Dataset Information

Artificial intelligence approach fighting COVID-19 with repurposing drugs.

ABSTRACT: Background:The ongoing COVID-19 pandemic has caused more than 193,825 deaths during the past few months. A quick-to-be-identified cure for the disease will be a therapeutic medicine that has prior use experiences in patients in order to resolve the current pandemic situation before it could become worsening. Artificial intelligence (AI) technology is hereby applied to identify the marketed drugs with potential for treating COVID-19. Methods:An AI platform was established to identify potential old drugs with anti-coronavirus activities by using two different learning databases; one consisted of the compounds reported or proven active against SARS-CoV, SARS-CoV-2, human immunodeficiency virus, influenza virus, and the other one containing the known 3C-like protease inhibitors. All AI predicted drugs were then tested for activities against a feline coronavirus in in vitro cell-based assay. These assay results were feedbacks to the AI system for relearning and thus to generate a modified AI model to search for old drugs again. Results:After a few runs of AI learning and prediction processes, the AI system identified 80 marketed drugs with potential. Among them, 8 drugs (bedaquiline, brequinar, celecoxib, clofazimine, conivaptan, gemcitabine, tolcapone, and vismodegib) showed in vitro activities against the proliferation of a feline infectious peritonitis (FIP) virus in Fcwf-4 cells. In addition, 5 other drugs (boceprevir, chloroquine, homoharringtonine, tilorone, and salinomycin) were also found active during the exercises of AI approaches. Conclusion:Having taken advantages of AI, we identified old drugs with activities against FIP coronavirus. Further studies are underway to demonstrate their activities against SARS-CoV-2 in vitro and in vivo at clinically achievable concentrations and doses. With prior use experiences in patients, these old drugs if proven active against SARS-CoV-2 can readily be applied for fighting COVID-19 pandemic.

SUBMITTER: Ke YY

PROVIDER: S-EPMC7227517 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Artificial intelligence approach fighting COVID-19 with repurposing drugs.

Ke Yi-Yu YY Peng Tzu-Ting TT Yeh Teng-Kuang TK Huang Wen-Zheng WZ Chang Shao-En SE Wu Szu-Huei SH Hung Hui-Chen HC Hsu Tsu-An TA Lee Shiow-Ju SJ Song Jeng-Shin JS Lin Wen-Hsing WH Chiang Tung-Jung TJ Lin Jiunn-Horng JH Sytwu Huey-Kang HK Chen Chiung-Tong CT

Biomedical journal 20200515 4

<h4>Background</h4>The ongoing COVID-19 pandemic has caused more than 193,825 deaths during the past few months. A quick-to-be-identified cure for the disease will be a therapeutic medicine that has prior use experiences in patients in order to resolve the current pandemic situation before it could become worsening. Artificial intelligence (AI) technology is hereby applied to identify the marketed drugs with potential for treating COVID-19.<h4>Methods</h4>An AI platform was established to identi ...[more]

PMID: 32426387

Similar Datasets

Project description:Background: Recently, Coronavirus Disease 2019 (COVID-19), caused by severe acute respiratory syndrome virus 2 (SARS-CoV-2), has affected more than 200 countries and lead to enormous losses. This study systematically reviews the application of Artificial Intelligence (AI) techniques in COVID-19, especially for diagnosis, estimation of epidemic trends, prognosis, and exploration of effective and safe drugs and vaccines; and discusses the potential limitations. Methods: We report this systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We searched PubMed, Embase and the Cochrane Library from inception to 19 September 2020 for published studies of AI applications in COVID-19. We used PROBAST (prediction model risk of bias assessment tool) to assess the quality of literature related to the diagnosis and prognosis of COVID-19. We registered the protocol (PROSPERO CRD42020211555). Results: We included 78 studies: 46 articles discussed AI-assisted diagnosis for COVID-19 with total accuracy of 70.00 to 99.92%, sensitivity of 73.00 to 100.00%, specificity of 25 to 100.00%, and area under the curve of 0.732 to 1.000. Fourteen articles evaluated prognosis based on clinical characteristics at hospital admission, such as clinical, laboratory and radiological characteristics, reaching accuracy of 74.4 to 95.20%, sensitivity of 72.8 to 98.00%, specificity of 55 to 96.87% and AUC of 0.66 to 0.997 in predicting critical COVID-19. Nine articles used AI models to predict the epidemic of the COVID-19, such as epidemic peak, infection rate, number of infected cases, transmission laws, and development trend. Eight articles used AI to explore potential effective drugs, primarily through drug repurposing and drug development. Finally, 1 article predicted vaccine targets that have the potential to develop COVID-19 vaccines. Conclusions: In this review, we have shown that AI achieved high performance in diagnosis, prognosis evaluation, epidemic prediction and drug discovery for COVID-19. AI has the potential to enhance significantly existing medical and healthcare system efficiency during the COVID-19 pandemic.

Project description:Background Coronavirus Disease 2019 (COVID-19) is a multi-systemic infection caused by the novel Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), that has become a pandemic. Although its prevailing symptoms include anosmia, ageusia, dry couch, fever, shortness of brief, arthralgia, myalgia, and fatigue, regional and methodological assessments vary, leading to heterogeneous clinical descriptions of COVID-19. Aging, uncontrolled diabetes, hypertension, obesity, and exposure to androgens have been correlated with worse prognosis in COVID-19. Abnormalities in the renin-angiotensin-aldosterone system (RAAS), angiotensin-converting enzyme-2 (ACE2) and the androgen-driven transmembrane serine protease 2 (TMPRSS2) have been elicited as key modulators of SARS-CoV-2. Main text While safe and effective therapies for COVID-19 lack, the current moment of pandemic urges for therapeutic options. Existing drugs should be preferred over novel ones for clinical testing due to four inherent characteristics: 1. Well-established long-term safety profile, known risks and contraindications; 2. More accurate predictions of clinical effects; 3. Familiarity of clinical management; and 4. Affordable costs for public health systems. In the context of the key modulators of SARS-CoV-2 infectivity, endocrine targets have become central as candidates for COVID-19. The only endocrine or endocrine-related drug class with already existing emerging evidence for COVID-19 is the glucocorticoids, particularly for the use of dexamethasone for severely affected patients. Other drugs that are more likely to present clinical effects despite the lack of specific evidence for COVID-19 include anti-androgens (spironolactone, eplerenone, finasteride and dutasteride), statins, N-acetyl cysteine (NAC), ACE inhibitors (ACEi), angiotensin receptor blockers (ARB), and direct TMPRSS-2 inhibitors (nafamostat and camostat). Several other candidates show less consistent plausibility. In common, except for dexamethasone, all candidates have no evidence for COVID-19, and clinical trials are needed. Conclusion While dexamethasone may reduce mortality in severely ill patients with COVID-19, in the absence of evidence of any specific drug for mild-to-moderate COVID-19, researchers should consider testing existing drugs due to their favorable safety, familiarity, and cost profile. However, except for dexamethasone in severe COVID-19, drug treatments for COVID-19 patients must be restricted to clinical research studies until efficacy has been extensively proven, with favorable outcomes in terms of reduction in hospitalization, mechanical ventilation, and death.

Dataset Information

Artificial intelligence approach fighting COVID-19 with repurposing drugs.

Publications

Artificial intelligence approach fighting COVID-19 with repurposing drugs.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets