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 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.

   Keywords: atopic dermatitis, bioinformatics, drug repurposing,
   functional annotation, genetic

Introduction

   Atopic Dermatitis (AD), also called atopic eczema, is a common chronic
   or relapsing skin inflammatory disease with characteristic acute
   flare-ups of eczematous pruritic lesions and dry skin ([39]1, [40]2).
   AD is the most common skin disease in children; the prevalence is
   15%~20% in children and 1%~3% in adults ([41]1). Approximately 80
   percent of the young patients remain symptomatic in adulthood, and they
   are mostly presented with lesions affecting the flexures, head, and
   neck ([42]3). Both genetic and environmental factors have been reported
   to be involved in the pathogenesis of AD ([43]4, [44]5). AD is a
   multifactorial disease with immunological processes, including type 1
   IgE dysfunction, cell-mediated immune response defects, and barrier
   dysfunction changes ([45]5). Genome-wide association studies (GWAS) and
   genetic association studies have reported many single nucleotide
   polymorphisms (SNPs) that were associated with AD pathogenesis,
   including Toll-like receptors (TLRs), IRF2, IL-4, IL-13, IL-25, IL-31,
   IL-33, IL1RL1/IL18R1/IL18RAP, STAT6, ORAI1 and TSLP ([46]6–[47]11).
   Findings from GWAS have shown the complex role of multiple loci in
   human AD susceptibility. New insights concerning the genetic,
   immunological, and environmental impacts of AD provide novel
   therapeutic strategies against AD ([48]12).

   Management of AD is dependent on the severity of the disease. Epidermal
   obstructions play an essential role in the onset of AD ([49]13). Two
   drugs have been approved by the U.S. Food and Drug Administration
   (FDA), which have increased treatment options for eczema. First of all,
   2% Crisaborole ointment is approved for mild to moderate AD in children
   ([50]14, [51]15). Furthermore, dupilumab is approved for adults with
   moderate to severe AD ([52]16). However, these drugs are effective in
   only about 20% of moderate to severe AD patients ([53]17). Therefore,
   developing new drugs for AD is urgent. Traditional drug discovery
   requires a long process (10~17 years) from an idea in the laboratory to
   a marketed drug with less than 10% overall probability of success
   ([54]18). There are notable advantages of drug repurposing over the
   traditional drug discovery process; for instance, repurposed drugs have
   already passed clinical trials for their first indications, which is
   more time and cost efficient for drug development ([55]19, [56]20). In
   addition, drug repurposing is able to reduce safety and pharmacokinetic
   uncertainties ([57]21). An example of clinically successful drug
   repurposing is ketoconazole for Cushing syndrome, initially used for
   fungal infection. Another example is raloxifene used initially for
   osteoporosis and is now successfully repurposed for breast cancer
   ([58]22).

   In 2014, Okada et al. proposed bioinformatics drug discovery
   methodologies for rheumatoid arthritis (RA). Data from GWAS
   meta-analysis in RA was applied to identify risk loci for functional
   annotations and drug repurposing. Results were further applied to
   investigate potential candidate drug targets for RA ([59]23). In the
   current study, we aimed to implement this bioinformatics strategy and
   identify AD’s biological candidate genes through an integrative gene
   network. Six functional annotations (missense mutations, cis-expression
   quantitative trait loci (cis-eQTL), a molecular pathway analysis,
   proteinprotein interactions (PPIs), genetic overlap with a knockout
   mouse phenotype, and primary immunodeficiencies (PIDs)) were used to
   discover biological AD risk genes.

