{"paper_id":"82e39097-c40a-4ccd-8dad-987dbd116cec","body_text":"Computational Drug Discovery in Patients with Endometriosis-Induced Infertility via Text Mining and Biomedical Databases | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Computational Drug Discovery in Patients with Endometriosis-Induced Infertility via Text Mining and Biomedical Databases Yingying Lin, Zhiwei Chen, Jianping Huang, Jiaoning Fang, Yuanjie Qi, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-744890/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Endometriosis (EMT) is the most common benign gynecological disease among women of reproductive age, causing infertility and seriously affects women's physical and mental health. However, the current treatment was not always effective. This study was designed to use publicly available data to identify drugs targeting the relevant gene with EMT-induced-infertility using computational tools. Methods: EMT and infertility genes were determined by text mining, and the GeneCodis program was used to analyzed gene ontology of the intersection of the two gene sets. A string database was used to analyze the protein-protein interaction network. The Drug-Gene Interaction database is queried for the rich gene set belonging to the identified pathways to find drug candidates that can be used in EMT-induced infertility. Results: Our analysis identified 550 genes common to both the EMT and infertility by text mining. Gene enrichment analysis and protein-protein interaction analysis found 39 genes potentially targetable by a total of 49 drugs that could be formulated for application, which have not been used in EMT-induced infertility. Conclusions: The findings from the present analysis can facilitate the Identification of existing drugs that have the potential of topical administration to improve EMT-induced infertility and present tremendous opportunities to study novel targets pharmacology using in silico text mining and pathway analysis tools. Theoretical Computer Science Computational Mathematics Drug Discovery, Design, & Development Differentially Expressed Genes Endometriosis Infertility Drugs Text mining. Figures Figure 1 Figure 2 Introduction Endometriosis (EMT) is the most common benign gynecological disease among women of reproductive age, with an estimated 10–15% [ 1 ] . It is a kind of chronic estrogen and progesterone-related inflammatory disease [ 2 ] characterized by dysmenorrhea, endometriosis-associated pain, and infertility [ 3 ] . It is reported that the infertility patients reduced by EMT are up to 25–50%, which seriously affects the quality of life of patients [ 4 ] . The American Society of Reproductive Medicine [ 5 ] proposes that EMT should be regarded as a chronic disease that requires lifelong treatment using medications with as few side effects as possible to avoid invasive procedures such as surgery. Patients with EMT-induced infertility are widely concerned because of their low fertility [ 6 ] . Treatment methods for EMT include surgery and drugs to improve the patient's pregnancy rate and clinical treatment effect. The commonly used drugs include oral contraceptive (OC), progestin and gonadotropin-releasing hormone analogs (GnRH-a). A meta-analysis indicated that GnRH-a is more suitable as a non-surgical treatment for EMT than progestin and OC according to the comprehensive efficacy and side effects [ 7 ] . GnRH-a can effectively improve the condition of EMT; however, bone loss and low estrogen levels caused by long-term use can not be ignored [ 8 , 9 ] . The search for a drug with positive efficacy, good tolerance, and high-cost performance is the development direction of comprehensive and individualized EMT treatment. However, It is difficult to develop new drugs because of their complicated pathogenesis. The traditional process of drug discovery and development is derived from physical experimentation and research of drug compounds, which requires much time, energy and financial resources. Fortunately, there has a prominent example of successful drug repurposing. It is reported that Sildenafil (Viagra), which was developed to treat angina, is effective in the treatment of erectile dysfunction [ 10 ] . Undoubtedly, reusing existing drugs to treat other diseases can be a lower cost and possibly a faster alternative. Following the repurposing paradigm, this study aimed to explore new drug treatment options for EMT-induced infertility by mining the available published literature associated with biological databases and other analytical tools. Text mining in the biomedical literature has been confirmed as an effective way to reveal the new relationship between genes and diseases [ 11 , 12 ] . When text mining is combined with biological knowledge and other analytical tools, new evidence on the potential to repurpose existing drugs can be obtained [ 13 ] . The computational prediction of multi-target drugs has established multi-pharmacology as a promising alternative to solve some complex drug discovery through text mining [ 14 ] . Biological knowledge can be assorted and categorized with gene ontology (GO) hierarchical relationships [ 15 ] . The definition of GO biological processes can provide a framework for mapping relationships between biological entities and text mining concepts [ 15 ] . It is reported that the cellular signaling pathway maps combined with text mining can be used to expand the information of a specific signal transduction pathway and find a target gene or a targetable regulator of the target gene [ 16 ] . Several successful research outcomes studies have been reported, in which some are discovering new uses for existing drugs were found using GO and pathway analysis to link drugs with diseases [ 17 , 18 ] . The priority of a group of genes can be further determined by evaluating their influence on protein interaction networks. This technique is useful because it demonstrated some novel relationships by analyzing gene interactions that tend to accumulate in the network due to certain characteristics of the disease or pathological conditions [ 16 ] . In this study, we performed queries using the search terms endometriosis and infertility to produce a preliminary list of genes in exploring potential drugs for EMT-induced infertility. We then validated the relationship between the identified genes and generated a priority target set by analyzing the signal pathway. We created a list of high-priority target genes with an in-depth analysis of the genes' functional enrichments. Candidate drugs were then derived from a summary of the data on potential drug-gene interactions. Materials And Methods Gene Collection The database genclip3 (http://ci.smu.edu.cn/genclip3/analysis.php）was used to perform text mining. We performed queries using the search terms endometriosis and infertility to produce a list of differentially expressed genes (DEGs). Then, we extracted all of the unique gene hits from each result. All of these genes were then used in the subsequent analyses. Gene Ontology and Pathway Analysis GeneCodis (http://genecodis.cnb.csic.es/), a web-based tool for integrating various sources of information with gene ontology and functional information [19] .It was used for the enrichment analysis of the genes corresponding to the EMT and infertility intersection. We used GeneCodis for enrichment analysis of the genes related to EMT-induced infertility. We put the genes obtained in the text mining step into the input set, and perform GO analysis on these. GO terms reflect genetic knowledge about the biological process (BP), cellular components (CC) and molecular function (MF) [21,22] . And then, this study selected the most significantly enriched biological processes. Genes with the selected annotations were performed for the next step and annotations of the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Moreover, the Kyoto Encyclopedia of Genes and Genomes (KEGG) [23] . It provides data resources of known biological pathways to annotate a set of genes. Protein Interaction Network. The STRING database (http://string‑db.org) integrates the protein-protein interactions of selected genes [20] . On the first page of the STRING database, we selected 'Multiple proteins' from the left menu bar, entered the genes chosen from the last step, and 'Homo sapiens' was selected as the organism. Regarding the confidence score, the stronger the evidence that two proteins interact with each other is, the higher the confidence scores observed. In this study, the confidence score is determined to medium (score 0.400). Although a lower confidence score may decrease the network's confidence, it may increase the inclusion criteria. Then, the protein-protein interaction network of the target gene is obtained. After that, PPI networks were built by using the Cytoscape software [24] . The plug-in Molecular Complex Detection (MCODE) built-in Cytoscape was used to select the significant gene modules of the PPI networks. The parameters were set as follows: MCODE scores >3 and the count of nodes >4. Finally, we selected one significant gene modules (including 40 genes) from the PPI networks for further validation analyses. Drug-gene Interactions We used DGIdb (http://www.dgidb.org), which aggregates drug-gene interaction data from 27 sources including DrugBank, PharmGKB, ChEMBL, NCBI Entrez, Ensembl, PubChem, various clinical trial databases, and literature available through NCBI PubMed to explore drug-gene interactions in the final list of genes, which were applied to find the potential targets in a search for ready-made drugs or small organic compounds [25] . Results Results of text mining, biological process and pathway analysis. In exploring potential drugs for EMT-induced infertility, 550 genes were found to be related to EMT-induced infertility from the text mining searches. We uploaded DEGs to the online website GeneCodis to identify GO Terms and KEGG pathways and classified them into three functional categories: biological process (BP), cellular component (CC), and molecular function (MF; Fig. 1 ). During this process, to ensure that only the most enriched annotations were chosen, a P‑value cutoff (P = 1.00E‑06) was set. As shown in Fig. 1 A and Table 1 , GO analysis showed that the DEGs were most significantly enriched in response to the organic substance. Moreover, the upregulated DEGs were significantly enriched in biological process, including muscle system process, muscle contraction, and regulation of muscle contraction (Fig. 1 A and Table 2 ); Among the most significantly enriched GO Terms above the cutoff, those most relevant to EMT-induced infertility based on the available literature and research were selected. Therefore, the six most enriched biological process annotations were: i) ‘response to organic substance’ (P = 3.90E-127);ii) ‘cellular response to chemical stimulus’ (P = 1.29E-112); iii) ‘regulation of cell proliferation’ (P = 1.43E-98); iv) ‘cellular response to organic substance’ (P = 1.01E-95); v) ‘cell proliferation’ (P = 3.58E-94); and vi) ‘response to oxygen-containing compound’ (P = 1.48E-92), containing 328, 304, 227, 261, 241 and 215 genes from the query set, respectively. And the six most enriched cellular component annotations were: i) ‘extracellular space’ (P = 1.87E-63);ii) ‘extracellular region’ (P = 2.15E-39); iii) ‘cell surface’ (P = 5.22E-38); iv) ‘extracellular region part’ (P = 1.70E-34); v) ‘external side of plasma membrane’ (P = 6.97E-22); and vi) ‘side of membrane’ (P = 2.16E-20), containing190, 305, 110, 265, 48 and 62 genes from the query set, respectively. What is more, the six most enriched molecular function annotations were: i) ‘receptor binding’ (P = 8.64E-75); ii) ‘cytokine receptor binding’ (P = 3.76E-48); iii) ‘cytokine activity’ (P = 5.29E-37); iv) ‘growth factor activity’ (P = 4.88E-24); v) ‘identical protein binding’ (P = 2.24E-19); and vi) ‘enzyme binding’ (P = 1.57E-18), containing200, 76, 60, 41, 111 and 130 genes from the query set, respectively. Table 1 The Gene Ontology of the genes involved in EM-induced infertility. Category Term Count PValue GOTERM_BP_FAT GO:0010033 ~ response to organic substance 328 3.90E-127 GOTERM_BP_FAT GO:0070887 ~ cellular response to chemical stimulus 304 1.29E-112 GOTERM_BP_FAT GO:0042127 ~ regulation of cell proliferation 227 1.43E-98 GOTERM_BP_FAT GO:0071310 ~ cellular response to organic substance 261 1.01E-95 GOTERM_BP_FAT GO:0008283 ~ cell proliferation 241 3.58E-94 GOTERM_BP_FAT GO:1901700 ~ response to oxygen-containing compound 215 1.48E-92 GOTERM_CC_FAT GO:0005615 ~ extracellular space 190 1.87E-63 GOTERM_CC_FAT GO:0005576 ~ extracellular region 305 2.15E-39 GOTERM_CC_FAT GO:0009986 ~ cell surface 110 5.22E-38 GOTERM_CC_FAT GO:0044421 ~ extracellular region part 265 1.70E-34 GOTERM_CC_FAT GO:0009897 ~ external side of plasma membrane 48 6.97E-22 GOTERM_CC_FAT GO:0098552 ~ side of membrane 62 2.16E-20 GOTERM_MF_FAT GO:0005102 ~ receptor binding 200 8.64E-75 GOTERM_MF_FAT GO:0005126 ~ cytokine receptor binding 76 3.76E-48 GOTERM_MF_FAT GO:0005125 ~ cytokine activity 60 5.29E-37 GOTERM_MF_FAT GO:0008083 ~ growth factor activity 41 4.88E-24 GOTERM_MF_FAT GO:0042802 ~ identical protein binding 111 2.24E-19 GOTERM_MF_FAT GO:0019899 ~ enzyme binding 130 1.57E-18 Abbreviation: EM,endometriosis. Table 2 Signaling Pathway Enrichment Analysis of involved in EM-induced infertility. Category Term Count PValue KEGG_PATHWAY bta04060:Cytokine-cytokine receptor interaction 65 2.41E-32 bta05200:Pathways in cancer 74 6.92E-23 bta05205:Proteoglycans in cancer 51 1.45E-21 bta05323:Rheumatoid arthritis 33 1.60E-18 bta05161:Hepatitis B 39 2.95E-17 bta04151:PI3K-Akt signaling pathway 60 6.44E-17 bta04668:TNF signaling pathway 33 1.14E-16 bta05142:Chagas disease (American trypanosomiasis) 33 7.56E-16 bta04066:HIF-1 signaling pathway 29 1.74E-14 bta05321:Inflammatory bowel disease (IBD) 24 2.40E-13 bta04068:FoxO signaling pathway 32 4.42E-13 bta05144:Malaria 21 4.53E-13 bta05133:Pertussis 24 2.34E-12 bta05140:Leishmaniasis 23 3.11E-12 bta04630:Jak-STAT signaling pathway 33 3.67E-12 bta05212:Pancreatic cancer 22 3.94E-12 bta05206:MicroRNAs in cancer 42 1.63E-11 bta05152:Tuberculosis 34 1.23E-10 In the process of KEGG pathway enrichment analysis, those most relevant to EMT-induced infertility based on the available literature and research were selected. The analysis of enriched pathway annotations resulted in 18 pathways containing a total of 74 unique genes (Table 2 ). The six most significantly enriched pathways were: i) ‘cytokine-cytokine receptor interaction’ (P = 2.41E-32); ii) ‘pathways in cancer’ (P = 6.92E-23); and iii) ‘proteoglycans in cancer’ (P = 1.45E-21), iv) ‘rheumatoid arthritis’ (P = 1.60E-18); v) ‘hepatitis B’ (P = 2.95E-17); and vi) ‘PI3K-Akt signaling pathway’ (P = 6.44E-17), containing 65, 74, 51, 33 ,39 and 60 genes from the query set, respectively. Results of protein-protein interaction. The protein-protein interaction network of the 452 target genes was illustrated based on the STRING online database ( http://string-db.org ) and Cytoscape software (Fig. 2 ). PPI network complexly contained 452 nodes and 2758 edges (Fig. 2 A). Finally, 39 genes were selected to form a strong interaction network based on the MCODE. The significant module (39 nodes 384 edges, Fig. 2 B) from the PPI network was selected, as determined by Cytoscape. The 39 genes included CXCR3, NPY, BMP4, IGFBP1, APOA1, IL6, PROC, APOE, POMC, TIMP1, AHSG, CXCL8, CX3CR1, TF, CXCL1, F5, DRD2, CCL5, GPER1, AFP, AGTR2, CXCL10, CXCL11, CXCL12, CCR5, CASR, SPP1, ALB, CCL19, CCL25, CCL21, C3, CCL20, AGT, LGALS1, MSLN, CSF1, BMP15 and MFGE8. Results of drug-gene interactions. They used the final list of 39 genes as the potential targets in the drug-gene interaction analysis, a list of 49 drugs meeting the standard requirements for drug treatments for EMT-induced infertility (Table 3 ). They included: Acepromazine, Acetophenazine, Alizapride, Amantadine hydrochloride, Amisulpride, Apomorphine, Aripiprazole, Bifeprunox, Brexpiprazole, Cabergoline, Cinacalcet, Clozapine, Haloperidol, Iloperidone, Levodopa, Levomepromazine, Lisuride, Loxapine, Maraviroc, Menadione, Mesoridazine, Minaprine, Olanzapine, Paliperidone, Perphenazine, Pimozide, Pipotiazine, Promazine, Propiomazine, Remoxipride, Risperidone, Rivanicline, Ropinirole, Rotigotine, Siltuximab, Sodium tetradecyl sulfate, Tasosartan, Thioproperazine, Thiothixene, Tinzaparin sodium, Triflupromazine, Velcalcetide and Zuclopenthixol. Table 3 Candidate drugs targeting genes with EM-induced infertility. Number Drug Gene Drug-gene interaction Score* Approved? Approved FDA Reference (PubMed ID) 1 Acepromazine DRD2 antagonist 7 Yes Yes 15694263 2 Acetophenazine DRD2 antagonist 9 Yes Yes 6147851 3 Alizapride DRD2 antagonist 5 Yes No 7865862 4 Amantadine hydrochloride DRD2 agonist 5 Yes No 10443547 5 Amisulpride DRD2 antagonist 8 Yes No 12404702 6 Apomorphine DRD2 agonist 12 Investigational Yes 11343576 7 Aripiprazole DRD2 antagonist 12 Yes Yes 9083792 8 Bifeprunox DRD2 agonist 6 Investigational No 17393144 9 Brexpiprazole DRD2 agonist 5 Yes No 24947465 10 Cabergoline DRD2 agonist 14 Yes Yes 12721865 11 Cinacalcet CASR agonist 15 Yes Yes 19261825 12 Clozapine DRD2 antagonist 14 Yes Yes 15781964 13 Dihydromorphine POMC agonist 3 Experimental No 6292632 14 Domperidone DRD2 antagonist 10 Investigational No 15894081 15 Droperidol DRD2 antagonist 8 Yes Yes 2527092 16 Drotrecogin alfa F5 inhibitor 13 Investigational Yes 11893230 17 Fluspirilene DRD2 antagonist 9 Yes Yes 8935801 18 Ginseng IL6 antagonist 3 Yes / 17436372 19 Haloperidol DRD2 antagonist 7 Yes Yes 12887421 20 Iloperidone DRD2 antagonist 7 Yes Yes 12861482 21 Levodopa DRD2 agonist 10 Yes Yes 11978145 22 Levomepromazine DRD2 antagonist 5 Yes No 2870716 23 Lisuride DRD2 agonist 6 Yes No 18691132 24 Loxapine DRD2 antagonist 13 Yes Yes 9570468 25 Maraviroc CCR5 antagonist 13 Yes Yes 16298345 26 Menadione PROC activator 8 Yes Yes 17215245 27 Mesoridazine DRD2 antagonist 8 Yes Yes 15357957 28 Minaprine DRD2 agonist 5 Yes No 17139284 29 Olanzapine DRD2 antagonist 15 Yes Yes 14575800 30 Paliperidone DRD2 antagonist 12 Yes Yes 11132243 31 Perphenazine DRD2 antagonist 12 Yes Yes 2573104 32 Pimozide DRD2 antagonist 14 Yes Yes 8301582 33 Pipotiazine DRD2 antagonist 9 Yes / 15694263 34 Promazine DRD2 antagonist 6 Yes Yes 17139284 35 Propiomazine DRD2 antagonist 5 Yes Yes 17139284 36 Remoxipride DRD2 antagonist 7 Yes No 8665533 37 Risperidone DRD2 antagonist 13 Investigational Yes 17059881 38 Rivanicline CXCL8 antagonist 2 Investigational No 16715250 39 Ropinirole DRD2 agonist 11 Investigational Yes 10446316 40 Rotigotine DRD2 agonist 7 Yes Yes 18691132 41 Siltuximab IL6 inhibitor 4 Yes Yes 8823310 42 Sodium tetradecyl sulfate PROC inhibitor 5 Yes Yes 11752352 43 Tasosartan AGTR2 antagonist 2 Yes No 11683476 44 Thioproperazine DRD2 antagonist 7 Yes No 15694263 45 Thiothixene DRD2 antagonist 9 Yes Yes 15694263 46 Tinzaparin sodium CXCL12 binder 2 Yes Yes 18991783 47 Triflupromazine DRD2 antagonist 6 Yes Yes 17139284 48 Velcalcetide CASR agonist 4 Investigational No 24235081 49 Zuclopenthixol DRD2 antagonist 8 Yes Yes 17535043 Each individual instance of a drug-gene interaction was evaluated in the context of the gene’s relationship to EMT-induced infertility and the drug’s relationship to the gene, thus ensuring any putative drug would be expected to have the desired directional effect on the condition. Drugs and compounds which met the criteria of targeting one of the candidate genes by an appropriate interaction were collected in a final list *The score is the combined number of database sources and PubMed references supporting a given interaction Potential gene targets of the drugs in this list are DRD2 (37 drugs), PROC, IL6 and CASR (2 drugs each), POMC, F5, CXCL8, CXCL12, CCR5and AGTR2 (1 drug each). Common previously approved uses for these drugs include the treatment of hemostasis, nausea, vomit, mental disease, chronic kidney disease, Parkinson's disease, and Alzheimer's disease. A total of 25 drugs on the list are used for anti-mental disease therapy. Next, we verified whether the drugs identified by our analysis could target pathways associated with EMT-induced infertility. Discussion Our purposes were to explore the potential drugs through silico text-mining and pathway analysis tools to identify existing drugs with the potential for topical administration and treatment with patients with EMT-induced infertility. The cause by EMT-induced infertility may be the following reasons: (1) Changes in the normal anatomy of the pelvic cavity cause obstruction of the fallopian tubes, which affects conception. (2) abnormal immune function and autoimmune response, such as the cytokines, inflammatory, including IL-6, IL-8 and VEGF, et al. And Iwabe et al. [ 26 ] found that IL-6 may promote the formation of autoantibodies and has embryotoxicity and inhibits early embryo implantation, which can cause EMT-induced infertility. (3) endocrine abnormalities: including ovarian hormone abnormalities and insufficient luteal function. Currently, The treatment of EMT-induced infertility is mainly anticipatory, drug treatment, surgical treatment, combined drug and surgical treatment and assisted reproductive technology treatments. Now, the drugs used for treatment are OC, drugs that inhibit the gonadal axis from antagonizing sex hormones, including mifepristone, danazol, gestrinone capsules and Gonadotropin-releasing hormone agonists and medicines that promote ovulation. De Ziegler et al. [ 27 ] found that using OC 6–8 weeks before assisted reproduction can also increase the pregnancy rate of assisted reproductive technology. Clinical studies have proved that mifepristone is effective in treating EMT-induced infertility [ 28 ] . A Cochrane study of 16 cases of preoperative and post-operative use of hormonal drugs to treat EMT-induced infertility, there is insufficient evidence that the drug can improve pregnancy rates [ 29 ] . Therefore, it is particularly important to find other safe and effective therapeutic drugs to treat EMT-induced infertility. In this study, we identified 39 genes following the gene set enrichment analysis representing a network (Fig. 2 ) targetable by the 49 drugs, all of which have not yet been tested for EMT-induced infertility (Table 3 ). The majority of potential drugs identified by the drug-gene interaction search are known to target DRD2. These drugs belong to treat mental disease. Besides, it also includes some drugs that target inflammatory factors, such as IL-6, CXCL8 and CXCL12. It is reported that dopamine receptor D2 (DRD2) is the main receptor for most antipsychotic drugs. Numerous documents have shown that DRD2 is essential for learning and memory, especially in the prefrontal cortex [ 30 ] . Activating DRD2 will also enable the corresponding downstream pathways, thereby mediating a series of downstream signaling effects such as the inhibition of inflammation and apoptosis [ 31 ] . Many scholars have pointed out that in various diseases, such as inflammatory bowel disease [ 32 ] , luteinizing granular cells [ 33 ] , and lung cancer [ 34 ] , dopamine inhibit VEGF-mediated vascular permeability and angiogenesis through DRD2 to treat diseases or inhibit tumors. The possible mechanism is that dopamine can inhibit VEGF-mediated microvascular permeability and endothelial cell proliferation and migration [ 35 ] . Some scholars also pointed out that dopamine can block VEGF-induced focal adhesion kinase (FAK) and mitogen-activated protein kinase (MAPK) phosphorylation in endothelial cells [ 36 ] . Angiogenesis is of great significance to the formation of ectopic intima, and many factors regulate it. As we knew, endothelial growth factor (VEGF) is one of the most important factors in angiogenesis. The expression of VEGF in the peritoneal fluid, ectopic endometrium, and eutopic endometrium of patients with endometriosis was significantly higher than in the control group. Based on this, it can be considered that the high expression of VEGF in patients with EMs indicates that it promotes angiogenesis in the disease. And it plays an important role in occurrence and development in EMT [ 37 ] . Therefore, we suspect that DRD2 may be involved in the incident and development of EMT through VEGF. Besides, the recruitment of inflammatory cells and the production of inflammatory factors, such as IL-6 and IL-8, play a key role in the formation of EMT. IL-6 mainly promotes cell growth, stimulates cell differentiation, and participates in acute inflammation. It induces local adhesion, fibrosis and immunological abnormalities in the pelvic cavity by mediating immune and inflammatory reactions, and promotes the formation and development of EMT [ 38 ] . What is more, it can increase the toxicity of early embryos and cause infertility [ 26 ] . IL-8 has angiogenic activity, increases the formation of microvessels in the abdominal cavity, and increases the acceptance of endometrial implants. It can promote the proliferation and adhesion of endometrial cells, leading to pelvic adhesion and fibrosis [ 39 ] . It can also increase the activity and infiltration capacity of matrix metallo proteinases(MMP) in endometrial stromal cells. It is a proteolytic enzyme that regulates the degradation and reconstruction of the extracellular matrix. It can make the endometrial fragments that flow back into the abdominal cavity with menstrual blood have a strong planting invasive ability plays an important role in the pathogenesis of EMT [ 40 ] . This study has the following limitations: 1. The database used in the research may have limited information on the annotation of gene functions or pathways. The services or effects of some genes cannot be verified through experiments; 2. For a given drug, not all existing gene interactions are known. Therefore, potentially useful drugs for EMT-induced infertility may be missed or ignored. The gene interaction may not have been fully elucidated, or the drug-gene interaction is suspected of producing harmful effects. Conclusions In conclusion, we have reported a method that has founded the panel of candidate drugs that target the genes/ pathways relevant to EMT-induced infertility. With the development and improvement of databases and analysis tools, such a method can be used regularly. Therefore, through this analysis, we have identified 49 drugs, which have not been used in EMT-induced infertility, which provides a basis for new trials and the development of novel targeted therapies as potential treatments for EMT-induced infertility. Declarations Acknowledgments Not applicable. Authors Contributions YL, ZC and LC was involved in the design of the study. JH, JF, YQ and LX were involved in data collection and analyzed the data. Both YL and ZC were involved in drafting the manuscript, LC and HL edit the manuscript. All authors have read and approved the final manuscript. The authors declare that all data were generated in-house and that no paper mill was used. Ethical Approval This study was approved by the Ethics Committee of Fujian Provincial Maternity and Children Hospital ( FMCH2018-155 ) and complied with the Declaration of Helsinki. Consent to Participate All individuals participating in this study provided written informed consent. Consent to Publish Not applicable. Funding No funding was received. Competing Interests The authors declare that they have no competing interests. Availability of data and materials Not applicable. References Wasson MN. Chronic pelvic pain due to postmenopausal endometriosis. J Minim Invasive Gynecol. (2019). doi: 10.1016/j.jmig.2019.08.015. Barra F, Scala C, Ferrero S.Current understand pharmacokinetics, clinical efficacy and safety of progestins for treating the pain associated with endometriosis[J]. Expert Opin Drug Metab Toxicol, 2018,14(4):399-415. Bedaiwy, M. A., Alfaraj, S., Yong, P., and Casper, R. (2017). New developments in the medical treatment of endometriosis. Fertil. Steril. 107, 555–565. doi: 10.1016/j.fertnstert.2016.12.025 As-Sanie S，Black R，Giudice LC, et al. Assessing research gaps and unmet needs in endometriosis [J]. Am J Obstet Gynecol，2019，221（2）：86-94. Practice Committee of the American Society for Reproductive Medicine.Treatment of pelvic pain associated with endometriosis: a committee opinion[J].Fertil Steril,2014，101(4):927-935. Ozkan S，Murk W，Arici A．Endometriosis and infertility: Epidemiology and evidence-based treatments［J］．Ann N Y Acad Sci， 2008，1127: 92-100． Jeng CJ, CHUANG L, SHEN J. A comparison of progestogens or oral contraceptives and gonadotropin-releasing hormone agonists for the treatment of endometriosis, a systematic review[J].Expert Opinion on Pharmacotherapy,2014:7. Seo JW, Lee DY, KIM SE, et al.Comparison of long-term use of combined oral contraceptive after gonadotropin-releasing hormone agonist plus add-back therapy versus dienogest to prevent recurrence of ovarian endometrioma after surgery[J]. Eur J Obstet Gynecol Reprod Biol,2019,236:53-57. BEDAIWY MA, ALLAIRE C, ALFARAJ S.Long-term medical management of endometriosis with dienogest and with a gonadotropin-releasing hormone agonist and add-back hormone therapy [J].Fertil Steril,2017,107(3):537-548. Moosavinasab S, Patterson J, Strouse R, Rastegar-Mojarad M, Regan K, Payne PR, Huang Y, Lin SM (2016) 'RE fine drugs': an interactive dashboard to access drug repurposing opportunities Database (Oxford). https://doi.org/10.1093/database/baw083 Mosca E, Bertoli G, Piscitelli E, et al. Identification of functionally related genes using data mining and data integration: a breast cancer case study. BMC Bioinformatics. 2009;10(suppl): S8. Yu S, Tranchevent LC, De Moor B, Moreau Y (2010) Gene prioritization and clustering by multi-view text mining. BMC Bioinf 11:28 Berg EL (2014) Systems biology in drug discovery and development. Drug Discov Today 19:113–125. Chaudhari R, Tan Z, Huang B, Zhang S. Computational polypharmacology: a new paradigm for drug discovery. Expert Opin Drug Discov. 2017;12:279e291. Andronis C, Sharma A, Virvilis V, Deftereos S, Persidis A (2011) Literature mining, ontologies and information visualization for drug repurposing. Brief Bioinform 12:357–368. Liu H, Beck TN, Golemis EA, Serebriiskii IG (2014) Integrating in silico resources to map a signaling network. Methods Mol Biol 1101:197–245. Cramer PE, Cirrito JR, Wesson DW, Lee CY, Karlo JC, Zinn AE, Casali BT, Restivo JL, Goebel WD, James MJ, Brunden KR, Wilson DA, Landreth GE (2012) ApoE-directed therapeutics rapidly clear beta-amyloid and reverse deficits in AD mouse models. Science 335:1503–1506. Tousi B (2015) The emerging role of bexarotene in the treatment of Alzheimer’s disease: current evidence. Neuropsychiatr Dis Treat 11:311–315. Tabas-Madrid D, Nogales-Cadenas R, Pascual-Montano A (2012) GeneCodis3: a non-redundant and modular enrichment analysis tool for functional genomics. Nucleic Acids Res 40:W478–W483. Szklarczyk D, Franceschini A, Wyder S, Forslund K, Heller D, Huerta‑CepasJ, Simonovic M, Roth A, Santos A, Tsafou KP, et al.: STRING v10: Protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res 43: D447‑D452, 2015. Hale M, Thapa I, Ghersi D. FunSet: an open-source software and web server for performing and displaying Gene Ontology enrichment analysis. BMC Bioinformatics 2019; 20:359-359. Carbon S, Dietze H, Lewis S, et al. Expand the Gene Ontology knowledgebase and resources. Nucleic Acids Res 2017; 45:D331-D338. Kanehisa M, Goto S. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res 2000; 28:27-30. Smoot M, Ono K, Ruscheinski J, et al. Cytoscape 2.8. Bioinformatics 2011; 27:431-432. Wagner AH, Coffman AC, Ainscough BJ, Spies NC, Skidmore ZL, Campbell KM, Krysiak K, Pan D, McMichael JF, Eldred JM, et al.: DGIdb 2.0: mining clinically relevant drug-gene interactions. Nucleic Acids Res 44: D1036‑D1044, 2016. wabe T, Harada T, Sakamoto Y, et al. Gonadotropin-releasing hormone agonist treatment reduced serum interleukin-6 concentrations in patients with ovarian endometriomas. Fertil Steril, 2003;80(2):300-304. de Ziegler D，Gayet V，Aubriot FX，et al．Use of oral contraceptives in women with endometriosis before assisted reproduction treatment improves outcomes［J］．Fertil Steril，2010，94 ( 7 ): 2796-2799． Minas V，Dada T．Laparoscopic treatment of endometriosis and effects on quality of life: A retrospective study using the short form EHP-5 endometriosis specific questionnaire［J］. J Obstet Gynaecol，2014，34( 4): 336-340． Yap C，Furness S，Farquhar C．Pre and post-operative medical therapy for endometriosis surgery [J/CD]. Cochrane Database Syst Ｒev，2004( 3): CD003678． C Missale 1, SR Nash, S W Robinson, M Jaber, M G Caron. Dopamine receptors: from structure to function．Physiol Rev. 1998 Jan;78(1):189-225. Xu TX, Sotnikova T D, Liang C, et al. Hyperdopaminergic tone erodes prefrontal long-term potential via a D2 receptor-operated protein phosphatase gate.[J]. Journal of Neuroscience, 2009, 29(45):14086-14099. Tolstanova G, Deng X, Ahluwalia A, et al. Role of Dopamine and D2 Dopamine Receptor in the Pathogenesis of Inflammatory Bowel Disease. Dig Dis Sci. 2015;60(10):2963-2975. Ferrero H, García-Pascual, Carmen M, Gómez, Raúl, et al. Dopamine receptor two activations inhibits ovarian vascular endothelial growth factor secretion invitro: implications for treatment of ovarian hyperstimulation syndrome with dopamine receptor two agonists[J]. Fertility & Sterility, 2014, 101(5):1411-1418.e2. Hoeppner L H, Wang Y, Sharma A, et al. Dopamine D2 receptor agonists inhibit lung cancer progression by reducing angiogenesis and tumor-infiltrating myeloid-derived suppressor cells[J]. Molecular Oncology, 2015, 9. Bhattacharya R, Sinha S, Yang SP, et al. The neurotransmitter dopamine modulates vascular permeability in the endothelium[J]. Journal of Molecular Signaling, 2008, 3:14-14. Sarkar C, Chakroborty D, Mitra R B, et al. dopamine in vivo inhibits VEGF-induced phosphorylation of VEGFR-2, MAPK, and focal adhesion kinase in endothelial cells[J]. Am J Physiol Heart Circ Physiol, 2004, 287(4): H1554. Huili Z. The role of VEGF in the pathogenesis of endometriosis[J]. Progress in Obstetrics and Gynecology, 2006. Sharpe-Timms K L, Zimmer R L, Ricke E A, et al. Endometriotic haptoglobin binds to peritoneal macrophages and alters their function in women with endometriosis[J]. Fertility & Sterility, 2002, 78(4):810-819. Arici A. Local cytokines in endometrial tissue: the role of interleukin-8 in the pathogenesis of endometriosis.[J]. Annals of the New York Academy of Sciences, 2010, 955(1):101-109. Mulayam N, Savlu A, Guzeloglu-Kayisli O, et al. Regulation of endometrial stromal cell matrix metalloproteinase activity and invasiveness by interleukin-8[J]. Fertility & Sterility, 2004, 81(Supp-S1):904-911. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-744890\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research\",\"associatedPublications\":[],\"authors\":[{\"id\":42737726,\"identity\":\"8ea8aab2-eb9d-4f40-8d93-b030ad5204d3\",\"order_by\":0,\"name\":\"Yingying Lin\",\"email\":\"\",\"orcid\":\"https://orcid.org/0000-0002-8631-5833\",\"institution\":\"Fujian Province Maternal and Child Health Hospital: Fujian Provincial Maternity and Children's Hospital\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Yingying\",\"middleName\":\"\",\"lastName\":\"Lin\",\"suffix\":\"\"},{\"id\":42737727,\"identity\":\"5d9e56e6-a43b-45a5-a88c-08dec57e98b6\",\"order_by\":1,\"name\":\"Zhiwei Chen\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Fujian Province Maternal and Child Health Hospital: Fujian Provincial Maternity and Children's Hospital\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Zhiwei\",\"middleName\":\"\",\"lastName\":\"Chen\",\"suffix\":\"\"},{\"id\":42737728,\"identity\":\"8f18c312-c4e1-46d6-a229-a53465080603\",\"order_by\":2,\"name\":\"Jianping Huang\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Fujian Medical University Union Hospital\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Jianping\",\"middleName\":\"\",\"lastName\":\"Huang\",\"suffix\":\"\"},{\"id\":42737729,\"identity\":\"7d058e2e-8801-4d61-a640-39069b26d41d\",\"order_by\":3,\"name\":\"Jiaoning Fang\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Fujian Province Maternal and Child Health Hospital: Fujian Provincial Maternity and Children's Hospital\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Jiaoning\",\"middleName\":\"\",\"lastName\":\"Fang\",\"suffix\":\"\"},{\"id\":42737730,\"identity\":\"1b85cf1c-7f68-4c7c-9f61-557bc1bcc3fd\",\"order_by\":4,\"name\":\"Yuanjie Qi\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Fujian Province Maternal and Child Health Hospital: Fujian Provincial Maternity and Children's Hospital\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Yuanjie\",\"middleName\":\"\",\"lastName\":\"Qi\",\"suffix\":\"\"},{\"id\":42737731,\"identity\":\"036e84c5-a9af-4223-b976-060ef532de82\",\"order_by\":5,\"name\":\"Li Xie\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Fujian Province Maternal and Child Health Hospital: Fujian Provincial Maternity and Children's Hospital\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Li\",\"middleName\":\"\",\"lastName\":\"Xie\",\"suffix\":\"\"},{\"id\":42737732,\"identity\":\"3718cb85-9533-4dab-87b7-6e538dcd5227\",\"order_by\":6,\"name\":\"Li Chen\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAz0lEQVRIiWNgGAWjYDACCSB+YHBAjp+Z+cCBDz+I1ZJgcMBYsr0t8eDMHqK1MBxI3HDmjPFhDjYidPDPbj72IKHgDmPDjZwPhxl4GOT5xQ4QsOTOsXSDBINnzIwzcjccLrBgMJw5OwG/FgOJHDOJBIPDbMwSQC0zeID+uk1QS/43kBYeNomcB0CSKC05bCAtEjw8ZxiI0yJxIw3ksGcGEuxtBsBAliDsF/4Zyc8kPvy5U7//MPPjDx9+2MjzSxPQgmEracpHwSgYBaNgFGAHAKg1SEpr3hsyAAAAAElFTkSuQmCC\",\"orcid\":\"https://orcid.org/0000-0001-9138-7789\",\"institution\":\"Fujian Province Maternal and Child Health Hospital: Fujian Provincial Maternity and Children's Hospital\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Li\",\"middleName\":\"\",\"lastName\":\"Chen\",\"suffix\":\"\"},{\"id\":42737733,\"identity\":\"021133b5-2b6a-4ac2-8ad1-1302d9157df0\",\"order_by\":7,\"name\":\"Hua Li\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Fujian Province Maternal and Child Health Hospital: Fujian Provincial Maternity and Children's Hospital\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Hua\",\"middleName\":\"\",\"lastName\":\"Li\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2021-07-23 10:34:23\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-744890/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-744890/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":12128610,\"identity\":\"25e2af11-5fe8-4fe0-8241-3e20aa29246b\",\"added_by\":\"auto\",\"created_at\":\"2021-08-04 23:24:31\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":1885610,\"visible\":true,\"origin\":\"\",\"legend\":\"Gene ontology analysis and significant enrichment of differentially expressed genes (DEGs) in EMT-induced-infertility. \\n(A) Gene ontology (GO) analysis classified DEGs into BP, CC, and MF groups.\\n(B) Significantly enriched signal pathway of differentially expressed genes (DEGs).\\n\",\"description\":\"\",\"filename\":\"Fig1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-744890/v1/eb29973add8ed5df62a408e7.png\"},{\"id\":12128411,\"identity\":\"272fbe27-f887-47a4-b966-8ac98649ad79\",\"added_by\":\"auto\",\"created_at\":\"2021-08-04 23:21:31\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":1586482,\"visible\":true,\"origin\":\"\",\"legend\":\"Protein-protein interaction (PPI) network of differentially expressed genes. (A) Based on the STRING online database, 452 DEGs were filtered into the DEGs PPI network.\\n(B) The most significant module from the PPI network.\\n\",\"description\":\"\",\"filename\":\"Fig2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-744890/v1/387cead2f358401ab1aed26c.png\"},{\"id\":13707992,\"identity\":\"6d6c716e-d7b1-42d6-94d0-d6e4c7cdfa93\",\"added_by\":\"auto\",\"created_at\":\"2021-09-17 14:05:36\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":1906947,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-744890/v1/55236cf5-f0ec-4c0f-96e2-e3ac35c193bd.pdf\"}],\"financialInterests\":\"\",\"formattedTitle\":\"\\u003cp\\u003eComputational Drug Discovery in Patients with Endometriosis-Induced Infertility via Text Mining and Biomedical Databases\\u003c/p\\u003e\",\"fulltext\":[{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003eEndometriosis (EMT) is the most common benign gynecological disease among women of reproductive age, with an estimated 10\\u0026ndash;15% \\u003csup\\u003e[\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e]\\u003c/sup\\u003e. It is a kind of chronic estrogen and progesterone-related inflammatory disease\\u003csup\\u003e[\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e]\\u003c/sup\\u003e characterized by dysmenorrhea, endometriosis-associated pain, and infertility\\u003csup\\u003e[\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e]\\u003c/sup\\u003e. It is reported that the infertility patients reduced by EMT are up to 25\\u0026ndash;50%, which seriously affects the quality of life of patients\\u003csup\\u003e[\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e]\\u003c/sup\\u003e. The American Society of Reproductive Medicine \\u003csup\\u003e[\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e]\\u003c/sup\\u003e proposes that EMT should be regarded as a chronic disease that requires lifelong treatment using medications with as few side effects as possible to avoid invasive procedures such as surgery.