Network pharmacology of bioactives from Sorghum bicolor with targets related to diabetes mellitus

New insight of chemical constituents in Persea americana fruit against obesity via integrated pharmacology

Persea americana fruit (PAF) is a favorable nutraceutical resource that comprises diverse unsaturated fatty acids (UFAs). UFAs are significant dietary supplementation, as they relieve metabolic disorders, including obesity (OB). In another aspect, this study was focused on the anti-OB efficacy of the non-fatty acids (NFAs) in PAF through network pharmacology (NP). Natural product activity & species source (NPASS), SwissADME, similarity ensemble approach (SEA), Swiss target prediction (STP), DisGeNET, and online Mendelian inheritance in man (OMIM) were utilized to gather significant molecules and its targets. The crucial targets were adopted to construct certain networks: protein-protein interaction (PPI), PAF-signaling pathways-targets-compounds (PSTC) networks, a bubble chart, molecular docking assay (MDA), and density function theory (DFT). Finally, the toxicities of the key compounds were validated by ADMETlab 2.0 platform. All 41 compounds in PAF conformed to Lipinski's rule, and the key 31 targets were identified between OB and PAF. On the bubble chart, PPAR signaling pathway had the highest rich factor, suggesting that the pathway might be an agonism for anti-OB. Conversely, estrogen signaling pathway had the lowest rich factor, indicating that the mechanism might be antagonism against OB. Likewise, the PSTC network represented that AKT1 had the greatest degree value. The MDA results showed that AKT1-gamma-tocopherol, PPARA-fucosterol, PPARD-stigmasterol, (PPARG)-fucosterol, (NR1H3)-campesterol, and ILK-alpha-tocopherol formed the most stable conformers. The DFT represented that the five molecules might be promising agents via multicomponent targeting. Overall, this study suggests that the NFAs in PAF might play important roles against OB.

Waste to Medicine: Evidence from Computational Studies on the Modulatory Role of Corn Silk on the Therapeutic Targets Implicated in Type 2 Diabetes Mellitus

Type 2 diabetes mellitus (T2DM) is characterized by insulin resistance and/or defective insulin production in the human body. Although the antidiabetic action of corn silk (CS) is well-established, the understanding of the mechanism of action (MoA) behind this potential is lacking. Hence, this study aimed to elucidate the MoA in different samples (raw and three extracts: aqueous, hydro-ethanolic, and ethanolic) as a therapeutic agent for the management of T2DM using metabolomic profiling and computational techniques. Ultra-performance liquid chromatography-mass spectrometry (UP-LCMS), in silico techniques, and density functional theory were used for compound identification and to predict the MoA. A total of 110 out of the 128 identified secondary metabolites passed the Lipinski's rule of five. The Kyoto Encyclopaedia of Genes and Genomes pathway enrichment analysis revealed the cAMP pathway as the hub signaling pathway, in which ADORA1, HCAR2, and GABBR1 were identified as the key target genes implicated in the pathway. Since gallicynoic acid (-48.74 kcal/mol), dodecanedioc acid (-34.53 kcal/mol), and tetradecanedioc acid (-36.80 kcal/mol) interacted well with ADORA1, HCAR2, and GABBR1, respectively, and are thermodynamically stable in their formed compatible complexes, according to the post-molecular dynamics simulation results, they are suggested as potential drug candidates for T2DM therapy via the maintenance of normal glucose homeostasis and pancreatic β-cell function.

Network pharmacology: a bright guiding light on the way to explore the personalized precise medication of traditional Chinese medicine

Network pharmacology can ascertain the therapeutic mechanism of drugs for treating diseases at the level of biological targets and pathways. The effective mechanism study of traditional Chinese medicine (TCM) characterized by multi-component, multi-targeted, and integrative efficacy, perfectly corresponds to the application of network pharmacology. Currently, network pharmacology has been widely utilized to clarify the mechanism of the physiological activity of TCM. In this review, we comprehensively summarize the application of network pharmacology in TCM to reveal its potential of verifying the phenotype and underlying causes of diseases, realizing the personalized and accurate application of TCM. We searched the literature using "TCM network pharmacology" and "network pharmacology" as keywords from Web of Science, PubMed, Google Scholar, as well as Chinese National Knowledge Infrastructure in the last decade. The origins, development, and application of network pharmacology are closely correlated with the study of TCM which has been applied in China for thousands of years. Network pharmacology and TCM have the same core idea and promote each other. A well-defined research strategy for network pharmacology has been utilized in several aspects of TCM research, including the elucidation of the biological basis of diseases and syndromes, the prediction of TCM targets, the screening of TCM active compounds, and the decipherment of mechanisms of TCM in treating diseases. However, several factors limit its application, such as the selection of databases and algorithms, the unstable quality of the research results, and the lack of standardization. This review aims to provide references and ideas for the research of TCM and to encourage the personalized and precise use of Chinese medicine.

