Abstract

   Background: Severe acute respiratory syndrome coronavirus-2
   (SARS-CoV-2), the etiological agent of coronavirus disease-2019
   (COVID-19), is a novel Betacoronavirus that was first reported in
   Wuhan, China in December of 2019. The virus has since caused a
   worldwide pandemic that highlights the need to quickly identify
   potential prophylactic or therapeutic treatments that can reduce the
   signs, symptoms, and/or spread of disease when dealing with a novel
   infectious agent. To combat this problem, we constructed a
   computational pipeline that uniquely combines existing tools to predict
   drugs and biologics that could be repurposed to combat an emerging
   pathogen.

   Methods: Our workflow analyzes RNA-sequencing data to determine
   differentially expressed genes, enriched Gene Ontology (GO) terms, and
   dysregulated pathways in infected cells, which can then be used to
   identify US Food and Drug Administration (FDA)-approved drugs that
   target human proteins within these pathways. We used this pipeline to
   perform a meta-analysis of RNA-seq data from cells infected with three
   Betacoronavirus species including severe acute respiratory syndrome
   coronavirus (SARS-CoV; SARS), Middle East respiratory syndrome
   coronavirus (MERS-CoV; MERS), and SARS-CoV-2, as well as respiratory
   syncytial virus and influenza A virus to identify therapeutics that
   could be used to treat COVID-19.

   Results: This analysis identified twelve existing drugs, most of which
   already have FDA-approval, that are predicted to counter the effects of
   SARS-CoV-2 infection. These results were cross-referenced with
   interventional clinical trials and other studies in the literature to
   identify drugs on our list that had previously been identified or used
   as treatments for COIVD-19 including canakinumab, anakinra,
   tocilizumab, sarilumab, and baricitinib.

   Conclusions: While the results reported here are specific to
   Betacoronaviruses, such as SARS-CoV-2, our bioinformatics pipeline can
   be used to quickly identify candidate therapeutics for future emerging
   infectious diseases.

   Keywords: bioinformatics, repurposing, coronavirus, SARS-CoV-2,
   COVID-19, virus, infection, therapeutic, target

Introduction

   Coronaviruses consist of a lipid envelope that contains a
   single-stranded positive-sense RNA genome that is approximately 30
   kilobases in length. Prior to 2019, six human coronavirus species had
   been discovered including HCoV-229E, HCoV-NL63, HCoV-HKU1, HCoV-OC43,
   SARS-CoV, and MERS-CoV. Four of these coronavirus species are endemic
   in humans and typically cause mild respiratory tract infections that
   present with cold-like symptoms but can cause more severe symptoms in
   immunocompromised individuals or infants ^[28]1, [29]2 . Two of these
   four endemic virus species are members of the Alphacoronavirus genus (
   HCoV-229E and HCoV-NL63), while the other two species are members of
   the Betacoronavirus genus ( HCoV-HKU1 and HCoV-OC43). The remaining two
   novel human coronavirus species discovered during this time are severe
   acute respiratory syndrome coronavirus ( SARS-CoV; SARS) and Middle
   East respiratory syndrome coronavirus ( MERS-CoV; MERS), which were
   emergent Betacoronaviruses responsible for epidemics in 2003 and 2012
   respectively ^[30]1, [31]3 . Human coronaviruses generally emerge from
   other animal hosts such as bats or mice, and typically pass through an
   intermediate host (e.g. civet cats for SARS and dromedary camels for
   MERS) before infecting a human host ^[32]1, [33]3 .

   In December 2019, a novel coronavirus was reported in Wuhan city, Hubei
   province, China and has since been named severe acute respiratory
   syndrome coronavirus-2 (SARS-CoV-2). SARS-CoV-2 is highly similar to
   other SARS-like viruses that have been isolated from bats previously,
   especially BatCoV RaTG13 with which it shares 96.3% identity ^[34]4,
   [35]5 . Initial infections with SARS-CoV-2 were traced back to the
   Huanan Seafood Wholesale Market and likely infected humans via a
   pangolin intermediate ^[36]6 . The global COVID-19 pandemic, as of
   February 14, 2021, has resulted in over 108 million cases and over 2.3
   million deaths worldwide. Approximately 7.6 million of the cases and
   over 500,000 deaths have occurred in the United States of America
   ^[37]7 .

