Drug repurposing based on the DTD-GNN graph neural network: revealing the relationships among drugs, targets and diseases.

MOTIVATION: The rational modelling of the relationship among drugs, targets and diseases is crucial for drug retargeting. While significant progress has been made in studying binary relationships, further research is needed to deepen our understanding of ternary relationships. The application of graph neural networks in drug retargeting is increasing, but further research is needed to determine the appropriate modelling method for ternary relationships and how to capture their complex multi-feature structure. RESULTS: The aim of this study was to construct relationships among drug, targets and diseases. To represent the complex relationships among these entities, we used a heterogeneous graph structure. Additionally, we propose a DTD-GNN model that combines graph convolutional networks and graph attention networks to learn feature representations and association information, facilitating a more thorough exploration of the relationships. The experimental results demonstrate that the DTD-GNN model outperforms other graph neural network models in terms of AUC, Precision, and F1-score. The study has important implications for gaining a comprehensive understanding of the relationships between drugs and diseases, as well as for further research and application in exploring the mechanisms of drug-disease interactions. The study reveals these relationships, providing possibilities for innovative therapeutic strategies in medicine.

Exploring cutting-edge strategies for drug repurposing in female cancers - An insight into the tools of the trade.

Female cancers, which include breast and gynaecological cancers, represent a significant global health burden for women. Despite advancements in research pertinent to unearthing crucial pathological characteristics of these cancers, challenges persist in discovering potential therapeutic strategies. This is further exacerbated by economic burdens associated with de novo drug discovery and clinical intricacies such as development of drug resistance and metastasis. Drug repurposing, an innovative approach leveraging existing FDA-approved drugs for new indications, presents a promising avenue to expedite therapeutic development. Computational techniques, including virtual screening and analysis of drug-target-disease relationships, enable the identification of potential candidate drugs. Integration of diverse data types, such as omics and clinical information, enhances the precision and efficacy of drug repurposing strategies. Experimental approaches, including high-throughput screening assays, in vitro, and in vivo models, complement computational methods, facilitating the validation of repurposed drugs. This review highlights various target mining strategies based on analysis of differential gene expression, weighted gene co-expression, protein-protein interaction network, and host-pathogen interaction, among others. To unearth drug candidates, the technicalities of leveraging information from databases such as DrugBank, STITCH, LINCS, and ChEMBL, among others are discussed. Further in silico validation techniques encompassing molecular docking, pharmacophore modelling, molecular dynamic simulations, and ADMET analysis are elaborated. Overall, this review delves into the exploration of individual case studies to offer a wide perspective of the ever-evolving field of drug repurposing, emphasizing the multifaceted approaches and methodologies employed for the same to confront female cancers.

Comparative analysis of gene and disease selection in genomic newborn screening studies.

Genomic newborn screening (gNBS) is on the horizon given the decreasing costs of sequencing and the advanced understanding of the impact of genetic variants on health and diseases. Key to ongoing gNBS pilot studies is the selection of target diseases and associated genes to be included. In this study, we present a comprehensive analysis of seven published gene-disease lists from gNBS studies, evaluating gene-disease count, composition, group proportions, and ClinGen curations of individual disorders. Despite shared selection criteria, we observe substantial variation in total gene count (median 480, range 237-889) and disease group composition. An intersection was identified for 53 genes, primarily inherited metabolic diseases (83%, 44/53). Each study investigated a subset of exclusive gene-disease pairs, and the total number of exclusive gene-disease pairs was positively correlated with the total number of genes included per study. While most pairs receive "Definitive" or "Strong" ClinGen classifications, some are labeled as "Refuted" (n = 5) or "Disputed" (n = 28), particularly in genetic cardiac diseases. Importantly, 17%-48% of genes lack ClinGen curation. This study underscores the current absence of consensus recommendations for selection criteria for target diseases for gNBS resulting in diversity in proposed gene-disease pairs, their coupling with gene variations and the use of ClinGen curation. Our findings provide crucial insights into the selection of target diseases and accompanying gene variations for future gNBS program, emphasizing the necessity for ongoing collaboration and discussion about criteria harmonization for panel selection to ensure the screening's objectivity, integrity, and broad acceptance.

Identification of Potential Therapeutic Targets on the Level of DNA/mRNAs, Proteins and Metabolites: A Systematic Mapping Review of Scientific Texts' Fragments from Open Targets.