\\u003c/p\\u003e \\u003cp\\u003ePatients with EMT-induced infertility are widely concerned because of their low fertility\\u003csup\\u003e[\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e]\\u003c/sup\\u003e. Treatment methods for EMT include surgery and drugs to improve the patient's pregnancy rate and clinical treatment effect. The commonly used drugs include oral contraceptive (OC), progestin and gonadotropin-releasing hormone analogs (GnRH-a). A meta-analysis indicated that GnRH-a is more suitable as a non-surgical treatment for EMT than progestin and OC according to the comprehensive efficacy and side effects\\u003csup\\u003e[\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e]\\u003c/sup\\u003e. GnRH-a can effectively improve the condition of EMT; however, bone loss and low estrogen levels caused by long-term use can not be ignored\\u003csup\\u003e[\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e]\\u003c/sup\\u003e. The search for a drug with positive efficacy, good tolerance, and high-cost performance is the development direction of comprehensive and individualized EMT treatment. However, It is difficult to develop new drugs because of their complicated pathogenesis.\\u003c/p\\u003e \\u003cp\\u003eThe traditional process of drug discovery and development is derived from physical experimentation and research of drug compounds, which requires much time, energy and financial resources. Fortunately, there has a prominent example of successful drug repurposing. It is reported that Sildenafil (Viagra), which was developed to treat angina, is effective in the treatment of erectile dysfunction\\u003csup\\u003e[\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e]\\u003c/sup\\u003e. Undoubtedly, reusing existing drugs to treat other diseases can be a lower cost and possibly a faster alternative. Following the repurposing paradigm, this study aimed to explore new drug treatment options for EMT-induced infertility by mining the available published literature associated with biological databases and other analytical tools.\\u003c/p\\u003e \\u003cp\\u003eText mining in the biomedical literature has been confirmed as an effective way to reveal the new relationship between genes and diseases\\u003csup\\u003e[\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e]\\u003c/sup\\u003e. When text mining is combined with biological knowledge and other analytical tools, new evidence on the potential to repurpose existing drugs can be obtained\\u003csup\\u003e[\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e]\\u003c/sup\\u003e. The computational prediction of multi-target drugs has established multi-pharmacology as a promising alternative to solve some complex drug discovery through text mining \\u003csup\\u003e[\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e]\\u003c/sup\\u003e. Biological knowledge can be assorted and categorized with gene ontology (GO) hierarchical relationships\\u003csup\\u003e[\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e]\\u003c/sup\\u003e. The definition of GO biological processes can provide a framework for mapping relationships between biological entities and text mining concepts\\u003csup\\u003e[\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e]\\u003c/sup\\u003e. It is reported that the cellular signaling pathway maps combined with text mining can be used to expand the information of a specific signal transduction pathway and find a target gene or a targetable regulator of the target gene\\u003csup\\u003e[\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e]\\u003c/sup\\u003e. Several successful research outcomes studies have been reported, in which some are discovering new uses for existing drugs were found using GO and pathway analysis to link drugs with diseases \\u003csup\\u003e[\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e]\\u003c/sup\\u003e. The priority of a group of genes can be further determined by evaluating their influence on protein interaction networks. This technique is useful because it demonstrated some novel relationships by analyzing gene interactions that tend to accumulate in the network due to certain characteristics of the disease or pathological conditions\\u003csup\\u003e[\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e]\\u003c/sup\\u003e.\\u003c/p\\u003e \\u003cp\\u003eIn this study, we performed queries using the search terms endometriosis and infertility to produce a preliminary list of genes in exploring potential drugs for EMT-induced infertility. We then validated the relationship between the identified genes and generated a priority target set by analyzing the signal pathway. We created a list of high-priority target genes with an in-depth analysis of the genes' functional enrichments. Candidate drugs were then derived from a summary of the data on potential drug-gene interactions.\\u003c/p\\u003e\"},{\"header\":\"Materials And Methods\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eGene Collection\\u003c/strong\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eThe database genclip3 (http://ci.smu.edu.cn/genclip3/analysis.php）was used to perform text mining. We performed queries using the search terms endometriosis and infertility to produce a list of differentially expressed genes (DEGs). Then, we extracted all of the unique gene hits from each result. All of these genes were then used in the subsequent analyses.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eGene Ontology and Pathway Analysis\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eGeneCodis (http://genecodis.cnb.csic.es/), a web-based tool for integrating various sources of information with gene ontology and functional information\\u003csup\\u003e[19]\\u003c/sup\\u003e.It was used for the enrichment analysis of the genes corresponding to the\\u0026nbsp;EMT\\u0026nbsp;and infertility intersection. We used GeneCodis for enrichment analysis of the genes related to EMT-induced infertility. We put the genes obtained in the text mining step into the input set, and perform GO analysis on these.\\u0026nbsp;GO terms reflect genetic knowledge about the biological process (BP), cellular components (CC) and molecular function (MF)\\u003csup\\u003e[21,22]\\u003c/sup\\u003e. And then, this study selected the most significantly enriched biological processes. Genes with the selected annotations were performed for the next step and annotations of the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways.\\u0026nbsp;Moreover, the Kyoto Encyclopedia of Genes and Genomes (KEGG)\\u003csup\\u003e[23]\\u003c/sup\\u003e.\\u0026nbsp;It provides data resources of known biological pathways to annotate a set of genes.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eProtein Interaction Network.\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe STRING database (http://string‑db.org) integrates the protein-protein interactions of selected genes\\u003csup\\u003e[20]\\u003c/sup\\u003e. On the first page of the STRING database, we selected \\u0026apos;Multiple proteins\\u0026apos; from the left menu bar, entered the genes chosen from the last step, and \\u0026apos;Homo sapiens\\u0026apos; was selected as the organism. Regarding the confidence score, the stronger the evidence that two proteins interact with each other is, the higher the confidence scores observed. In this study, the confidence score is determined to medium (score 0.400). Although a lower confidence score may decrease the network\\u0026apos;s confidence, it may increase the inclusion criteria. Then, the protein-protein interaction network of the target gene is obtained.\\u0026nbsp;After that, PPI networks were built by using the Cytoscape software\\u003csup\\u003e[24]\\u003c/sup\\u003e.\\u0026nbsp;The plug-in Molecular Complex Detection (MCODE) built-in Cytoscape was used to select the significant gene modules of the PPI networks. The parameters were set as follows: MCODE scores \\u0026gt;3 and the count of nodes \\u0026gt;4. Finally, we selected one significant gene modules (including 40 genes) from the PPI networks for further validation analyses.\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eDrug-gene Interactions\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eWe used DGIdb (http://www.dgidb.org), which aggregates drug-gene interaction data from 27 sources including DrugBank, PharmGKB, ChEMBL, NCBI Entrez, Ensembl, PubChem, various clinical trial databases, and literature available through NCBI PubMed to explore drug-gene interactions in the final list of genes, which were applied to find the potential targets in a search for ready-made drugs or small organic compounds\\u003csup\\u003e[25]\\u003c/sup\\u003e.\\u0026nbsp;\\u003c/p\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cp\\u003e \\u003cb\\u003eResults of text mining, biological process and pathway analysis.\\u003c/b\\u003e \\u003c/p\\u003e \\u003cp\\u003eIn exploring potential drugs for EMT-induced infertility, 550 genes were found to be related to EMT-induced infertility from the text mining searches.\\u003c/p\\u003e \\u003cp\\u003eWe uploaded DEGs to the online website GeneCodis to identify GO Terms and KEGG pathways and classified them into three functional categories: biological process (BP), cellular component (CC), and molecular function (MF; Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). During this process, to ensure that only the most enriched annotations were chosen, a P‑value cutoff (P\\u0026thinsp;=\\u0026thinsp;1.00E‑06) was set. As shown in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eA and Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e, GO analysis showed that the DEGs were most significantly enriched in response to the organic substance. Moreover, the upregulated DEGs were significantly enriched in biological process, including muscle system process, muscle contraction, and regulation of muscle contraction (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eA and Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e); Among the most significantly enriched GO Terms above the cutoff, those most relevant to EMT-induced infertility based on the available literature and research were selected. Therefore, the six most enriched biological process annotations were: i) \\u0026lsquo;response to organic substance\\u0026rsquo; (P\\u0026thinsp;=\\u0026thinsp;3.90E-127);ii) \\u0026lsquo;cellular response to chemical stimulus\\u0026rsquo; (P\\u0026thinsp;=\\u0026thinsp;1.29E-112); iii) \\u0026lsquo;regulation of cell proliferation\\u0026rsquo; (P\\u0026thinsp;=\\u0026thinsp;1.43E-98); iv) \\u0026lsquo;cellular response to organic substance\\u0026rsquo; (P\\u0026thinsp;=\\u0026thinsp;1.01E-95); v) \\u0026lsquo;cell proliferation\\u0026rsquo; (P\\u0026thinsp;=\\u0026thinsp;3.58E-94); and vi) \\u0026lsquo;response to oxygen-containing compound\\u0026rsquo; (P\\u0026thinsp;=\\u0026thinsp;1.48E-92), containing 328, 304, 227, 261, 241 and 215 genes from the query set, respectively. And the six most enriched cellular component annotations were: i) \\u0026lsquo;extracellular space\\u0026rsquo; (P\\u0026thinsp;=\\u0026thinsp;1.87E-63);ii) \\u0026lsquo;extracellular region\\u0026rsquo; (P\\u0026thinsp;=\\u0026thinsp;2.15E-39); iii) \\u0026lsquo;cell surface\\u0026rsquo; (P\\u0026thinsp;=\\u0026thinsp;5.22E-38); iv) \\u0026lsquo;extracellular region part\\u0026rsquo; (P\\u0026thinsp;=\\u0026thinsp;1.70E-34); v) \\u0026lsquo;external side of plasma membrane\\u0026rsquo; (P\\u0026thinsp;=\\u0026thinsp;6.97E-22); and vi) \\u0026lsquo;side of membrane\\u0026rsquo; (P\\u0026thinsp;=\\u0026thinsp;2.16E-20), containing190, 305, 110, 265, 48 and 62 genes from the query set, respectively. What is more, the six most enriched molecular function annotations were: i) \\u0026lsquo;receptor binding\\u0026rsquo; (P\\u0026thinsp;=\\u0026thinsp;8.64E-75); ii) \\u0026lsquo;cytokine receptor binding\\u0026rsquo; (P\\u0026thinsp;=\\u0026thinsp;3.76E-48); iii) \\u0026lsquo;cytokine activity\\u0026rsquo; (P\\u0026thinsp;=\\u0026thinsp;5.29E-37); iv) \\u0026lsquo;growth factor activity\\u0026rsquo; (P\\u0026thinsp;=\\u0026thinsp;4.88E-24); v) \\u0026lsquo;identical protein binding\\u0026rsquo; (P\\u0026thinsp;=\\u0026thinsp;2.24E-19); and vi) \\u0026lsquo;enzyme binding\\u0026rsquo; (P\\u0026thinsp;=\\u0026thinsp;1.57E-18), containing200, 76, 60, 41, 111 and 130 genes from the query set, respectively.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eThe Gene Ontology of the genes involved in EM-induced infertility.