A novel network pharmacology strategy to decode mechanism of Wuling Powder in treating liver cirrhosis.

BACKGROUND: Liver cirrhosis is a chronic liver disease with hepatocyte necrosis and lesion. As one of the TCM formulas Wuling Powder (WLP) is widely used in the treatment of liver cirrhosis. However, it's key functional components and action mechanism still remain unclear. We attempted to explore the Key Group of Effective Components (KGEC) of WLP in the treatment of Liver cirrhosis through integrative pharmacology combined with experiments. METHODS: The components and potential target genes of WLP were extracted from published databases. A novel node importance calculation model considering both node control force and node bridging force is designed to construct the Function Response Space (FRS) and obtain key effector proteins. The genetic knapsack algorithm was employed to select KGEC. The effectiveness and reliability of KGEC were evaluated at the functional level by using gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Finally, the effectiveness and potential mechanism of KGEC were confirmed by CCK-8, qPCR and Western blot. RESULTS: 940 effective proteins were obtained in FRS. KEGG pathways and GO terms enrichments analysis suggested that effective proteins well reflect liver cirrhosis characteristics at the functional level. 29 components of WLP were defined as KGEC, which covered 100% of the targets of the effective proteins. Additionally, the pathways enriched for the KGEC targets accounted for 83.33% of the shared genes between the targets and the pathogenic genes enrichment pathways. Three components scopoletin, caryophyllene oxide, and hydroxyzinamic acid from KGEC were selected for in vivo verification. The qPCR results demonstrated that all three components significantly reduced the mRNA levels of COL1A1 in TGF-ß1-induced liver cirrhosis model. Furthermore, the Western blot assay indicated that these components acted synergistically to target the NF-κB, AMPK/p38, cAMP, and PI3K/AKT pathways, thus inhibiting the progression of liver cirrhosis. CONCLUSION: In summary, we have developed a new model that reveals the key components and potential mechanisms of WLP for the treatment of liver cirrhosis. This model provides a reference for the secondary development of WLP and offers a methodological strategy for studying TCM formulas.

Screening of the key response component groups and mechanism verification of Huangqi-Guizhi-Wuwu-Decoction in treating rheumatoid arthritis based on a novel computational pharmacological model.

BACKGROUND: Rheumatoid arthritis (RA) is a chronic autoimmune disease characterized by the destruction of synovial tissue and articular cartilage. Huangqi-Guizhi-Wuwu-Decoction (HGWD), a formula of Traditional Chinese Medicine (TCM), has shown promising clinical efficacy in the treatment of RA. However, the synergistic effects of key response components group (KRCG) in the treatment of RA have not been well studied. METHODS: The components and potential targets of HGWD were extracted from published databases. A novel node influence calculation model that considers both the node control force and node bridging force was designed to construct the core response space (CRS) and obtain key effector proteins. An increasing coverage coefficient (ICC) model was employed to select the KRCG. The effectiveness and potential mechanism of action of KRCG were confirmed using CCK-8, qPCR, and western blotting. RESULTS: A total of 796 key effector proteins were identified in CRS. The Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses confirmed their effectiveness and reliability. In addition, 59 components were defined as KRCG, which contributed to 85.05% of the target coverage of effective proteins. Of these, 677 targets were considered key reaction proteins, and their enriched KEGG pathways accounted for 84.89% of the pathogenic genes and 87.94% of the target genes. Finally, four components (moupinamide, 6-Paradol, hydrocinnamic acid, and protocatechuic acid) were shown to inhibit the inflammatory response in RA by synergistically targeting the cAMP, PI3K-Akt, and HIF-1α pathways. CONCLUSIONS: We have introduced a novel model that aims to optimize and analyze the mechanisms behind herbal formulas. The model revealed the KRCG of HGWD for the treatment of RA and proposed that KRCG inhibits the inflammatory response by synergistically targeting cAMP, PI3K-Akt, and HIF-1α pathways. Overall, the novel model is plausible and reliable, offering a valuable reference for the secondary development of herbal formulas.

System pharmacology-based determination of the functional components and mechanisms in chronic heart failure treatment: an example of Zhenwu decoction.

