Computational drug discovery and repurposing for the treatment of COVID-19: A systematic review

被引:99
|
作者
Mohamed, Kawthar [1 ,2 ]
Yazdanpanah, Niloufar [1 ,2 ]
Saghazadeh, Amene [1 ,2 ]
Rezaei, Nima [2 ,3 ,4 ]
机构
[1] Universal Sci Educ & Res Network USERN, Systemat Review & Meta Anal Expert Grp SRMEG, Tehran, Iran
[2] Univ Tehran Med Sci, Res Ctr Immunodeficiencies, Childrens Med Ctr, Tehran, Iran
[3] Univ Tehran Med Sci, Sch Med, Dept Immunol, Tehran, Iran
[4] Universal Sci Educ & Res Network USERN, Network Immun Infect Malignancy & Autoimmun NIIMA, Tehran, Iran
关键词
Drug discovery; Drug repurposing; Computational methods; SARS-CoV-2; COVID-2019; SPIKE PROTEIN; SARS-COV; CORONAVIRUS; TRANSMISSION; SARS-COV-2; REMDESIVIR; VACCINE; TARGET;
D O I
10.1016/j.bioorg.2020.104490
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
Background: Since the beginning of the novel coronavirus (SARS-CoV-2) disease outbreak, there has been an increasing interest in finding a potential therapeutic agent for the disease. Considering the matter of time, the computational methods of drug repurposing offer the best chance of selecting one drug from a list of approved drugs for the life-threatening condition of COVID-19. The present systematic review aims to provide an overview of studies that have used computational methods for drug repurposing in COVID-19. Methods: We undertook a systematic search in five databases and included original articles in English that applied computational methods for drug repurposing in COVID-19. Results: Twenty-one original articles utilizing computational drug methods for COVID-19 drug repurposing were included in the systematic review. Regarding the quality of eligible studies, high-quality items including the use of two or more approved drug databases, analysis of molecular dynamic simulation, multi-target assessment, the use of crystal structure for the generation of the target sequence, and the use of AutoDock Vina combined with other docking tools occurred in about 52%, 38%, 24%, 48%, and 19% of included studies. Studies included repurposed drugs mainly against non-structural proteins of SARS-CoV2: the main 3C-like protease (Lopinavir, Ritonavir, Indinavir, Atazanavir, Nelfinavir, and Clocortolone), RNA-dependent RNA polymerase (Remdesivir and Ribavirin), and the papain-like protease (Mycophenolic acid, Telaprevir, Boceprevir, Grazoprevir, Darunavir, Chloroquine, and Formoterol). The review revealed the best-documented multi-target drugs repurposed by computational methods for COVID-19 therapy as follows: antiviral drugs commonly used to treat AIDS/HIV (Atazanavir, Efavirenz, and Dolutegravir Ritonavir, Raltegravir, and Darunavir, Lopinavir, Saquinavir, Nelfinavir, and Indinavir), HCV (Grazoprevir, Lomibuvir, Asunaprevir, Ribavirin, and Simeprevir), HBV (Entecavir), HSV (Penciclovir), CMV (Ganciclovir), and Ebola (Remdesivir), anticoagulant drug (Dabigatran), and an antifungal drug (Itraconazole). Conclusions: The present systematic review provides a list of existing drugs that have the potential to influence SARS-CoV2 through different mechanisms of action. For the majority of these drugs, direct clinical evidence on their efficacy for the treatment of COVID-19 is lacking. Future clinical studies examining these drugs might come to conclude, which can be more useful to inhibit COVID-19 progression.
引用
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页数:15
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