Luteolin is a potential inhibitor of COVID-19: An in silico analysis

The severe respiratory syndrome 2019 novel coronavirus disease (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread explosively, raising global health concerns. Luteolin shows antiviral properties, but its effect on SARS-CoV-2 and the associated mechanisms are not elucidated. We used network pharmacology, molecular docking and molecular dynamics to provide potential molecular support of luteolin (3,4,5,7-tetrahydroxyflavone) (LUT) against COVID-19. We employed network pharmacology, molecular docking, and molecular dynamics techniques to investigate how LUT affected COVID-19. Several databases were queried to determine potential target proteins related to LUT and COVID-19. Protein-protein interaction network was constructed, and core targets were filtered by degree value. Following that, functional enrichment was conducted. Molecular docking was utilized to ensure LUT was compatible with core target proteins. Finally, molecular dynamics was used to analyze the effects of the LUT on the optimal hub target. A total of 64 potential target genes for treating COVID-19 were identified, of which albumin, RAC-alpha serine/threonine-protein kinase, caspase-3, epidermal growth factor receptor, heat shock protein HSP 90-alpha, and mitogen-activated protein kinase 1 might be the most promising. In addition, molecular docking results showed that LUT could interact with SARS-CoV-2 major protease 3CL. LUT can bind to the active sites of 3CL protease and mitogen-activated protein kinase 1, showing an anti-SARS-CoV-2 potential.

Exploring the protective effect of Sangggua Drink against type 2 diabetes mellitus in db/db mice using a network pharmacological approach and experimental validation

Sanggua Drink (SGD) is an experienced formula for clinical treatment of type 2 diabetes mellitus (T2DM). Network pharmacology and experiments were combined to explore the potential mechanism of action of SGD on T2DM. The material basis and action mechanism of SGD were investigated to reveal the active components of SGD, potential target prediction was conducted from TargetNet, PharmMapper; Cytoscape was used to construct PPI network and component-target-pathway (C-T-P) network diagram to interpret biological processes and enrich action pathways. 54 compounds and 41 key target proteins were screened, and a total of 98 signaling pathways were obtained. In vivo experiments, the levels of p-AMPK (P < 0.01), p-ACC and p-AKT were significantly increased in the mice with SGD intervention compared to the db/db mice, while level of FOXO1 were decreased. The results suggested that SGD might improve insulin resistance and glucose metabolism in T2DM mice by activating the AMPK/Akt signaling pathway.

Recent Advances in the Synthesis of 3,4-Dihydropyran-2-Ones Organocatalyzed by N-Heterocyclic Carbenes

In recent years, N-heterocyclic carbenes (NHC) have gained recognition as versatile molecules capable of acting as organocatalysts in various reactions, particularly through the activation of aldehydes via Breslow-type adducts. This organocatalytic activation has enabled the production of numerous 3,4-dihydropyran-2-ones and related derivatives. In this review, we provide an overview of the production of 3,4-dihydropyran-2-ones and derivatives via organocatalytic processes involving NHCs over the past eight years. These processes involve the use of a diverse range of substrates, catalysts, and reaction conditions, which can be classified into [4+2]-and [3+3]-type cycloadditions, primarily aimed at synthesizing this skeleton due to its biological activity and multiple stereocenters. These processes are scaled up to the gram scale, and the resulting products are often directed towards epimerization and functionalization to produce more complex molecules with potential applications in the biological field. Finally, we provide a perspective and the future directions of this topic in organic synthesis.

In Silico Molecular Docking of Phytochemicals for Type 2 Diabetes Mellitus Therapy: A Network Pharmacology Approach

Identification of potential lead molecules in herbal medicines is crucial not only for validation but also for drug discovery. This study was focused on identifying the therapeutic mechanisms of 10 common herbs used to treat type 2 diabetes mellitus (T2DM) using network pharmacology and docking studies. Details pertaining to medicinal plants and their phytoconstituents were obtained from Indian Medicinal Plants, Phytochemistry, and Therapeutics and Dr. Duke's database, respectively. MolSoft was used to assess their drug likeness. Prediction of protein targets for the screened phytochemicals and the list of target genes involved in T2DM were obtained using Swiss TargetPrediction and GeneCards respectively. STRING; Cytoscape; Database for Annotation, Visualization, and Integrated Discovery; and PyRx were used for network construction, network analysis, gene ontology analysis, and molecular docking, respectively. The protein targets MAPK1, AKT1, PI3K, and EGFR were identified to play a crucial role in the progression of T2DM. Furthermore, molecular docking indicated that nimbaflavone exhibited high binding affinities for MAPK1 (-8.7 kcal/mole) and PI3K (-9.6 kcal/mole), whereas rutin and 10-hydroxyaloin-B showed high binding affinities for AKT1 (-7.4 kcal/mole) and EGFR (-8.1 kcal/mole), respectively. The findings from this study suggest that flavonoids are the major phytoconstituents that display antidiabetic activity by interacting with key protein molecules related to the MAPK and PI3K-AKT signaling pathways, thereby aiding in the treatment of T2DM. The activation of these pathways alters Ras-GTPase activity and enhances the expression of GLUT4, a glucose transporter, resulting in the uptake of glucose from the bloodstream.