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"4\\\"\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCategory\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eTerm\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eCount\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003ePValue\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eGOTERM_BP_FAT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eGO:0010033\\u0026thinsp;~\\u0026thinsp;response to organic substance\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e328\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e3.90E-127\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eGOTERM_BP_FAT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eGO:0070887\\u0026thinsp;~\\u0026thinsp;cellular response to chemical stimulus\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e304\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.29E-112\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eGOTERM_BP_FAT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eGO:0042127\\u0026thinsp;~\\u0026thinsp;regulation of cell proliferation\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e227\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.43E-98\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eGOTERM_BP_FAT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eGO:0071310\\u0026thinsp;~\\u0026thinsp;cellular response to organic substance\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e261\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.01E-95\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eGOTERM_BP_FAT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eGO:0008283\\u0026thinsp;~\\u0026thinsp;cell proliferation\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e241\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e3.58E-94\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eGOTERM_BP_FAT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eGO:1901700\\u0026thinsp;~\\u0026thinsp;response to oxygen-containing compound\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e215\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.48E-92\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eGOTERM_CC_FAT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eGO:0005615\\u0026thinsp;~\\u0026thinsp;extracellular space\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e190\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.87E-63\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eGOTERM_CC_FAT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eGO:0005576\\u0026thinsp;~\\u0026thinsp;extracellular region\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e305\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2.15E-39\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eGOTERM_CC_FAT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eGO:0009986\\u0026thinsp;~\\u0026thinsp;cell surface\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e110\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e5.22E-38\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eGOTERM_CC_FAT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eGO:0044421\\u0026thinsp;~\\u0026thinsp;extracellular region part\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e265\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.70E-34\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eGOTERM_CC_FAT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eGO:0009897\\u0026thinsp;~\\u0026thinsp;external side of plasma membrane\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e48\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e6.97E-22\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eGOTERM_CC_FAT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eGO:0098552\\u0026thinsp;~\\u0026thinsp;side of membrane\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e62\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2.16E-20\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eGOTERM_MF_FAT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eGO:0005102\\u0026thinsp;~\\u0026thinsp;receptor binding\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e200\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e8.64E-75\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eGOTERM_MF_FAT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eGO:0005126\\u0026thinsp;~\\u0026thinsp;cytokine receptor binding\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e76\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e3.76E-48\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eGOTERM_MF_FAT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eGO:0005125\\u0026thinsp;~\\u0026thinsp;cytokine activity\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e60\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e5.29E-37\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eGOTERM_MF_FAT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eGO:0008083\\u0026thinsp;~\\u0026thinsp;growth factor activity\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e41\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e4.88E-24\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eGOTERM_MF_FAT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eGO:0042802\\u0026thinsp;~\\u0026thinsp;identical protein binding\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e111\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2.24E-19\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eGOTERM_MF_FAT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eGO:0019899\\u0026thinsp;~\\u0026thinsp;enzyme binding\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e130\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.57E-18\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003ctfoot\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"4\\\"\\u003eAbbreviation: EM,endometriosis.\\u003c/td\\u003e\\u003c/tr\\u003e \\u003c/tfoot\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 2\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eSignaling Pathway Enrichment Analysis of involved in EM-induced infertility.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"4\\\"\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCategory\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eTerm\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eCount\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003ePValue\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"17\\\" rowspan=\\\"18\\\"\\u003e \\u003cp\\u003eKEGG_PATHWAY\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ebta04060:Cytokine-cytokine receptor interaction\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e65\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2.41E-32\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ebta05200:Pathways in cancer\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e74\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e6.92E-23\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ebta05205:Proteoglycans in cancer\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e51\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.45E-21\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ebta05323:Rheumatoid arthritis\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e33\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.60E-18\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ebta05161:Hepatitis B\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e39\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2.95E-17\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ebta04151:PI3K-Akt signaling pathway\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e60\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e6.44E-17\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ebta04668:TNF signaling pathway\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e33\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.14E-16\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ebta05142:Chagas disease (American trypanosomiasis)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e33\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e7.56E-16\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ebta04066:HIF-1 signaling pathway\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e29\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.74E-14\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ebta05321:Inflammatory bowel disease (IBD)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e24\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2.40E-13\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ebta04068:FoxO signaling pathway\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e32\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e4.42E-13\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ebta05144:Malaria\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e21\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e4.53E-13\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ebta05133:Pertussis\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e24\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2.34E-12\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ebta05140:Leishmaniasis\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e23\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e3.11E-12\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ebta04630:Jak-STAT signaling pathway\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e33\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e3.67E-12\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ebta05212:Pancreatic cancer\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e22\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e3.94E-12\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ebta05206:MicroRNAs in cancer\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e42\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.63E-11\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ebta05152:Tuberculosis\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e34\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.23E-10\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eIn the process of KEGG pathway enrichment analysis, those most relevant to EMT-induced infertility based on the available literature and research were selected. The analysis of enriched pathway annotations resulted in 18 pathways containing a total of 74 unique genes (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). The six most significantly enriched pathways were: i) \\u0026lsquo;cytokine-cytokine receptor interaction\\u0026rsquo; (P\\u0026thinsp;=\\u0026thinsp;2.41E-32); ii) \\u0026lsquo;pathways in cancer\\u0026rsquo; (P\\u0026thinsp;=\\u0026thinsp;6.92E-23); and iii) \\u0026lsquo;proteoglycans in cancer\\u0026rsquo; (P\\u0026thinsp;=\\u0026thinsp;1.45E-21), iv) \\u0026lsquo;rheumatoid arthritis\\u0026rsquo; (P\\u0026thinsp;=\\u0026thinsp;1.60E-18); v) \\u0026lsquo;hepatitis B\\u0026rsquo; (P\\u0026thinsp;=\\u0026thinsp;2.95E-17); and vi) \\u0026lsquo;PI3K-Akt signaling pathway\\u0026rsquo; (P\\u0026thinsp;=\\u0026thinsp;6.44E-17), containing 65, 74, 51, 33 ,39 and 60 genes from the query set, respectively.\\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eResults of protein-protein interaction.\\u003c/b\\u003e \\u003c/p\\u003e \\u003cp\\u003eThe protein-protein interaction network of the 452 target genes was illustrated based on the STRING online database (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttp://string-db.org\\u003c/span\\u003e\\u003c/span\\u003e) and Cytoscape software (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). PPI network complexly contained 452 nodes and 2758 edges (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eA). Finally, 39 genes were selected to form a strong interaction network based on the MCODE. The significant module (39 nodes 384 edges, Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eB) from the PPI network was selected, as determined by Cytoscape. The 39 genes included CXCR3, NPY, BMP4, IGFBP1, APOA1, IL6, PROC, APOE, POMC, TIMP1, AHSG, CXCL8, CX3CR1, TF, CXCL1, F5, DRD2, CCL5, GPER1, AFP, AGTR2, CXCL10, CXCL11, CXCL12, CCR5, CASR, SPP1, ALB, CCL19, CCL25, CCL21, C3, CCL20, AGT, LGALS1, MSLN, CSF1, BMP15 and MFGE8.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eResults of drug-gene interactions.\\u003c/b\\u003e \\u003c/p\\u003e \\u003cp\\u003eThey used the final list of 39 genes as the potential targets in the drug-gene interaction analysis, a list of 49 drugs meeting the standard requirements for drug treatments for EMT-induced infertility (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e). They included: Acepromazine, Acetophenazine, Alizapride, Amantadine hydrochloride, Amisulpride, Apomorphine, Aripiprazole, Bifeprunox, Brexpiprazole, Cabergoline, Cinacalcet, Clozapine, Haloperidol, Iloperidone, Levodopa, Levomepromazine, Lisuride, Loxapine, Maraviroc, Menadione, Mesoridazine, Minaprine, Olanzapine, Paliperidone, Perphenazine, Pimozide, Pipotiazine, Promazine, Propiomazine, Remoxipride, Risperidone, Rivanicline, Ropinirole, Rotigotine, Siltuximab, Sodium tetradecyl sulfate, Tasosartan, Thioproperazine, Thiothixene, Tinzaparin sodium, Triflupromazine, Velcalcetide and Zuclopenthixol.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab3\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 3\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eCandidate drugs targeting genes with EM-induced infertility.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"8\\\"\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNumber\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eDrug\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eGene\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eDrug-gene interaction\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eScore*\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eApproved?