{"doi":"10.3390/pr10020326","title":"Network-Based Approach to Repurpose Approved Drugs for COVID-19 by Integrating GWAS and Text Mining Data","abstract":"The coronavirus disease 19 (COVID-19) is a global pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which has a rapidly increasing prevalence and has caused significant morbidity/mortality. Despite the availability of many vaccines that can offer widespread immunization, it is also important to reach effective treatment for COVID-19 patients. However, the development of novel drug therapeutics is usually a time-consuming and costly process, and therefore, repositioning drugs that were previously approved for other purposes could have a major impact on the fight against COVID-19. Here, we first identified lung-specific gene regulatory/interaction subnetworks (COVID-19-related genes modules) enriched for COVID-19-associated genes obtained from GWAS and text mining. We then screened the targets of 220 approved drugs from DrugBank, obtained their drug-induced gene expression profiles in the LINCS database, and constructed lung-specific drug-related gene modules. By applying an integrated network-based approach to quantify the interactions of the COVID-19-related gene modules and drug-related gene modules, we prioritized 13 approved drugs (e.g., alitretinoin, clocortolone, terazosin, doconexent, and pergolide) that could potentially be repurposed for the treatment of COVID-19. These findings provide important and timely insights into alternative therapeutic options that should be further explored as COVID-19 continues to spread.","journal":"Processes","year":2022,"id":279791,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":3,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9536,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2022-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":490571,"name":"Hui Li","orcid":"0000-0003-4759-1779","position":1,"is_corresponding":false},{"id":675509,"name":"Danyang Liu","orcid":"0000-0001-8015-4103","position":2,"is_corresponding":false},{"id":490575,"name":"Wan‐Qiang Lv","orcid":"0000-0001-8620-8889","position":3,"is_corresponding":false},{"id":718136,"name":"Shengran Wang","orcid":"0000-0001-5539-4471","position":4,"is_corresponding":false},{"id":52792,"name":"Jiachen Liu","orcid":"0000-0002-0263-3704","position":5,"is_corresponding":false},{"id":268245,"name":"Jonathan Greenbaum","orcid":"0000-0002-7817-5981","position":6,"is_corresponding":false},{"id":268248,"name":"Hui Shen","orcid":"0000-0003-0335-6064","position":7,"is_corresponding":false},{"id":418030,"name":"Hong‐Mei Xiao","orcid":"0000-0002-8121-9498","position":8,"is_corresponding":false},{"id":85487,"name":"Hong‐Wen Deng","orcid":"0000-0002-0387-8818","position":9,"is_corresponding":false},{"id":675510,"name":"Shuang Liang","orcid":"0000-0002-4906-7201","position":0,"is_corresponding":true}],"reference_count":81,"raw_metadata":null,"created_at":"2026-07-19T00:28:55.546964Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}