{"id":58026,"date":"2024-06-25T10:38:07","date_gmt":"2024-06-25T10:38:07","guid":{"rendered":"https:\/\/biomedpharmajournal.org\/?p=58026"},"modified":"2024-07-03T17:43:31","modified_gmt":"2024-07-03T17:43:31","slug":"omics-based-analysis-of-bhadradarvadi-kashayam-in-managing-rheumatoid-arthritis-via-cxcl8-cxcr1-2-axis-mapk-and-nf-%ce%bab-signaling-pathways-a-network-pharmacology-approach","status":"publish","type":"post","link":"https:\/\/biomedpharmajournal.org\/staging\/vol17no2\/omics-based-analysis-of-bhadradarvadi-kashayam-in-managing-rheumatoid-arthritis-via-cxcl8-cxcr1-2-axis-mapk-and-nf-%ce%bab-signaling-pathways-a-network-pharmacology-approach\/","title":{"rendered":"Omics-based Analysis of Bhadradarvadi Kashayam in Managing Rheumatoid Arthritis via CXCL8-CXCR1\/2 axis, MAPK and NF-\u03baB Signaling Pathways – A Network Pharmacology Approach"},"content":{"rendered":"\n

Introduction<\/strong><\/p>\n\n\n\n

Rheumatoid arthritis (RA) is a chronic\nautoimmune disease designated with symmetrical inflammation, progressive\ndisability, bone erosion and cartilage destruction1\u20133<\/sup>. As an\nincreasingly common inflammatory disease, the average prevalence of RA is about\n0.5%-1.0% worldwide with the male-to-female ratio nearly 1:4, and the incident\nrate nearing approximately 0.6% in India4<\/sup>. However, the pathogenesis\nand\/or etiology of RA remains convoluted and unclear5<\/sup>. Several\nstudies have reported that a variety of immune cells and inflammatory mediators\nare critically implicated in the disease development and progression6,7<\/sup>.\nThe processes are driven through the secretion of various pro-inflammatory\ncytokines and inflammatory enzymes by monocytes\/macrophages in synovial fluid.\nThese inflammatory mediators play a critical role in regulating the growth and\nproliferation of immune T cells, which further stimulates the release of\ntissue-degrading enzymes, all of which enhance the inflammatory reaction and\njoint damage8<\/sup>. Hence, aberrant activation of various important regulatory\nsignaling cascades including mitogen-activated protein kinase (MAPK), toll-like\nreceptor, CXCL8-CXCR1\/2 axis, and nuclear factor kappa B (NF\u03baB) pathways are\nrecognized as significant contributors to accelerating the progression of RA9,10<\/sup>.\n<\/p>\n\n\n\n

Currently, non-steroidal\nanti-inflammatory medications (NSAIDs), disease-modifying anti-rheumatic\nmedicines (DMARDs), biologics, and immunosuppressants have been prescribed for\nthe treatment of RA11<\/sup>. Even though they can mitigate the symptoms\nincluding inflammation and pain, they are found to come up with noteworthy side\neffects such as hepatic and pulmonary toxicity, peptic ulcers, infections,\ngastro-intestinal damage, renal failure and so forth12\u201314<\/sup>.\nTherefore, an exploration for efficient alternative and additional therapeutic\noptions that aim to mitigate the symptoms concomitant with RA instead of\naffording a complete cure to the patients still endures.<\/p>\n\n\n\n

In the current situation,\ncomplementary and alternative medicine (CAM) are getting greater prominence by\nproviding a variety of treatment choices that outperforms traditional\nmedications. The usage of CAM is on an increasing trend in all continents and is\nnot restricted only to Asia15<\/sup>. As reported by National Health\nInterview Survey (NHIS) from the Centers for Disease Control (CDC) in 2007,\nmore than 200K US adults selected to use Ayurvedic medications in the past year\nto treat their health issues. Seventy percent of the Indian population relies\non the system of medicine that was traditionally considered like Ayurveda,\nhomeopathy, yoga and Siddha16\u201318<\/sup>. Of the known traditional medical\npractices, Ayurveda is among the holistic medicinal approach and is in practice\nsince olden times and emerged from a verbal tradition that was subsequently\nchronicled in the ancient Indian texts called Vedas, which were originally\nwritten in Sanskrit. It always emphasizes the significance of endorsing health\nby taking a holistic outlook of mind, body, and spirit as well as using natural\nremedies acquired from medicinal plants and minerals. Ayurvedic system of\nmedicine is very less studied and documented due to the difficulty in\nunderstanding strict logical descriptions and less conceptual development.\nAyurvedic pharmacology (Dravyaguna) encircles with three concepts i) taste\n(rasa), ii) properties (guna), iii) active principles (virya), iv)\nbiotransformation (vipak) and v) specific actions (prabhav)19<\/sup> and it\naffords to bring a favorable cure to an array of ailments which include RA20,21<\/sup>.\nOne of the highly cited oral medications for RA is Bhadradarvadi decoction\n(BDK), a polyherbal concoction of fifteen herbs with influential active\ningredients. <\/p>\n\n\n\n

A blend of system biology with the\nstudy of levels of biological information along with the clinical pharmacology\nprofile, brings in the module of system pharmacology, that investigates the\nrelationship between molecular properties and their pharmacological effect. The\narena integrates the omics (proteomics, genomics, metabolomics, and\ntranscriptomics) outlines and metabolites to construct networks to ingress the\nunderlying drug mechanisms22\u201324<\/sup>. In addition to finding unique drug\ntargets that are novel, the network medicine strategy can be efficiently used\nto establish combinations of drugs in conditions like neurovascular diseases,\nCVD and cancer25\u201328<\/sup>. Consequently, the strategy could be extended to\nexplore the involvement of plant-derived medicines in holistically treating a\nvariety of diseases.<\/p>\n\n\n\n

The herbal formulations encircle\nperplexed chemical systems incorporating a mixture of a wide array of\ncompounds. The formulations derived from medicinal plants were deemed to work\non a web of targets specific to the phenotype of diseases. The mode of action\nand efficacy of the herbal components could be predicted by network-based\nmethods. The investigation of the network uncovers the effective therapeutic\nmechanism of the herbal compositions and the adeptness to comprehend the\nunexposed pharmacological properties. The network medicine strategy identifies\nthe prospective on and off-targets that can be used to create a sensible design\nof drug interactions. The present study employs a network pharmacology strategy\nto probe into the mode of action of the polyherbal formulation of BDK in\ntreating RA. The models of pharmacokinetic properties such as oral bioavailability\n(OB), drug-likeliness (DL), and ADMET parameters were evaluated to explore the\ntherapeutic effects of the formulation. In this study, we attempted to utilize\nsystematic pharmacology and network analysis to interpret the connections at\nthe molecular level generated by several bioactive components of the herbal\nformulation to comprehend its mechanism of action.<\/p>\n\n\n\n