Chronic heart failure (CHF) is the primary cause of death among patients with cardiovascular diseases, representing the advanced stage in the development of several cardiovascular conditions. Zhenwu decoction (ZWD) has gained widespread recognition as an efficacious remedy for CHF due to its potent therapeutic properties and absence of adverse effects. Nevertheless, the precise molecular mechanisms underlying its actions remain elusive. This study endeavors to unravel the intricate pharmacological underpinnings of five herbs within ZWD concerning CHF through an integrated approach. Initially, pertinent data regarding ZWD and CHF were compiled from established databases, forming the foundation for constructing an intricate network of active component-target interactions. Subsequently, a pioneering method for evaluating node significance was formulated, culminating in the creation of core functional association space (CFAS). To discern vital components, a novel dynamic programming algorithm was devised and used to determine the core component group (CCG) within the CFAS. Enrichment analysis of the CCG targets unveiled the potential coordinated molecular mechanisms of ZWD, illuminating its capacity to ameliorate CHF by modulating genes and related signaling pathways involved in pathological remodeling. Notable pathways encompass PI3K-Akt, diabetic cardiomyopathy, cAMP and MAPK signaling. Concluding the computational analyses, in vitro experiments were executed to assess the effects of vanillic acid, paradol, 10-gingerol and methyl cinnamate. Remarkably, these compounds demonstrated efficacy in reducing the production of ANP and BNP within isoprenaline-induced AC 16 cells, further validating their potential therapeutic utility. This investigation underscores the efficacy of the proposed model in enhancing the precision and reliability of CCG selection within ZWD, thereby presenting a novel avenue for mechanistic inquiries, compound refinement and the secondary development of TCM herbs.

Corrigendum: An integrative pharmacology model for decoding the underlying Therapeutic Mechanisms of Ermiao Powder for Rheumatoid arthritis.

[This corrects the article DOI: 10.3389/fphar.2022.801350.].

Screening of key functional components of Taohong Siwu Decoction on ischemic stroke treatment based on multiobjective optimization approach and experimental validation.

BACKGROUND: Taohong Siwu Decoction (THSWD) is a widely used traditional Chinese medicine (TCM) prescription in the treatment of ischemic stroke. There are thousands of chemical components in THSWD. However, the key functional components are still poorly understood. This study aimed to construct a mathematical model for screening of active ingredients in TCM prescriptions and apply it to THSWD on ischemic stroke. METHODS: Botanical drugs and compounds in THSWD were acquired from multiple public TCM databases. All compounds were initially screened by ADMET properties. SEA, HitPick, and Swiss Target Prediction were used for target prediction of the filtered compounds. Ischemic stroke pathological genes were acquired from the DisGeNet database. The compound-target-pathogenic gene (C-T-P) network of THSWD was constructed and then optimized using the multiobjective optimization (MOO) algorithm. We calculated the cumulative target coverage score of each compound and screened the top compounds with 90% coverage. Finally, verification of the neuroprotective effect of these compounds was performed with the oxygen-glucose deprivation and reoxygenation (OGD/R) model. RESULTS: The optimized C-T-P network contains 167 compounds, 1,467 predicted targets, and 1,758 stroke pathological genes. And the MOO model showed better optimization performance than the degree model, closeness model, and betweenness model. Then, we calculated the cumulative target coverage score of the above compounds, and the cumulative effect of 39 compounds on pathogenic genes reached 90% of all compounds. Furthermore, the experimental results showed that decanoic acid, butylphthalide, chrysophanol, and sinapic acid significantly increased cell viability. Finally, the docking results showed the binding modes of these four compounds and their target proteins. CONCLUSION: This study provides a methodological reference for the screening of potential therapeutic compounds of TCM. In addition, decanoic acid and sinapic acid screened from THSWD were found having potential neuroprotective effects first and verified with cell experiments, however, further in vitro and in vivo studies are needed to explore the precise mechanisms involved.

Decoding the key compounds and mechanism of Shashen Maidong decoction in the treatment of lung cancer.

