Drug Repurposing for Atopic Dermatitis by Integration of Gene Networking and Genomic Information

被引:18
作者
Adikusuma, Wirawan [1 ,2 ]
Irham, Lalu Muhammad [1 ,3 ]
Chou, Wan-Hsuan [1 ]
Wong, Henry Sung-Ching [1 ]
Mugiyanto, Eko [4 ,5 ]
Ting, Jafit [1 ]
Perwitasari, Dyah Aryani [3 ]
Chang, Wei-Pin [6 ]
Chang, Wei-Chiao [1 ,7 ,8 ,9 ,10 ]
机构
[1] Taipei Med Univ, Sch Pharm, Dept Clin Pharm, Taipei, Taiwan
[2] Univ Muhammadiyah Mataram, Fac Hlth Sci, Dept Pharm, Mataram, Indonesia
[3] Univ Ahmad Dahlan, Fac Pharm, Yogyakarta, Indonesia
[4] Taipei Med Univ, Coll Pharm, Program Clin Drug Dev Herbal Med, Taipei, Taiwan
[5] Univ Muhammadiyah Pekajangan Pekalongan, Fac Hlth Sci, Dept Pharm, Pekalongan, Indonesia
[6] Taipei Med Univ, Coll Management, Sch Hlth Care Adm, Taipei, Taiwan
[7] Taipei Med Univ, Taipei Med Univ TMU Res Ctr Canc Translat Med, Taipei, Taiwan
[8] Taipei Med Univ, Wan Fang Hosp, Dept Pharm, Taipei, Taiwan
[9] Taipei Med Univ, Wan Fang Hosp, Integrat Res Ctr Crit Care, Taipei, Taiwan
[10] Natl Def Med Ctr, Dept Pharmacol, Taipei, Taiwan
关键词
atopic dermatitis; bioinformatics; drug repurposing; functional annotation; genetic; SUSCEPTIBILITY LOCI; WIDE ASSOCIATION; CRISABOROLE OINTMENT; SAFETY; PERSISTENCE; CHILDREN; EFFICACY; VARIANT; SKIN;
D O I
10.3389/fimmu.2021.724277
中图分类号
R392 [医学免疫学]; Q939.91 [免疫学];
学科分类号
100102 ;
摘要
Atopic Dermatitis (AD) is a chronic and relapsing skin disease. The medications for treating AD are still limited, most of them are topical corticosteroid creams or antibiotics. The current study attempted to discover potential AD treatments by integrating a gene network and genomic analytic approaches. Herein, the Single Nucleotide Polymorphism (SNPs) associated with AD were extracted from the GWAS catalog. We identified 70 AD-associated loci, and then 94 AD risk genes were found by extending to proximal SNPs based on r(2) > 0.8 in Asian populations using HaploReg v4.1. Next, we prioritized the AD risk genes using in silico pipelines of bioinformatic analysis based on six functional annotations to identify biological AD risk genes. Finally, we expanded them according to the molecular interactions using the STRING database to find the drug target genes. Our analysis showed 27 biological AD risk genes, and they were mapped to 76 drug target genes. According to DrugBank and Therapeutic Target Database, 25 drug target genes overlapping with 53 drugs were identified. Importantly, dupilumab, which is approved for AD, was successfully identified in this bioinformatic analysis. Furthermore, ten drugs were found to be potentially useful for AD with clinical or preclinical evidence. In particular, we identified filgotinub and fedratinib, targeting gene JAK1, as potential drugs for AD. Furthermore, four monoclonal antibody drugs (lebrikizumab, tralokinumab, tocilizumab, and canakinumab) were successfully identified as promising for AD repurposing. In sum, the results showed the feasibility of gene networking and genomic information as a potential drug discovery resource.</p>
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页数:9
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