   Common symptoms of COVID-19 include fever, dry cough, dyspnea, sore
   throat, myalgia, fatigue, and in some cases diarrhea ^[38]4, [39]5,
   [40]8– [41]11 . SARS-CoV-2 is spread through aerosols, droplets, direct
   contact between people, and fomites ^[42]4, [43]12 . Other studies
   suggest a fecal-oral transmission route is possible due to the presence
   of SARS-CoV-2 in stool samples of infected patients ^[44]12, [45]13 .
   SARS-CoV-2 infects cells by binding to the membrane-associated
   Angiotensin-converting enzyme 2 (ACE2) receptor, which generally plays
   a role in the renin-angiotensin-aldosterone system to regulate blood
   pressure and fluid balance in the body ^[46]14, [47]15 . ACE2 receptors
   are known to be expressed in lung, renal, cardiac, vascular,
   intestinal, and placental tissues ^[48]14, [49]15 .

   Both the widespread effects of COVID-19 and the initial highly
   susceptible population emphasized the need to identify potential drug
   treatments for emerging diseases--particularly before vaccines become
   available. The aim of many recent and ongoing clinical trials is to
   quantify the efficacy of various therapeutics for COVID-19 ^[50]16 .
   Many vaccines are in various stages of preclinical (at least 139) or
   clinical (at least 25) development ^[51]17 , as well as some that have
   gained emergency use authorization by the US Food and Drug
   Administration (FDA).

   The COVID-19 pandemic, caused by the SARS-CoV-2 virus, has highlighted
   the need to quickly and accurately identify therapeutics that can be
   repurposed to combat the signs, symptoms, and spread of disease. One
   method for predicting potential therapeutics is to identify host
   pathways that are dysregulated by infections and then find existing
   drugs that target those pathways. Programs to perform this analysis
   include DrugThatGene and GPSnet, which have both been used to identify
   potential drugs to target cancers ^[52]18, [53]19 . The aim of this
   study was to construct a bioinformatics workflow that uniquely combines
   existing tools, databases, and programming libraries with custom
   scripts to predict potential human therapeutic targets for multiple
   members of the Betacoronavirus genus, which includes the SARS-CoV,
   SARS-CoV-2, and MERS-CoV species. Our unique combination of tools
   consists of a consistent and robust RNA-seq preprocessing workflow as
   well as an intracellular signaling pathway perturbation method that
   enables us to account for the role of protein-protein interaction
   networks instead of merely enriching for differentially expressed
   genes. We then applied this workflow to a use case involving a
   meta-analysis of coronaviruses and other respiratory viruses. This
   workflow first performs an analysis of human genes and significant
   signaling pathways that play a role in infection and pathogenesis. The
   pathway information is then used to predict relevant human drug targets
   and the associated small molecules or biologics that bind to the target
   of interest.

   The rationale for identifying drug targets from multiple intersecting
   signaling pathways is based on the theory that a protein which
   participates in multiple affected pathways during viral infection has a
   higher likelihood of playing an important role in viral pathogenesis
   and replication. Targeting one or more of these proteins that act as
   “key hubs” with a therapeutic would therefore have a higher chance of
   reducing viral processes and the ensuing disease. Similarly, host
   proteins that participate in multiple pathways across various related
   viruses likely represent an evolutionarily conserved host-pathogen
   interaction that can be therapeutically modulated. We expect virus
   resistance to these host-based drugs to be relatively infrequent since
   they target relevant host processes. Our workflow could therefore be
   applicable not only to improving therapeutic treatment during infection
   with existing or emerging coronaviruses, such as SARS-CoV-2, but to
   rapidly identifying potential treatments for pathogens that may emerge
   in the future.

Methods

Included datasets

   A search of the Gene Expression Omnibus (GEO) database, hosted at the
   National Center for Biotechnology Information (NCBI;
   [54]https://www.ncbi.nlm.nih.gov/geo/) ^[55]20 , was performed in
   mid-2020 to find RNA-sequencing datasets for various viruses including
   “MERS”, “SARS”, and “coronavirus”. The corresponding sequencing data
   for four GEO series were retrieved from the Sequence Read Archive (SRA;
   [56]https://www.ncbi.nlm.nih.gov/sra) at NCBI ^[57]21 : [58]GSE122876,
   [59]GSE56192, [60]GSE147507, [61]GSE139516 ^[62]22– [63]24 . These
   datasets were generated from cell cultures or patients infected with
   one of: respiratory syncytial virus (RSV), influenza A virus (IAV),
   MERS, SARS, and SARS-CoV-2. The SRA files were downloaded and converted
   to fastq format using version 2.10.1 of the NCBI sratools software
   package ( [64]https://github.com/ncbi/sra-tools).