BACKGROUND: Lung cancer is a malignant tumour with the fastest increase in morbidity and mortality around the world. The clinical treatments available have significant side effects, thus it is desirable to identify alternative modalities to treat lung cancer. Shashen Maidong decoction (SMD) is a commonly used traditional Chinese medicine (TCM) formula for treating lung cancer in the clinic. While the key functional components (KFC) and the underlying mechanisms of SMD treating lung cancer are still unclear. METHODS: We propose a new integrated pharmacology model, which combines a novel node-importance calculation method and the contribution decision rate (CDR) model, to identify the KFC of SMD and to deduce their mechanisms in the treatment of lung cancer. RESULTS: The enriched effective Gene Ontology (GO) terms selected from our proposed node importance detection method could cover 97.66% of enriched GO terms of reference targets. After calculating CDR of active components in key functional network, the first 82 components covered 90.25% of the network information, which were defined as KFC. 82 KFC were subjected to functional analysis and experimental validation. 5-40 µM protocatechuic acid, 100-400 µM paeonol or caffeic acid exerted significant inhibitory activity on the proliferation of A549 cells. The results show that KFC play an important therapeutic role in the treatment of lung cancer by targeting Ras, AKT, IKK, Raf1, MEK, and NF-κB in the PI3K-Akt, MAPK, SCLC, and NSCLC signaling pathways active in lung cancer. CONCLUSIONS: This study provides a methodological reference for the optimization and secondary development of TCM formulas. The strategy proposed in this study can be used to identify key compounds in the complex network and provides an operable test range for subsequent experimental verification, which greatly reduces the experimental workload.

A Metabolic Driven Bio-Responsive Hydrogel Loading Psoralen for Therapy of Rheumatoid Arthritis.

Overexpressed matrix metalloproteinases, hypoxia microenvironment, and metabolic abnormality are important pathological signs of rheumatoid arthritis (RA). Designing a delivery carrier according to the pathological characteristics of RA that can control drug release in response to disease severity may be a promising treatment strategy. Psoralen is the main active ingredient isolated from Psoralea corylifolia L. and possesses excellent anti-inflammatory activities as well as improving bone homeostasis. However, the specific underlying mechanisms, particularly the possible relationships between the anti-RA effects of psoralen and related metabolic network, remain largely unexplored. Furthermore, psoralen shows systemic side effects and has unsatisfactory solubility. Therefore, it is desirable to develop a novel delivery system to maximize psoralen's therapeutic effect. In this study, a self-assembled degradable hydrogel platform is developed that delivers psoralen and calcium peroxide to arthritic joints and controls the release of psoralen and oxygen according to inflammatory stimulation, to regulate homeostasis and the metabolic disorder of the anoxic arthritic microenvironment. Therefore, the hydrogel drug delivery system based on the responsiveness of the inflammatory microenvironment and regulation of metabolism provides a new therapeutic strategy for RA treatment.

Decoding the underlying mechanisms of Di-Tan-Decoction in treating intracerebral hemorrhage based on network pharmacology.

BACKGROUND: Chinese medicine usually acts as "multi-ingredients, multi-targets and multi-pathways" on complex diseases, and these action modes reflect the coordination and integrity of the treatment process with traditional Chinese medicine (TCM). System pharmacology is developed based on the cross-disciplines of directional pharmacology, system biology, and mathematics, has the characteristics of integrity and synergy in the treatment process of TCM. Therefore, it is suitable for analyzing the key ingredients and mechanisms of TCM in treating complex diseases. Intracerebral Hemorrhage (ICH) is one of the leading causes of death in China, with the characteristics of high mortality and disability rate. Bring a significant burden on people and society. An increasing number of studies have shown that Chinese medicine prescriptions have good advantages in the treatment of ICH, and Ditan Decoction (DTT) is one of the commonly used prescriptions in the treatment of ICH. Modern pharmacological studies have shown that DTT may play a therapeutic role in treating ICH by inhibiting brain inflammation, abnormal oxidative stress reaction and reducing neurological damage, but the specific key ingredients and mechanism are still unclear. METHODS: To solve this problem, we established PPI network based on the latest pathogenic gene data of ICH, and CT network based on ingredient and target data of DTT. Subsequently, we established optimization space based on PPI network and CT network, and constructed a new model for node importance calculation, and proposed a calculation method for PES score, thus calculating the functional core ingredients group (FCIG). These core functional groups may represent DTT therapy for ICH. RESULTS: Based on the strategy, 44 ingredients were predicted as FCIG, results showed that 80.44% of the FCIG targets enriched pathways were coincided with the enriched pathways of pathogenic genes. Both the literature and molecular docking results confirm the therapeutic effect of FCIG on ICH via targeting MAPK signaling pathway and PI3K-Akt signaling pathway. CONCLUSIONS: The FCIG obtained by our network pharmacology method can represent the effect of DTT in treating ICH. These results confirmed that our strategy of active ingredient group optimization and the mechanism inference could provide methodological reference for optimization and secondary development of TCM.

Quantification of promoting efficiency and reducing toxicity of Traditional Chinese Medicine: A case study of the combination of Tripterygium wilfordii hook. f. and Lysimachia christinae hance in the treatment of lung cancer.