Materials and Methods<\/strong><\/p>\n\n\n\n

Profiling of\ncompound<\/strong><\/p>\n\n\n\n

BDK is prepared from the combination\nof fifteen herbs Lignum of Cedrus deodara (CD), Radix of Abutilon Indicum (AI),\nRadix of Valeriana jatamansi (Vj), Radix of Desmodium gangeticum (Dg), Radix of\nSaussurea costus (Sc), Radix of Gmelina arborea (Ga), Radix of Aegle marmelos\n(Am), Radix of Stereospermum colors (Sc), Radix of Oroxylum indicum (Oi), Radix\nof Premna corymbosa (Pc), Radix of Solanum anguivi (Sa), Radix of Solanum\nsurattense (Ss), Fructus of Tribulus terrestris (Tt), Radix of Pseudarthria\nviscida (Pv) and Radix of Sida cordifolia (Scf)29<\/sup>. The bioactive\ncompounds from these fifteen plants were retrieved from the databases such as\nTraditional Chinese Medicine Systems Pharmacology (TCMSP)30<\/sup>,\nUniversal Natural Products Database (UNPD)22<\/sup>, and Dr. Duke’s\nPhytochemical and Ethnobotanical Databases31<\/sup>. The compound\nstructures were fetched from the Chemical book, PubChem and drawn using the\nChemsketch tool.<\/p>\n\n\n\n

Oral\nbioavailability (OB) and drug-likeness (DL) assessment<\/strong><\/p>\n\n\n\n

Traditional medicine formula\ndecoctions are mostly orally administered, which have a greater number of\ncompounds, but only a part of them create a therapeutic effect. OB signifies\nthe capability of a compound to disseminate in the body after oral ingestion.\nOB can determine the ability of active compounds in a formula to be transported\nthroughout the body and cause a physiochemical effect. Analysis of several\nmolecular descriptors incited a series of rules connecting them with OB\nincluding Lipinski\u2019s rule of 532<\/sup>. In this study, the QikProp filter\nfrom Schr\u00f6dinger software (Schrodinger, LLC, New York, USA) was used with a set\nof descriptors (% human oral absorption, and rule of 5) were selected to\ndelineate the activity, permeability, and metabolism. Together with Lipinski\u2019s\nparameters, the other molecular descriptors supporting OB as recommended by\nVeber were also calculated33<\/sup>. Pharmacological descriptors including\nmolecular weight (MW), Ghose-Crippen-Viswanadhan octanol-water partition\ncoefficient (ALogP), number of donor atoms for hydrogen bond (nHDon), number of\nacceptor atoms for hydrogen bond (nHAcc), and total polar surface area (TPSA) were\npredicted using PaDEL descriptor tool (version 2.18)34<\/sup>.<\/p>\n\n\n\n

DL is a pointer for identifying the likeness of a compound, which possess functional groups and physicochemical properties similar to the conventional drugs and could have a possible desired therapeutic effect. Evaluating DL according to the concept of desirability termed as a quantitative estimate of drug-likeness (QED), aids to investigate the chemical homology of the compounds with known drugs. The individual desirability functions were calculated for the molecular descriptors MW, RotB, ALogP, nHDon, nHAcc, and TPSA. The QED score is calculated by obtaining the geometric mean of all individual desirability functions using the following equation: <\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

Where di is calculated with the desirability score of the nth molecular descriptor. The QED score ranges from 0 (complete unfavorable properties) to 0.49 (all favorable properties). DruLiTo tool was utilized to calculate the score.<\/p>\n\n\n\n

Toxicity prediction<\/strong><\/p>\n\n\n\n

Furthermore, to the above assessments,\nthe compounds chosen were evaluated for the probable effect of toxicity. The\ncompound\u2019s toxicity was determined by utilizing the ProTox-II (prediction of\ntoxicity of chemicals) server35<\/sup>. The server predicts rodent oral\ntoxicity using a globally harmonized system of classification of labeling of\nchemicals (GHS), hepatotoxicity using the Random Forest (RF) algorithm and\nimmunotoxicity using the Bernoulli-Na\u00efve Bayes algorithm.<\/p>\n\n\n\n

Target fishing\nfor potential active compounds<\/strong><\/p>\n\n\n\n

The archetype of \u201cone drug \u2013 one\ntarget \u2013 one disease\u201d has been transfigured to \u201cone drug \u2013 multi targets \u2013\nmulti diseases\u201d owing to the progression in the studies and approaches of\npolypharmacology. To discern the multi-target hypothesis, the bioactive compounds\nwere further assessed and the protein targets were recognized. The protein\ntargets for the identified compounds were gathered from several resources\nincluding extensive literature hunts and database searches from TCMSP,\nTherapeutic Target Database (TTD)36<\/sup>, and Traditional Chinese\nMedicine Integrated Database (TCMID)37<\/sup>. The compounds with yet\nunknown targets were imposed to reverse drug targeting strategy using STITCH\n(search tool for interacting chemicals) and BindingDB38<\/sup>. Based on\nthe similarity index (>0.85) and other parameters like the Tanimoto index\n(>0.9) from Binding DB and STITCH respectively, the targets for the\ncompounds were obtained.<\/p>\n\n\n\n

Enrichment and\ncomorbidities analysis<\/strong><\/p>\n\n\n\n

The Gene Ontology (GO) functional and\npathway enrichment analysis provides a standardized depiction and annotation\nfor genes and gene products that can possess the response to a biological\nquery. Over GO analysis, researchers can retrieve complete insights into\nvarious aspects including cellular component (CC), biological process (BP), and\nmolecular function (MF). GO and pathway analysis was performed using ToppGene\nSuite (a one-stop portal for functional and pathway enrichment analysis)39<\/sup>.\nThe portal identifies the functional and pathway enrichment based on various\ndatabases including the Kyoto Encyclopedia of Genes and Genomes (KEGG),\nReactome, and so forth. GO and KEGG pathway enrichment analysis for the\npredicted target proteins from the compounds were performed using the ToppFun\ntool in the Toppgene Suite. The default parameters like the probability density\nfunction for the p-value scheme and FDR correction were chosen. Gene count\n>2 and FDR B&H (q value) as 0.01 were selected as the significant\ncut-off limit for enrichment analysis. Further, the targets were explored to\nidentify their concomitant diseases using DisGeNET (v7.0), which integrates\nhuman disease-gene correlations and their variants. Comorbidities associated\nwith RA were filtered out to spotlight the intricacies of the target proteins\nby involving manifold diseases. <\/p>\n\n\n\n