Database records contain useful information, which is readily available, but, unfortunately, limited compared to the source (publications). Our study reviewed the text fragments supporting the association between the biological macromolecules and diseases from Open Targets to map them on the biological level of study (DNA/RNA, proteins, metabolites). We screened records using a dictionary containing terms related to the selected levels of study, reviewed 600 hits manually and used machine learning to classify 31,260 text fragments. Our results indicate that association studies between diseases and macromolecules conducted on the level of DNA and RNA prevail, followed by the studies on the level of proteins and metabolites. We conclude that there is a clear need to translate the knowledge from the DNA/RNA level to the evidence on the level of proteins and metabolites. Since genes and their transcripts rarely act in the cell by themselves, more direct evidence may be of greater value for basic and applied research.

Analysis of Pharmacological Activities and Mechanisms of Essential Oil in Leaves of C. grandis 'Tomentosa' by GC-MS/MS and Network Pharmacology.

BACKGROUND: Citrus grandis 'Tomentosa,' a fruit epicarp of C. grandis 'Tomentosa' or C. grandis (L.) Osbeck is widely used in health food and medicine. Based on our survey results, there are also rich essential oils with bioactivities in leaves, but the chemical compounds in this part and relevant pharmacological activities have never been studied systematically. Therefore, this study was to preliminarily decipher the pharmacological activities and mechanisms of the essential oil in leaves of C. grandis 'Tomentosa' by an integrated network pharmacology approach. METHODS: Essential oil compositions from leaves ofC. grandis 'Tomentosa' were identified using GC-MS/MS. And then, the targets of these oil compositions were predicted and screened from TCMSP, SwissTargetPrediction, STITCH and SEA databases. STRING database was used to construct the protein-protein interaction networks, and the eligible protein targets were input into WebGestalt 2019 to carry out GO enrichment and KEGG pathway enrichment analysis. Based on the potential targets, disease enrichment information was obtained by TTD databases. Cytoscape software was used to construct the component-target-disease network diagrams. RESULTS: Finally, 61 essential oil chemical components were identified by GC-MS/MS, which correspond to 679 potential targets. Biological function analysis showed 12, 19, and 12 GO entries related to biological processes, cell components and molecular functions, respectively. 43 KEGG pathways were identified, of which the most significant categories were terpenoid backbone biosynthesis, TNF signaling pathway and leishmaniasis. The component-target-disease network diagram revealed that the essential oil compositions in leaves of C. grandis 'Tomentosa' could treat tumors, immune diseases, neurodegenerative diseases and respiratory diseases, which were highly related to CHRM1, PTGS2, CASP3, MAP2K1 and CDC25B. CONCLUSION: This study may provide new insight into C. grandis 'Tomentosa' or C. grandis (L.) Osbeck and may provide useful information for future utilization and development.

Drug-Disease Severity and Target-Disease Severity Interaction Networks in COVID-19 Patients.

Drug interactions with other drugs are a well-known phenomenon. Similarly, however, pre-existing drug therapy can alter the course of diseases for which it has not been prescribed. We performed network analysis on drugs and their respective targets to investigate whether there are drugs or targets with protective effects in COVID-19, making them candidates for repurposing. These networks of drug-disease interactions (DDSIs) and target-disease interactions (TDSIs) revealed a greater share of patients with diabetes and cardiac co-morbidities in the non-severe cohort treated with dipeptidyl peptidase-4 (DPP4) inhibitors. A possible protective effect of DPP4 inhibitors is also plausible on pathophysiological grounds, and our results support repositioning efforts of DPP4 inhibitors against SARS-CoV-2. At target level, we observed that the target location might have an influence on disease progression. This could potentially be attributed to disruption of functional membrane micro-domains (lipid rafts), which in turn could decrease viral entry and thus disease severity.

Analysis of occupational health examination results of radiation workers in medical institutions of Shenzhen in 2020 / 中国辐射卫生

Objective To analyze the medical examinations of radiation worker in medical institutions and provide some basic data for radiation protection management. Methods The occupational health examination of 3568 radiation workers from 681 medical institutions who came to our hospital for occupational health examination from January 1 to December 31 in 2020 were summarized and analyzed. Results There was no case of suspected occupational radiation sickness. The abnormal rate was in the range of 2.17%～2.99%, the rate of occupational contraindicated was about 1.44%～2.17%. The total review rate was about 13.00%, more than 79.48% of the radiation workers were checked out other diseases or abnormal. The abnormal examination items are mainly ophthalmology, B ultrasound of liver, gallbladder, spleen and pancreas, liver function, electrocardiogram, blood routine, urine routine, blood pressure, B ultrasound of both kidneys and kidney function. The abnormal rate of ophthalmology in each level of institutions was decreased with the increase of the length of service, while the abnormal results of B-ultrasound of liver, gallbladder, spleen and pancreas, blood pressure, B-ultrasound of both kidneys and renal function were increased with the increase of service. Conclusion Maybe the radiation protection of radiation workers in medical institutions was well in Shenzhen, but there were different effects of the health status of the staff. Therefore, it is important to further strengthen the occupational health monitoring management.