Traditional Chinese medicine (TCM) usually acts in the form of compound prescriptions in the treatment of complex diseases. The herbs contained in each prescription have the dual nature of efficiency and toxicity due to their complex chemical component, and the principle of prescription is usually to increase efficiency and reduce toxicity. At present, the studies on prescriptions have mainly focused on the consideration of the material basis and possible mechanism of the action mode, but the quantitative research on the compatibility rule of increasing efficiency and reducing toxicity is still the tip of the iceberg. With the extensive application of computational pharmacology technology in the research of TCM prescriptions, it is possible to quantify the mechanism of synergism and toxicity reduction of the TCM formula. Currently, there are some classic drug pairs commonly used to treat complex diseases, such as Tripterygium wilfordii Hook. f. with Lysimachia christinae Hance for lung cancer, Aconitum carmichaelii Debeaux with Glycyrrhiza uralensis Fisch. in the treatment of coronary heart disease, but there is a lack of systematic quantitative analysis model and strategy to quantitatively study the compatibility rule and potential mechanism of synergism and toxicity reduction. To address this issue, we designed an integrated model which integrates matrix decomposition and shortest path propagation, taking into account both the crosstalk of the effective network and the propagation characteristics. With the integrated model strategy, we can quantitatively detect the possible mechanisms of synergism and attenuation of Tripterygium wilfordii Hook. f. and Lysimachia christinae Hance in the treatment of lung cancer. The results showed the compatibility of Tripterygium wilfordii Hook. f. and Lysimachia christinae Hance could increase the efficacy and decrease the toxicity of lung cancer treatment through MAPK pathway and PD-1 checkpoint pathway in lung cancer.

Decoding the Key Functional Combined Components Group and Uncovering the Molecular Mechanism of Longdan Xiegan Decoction in Treating Uveitis.

Objective: Longdan Xiegan Decoction (LXD) is a famous herbal formula in China. It has been proved that LXD has been shown to have a significant inhibitory effect on suppresses the inflammatory cells associated with uveitis. However, the key functional combination of component groups and their possible mechanisms remain unclear. Methods: The community detecting model of the network, the functional response space, and reverse prediction model were utilized to decode the key components group (KCG) and possible mechanism of LXD in treating uveitis. Finally, MTT assay, NO assay and ELISA assay were applied to verify the effectiveness of KCG and the accuracy of our strategy. Results: In the components-targets-pathogenic genes-disease (CTP) network, a combination of Huffman coding and random walk algorithm was used and eight foundational acting communities (FACs) were discovered with important functional significance. Verification has shown that FACs can represent the corresponding C-T network for treating uveitis. A novel node importance calculation method was designed to construct the functional response space and pick out 349 effective proteins. A total of 54 components were screened and defined as KCG. The pathway enrichment results showed that KCG and their targets enriched signal pathways of IL-17, Toll-like receptor, and T cell receptor played an important role in the pathogenesis of uveitis. Furthermore, experimental verification results showed that important KCG quercetin and sitosterol markedly inhibited the production of nitric oxide and significantly regulated the level of TNF-α and IFN-γ in Lipopolysaccharide-induced RAW264.7 cells. Discussion: In this research, we decoded the potential mechanism of the multi-components-genes-pathways of LXD's pharmacological action mode against uveitis based on an integrated pharmacology approach. The results provided a new perspective for the future studies of the anti-uveitis mechanism of traditional Chinese medicine.

ReDisX, a machine learning approach, rationalizes rheumatoid arthritis and coronary artery disease patients uniquely upon identifying subpopulation differentiation markers from their genomic data.

Diseases originate at the molecular-genetic layer, manifest through altered biochemical homeostasis, and develop symptoms later. Hence, symptomatic diagnosis is inadequate to explain the underlying molecular-genetic abnormality and individual genomic disparities. The current trends include molecular-genetic information relying on algorithms to recognize the disease subtypes through gene expressions. Despite their disposition toward disease-specific heterogeneity and cross-disease homogeneity, a gap still exists in describing the extent of homogeneity within the heterogeneous subpopulation of different diseases. They are limited to obtaining the holistic sense of the whole genome-based diagnosis resulting in inaccurate diagnosis and subsequent management. Addressing those ambiguities, our proposed framework, ReDisX, introduces a unique classification system for the patients based on their genomic signatures. In this study, it is a scalable machine learning algorithm deployed to re-categorize the patients with rheumatoid arthritis and coronary artery disease. It reveals heterogeneous subpopulations within a disease and homogenous subpopulations across different diseases. Besides, it identifies granzyme B (GZMB) as a subpopulation-differentiation marker that plausibly serves as a prominent indicator for GZMB-targeted drug repurposing. The ReDisX framework offers a novel strategy to redefine disease diagnosis through characterizing personalized genomic signatures. It may rejuvenate the landscape of precision and personalized diagnosis and a clue to drug repurposing.