Protein-protein\ninteraction (PPI) network construction<\/strong><\/p>\n\n\n\n

Upholding cellular homeostasis needs a\nblend of proteins with other molecules including genes, small particles, and\nother proteins. A PPI network was ascertained to explicate the connection\nbetween the human proteins and their predicted targets, as well as it has\nloomed as an ideal method for drug discovery40<\/sup>. Interacting partners\nfor the target proteins of related ingredients were obtained using the \u201cSearch\ntool for retrieval of interacting proteins\u201d (STRING v11.5), which is a database\nfor constructing a protein-protein interaction network. The species were set as\n\u201cHomo sapiens\u201d and the interacting proteins retrieved with a confidence score\ncut-off as >0.8, which ensures to be reported through experiments and\ndatabases were only considered for the analysis. The networks were then\nconstructed, visualized, and further analyzed using Cytoscape v3.8.241<\/sup>.\n<\/p>\n\n\n\n

Hub protein\nidentification and cluster analysis<\/strong><\/p>\n\n\n\n

In the PPI network, each node\nspecifies a target protein and an edge specifies the interaction between the\ntarget proteins. Hub proteins are highly influential proteins having high\ninteraction with a large number of partners that could play a crucial role in\ndisease progression. The highly influential proteins from the constructed\nnetwork were analyzed through the cytoHubba42<\/sup> plugin of Cytoscape\nfor computing the topological metrics such as degree centrality (t),\nbetweenness centrality (CB), closeness centrality (CC) and maximal clique\ncentrality (CMC) measures. <\/p>\n\n\n\n

Degree centrality (t), indicates the number of interactions upon by a node or the edges connected with other nodes in the network and assists in calculating the node impact in regulating the network.<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

where Tu represents the node-set\ncomprising all the neighbors of node u, and x(u, v) denotes the edge weight\nconnecting node u along with node v.<\/p>\n\n\n\n

Betweenness centrality (CB) calculates the number of times a node tumbles on the shortest path with other neighboring nodes. It depicts a node\u2019s competency to regulate the signal processing and data drift in the network.<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

where \u03c3tg (u) represents the number of\ninteractions from t to g that passes through u, and \u03c3tg is the sum of all the\ninteractions between node t and g.<\/p>\n\n\n\n

Closeness centrality (CC) measures the distances of all the shortest paths from one node to all the other nodes in a network. Therefore, that highest centrality is nearer to the other nodes. It is measured as<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

where d(z,y) denotes the distance\nbetween y and z nodes and N is the number of nodes in the network.<\/p>\n\n\n\n

Maximal clique centrality (CMC) measures the level of the highly modular nodes in the complex network by identifying a large number of vertices (clique in a graph) within a network and a larger number of links between these vertices. It is represented as<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

where S(u) represents a collection of maximal cliques which consist of u, and (|C|-1)! is the multiplication of all positive integers <|C|. In case, there is no edge between the neighbors of node u, then CMC<\/sub><\/em>(u<\/em>)=degree.<\/p>\n\n\n\n

Further, clusters or topology modules\nof the PPI network signify the highly interconnected regions, which render to\nreflect molecular biological functions and essential protein progressions. In\nour network pharmacology study, highly interconnected regions in the PPI\nnetwork were filtered by the MCODE (Molecular Complex Detection) plugin to\nidentify the densely interconnected regions. MCODE with the node cut-off = 0.2,\nfluff-density cut-off = 0.2, K-core = 2, and a max-depth = 100 was initialized\nas advanced options. <\/p>\n\n\n\n

Pathway mapping<\/strong><\/p>\n\n\n\n

We have explored all the RA pathways\nassociated with the highly influential proteins; they were impelled by the\nmultifaceted disease via modulating certain pathways. The obtained hub proteins\nalong with their correlated bioactive compounds were investigated by mapping\nonto the pathways that are closely related and regulating the disease\nprogression with the help of extensive literature studies manually.<\/p>\n\n\n\n

Validation of\ncompound-target interaction <\/strong><\/p>\n\n\n\n

Molecular docking technology toils\nbased on the \u201cLock-Key principle\u201d to predict the small molecule ligands-protein\nreceptors association by calculating the repulsion, spatial effect, hydrogen\nbonds, molecular flexibility, hydrophobic interaction, and affinity score. The\nstructure of identified hub proteins and the active compounds were retrieved\nfrom the Protein Data Bank database (PDB, https:\/\/www.rcsb.org\/) and PubChem\ndatabase (https:\/\/pubchem.ncbi.nlm.nih.gov\/), respectively. The protein\nstructures were prepared by the addition of hydrogen molecules as well as the\nremoval of water molecules in the receptor. Molecular docking was performed\nwith the selected hub proteins and the ligand using the AutoDock Vina 4.2.643<\/sup>.\nLigand-binding affinities or binding energies between the receptor and ligand\nmolecule were attained as negative Gibbs free energy (\u0394G) scores (kcal\/mol).\nThe interactions of the post-docking\/docked complex were visualized by\nDiscovery Studio Visualizer. Ultimately, the potent inhibitor has been prompted\nfrom the docked complex according to the hydrophobic interaction, hydrogen bond\nand binding score for RA treatment.<\/p>\n\n\n\n

Results <\/strong><\/p>\n\n\n\n

Screening of\nactive compounds in BDK<\/strong><\/p>\n\n\n\n

A total of 205 BDK ingredients were\nobtained after structural confirmation and removal of duplicates. Further,\nscreening was performed based on OB, DL and ADMET-related parameters, which\nobtained 57 compounds and the same is listed in Table.1, including 9 AMs, 1 AI,\n6 CDs, 2 DGs, 2 GAs, 3 OIs, 1 PC, 7 PVs, 1 SA, 10 SCs, 6 SCFs, 1 SCO, 2 SSs, 2\nTTs, and 6 VJs. The IDs of the molecules that were distributed by 2 or 3\nmedicinal herbs were indicated accordingly. The average QED value of the 57\ncompounds was 0.604. 57 compounds had a QED value = or > 0.49 and < 0.6;\nonly biochanin A (OI) had a higher QED value than 0.8, which was 0.88. <\/p>\n\n\n\n

Construction of\ncompound-target (C-T) network<\/strong><\/p>\n\n\n\n

The construction of a compound-target\nnetwork (C-T) helped us to highlight the inter-relation of the BDK active\ningredient and the respective candidate target. After idleness elimination, the\nC-T network was obtained by connecting 57 compounds and 377 protein targets.\nThe network consisted of 434 nodes and 814 edges with an average degree of 3.75\nnodes per target and 18.27 edges per compound (Fig. 1), suggesting that a\ntarget could be common to multiple compounds, including synergistic action of\nBDK active ingredients. As exposed in this network, the degree value of\ncryptomeridol, 1,1-diethoxy-3-methyl butane, deodarone, nerolidol and coumaran\nwere 37, 30, 30, 26 and 25, respectively, and they interact with many protein\ntargets. This could justify the pleiotropic effects revealed by the active\ningredients of BDK according to the prime positioning in the network.<\/p>\n\n\n\n

