Abstract Background Paeoniae Radix Alba, the root of the plant Paeonia lactiflora Pall, is a common blood-enriching drug in traditional Chinese medicine. Its effectiveness in the clinical treatment of anaemia is remarkable, but its potential pharmacologic mechanism has not been clarified. Methods In this study, the potential pharmacologic mechanism of Paeoniae Radix Alba in the treatment of iron-deficiency anaemia was preliminarily elucidated through systematic and comprehensive network pharmacology. Results Specifically, we obtained 15 candidate active ingredients from among 146 chemical components in Paeoniae Radix Alba. The ingredients were predicted to target 77 genes associated with iron-deficiency anaemia. In-depth analyses of these targets revealed that they were mostly associated with energy metabolism, cell proliferation, and stress responses, suggesting that Paeoniae Radix Alba helps alleviate iron-deficiency anaemia by affecting these processes. In addition, we conducted a core target analysis and a cluster analysis of protein-protein interaction (PPI) networks. The results showed that four pathways, the p53 signalling pathway, the IL-17 signalling pathway, the TNF signalling pathway and the AGE-RAGE signalling pathway in diabetic complications, may be major pathways associated with the ameliorative effects of Paeoniae Radix Alba on iron-deficiency anaemia. Moreover, molecular docking verified the credibility of the network for molecular target prediction. Conclusions Overall, this study predicted the functional ingredients in Paeoniae Radix Alba and their targets and uncovered the mechanism of action of this drug, providing new insights for advanced research on Paeoniae Radix Alba and other traditional Chinese medicines. Keywords: Paeoniae Radix Alba, Network pharmacology, Iron-deficiency anaemia Background Worldwide, 46% of children aged 5 to 14 years and 48% of pregnant women suffer from iron-deficiency anaemia (IDA) [[33]1, [34]2]. IDA is one of the most widespread nutritional deficiency diseases [[35]3] and can cause cognitive deficiency and irreversible auditory and visual system damage in infants [[36]4, [37]5]. Pregnant women with anaemia may give birth to infants with foetal dysplasia and low birth weight [[38]6, [39]7]. Iron supplements are widely used to treat IDA [[40]8]. However, long-term use of supplements containing ferrous salts can cause side effects such as epigastric pain, diarrhoea and constipation [[41]9, [42]10]. Thus, identification of a good alternative supplement with fewer side effects has become an important research objective. In China, traditional Chinese medicine (TCM) is not simply a cultural practice, it also has a history of thousands of years of use for the treatment of various diseases. Under the guidance of the overall concepts and principles of syndrome differentiation and treatment, TCM has achieved satisfactory clinical results for anaemia treatment. Paeoniae Radix Alba (PRA), the root of the plant Paeonia lactiflora Pall (family Ranunculaceae), is a TCM with the functions of nourishing blood, astringing Yin, preventing perspiration, regulating menstruation, extinguishing liver wind and relieving pain [[43]11, [44]12]. In recent years, an unconventional novel analytical technique called network pharmacology has been widely used in TCM research [[45]13–[46]16]. Combined with extensive data analysis, network pharmacology can systematically determine the effects and mechanisms of drugs employed to treat complex diseases at the molecular, cellular, tissue, and biological levels [[47]17]. Although PRA is noteworthy in treating anaemia, for IDA, the active compositions, drug targets, and exact molecular mechanism are still unclear [[48]12, [49]18–[50]20]. In this study, network pharmacology was utilized to analyse the active ingredients, drug targets and key pathways of PRA in the treatment of IDA, as shown in Fig. [51]1. This study provides a new perspective for studying the mechanisms of TCMs. Fig. 1. [52]Fig. 1 [53]Open in a new tab Diagram of the study design. Step 1: Gathered the chemical composition of PRA from three databases (TCMSP, ETCM, BATMAN-TCM), and collected IDA-related targets from four databases (DrugBank, GeneCards, NCBI, DisGeNET). Step 2: Select the candidate components and take the intersection of the component-target and the IDA-target. Step 3: KEGG and