Mathematical algorithm-based identification of the functional components and mechanisms in depression treatment: An example of Danggui-Shaoyao-San.

Depression, a complex epidemiological mental disorder, affects around 350 million people worldwide. Despite the availability of antidepressants based on monoamine hypothesis of depression, most patients suffer side effects from these drugs, including psychomotor impairment and dependence liability. Traditional Chinese medicine (TCM) is receiving more and more attention due to the advantages of high therapeutic performance and few side effects in depression treatment. However, complex multicomponents and multi-targets in TCM hinder our ability to identify the functional components and molecular mechanisms of its efficacy. In this study, we designed a novel strategy to capture the functional components and mechanisms of TCM based on a mathematical algorithm. To establish proof of principle, the TCM formula Danggui-Shaoyao-San (DSS), which possesses remarkable antidepressant effect but its functional components and mechanisms are unclear, is used as an example. According to the network motif detection algorithm, key core function motifs (CIM) of DSS in treating depression were captured, followed by a functional analysis and verification. The results demonstrated that 198 pathways were enriched by the target genes of the CIM, and 179 coincided with the enriched pathways of pathogenic genes, accounting for 90.40% of the gene enrichment pathway of the C-T network. Then the functional components group (FCG) comprising 40 components was traced from CIM based on the target coverage accumulation algorithm, after which the pathways enriched by the target genes of FCG were selected to elucidate the potential mechanisms of DSS in treating depression. Finally, the pivotal components in FCG of DSS and the related pathways were selected for experimental validation in vitro and in vivo. Our results indicated good accuracy of the proposed mathematical algorithm in sifting the FCG from the TCM formula, which provided a methodological reference for discovering functional components and interpreting molecular mechanisms of the TCM formula in treating complex diseases.

Computational Metabolomics Reveals the Potential Mechanism of Matrine Mediated Metabolic Network Against Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is a complex issue in cancer treatment in the world at present. Matrine is the main active ingredient isolated from Sophora flavescens air and possesses excellent antitumor effects in HCC. However, the specific underlying mechanisms, especially the possible relationships between the anti-HCC effect of matrine and the related metabolic network of HCC, are not yet clear and need further clarification. In this study, an integrative metabolomic-based bioinformatics algorithm was designed to explore the underlying mechanism of matrine on HCC by regulating the metabolic network. Cell clone formation, invasion, and adhesion assay were utilized in HCC cells to evaluate the anti-HCC effect of matrine. A cell metabolomics approach based on LC-MS was used to obtain the differential metabolites and metabolic pathways regulated by matrine. The maximum activity contribution score model was developed and applied to calculate high contribution target genes of matrine, which could regulate a metabolic network based on the coexpression matrix of matrine-regulated metabolic genes and targets. Matrine significantly repressed the clone formation and invasion, enhanced cell-cell adhesion, and hampered cell matrix adhesion in SMMC-7721 cells. Metabolomics results suggested that matrine markedly regulated the abnormal metabolic network of HCC by regulating the level of choline, creatine, valine, spermidine, 4-oxoproline, D-(+)-maltose, L-(-)-methionine, L-phenylalanine, L-pyroglutamic acid, and pyridoxine, which are involved in D-glutamine and D-glutamate metabolism, glycine, serine and threonine metabolism, arginine and proline metabolism, etc. Our proposed metabolomic-based bioinformatics algorithm showed that the regulating metabolic networks of matrine exhibit anti-HCC effects through acting on MMP7, ABCC1, PTGS1, etc. At last, MMP7 and its related target ß-catenin were validated. Together, the metabolomic-based bioinformatics algorithm reveals the effects of the regulating metabolic networks of matrine in treating HCC relying on the unique characteristics of the multitargets and multipathways of traditional Chinese medicine.

Detecting Key Functional Components Group and Speculating the Potential Mechanism of Xiao-Xu-Ming Decoction in Treating Stroke.