Methods

Study Design

   A descriptive scheme of the current drug repurposing study for AD was
   shown in [60]Figure 1 . The SNPs with significant association with AD
   (p < 10^-5) were queried from the National Human Genome Research
   Institute (NHGRI) GWAS catalog database ([61]http://www.ebi.ac.uk/gwas)
   ([62]24) on January 7, 2019. The SNPs adjacent to the AD associated
   SNPs were included based on Linkage Disequilibrium (LD) characteristic
   to define the AD risk SNPs. It was conducted using HaploReg (v4.1)
   ([63]25) with the criterion of r^2 ≥ 0.8 in Asian (ASN) populations
   from the 1000 Genome Project Phase I data. The AD risk SNPs were
   classified into missense (or nonsense), synonymous or non-coding (with
   or without cis-eQTL) SNPs. In addition, the genes encoded by the AD
   risk SNPs will be used for further analyses. The AD risk genes were
   subsequently prioritized based on six functional annotation criteria.
   Accordingly, those genes with one functional annotation obtained one
   point (score), and those genes which met criteria with a score of ≥ 2
   were defined as “biological AD risk genes”. In our analyses, we set the
   threshold of biological score ≥ 2 to find a much higher number of genes
   as the biological AD risk genes. The STRING database ([64]26) was used
   to expand biological risk genes. The extended list was further defined
   as drug target genes. We mapped those drug target genes to DrugBank
   ([65]27) and Therapeutic Target Database (TTD) ([66]28). The drugs
   identified were examined for their clinical status, according to
   [67]ClinicalTrials.gov.

Figure 1.

   [68]Figure 1
   [69]Open in a new tab

   An overview of drug repurposing for atopic dermatitis (AD). The study
   design utilizing by GWAS Catalog and various databases: DrugBank
   database, Therapeutic Target Database (TTD), ClinicalTrial.gov, and
   PubMed.

Functional Annotation of AD Risk Genes

   Six biological functional annotations were used to build a scoring
   system representing the most probable candidate genes as AD targets.
   The six biological functional annotations were as follows: (i) missense
   or nonsense variants: We used RStudio v3.4.3 and the HaploR package
   ([70]29), which contain annotations of functional consequences from a
   database of SNPs (dbSNPs) to perform this biological functional
   annotation. Missense or nonsense variants can be one method for
   functional annotation because the change in the amino acid sequence may
   affect protein function ([71]30). If a gene had any missense or
   nonsense AD risk SNP, it was assigned with one point; (ii) cis-eQTLs: A
   SNP with cis-eQTL effect is associated with the expression of the gene
   at where the SNP is located. The polymorphism is associated with the
   change in gene expression in the target tissue, resulting in biological
   implications. If a gene had any AD risk SNP with cis-eQTL effect in the
   whole blood, it was assigned one point; (iii) KO mice: The gene from
   human Ensemble ID was converted to mouse Ensemble ID using BioMart to
   query the mouse phenotype ([72]31). The data source was Mammalian
   Phenotype Ontology (MP), with information on mice and other mammal
   phenotypes ([73]32). The gene set, with an FDR of < 0.05 in the
   enrichment analysis, were considered significant. (iv) gene ontology:
   The data source was the biological process category of Gene Ontology
   (GO). The significance of an FDR < 0.05 was set ([74]33); (v) molecular
   pathways: Enrichment analysis was performed on molecular pathways using
   the Kyoto Encyclopedia of Genes and Genomes (KEGG), an online
   biochemical route database. The genes enriched in the KEGG pathway (FDR
   < 0.05) were assigned with one point ([75]33); and (vi) PID: The PID
   was the final annotation to prioritize the AD risk genes. PID genes
   were collected by the IUIS until 2013 ([76]34). Enrichment analysis on
   the data was conducted using a hypergeometric test; a p-value < 0.05
   was used in this step as the significance criterion.

Expansion Network by STRING Database

   The AD biological risk genes were expanded using the STRING database to
   obtain more candidate drug target genes. The purpose of the STRING
   database ([77]https://string-db.org/) is to integrate functional
   interactions related to protein expression by including and arranging
   predicted protein-protein association-related data ([78]26). We
   inputted the list of biological AD risk genes selected in the previous
   steps and set the criterion of 50 interactions. In this way, we were
   able to increase the number of genes. A larger number of
   disease-protein networks have greater potential to identify novel
   therapeutic targets for diseases ([79]35).

Identification of Drug Target Genes by Using Drugbank and TTD

   After completing gene expansion based on PPI information from the
   STRING database, we conducted an overlapping analysis. The sources of
   data for the overlapping analysis were the DrugBank database and TTD.
   Drugbank 5.0 database ([80]www.drugbank.ca) containing around 17,000
   associations of drug targets and data on more than 10,000 drug
   compounds ([81]27). TTD ([82]http://bidd.nus.edu.sg/group/cjttd/)
   provides information about the 3,101 targets of 34,019 clinically
   approved and experimental drugs ([83]28). The target genes were used to
   query the databases according to several parameters, such as drugs with
   pharmacological activity, human efficacy, and annotations of approved,
   in clinical trials or experimental drugs.

