Drug Target Information Research Articles

Multi-Omics Analysis Identified Drug Repurposing Targets for Chronic Obstructive Pulmonary Disease.

Despite recent advances in chronic obstructive pulmonary disease (COPD) research, few studies have identified the potential therapeutic targets systematically by integrating multiple-omics datasets. This project aimed to develop a systems biology pipeline to identify biologically relevant genes and potential therapeutic targets that could be exploited to discover novel COPD treatments via drug repurposing or de novo drug discovery. A computational method was implemented by integrating multi-omics COPD data from unpaired human samples of more than half a million subjects. The outcomes from genome, transcriptome, proteome, and metabolome COPD studies were included, followed by an in silico interactome and drug-target information analysis. The potential candidate genes were ranked by a distance-based network computational model. Ninety-two genes were identified as COPD signature genes based on their overall proximity to signature genes on all omics levels. They are genes encoding proteins involved in extracellular matrix structural constituent, collagen binding, protease binding, actin-binding proteins, and other functions. Among them, 70 signature genes were determined to be druggable targets. The in silico validation identified that the knockout or over-expression of SPP1, APOA1, CTSD, TIMP1, RXFP1, and SMAD3 genes may drive the cell transcriptomics to a status similar to or contrasting with COPD. While some genes identified in our pipeline have been previously associated with COPD pathology, others represent possible new targets for COPD therapy development. In conclusion, we have identified promising therapeutic targets for COPD. This hypothesis-generating pipeline was supported by unbiased information from available omics datasets and took into consideration disease relevance and development feasibility.

Mechanisms of Luteolin Against Gastro-Esophageal Reflux Disease Based on Network Pharmacology, Molecular Docking, and Molecular Dynamics Simulation.

Luteolin is a naturally occurring flavonoid. The effectiveness of luteolin-rich drugs in treating gastro-esophageal reflux disease (GERD) through traditional Chinese medicine has been demonstrated. This study aimed to identify the potential targets and mechanisms of action of luteolin for the treatment of GERD. An innovative approach combining network pharmacology and molecular dynamics was used to explore the potential therapeutic mechanisms of luteolin and to facilitate the further development of GERD treatment. Drug and disease target information was screened from public databases to obtain 159 intersecting targets through the construction of Venn diagrams. Subsequently, a protein‒protein interaction (PPI) network was constructed, and 10 core targets were identified. Through Gene Ontology (GO) functional and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses, relevant biological processes, cellular components, and molecular functions related to the treatment of GERD were identified and revealed. KEGG pathway analyses showed enrichment of signaling pathways, including the TNF, IL-17, NF-kappa B, and Toll-like receptor pathways. The molecular docking results indicated that luteolin can effectively bind to 10 core targets. Finally, molecular dynamics simulations confirmed the formation of stable protein‒ligand complexes when IL6 binds to luteolin. In conclusion, network pharmacology, molecular docking, and molecular dynamics simulations were utilized to investigate the mechanism by which luteolin treats GERD. These findings establish a theoretical foundation for future research on the efficacy of luteolin in treating GERD.

Knowledge-embedded constrained multiobjective evolutionary algorithm based on structural network control principles for personalized drug targets recognition in cancer

The structural network control principle for identifying personalized drug targets (SNCPDTs) is a kind of constrained multiobjective optimization (CMO) problem with NP-hard features, which makes traditional mathematical methods difficult to adopt. Therefore, this study designs a knowledge-embedded multitasking constrained multiobjective evolutionary algorithm (KMCEA) to solve the SNCPDTs by mining relevant knowledge. Specifically, the relationships between two optimization objectives (minimizing the number of driver nodes and maximizing prior-known drug-target information) and constraints (guaranteeing network control) are analyzed from the perspective of CMO. We find that two objectives are difficult to optimize; thus two single-objective auxiliary tasks are created to optimize two objectives respectively, so as to maintain diversity along the Pareto front. Furthermore, we find that two optimization objectives have a complex reverse relation and a simple positive relation with constraints, respectively; thus, a population initialization method and a local auxiliary task are designed for two single-objective auxiliary tasks, respectively, so as to improve the performance of the algorithm on two objective functions. Finally, KMCEA is used to solve two kinds of models with three kinds of datasets. Compared with various methods, KMCEA can not only effectively discover clinical combinatorial drugs but also better solve the SNCPDTs regarding convergence and diversity.