GO enrichment analysis Methods Data sources Ingredients of PRA Information on the chemical composition of PRA was gathered from three databases: the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP, a unique pharmacologic platform for Chinese herbal medicine that can be used to search for the relationships among drugs, targets and diseases), the Encyclopedia of Traditional Chinese Medicine (ETCM, a database of commonly used herbs and herbal formulations that includes standardized information and ingredient information), and the Bioinformatics Analysis Tool for Molecular mechANisms of TCM (BATMAN-TCM, a biological online networking tool that provides users with basic information about herbs, such as their ingredients, targets, and disease relationships) [[54]21–[55]23]. In this experiment, “baishao” was invoked as the keyword, and the structures of the components were saved in MOL2 format. The structures of the components were verified with ChemSpider and SciFinder. IDA-related targets Targets of IDA were identified with four databases: GeneCards (a searchable, free, and comprehensive database that provides users with ample information for annotating and predicting human genes), DrugBank (a comprehensive, and freely accessible online database that includes information on drugs and drug targets), DisGeNET (a platform to explore the relationships between genes and diseases), and the NCBI database (National Center for Biotechnology Information, a platform integrating the PubMed, Bookshelf, Blast, Genome and other databases; users can search different targets through different databases of the platform) [[56]24–[57]26]. In this study, we used “iron-deficiency anaemia” as a keyword, searched the “gene” database type, and limited the species to “Homo sapiens” to identify IDA-related genes. Data preprocessing Screening of active ingredients of PRA Screening of dynamic components can be conducted on the basis of the five rules of Lipinski: a molecular weight (MW) < 500, a hydrogen acceptor number ≤ 10, a hydrogen donor (HDon) number ≤ 5, a log P value of − 2 ~ 5, and a rotatable hydrogen bond number (RBN) ≤ 10. If a compound does not violate two or more of the above principles, it can be considered that the compound has soothing properties [[58]27]. Functional components can also be screened according to oral bioavailability (OB; > 30%) and drug likeness (DL; > 0.18) [[59]28]. The OB value is an important indicator for evaluation of the internal conversion of drugs [[60]29]; a higher OB value of a drug is associated with a higher utilization rate of the drug after oral administration and a greater possibility of clinical application [[61]30, [62]31]. In this experiment, an OB > 30% and a DL > 0.18 were utilized as the criteria for screening of functional components. A literature review was used to supplement the information on the active components. Target prediction for the bioactive ingredients of PRA At present, the methods and techniques of drug target prediction can be divided into four parts according to their principles: (1) ligand prediction based on chemical structure similarity and pharmacophore models; (2) machine intelligence learning and prediction, for which standardized names and clear molecular target correspondence are required; (3) molecular docking, in which receptors are used to make predictions; and (4) combined prediction [[63]32]. Building on the current conditions and constraints, we selected ligand prediction as the main method and supplemented it with data from DrugBank. First, the TCMSP, BATMAN-TCM, and ETCM were chosen as databases for the chemical components of PRA. The active components were screened with the criteria of an OB > 30% and a DL > 0.18, and the component targets were then rigorously predicted by SwissTargetPrediction (which compares the components to a library containing 28,000 compounds by two-dimensional and three-dimensional similarity and further predicts any applicable molecular targets from among more than 2000 targets in five different organisms) and Stitch (which can randomly select at least four predicted linked proteins based on a single protein name, multiple protein names, or amino acid sequences with moderate or better confidence) [[64]33]. In addition, listed or laboratory-verified targets in DrugBank were identified as supplementary data. In this experiment, we