COVID-19 Knowledge Extractor (COKE): A Tool and a Web Portal to Extract Drug - Target Protein Associations from the CORD-19 Corpus of Scientific Publications on COVID-19.

Objective: The COVID-19 pandemic has catalyzed a widespread effort to identify drug candidates and biological targets of relevance to SARS-COV-2 infection, which resulted in large numbers of publications on this subject. We have built the COVID-19 Knowledge Extractor (COKE), a web application to extract, curate, and annotate essential drug-target relationships from the research literature on COVID-19 to assist drug repurposing efforts. Materials and Methods: SciBiteAI ontological tagging of the COVID Open Research Dataset (CORD-19), a repository of COVID-19 scientific publications, was employed to identify drug-target relationships. Entity identifiers were resolved through lookup routines using UniProt and DrugBank. A custom algorithm was used to identify co-occurrences of protein and drug terms, and confidence scores were calculated for each entity pair. Results: COKE processing of the current CORD-19 database identified about 3,000 drug-protein pairs, including 29 unique proteins and 500 investigational, experimental, and approved drugs. Some of these drugs are presently undergoing clinical trials for COVID-19. Discussion: The rapidly evolving situation concerning the COVID-19 pandemic has resulted in a dramatic growth of publications on this subject in a short period. These circumstances call for methods that can condense the literature into the key concepts and relationships necessary for insights into SARS-CoV-2 drug repurposing. Conclusion: The COKE repository and web application deliver key drug - target protein relationships to researchers studying SARS-CoV-2. COKE portal may provide comprehensive and critical information on studies concerning drug repurposing against COVID-19. COKE is freely available at https://coke.mml.unc.edu/ and the code is available at https://github.com/DnlRKorn/CoKE.

COVID-KOP: Integrating Emerging COVID-19 Data with the ROBOKOP Database.

In response to the COVID-19 pandemic, we established COVID-KOP, a new knowledgebase integrating the existing ROBOKOP biomedical knowledge graph with information from recent biomedical literature on COVID-19 annotated in the CORD-19 collection. COVID-KOP can be used effectively to test new hypotheses concerning repurposing of known drugs and clinical drug candidates against COVID-19. COVID-KOP is freely accessible at https://covidkop.renci.org/. For code and instructions for the original ROBOKOP, see: https://github.com/NCATS-Gamma/robokop.

Associating 197 Chinese herbal medicine with drug targets and diseases using the similarity ensemble approach.

Chinese herbal medicine (CHM) addresses complex diseases through polypharmacological interactions. However, systematic studies of herbal medicine pharmacology remain challenging due to the complexity of CHM ingredients and their interactions with various targets. In this study, we aim to address this challenge with computational approaches. We investigated the herb-target-disease associations of 197 commonly prescribed CHMs using the similarity ensemble approach and DisGeNET database. We demonstrated that this method can be applied to associate herbs with their putative targets. In the case study of three well-known herbs, Radix Glycyrrhizae, Flos Lonicerae, and Rhizoma Coptidis, approximately 70% of the predicted targets were supported by scientific literature. By linking 406 targets to 2439 annotated diseases, we further analyzed the pharmacological functions of 197 herbs. Finally, we proposed a strategy of target-oriented herbal formula design and illustrated the target profiles for four common chronic diseases, namely, Alzheimer's disease, depressive disorder, hypertensive disease, and non-insulin-dependent diabetes mellitus. This computational approach holds great potential in the target identification of herbs, understanding the molecular mechanisms of CHM, and designing novel herbal formulas.

Predicting associations among drugs, targets and diseases by tensor decomposition for drug repositioning.

BACKGROUND: Development of new drugs is a time-consuming and costly process, and the cost is still increasing in recent years. However, the number of drugs approved by FDA every year per dollar spent on development is declining. Drug repositioning, which aims to find new use of existing drugs, attracts attention of pharmaceutical researchers due to its high efficiency. A variety of computational methods for drug repositioning have been proposed based on machine learning approaches, network-based approaches, matrix decomposition approaches, etc. RESULTS: We propose a novel computational method for drug repositioning. We construct and decompose three-dimensional tensors, which consist of the associations among drugs, targets and diseases, to derive latent factors reflecting the functional patterns of the three kinds of entities. The proposed method outperforms several baseline methods in recovering missing associations. Most of the top predictions are validated by literature search and computational docking. Latent factors are used to cluster the drugs, targets and diseases into functional groups. Topological Data Analysis (TDA) is applied to investigate the properties of the clusters. We find that the latent factors are able to capture the functional patterns and underlying molecular mechanisms of drugs, targets and diseases. In addition, we focus on repurposing drugs for cancer and discover not only new therapeutic use but also adverse effects of the drugs. In the in-depth study of associations among the clusters of drugs, targets and cancer subtypes, we find there exist strong associations between particular clusters. CONCLUSIONS: The proposed method is able to recover missing associations, discover new predictions and uncover functional clusters of drugs, targets and diseases. The clustering of drugs, targets and diseases, as well as the associations among the clusters, provides a new guiding framework for drug repositioning.

