dc.creator | Charitou T., Kontou P.I., Tamposis I.A., Pavlopoulos G.A., Braliou G.G., Bagos P.G. | en |
dc.date.accessioned | 2023-01-31T07:43:21Z | |
dc.date.available | 2023-01-31T07:43:21Z | |
dc.date.issued | 2022 | |
dc.identifier | 10.1038/s41397-022-00289-1 | |
dc.identifier.issn | 1470269X | |
dc.identifier.uri | http://hdl.handle.net/11615/72544 | |
dc.description.abstract | Available drugs have been used as an urgent attempt through clinical trials to minimize severe cases of hospitalizations with Coronavirus disease (COVID-19), however, there are limited data on common pharmacogenomics affecting concomitant medications response in patients with comorbidities. To identify the genomic determinants that influence COVID-19 susceptibility, we use a computational, statistical, and network biology approach to analyze relationships of ineffective concomitant medication with an adverse effect on patients. We statistically construct a pharmacogenetic/biomarker network with significant drug-gene interactions originating from gene-disease associations. Investigation of the predicted pharmacogenes encompassing the gene-disease-gene pharmacogenomics (PGx) network suggests that these genes could play a significant role in COVID-19 clinical manifestation due to their association with autoimmune, metabolic, neurological, cardiovascular, and degenerative disorders, some of which have been reported to be crucial comorbidities in a COVID-19 patient. © 2022, The Author(s), under exclusive licence to Springer Nature Limited. | en |
dc.language.iso | en | en |
dc.source | Pharmacogenomics Journal | en |
dc.source.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139118298&doi=10.1038%2fs41397-022-00289-1&partnerID=40&md5=7ad2e53232de1b88bb2c85fa4a6f8eda | |
dc.subject | acetylsalicylic acid | en |
dc.subject | alpha2 integrin | en |
dc.subject | anakinra | en |
dc.subject | apolipoprotein C1 | en |
dc.subject | apolipoprotein C3 | en |
dc.subject | apolipoprotein E | en |
dc.subject | atazanavir | en |
dc.subject | azithromycin | en |
dc.subject | bamlanivimab | en |
dc.subject | baricitinib | en |
dc.subject | beta interferon | en |
dc.subject | beta3 integrin | en |
dc.subject | biological marker | en |
dc.subject | canakinumab | en |
dc.subject | casirivimab plus imdevimab | en |
dc.subject | chloroquine | en |
dc.subject | colchicine | en |
dc.subject | cytochrome P450 2C19 | en |
dc.subject | cytochrome P450 2C9 | en |
dc.subject | cytochrome P450 2D6 | en |
dc.subject | cytochrome P450 3A4 | en |
dc.subject | cytochrome P450 3A5 | en |
dc.subject | dalteparin | en |
dc.subject | dexamethasone | en |
dc.subject | endothelial nitric oxide synthase | en |
dc.subject | enoxaparin | en |
dc.subject | favipiravir | en |
dc.subject | glucuronosyltransferase 1A1 | en |
dc.subject | glucuronosyltransferase 1A3 | en |
dc.subject | glucuronosyltransferase 1A6 | en |
dc.subject | glucuronosyltransferase 1A7 | en |
dc.subject | glutathione peroxidase 1 | en |
dc.subject | HLA DQA1 antigen | en |
dc.subject | HLA DRB1 antigen | en |
dc.subject | hydrocortisone | en |
dc.subject | hydroxychloroquine | en |
dc.subject | insulin receptor substrate 1 | en |
dc.subject | ivermectin | en |
dc.subject | lopinavir plus ritonavir | en |
dc.subject | methylprednisolone | en |
dc.subject | multidrug resistance associated protein 1 | en |
dc.subject | nitazoxanide | en |
dc.subject | reduced folate carrier | en |
dc.subject | remdesivir | en |
dc.subject | ribavirin | en |
dc.subject | ruxolitinib | en |
dc.subject | sarilumab | en |
dc.subject | SARS-CoV-2 vaccine | en |
dc.subject | solute carrier organic anion transporter 1B1 | en |
dc.subject | tocilizumab | en |
dc.subject | transporter associated with antigen processing 1 | en |
dc.subject | vasculotropin A | en |
dc.subject | vitamin D receptor | en |
dc.subject | Article | en |
dc.subject | clinical decision making | en |
dc.subject | clinical feature | en |
dc.subject | clinical outcome | en |
dc.subject | controlled study | en |
dc.subject | coronavirus disease 2019 | en |
dc.subject | data mining | en |
dc.subject | drug efficacy | en |
dc.subject | drug repositioning | en |
dc.subject | gene identification | en |
dc.subject | gene interaction | en |
dc.subject | genetic association | en |
dc.subject | genetic variation | en |
dc.subject | genome-wide association study | en |
dc.subject | human | en |
dc.subject | infection sensitivity | en |
dc.subject | pandemic | en |
dc.subject | pharmacogenetic testing | en |
dc.subject | protein protein interaction | en |
dc.subject | risk factor | en |
dc.subject | systems biology | en |
dc.subject | treatment response | en |
dc.subject | drug therapy | en |
dc.subject | genomics | en |
dc.subject | pharmacogenetics | en |
dc.subject | COVID-19 | en |
dc.subject | Data Mining | en |
dc.subject | Genomics | en |
dc.subject | Humans | en |
dc.subject | Pharmacogenetics | en |
dc.subject | Springer Nature | en |
dc.title | Drug genetic associations with COVID-19 manifestations: a data mining and network biology approach | en |
dc.type | journalArticle | en |