converted the dynamic component into the “SMILES” format and selected Homo sapiens as the species. To ensure the accuracy of the results, we used P > 0.5 as the constraint condition for the predicted targets, yielding moderate credibility. After that, we obtained the drug active ingredient targets. Second, to consolidate and standardize the data, we identified the gene names of the predicted target proteins with Universal Protein (UniProt), a comprehensive resource of protein sequences and annotation data. UniProt is a compilation of the UniProt Knowledgebase, the UniProt Reference Cluster, and the UniProt Archive [[65]34]. We restricted the species to humans and created a protein-gene document. Finally, to identify the universal targets between IDA and PRA, we uploaded the two target networks to the Venny 2.1 online server and obtained 77 common targets (Table [66]2) [[67]35]. We then used the WEB-based Gene SeT AnaLysis Toolkit (WebGestalt) online tool to carry out Gene Ontology (GO) analysis on the shared targets for the Biological Process (BP), Cellular Component (CC), Molecular Function (MF) GO categories [[68]36]. Enriched terms in the BP, CC, and MF categories were selected for display. Additionally, we utilized ClueGo in Cytoscape (v 3.6.1) for pathway analysis using data from the Kyoto Encyclopedia of Genes and Genomes (KEGG). Table 2. Common targets between PRA and IDA NO. Protein ID Gene name Protein name Protein Class 1 [69]P31645 SLC6A4 solute carrier family 6 member 4 transporter 2 [70]Q01959 SLC6A3 solute carrier family 6 member 3 transporter 3 [71]P23975 SLC6A2 solute carrier family 6 member 2 transporter 4 [72]P33527 ABCC1 ATP binding cassette subfamily C member 1 transporter 5 [73]P19793 RXRA retinoid X receptor alpha nucleic acid binding; receptor; transcription factor 6 [74]P03372 ESR1 estrogen receptor 1 nucleic acid binding; receptor; transcription factor 7 [75]P05412 JUN Jun proto-oncogene, AP-1 transcription factor subunit nucleic acid binding; transcription factor 8 [76]P10275 AR androgen receptor nucleic acid binding; receptor; transcription factor 9 [77]P37231 PPARG peroxisome proliferator activated receptor gamma nucleic acid binding; receptor; transcription factor 10 [78]P27338 MAOB monoamine oxidase B nucleic acid binding; oxidoreductase; transferase 11 [79]P55055 NR1H2 nuclear receptor subfamily 1 group H member 2 nucleic acid binding; receptor;transcription factor 12 [80]O75469 NR1I2 nuclear receptor subfamily 1 group I member 2 nucleic acid binding; receptor; transcription factor 13 [81]P06746 POLB DNA polymerase beta nucleic acid binding 14 [82]O95342 ABCB11 ATP binding cassette subfamily B member 11 hydrolase; protease 15 [83]Q2M3G0 ABCB5 ATP binding cassettesubfamily B member 5 hydrolase; protease 16 [84]P22303 ACHE Acetylcholinesterase hydrolase; protease 17 [85]P03956 MMP1 matrix metallopeptidase 1 hydrolase; protease 18 [86]P00734 F2 coagulation factor II, thrombin hydrolase; protease 19 [87]P08253 MMP2 matrix metallopeptidase 2 hydrolase; protease 20 [88]P08183 ABCB1 ATP binding cassette subfamily B member 1 hydrolase; protease 21 [89]P08709 F7 coagulation factor VII hydrolase; protease 22 [90]P45983 MAPK8 mitogen-activated protein kinase 8 kinase; transferase 23 [91]Q13315 ATM ATM serine/threonine kinase kinase;nucleic acid binding; transferase 24 [92]P06493 CDK1 cyclin dependent kinase 1 kinase; transferase 25 [93]P48736 PIK3CG phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit gamma kinase; transferase 26 [94]P47989 XDH xanthine dehydrogenase oxidoreductase 27 [95]P04040 CAT catalase oxidoreductase 28 [96]P09917 ALOX5 arachidonate 5-lipoxygenase oxidoreductase 29 [97]P08684 CYP3A4 cytochrome P450 family 3 subfamily A member 4 oxidoreductase 30 [98]P16050 ALOX15 arachidonate 15-lipoxygenase oxidoreductase 31 [99]P23219 PTGS1 prostaglandin-endoperoxide synthase 1 oxidoreductase 32 [100]P05177 CYP1A2 cytochrome P450 family 1 subfamily A member 2 oxidoreductase 33 [101]P35354 PTGS2 prostaglandin-endoperoxide synthase 2 oxidoreductase 34 [102]Q16850 CYP51A1 cytochrome P450 family 51 subfamily A member 1 oxidoreductase 35 [103]P09601 HMOX1 heme oxygenase 1 oxidoreductase 36 [104]P18054 ALOX12 arachidonate 12-lipoxygenase, 12S type oxidoreductase 37 [105]P14550 AKR1A1 aldo-keto reductase family 1 member A1 oxidoreductase 38 [106]O60218 AKR1B10 aldo-keto reductase family 1 member