DRSPRING: Graph convolutional network (GCN)-Based drug synergy prediction utilizing drug-induced gene expression profile

Great efforts have been made over the years to identify novel drug pairs with synergistic effects. Although numerous computational approaches have been proposed to analyze diverse types of biological big data, the pharmacogenomic profiles, presumably the most direct proxy of drug effects, have been rarely used due to the data sparsity problem. In this study, we developed a composite deep-learning-based model that predicts the drug synergy effect utilizing pharmacogenomic profiles as well as molecular properties. Graph convolutional network (GCN) was used to represent and integrate the chemical structure, genetic interactions, drug-target information, and gene expression profiles of cell lines. Insufficient amount of pharmacogenomic data, i.e., drug-induced expression profiles from the LINCS project, was resolved by augmenting the data with the predicted profiles. Our method learned and predicted the Loewe synergy score in the DrugComb database and achieved a better or comparable performance compared to other published methods in a benchmark test. We also investigated contribution of various input features, which highlighted the value of basal gene expression and pharmacogenomic profiles of each cell line. Importantly, DRSPRING (DRug Synergy PRediction by INtegrated GCN) can be applied to any drug pairs and any cell lines, greatly expanding its applicability compared to previous methods.

Multiple receptor tyrosine kinases regulate dengue infection of hepatocytes.

Dengue is an arboviral disease causing severe illness in over 500,000 people each year. Currently, there is no way to constrain dengue in the clinic. Host kinase regulators of dengue virus (DENV) infection have the potential to be disrupted by existing therapeutics to prevent infection and/or disease progression. To evaluate kinase regulation of DENV infection, we performed kinase regression (KiR), a machine learning approach that predicts kinase regulators of infection using existing drug-target information and a small drug screen. We infected hepatocytes with DENV in vitro in the presence of a panel of 38 kinase inhibitors then quantified the effect of each inhibitor on infection rate. We employed elastic net regularization on these data to obtain predictions of which of 291 kinases are regulating DENV infection. Thirty-six kinases were predicted to have a functional role. Intriguingly, seven of the predicted kinases - EPH receptor A4 (EPHA4), EPH receptor B3 (EPHB3), EPH receptor B4 (EPHB4), erb-b2 receptor tyrosine kinase 2 (ERBB2), fibroblast growth factor receptor 2 (FGFR2), Insulin like growth factor 1 receptor (IGF1R), and ret proto-oncogene (RET) - belong to the receptor tyrosine kinase (RTK) family, which are already therapeutic targets in the clinic. We demonstrate that predicted RTKs are expressed at higher levels in DENV infected cells. Knockdown of EPHB4, ERBB2, FGFR2, or IGF1R reduces DENV infection in hepatocytes. Finally, we observe differential temporal induction of ERBB2 and IGF1R following DENV infection, highlighting their unique roles in regulating DENV. Collectively, our findings underscore the significance of multiple RTKs in DENV infection and advocate further exploration of RTK-oriented interventions against dengue.