Stroke is a cerebrovascular event with cerebral blood flow interruption which is caused by occlusion or bursting of cerebral vessels. At present, the main methods in treating stroke are surgical treatment, statins, and recombinant tissue-type plasminogen activator (rt-PA). Relatively, traditional Chinese medicine (TCM) has widely been used at clinical level in China and some countries in Asia. Xiao-Xu-Ming decoction (XXMD) is a classical and widely used prescription in treating stroke in China. However, the material basis of effect and the action principle of XXMD are still not clear. To solve this issue, we designed a new system pharmacology strategy that combined targets of XXMD and the pathogenetic genes of stroke to construct a functional response space (FRS). The effective proteins from this space were determined by using a novel node importance calculation method, and then the key functional components group (KFCG) that could mediate the effective proteins was selected based on the dynamic programming strategy. The results showed that enriched pathways of effective proteins selected from FRS could cover 99.10% of enriched pathways of reference targets, which were defined by overlapping of component targets and pathogenetic genes. Targets of optimized KFCG with 56 components can be enriched into 166 pathways that covered 80.43% of 138 pathways of 1,012 pathogenetic genes. A component potential effect score (PES) calculation model was constructed to calculate the comprehensive effective score of components in the components-targets-pathways (C-T-P) network of KFCGs, and showed that ferulic acid, zingerone, and vanillic acid had the highest PESs. Prediction and docking simulations show that these components can affect stroke synergistically through genes such as MEK, NFκB, and PI3K in PI3K-Akt, cAMP, and MAPK cascade signals. Finally, ferulic acid, zingerone, and vanillic acid were tested to be protective for PC12 cells and HT22 cells in increasing cell viabilities after oxygen and glucose deprivation (OGD). Our proposed strategy could improve the accuracy on decoding KFCGs of XXMD and provide a methodologic reference for the optimization, mechanism analysis, and secondary development of the formula in TCM.

Uncovering the potential mechanism of Xue Fu Zhu Yu Decoction in the treatment of intracerebral hemorrhage.

BACKGROUND: Chinese herbal medicine (CHM) is characterized by "multi- compounds, multi-targets and multi-pathway", which has advanced benefits for preventing and treating complex diseases, but there still exists unsolved issues, mainly include unclear material basis and underlying mechanism of prescription. Integrated pharmacology is a hot cross research area based on system biology, mathematics and poly-pharmacology. It can systematically and comprehensively investigate the therapeutic reaction of compounds or drugs on pathogenic genes network, and is especially suitable for the study of complex CHM systems. Intracerebral Hemorrhage (ICH) is one of the main causes of death among Chinese residents, which is characterized with high mortality and high disability rate. In recent years, the treatment of ICH by CHM has been deeply researched. Xue Fu Zhu Yu Decoction (XFZYD), one of the commonly used prescriptions in treating ICH at clinic level, has not been clear about its mechanism. METHODS: Here, we established a strategy, which based on compounds-targets, pathogenetic genes, network analysis and node importance calculation. Using this strategy, the core compounds group (CCG) of XFZYD was predicted and validated by in vitro experiments. The molecular mechanism of XFZYD in treating ICH was deduced based on CCG and their targets. RESULTS: The results show that the CCG with 43 compounds predicted by this model is highly consistent with the corresponding Compound-Target (C-T) network in terms of gene coverage, enriched pathway coverage and accumulated contribution of key nodes at 89.49%, 88.72% and 90.11%, respectively, which confirmed the reliability and accuracy of the effective compound group optimization and mechanism speculation strategy proposed by us. CONCLUSIONS: Our strategy of optimizing the effective compound groups and inferring the mechanism provides a strategic reference for explaining the optimization and inferring the molecular mechanism of prescriptions in treating complex diseases of CHM.

Elucidating the Synergistic Effect of Multiple Chinese Herbal Prescriptions in the Treatment of Post-stroke Neurological Damage.