\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eApproved FDA\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003eReference (PubMed ID)\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eAcepromazine\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eDRD2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eantagonist\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e7\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e15694263\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eAcetophenazine\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eDRD2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eantagonist\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e9\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e6147851\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eAlizapride\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eDRD2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eantagonist\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eNo\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e7865862\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eAmantadine hydrochloride\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eDRD2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eagonist\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eNo\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e10443547\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eAmisulpride\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eDRD2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eantagonist\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eNo\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e12404702\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eApomorphine\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eDRD2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eagonist\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e12\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eInvestigational\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e11343576\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e7\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eAripiprazole\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eDRD2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eantagonist\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e12\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e9083792\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eBifeprunox\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eDRD2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eagonist\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eInvestigational\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eNo\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e17393144\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e9\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eBrexpiprazole\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eDRD2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eagonist\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eNo\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e24947465\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e10\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eCabergoline\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eDRD2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eagonist\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e14\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e12721865\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e11\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eCinacalcet\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eCASR\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eagonist\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e15\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e19261825\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e12\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eClozapine\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eDRD2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eantagonist\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e14\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e15781964\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e13\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eDihydromorphine\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003ePOMC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eagonist\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eExperimental\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eNo\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e6292632\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e14\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eDomperidone\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eDRD2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eantagonist\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e10\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eInvestigational\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eNo\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e15894081\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e15\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eDroperidol\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eDRD2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eantagonist\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e2527092\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e16\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eDrotrecogin alfa\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eF5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003einhibitor\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e13\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eInvestigational\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e11893230\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e17\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eFluspirilene\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eDRD2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eantagonist\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e9\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e8935801\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e18\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eGinseng\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eIL6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eantagonist\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e/\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e17436372\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e19\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eHaloperidol\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eDRD2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eantagonist\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e7\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e12887421\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e20\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eIloperidone\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eDRD2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eantagonist\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e7\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e12861482\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e21\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eLevodopa\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eDRD2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eagonist\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e10\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e11978145\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e22\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eLevomepromazine\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eDRD2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eantagonist\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eNo\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e2870716\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e23\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eLisuride\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eDRD2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eagonist\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eNo\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e18691132\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e24\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eLoxapine\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eDRD2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eantagonist\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e13\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e9570468\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e25\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMaraviroc\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eCCR5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eantagonist\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e13\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e16298345\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e26\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMenadione\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003ePROC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eactivator\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e17215245\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e27\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMesoridazine\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eDRD2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eantagonist\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e15357957\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e28\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMinaprine\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eDRD2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eagonist\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eNo\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e17139284\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e29\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eOlanzapine\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eDRD2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eantagonist\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e15\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e14575800\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e30\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePaliperidone\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eDRD2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eantagonist\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e12\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e11132243\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e31\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePerphenazine\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eDRD2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eantagonist\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e12\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e2573104\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e32\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePimozide\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eDRD2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eantagonist\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e14\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e8301582\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e33\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePipotiazine\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eDRD2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eantagonist\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e9\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e/\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e15694263\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e34\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePromazine\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eDRD2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eantagonist\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e17139284\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e35\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePropiomazine\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eDRD2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eantagonist\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e17139284\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e36\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eRemoxipride\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eDRD2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eantagonist\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e7\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eNo\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e8665533\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e37\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eRisperidone\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eDRD2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eantagonist\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e13\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eInvestigational\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e17059881\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e38\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eRivanicline\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eCXCL8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eantagonist\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eInvestigational\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eNo\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e16715250\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e39\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eRopinirole\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eDRD2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eagonist\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e11\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eInvestigational\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e10446316\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e40\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eRotigotine\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eDRD2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eagonist\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e7\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e18691132\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e41\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eSiltuximab\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eIL6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003einhibitor\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e8823310\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e42\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eSodium