Research on the Mechanism of Si Xian Decoction in Treating Acute Leukemia Based on Network Pharmacology and Molecular Docking Technology

Abstract Objective Our objective was to investigate the mechanism of action of the Si Xian Decoction (SXD) in treating acute leukemia (AL) using network pharmacology and molecular docking techniques. Methods The chemical components of the four medicinal herbs of Shengdi (Rehmanniae Radix), Baimaogen (Imperatae Rhizoma), Xiaoji (Cirsii Herba), and Pugongying (Taraxaci Herba) in the SXD were obtained from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP), Bioinformatics Analysis Tool for Molecular mechANism of Traditional Chinese Medicine (BATMAN-TCM), and Encyclopedia of Traditional Chinese Medicine (ETCM). A natural active chemical component dataset for the SXD was established. Human Gene Database (Gencards), Database of Gene-Disease Associations (DisGeNET), Database for Drug and Drug Target Information (DrugBank), and Human Disease Database (MalaCards) were searched to obtain AL-related targets and to establish a disease target database. After obtaining the intersection targets of drugs and diseases, a Venn diagram of the common targets was drawn online. A drug-disease protein interaction network was constructed using the String 11.5 platform, and a “drug-disease-target-signal pathway” network was built using Cytoscape 3.8.2 software to obtain relevant target network topology parameters. Results By searching the TCMSP, BATMAN-TCM, and ETCM databases, 30 active components of the SXD and 677 related targets were obtained. From Gencards, DrugBank, MalaCards, and DisGeNET databases, 12,110 potential AL disease targets were obtained. Using the ClusterProfiler package of the R4.2.2 platform, 1,011 entries of gene ontology information were enriched, including 467 biological process entries, 236 molecular function entries, and 308 cellular component entries. Additionally, 220 enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathways were obtained, mainly involving chemical carcinogen receptor activation, lipid and atherosclerosis, fluid shear stress and atherosclerosis, prostate cancer, and the role of the advanced glycation end products-receptor for advanced glycation end products (AGE-RAGE) signaling pathway in diabetic complications. Network topology analysis revealed that the main active components of SXD treating AL include γ-aminobutyric acid, adenosine, quercetin, scopolamine, and taraxasterol. Conclusion The treatment of AL with the SXD is a process of multicomponent, multitarget, and multisignal pathway coordination. Network pharmacology provides a solid research basis for elucidating the mechanism of action of SXD in the treatment of AL.

DGDRP: drug-specific gene selection for drug response prediction via re-ranking through propagating and learning biological network.

Introduction: Drug response prediction, especially in terms of cell viability prediction, is a well-studied research problem with significant implications for personalized medicine. It enables the identification of the most effective drugs based on individual genetic profiles, aids in selecting potential drug candidates, and helps identify biomarkers that predict drug efficacy and toxicity.A deeper investigation on drug response prediction reveals that drugs exert their effects by targeting specific proteins, which in turn perturb related genes in cascading ways. This perturbation affects cellular pathways and regulatory networks, ultimately influencing the cellular response to the drug. Identifying which genes are perturbed and how they interact can provide critical insights into the mechanisms of drug action. Hence, the problem of predicting drug response can be framed as a dual problem involving both the prediction of drug efficacy and the selection of drug-specific genes. Identifying these drug-specific genes (biomarkers) is crucial because they serve as indicators of how the drug will affect the biological system, thereby facilitating both drug response prediction and biomarker discovery.Methods: In this study, we propose DGDRP (Drug-specific Gene selection for Drug Response Prediction), a graph neural network (GNN)-based model that uses a novel rank-and-re-rank process for drug-specific gene selection. DGDRP first ranks genes using a pathway knowledge-enhanced network propagation algorithm based on drug target information, ensuring biological relevance. It then re-ranks genes based on the similarity between gene and drug target embeddings learned from the GNN, incorporating semantic relationships. Thus, our model adaptively learns to select drug mechanism-associated genes that contribute to drug response prediction. This integrated approach not only improves drug response predictions compared to other gene selection methods but also allows for effective biomarker discovery.Discussion: As a result, our approach demonstrates improved drug response predictions compared to other gene selection methods and demonstrates comparability with state-of-the-art deep learning models. Case studies further support our method by showing alignment of selected gene sets with the mechanisms of action of input drugs.Conclusion: Overall, DGDRP represents a deep learning based re-ranking strategy, offering a robust gene selection framework for more accurate drug response prediction. The source code for DGDRP can be found at: https://github.com/minwoopak/heteronet.