Hyphenated chromatography detection and compound-target-disease investigation on herb-pair Chuanxiong Rhizoma - Xiangfu Rhizoma.

ETHNOPHARMACOLOGY RELEVANCE: The combination of Chuanxiong Rhizoma (Ligusticum chuanxiong Hort., umbelliferae) with Xiangfu Rhizoma (the rhizoma of Cyperus rotundus L., Cyperaceae), is deemed as CR-XR herb-pair (Yaodui) in China. Their compatible mechanism needs a further research using modern analytical techniques and bioinformatic tool. METHODS: Head Space- Solid Phase Micro Extraction coupled with Gas Chromatography/Mass Spectrometer detection (HS-SPME-GC/MS) and Liquid Chromatography coupled to quadrupole Time of Flight - Mass Spectrometry (LC-qTOF-MS) were applied in an accurate identification of the absorbed phytochemicals in mice serum; Their potential targets were available after compound-protein interaction (CPI) prediction and molecular docking verification; Then the corresponding disease types, as well as the relevant Traditional Chinese Medicine (Zhongyi) syndromes (Zheng), were matched from databases and references. RESULTS: Resolution from hyphenated chromatographic datasets, thirty-eight phytochemicals were detected in serum samples from mice. Seventy potential target proteins were thereby found through a bioinformatic calculation, which mainly focused on circulatory, endocrine and nervous diseases in Western medicine, also related with Qizhi and Xueyu Zheng from the perspective of Zhongyi. Part of the relationships among compound-Target-Disease have been confirmed by literatures. These virtual data were sketched out as 'The active Compound - potential Target' network, 'Target - Disease' network and 'Target - Zhongyi Disease' network, in which the network topology was used to analyze them. CONCLUSIONS: Our work successfully explained the compatible mechanism of CR-XR Yaodui, which exert 'multi-components, multi targets' in treating Qizhi and Xueyu Zheng.

[Mechanism of Gardenia jasminoides against cholestasis based on network pharmacology].

To screen the active ingredients of Gardenia jasminoides and potential targets,and investigate the mechanisms against cholestasis based on network pharmacology technology. Twenty-one active components of G. jasminoides were retrieved and the target sites were screened by using Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform( TCMSP). Cytoscape3. 2. 1 was used to construct the component-target network. Two hundred and eight targets related to cholestasis were searched and screened through Dis Ge NET,KEGG and OMIM databases. The key targets of G. jasminoides components and cholestasis were integrated and screened,and the component-target-disease network was constructed with Cytoscape 3. 2. 1 software to screen out the core network whose freedom degree was greater than the average value. The Clue GO plug-in of Cytoscape 3. 2. 1 software was used to analyze the biological processes and pathway enrichment of G. jasminoides in regulation of cholestasis. GO biological process analysis revealed 17 biological processes,involving 3 signaling biological processes related to cholestasis,i.e. acute inflammatory response,positive regulation of reactive oxygen species metabolic process,and nitric oxide biosynthetic process. KEGG-KEEG-305 terms and REACTOME pathways analysis revealed 17 regulatory pathways,involving 4 signaling pathways related to cholestasis,i.e. metabolism of xenobiotics by cytochrome P450,nuclear receptor transcription pathway,GPVI-mediated activation cascade and platelet activation. It was found that aqueous extract of G. jasminoides could improve serum biochemical abnormalities in ANIT-induced cholestasis rats. Aqueous extract of G. jasminoides could decrease the protein and mRNA expression levels of ESR1 in liver tissues,and increase the protein and mRNA expression levels of PPARG,NOS2,F2 R,NOS3,and NR3 C1. To sum up,the possible mechanisms of G. jasminoides against cholestasis may be related with the above three processes and four pathways.