tetradecyl sulfate\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003ePROC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003einhibitor\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e11752352\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e43\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eTasosartan\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eAGTR2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eantagonist\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eNo\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e11683476\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e44\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eThioproperazine\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eDRD2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eantagonist\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e7\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eNo\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e15694263\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e45\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eThiothixene\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eDRD2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eantagonist\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e9\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e15694263\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e46\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eTinzaparin sodium\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eCXCL12\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003ebinder\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e18991783\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e47\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eTriflupromazine\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eDRD2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eantagonist\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e17139284\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e48\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eVelcalcetide\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eCASR\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eagonist\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eInvestigational\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eNo\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e24235081\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e49\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eZuclopenthixol\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eDRD2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eantagonist\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e17535043\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003ctfoot\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"8\\\"\\u003eEach individual instance of a drug-gene interaction was evaluated in the context of the gene\\u0026rsquo;s relationship to EMT-induced infertility and the drug\\u0026rsquo;s relationship to the gene, thus ensuring any putative drug would be expected to have the desired directional effect on the condition. Drugs and compounds which met the criteria of targeting one of the candidate genes by an appropriate interaction were collected in a final list\\u003c/td\\u003e\\u003c/tr\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"8\\\"\\u003e*The score is the combined number of database sources and PubMed references supporting a given interaction\\u003c/td\\u003e\\u003c/tr\\u003e \\u003c/tfoot\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003ePotential gene targets of the drugs in this list are DRD2 (37 drugs), PROC, IL6 and CASR (2 drugs each), POMC, F5, CXCL8, CXCL12, CCR5and AGTR2 (1 drug each). Common previously approved uses for these drugs include the treatment of hemostasis, nausea, vomit, mental disease, chronic kidney disease, Parkinson's disease, and Alzheimer's disease. A total of 25 drugs on the list are used for anti-mental disease therapy. Next, we verified whether the drugs identified by our analysis could target pathways associated with EMT-induced infertility.\\u003c/p\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eOur purposes were to explore the potential drugs through silico text-mining and pathway analysis tools to identify existing drugs with the potential for topical administration and treatment with patients with EMT-induced infertility. The cause by EMT-induced infertility may be the following reasons: (1) Changes in the normal anatomy of the pelvic cavity cause obstruction of the fallopian tubes, which affects conception. (2) abnormal immune function and autoimmune response, such as the cytokines, inflammatory, including IL-6, IL-8 and VEGF, et al. And Iwabe et al.\\u003csup\\u003e[\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e]\\u003c/sup\\u003e found that IL-6 may promote the formation of autoantibodies and has embryotoxicity and inhibits early embryo implantation, which can cause EMT-induced infertility. (3) endocrine abnormalities: including ovarian hormone abnormalities and insufficient luteal function. Currently, The treatment of EMT-induced infertility is mainly anticipatory, drug treatment, surgical treatment, combined drug and surgical treatment and assisted reproductive technology treatments. Now, the drugs used for treatment are OC, drugs that inhibit the gonadal axis from antagonizing sex hormones, including mifepristone, danazol, gestrinone capsules and Gonadotropin-releasing hormone agonists and medicines that promote ovulation. De Ziegler et al.\\u003csup\\u003e[\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e]\\u003c/sup\\u003e found that using OC 6\\u0026ndash;8 weeks before assisted reproduction can also increase the pregnancy rate of assisted reproductive technology. Clinical studies have proved that mifepristone is effective in treating EMT-induced infertility\\u003csup\\u003e[\\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e]\\u003c/sup\\u003e.\\u003c/p\\u003e \\u003cp\\u003eA Cochrane study of 16 cases of preoperative and post-operative use of hormonal drugs to treat EMT-induced infertility, there is insufficient evidence that the drug can improve pregnancy rates \\u003csup\\u003e[\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e]\\u003c/sup\\u003e. Therefore, it is particularly important to find other safe and effective therapeutic drugs to treat EMT-induced infertility. In this study, we identified 39 genes following the gene set enrichment analysis representing a network (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e) targetable by the 49 drugs, all of which have not yet been tested for EMT-induced infertility (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e). The majority of potential drugs identified by the drug-gene interaction search are known to target DRD2. These drugs belong to treat mental disease. Besides, it also includes some drugs that target inflammatory factors, such as IL-6, CXCL8 and CXCL12.\\u003c/p\\u003e \\u003cp\\u003eIt is reported that dopamine receptor D2 (DRD2) is the main receptor for most antipsychotic drugs. Numerous documents have shown that DRD2 is essential for learning and memory, especially in the prefrontal cortex \\u003csup\\u003e[\\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e]\\u003c/sup\\u003e. Activating DRD2 will also enable the corresponding downstream pathways, thereby mediating a series of downstream signaling effects such as the inhibition of inflammation and apoptosis\\u003csup\\u003e[\\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e]\\u003c/sup\\u003e. Many scholars have pointed out that in various diseases, such as inflammatory bowel disease\\u003csup\\u003e[\\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e]\\u003c/sup\\u003e, luteinizing granular cells\\u003csup\\u003e[\\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e33\\u003c/span\\u003e]\\u003c/sup\\u003e, and lung cancer\\u003csup\\u003e[\\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e34\\u003c/span\\u003e]\\u003c/sup\\u003e, dopamine inhibit VEGF-mediated vascular permeability and angiogenesis through DRD2 to treat diseases or inhibit tumors. The possible mechanism is that dopamine can inhibit VEGF-mediated microvascular permeability and endothelial cell proliferation and migration\\u003csup\\u003e[\\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e35\\u003c/span\\u003e]\\u003c/sup\\u003e. Some scholars also pointed out that dopamine can block VEGF-induced focal adhesion kinase (FAK) and mitogen-activated protein kinase (MAPK) phosphorylation in endothelial cells\\u003csup\\u003e[\\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e36\\u003c/span\\u003e]\\u003c/sup\\u003e. Angiogenesis is of great significance to the formation of ectopic intima, and many factors regulate it. As we knew, endothelial growth factor (VEGF) is one of the most important factors in angiogenesis. The expression of VEGF in the peritoneal fluid, ectopic endometrium, and eutopic endometrium of patients with endometriosis was significantly higher than in the control group. Based on this, it can be considered that the high expression of VEGF in patients with EMs indicates that it promotes angiogenesis in the disease. And it plays an important role in occurrence and development in EMT\\u003csup\\u003e[\\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e37\\u003c/span\\u003e]\\u003c/sup\\u003e. Therefore, we suspect that DRD2 may be involved in the incident and development of EMT through VEGF.\\u003c/p\\u003e \\u003cp\\u003eBesides, the recruitment of inflammatory cells and the production of inflammatory factors, such as IL-6 and IL-8, play a key role in the formation of EMT. IL-6 mainly promotes cell growth, stimulates cell differentiation, and participates in acute inflammation. It induces local adhesion, fibrosis and immunological abnormalities in the pelvic cavity by mediating immune and inflammatory reactions, and promotes the formation and development of EMT\\u003csup\\u003e[\\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e38\\u003c/span\\u003e]\\u003c/sup\\u003e. What is more, it can increase the toxicity of early embryos and cause infertility\\u003csup\\u003e[\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e]\\u003c/sup\\u003e. IL-8 has angiogenic activity, increases the formation of microvessels in the abdominal cavity, and increases the acceptance of endometrial implants. It can promote the proliferation and adhesion of endometrial cells, leading to pelvic adhesion and fibrosis\\u003csup\\u003e[\\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e39\\u003c/span\\u003e]\\u003c/sup\\u003e. It can also increase the activity and infiltration capacity of matrix metallo proteinases(MMP) in endometrial stromal cells. It is a proteolytic enzyme that regulates the degradation and reconstruction of the extracellular matrix. It can make the endometrial fragments that flow back into the abdominal cavity with menstrual blood have a strong planting invasive ability plays an important role in the pathogenesis of EMT\\u003csup\\u003e[\\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e40\\u003c/span\\u003e]\\u003c/sup\\u003e.\\u003c/p\\u003e \\u003cp\\u003eThis study has the following limitations: 1. The database used in the research may have limited information on the annotation of gene functions or pathways. The services or effects of some genes cannot be verified through experiments; 2. For a given drug, not all existing gene interactions are known. Therefore, potentially useful drugs for EMT-induced infertility may be missed or ignored. The gene interaction may not have been fully elucidated, or the drug-gene interaction is suspected of producing harmful effects.\\u003c/p\\u003e\"},{\"header\":\"Conclusions\",\"content\":\"\\u003cp\\u003eIn conclusion, we have reported a method that has founded the panel of candidate drugs that target the genes/ pathways relevant to EMT-induced infertility. With the development and improvement of databases and analysis tools, such a method can be used regularly. Therefore, through this analysis, we have identified 49 drugs, which have not been used in EMT-induced infertility, which provides a basis for new trials and the development of novel targeted therapies as potential treatments for EMT-induced infertility.