B10 oxidoreductase 39 [107]P04141 CSF2 colony stimulating factor 2 signaling molecule 40 [108]P10415 BCL2 BCL2, apoptosis regulator signaling molecule 41 [109]P01375 TNF tumor necrosis facto signaling molecule 42 [110]P27487 DPP4 dipeptidyl peptidase 4 enzyme modulator; hydrolase; protease 43 [111]P05121 SERPINE1 serpin family E member 1 enzyme modulator 44 [112]P42574 CASP3 caspase 3 enzyme modulator; hydrolase; protease 45 [113]P07550 ADRB2 adrenoceptor beta 2 receptor 46 [114]P25105 PTAFR platelet activating factor receptor receptor 47 [115]P08238 HSP90AB1 heat shock protein 90 alpha family class B member 1 chaperone 48 [116]P04637 TP53 tumor protein p53 transcription factor 49 [117]P17931 LGALS3 galectin 3 cell adhesion molecule; signaling molecule 50 [118]P05231 IL6 interleukin 6 None 51 [119]P00918 CA2 carbonic anhydrase 2 None 52 [120]P00915 CA1 carbonic anhydrase 1 None 53 [121]P01130 LDLR low density lipoprotein receptor None 54 [122]P11388 TOP2A DNA topoisomerase II alpha None 55 [123]P35503 UGT1A3 UDP glucuronosyltransferase family 1 member A3 None 56 [124]O60656 UGT1A9 UDP glucuronosyltransferase family 1 member A9 None 57 [125]P04035 HMGCR 3-hydroxy-3-methylglutaryl -CoA reductase None 58 [126]P10636 MAPT microtubule associated protein tau None 59 [127]P26358 DNMT1 DNA methyltransferase 1 None 60 [128]P35228 NOS2 nitric oxide synthase 2 None 61 [129]P05093 CYP17A1 Cytochrome P450 family 17 subfamily A member 1 None 62 [130]P16581 SELE selectin E None 63 [131]P04114 APOB apolipoprotein B None 64 [132]P17612 PRKACA protein kinase cAMP-activated catalytic subunit alpha None 65 [133]O00255 MEN1 menin 1 None 66 [134]P08700 IL3 interleukin 3 None 67 [135]P19320 VCAM1 vascular cell adhesion molecule 1 None 68 [136]Q9HAW9 UGT1A8 UDP glucuronosyl transferase family 1member A8 None 69 [137]P29474 NOS3 nitric oxide synthase 3 None 70 [138]Q16790 CA9 carbonic anhydrase 9 None 71 [139]P83111 LACTB lactamase beta None 72 [140]P06276 BCHE butyrylcholinesterase None 73 [141]P09211 GSTP1 glutathione S-transferase pi 1 None 74 [142]P06213 INSR insulin receptor None 75 [143]P14679 TYR tyrosinase None 76 [144]Q12809 KCNH2 potassium voltage-gated channel subfamily H member 2 None 77 [145]P27169 PON1 paraoxonase 1 None [146]Open in a new tab Protein-protein interaction (PPI) network construction for IDA and PRA Physiological processes are not only affected by single signals; rather, the expression and function of a gene/protein are often impacted by multiple genes [[147]37]. PPI networks are interaction networks between targets and proteins [[148]38]. We uploaded the obtained targets to the tool of Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) Version 11.0 to develop a PPI network. STRING calculates comprehensive scores and probabilities based on distinct lines of evidence and corrects for the probabilities of random interactions. A minimum score of 0.4 indicates moderate credibility, a minimum score of 0.7 indicates high credibility, and a minimum score of 0.9 indicates the highest credibility [[149]39]. In this study, we constructed a PPI network with a score of 0.4; thus, it was a moderate-credibility network. Cluster analyses for the PPI network Clustering refers to the identification of highly correlated groups of different compounds or objects with similar basic attributes [[150]40]. Cluster analysis, an important classification method, indicates the effectiveness of the classification used for the PPI network. Many algorithms for Cytoscape PPI network clustering analysis have been reported, but previous studies have shown that the molecular complex detection (MCODE) algorithm for protein complex detection is the most reliable for gene network module analysis [[151]41, [152]42]; thus, we choose MCODE for our PPI network cluster analysis. GO function and KEGG pathway enrichment analyses To determine the commonality among targets, the GO and KEGG pathways of clustered targets are commonly analysed [[153]42]. WebGestalt is a feature-rich web analytics tool; as of 14 January 2019, it covers 354 databases that support 12 organisms and 321,251 functional classifications. It also enables analysis of genes that are not in the database and of data from experimental organisms on the platform [[154]36]. In this study, we examined the enriched GO functions for each target classification and conducted KEGG pathway analysis of