Prioritization of Drug Repurposed for AD

   All drug targets for AD were confirmed in ClinicalTrials.gov
   ([84]https://clinicaltrials.gov/) on January 7, 2019 to check whether
   each drug is under clinical investigation for AD or other diseases.
   ClinicalTrials.gov is a comprehensive database that documents drugs
   under clinical investigations in human subjects.

Results

AD Risk SNPs and Genes Detected by GWAS

   The bioinformatics drug discovery methodologies for AD was shown in
   [85]Figure 1 . This study obtained 70 AD associated SNPs from the NHGRI
   with GWAS p-values of less than 1 x 10^-5 ([86] Supplementary Table 1
   ). The selection of SNPs was based on the disease/trait attribute of
   “Atopic dermatitis.” Next, we expand the number of SNPs by HaploReg
   v4.1 based on the characteristic of r^2 > 0.8 in Asian populations;
   hence, we obtained 94 AD risk genes ([87] Supplementary Table 2 ).

Functional Annotation of AD Risk Genes

   Six biological functional annotations were applied to prioritize
   biological AD risk genes. One point was given for each functional
   annotation. We scored each of the 94 candidate genes by adopting the
   following six criteria: (1) genes with any missense AD risk variant
   (n=11); (2) cis-eQTL genes (n=20); (3) genes in any enriched knockout
   mouse phenotype (n=19); (4) genes involved in the enriched GO terms
   (n=26); (5) genes involved in any enriched KEGG pathway (n=19); and (6)
   PID genes (n=3) ([88] Supplementary Table 3 ). Thus, each gene earned a
   score based on the number of criteria fulfilled (score ranging from 0
   to 6 for each gene). As shown in [89]Figure 2 , 48 genes are with a
   score of 0, 19 genes with a score of 1, and 27 genes with total scores
   ≥ 2. The 27 genes with a score ≥ 2 were defined as “biological AD risk
   genes.” As shown in [90]Table 1 , the top five biological AD risk genes
   include interleukin 7 receptor (IL7R), interleukin 6 receptor (IL6R),
   interleukin 18 receptor 1 (IL18R1), interleukin 2 receptor subunit
   alpha (IL2RA), and signal transducer and activator of transcription 3
   (STAT3).

Figure 2.

   [91]Figure 2
   [92]Open in a new tab

   Histogram distribution of gene score. The figure showed that genes with
   a score of 0 were 48, while those with a score of 1 were 19, and there
   were 27 genes with total scores ≥ 2 were described as “Biological AD
   risk genes”.

Table 1.

   Biological atopic dermatitis risk genes.
   Gencode Id Gencode Name Score Biological criteria
   Missense/Nonsense Cis-eQTL KO mice PPI KEGG PID
   ENSG00000168685 IL7R 6
   ENSG00000160712 IL6R 5
   ENSG00000115604 IL18R1 4
   ENSG00000134460 IL2RA 4
   ENSG00000168610 STAT3 4
   ENSG00000169194 IL13 4
   ENSG00000092020 PPP2R3C 3
   ENSG00000109471 IL2 3
   ENSG00000113520 IL4 3
   ENSG00000115594 IL1R1 3
   ENSG00000134954 ETS1 3
   ENSG00000138684 IL21 3
   ENSG00000157456 CCNB2 3
   ENSG00000172673 THEMIS 3
   ENSG00000227507 LTB 3
   ENSG00000258366 RTEL1 3
   ENSG00000026036 RTEL1 2
   ENSG00000113522 RAD50 2
   ENSG00000115602 IL1RL1 2
   ENSG00000115607 IL18RAP 2
   ENSG00000168477 TNXB 2
   ENSG00000182261 NLRP10 2
   ENSG00000196126 HLA-DRB1 2
   ENSG00000197114 ZGPAT 2
   ENSG00000204315 FKBPL 2
   ENSG00000204525 HLA-C 2
   ENSG00000213654 GPSM3 2
   [93]Open in a new tab

   Cis-eQTL, cis-expression quantitative trait locus; KO mice, knockout
   mouse phenotype; PPI, protein-protein interaction; KEGG, Kyoto
   Encyclopedia of Genes and Genomes; PID, primary immunodeficiency.