Background: Traditional Chinese medicine (TCM) has been widely used in the treatment of human diseases. However, the synergistic effects of multiple TCM prescriptions in the treatment of stroke have not been thoroughly studied. Objective of the study: This study aimed to reveal the mechanisms underlying the synergistic effects of these TCM prescriptions in stroke treatment and identify the active compounds. Methods: Herbs and compounds in the Di-Tan Decoction (DTD), Xue-Fu Zhu-Yu Decoction (XFZYD), and Xiao-Xu-Ming Decoction (XXMD) were acquired from the TCMSP database. SEA, HitPick, and TargetNet web servers were used for target prediction. The compound-target (C-T) networks of three prescriptions were constructed and then filtered using the collaborative filtering algorithm. We combined KEGG enrichment analysis, molecular docking, and network analysis approaches to identify active compounds, followed by verification of these compounds with an oxygen-glucose deprivation and reoxygenation (OGD/R) model. Results: The filtered DTD network contained 39 compounds and 534 targets, the filtered XFZYD network contained 40 compounds and 508 targets, and the filtered XXMD network contained 55 compounds and 599 targets. The filtered C-T networks retained approximately 80% of the biological functions of the original networks. Based on the enriched pathways, molecular docking, and network analysis results, we constructed a complex network containing 3 prescriptions, 14 botanical drugs, 26 compounds, 13 targets, and 5 pathways. By calculating the synergy score, we identified the top 5 candidate compounds. The experimental results showed that quercetin, baicalin, and ginsenoside Rg1 independently and synergistically increased cell viability. Conclusion: By integrating pharmacological and chemoinformatic approaches, our study provides a new method for identifying the effective synergistic compounds of TCM prescriptions. The filtered compounds and their synergistic effects on stroke require further research.

An Integrative Pharmacology Model for Decoding the Underlying Therapeutic Mechanisms of Ermiao Powder for Rheumatoid Arthritis.

As a systemic inflammatory arthritis disease, rheumatoid arthritis (RA) is complex and hereditary. Traditional Chinese medicine (TCM) has evident advantages in treating complex diseases, and a variety of TCM formulas have been reported that have effective treatment on RA. Clinical and pharmacological studies showed that Ermiao Powder, which consists of Phellodendron amurense Rupr. (PAR) and Atractylodes lancea (Thunb.) DC. (ALD), can be used in the treatment of RA. Currently, most studies focus on the anti-inflammatory mechanism of PAR and ALD and are less focused on their coordinated molecular mechanism. In this research, we established an integrative pharmacological strategy to explore the coordinated molecular mechanism of the two herbs of Ermiao Powder in treating RA. To explore the potential coordinated mechanism of PAR and ALD, we firstly developed a novel mathematical model to calculate the contribution score of 126 active components and 85 active components, which contributed 90% of the total contribution scores that were retained to construct the coordinated functional space. Then, the knapsack algorithm was applied to identify the core coordinated functional components from the 85 active components. Finally, we obtained the potential coordinated functional components group (CFCG) with 37 components, including wogonin, paeonol, ethyl caffeate, and magnoflorine. Also, functional enrichment analysis was performed on the targets of CFCG to explore the potential coordinated molecular mechanisms of PAR and ALD. The results indicated that the CFCG could treat RA by coordinated targeting to the genes involved in immunity and inflammation-related signal pathways, such as phosphatidylinositol 3­kinase/protein kinase B signaling pathway, mitogen-activated protein kinase signaling pathway, tumor necrosis factor signaling pathway, and nuclear factor-kappa B signaling pathway. The docking and in vitro experiments were used to predict the affinity and validate the effect of CFCG and further confirm the reliability of our method. Our integrative pharmacological strategy, including CFCG identification and verification, can provide the methodological references for exploring the coordinated mechanism of TCM in treating complex diseases and contribute to improving our understanding of the coordinated mechanism.

Uncovering Hidden Mechanisms of Different Prescriptions Treatment for Osteoporosis via Novel Bioinformatics Model and Experiment Validation.