the targets with WebGestalt. Drug-ingredient-target-pathway-disease (D-I-T-P-D) network construction The network obtained from the above experiment was introduced into Cytoscape (v 3.6.1), and the “merge” tool was utilized to merge the network. The D-I-T-P-D network was obtained. Molecular docking verification The LibDock module of Discovery Studio 2016 was used to verify the molecular docking based on the functional components of PRA, and a heat map was constructed from the component-core target docking scores. Results Component-target networks of PRA We collected data on the chemical components of PRA from three databases, namely, the TCMSP, ETCM, and BATMAN-TCM. The numbers of chemical components derived from these three databases were 85, 59, and 35, respectively. After removing duplicates, we obtained 146 chemical components. Through screening of OB values and DL values, 13 qualified chemical components were obtained (Table [155]1). The literature shows that albiflorin and paeoniflorin are the active ingredients of PRA responsible for its ameliorative effects on anaemia [[156]20], and gallic acid has anti-inflammatory, antioxidant and antitumour effects [[157]43]. Therefore, these chemical components were also considered candidate components, and their structures were verified with SciFinder and ChemSpider. Table 1. Candidate active ingredients of PRA NO. ID Name OB% DL 1 MOL001910 11alpha,12alpha-epoxy-3beta-23-dihydroxy-30-norolean-20-en-28,12beta-ol ide 64.77 0.38 2 MOL001919 (3S,5R,8R,9R,10S,14S)-3,17-dihydroxy-4,4,8,10,14-pentamethyl-2,3,5,6,7, 9-hexahydro-1H-cyclopenta [a]phenanthrene-15,16-dione 43.56 0.53 3 MOL001918 paeoniflorigenone 87.59 0.37 4 MOL001921 Lactiflorin 49.12 0.8 5 MOL001924 paeoniflorin 53.87 0.79 6 MOL001925 paeoniflorin_qt 68.18 0.4 7 MOL001928 albiflorin_qt 66.64 0.33 8 MOL001930 benzoylpaeoniflorin 31.27 0.75 9 MOL000211 Mairin 55.38 0.78 10 MOL000358 beta-sitosterol 36.91 0.75 11 MOL000359 sitosterol 36.91 0.75 12 MOL000422 kaempferol 41.88 0.24 13 MOL000492 (+)-catechin 54.83 0.24 14 MOL001927 Albiflorin 12.09 0.77 15 MOL000513 gallic acid 31.69 0.04 [158]Open in a new tab To more intuitively indicate the relationships between components and targets, we constructed a component-target network diagram with Cytoscape (v 3.6.1) [[159]44] that contained 178 nodes and 264 edges. In this network diagram, we found that the median degree of connectivity among 12 components was greater than 6; specifically, kaempferol, beta-sitosterol, (+)-catechin and gallic acid exhibited 61, 38, 32 and 32 degrees of connectivity, respectively, indicating that these four components are important active ingredients in PRA. Target networks associated with IDA The development of a disease is usually associated with multiple genes or proteins, as is the case for IDA. In this study, we identified 1923, 60, 25 and 29 IDA-related genes from the GeneCards, DisGeNET, NCBI and DrugBank databases, respectively. Duplicates were removed, and 1939 related genes were obtained. A total of 77 genes were shared between PRA targets and IDA-related genes (Table [160]2). To investigate the relationships between the 77 common targets and IDA, we conducted GO and KEGG analyses of the shared targets (Fig. [161]2). Ultimately, we obtained 12 enriched BP terms, 19 enriched CC terms, 16 enriched MF terms and 40 enriched KEGG pathways. The BP category results mainly indicated enrichment for the biological regulation (73/77), metabolic process (72/77), response to stimulus (71/77), multicellular organismal process (76/77), localization (56/77), developmental process (55/77), and cell communication (53/77) terms. The membrane (58/77), endomembrane system (39/77) and membrane-enclosed lumen (34/77) terms were significantly enriched in the CC category. The protein binding (70/77) and ion binding (60/77) terms were the primary enriched MF terms identified in our study. In addition, nitrogen metabolism was the most significantly enriched pathway. This suggests that nitrogen metabolism may be the core process affecting IDA. IDA development has been found to play roles in a variety of diseases, such as hepatitis B, amoebiasis, toxoplasmosis, malaria, African trypanosomiasis, and prostate cancer, suggesting that IDA may be affected by one or more diseases. The NF-kappa B signalling pathway, the HIF-1 signalling pathway, the AGE-RAGE signalling