   Summary scores obtained from 6 criteria are shown. Filled boxes
   indicate fulfilled criteria.

Expansion Network by STRING Database

   We utilized the STRING database to integrate publicly accessible
   sources of information on direct (physical) and indirect (functional)
   protein-protein interactions ([94]26). Twenty-seven biological AD risk
   genes were expanded by using the STRING database. Fifty interactions
   were selected to perform the calculation and expand the number of
   genes. This study obtained 76 genes as the drug target genes through
   the STRING database ([95] Supplementary Table 4 ). These drug target
   genes were used for further analysis.

Prioritization of Drug Repurposed for AD

   In this step, we obtained 2053 interaction pairs with 76 drug target
   genes from the curated PPI networking ([96] Supplementary Table 5 ).
   Furthermore, 76 drug target genes were mapped to DrugBank and TTD.
   However, not all drug target genes are druggable; only 25 drug target
   genes were found to bind to 53 drugs based on DrugBank and TTD. The
   identified target-drug pairs are listed in [97]Supplementary Table 6 .
   Among them, dupilumab was clinically approved for AD ([98] Figure 3 );
   dupilumab blocks interleukin-4 (IL-4) and interleukin-13 (IL-13)
   signaling by binding to interleukin 4 receptor (IL4R). IL-4 and IL-13
   are the main drivers of the clinical symptoms of AD ([99]36). It is
   noteworthy that dupilumab is an effective drug for moderate to severe
   AD ([100]16). As dupilumab was identified from our analysis, the
   feasibility of gene-based drug repurposing was confirmed.

Figure 3.

   [101]Figure 3
   [102]Open in a new tab

   Connections between biological AD risk genes and drugs available for
   AD. Representative connections between AD biological genes (green);
   genes in PPIs (yellow); target drugs (blue); indication (orange).

   In addition, as shown in [103]Table 2 , ten drugs were found to be
   potentially useful for AD with clinical or preclinical evidence. Among
   them, seven drugs were approved for diseases other than AD, including
   baricitinib ([104]NCT03334422, [105]NCT03334396), tofacitinib
   ([106]NCT02001181), ruxolitinib ([107]NCT03745651, [108]NCT03745638),
   upadacitinib ([109]NCT04195698), lebrikizumab ([110]NCT04392154),
   tralokinumab ([111]NCT03160885), and pitrakinra ([112]NCT00676884).
   Furthermore, the usage of tocilizumab was supported by case series
   ([113]42). The effectiveness of momelotinib and canakinumab on AD were
   supported with animal studies ([114]43, [115]44). These drugs
   correspond to 5 gene targets, Janus kinase 1 (JAK1), IL13, IL4, IL6R,
   and 1L1B, which have the potential to be repurposed for the treatment
   of AD ([116] Figure 4 ). Examples of repurposed drugs are ruxolitinib
   and momelotinib, which are approved for myelofibrosis, targeting the
   JAK1/JAK2 gene pathway. Ruxolitinib is under clinical investigation for
   AD in phase III trials ([117]NCT03745651, [118]NCT03745638), while
   momelitinib has been reported to be repurposed for AD therapy in
   preclinical investigations ([119]44). Both ruxolitinib and momelitinib
   are selective inhibitors of JAK1 and JAK2 that showed the potential
   inhibition for proinflammatory cytokine signaling in AD’s pathogenesis
   ([120] Table 2 ).

Table 2.