Functional and\npathway enrichment transcripts of targets<\/strong><\/p>\n\n\n\n

GO enrichment analysis was performed\non the 377 targets to understand the biological functions involved in it, using\nthree functional classes such as biological process (BP), cellular component\n(CC) and molecular function (MF). Ultimately, 2416 BPs, 230 CCs, and 367 MFs\nwere enriched among the potential targets. The top 20 significantly enriched GO\nterms within BP, CC and MF were displayed in Tables 2, 3, and 4. BP was predominantly\nrelated to response to oxygen-containing compounds, response to nitrogen\ncompounds, regulation of cell death, regulation of cell communication, and so\nforth. CC was mainly intricate in membrane stack, membrane switch, and membrane\nregion. MF was associated with neurotransmitter receptor activity, signaling\nreceptor activity, amino acid binding, oxidoreductase activity and so forth.<\/p>\n\n\n\n

Furthermore, KEGG enrichment analysis\nwas performed to identify the pathways impacted substantially by BDK and the\ntop ones were TNF signaling pathway, MAPK signaling pathway, IL-17 signaling\npathway, PI3K-Akt signaling pathway, rheumatoid arthritis, apoptosis and so\nforth. Among the top 20 highly enriched pathways, seven were directly related\nto the inflammatory condition in RA including the HIF-1 signaling pathway, TNF\nsignaling pathway, IL-17 signaling pathway, PI3K-Akt signaling pathway, MAPK\nsignaling pathway, NF\u03baB signaling pathway, and JAK-STAT signaling pathway. BDK\nmay probably endeavor an anti-arthritic effect mainly over modulating\ninflammation, which could be validated by in vitro and in vivo experiments.<\/p>\n\n\n\n

Targets\ndiseaseome \u2013 concerning RA with its comorbidities<\/strong><\/p>\n\n\n\n

Rheumatoid arthritis is recurrent\nmanifold-syndromic and therefore influences the chances of having associated\ndiseases coupled with its manifestations. Our dataset of BDK target proteins\nwas traced for its association and was delivered with 198 different diseases,\nwhich were further categorized into four groups rheumatoid arthritis,\nRA-associated diseases (heart diseases, diabetes, joint pain, and irritable\nbowel disease (IBD)), autoimmune diseases (Lupus erythematosus, celiac disease,\nSjogren\u2019s syndrome, multiple sclerosis and Addison disease) and other diseases\n(hepatitis, cancer, thyroid diseases, leprosy, Alzheimer disease,\natherosclerosis and Gaucher disease). The categorization identified 136 targets\npossibly responsible for rheumatoid arthritis, 71 targets for RA-associated\ndiseases, 172 targets for autoimmune diseases, and 292 targets for other diseases,\nrespectively. <\/p>\n\n\n\n

PPI analysis and\nrevelation of highly influential proteins<\/strong><\/p>\n\n\n\n

To meticulously comprehend the\nmechanism of action of BDK on rheumatoid arthritis, the interplay and crosstalk\nbetween proteins must be elucidated. The PPI (T-IP) network aids in the\ndiscernment of the biological activities of the cell and its interior and\nexterior response to the environment. Therefore, the PPI network of 377\nputative therapeutic targets and the interacting partners (PPI data) which were\nobtained from the STRING database was constructed. The PPI network was\nconstructed using Cytoscape and composed of 377 nodes and 3992 edges with a\nclustering coefficient estimated to be 0.302. Subsequently, the topological\nparameters of the network were analyzed using NetworkAnalyzer and illustrated\nin Table 1. Based on different algorithms such as high centrality measures like\nMCC, degree, closeness and betweenness in the cytoHubba plugin of Cytoscape,\nthe top 20 hub proteins were identified in each category as illustrated in Table\n2. A total of 4 hub proteins commonly occurred almost in three categories\nimplying their prominence in sustaining the highly interconnected structure and\ninteraction. The commonly appeared hub proteins were MAPK1, MAPK14, FYN, and\nCXCL8 which are highlighted in Table 2. Moreover, the mainstream of the hub\nproteins was found to be actively engaged in the regulation of cell\nproliferation, response to cytokine and chemokine, immune and inflammatory\nresponse, kinase activity, cell-cell signaling, stress-activated protein kinase\nsignaling cascade, and transcription factor binding.<\/p>\n\n\n\n

The clustering modules were\nascertained from the PPI network consisting of 377 proteins (nodes). Using the\nMCODE plugin for cluster analysis, the top three highly interconnected sub-networks\n(modules) were obtained from the main network based on the clustering score,\neach one was autonomous of the others and exerted a distinctive task. Notably,\ninteracting partners of interlinked hub proteins (MAPK1 and MAPK14) were\nobserved in the first module with a clustering score of 11.808 (Fig.2A), the\nsecond module with a cluster score of 7.576 containing the interconnected hub\nprotein CXCL8 (Fig.2B), whereas the third module with a cluster score of 7.037\nincluding the connected hub protein FYN (Fig.2C). These three highly\ninterlinked modules again highlight the significance of hub proteins in the\nglobal network structure and are enriched in topmost ontologies including\nresponse to cytokine and chemokine, immune and inflammatory response, cellular\nresponse to endogenous stimuli, protein kinase activity, and cell-cell\nsignaling.<\/p>\n\n\n\n

Mapping the\nconnectivity to design a molecular roadmap<\/strong><\/p>\n\n\n\n

The hub proteins identified from the\ndataset were mapped with RA-related pathways including MAPK signaling,\nCXCL8-CXCR1\/2 axis, toll-like receptor signaling and NF\u03baB signaling cascades\nalong with their associated bioactive ingredients to target the molecules\npromoting disease progression. Among the highly influential proteins, CXCL8\nplays a pleiotropic role in immune-inflammatory response related to disease\npathogenesis through several pathways including MAPK and NF-\u03baB signaling\npathways. Involvement of other hub proteins like MAPK1, MAPK14, and FYN was\nfound to play a crucial role in regulating the production of pro-inflammatory\ncytokines including TNF-\u03b1, IL-1\u03b2, IL-6 and IL-17, which ultimately promotes\nsynovial hyperplasia, inflammation and bone erosion. <\/p>\n\n\n\n

Validation of\nmolecular docking<\/strong><\/p>\n\n\n\n

To validate the network pharmacology\nprediction results, we employed AutoDock Vina to assess the physical\ninteraction between the hub proteins and prime active ingredients of BDK. The\nhub proteins along with their interlinked bioactive compounds were selected\nfrom the network analysis to identify the binding activity. The binding potency\nbetween the compound and the protein was indicated as the lower the binding\nenergy (> negative value), the higher the binding ability. The calculated\nbinding energies between the bioactive compounds and their corresponding\nproteins were displayed in Table 3. The binding energy of kanakonol with MAPK1,\ncedrol\/cryptomeridol with MAPK14, deodarone with FYN, and hypaphorine with\nCXCL8 was the least and their score was -6.6 kcal\/mol, -7.8 kcal\/mol, -8.8\nkcal\/mol, and -6.5 kcal\/mol, respectively (Fig. 3), which suggests that these\ncompounds could have a strong binding affinity towards their target proteins.<\/p>\n\n\n\n