Mechanism of Gardenia jasminoides against cholestasis based on network pharmacology / 中国中药杂志

To screen the active ingredients of Gardenia jasminoides and potential targets,and investigate the mechanisms against cholestasis based on network pharmacology technology. Twenty-one active components of G. jasminoides were retrieved and the target sites were screened by using Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform( TCMSP). Cytoscape3. 2. 1 was used to construct the component-target network. Two hundred and eight targets related to cholestasis were searched and screened through Dis Ge NET,KEGG and OMIM databases. The key targets of G. jasminoides components and cholestasis were integrated and screened,and the component-target-disease network was constructed with Cytoscape 3. 2. 1 software to screen out the core network whose freedom degree was greater than the average value. The Clue GO plug-in of Cytoscape 3. 2. 1 software was used to analyze the biological processes and pathway enrichment of G. jasminoides in regulation of cholestasis. GO biological process analysis revealed 17 biological processes,involving 3 signaling biological processes related to cholestasis,i.e. acute inflammatory response,positive regulation of reactive oxygen species metabolic process,and nitric oxide biosynthetic process. KEGG-KEEG-305 terms and REACTOME pathways analysis revealed 17 regulatory pathways,involving 4 signaling pathways related to cholestasis,i.e. metabolism of xenobiotics by cytochrome P450,nuclear receptor transcription pathway,GPVI-mediated activation cascade and platelet activation. It was found that aqueous extract of G. jasminoides could improve serum biochemical abnormalities in ANIT-induced cholestasis rats. Aqueous extract of G. jasminoides could decrease the protein and mRNA expression levels of ESR1 in liver tissues,and increase the protein and mRNA expression levels of PPARG,NOS2,F2 R,NOS3,and NR3 C1. To sum up,the possible mechanisms of G. jasminoides against cholestasis may be related with the above three processes and four pathways.

Literature evidence in open targets - a target validation platform.

BACKGROUND: We present the Europe PMC literature component of Open Targets - a target validation platform that integrates various evidence to aid drug target identification and validation. The component identifies target-disease associations in documents and ranks the documents based on their confidence from the Europe PMC literature database, by using rules utilising expert-provided heuristic information. The confidence score of a given document represents how valuable the document is in the scope of target validation for a given target-disease association by taking into account the credibility of the association based on the properties of the text. The component serves the platform regularly with the up-to-date data since December, 2015. RESULTS: Currently, there are a total number of 1168365 distinct target-disease associations text mined from >26 million PubMed abstracts and >1.2 million Open Access full text articles. Our comparative analyses on the current available evidence data in the platform revealed that 850179 of these associations are exclusively identified by literature mining. CONCLUSIONS: This component helps the platform's users by providing the most relevant literature hits for a given target and disease. The text mining evidence along with the other types of evidence can be explored visually through https://www.targetvalidation.org and all the evidence data is available for download in json format from https://www.targetvalidation.org/downloads/data .

The role of methylene blue in the pediatric patient with vasoplegic syndrome.

Patients with vasoplegic syndrome (VPS) in the post-cardiopulmonary bypass setting usually require escalating vasopressor support. The utilization of methylene blue (MB) in the treatment of VPS in the adult population has been well described. We present a 5-year-old girl who developed vasodilatory shock due to VPS that was resistant to escalating doses of adrenergic agonists following cardiac transplantation. After receiving 1 mg/kg of MB, there was a significant improvement in the patient's mean arterial pressure which allowed for progressive weaning of the vasopressor support. To date, there are limited data regarding the use of MB in pediatric patients with VPS following cardiothoracic surgery. The cellular mechanisms of MB in VPS are discussed and reports of its use in the adult and pediatric population are reviewed. Dosing regimens and potential adverse effects of MB are presented.

A Study on Major Health Components of National Health Examination Survey in Korea / 예방의학회지

The purpose of this study was to identify the major health components and measurements to be conducted in National Health Examination Survey(KNHES). The prevalence and severity of disease, acceptability of population and the possibility, of standardization of measurement were considered as guideline for selecting the components. On the base of magnitude and severity of disease, chronic liver disease, hepatic cancer, gastric ulcer, stomach cancer, essential hypertension, cerebrovascular disease, ischemic heart disease, pulmonary tuberculosis, lung cancer, DM, breast cancer, cervical cancer, arthritis and intervertebral disc disorder were selected as the preliminary target diseases. Questionnaire survey for 648 persons in 'K' city and medical specialists in five clinical societies were conducted for evaluating the acceptability of general population for the measurements and the possibility of standardization for the procedures. In conclusion, the major target diseases were chronic liver disease, hypertension and DM and the total cholesterol, high density lipoprotein, triglyceride, total protein, albumin, hemoglobulin, hematocrit, platlet count, anti-HBs, HBsAg, height and weight were selected for basic physical components.