pathway in diabetic complications, the pentose and glucuronate interconversion pathways, and the IL-17 signalling pathway were also identified in this study. The results show that the TCM PRA affects multiple pathways and processes in the context of IDA treatment. Fig. 2. [162]Fig. 2 [163]Open in a new tab IDA-related target network. a Four disease-related gene target databases. b IDA target network containing 1940 nodes and 1939 edges. c Seventy-seven common targets between IDA and PRA. d GO and e KEGG pathway enrichment analysis results for PRA-targeted genes associated with IDA PRA-IDA PPI networks To develop a better understanding of the association between PRA and IDA, we analysed the relationships between them through assessment of their core targets. The screening condition of a degree centrality (DC) > 2× the average degree for the core targets yielded 12 strategic targets. The results of the GO function and KEGG pathway enrichment analyses were very similar to the enrichment results for the 77 targets (Fig. [164]3). The top eight functional terms were the biological regulation (12/12), metabolic process (12/12), response to stimulus (12/12), membrane-enclosed lumen (9/12), cytosol (8/12), endomembrane system (8/12), protein binding (12/12), and ion binding (8/12) terms. These enriched terms were highly correlated with anti-inflammatory activity, especially in the context of chronic or allergic rhinitis. Fig. 3. [165]Fig. 3 [166]Open in a new tab Enrichment analysis of 12 core targets. a Putative target PPI network of PRA. b IDA-related PPI network. c and d Analysis network; targets with DC values > 27.789 were considered core targets. e GO and f KEGG pathway analysis results The 12 targets were enriched in 10 KEGG pathways with significant false discovery rate (FDR)-adjusted P-values, including pertussis and the TNF signalling pathway (Fig. [167]4). The details of the KEGG pathways are outlined in Additional file [168]3. Fig. 4. [169]Fig. 4 [170]Open in a new tab TNF signalling pathway. As shown in the figure, PRA may treat IDA by inhibiting the TNF signalling pathway Enrichment analyses of 75 core targets The PPI network comparison of IDA and PRA revealed 75 core targets. To elucidate the biological functions of these targets, we divided the 75 targets into four clusters and subjected them to GO and KEGG pathway analyses (Fig. [171]5). Based on the GO term results, we found that biological regulation-related processes, such as gene expression, smooth muscle cell proliferation, and nitric oxide biosynthesis; metabolic processes, such as aerobic metabolism and steroid metabolism; responses to stimuli such as hypoxia, oestradiol, and lipopolysaccharide; and other processes, such as enzyme binding, protein homodimerization activity, iron ion binding, RNA polymerase II transcription factor activity, and ligand-activated sequence-specific DNA binding, were enriched for our clusters, suggesting that PRA may help alleviate IDA by affecting enzymes, iron ion binding, stress responses and nitric oxide biosynthesis. Fig. 5. [172]Fig. 5 [173]Open in a new tab Enrichment analysis of 75 core targets. A IDA-PRA PPI network. B Clusters of the PPI network. C GO and D KEGG pathway analysis results for each cluster. Due to space constraints, other Cluster analysis (b, c, d) is shown in file [174]3 The occurrence and development of diseases can be affected by other diseases and processes. In our study, we found that African trypanosomiasis, malaria, amoebiasis, colorectal cancer, pertussis, hepatitis B, serotonergic synapse-related processes, ovarian steroidogenesis, apoptosis, and cell proliferation could also indirectly affect the development of IDA, suggesting that PRA alleviates IDA by affecting cell-, nerve-, and inflammation-related processes. In addition, we believe that the top four KEGG pathways identified for these clusters, namely, the HIF-1 signalling pathway, the AGE-RAGE signalling pathway in diabetic complications, the TNF signalling pathway, and the IL-17 signalling pathway, might play significant roles in IDA treatment. D-I-T-P-D network construction On the basis of the PPI targets and pathway analyses, a D-I-T-P-D network was constructed using Cytoscape (v 3.6.1). As illustrated in Fig. [175]6, this D-I-T-P-D network had 108 nodes and 785 edges. The dark cyan circles, red triangles, celadon ellipses, yellow