Drug target, class level, and PathFX pathway information share utility for machine learning prediction of common drug-induced side effects

Drug target, class level, and PathFX pathway information share utility for machine learning prediction of common drug-induced side effects

Computer Assisted Drug Design of AITC for Treatment of Cancer: A Combined Molecular Target Identification Study and Drug-Target Prediction

Drugs can influence the whole biological system by targeting interaction reactions. However, current databases have minimal drug target information and only a few are validated. This emerges a need to develop new drugs that predict true interaction pairs with high accuracy. Currently, network pharmacology has been used in identifying potential drug targets for allylisothiocyanate (AITC) and in predicting the spread of its activity toward bladder cancer whereas the mechanisms of isothiocyanates remain largely undefined. In this study, we present a computational method to predict targets for AITC. Results showed 13 differentially expressed hubs confirmed by Cytoscape (3.7.2). We further observed that the obtained targets were found to involve a type of Ontology of classification- molecular function, cellular component, biological process and protein class. Pathway enrichment was done by DAVID. This study identifies key genes and pathways involved in bladder cancer which helps us to understand the progression and molecular mechanism underlying bladder cancer. The obtained key genes may serve as active therapeutic targets for bladder cancer treatment. AITC may directly regulate several biological processes and their genes in bladder cancer.

Improved drug response prediction by drug target data integration via network-based profiling.

Drug response prediction (DRP) is important for precision medicine to predict how a patient would react to a drug before administration. Existing studies take the cell line transcriptome data, and the chemical structure of drugs as input and predict drug response as IC50 or AUC values. Intuitively, use of drug target interaction (DTI) information can be useful for DRP. However, use of DTI is difficult because existing drug response database such as CCLE and GDSC do not have information about transcriptome after drug treatment. Although transcriptome after drug treatment is not available, if we can compute the perturbation effects by the pharmacologic modulation of target gene, we can utilize the DTI information in CCLE and GDSC. In this study, we proposed a framework that can improve existing deep learning-based DRP models by effectively utilizing drug target information. Our framework includes NetGP, a module to compute gene perturbation scores by the network propagation technique on a network. NetGP produces genes in a ranked list in terms of gene perturbation scores and the ranked genes are input to a multi-layer perceptron to generate a fixed dimension vector for the integration with existing DRP models. This integration is done in a model-agnostic way so that any existing DRP tool can be incorporated. As a result, our framework boosts the performance of existing DRP models, in 64 of 72 comparisons. The performance gains are larger especially for test scenarios with samples with unseen drugs by large margins up to 34% in Pearson's correlation coefficient.

Distinct transcriptional signatures in purified circulating immune cells drive heterogeneity in disease location in IBD

ObjectiveTo infer potential mechanisms driving disease subtypes among patients with inflammatory bowel disease (IBD), we profiled the transcriptome of purified circulating monocytes and CD4 T-cells.DesignRNA extracted from purified monocytes and...

ACDA: implementation of an augmented drug synergy prediction algorithm.

Drug synergy prediction is approached with machine learning techniques using molecular and pharmacological data. The published Cancer Drug Atlas (CDA) predicts a synergy outcome in cell-line models from drug target information, gene mutations and the models' monotherapy drug sensitivity. We observed low performance of the CDA, 0.339, measured by Pearson correlation of predicted versus measured sensitivity on DrugComb datasets. We augmented the approach CDA by applying a random forest regression and optimization via cross-validation hyper-parameter tuning and named it Augmented CDA (ACDA). We benchmarked the ACDA's performance, which is 68% higher than that of the CDA when trained and validated on the same dataset spanning 10 tissues. We compared the performance of ACDA to one of the winning methods of the DREAM Drug Combination Prediction Challenge, the performance of which was lower than ACDA in 16 out of 19 cases. We further trained the ACDA on Novartis Institutes for BioMedical Research PDX encyclopedia data and generated sensitivity predictions for PDX models. Finally, we developed a novel approach to visualize synergy-prediction data. The source code is available at https://github.com/TheJacksonLaboratory/drug-synergy and the software package at PyPI. Supplementary data are available at Bioinformatics Advances online.