Network Pharmacology- and Molecular Dynamics Simulation-Based Bioprospection of Aspalathus linearis for Type-2 Diabetes Care

The medicinal herb Aspalathus linearis (rooibos) is globally recognized in type-2 diabetes mellitus (T2DM) treatment due to its known and distinctive compounds. This work utilized network pharmacology (NP) coupled with molecular dynamics simulation in gaining new insight into the anti-diabetic molecular mechanism of action of rooibos teas. It looked at the interactions between rooibos constituents with various relevant protein receptors and signaling routes associated with T2DM progression. The initial analysis revealed 197 intersecting gene targets and 13 bioactive rooibos constituents linked to T2DM. The interactions between proteins and compounds to the target matrix were generated with the Cystoscope platform and STRING database. These analyses revealed intersecting nodes active in T2DM and hypoxia-inducible factor 1 (HIF-1) as an integral receptors target. In addition, KEGG analysis identified 11 other pathways besides the hub HIF-1 signaling route which may also be targeted in T2DM progression. In final molecular docking and dynamics simulation analysis, a significant binding affinity was confirmed for key compound-protein matrices. As such, the identified rooibos moieties could serve as putative drug candidates for T2DM control and therapy. This study shows rooibos constituents' interaction with T2DM-linked signaling pathways and target receptors and proposes vitexin, esculin and isovitexin as well as apigenin and kaempferol as respective pharmacologically active rooibos compounds for the modulation of EGFR and IGF1R in the HIF-1 signaling pathway to maintain normal homeostasis and function of the pancreas and pancreatic β-cells in diabetics.

Integrating experimental model, LC-MS/MS chemical analysis, and systems biology approach to investigate the possible antidiabetic effect and mechanisms of Matricaria aurea (Golden Chamomile) in type 2 diabetes mellitus

Type 2 diabetes mellitus (T2DM) is a heterogeneous disease with numerous abnormal targets and pathways involved in insulin resistance, low-grade inflammation, oxidative stress, beta cell dysfunction, and epigenetic factors. Botanical drugs provide a large chemical space that can modify various targets simultaneously. Matricaria aurea (MA, golden chamomile) is a widely used herb in Middle Eastern communities for many ailments, including diabetes mellitus, without any scientific basis to support this tradition. For the first time, this study aimed to investigate the possible antidiabetic activity of MA in a type 2 diabetic rat model, identify chemical constituents by LC-MS/MS, and then elucidate the molecular mechanism(s) using enzyme activity assays, q-RTPCR gene expression analysis, network pharmacology analysis, and molecular docking simulation. Our results demonstrated that only the polar hydroethanolic extract of MA had remarkable antidiabetic activity. Furthermore, it improved dyslipidemia, insulin resistance status, ALT, and AST levels. LC-MS/MS analysis of MA hydroethanolic extract identified 62 compounds, including the popular chamomile flavonoids apigenin and luteolin, other flavonoids and their glycosides, coumarin derivatives, and phenolic acids. Based on pharmacokinetic screening and literature, 46 compounds were chosen for subsequent network analysis, which linked to 364 candidate T2DM targets from various databases and literature. The network analysis identified 123 hub proteins, including insulin signaling and metabolic proteins: IRS1, IRS2, PIK3R1, AKT1, AKT2, MAPK1, MAPK3, and PCK1, inflammatory proteins: TNF and IL1B, antioxidant enzymes: CAT and SOD, and others. Subsequent filtering identified 40 crucial core targets (major hubs) of MA in T2DM treatment. Functional enrichment analyses of the candidate targets revealed that MA targets were mainly involved in the inflammatory module, energy-sensing/endocrine/metabolic module, and oxidative stress module. q-RTPCR gene expression analysis showed that MA hydroethanolic extract was able to significantly upregulate PIK3R1 and downregulate IL1B, PCK1, and MIR29A. Moreover, the activity of the antioxidant hub enzymes was substantially increased. Molecular docking scores were also consistent with the networks’ predictions. Based on experimental and computational analysis, this study revealed for the first time that MA exerted antidiabetic action via simultaneous modulation of multiple targets and pathways, including inflammatory pathways, energy-sensing/endocrine/metabolic pathways, and oxidative stress pathways.