inverted triangles, and cyan diamond represent PRA, PRA ingredients, target genes, pathways, and IDA, respectively. Fig. 6. [176]Fig. 6 [177]Open in a new tab Construction of the D-I-T-P-D network. a Ingredient-PRA (ingredient-drug [I-D]) network. b Ingredient-target (I-T) network. c Target-pathway-disease (T-P-D) network. d D-I-T-P-D network containing 108 nodes and 785 edges Component-core target docking scores The docking score diagram is given in Fig. [178]7a. Notably, each active component and core target had good docking ability. PPARG, CYP3A4, and TNF may be the most important targets. Paeoniflorin, benzoylpaeoniflorin, and albiflorin may be the components associated with the ameliorative effects of PRA on IDA, which further confirms the reliability of the network pharmacology. MOL001910 and MOL001919 were replaced by 11α and 3S, respectively. The ligands paeoniflorigenone and lactiflorin could not be constructed, so docking verification was not carried out. Example of molecular docking: benzoylpaeoniflorin-TNF, Fig. [179]7b. Fig. 7. Fig. 7 [180]Open in a new tab a Component-core target docking scores. In this heat map, the docking score between the component and the target is converted by Log10, and the color is closer to red, indicating that the docking score between the component and the target is high and the docking strength is high. b benzoylpaeoniflorin-TNF. A two-dimensional diagram of the interaction between benzoylpaeoniflorin and TNF. Most of the oxygen atoms in benzoylpaeoniflorin form hydrogen bonds with ARG98, ARG103, GLN102, and some benzene rings form Pi-Sigma with CYS101 Discussion IDA is a condition that occurs among children in both developing and developed countries, leading to impaired development, activity intolerance, behavioural changes, irritability, and reduced learning ability; women and elderly individuals are also affected [[181]45, [182]46]. The iron deficiency associated with IDA can be classified as either absolute or functional iron deficiency: absolute iron deficiency is defined as a severe reduction in or loss of iron reserves in the bone marrow, liver, and spleen, while functional iron deficiency is defined as deficiency due to inadequate intake, malabsorption, or metabolic disorders [[183]47–[184]49]. Iron is an important component of human metabolism that plays crucial roles in cellular respiration, DNA synthesis, cell proliferation and oxygen storage [[185]50]. Disorders in iron absorption and metabolism result in severe oxidative stress and tissue damage [[186]51]. PRA is a blood tonic drug that regulates menstruation, but its precise mechanism of action is not yet clear [[187]11]. Therefore, it is imperative to explore the mechanism of PRA in IDA treatment by using network pharmacology methods combined with functional ingredient screening, drug target prediction, and network and pathway analyses. In this study, we identified 77 common targets between PRA and IDA. These targets were mainly enriched for energy metabolism-, cell proliferation-, and apoptosis-related terms. The main properties of these targets were associated with nucleic acid binding, receptors, and transcription factors (9/77); hydrolases and proteases (8/77); and oxidoreductases (13/77). Many other targets had unknown or other attributes. We reviewed several literature sources and found that most of the components downregulate TNF, IL6, PTGS2, CYP3A4, and CASP3 and upregulate PPARG and CAT, further indicating the reliability of the target prediction, as detailed in Table [188]3. In addition, we found that IL6 can induce the growth of myeloma and plasma cell tumours and induce the differentiation of nerve cells and that its overexpression in inflammation sites is a main cause of anaemia and chronic inflammation [[189]88, [190]89]. TP53, which is involved in a variety of cell death pathways and can be used as a marker of neuronal injury, has been widely studied in the context of cancer treatment [[191]90, [192]91]. TNF and IL6 are involved in the occurrence and development of chronic anaemia/IDA, and the level of TNF expression can reflect the degree of disease of patients with aplastic anaemia [[193]92–[194]94]. IL3 and TNF participate in relieving blood deficiencies in mice caused by cyclophosphamide [[195]19]. Table 3. Correlations between components and targets Components Downregulate Upregulate References