A network-based approach for isolating the chronic inflammation gene signatures underlying complex diseases towards finding new treatment opportunities.

Complex diseases are associated with a wide range of cellular, physiological, and clinical phenotypes. To advance our understanding of disease mechanisms and our ability to treat these diseases, it is critical to delineate the molecular basis and therapeutic avenues of specific disease phenotypes, especially those that are associated with multiple diseases. Inflammatory processes constitute one such prominent phenotype, being involved in a wide range of health problems including ischemic heart disease, stroke, cancer, diabetes mellitus, chronic kidney disease, non-alcoholic fatty liver disease, and autoimmune and neurodegenerative conditions. While hundreds of genes might play a role in the etiology of each of these diseases, isolating the genes involved in the specific phenotype (e.g., inflammation “component”) could help us understand the genes and pathways underlying this phenotype across diseases and predict potential drugs to target the phenotype. Here, we present a computational approach that integrates gene interaction networks, disease-/trait-gene associations, and drug-target information to accomplish this goal. We apply this approach to isolate gene signatures of complex diseases that correspond to chronic inflammation and use SAveRUNNER to prioritize drugs to reveal new therapeutic opportunities.

Study on the mechanism of Astragalus-Panax notoginseng in the treatment of gastric ulcer based on network pharmacology

Objective:To explore the possible mechanism of Astragalus Panax notoginseng in the treatment of GU by using network pharmacology. Methods: combined with the network pharmacology technology, the active components were obtained with the help of tcmsp database, OB≥30% and DL≥0.18 were set, the potential active components were screened, and the drug target information was obtained; After screening the genes, disenem and other important targets in the database; Using venny2 1 software, draw Wayne diagram and obtain drug disease common targets; Using Cytoscape 3.7.2 software, construct the "drug component target disease" network diagram, screen the core genes, and conduct go analysis and KEGG analysis. Results: 27 potential active components, 488 drug targets and 118 drug disease common targets were got. Go enrichment analysis showed that the intersection gene set was enriched to 1948 biological processes, 45 cell component expression processes and 106 processes related to molecular function. KEGG enrichment analysis is mainly based on 146 related signal pathways. The signal pathways of Astragalus Panax notoginseng in the treatment of Gu mainly involve proteoglycans in cancer, prostate cancer, EGFR tyrosine kinase inhibitor resistance and other signal pathways. Conclusion: Astragalus-Panax notoginseng drug pair can treat gastric ulcer through multiple components acting on multiple targets and multiple signal pathways, which preliminarily reveals its mechanism of action and provides a support for further empirical research.

Study on the mechanism of Astragalus-Panax notoginseng in the treatment of gastric ulcer based on network pharmacology

Objective:To explore the possible mechanism of Astragalus Panax notoginseng in the treatment of GU by using network pharmacology. Methods: combined with the network pharmacology technology, the active components were obtained with the help of tcmsp database, OB≥30% and DL≥0.18 were set, the potential active components were screened, and the drug target information was obtained; After screening the genes, disenem and other important targets in the database; Using venny2 1 software, draw Wayne diagram and obtain drug disease common targets; Using Cytoscape 3.7.2 software, construct the "drug component target disease" network diagram, screen the core genes, and conduct go analysis and KEGG analysis. Results: 27 potential active components, 488 drug targets and 118 drug disease common targets were got. Go enrichment analysis showed that the intersection gene set was enriched to 1948 biological processes, 45 cell component expression processes and 106 processes related to molecular function. KEGG enrichment analysis is mainly based on 146 related signal pathways. The signal pathways of Astragalus Panax notoginseng in the treatment of Gu mainly involve proteoglycans in cancer, prostate cancer, EGFR tyrosine kinase inhibitor resistance and other signal pathways. Conclusion: Astragalus-Panax notoginseng drug pair can treat gastric ulcer through multiple components acting on multiple targets and multiple signal pathways, which preliminarily reveals its mechanism of action and provides a support for further empirical research.