Unraveling the Mechanisms of Clinical Drugs-Induced Neural Tube Defects Based on Network Pharmacology and Molecular Docking Analysis

Chemotherapeutic agents such as methotrexate (MTX), raltitrexed (RTX), 5-fluorouracil (5-FU), hydroxyurea (HU), and retinoic acid (RA), and valproic acid (VPA), an antiepileptic drug, all can cause malformations in the developing central nervous system (CNS), such as neural tube defects (NTDs). However, the common pathogenic mechanisms remain unclear. This study aimed to explore the mechanisms of NTDs caused by MTX, RTX, 5-FU, HU, RA, and VPA (MRFHRV), based on network pharmacology and molecular biology experiments. The MRFHRV targets were integrated with disease targets, to find the potential molecules related to MRFHRV-induced NTDs. Protein-protein interaction analysis and molecular docking were performed to analyze these common targets. Utilizing the kyoto encyclopedia of genes and genomes (KEGG) signaling pathways, we analyzed and searched the possible causative pathogenic mechanisms by crucial targets and the signaling pathway. Results showed that MRFHRV induced NTDs through several key targets (including TP53, MAPK1, HSP90AA1, ESR1, GRB2, HDAC1, EGFR, PIK3CA, RXRA, and FYN) and multiple signaling pathways such as PI3K/Akt pathway, suggesting that abnormal proliferation and differentiation could be critical pathogenic contributors in NTDs induced by MRFHRV. These results were further validated by CCK8 assay in mouse embryonic stem cells and GFAP staining in embryonic brain tissue. This study indicated that chemotherapeutic and antiepileptic agents induced NTDs might through predicted targets TP53, MAPK1, GRB2, HDAC1, EGFR, PIK3CA, RXRA, and FYN and multiple signaling pathways. More caution was required for the clinical administration for women with childbearing potential and pregnant.

Antidiabetic Effect of Rehmanniae Radix Based on Regulation of TRPV1 and SCD1

Purpose: This study aimed to disclose the antidiabetic mechanisms of Rehmanniae Radix (RR). Methods: The antidiabetic effect of RR was studied in Streptozocin (STZ)-induced diabetes mellitus (DM) rats and HepG2 cells with insulin resistance (IR). Antidiabetic targets and signaling pathways of RR were confirmed by the network pharmacology and transcriptome analysis as well as HK2 cells induced by high glucose (HG). Results: After the DM rats were administrated RR extract (RRE) for 4 weeks, their body weight was 10.70 ± 2.00% higher than those in the model group, and the fasting blood glucose (FBG), AUC of the oral glucose tolerance test, and insulin sensitivity test values were 73.23 ± 3.33%, 12.31 ± 2.29%, and 13.61 ± 5.60% lower in the RRE group, respectively. When compared with the model group, an increase of 45.76 ± 3.03% in the glucose uptake of HepG2 cells with IR was seen in the RRE group. The drug (RR)-components-disease (DM)-targets network with 18 components and 58 targets was established. 331 differentially expressed genes (DEGs) were identified. TRPV1 and SCD1 were important DEGs by the intersectional analysis of network pharmacology and renal transcriptome. The TRPV1 overexpression significantly inhibited apoptosis and oxidative stress of the HK2 cells induced by HG, while SCD1 overexpression induced apoptosis and oxidative stress of the HK2 cells induced by low and high glucose. When compared to the HG group, the mRNA and protein expressions of TRPV1 in the presence of RRE (100 μg/ml) increased by 3.94 ± 0.08 and 2.83 ± 0.40 folds, respectively. Conclusion: In summary, RR displayed an inspiring antidiabetic effect by reducing FBG and IR, upregulating the mRNA and protein expressions of TRPV1, and downregulating mRNA expression of SCD1. Induction of TRPV1 and inhibition of SCD1 by RR was possibly one of its antidiabetic mechanisms.

Network Pharmacology Approach for Medicinal Plants: Review and Assessment

Natural products have played a critical role in medicine due to their ability to bind and modulate cellular targets involved in disease. Medicinal plants hold a variety of bioactive scaffolds for the treatment of multiple disorders. The less adverse effects, affordability, and easy accessibility highlight their potential in traditional remedies. Identifying pharmacological targets from active ingredients of medicinal plants has become a hot topic for biomedical research to generate innovative therapies. By developing an unprecedented opportunity for the systematic investigation of traditional medicines, network pharmacology is evolving as a systematic paradigm and becoming a frontier research field of drug discovery and development. The advancement of network pharmacology has opened up new avenues for understanding the complex bioactive components found in various medicinal plants. This study is attributed to a comprehensive summary of network pharmacology based on current research, highlighting various active ingredients, related techniques/tools/databases, and drug discovery and development applications. Moreover, this study would serve as a protocol for discovering novel compounds to explore the full range of biological potential of traditionally used plants. We have attempted to cover this vast topic in the review form. We hope it will serve as a significant pioneer for researchers working with medicinal plants by employing network pharmacology approaches.

