AKT signaling pathway expression in the colon tissue.

CONCLUSIONS

In this study, the active components and main targets of DH for UC treatment were initially forecasted, and the potential mechanism was investigated through network pharmacology. These findings offer an experimental foundation for the clinical utilization of DH.

Integrating network pharmacology with molecular docking to rationalize the ethnomedicinal use of Alchornea laxiflora (Benth.) Pax & K. Hoffm. for efficient treatment of depression

Background: Alchornea laxiflora (Benth.) Pax & K. Hoffm. (A. laxiflora) has been indicated in traditional medicine to treat depression. However, scientific rationalization is still lacking. Hence, this study aimed to investigate the antidepressant potential of A. laxiflora using network pharmacology and molecular docking analysis. Materials and methods: The active compounds and potential targets of A. laxiflora and depression-related targets were retrieved from public databases, such as PubMed, PubChem, DisGeNET, GeneCards, OMIM, SwissTargetprediction, BindingDB, STRING, and DAVID. Essential bioactive compounds, potential targets, and signaling pathways were predicted using in silico analysis, including BA-TAR, PPI, BA-TAR-PATH network construction, and GO and KEGG pathway enrichment analysis. Later on, with molecular docking analysis, the interaction of essential bioactive compounds of A. laxiflora and predicted core targets of depression were verified. Results: The network pharmacology approach identified 15 active compounds, a total of 219 compound-related targets, and 14,574 depression-related targets with 200 intersecting targets between them. SRC, EGFR, PIK3R1, AKT1, and MAPK1 were the core targets, whereas 3-acetyloleanolic acid and 3-acetylursolic acid were the most active compounds of A. laxiflora with anti-depressant potential. GO functional enrichment analysis revealed 129 GO terms, including 82 biological processes, 14 cellular components, and 34 molecular function terms. KEGG pathway enrichment analysis yielded significantly enriched 108 signaling pathways. Out of them, PI3K-Akt and MAPK signaling pathways might have a key role in treating depression. Molecular docking analysis results exhibited that core targets of depression, such as SRC, EGFR, PIK3R1, AKT1, and MAPK1, bind stably with the analyzed bioactive compounds of A. laxiflora. Conclusion: The present study elucidates the bioactive compounds, potential targets, and pertinent mechanism of action of A. laxiflora in treating depression. A. laxiflora might exert an antidepressant effect by regulating PI3K-Akt and MAPK signaling pathways. However, further investigations are required to validate.

Integrating artificial intelligence into the modernization of traditional Chinese medicine industry: a review

Traditional Chinese medicine (TCM) is the practical experience and summary of the Chinese nation for thousands of years. It shows great potential in treating various chronic diseases, complex diseases and major infectious diseases, and has gradually attracted the attention of people all over the world. However, due to the complexity of prescription and action mechanism of TCM, the development of TCM industry is still in a relatively conservative stage. With the rise of artificial intelligence technology in various fields, many scholars began to apply artificial intelligence technology to traditional Chinese medicine industry and made remarkable progress. This paper comprehensively summarizes the important role of artificial intelligence in the development of traditional Chinese medicine industry from various aspects, including new drug discovery, data mining, quality standardization and industry technology of traditional Chinese medicine. The limitations of artificial intelligence in these applications are also emphasized, including the lack of pharmacological research, database quality problems and the challenges brought by human-computer interaction. Nevertheless, the development of artificial intelligence has brought new opportunities and innovations to the modernization of traditional Chinese medicine. Integrating artificial intelligence technology into the comprehensive application of Chinese medicine industry is expected to overcome the major problems faced by traditional Chinese medicine industry and further promote the modernization of the whole traditional Chinese medicine industry.

On-line extraction and determination of coumarins compounds from mouse plasma based on a homemade phenyl-hybrid monolithic adsorbent

A phenyl-hybrid monolithic adsorbent was prepared using an organic monomer of ethylene glycol phenyl ether acrylate and inorganic monomers of tetramethoxysilane and vinyltrimethoxysilane, via polycondensation and polymerization in a stainless-steel column, which shows porous structure and multiple functional groups, according to the measurements of scanning electron microscopy, nitrogen adsorption-desorption method and infrared spectroscopy. The resulting hybrid phenyl-based monolith was used as a solid-phase extraction column, combining with an analytical column in conjunction with high-performance liquid chromatography system for the on-line extraction and determination of coumarins (praeruptorin A and praeruptorin B) in Peucedani Radix from mouse plasma. The homemade hybrid monolithic solid-phase extraction column exhibits good removal ability for the sample matrices, as well as unique selectivity for the two praeruptorins. Methodology validation results indicate that the present method is applicable for the on-line extraction and quantitative analysis of praeruptorin A and praeruptorin B in Peucedani Radix from mouse plasma with a limit of quantitation 0.06 μg/mL and a linear range 0.06-5 μg/mL (r＞0.999), thus indicating the present method is a promising and alternative method for the quantitative determination of similar target components with micro or trace concentration from complex extract solution and plasma.