Predicting Rheumatoid Arthritis Associated Significant Amino-Acid Residues Using Residue-Residue Interaction Analysis

Rheumatoid Arthritis (RA) is a prevalent autoimmune and inflammatory disease that requires restructuring. A lot of research information is available, but a clear etiology and drug target information is still unclear. A bottom-up approach can add more information to existing knowledge about RA. One better way of understanding the disease-related mechanism and drug objectives can be a detailed residue-residue interaction of the proteins involved with RA. In the current research work, we have studied the significant proteins reported in the Indian population that are involved in RA progression and have represented each of them as a complex network of amino acid residues to understand the significance of individual residues in the network. We implied the graph theory approach to identity central important residue, based on topological properties of the network. This approach allows us to look at a more precise method to identify potential drug targets. Our result identified leucine, phenylalanine, tyrosine, and tryptophan as essential nodes in the network, their activity was mainly connected with immune system. Understanding the function of these amino acids in CTLA4, CD40, IRF5, IL2RB, and TRAF could lead to a new treatment options in the fight against Rheumatoid Arthritis. Bangladesh Journal of Medical Science Vol. 21 No. 04 October’22 Page : 698-710

Multidrug representation learning based on pretraining model and molecular graph for drug interaction and combination prediction.

Approaches for the diagnosis and treatment of diseases often adopt the multidrug therapy method because it can increase the efficacy or reduce the toxic side effects of drugs. Using different drugs simultaneously may trigger unexpected pharmacological effects. Therefore, efficient identification of drug interactions is essential for the treatment of complex diseases. Currently proposed calculation methods are often limited by the collection of redundant drug features, a small amount of labeled data and low model generalization capabilities. Meanwhile, there is also a lack of unique methods for multidrug representation learning, which makes it more difficult to take full advantage of the originally scarce data. Inspired by graph models and pretraining models, we integrated a large amount of unlabeled drug molecular graph information and target information, then designed a pretraining framework, MGP-DR (Molecular Graph Pretraining for Drug Representation), specifically for drug pair representation learning. The model uses self-supervised learning strategies to mine the contextual information within and between drug molecules to predict drug-drug interactions and drug combinations. The results achieved promising performance across multiple metrics compared with other state-of-the-art methods. Our MGP-DR model can be used to provide a reliable candidate set for the combined use of multiple drugs. Code of the model, datasets and results can be downloaded from GitHub (https://github.com/LiangYu-Xidian/MGP-DR). Supplementary data are available at Bioinformatics online.

Multi-modal optimization to identify personalized biomarkers for disease prediction of individual patients with cancer.

Finding personalized biomarkers for disease prediction of patients with cancer remains a massive challenge in precision medicine. Most methods focus on one subnetwork or module as a network biomarker; however, this ignores the early warning capabilities of other modules with different configurations of biomarkers (i.e. multi-modal personalized biomarkers). Identifying such modules would not only predict disease but also provide effective therapeutic drug target information for individual patients. To solve this problem, we developed a novel model (denoted multi-modal personalized dynamic network biomarkers (MMPDNB)) based on a multi-modal optimization mechanism and personalized dynamic network biomarker (PDNB) theory, which can provide multiple modules of personalized biomarkers and unveil their multi-modal properties. Using the genomics data of patients with breast or lung cancer from The Cancer Genome Atlas database, we validated the effectiveness of the MMPDNB model. The experimental results showed that compared with other advanced methods, MMPDNB can more effectively predict the critical state with the highest early warning signal score during cancer development. Furthermore, MMPDNB more significantly identified PDNBs containing driver and biomarker genes specific to cancer tissues. More importantly, we validated the biological significance of multi-modal PDNBs, which could provide effective drug targets of individual patients as well as markers for predicting early warning signals of the critical disease state. In conclusion, multi-modal optimization is an effective method to identify PDNBs and offers a new perspective for understanding tumor heterogeneity in cancer precision medicine.