Metabolomics-based profiling of 4 avocado varieties using HPLC-MS/MS and GC/MS and evaluation of their antidiabetic activity

Seven avocado "Persea americana" seeds belonging to 4 varieties, collected from different localities across the world, were profiled using HPLC-MS/MS and GC/MS to explore the metabolic makeup variabilities and antidiabetic potential. For the first time, 51 metabolites were tentatively-identified via HPLC-MS/MS, belonging to different classes including flavonoids, biflavonoids, naphthodianthrones, dihydrochalcones, phloroglucinols and phenolic acids while 68 un-saponified and 26 saponified compounds were identified by GC/MS analysis. The primary metabolic variabilities existing among the different varieties were revealed via GC/MS-based metabolomics assisted by unsupervised pattern recognition methods. Fatty acid accumulations were proved as competent, and varietal-discriminatory metabolites. The antidiabetic potential of the different samples was explored using in-vitro amylase and glucosidase inhibition assays, which pointed out to Gwen (KG) as the most potent antidiabetic sample. This could be attributed to its enriched content of poly-unsaturated fatty acids and polyphenolics. Molecular docking was then performed to predict the most promising phytoligands in KG variety to be posed as antidiabetic drug leads. The highest in-silico α-amylase inhibition was observed with chrysoeriol-4'-O-pentoside-7-O-rutinoside, apigenin-7-glucuronide and neoeriocitrin which might serve as potential drug leads for the discovery of new antidiabetic remedies.

Uncovering a Hub Signaling Pathway of Antimicrobial-Antifungal-Anticancer Peptides’ Axis on Short Cationic Peptides via Network Pharmacology Study

Short cationic peptides (SCPs) with therapeutic efficacy of antimicrobial peptides (AMPs), antifungal peptides (AFPs), and anticancer peptides (ACPs) are known as an enhancement of the host defense system. Here, we investigated the uppermost peptide(s), hub signaling pathway(s), and their associated target(s) through network pharmacology. Firstly, we selected SCPs with positive amino acid residues on N- and C- terminals under 500 Dalton via RStudio. Secondly, the overlapping targets between the bacteria-responsive targets (TTD and OMIM) and AMPs’ targets were visualized by VENNY 2.1. Thirdly, the overlapping targets between AFPs’ targets and fungal-responsive targets were exhibited by VENNY 2.1. Fourthly, the overlapping targets between cancer-related targets (TTD and OMIM) and fungal-responsive targets were displayed by VENNY 2.1. Finally, a molecular docking study (MDS) was carried out to discover the most potent peptides on a hub signaling pathway. A total of 1833 SCPs were identified, and AMPs’, AFPs’, and ACPs’ filtration suggested that 197 peptides (30 targets), 81 peptides (6 targets), and 59 peptides (4 targets) were connected, respectively. The AMPs―AFPs―ACPs’ axis indicated that 27 peptides (2 targets) were associated. Each hub signaling pathway for the enhancement of the host defense system was “Inactivation of Rap1 signaling pathway on AMPs”, “Activation of Notch signaling pathway on AMPs―AFPs’ axis”, and “Inactivation of HIF-1 signaling pathway on AMPs―AFPs―ACPs’ axis”. The most potent peptides were assessed via MDS; finally, HPIK on STAT3 and HVTK on NOS2 and on HIF-1 signaling pathway were the most stable complexes. Furthermore, the two peptides had better affinity scores than standard inhibitors (Stattic, 1400 W). Overall, the most potent SCPs for the human defense system were HPIK on STAT3 and HVTK on NOS2, which might inactivate the HIF-1 signaling pathway.

Evaluation of RevX Solution Extract as a Potential Inhibitor of the Main Protease of SARS-CoV-2—In vitro Study and Molecular Docking

The main protease (M^pro) of SARS-CoV-2 is a protease necessary for viral polyprotein processing and maturation. M^pro cleaves the polypeptide sequence after the glutamine residues. There is no known cellular protease with this substrate specificity in humans; therefore, it is considered an attractive drug target. Previously, fermented sorghum extract RevX (trademark of Revolutrx INC.) solution significantly alleviated physical decline and complications in a patient with lung adenocarcinoma, suggesting the role of bioactive components in RevX solution. To further explore whether the bioactive components in RevX solution exhibit other biological activities, such as antiviral effects, we investigated its inhibitory effect on the M^pro of SARS-CoV-2 virus. We report herein that the solid extract of the RevX solution exhibits an efficacious M^pro inhibitory activity, with IC₅₀ of 2.07 ± 0.38 μg/mL. Molecular docking of sterol-like components in the RevX extracts identified by MS shows that the three sterol-like molecules can bind to the active region of the GC376-M^pro complex, supporting the structure-function relationship. Combined with its ability to significantly alleviate the body's immunity decline and to inhibit the activity of SARS-CoV-2 M^pro, RevX solution may provide a possible alternative supportive treatment for patients with COVID-19.