Differential expression analysis

   The Automated Reproducible MOdular Workflow for Preprocessing and
   Differential Analysis of RNA-seq Data (ARMOR) workflow was used to
   preprocess and analyze the fastq files against the Ensembl reference
   transcriptome for Homo sapiens build GRCh38, release 98
   [GCA_000001405.15] ^[65]25 . Briefly, this automated snakemake-based
   workflow performs quality control of the reads with fastQC (
   [66]www.bioinformatics.babraham.ac.uk/projects/fastqc/), trims the
   adapters and poor-quality regions with TrimGalore! (
   [67]https://www.bioinformatics.babraham.ac.uk/projects/trim_galore/),
   maps & quantifies reads to the human transcriptome using Salmon ^[68]26
   , performs differential expression using edgeR ^[69]27 , identifies
   differential transcript usage with DRIMseq ^[70]28 , and enriches on
   Gene Ontology terms ^[71]29 and Hallmark gene sets ^[72]30 using the
   Camera algorithm to adjust for inter-gene correlation ^[73]31 .
   Differential expression was performed by calculating log2 fold-change
   and the associated p-values from infected samples versus mock-infected
   samples. The significance threshold for differential gene expression
   and Gene Ontology enrichment was defined as an false discovery rate
   (FDR)-corrected p-value < 0.05. Gene Ontology enrichment results from
   all datasets were then combined to identify shared terms.

Pathway enrichment analysis

   Once the differentially expressed gene lists were constructed, the
   Ensembl identifiers for these human gene lists were mapped to the
   corresponding NCBI Entrez Gene identifiers using BioMart and
   Bioconductor ^[74]32, [75]33 prior to pathway enrichment using
   Signaling Pathway Impact Analysis (SPIA) ^[76]34 . Briefly, this
   pathway analysis combines robust statistics and bootstrapping to
   identify enriched pathways from lists of genes by incorporating the
   directionality of expression. Public pathway databases in the Graphite
   package in R Bioconductor were used by SPIA for enrichment with those
   surpassing a Bonferroni-corrected p-value < 0.05 being retained ^[77]35
   . These databases include: KEGG ^[78]36 , Reactome ^[79]37 , Panther
   ^[80]38 , NCI ^[81]39 , and [82]BioCarta. All pathways from each
   dataset were then compared to identify those that were shared among the
   various datasets, as well as those that were unique only to infection
   with SARS-CoV-2.

Prediction of relevant drug targets

   The output from the SPIA pathway enrichment were then used as input for
   a custom bioinformatics pipeline to identify existing drugs and
   biologics that could be repurposed to reduce signs, symptoms, and/or
   replication of coronaviruses. Specifically, this pipeline iteratively
   1) retrieves the genes that participate in each statistically
   significant signaling pathway, 2) maps the gene identifiers to the
   corresponding UniProt protein identifier ^[83]40 , 3) searches the
   opentargets resource ( [84]www.opentargets.org ^[85]41 ) to identify
   known drug targets and therapeutics, and 4) generates a table with
   various attributes of the target and small molecule/biologic treatment.
   These data were then integrated with the pathway comparisons across the
   various datasets and analyzed to determine what drugs would affect the
   pathways dysregulated in the greatest number of viruses and could be
   used as potential therapeutics for SARS-CoV-2 infection. The results
   were ranked by how many times a given protein target appeared in the
   results across the relevant virus taxa. The top ranked small
   molecules/biologic treatments were analyzed to determine which were
   predicted to reverse the effects of the viral infection on a given
   pathway. Manual review of high-ranking hits was then performed to
   determine the existing indication(s) for each treatment, followed by a
   literature search to determine which, if any, of the therapeutics
   identified had previously been used or considered for the treatment of
   COVID-19. Code for this workflow can be found on GitHub:
   [86]https://github.com/bpickett/Pathway2Targets.

Results

Differential expression analysis

   Each of the algorithmic components of our computational workflow was
   dependent on first calculating differentially expressed (DE) genes for
   each of the datasets. We consequently began by using the same
   computational workflow to generate the DE genes from the raw
   transcriptomic data for human cells infected with one of: RSV, IAV,
   MERS, SARS, and SARS-CoV-2. Each set of results was specific to the
   comparison that was performed (e.g. mock-infected vs. infected) and
   included both log2 fold-change values and FDR-corrected p-values. We
   then integrated and compared the differential expression results across
   all virus comparisons to facilitate downstream comparisons.