   Pharmacological therapies in development for the treatment of Atopic
   Dermatitis.
   Drug candidate Target Possible mechanism of action on Atopic Dermatitis
   Disease indication Level of evidence NCT Number/PubMed ID
   Baricitinib JAK1/JAK2 Inhibitor of JAK1 dan JAK2 mediated signaling in
   the immunopathology of AD ([121]37) Rheumatoid arthritis Phase III
   completed [122]NCT03334422, [123]NCT03334396,
   Tofacitinib JAK1/JAK2 Inhibitor of JAK1 and JAK2 inhibits cytokine IL-4
   directly ([124]38). Rheumatoid arthritis Phase II completed
   [125]NCT02001181
   Tralokinumab 1L13 Inhibitor of IL-13 by maintaining inflammatory
   reaction and major skin effects ([126]39). Asthma Phase III completed
   [127]NCT03160885
   Ruxolitinib JAK1/JAK2 a selective inhibitor of JAK1 and JAK2 potently
   inhibits proinflammatory cytokine signaling ([128]40) Myelofibrosis
   Phase III ongoing [129]NCT03745651, [130]NCT03745638
   Upadacitinib JAK1 a selective inhibitor of JAK1 inhibited the
   production of proinflammatory Th2 cytokines such as IL-4 ([131]41).
   Rheumatoid arthritis Phase III ongoing [132]NCT04195698 
   Lebrikizumab IL13 Inhibitor of IL-13 by maintaining inflammatory
   reaction and major skin effects ([133]39). Asthma Phase III ongoing
   [134]NCT04392154
   Pitrakinra IL13/IL4 IL-4/IL-13 inhibitory activity may reduce
   inflammation caused by allergens ([135]39). Asthma Phase II completed
   [136]NCT00676884
   Tocilizumab^# IL6R inhibits IL-6 binding to soluble IL6R ([137]42).
   Rheumatoid arthritis Case series 21962991
   Canakinumab* IL1B IL-1β induced TSLP production and stimulated
   keratinocytes ([138]43). Familial Cold Autoinflammatory Syndrome (FCAS)
   – 30937919
   Momelotinib* JAK1/JAK2 JAK1 and JAK2 inhibitor could reduce
   inflammatory cytokine expression, including IL4, IL5, IFN-γ, and TSLP
   ([139]44). Myelofibrosis – 30544712
   [140]Open in a new tab

   *Represents under preclinical investigation, ^#case series.

Figure 4.

   [141]Figure 4
   [142]Open in a new tab

   Connections between biological AD risk genes and drugs are potentially
   useful for AD. Representative connections between AD biological genes
   (green); genes in PPIs (yellow); target drug for drugs under clinical
   trial (blue); the drug was supported by case series (pink), and drugs
   with preclinical data for AD (grey); indication (orange). Grey lines
   indicate connections. All other biological gene-drug connections are
   through the PPI network.

   Importantly, we further found 42 unknown anti-AD drugs mapping to 17
   drug targets prioritized in our study. The 17 targets are interleukin 1
   receptor-associated kinase 1 (IRAK1), interleukin 2 receptor subunit
   alpha (IL2RA), interleukin 2 receptor subunit beta (IL2RB), interleukin
   2 receptor subunit gamma (IL2RG), interleukin 6 (IL6), epidermal growth
   factor receptor (EGFR), interleukin 1 beta (IL1B), interleukin 6
   receptor (IL6R), interleukin 1 receptor antagonist (IL1RN), interleukin
   2 (IL2), Janus kinase 1 (JAK1), major histocompatibility complex, class
   II, DR beta 1 (HLA-DRB1), protein kinase, membrane-associated
   tyrosine/threonine 1 (PKMYT1), cyclin-dependent kinase 2 (CDK2),
   baculoviral IAP repeat-containing 5 (BIRC5), beta-2-microglobulin
   (B2M), and aurora kinase B (AURKB) ([143] Figure 5 ).

Figure 5.

   Figure 5
   [144]Open in a new tab

   Connections between biological AD risk genes and drugs that were
   approved for other indications. Representative connections between AD
   biological genes (green); genes in PPIs (yellow); target drugs (blue);
   indication (orange). Grey lines indicate connections. Only IL1R1,
   BIRC5, B2M, and AURKB are directly connected to a biological gene-drug
   (anakinra, reserpine, berberine, copper, hesperidin); all other
   biological gene-drug connections are through the PPI network.

Discussion

   This study focused on repurposing AD drugs based on candidate gene
   prioritization from the GWAS-identified loci. Using in silico
   pipelines, we built a scoring system that used six functional
   annotation for candidate drug prediction. We successfully identified
   the AD-approved drug, dupilumab, in our study. Furthermore, another ten
   drugs identified are potentially useful for AD according to preclinical
   and clinical trial evidence, including baricitinib, tofacitinib,
   ruxolitinib, upadacitinib, lebrikizumab, tralokinumab, pitrakinra,
   tocilizumab, momelitinib, and canakinumab, as depicted in [145]Table 2
   . As such, our genetic-driven drug discovery method indicated that a
   combination of GWAS-based approaches and six functional annotations is
   able to identify potential candidate drugs for AD effectively.