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The unique substrate specificity of SARS-CoV-2 M^pro makes it a potential target for drug design.

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Fermented sorghum extract RevX solution enhances adjuvant therapy of lung adenocarcinoma, suggesting the role of bioactive components in RevX solution.

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The solid extract of RevX showed potent M^pro inhibitory activity with IC₅₀ of 2.07 ± 0.38 μg/mL.

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The three sterol-like structures of RevX extract showed a similar binding cavity to Mpro-GC376, suggesting its putative inhibitory activity.

Mortality-based definition of renal hyperfiltration in middle-aged men: a 35-year cohort from Finland

Background

While the impact of low glomerular filtration rate (eGFR) on various outcomes has been extensively studied, the other adverse occurrence, renal hyperfiltration (RHF), remains understudied, poorly defined, and, therefore, its impact on mortality unestablished.

Methods

Using a population-based subcohort from the Kuopio Ischaemic Disease Risk Factor Study restricted to non-diabetic Finnish men aged 54 or 55 years, we followed up n = 1179 study participants for up to 35 years. We evaluated the hazard of all-cause mortality associated to RHF at different cutoff points defining eGFR. Based on models’ accuracy we suggested an optimal eGFR cutoff point for the definition of RHF. We divided the RHF category to three subgroups and evaluated them in terms of baseline characteristics and mortality hazard.

Results

The eGFR value of 97 mL/min/1.73 m² corresponded to the models with the highest accuracy. Overall RHF associated with an increased risk of mortality (hazard ratio [HR] 1.42; 95% confidence interval [CI] 1.21 to 1.67). Moderate RHF associated with a decreased HR of mortality when compared to mild (0.64; 95% CI 0.46 to 0.9) or to extreme RHF (0.61; 95% CI 0.43 to 0.85), suggesting a rather U-shaped relationship between RHF’s eGFR values and mortality hazard.

Conclusion

The burden of increased eGFR within what is still considered normal eGFR category was highly underestimated. RHF’s eGFR values had a U-shaped association with the risk of overall mortality. A more uniform consensual definition of RHF is needed, as higher to normal eGFR values that are not without consequences.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11255-021-03048-6.

Mechanisms of Rhizoma Coptidis against type 2 diabetes mellitus explored by network pharmacology combined with molecular docking and experimental validation

This study systematically explored the underlying mechanism of Rhizoma Coptidis against type 2 diabetes mellitus (T2DM) by using network pharmacology and molecular docking and experimental validation. We retrieved and screened active compounds of Rhizoma Coptidis and corresponding T2DM-related targets across multiple databases. PPI networks of the genes were constructed using STRING, and the core targets were screened via topological analysis. GO and KEGG enrichment analyses were performed by using DAVID. Finally, molecular docking and experimental studies were performed after bioinformatic analysis for verification. There were 14 active compounds and 19 core targets of Rhizoma Coptidis-T2DM, of which quercetin was identified as the main compound and IL6, VEGFA and TNF were the most significant core targets. GO and KEGG enrichment analyses showed that Rhizoma Coptidis ameliorated T2DM by regulating multiple biological processes and pathways. Docking studies indicated that IL6, VEGFA and TNF could stably bind with all active compounds of Rhizoma Coptidis. The results of our experiments revealed that Rhizoma Coptidis could inhibit the expression of IL6 and TNFα and enhance islet cell viability. This study suggests anti-inflammatory therapeutic effects of Rhizoma Coptidis on T2DM, thereby providing a scientific basis and new insight for further research on the antidiabetic effect of Rhizoma Coptidis.

Network Pharmacology-Based Study to Uncover Potential Pharmacological Mechanisms of Korean Thistle (Cirsium japonicum var. maackii (Maxim.) Matsum.) Flower against Cancer

Cirsium japonicum var. maackii (Maxim.) Matsum. or Korean thistle flower is a herbal plant used to treat tumors in Korean folk remedies, but its essential bioactives and pharmacological mechanisms against cancer have remained unexplored. This study identified the main compounds(s) and mechanism(s) of the C. maackii flower against cancer via network pharmacology. The bioactives from the C. maackii flower were revealed by gas chromatography-mass spectrum (GC-MS), and SwissADME evaluated their physicochemical properties. Next, target(s) associated with the obtained bioactives or cancer-related targets were retrieved by public databases, and the Venn diagram selected the overlapping targets. The networks between overlapping targets and bioactives were visualized, constructed, and analyzed by RPackage. Finally, we implemented a molecular docking test (MDT) to explore key target(s) and compound(s) on AutoDockVina and LigPlot+. GC-MS detected a total of 34 bioactives and all were accepted by Lipinski's rules and therefore classified as drug-like compounds (DLCs). A total of 597 bioactive-related targets and 4245 cancer-related targets were identified from public databases. The final 51 overlapping targets were selected between the bioactive targets network and cancer-related targets. With Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, a total of 20 signaling pathways were manifested, and a hub signaling pathway (PI3K-Akt signaling pathway), a key target (Akt1), and a key compound (Urs-12-en-24-oic acid, 3-oxo, methyl ester) were selected among the 20 signaling pathways via MDT. Overall, Urs-12-en-24-oic acid, 3-oxo, methyl ester from the C. maackii flower has potent anti-cancer efficacy by inactivating Akt1 on the PI3K-Akt signaling pathway.