Discussion<\/strong><\/p>\n\n\n\n

From the standpoint of traditional\npharmacology, TIM patterns are just too intricate and extensive to comprehend.\nHowever, systems pharmacology-based strategy consents this issue to be tackled.\nHerbal decoctions also consist of several active ingredients that encounter a\nwide range of biological targets implicated in disease pathogenesis.\nFirst-ever, the present study executes a systems pharmacology approach to\nmeticulously unveil the mechanism of action of BDK decoction (Ayurveda\nformulation) against RA. A total of 205 compounds were retrieved from various\ndatabases including TCMSP, UNPD, and Dr. Duke’s Phytochemical and Ethnobotanical\nDatabases and we screened out 57 compounds that were found to possess OB, DL\nand ADMET properties. Among the 57 active compounds, six compounds including\nnerolidol44,45<\/sup>, alpha-phellandrene46<\/sup>, camphene, coumaran,\nmyrcene and naphthalene47<\/sup> exhibit diverse biological activity\nincluding anti-inflammatory, anti-oxidant, promoting immune responses, and\ninduces mitochondrial apoptosis. Evidence also showed that some other compounds\nlike camphene and myrcene are reported to have anti-inflammatory and\nantioxidant activities19<\/sup>. Interestingly, they also exhibited\npromising therapeutic impacts on rheumatoid arthritis. For instance, Zou\nrevealed that \u03b2-elemene could be efficient in stimulating mitochondrial\napoptosis of rheumatoid arthritis fibroblast-like synoviocytes (RA-FLS), which\nis regulated via the initiation of ROS production and activation of p38 MAPK\nsignaling48<\/sup>. Hence, these compounds must be given high priority\nwhile exploring natural products, which can attenuate the rheumatic condition\nfor further investigation.<\/p>\n\n\n\n

To comprehend intuitively the\npotential mechanism of BDK, a PPI network was constructed for 377 targets, to\nrepresent the interplay link between the proteins. The top 20 hub proteins were\nscreened out by investigating each topological attribute in the PPI network.\nThe frequently occurring 4 highly influential proteins MAPK1, MAPK14, FYN, and\nCXCL8 were recognized, which could play a crucial role in BDK-mediated\nalleviation of the rheumatic condition. MAPK1 (ERK2) and MAPK14 (p38\u03b1) are the\nkey members of the mitogen-activated protein kinase (MAPK) family49,50<\/sup>.\nThese proteins are involved in various events including regulation of cell\nproliferation, cell differentiation and apoptosis, triggering growth factors\nand pro-inflammatory cytokines, and may initiate ERK signaling pathway,\nsubsequently related to the chronic inflammatory response51,52<\/sup>. In\nthe tissues of damaged joints, MAPKs are implicated in the downstream IL-1,\nIL-17, and TNF-\u03b1 receptors signaling cascade as well as regulate the production\nof the pro-inflammatory cytokines53<\/sup>. ERK2 (MAPK1) controls the\ngeneration of TNF-\u03b1, IL-23, IL-12, and IL-6 in lipopolysaccharide (LPS) induced\nmacrophages54<\/sup>. It also regulates the COX-2-dependent PGE2 production\nowing to the release of epidermal growth factor in RA-FLS of patients55<\/sup>.\nMAPK14 (p38\u03b1) are predominantly activated by several inflammatory cytokines\n(IL-1\u03b2, TNF-\u03b1, IL-8, IL-6, MMP1 and MMP3) and modulate their expression that\nconsequently plays a critical role in immune-inflammatory response related to\nautoimmune diseases56,57<\/sup>. In the inflamed joint tissues, IL-1\u03b2 and\nTNF-\u03b1 arbitrate p38-MAPK-dependent production of IL-6, IL-8, and receptor\nactivator of nuclear factor-\u03ba B ligand (RANKL) in osteocytes and stroma cells\nof bone marrow, which eventually promote cartilage and bone degradation58<\/sup>.<\/p>\n\n\n\n

Among the highly influential proteins,\nCXCL8 (IL8) is one of the most extensively studied chemokines, released by\nvarious cell types such as macrophages, monocytes, fibroblasts and T\nlymphocytes26,59<\/sup>. CXCL8 is activated by several factors including\ncellular stress, lipopolysaccharide60<\/sup>, bacterial particles61<\/sup>,\nas well as by various cytokines (IL-1\u03b2, TNF-\u03b1, IL-6, interferon-\u03b3, and so\nforth) (60). CXC chemokine receptors type 1 (CXCR1) and type 2 (CXCR2) are\nmembers of the G protein-coupled receptors (GPCR) family, which have a strong\naffinity for CXCL862<\/sup>. The CXCL8-CXCR1\/CXCR2 axis has a pleiotropic\nrole in the immune response including upregulation of granulocytes and\nneutrophils towards the inflammation site63<\/sup>, tending to pathogen\nelimination that results in high severity and chronic inflammatory immune\nresponse and tissue destruction and thus plays a vital role in all types of\ninflammatory disorders, for instance: arthritis64<\/sup>, bacterial\ninfections65,66<\/sup>, sepsis67<\/sup>, ulcerative colitis68<\/sup>,\nand so forth. The CXCL8-CXCR1\/CXCR2 axis also pertains to regulating\nangiogenesis, cell migration and immune cell infiltration during chronic\ninflammation46,69<\/sup>. The production of CXCL8 is intimately connected\nto the MAPK signaling pathway. ERK and JNK promote CXCL8 gene transcription via\nstimulating activating protein-1 (AP-1), a transcription activator primarily\ncomprised of Jun and Fos proteins. The MAPK signaling pathway activates c-Jun\nand c-fos transcription factors, facilitating the translocation into the\nnucleus to create AP-1, which interacts with DNA target sequences and further\npromotes CXCL8 transcription. Subsequently, this leads to the regulation of\ncellular processes including migration, differentiation, proliferation,\napoptosis, stress, and inflammation reaction70,71<\/sup>. Another hub\nprotein Fyn regulates the various cellular functions including cell adhesion,\nsurvival, growth, motility, cytoskeletal remodeling and T-cell receptor\nsignaling, which are immensely stretched to several pathological conditions.\nFyn also upregulates the pro-inflammatory cytokines in macrophages, mast cells,\nand natural killer cells, which enhances the immune response that aggravates\ninflammatory disease conditions. Mkaddem revealed that downregulation and\nchronic activation of immunoreceptor tyrosine-based activation motif\n(ITAM)-containing immunoreceptor has been reported to be responsible for\nautoimmune and inflammatory disorders. ITAM present in the aggregated\nimmunoreceptors can be phosphorylated by Fyn kinase72<\/sup>. Taken\ntogether, these recognized highly influential proteins, contribute to the\nexplicit or implicit effect associated with MAPK signaling cascade on\ninflammation-induced arthritis, thus proving the rationale of the network pharmacology\nprediction outcomes.<\/p>\n\n\n\n