   Among the targets identified, the roles of IL1B and EGFR in AD have
   been supported by preclinical evidence. Schwartz et al. reported that
   anti-IL-1β antibody is able to alleviate dermatitis in the mice model
   ([146]43). Furthermore, previous studies showed that EGFR is involved
   in the pathogenesis of AD ([147]45). In this study, several drugs are
   linked to EGFR. An example is foreskin keratinocytes, a type of skin
   cell-cultured as a wound healing and closure. Foreskin keratinocytes
   are a key component of a number of skin replacements utilized for
   various indications ([148]46). Keratinocytes are generated from
   neonatal foreskins and utilized to make a drug called Apligraf, which
   is a mixture of neonatal foreskin fibroblasts and keratinocytes. Orcel
   is another skin substitute that combines fibroblasts and keratinocytes
   produced from the neonatal foreskin, similar to Apligraf ([149]47). In
   mice model of acute AD, EGFR signaling significantly decreases
   allergen-induced IL-6 production and Th17 responses in the skin,
   demonstrating that EGF has an immunomodulatory impact and is protective
   in the inflamed skin tissue ([150]45). In addition, our bioinformatic
   networking analysis specifically identified JAK as a potential target
   for AD. JAK1 is an upstream regulator of cytokine secretion and immune
   activation. Hence, the effects of JAK inhibitors targeting the multiple
   immune pathways are a critical mechanism for AD ([151]48). Consistent
   with previous findings, a preclinical study demonstrated that
   disrupting JAK1 signaling is helpful to reduce persistent itch through
   sensory neurons and immune pathways involving T[H]2 cytokines
   ([152]49).

   Indeed, from the drugs identified through our analysis, five small
   molecule drugs targeting JAK have been supported with clinical and
   preclinical data in AD. These JAK inhibitors are ruxolitinib,
   baricitinib, upadacitinib, tofacitinib, and momelitinib. JAK inhibitors
   have emerged as a promising therapy option for AD ([153]50). Both oral
   or topical applications have dramatically improved the clinical
   outcomes of individuals with insufficient responses to previous
   medicines in randomized controlled trials ([154]51). In light of the
   importance of JAK/STAT pathways for AD ([155]52), filgotinib and
   fedratinib were identified as high potential drugs to repurpose for AD
   in this study. Filgotinib is a selective inhibitor of JAK1 approved for
   RA treatment, similar to the marketed drug upadacitinib ([156]53). In
   phase III randomized controlled trials, upadacitinib was shown to be
   more effective and well-tolerated than dupilumab for moderate to severe
   AD ([157]NCT03738397). On the other hand, fedratinib is a selective
   inhibitor of JAK2 with a similar mechanism of ruxolitinib ([158]54).
   Ruxolitinib is a selective JAK1 and JAK2 inhibitor. Topical Ruxolitinib
   Evaluation in Atopic Dermatitis studies (TRuE-AD) demonstrated the
   safety and efficacy from clinical trials ([159]55).

   Another category of drugs identified is monoclonal antibody (mAb)
   drugs. Four monoclonal antibody drugs (lebrikizumab, tralokinumab,
   tocilizumab, and canakinumab) were successfully identified as the most
   promising drug for AD repurposing. Lebrikizumab ([160]NCT04392154) and
   tralokinumab ([161]NCT03160885) are human monoclonal antibodies
   targeting IL-13 under phases III clinical trials for AD. Tocilizumab is
   IL-6R inhibiting monoclonal antibodies and is commonly used in RA.
   Importantly, functional IL-6 receptor (IL6R) variant have already
   reported as a risk variant that was associated with persistent AD
   ([162]56). Thus, blocking of IL-6 signaling is very likely as a novel
   therapeutic approach for AD. The other mAb is canakinumab (anti-IL-1β)
   that has been reported as a potential treatment of inflammatory
   disorders ([163]57).