A network pharmacology analysis on drug-like compounds from Ganoderma lucidum for alleviation of atherosclerosis

Ganoderma lucidum (GL) is known as a potent alleviator against chronic inflammatory disease like atherosclerosis (AS), but its mechanisms against AS have not been unveiled. This research aimed to identify the key compounds(s) and mechanism(s) of GL against AS through network pharmacology. The compounds from GL were identified by gas chromatography-mass spectrum (GC-MS), and SwissADME screened their physicochemical properties. Then, the target(s) associated with the screened compound(s) or AS related targets were identified by public databases, and we selected the overlapping targets using a Venn diagram. The networks between overlapping targets and compounds were visualized, constructed, and analyzed by RStudio. Finally, we performed a molecular docking test (MDT) to explore key target(s), compound(s), on AutoDockVina. A total of 35 compounds in GL were detected via GC-MS, and 34 compounds (accepted by Lipinski's rule) were selected as drug-like compounds (DLCs). A total of 34 compounds were connected to the number of 785 targets, and DisGeNET and Online Mendelian Inheritance in Man (OMIM) identified 2,606 AS-related targets. The final 98 overlapping targets were extracted between the compounds-targets and AS-related targets. On Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, the number of 27 signaling pathways were sorted out, and a hub signaling pathway (MAPK signaling pathway), a core gene (PRKCA), and a key compound (Benzamide, 4-acetyl-N-[2,6-dimethylphenyl]) were selected among the 27 signaling pathways via MDT. Overall, we found that the identified 3 DLCs from GL have potent anti-inflammatory efficacy, improving AS by inactivating the MAPK signaling pathway. PRACTICAL APPLICATIONS: Ganoderma lucidum (GL) has been used as a medicinal or edible mushroom for chronic inflammatory patients: diabetes mellitus and dyslipidemia, especially atherosclerosis (AS). Until now, the majority of mushroom research has been implemented regarding β-glucan derivatives with very hydrophilic physicochemical properties. It implies that β-glucan or its derivatives have poor bioavailability. Hence, we have involved GC-MS in identifying lipophilic compounds from GL, which filtered them in silico to sort drug-like compounds (DLCs). Then, we retrieved targets associated with the DLCs, and identified a key signaling pathway, key targets, and key compounds against AS. In this paper, we utilized bioinformatics and network pharmacology theory to understand the uncovered pharmacological mechanism of GL on AS. To sum things up, our analysis elucidates the relationships between signaling pathways, targets, and compounds in GL. Ultimately, this work provides biochemical evidence to identify the therapeutic effect of GL on AS, and a scientific basis for deciphering the key mechanism on DLCs of GL against AS.

Uncovering Mechanisms of Zanthoxylum piperitum Fruits for the Alleviation of Rheumatoid Arthritis Based on Network Pharmacology

Zanthoxylum piperitum fruits (ZPFs) have been demonstrated favorable clinical efficacy on rheumatoid arthritis (RA), but its compounds and mechanisms against RA have not been elucidated. This study was to investigate the compounds and mechanisms of ZPFs to alleviate RA via network pharmacology. The compounds from ZPFs were detected by gas chromatography-mass spectrometry (GC-MS) and screened to select drug-likeness compounds through SwissADME. Targets associated with bioactive compounds or RA were identified utilizing bioinformatics databases. The signaling pathways related to RA were constructed; interactions among targets; and signaling pathways-targets-compounds (STC) were analyzed by RPackage. Finally, a molecular docking test (MDT) was performed to validate affinity between targets and compounds on key signaling pathway(s). GC-MS detected a total of 85 compounds from ZPFs, and drug-likeness properties accepted all compounds. A total of 216 targets associated with compounds 3377 RA targets and 101 targets between them were finally identified. Then, a bubble chart exhibited that inactivation of MAPK (mitogen-activated protein kinase) and activation of PPAR (peroxisome proliferator-activated receptor) signaling pathway might be key pathways against RA. Overall, this work suggests that seven compounds from ZPFs and eight targets might be multiple targets on RA and provide integrated pharmacological evidence to support the clinical efficacy of ZPFs on RA.