Comparison of different extraction methods of active ingredients of Chinese medicine and natural products

Like other traditional medicine in the world, Chinese traditional medicine (CTM) has a long history, which is a treasure of the combination of medicine and Chinese classical culture even more than 5000 years. For thousands of years, CTM has made great contributions to the reproduction and health of the Chinese people. It was an efficient therapeutic tool under the guidance of Chinese traditional medical theory, its source is generally natural products, but there are also a small number of it are natural products after some processing methods. In fact, the definition of Chinese medicine (CM) includes both traditional and new CM developed by modern technology. It is well known that the chemical composition of most CM and natural products is very complex, for example, a single herb may contain hundreds of different chemicals, including active ingredients, side effects, and even toxic ingredients. Therefore, the extraction process is particularly crucial for the quality and clinical efficacy of CM and natural products. In this work, a new classification method was proposed to divide the extraction technologies of CM and natural products into 21 kinds in recent years and analyze their status, advantages, and disadvantages. Then put forward a new technical route based on ultra-high-pressure extraction technology for rapid extraction else while removing harmful impurities and making higher utilization of CM and natural products. It is a useful exploration for the extraction industry of medicinal materials and natural products in the world.

Microbial and Host Metabolites at the Backstage of Fever: Current Knowledge about the Co-Ordinate Action of Receptors and Molecules Underlying Pathophysiology and Clinical Implications

Fever represents an elevation of body temperature, that exerts a protective effect against pathogens. Innate immune cells and neurons are implicated in the regulation of body temperature. Pathogen-associated molecular patterns, i.e., lipopolysaccharides from Gram-negative bacteria and peptidoglycan and lipoteichoic acid from Gram-positive bacteria are exogenous pyrogens, that bind to Toll-like receptors on immune and non-immune cells. The subsequent release of pro-inflammatory cytokines [interleukin-1 (IL-1), IL-6 and Tumor necrosis factor-alpha] and their passage through the brain trigger the febrile response. In fact, neurons of the pre-optic area produce prostaglandin E2 (PGE2), that, in turn, bind to the PGE2 receptors; thus, generating fever. Apart from classical non-steroidal anti-inflammatory drugs, i.e., aspirin and acetaminophen, various botanicals are currently used as antipyretic agents and, therefore, their mechanisms of action will be elucidated.

Leaf Extract of Perilla frutescens (L.) Britt Promotes Adipocyte Browning via the p38 MAPK Pathway and PI3K-AKT Pathway

The leaf of Perilla frutescens (L.) Britt (PF) has been reported to negatively affect adipocyte formation, inhibit body-fat formation, and lower body weight. However, its effect on adipocyte browning remains unknown. Thus, the mechanism of PF in promoting adipocyte browning was investigated. The ingredients of PF were acquired from the online database and filtered with oral bioavailability and drug-likeness criteria. The browning-related target genes were obtained from the Gene Card database. A Venn diagram was employed to obtain the overlapped genes that may play a part in PF promoting adipocyte browning, and an enrichment was analysis conducted based on these overlapped genes. A total of 17 active ingredients of PF were filtered, which may regulate intracellular receptor-signaling pathways, the activation of protein kinase activity, and other pathways through 56 targets. In vitro validation showed that PF promotes mitochondrial biogenesis and upregulates brite adipocyte-related gene expression. The browning effect of PF can be mediated by the p38 MAPK pathway as well as PI3K-AKT pathway. The study revealed that PF could promote adipocyte browning through multitargets and multipathways. An in vitro study validated that the browning effect of PF can be mediated by both the P38 MAPK pathway and the PI3K-AKT pathway.

Mechanism of Chaihu Longgu oyster adjusted decoction for the treatment of depression based on network pharmacology and molecular docking technology

Background

Depression is a common clinical psychiatric disorder that is responsible for health-related disease burdens globally. According to traditional Chinese medicine (TCM), mental disorders and qi stagnation are important pathogenic mechanisms of depression. The Chaihu Longgu Oyster Decoction, which has been documented in the Shanghanlun (Treatise on Typhoid), is widely used to treat various affective disorders.

Methods

Network pharmacology and molecular docking technology were used to investigate the material basis and mechanism of action of the Chaihu Longgu oyster adjusted decoction in treating depression. The main pharmacological substance bases, possible targets, and pathways of Chaihu Longgu oyster adjusted decoction in treating depression were visualized by constructing a "component-pathway-target" network.

Results

Quercetin, 7-methoxy-2-methylisoflavone, baicalein, kaempferol, and lignan are the main practical chemical components in Chaihu Longgu oyster adjusted decoction. The Chaihu Longgu oyster adjusted decoction regulates 74 protein targets and 142 pathways associated with depression. Its molecular mechanism involves inhibiting neuroinflammation and improving neurotransmitter function, neuroplasticity, etc.

Conclusions

The underlying mechanism of the anti-depressive effect of the Chaihu Longgu oyster adjusted decoction may involve neuroinflammatory response reduction and improvement of neurotransmitter function and neuroplasticity. This study revealed the mechanism of action of the Chaihu Longgu oyster adjusted decoction in the treatment of depression through network pharmacology, which provides a scientific basis for clinical application.