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgments\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAuthors Contributions\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eYL, ZC and LC was involved in the design of the study. JH, JF, YQ and LX were involved in data collection and analyzed the data. Both YL and ZC were involved in drafting the manuscript, LC and HL edit the manuscript. All authors have read and approved the final manuscript. The authors declare that all data were generated in-house and that no paper mill was used.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eEthical Approval\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis study was approved by the Ethics Committee of Fujian Provincial Maternity and Children Hospital ( FMCH2018-155 ) and complied with the Declaration of Helsinki.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConsent to Participate\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eAll individuals participating in this study provided written informed consent.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConsent to Publish\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNo funding was received.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCompeting Interests\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors declare that they have no competing interests.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAvailability of data and materials\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n \\u003cli\\u003eWasson MN. Chronic pelvic pain due to postmenopausal endometriosis. J Minim Invasive Gynecol. (2019). doi: 10.1016/j.jmig.2019.08.015.\\u003c/li\\u003e\\n \\u003cli\\u003eBarra F, Scala C, Ferrero S.Current understand pharmacokinetics, clinical efficacy and safety of progestins for treating the pain associated with endometriosis[J]. Expert Opin Drug Metab Toxicol, 2018,14(4):399-415.\\u003c/li\\u003e\\n \\u003cli\\u003eBedaiwy, M. A., Alfaraj, S., Yong, P., and Casper, R. (2017). New developments in the medical treatment of endometriosis. Fertil. Steril. 107, 555\\u0026ndash;565. doi: 10.1016/j.fertnstert.2016.12.025\\u003c/li\\u003e\\n \\u003cli\\u003eAs-Sanie S，Black R，Giudice LC, et al. Assessing research gaps and unmet needs in endometriosis [J]. Am J Obstet Gynecol，2019，221（2）：86-94.\\u003c/li\\u003e\\n \\u003cli\\u003ePractice Committee of the American Society for Reproductive Medicine.Treatment of pelvic pain associated with endometriosis: a committee opinion[J].Fertil Steril,2014，101(4):927-935.\\u003c/li\\u003e\\n \\u003cli\\u003eOzkan S，Murk W，Arici A．Endometriosis and infertility: Epidemiology and evidence-based treatments［J］．Ann N Y Acad Sci，\\u0026nbsp;2008，1127: 92-100．\\u003c/li\\u003e\\n \\u003cli\\u003eJeng CJ, CHUANG L, SHEN J. A comparison of progestogens or oral contraceptives and gonadotropin-releasing hormone agonists for the treatment of endometriosis, a systematic review[J].Expert Opinion on Pharmacotherapy,2014:7.\\u003c/li\\u003e\\n \\u003cli\\u003eSeo JW, Lee DY, KIM SE, et al.Comparison of long-term use of combined oral contraceptive after gonadotropin-releasing hormone agonist plus add-back therapy versus dienogest to prevent recurrence of ovarian endometrioma after surgery[J]. Eur J Obstet Gynecol Reprod Biol,2019,236:53-57.\\u003c/li\\u003e\\n \\u003cli\\u003eBEDAIWY MA, ALLAIRE C, ALFARAJ S.Long-term medical management of endometriosis with dienogest and with a gonadotropin-releasing hormone agonist and add-back hormone therapy [J].Fertil Steril,2017,107(3):537-548.\\u003c/li\\u003e\\n \\u003cli\\u003eMoosavinasab S, Patterson J, Strouse R, Rastegar-Mojarad M, Regan K, Payne PR, Huang Y, Lin SM (2016) \\u0026apos;RE fine drugs\\u0026apos;: an interactive dashboard to access drug repurposing opportunities Database (Oxford). https://doi.org/10.1093/database/baw083\\u003c/li\\u003e\\n \\u003cli\\u003eMosca E, Bertoli G, Piscitelli E, et al. Identification of functionally related genes using data mining and data integration: a breast cancer case study. BMC Bioinformatics. 2009;10(suppl): S8.\\u003c/li\\u003e\\n \\u003cli\\u003eYu S, Tranchevent LC, De Moor B, Moreau Y (2010) Gene prioritization and clustering by multi-view text mining. BMC Bioinf 11:28\\u003c/li\\u003e\\n \\u003cli\\u003eBerg EL (2014) Systems biology in drug discovery and development. Drug Discov Today 19:113\\u0026ndash;125.\\u003c/li\\u003e\\n \\u003cli\\u003eChaudhari R, Tan Z, Huang B, Zhang S. Computational polypharmacology: a new paradigm for drug discovery. Expert Opin Drug Discov. 2017;12:279e291.\\u003c/li\\u003e\\n \\u003cli\\u003eAndronis C, Sharma A, Virvilis V, Deftereos S, Persidis A (2011) Literature mining, ontologies and information visualization for drug repurposing. Brief Bioinform 12:357\\u0026ndash;368.\\u003c/li\\u003e\\n \\u003cli\\u003eLiu H, Beck TN, Golemis EA, Serebriiskii IG (2014) Integrating in silico resources to map a signaling network. Methods Mol Biol 1101:197\\u0026ndash;245.\\u003c/li\\u003e\\n \\u003cli\\u003eCramer PE, Cirrito JR, Wesson DW, Lee CY, Karlo JC, Zinn AE, Casali BT, Restivo JL, Goebel WD, James MJ, Brunden KR, Wilson DA, Landreth GE (2012) ApoE-directed therapeutics rapidly clear beta-amyloid and reverse deficits in AD mouse models. Science 335:1503\\u0026ndash;1506.\\u003c/li\\u003e\\n \\u003cli\\u003eTousi B (2015) The emerging role of bexarotene in the treatment of Alzheimer\\u0026rsquo;s disease: current evidence. Neuropsychiatr Dis Treat 11:311\\u0026ndash;315.\\u003c/li\\u003e\\n \\u003cli\\u003eTabas-Madrid D, Nogales-Cadenas R, Pascual-Montano A (2012) GeneCodis3: a non-redundant and modular enrichment analysis tool for functional genomics. Nucleic Acids Res 40:W478\\u0026ndash;W483.\\u003c/li\\u003e\\n \\u003cli\\u003eSzklarczyk D, Franceschini A, Wyder S, Forslund K, Heller D, Huerta‑CepasJ, Simonovic M, Roth A, Santos A, Tsafou KP, et al.: STRING v10: Protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res 43: D447‑D452, 2015.\\u003c/li\\u003e\\n \\u003cli\\u003eHale M, Thapa I, Ghersi D. FunSet: an open-source software and web server for performing and displaying Gene Ontology enrichment analysis. BMC Bioinformatics 2019; 20:359-359.\\u003c/li\\u003e\\n \\u003cli\\u003eCarbon S, Dietze H, Lewis S, et al. Expand the Gene Ontology knowledgebase and resources. Nucleic Acids Res 2017; 45:D331-D338.\\u003c/li\\u003e\\n \\u003cli\\u003eKanehisa M, Goto S. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res 2000; 28:27-30.\\u003c/li\\u003e\\n \\u003cli\\u003eSmoot M, Ono K, Ruscheinski J, et al. Cytoscape 2.8. Bioinformatics 2011; 27:431-432.\\u003c/li\\u003e\\n \\u003cli\\u003eWagner AH, Coffman AC, Ainscough BJ, Spies NC, Skidmore ZL, Campbell KM, Krysiak K, Pan D, McMichael JF, Eldred JM, et al.: DGIdb 2.0: mining clinically relevant drug-gene interactions. Nucleic Acids Res 44: D1036‑D1044, 2016.\\u003c/li\\u003e\\n \\u003cli\\u003ewabe T, Harada T, Sakamoto Y, et al. Gonadotropin-releasing hormone agonist treatment reduced serum interleukin-6 concentrations in patients with ovarian endometriomas. Fertil Steril, 2003;80(2):300-304.\\u003c/li\\u003e\\n \\u003cli\\u003ede Ziegler D，Gayet V，Aubriot FX，et al．Use of oral contraceptives in women with endometriosis before assisted reproduction treatment improves outcomes［J］．Fertil Steril，2010，94 ( 7 ): 2796-2799．\\u003c/li\\u003e\\n \\u003cli\\u003eMinas V，Dada T．Laparoscopic treatment of endometriosis and effects on quality of life: A retrospective study using the short form EHP-5 endometriosis specific questionnaire［J］. J Obstet Gynaecol，2014，34( 4): 336-340．\\u003c/li\\u003e\\n \\u003cli\\u003eYap C，Furness S，Farquhar C．Pre and post-operative medical therapy for endometriosis surgery [J/CD]. Cochrane Database Syst Ｒev，2004( 3): CD003678．\\u003c/li\\u003e\\n \\u003cli\\u003eC Missale 1, SR Nash, S W Robinson, M Jaber, M G Caron. Dopamine receptors: from structure to function．Physiol Rev. 1998 Jan;78(1):189-225.\\u003c/li\\u003e\\n \\u003cli\\u003eXu TX, Sotnikova T D, Liang C, et al. Hyperdopaminergic tone erodes prefrontal long-term potential via a D2 receptor-operated protein phosphatase gate.[J]. Journal of Neuroscience, 2009, 29(45):14086-14099.\\u003c/li\\u003e\\n \\u003cli\\u003eTolstanova G, Deng X, Ahluwalia A, et al. Role of Dopamine and D2 Dopamine Receptor in the Pathogenesis of Inflammatory Bowel Disease.\\u0026nbsp;Dig Dis Sci. 2015;60(10):2963-2975.\\u003c/li\\u003e\\n \\u003cli\\u003eFerrero H, Garc\\u0026iacute;a-Pascual, Carmen M, G\\u0026oacute;mez, Ra\\u0026uacute;l, et al. Dopamine receptor two activations inhibits ovarian vascular endothelial growth factor secretion invitro: implications for treatment of ovarian hyperstimulation syndrome with dopamine receptor two agonists[J]. Fertility \\u0026amp; Sterility, 2014, 101(5):1411-1418.e2.\\u003c/li\\u003e\\n \\u003cli\\u003eHoeppner L H, Wang Y, Sharma A, et al. Dopamine D2 receptor agonists inhibit lung cancer progression by reducing angiogenesis and tumor-infiltrating myeloid-derived suppressor cells[J]. Molecular Oncology, 2015, 9.\\u003c/li\\u003e\\n \\u003cli\\u003eBhattacharya R, Sinha S, Yang SP, et al. The neurotransmitter dopamine modulates vascular permeability in the endothelium[J]. Journal of Molecular Signaling, 2008, 3:14-14.\\u003c/li\\u003e\\n \\u003cli\\u003eSarkar C, Chakroborty D, Mitra R B, et al. dopamine in vivo inhibits VEGF-induced phosphorylation of VEGFR-2, MAPK, and focal adhesion kinase in endothelial cells[J]. Am J Physiol Heart Circ Physiol, 2004, 287(4): H1554.\\u003c/li\\u003e\\n \\u003cli\\u003eHuili Z. The role of VEGF in the pathogenesis of endometriosis[J]. Progress in Obstetrics and Gynecology, 2006.\\u003c/li\\u003e\\n \\u003cli\\u003eSharpe-Timms K L, Zimmer R L, Ricke E A, et al. Endometriotic haptoglobin binds to peritoneal macrophages and alters their function in women with endometriosis[J]. Fertility \\u0026amp; Sterility, 2002, 78(4):810-819.\\u003c/li\\u003e\\n \\u003cli\\u003eArici A. Local cytokines in endometrial tissue: the role of interleukin-8 in the pathogenesis of endometriosis.[J]. Annals of the New York Academy of Sciences, 2010, 955(1):101-109.\\u003c/li\\u003e\\n \\u003cli\\u003eMulayam N, Savlu A, Guzeloglu-Kayisli O, et al. Regulation of endometrial stromal cell matrix metalloproteinase activity and invasiveness by interleukin-8[J]. Fertility \\u0026amp; Sterility, 2004, 81(Supp-S1):904-911.\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Differentially Expressed Genes, Endometriosis, Infertility, Drugs, Text mining.\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-744890/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-744890/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003e\\u003cstrong\\u003eBackground: \\u003c/strong\\u003eEndometriosis (EMT) is the most common benign gynecological disease among women of reproductive age, causing infertility and seriously affects women's physical and mental health. However, the current treatment was not always effective. This study was designed to use publicly available data to identify drugs targeting the relevant gene with EMT-induced-infertility using computational tools.\\u003c/p\\u003e\\u003cp\\u003e\\u003cstrong\\u003eMethods: \\u003c/strong\\u003eEMT and infertility genes were determined by text mining, and the GeneCodis program was used to analyzed gene ontology of the intersection of the two gene sets. A string database was used to analyze the protein-protein interaction network. The Drug-Gene Interaction database is queried for the rich gene set belonging to the identified pathways to find drug candidates that can be used in EMT-induced infertility.\\u003c/p\\u003e\\u003cp\\u003e\\u003cstrong\\u003eResults: \\u003c/strong\\u003e\\u003c/p\\u003e\\u003cp\\u003eOur analysis identified 550 genes common to both the EMT and infertility by text mining. Gene enrichment analysis and protein-protein interaction analysis found 39 genes potentially targetable by a total of 49 drugs that could be formulated for application, which have not been used in EMT-induced infertility. \\u003c/p\\u003e\\u003cp\\u003e\\u003cstrong\\u003eConclusions:\\u003c/strong\\u003e The findings from the present analysis can facilitate the Identification of existing drugs that have the potential of topical administration to improve EMT-induced infertility and present tremendous opportunities to study novel targets pharmacology using in silico text mining and pathway analysis tools.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Computational Drug Discovery in Patients with Endometriosis-Induced Infertility via Text Mining and Biomedical Databases\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2021-08-04 23:21:29\",\"doi\":\"10.21203/rs.3.rs-744890/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"73f41e4b-ac9d-4a56-b3d6-aeb69d3c0372\",\"owner\":[],\"postedDate\":\"August 4th, 2021\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[{\"id\":6205937,\"name\":\"Theoretical Computer Science\"},{\"id\":6205938,\"name\":\"Computational Mathematics\"},{\"id\":6205939,\"name\":\"Drug Discovery, Design, \\u0026 Development\"}],\"tags\":[],\"updatedAt\":\"2021-08-20T01:15:43+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2021-08-04 23:21:29\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-744890\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-744890\",\"identity\":\"rs-744890\",\"version\":[\"v1\"]},\"buildId\":\"zG6E5Sm6oA1kbPE6fP8qF\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC0","license_restricted":false}