We noted that the identified hub proteins are associated with rheumatoid arthritis and other inflammatory diseases, mediated by several pathways including TNF signaling pathway, IL-17 signaling pathway, toll-like receptor signaling pathway, IL-8 mediated signaling events and NOD-like receptor signaling pathway to name a few. The corresponding compounds of hub proteins or the synergistic effect of the compounds such as alpha-phellandrene, caffeic acid, cryptomeridol, kanokonol, cedrol, deodarone, hypaphorine and nerolidol, present in BDK decoction could facilitate the attenuation of rheumatic disease condition through the regulation of inflammatory-related pathways. Yangxinshi tablet was investigated to be efficient and reliable as a cardiovascular nourishing agent when administered at the time of cardiac arrest and enhances the immunological system [20]. Likewise, Zhang also established the protein interaction network of the potential targets of Wu-tou decoction as well as RA-associated targets by maintaining the coordination between immune and endocrine systems73<\/sup>. Therefore, multi-component herbal medicine is likely effective in managing the inflammatory disorders like rheumatoid arthritis primarily through two mechanisms: induction of immunomodulatory agents (like MAPK1, MAPK14, CXCL8, FYN) which inevitably assist in advancing the innate and adaptive immune system, and in the interim, regulation of pro-inflammatory mediators (like TNF-\u03b1, Il-1\u03b2, IL-6, IL-17, IL-8, COX-2) and inflammatory cytokines by herbal ingredients will possibly assist in reducing inflammation. In the future, the predicted targets and anti-inflammatory mechanism of BDK need to be validated along with their biological process and molecular function by a more comprehensive wet-lab experiment.<\/p>\n\n\n\n\n\n
\"\"<\/td>\n\n

Figure 1: Compound-Target (C-T) network connecting 57 compounds and 377 protein targets.<\/strong><\/p>\n

<\/p>\n

Click here to view Figure<\/a><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n\n
\"\"<\/td>\n\n

Figure 2: Clustering module analysis on the PPI network by MCODE. Nodes are displayed in turquoise green and the edges in pink colour.<\/strong><\/p>\n

<\/p>\n

Click here to view Figure<\/a><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n\n
\"\"<\/td>\n\n

Figure 3: Two-dimensional and three-dimensional view illustrates the docking results of hub proteins and their corresponding active compounds.<\/strong><\/p>\n

<\/p>\n

Click here to view Figure<\/a><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n

Table 1: Topological parameters of the T-IP network<\/strong><\/p>\n\n\n\n\n\n\n\n\n\n\n\n
\n

Topological parameters<\/strong><\/p>\n<\/td>\n

\n

Comprehended values<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

Number of nodes<\/strong><\/p>\n<\/td>\n

\n

377<\/p>\n<\/td>\n<\/tr>\n

\n

Number of edges<\/strong><\/p>\n<\/td>\n

\n

3992<\/p>\n<\/td>\n<\/tr>\n

\n

Clustering co-efficient<\/strong><\/p>\n<\/td>\n

\n

0.302<\/p>\n<\/td>\n<\/tr>\n

\n

Network density<\/strong><\/p>\n<\/td>\n

\n

0.002<\/p>\n<\/td>\n<\/tr>\n

\n

Characteristic path length<\/strong><\/p>\n<\/td>\n

\n

4.987<\/p>\n<\/td>\n<\/tr>\n

\n

Average number of neighbors<\/strong><\/p>\n<\/td>\n

\n

6.702<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n\n\n

Table 2: The top 20 highly influential proteins (common between at least 3 \u2013 highlighted in bold) identified using distinct algorithms of Cytohubba plugin.<\/strong><\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
\n