   Although our approaches indicated that the utilization of GWAS data is
   a potential way of drug mining, there were some limitations. First, by
   using GWAS data, some SNPs are without biological relevance, and not
   all drug target genes we identified are directly druggable. Secondly,
   therapeutic drugs identified through in silico pipelines have not been
   validated in molecular mechanisms or animal models. Therefore, further
   investigations are necessary to determine the effects of candidate
   drugs in clinical applications.

Conclusion

   Drug repurposing offers valuable advantages in the drug development
   process, such as reduced time and cost, and increased success rates. In
   the current study, we combine the drug repurposing pipeline with
   integrative bioinformatics methodologies to identify drugs with novel
   indications for AD. We found JAK1 inhibitors are particularly important
   with their involvement in several immune pathways. The results further
   confirms the feasibility of the application of gene networking and
   genomic information for drug repurposing.

Data Availability Statement

   The datasets presented in this study can be found in online
   repositories. The names of the repository/repositories and accession
   number(s) can be found in the article/[164]Supplementary Material.

Author Contributions

   WA, HW, W-HC, W-PC and W-CC conceptualized, conceived and designed the
   study. WA and LMI performed the analysis. WA, LMI, W-HC, HW, JT, and
   W-CC curated the data. WA wrote the original draft. WA, LMI, W-HC, HW,
   EM, JT, DP, W-PC, and W-CC interpreted the data. WA, LMI, W-HC, HW, EM,
   JT, DP, W-PC, and W-CC revised the manuscript. W-CC, W-PC, and DP
   provided the funding. W-PC and W-CC supervised and coordinated this
   study. All authors have made significant contributions to this study.
   All authors contributed to the article and approved the submitted
   version.

Funding

   This work was supported by grants from the Health and welfare surcharge
   of tobacco products grant (MOHW110-TDU-B-212-144014;
   MOHW110-TDU-B-212-144020), Ministry of Science and Technology, Taiwan
   (MOST109-2314-B-038-131 and MOST110-2628-B-038-020), and Taipei Medical
   University, Taiwan (12310-106079; Yusuke Nakamura Chair Professorship).

Conflict of Interest

   The authors declare that the research was conducted in the absence of
   any commercial or financial relationships that could be construed as a
   potential conflict of interest.

Publisher’s Note

   All claims expressed in this article are solely those of the authors
   and do not necessarily represent those of their affiliated
   organizations, or those of the publisher, the editors and the
   reviewers. Any product that may be evaluated in this article, or claim
   that may be made by its manufacturer, is not guaranteed or endorsed by
   the publisher.

Supplementary Material

   The Supplementary Material for this article can be found online at:
   [165]https://www.frontiersin.org/articles/10.3389/fimmu.2021.724277/ful
   l#supplementary-material
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Abbreviations

   AD, Atopic Dermatitis; AURKB, aurora kinase B; BIRC5, baculoviral IAP
   repeat containing 5; B2M, beta-2-microglobulin; CDK2, cyclin dependent
   kinase 2; cis-eQTL, cis-expression quantitative trait loci; EGFR,
   epidermal growth factor receptor; HLA-DRB1, major histocompatibility
   complex, class II, DR beta 1; IL-13, interleukin-13; IL-4,
   interleukin-4; IL-6R, interleukin-6 receptor; IL1R1, interleukin-1
   receptor type I; IL1B, interleukin-1 beta; IUIS, international union of
   immunological societies; IRAK1, interleukin 1 receptor associated
   kinase; IL2RA, interleukin 2 receptor subunit alpha; IL2RB, interleukin
   2 receptor subunit beta; IL2RG, interleukin 2 receptor subunit gamma;
   IL6, interleukin 6; IL1B, interleukin 1 beta; IL6R, interleukin 6
   receptor; IL1RN, interleukin 1 receptor antagonist, IL2, interleukin 2;
   JAK, Janus Kinase; KEGG, Kyoto encyclopedia of genes and genomes;
   KOmice, knockout mouse phenotypes; MP, mammalian phenotype ontology;
   NHGRI, National Human Genome Research Institute; ORA,
   over-representation analysis; PIDs, primary immunodeficiency’s; PPIs,
   protein-protein interactions; PKMYT1, membrane associated
   tyrosine/threonine 1; SNPs, Single nucleotide polymorphisms; TTD,
   therapeutic target database.

References