Network Pharmacology Study to Interpret Signaling Pathways of Ilex cornuta Leaves against Obesity

Ilex cornuta Leaves (ICLs) are a representative and traditional prescription for controlling obesity. Nevertheless, the corresponding therapeutic compounds and related pharmacological mechanisms of such medication remain undocumented. The compounds from ICLs were identified by gas chromatography-mass spectrum (GC-MS), and SwissADME confirmed their physicochemical properties. Next, the target proteins related to compounds or obesity-associated proteins were retrieved from public databases. RPackage constructed the protein–protein interaction (PPI) network, a bubble chart, and signaling pathways–target proteins–compounds (STC) network. Lastly, a molecular docking test (MDT) was performed to evaluate the affinity between target proteins and ligands from ICLs. GC-MS detected a total of 51 compounds from ICLs. The public databases identified 219 target proteins associated with selective compounds, 3028 obesity-related target proteins, and 118 overlapping target proteins. Moreover, the STC network revealed 42 target proteins, 22 signaling pathways, and 39 compounds, which were viewed to be remedially significant. The NOD-like receptor (NLR) signaling pathway was considered a key signaling pathway from the bubble chart. In parallel, the MDT identified three target proteins (IL6, MAPK1, and CASP1) on the NLR signaling pathway and four compounds against obesity. Overall, four compounds from ICLs might show anti-obesity synergistic efficacy by inactivating the NLR signaling pathway.

Network pharmacology approach to decipher signaling pathways associated with target proteins of NSAIDs against COVID-19

Non-steroidal anti-inflammatory drugs (NSAIDs) showed promising clinical efficacy toward COVID-19 (Coronavirus disease 2019) patients as potent painkillers and anti-inflammatory agents. However, the prospective anti-COVID-19 mechanisms of NSAIDs are not evidently exposed. Therefore, we intended to decipher the most influential NSAIDs candidate(s) and its novel mechanism(s) against COVID-19 by network pharmacology. FDA (U.S. Food & Drug Administration) approved NSAIDs (19 active drugs and one prodrug) were used for this study. Target proteins related to selected NSAIDs and COVID-19 related target proteins were identified by the Similarity Ensemble Approach, Swiss Target Prediction, and PubChem databases, respectively. Venn diagram identified overlapping target proteins between NSAIDs and COVID-19 related target proteins. The interactive networking between NSAIDs and overlapping target proteins was analyzed by STRING. RStudio plotted the bubble chart of the KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment analysis of overlapping target proteins. Finally, the binding affinity of NSAIDs against target proteins was determined through molecular docking test (MDT). Geneset enrichment analysis exhibited 26 signaling pathways against COVID-19. Inhibition of proinflammatory stimuli of tissues and/or cells by inactivating the RAS signaling pathway was identified as the key anti-COVID-19 mechanism of NSAIDs. Besides, MAPK8, MAPK10, and BAD target proteins were explored as the associated target proteins of the RAS. Among twenty NSAIDs, 6MNA, Rofecoxib, and Indomethacin revealed promising binding affinity with the highest docking score against three identified target proteins, respectively. Overall, our proposed three NSAIDs (6MNA, Rofecoxib, and Indomethacin) might block the RAS by inactivating its associated target proteins, thus may alleviate excessive inflammation induced by SARS-CoV-2.

A network pharmacology study on main chemical compounds from Hibiscus cannabinus L. leaves

Hibiscus cannabinus L. leaves (HCLLs) are considered a favorable source of natural antiobesity substances. However, actual bioactive compound(s) in it and their mechanism(s) against obesity have not been confirmed. Hence, network pharmacology was conducted to identify its key compounds and mechanism(s) against obesity. Compounds in HCLLs were identified through GC-MS analysis and screened by Lipinski's rule. Genes related to the selected compounds and obesity were obtained from public databases, and overlapping genes between HCLL compound-related genes and obesity target genes were selected using a Venn diagram. The networking between selected compounds and overlapping genes was then constructed, visualized, and analyzed by RStudio. Finally, the binding affinity between compounds and genes was evaluated via molecular docking (MD). A total of 30 compounds in HCLLs were detected via GC-MS, and Lipinski's rule accepted all compounds. The compound-related genes (570 genes) and obesity targeted genes (3028 genes) were identified, and between them, 64 overlapping genes were selected. Gene Set Enrichment Analysis (GSEA) displayed that the mechanisms of HCLLs against obesity were associated with 13 signaling pathways on 22 compounds in HCLLs. Superficially, AKT1, vitamin E, and RAS signaling pathways were noted as a hub gene, an uppermost bioactive compound, and a hub signaling pathway, respectively. However, the binding affinity of ligands and proteins on the RAS signaling pathway was very low; instead, the PPAR signalling pathway was evaluated with potent efficacy against obesity through MD. On the PPAR signaling pathway, α-amyrin was found as the most significant compound for the amelioration of obesity. α-Amyrin manifested the strongest binding affinity on six target proteins associated with the PPAR signaling pathway. Our study suggests that an auxiliary (PPAR) signaling pathway of HCLLs might intervene efficiently against obesity over the hub (RAS) signaling pathway.