Degree<\/strong><\/p>\n<\/td>\n

\n

Betweeness<\/strong><\/p>\n<\/td>\n

\n

Closeness<\/strong><\/p>\n<\/td>\n

\n

MCC<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

Terms<\/strong><\/p>\n<\/td>\n

\n

Score<\/strong><\/p>\n<\/td>\n

\n

Terms<\/strong><\/p>\n<\/td>\n

\n

Score<\/strong><\/p>\n<\/td>\n

\n

Terms<\/strong><\/p>\n<\/td>\n

\n

Score<\/strong><\/p>\n<\/td>\n

\n

Terms<\/strong><\/p>\n<\/td>\n

\n

Score<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

PPARA<\/p>\n<\/td>\n

\n

1187<\/p>\n<\/td>\n

\n

PPARA<\/p>\n<\/td>\n

\n

1174870.293<\/p>\n<\/td>\n

\n

MAPK3<\/p>\n<\/td>\n

\n

1516.33333<\/p>\n<\/td>\n

\n

CCR2<\/p>\n<\/td>\n

\n

40901508<\/p>\n<\/td>\n<\/tr>\n

\n

PTPN1<\/p>\n<\/td>\n

\n

865<\/p>\n<\/td>\n

\n

MAPK14<\/p>\n<\/td>\n

\n

960585.1948<\/p>\n<\/td>\n

\n

MAPK1<\/p>\n<\/td>\n

\n

1491.96667<\/p>\n<\/td>\n

\n

CXCR3<\/p>\n<\/td>\n

\n

40857927<\/p>\n<\/td>\n<\/tr>\n

\n

MAPK14<\/p>\n<\/td>\n

\n

853<\/p>\n<\/td>\n

\n

MAPK1<\/p>\n<\/td>\n

\n

725003.9293<\/p>\n<\/td>\n

\n

AKT1<\/p>\n<\/td>\n

\n

1480.71905<\/p>\n<\/td>\n

\n

CCR5<\/p>\n<\/td>\n

\n

40712592<\/p>\n<\/td>\n<\/tr>\n

\n

JAK2<\/p>\n<\/td>\n

\n

765<\/p>\n<\/td>\n

\n

HIF1A<\/p>\n<\/td>\n

\n

704480.7551<\/p>\n<\/td>\n

\n

MAPK14<\/p>\n<\/td>\n

\n

1476.13571<\/p>\n<\/td>\n

\n

CCR3<\/p>\n<\/td>\n

\n

39850964<\/p>\n<\/td>\n<\/tr>\n

\n

JAK1<\/p>\n<\/td>\n

\n

761<\/p>\n<\/td>\n

\n

TP53<\/p>\n<\/td>\n

\n

678181.2047<\/p>\n<\/td>\n

\n

STAT3<\/p>\n<\/td>\n

\n

1475.56667<\/p>\n<\/td>\n

\n

CXCR2<\/p>\n<\/td>\n

\n

39513387<\/p>\n<\/td>\n<\/tr>\n

\n

PARP1<\/p>\n<\/td>\n

\n

755<\/p>\n<\/td>\n

\n

MDM2<\/p>\n<\/td>\n

\n

601992.2102<\/p>\n<\/td>\n

\n

PIK3CA<\/p>\n<\/td>\n

\n

1465.51667<\/p>\n<\/td>\n

\n

CXCL8<\/p>\n<\/td>\n

\n

38540127<\/p>\n<\/td>\n<\/tr>\n

\n

HIF1A<\/p>\n<\/td>\n

\n

732<\/p>\n<\/td>\n

\n

CXCL8<\/p>\n<\/td>\n

\n

568776.8803<\/p>\n<\/td>\n

\n

JUN<\/p>\n<\/td>\n

\n

1465.3<\/p>\n<\/td>\n

\n

CXCR1<\/p>\n<\/td>\n

\n

37922604<\/p>\n<\/td>\n<\/tr>\n

\n

JAK3<\/p>\n<\/td>\n

\n

636<\/p>\n<\/td>\n

\n

CDK2<\/p>\n<\/td>\n

\n

542526.8383<\/p>\n<\/td>\n

\n

NFKB1<\/p>\n<\/td>\n

\n

1461.7<\/p>\n<\/td>\n

\n

CCR9<\/p>\n<\/td>\n

\n

27105305<\/p>\n<\/td>\n<\/tr>\n

\n

ESR1<\/p>\n<\/td>\n

\n

538<\/p>\n<\/td>\n

\n

GAPDH<\/p>\n<\/td>\n

\n

536164.1021<\/p>\n<\/td>\n

\n

SRC<\/p>\n<\/td>\n

\n

1457.93571<\/p>\n<\/td>\n

\n

CCR1<\/p>\n<\/td>\n

\n

20743064<\/p>\n<\/td>\n<\/tr>\n

\n

CDK2<\/p>\n<\/td>\n

\n

524<\/p>\n<\/td>\n

\n

PTGS2<\/p>\n<\/td>\n

\n

520864.4916<\/p>\n<\/td>\n

\n

TP53<\/p>\n<\/td>\n

\n

1457.23333<\/p>\n<\/td>\n

\n

CCR4<\/p>\n<\/td>\n

\n

19162491<\/p>\n<\/td>\n<\/tr>\n

\n

PTGS2<\/p>\n<\/td>\n

\n

457<\/p>\n<\/td>\n

\n

AKT1<\/p>\n<\/td>\n

\n

513678.1772<\/p>\n<\/td>\n

\n

CREBBP<\/p>\n<\/td>\n

\n

1452.16905<\/p>\n<\/td>\n

\n

ADORA3<\/p>\n<\/td>\n

\n

13848675<\/p>\n<\/td>\n<\/tr>\n

\n

ALOX5<\/p>\n<\/td>\n

\n

448<\/p>\n<\/td>\n

\n

HSPD1<\/p>\n<\/td>\n

\n

491127.8147<\/p>\n<\/td>\n

\n

NR3C1<\/p>\n<\/td>\n

\n

1445.51667<\/p>\n<\/td>\n

\n

GRIN2A<\/p>\n<\/td>\n

\n

2993139<\/p>\n<\/td>\n<\/tr>\n

\n

MAPK1<\/p>\n<\/td>\n

\n

420<\/p>\n<\/td>\n

\n

ESR1<\/p>\n<\/td>\n

\n

491100.5705<\/p>\n<\/td>\n

\n

FOS<\/p>\n<\/td>\n

\n

1433.08333<\/p>\n<\/td>\n

\n

GRIN1<\/p>\n<\/td>\n

\n

2993112<\/p>\n<\/td>\n<\/tr>\n

\n

PTGS1<\/p>\n<\/td>\n

\n

395<\/p>\n<\/td>\n

\n

STAT3<\/p>\n<\/td>\n

\n

490452.6036<\/p>\n<\/td>\n

\n

MAPK11<\/p>\n<\/td>\n

\n

1430.26905<\/p>\n<\/td>\n

\n

GRIN2D<\/p>\n<\/td>\n

\n

2993064<\/p>\n<\/td>\n<\/tr>\n

\n

MAPK8<\/p>\n<\/td>\n

\n

390<\/p>\n<\/td>\n

\n

CREBBP<\/p>\n<\/td>\n

\n

480520.6511<\/p>\n<\/td>\n

\n

TNF<\/p>\n<\/td>\n

\n

1420.9381<\/p>\n<\/td>\n

\n

GRIN2C<\/p>\n<\/td>\n

\n

2993064<\/p>\n<\/td>\n<\/tr>\n

\n

EGFR<\/p>\n<\/td>\n

\n

381<\/p>\n<\/td>\n

\n

SRC<\/p>\n<\/td>\n

\n

473425.1257<\/p>\n<\/td>\n

\n

IL2<\/p>\n<\/td>\n

\n

1420.88333<\/p>\n<\/td>\n

\n

GRIA3<\/p>\n<\/td>\n

\n

2989139<\/p>\n<\/td>\n<\/tr>\n

\n

IL2<\/p>\n<\/td>\n

\n

357<\/p>\n<\/td>\n

\n

FYN<\/p>\n<\/td>\n

\n

459267.1117<\/p>\n<\/td>\n

\n

FYN<\/p>\n<\/td>\n

\n

1419.76667<\/p>\n<\/td>\n

\n

GRIA4<\/p>\n<\/td>\n

\n

2989130<\/p>\n<\/td>\n<\/tr>\n

\n

MMP9<\/p>\n<\/td>\n

\n

342<\/p>\n<\/td>\n

\n

NR3C1<\/p>\n<\/td>\n

\n

456976.7236<\/p>\n<\/td>\n

\n

NFKBIA<\/p>\n<\/td>\n

\n

1411.80238<\/p>\n<\/td>\n

\n

GRIA2<\/p>\n<\/td>\n

\n

2984050<\/p>\n<\/td>\n<\/tr>\n

\n

FYN<\/p>\n<\/td>\n

\n

338<\/p>\n<\/td>\n

\n

MAPK3<\/p>\n<\/td>\n

\n

451730.9712<\/p>\n<\/td>\n

\n

CXCL8<\/p>\n<\/td>\n

\n

1409.63571<\/p>\n<\/td>\n

\n

GRIN2B<\/p>\n<\/td>\n

\n

2912542<\/p>\n<\/td>\n<\/tr>\n

\n

PTPN2<\/p>\n<\/td>\n

\n

333<\/p>\n<\/td>\n

\n

EPRS<\/p>\n<\/td>\n

\n

425404.4823<\/p>\n<\/td>\n

\n

PDPK1<\/p>\n<\/td>\n

\n

1406.65<\/p>\n<\/td>\n

\n

NEFL<\/p>\n<\/td>\n

\n

2906643<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n

Table 3: The binding affinity and interactions of selected top hit hub proteins docked with potential phytocompounds of Badradarvadi concoction.<\/strong><\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
\n