Finding the “switch” in platelet activation: prediction of key mediators involved in reversal of platelet activation using a novel network biology approach

The cAMP-protein kinase A (PKA) pathway in platelets is important for both platelet activation and inactivation. We hypothesize that proteins/processes downstream of the cAMP-PKA pathway that are regulated after platelet activation ánd subsequent inactivation can serve as a “switch” in platelet activation and inhibition. We used a STRING-based protein-protein interaction network from proteins of interest distilled from publicly available quantitative platelet proteome datasets. The protein network was integrated with biological pathway information by functional enrichment analysis, phosphorylation by PKA, and drug-target information. Functional enrichment analysis revealed biological processes related to vesicle secretion and cytoskeletal reorganization to be overrepresented among these 30 proteins coinciding with topological clusters in the network. Our method identified proteins/processes with functions related to vesicle transport, cyclin-dependent protein kinases, tight junctions, and small GTPases as potential switches in platelet activation and inhibition. Next to established enzymes in cAMP-PKA signaling, such as PDE3A, proteins with an unknown/less well-known role in platelet biology, such as Stonin-2 and ABLIM-3, emerged from our analysis as interesting candidates for reversal of platelet activation. Our method can be used to repurpose existing datasets and provide a coherent overview of mechanisms involved to predict novel connections, by visually integrating multiple datasets. SignificanceThis article presents a novel approach of visually incorporating multiple existing tools and proteomics datasets and in doing so provides novel insight into the complex molecular mechanisms involved in platelet activation. Using our approach, we also highlight several interesting candidates for future research into pathologies with high platelet reactivity.

Use the Chinese herbal compound regulatory network to verify the relationship between the Jun, Chen, Zuo, and Shi of Xiaochaihu Decoction in treating hepatitis

PurposeTo verify the relationship between the monarch(Jun), minister(Chen), assistant(Zuo), and guide(Shi) of Xiaochaihu Decoction in treating hepatitis through the regulation network of traditional Chinese Pharmacy compound. MethodsFirst, We use the data in the TCMSP database to obtain the effective ingredients and targets of Xiaochaihu Decoction, and screen the effective ingredients. Then, the genes related to hepatitis diseases obtain from the five databases, and the intersection genes with the target genes Xiaochaihu Decoction were screened. Third, We draw the "traditional Chinese Pharmacy - compound - target (gene)" network diagram of Xiaochaihu Decoction and hepatitis, then we get the network core of the network. Fourth, We perform GO enrichment analysis and KEGG enrichment analysis on the intersection genes. Fifth, We use molecular docking technology to verify the interaction between crucial targets and compounds. Finally, get data from the Oncomine database for Meta-analysis. We have verified the gene expression and protein expression determination of AKT, IKKs, IκBα through PCR and WB experiments. ResultsWe screened and obtained the drug target information of Xiaochaihu Decoction, and we finally obtained a total of 163 active ingredients and 6854 hepatitis-related genes. There are 193 genes common to drug targets and hepatitis. We obtained 18 core genes from the Chinese herbal compound regulatory network diagram. The results of Meta-analysis and molecular docking are both ideal. It proved that Xiaochaihu Decoction has an inhibitory effect on the TNF signaling pathway and the gene expression of AKT, IKKs, and IκBα. The Jun (Monarch) Chen (Minister) medicine plays an essential guiding role in Xiaochaihu Decoction. ConclusionsThe results obtained through the Chinese herbal compound regulatory network analysis can scientifically explain the relationship between the Jun (Monarch), Chen (Minister), Zuo (Assistant), and Shi (Guide) of Xiaochaihu Decoction.