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

Abstract

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 r2 > 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.

Integration of genomic variants and bioinformatic-based approach to drive drug repurposing for multiple sclerosis

“…Primary immunodeficiency (PID) was the last annotation criterion. It refers to inborn immunity diseases that are genetic disorders associated with increased severity [ 13 , 15 ]. Data enrichment analysis was performed using the hypergeometric test; p < 0.05 was used in this stage as the significance criterion [ 13 ].…”

“…It refers to inborn immunity diseases that are genetic disorders associated with increased severity [ 13 , 15 ]. Data enrichment analysis was performed using the hypergeometric test; p < 0.05 was used in this stage as the significance criterion [ 13 ]. It is important to note that each functional annotation is assigned a score of 1, and genes with a score ≥2 are defined as “biological MS risk genes”.…”

“…Genome-wide association study (GWAS) can potentially be leveraged for precision drug repurposing by applying functional annotation [ 11 ]. Several studies were applied to the risk variants from GWAS, and were able to prioritize the biological risk genes based on the functional annotations to drive drug repurposing for various diseases, including chronic hepatitis B [ 12 ], atopic dermatitis [ 13 ], asthma [ 14 ], colorectal cancer [ 15 ] and rheumatoid arthritis [ 16 ]. Of interest to this study, GWAS has revolutionized MS genetic analyses, including the MS variants.…”

Genomic-Analysis-Oriented Drug Repurposing in the Search for Novel Antidepressants

“…A popular method, established by Okada , et al [ 54 ], involves utilizing a scoring system comprising eight functional annotations based on genomic analysis to prioritize target genes and discover drug repurposing candidates; the method was originally used to identify candidates for the treatment of rheumatoid arthritis according to SNP data collected from genome-wide association studies. Other studies have adapted Okada’s approach to use five sets of functional annotations to discover drug repurposing candidates for the treatment of atopic dermatitis [ 55 ] and asthma [ 56 ]. Functional annotations are considered crucial for evaluating diseases.…”

StarGazer: A Hybrid Intelligence Platform for Drug Target Prioritization and Digital Drug Repositioning Using Streamlit

“…In light of this, we present our interactive dashboard, StarGazer, which aims to address these challenges by integrating three different datatypes (i.e., disease-target association, target druggability, and target protein-protein interaction) into a novel scoring system, utilizing real-time API calls and Python-based Streamlit technology. While these types of datasets have been used for numerous repositioning studies separately ( Liu et al, 2014 ; Khaladkar et al, 2017 ; Hermawan et al, 2020 ; Wijetunga et al, 2020 ; Adikusuma et al, 2021 ; Attique et al, 2021 ; Ghoussaini et al, 2021 ; Portelli et al, 2021 ; Tan et al, 2021 ; Varghese and Majumdar, 2022 ; Zhao et al, 2022 ), StarGazer represents the first ever integration of the PheWAS catalog, Open Targets, STRING and Pharos, all of which are well-curated, well-studied, open access databases. Furthermore, computational repositioning studies focus largely on singular diseases, phenotypes or drugs, but StarGazer is equipped for flexible investigation into any of the 1,844 phenotypes and traits within the dashboard.…”