S. No<\/strong><\/p>\n<\/td>\n

\n

Target<\/strong><\/p>\n<\/td>\n

\n

Compound\/Ligand<\/strong><\/p>\n<\/td>\n

\n

PubChem Id<\/strong><\/p>\n<\/td>\n

\n

Binding affinity (kcal\/Mol)<\/strong><\/p>\n<\/td>\n

\n

No. of Hydrogen bonds<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

1<\/p>\n<\/td>\n

\n

MAPK1
(PDB ID: 3W55)<\/p>\n<\/td>\n

\n

Alpha-Phellandrene<\/p>\n<\/td>\n

\n

7460<\/p>\n<\/td>\n

\n

-6.1<\/p>\n<\/td>\n

\n

0<\/p>\n<\/td>\n<\/tr>\n

\n

Caffeic acid<\/p>\n<\/td>\n

\n

689043<\/p>\n<\/td>\n

\n

-6.1<\/p>\n<\/td>\n

\n

0<\/p>\n<\/td>\n<\/tr>\n

\n

Cryptomeridol<\/p>\n<\/td>\n

\n

165258<\/p>\n<\/td>\n

\n

-6<\/p>\n<\/td>\n

\n

1<\/p>\n<\/td>\n<\/tr>\n

\n

Ephedrine<\/p>\n<\/td>\n

\n

9294<\/p>\n<\/td>\n

\n

-6<\/p>\n<\/td>\n

\n

1<\/p>\n<\/td>\n<\/tr>\n

\n

Kanokonol<\/p>\n<\/td>\n

\n

46173905<\/p>\n<\/td>\n

\n

-6.6<\/p>\n<\/td>\n

\n

1<\/p>\n<\/td>\n<\/tr>\n

\n

Linalol<\/p>\n<\/td>\n

\n

6549<\/p>\n<\/td>\n

\n

-5.7<\/p>\n<\/td>\n

\n

0<\/p>\n<\/td>\n<\/tr>\n

\n

2<\/p>\n<\/td>\n

\n

MAPK14
(PDB ID: 5ETI)<\/p>\n<\/td>\n

\n

Alpha-Phellandrene<\/p>\n<\/td>\n

\n

7460<\/p>\n<\/td>\n

\n

-6.1<\/p>\n<\/td>\n

\n

0<\/p>\n<\/td>\n<\/tr>\n

\n

1,1-diethoxy-3-methylbutane<\/p>\n<\/td>\n

\n

19695<\/p>\n<\/td>\n

\n

-5<\/p>\n<\/td>\n

\n

1<\/p>\n<\/td>\n<\/tr>\n

\n

Cedrol<\/p>\n<\/td>\n

\n

65575<\/p>\n<\/td>\n

\n

-7.1<\/p>\n<\/td>\n

\n

1<\/p>\n<\/td>\n<\/tr>\n

\n

Cryptomeridol<\/p>\n<\/td>\n

\n

165258<\/p>\n<\/td>\n

\n

-6.2<\/p>\n<\/td>\n

\n

0<\/p>\n<\/td>\n<\/tr>\n

\n

Deodarone<\/p>\n<\/td>\n

\n

14657303<\/p>\n<\/td>\n

\n

-7<\/p>\n<\/td>\n

\n

1<\/p>\n<\/td>\n<\/tr>\n

\n

2,3-Dihydro-Benzofuran<\/p>\n<\/td>\n

\n

10329<\/p>\n<\/td>\n

\n

-5.5<\/p>\n<\/td>\n

\n

0<\/p>\n<\/td>\n<\/tr>\n

\n

Kanokonol<\/p>\n<\/td>\n

\n

46173905<\/p>\n<\/td>\n

\n

-6.7<\/p>\n<\/td>\n

\n

1<\/p>\n<\/td>\n<\/tr>\n

\n

Nerolidol<\/p>\n<\/td>\n

\n

5284507<\/p>\n<\/td>\n

\n

-5.7<\/p>\n<\/td>\n

\n

1<\/p>\n<\/td>\n<\/tr>\n

\n

3<\/p>\n<\/td>\n

\n

FYN
(PDB ID: 2DQ7)<\/p>\n<\/td>\n

\n

Deodarone<\/p>\n<\/td>\n

\n

14657303<\/p>\n<\/td>\n

\n

-7.5<\/p>\n<\/td>\n

\n

1<\/p>\n<\/td>\n<\/tr>\n

\n

Ephedrine<\/p>\n<\/td>\n

\n

9294<\/p>\n<\/td>\n

\n

-5.9<\/p>\n<\/td>\n

\n

1<\/p>\n<\/td>\n<\/tr>\n

\n

Alpha-Phellandrene<\/p>\n<\/td>\n

\n

7460<\/p>\n<\/td>\n

\n

-5.9<\/p>\n<\/td>\n

\n

0<\/p>\n<\/td>\n<\/tr>\n

\n

4<\/p>\n<\/td>\n

\n

CXCL8
(PDB ID: 5D14)<\/p>\n<\/td>\n

\n

Hypaphorine<\/p>\n<\/td>\n

\n

442106<\/p>\n<\/td>\n

\n

-6.5<\/p>\n<\/td>\n

\n

1<\/p>\n<\/td>\n<\/tr>\n

\n

2,3-Dihydro-Benzofuran<\/p>\n<\/td>\n

\n

10329<\/p>\n<\/td>\n

\n

-5.5<\/p>\n<\/td>\n

\n

0<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n

Conclusion<\/strong><\/p>\n\n\n\n

This study for the first time\nelucidated the therapeutic mechanism of BDK against rheumatoid arthritis using\nnetwork pharmacology approach employing multiple bioinformatic tools. We\nidentified 57 active ingredients of BDK effective against RA and MAPK1, MAPK14,\nFYN and CXCL8 were identified as crucial proteins that are involved in the main\nsignal transduction pathway of the disease like MAPK signaling cascade,\nCXCL8-CXCR1\/CXCR2 axis and TNF signaling pathway which were targeted by BDK to\nemploy its anti-inflammatory and anti-apoptotic effect on RA. Our study offers\ntheoretical support for the pharmacological basis and clinical application for\nfuture research, wherein the results of which would support and validate our\nstudy-derived conclusion.<\/p>\n\n\n\n

Acknowledgement<\/strong><\/p>\n\n\n\n

The authors are grateful to the\nmanagement of VIT for providing the facilities to carry out this research work.<\/p>\n\n\n\n

Funding support<\/strong><\/p>\n\n\n\n

The authors received no specific\nfunding for this work.<\/p>\n\n\n\n

Authors\u2019\ncontributions<\/strong><\/p>\n\n\n\n

Mohamed Thoufic Ali A M: performed experiments\nand wrote the manuscript.<\/p>\n\n\n\n

Vino S: conceptualized and designed\nthe work, formal analysis, writing \u2013 review & editing.<\/p>\n\n\n\n

Conflict of\ninterest<\/strong><\/p>\n\n\n\n

The authors declare that there is NO\nconflict of interest.<\/p>\n\n\n\n

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