Osteoporosis (OP) is a systemic disease susceptible to fracture due to the decline of bone mineral density and bone mass, the destruction of bone tissue microstructure, and increased bone fragility. At present, the treatments of OP mainly include bisphosphonates, hormone therapy, and RANKL antibody therapy. However, these treatments have observable side effects and cannot fundamentally improve bone metabolism. Currently, the prescription of herbal medicine and their derived proprietary Chinese medicines are playing increasingly important roles in the treatment of OP due to their significant curative effects and few side effects. Among these prescriptions, Gushukang Granules (GSK), Xianling Gubao Capsules (XLGB), and Er-xian Decoction (EXD) are widely employed at the clinic on therapy of OP, which also is in line with the compatibility principle of "different treatments for the same disease" in herbal medicine. However, at present, the functional interpretation of "different treatments for the same disease" in herbal medicine still lacks systematic quantitative research, especially on the detection of key component groups and mechanisms. To solve this problem, we designed a new bioinformatics model based on random walk, optimized programming, and information gain to analyze the components and targets to figure out the Functional Response Motifs (FRMs) of different prescriptions for the therapy of OP. The distribution of high relevance score, the number of reported evidence, and coverage of enriched pathways were performed to verify the precision and reliability of FRMs. At the same time, the information gain and target influence of each component was calculated, and the key component groups in all FRMs of each prescription were screened to speculate the potential action mode of different prescriptions on the same disease. Results show that the relevance score and the number of reported evidence of high reliable genes in FRMs were higher than those of the pathogenic genes of OP. Furthermore, the gene enrichment pathways in FRMs could cover 79.6, 81, and 79.5% of the gene enrichment pathways in the component-target (C-T) network. Functional pathway enrichment analysis showed that GSK, XLGB, and EXD all treat OP through osteoclast differentiation (hsa04380), calcium signaling pathway (hsa04020), MAPK signaling pathway (hsa04010), and PI3K-Akt signaling pathway (hsa04151). Combined with experiments, the key component groups and the mechanism of "different treatments for the same disease" in the three prescriptions and proprietary Chinese medicines were verified. This study provides methodological references for the optimization and mechanism speculation of Chinese medicine prescriptions and proprietary Chinese medicines.

Computational Network Pharmacology-Based Strategy to Capture Key Functional Components and Decode the Mechanism of Chai-Hu-Shu-Gan-San in Treating Depression.

Traditional Chinese medicine (TCM) usually plays therapeutic roles on complex diseases in the form of formulas. However, the multicomponent and multitarget characteristics of formulas bring great challenges to the mechanism analysis and secondary development of TCM in treating complex diseases. Modern bioinformatics provides a new opportunity for the optimization of TCM formulas. In this report, a new bioinformatics analysis of a computational network pharmacology model was designed, which takes Chai-Hu-Shu-Gan-San (CHSGS) treatment of depression as the case. In this model, effective intervention space was constructed to depict the core network of the intervention effect transferred from component targets to pathogenic genes based on a novel node importance calculation method. The intervention-response proteins were selected from the effective intervention space, and the core group of functional components (CGFC) was selected based on these intervention-response proteins. Results show that the enriched pathways and GO terms of intervention-response proteins in effective intervention space could cover 95.3 and 95.7% of the common pathways and GO terms that respond to the major functional therapeutic effects. Additionally, 71 components from 1,012 components were predicted as CGFC, the targets of CGFC enriched in 174 pathways which cover the 86.19% enriched pathways of pathogenic genes. Based on the CGFC, two major mechanism chains were inferred and validated. Finally, the core components in CGFC were evaluated by in vitro experiments. These results indicate that the proposed model with good accuracy in screening the CGFC and inferring potential mechanisms in the formula of TCM, which provides reference for the optimization and mechanism analysis of the formula in TCM.

A System Pharmacology Model for Decoding the Synergistic Mechanisms of Compound Kushen Injection in Treating Breast Cancer.

Breast cancer (BC) is one of the most common malignant tumors among women worldwide and can be treated using various methods; however, side effects of these treatments cannot be ignored. Increasing evidence indicates that compound kushen injection (CKI) can be used to treat BC. However, traditional Chinese medicine (TCM) is characterized by "multi-components" and "multi-targets", which make it challenging to clarify the potential therapeutic mechanisms of CKI on BC. Herein, we designed a novel system pharmacology strategy using differentially expressed gene analysis, pharmacokinetics synthesis screening, target identification, network analysis, and docking validation to construct the synergy contribution degree (SCD) and therapeutic response index (TRI) model to capture the critical components responding to synergistic mechanisms of CKI in BC. Through our designed mathematical models, we defined 24 components as a high contribution group of synergistic components (HCGSC) from 113 potentially active components of CKI based on ADME parameters. Pathway enrichment analysis of HCGSC targets indicated that Rhizoma Heterosmilacis and Radix Sophorae Flavescentis could synergistically target the PI3K-Akt signaling pathway and the cAMP signaling pathway to treat BC. Additionally, TRI analysis showed that the average affinity of HCGSC and targets involved in the key pathways reached -6.47 kcal/mmol, while in vitro experiments proved that two of the three high TRI-scored components in the HCGSC showed significant inhibitory effects on breast cancer cell proliferation and migration. These results demonstrate the accuracy and reliability of the proposed strategy.