   We began by identifying genes that were either up- or down-regulated
   across a large number of studies. Specifically, we calculated the
   number of DE genes (FDR-corrected p-value < 0.05) that significantly
   changed during infection with influenza A (4205 genes), RSV (3661
   genes), MERS (range across studies: 1222-13006 genes), SARS-CoV (range
   across studies: 2130-5557 genes), or SARS-CoV-2 (range across studies:
   427-6933 genes). Out of the 15 studies evaluated, we observed 11 genes
   that displayed increased expression across 10-13 study comparisons
   including: TRIM25, C3, NCOA7, PTAFR, TNFAIP3, EIF2AK2, HELZ2, HBEGF,
   NFKB2, REL and VEGFC. We found an additional 18 genes that were
   upregulated across 9 of the 15 comparisons including: IFIT2, EGFR, and
   FOSL1. In contrast, we identified 14 genes that showed decreased
   expression across 11-12 comparisons including: EIF4H, APH1A, NME2,
   SEC61B, TUBB4B, CHCHD2, TUBA1C, TP53I3, H2AFZ, PEBP1, HOXB6, TPM2,
   CBR1, and SIVA1.

   We next analyzed the DE lists to identify significant genes that were
   shared across infections involving only the Betacoronaviruses including
   SARS-CoV, MERS-CoV, and SARS-CoV-2. This analysis revealed 12 genes
   that were upregulated in at least nine of thirteen comparisons
   including: TRIM25, REL, DNASE1, C3, NCOA7, PTAFR, EIF2AK2, HELZ2,
   NFKB2, ZNF385C, ZC3H12A, and OR7E122P. In contrast, the 10 genes that
   were found to be downregulated in ten of the thirteen comparisons
   involving any of the Betacoronaviruses included: CHCHD2, SMIM3, EIF4H,
   NME2, CBR1, SPARC, DSTN, CDC123, TIMM17A, and PDAP1.

   We then performed a similar analysis to identify the five statistically
   significant DE genes (FDR-corrected p-value < 0.05) that displayed the
   highest and lowest average fold-change values during infection across
   all comparisons of SARS-CoV-2, Betacoronaviruses, or all viruses (
   [87]Figure 1). This analysis showed that a subset of the genes such as
   CXCL11, which is induced by interferon and is involved in T-cell
   signaling, displayed similar fold-change values across multiple
   comparisons. This finding was somewhat expected since certain genes
   involved in the host innate immune response are expected to be
   modulated during virus infection. However, we also observed that genes
   such as MUC3A, PCSK5, MRC1, and CLEC3B displayed somewhat different
   average fold-change values across the included comparisons. This
   observation was also expected given the diverse virus replication
   requirements and the resulting host intracellular transcriptional
   response that occurs during infections with human viral pathogens.

Figure 1. Heatmap of human transcriptional biomarkers associated with
infection by various virus taxa.

   [88]Figure 1.
   [89]Open in a new tab

   The five genes with the highest and lowest log2 fold-change values for
   each virus comparison were identified and compared against expression
   in the other datasets. Squares in red or blue represent genes that are
   upregulated or downregulated (respectively) in the relevant comparison.

Enrichment of annotated terms

   Given the number of DE genes involved in the analysis, manual
   interpretation of the results would be intractable. We therefore
   performed an analysis to determine which Gene Ontology (GO) terms and
   Hallmark gene sets were specifically enriched to better understand
   which biological processes and molecular functions were represented by
   the genes in each of the DE gene lists. Overall, we observed a superset
   of 580 terms that were enriched in any of the included viral
   infections. The enrichment results for each taxon of virus were then
   reviewed to identify annotated terms that were shared between multiple
   types of viral infections, shared amongst coronaviruses, or specific to
   SARS-CoV-2.

   We found several notable terms that were shared among the results
   obtained from infection of multiple unrelated taxa of viruses. These
   terms referred to functions such as the host interferon response,
   regulation of virus response, chemokine activity, and immune cell
   migration that were positively enriched in both the RSV infection of
   A549 cells and the SARS-CoV-2 infection of either A549 or NHBE cells.
   As expected, the statistical significance of these terms supports the
   important role that the human immune system plays in response to virus
   infection, which is an expected result and validates the upstream DE
   gene analysis.

   Our method identified no significant GO terms that were shared across
   all Betacoronavirus comparisons. Interestingly, we found that each
   coronavirus species had its own set of uniquely enriched terms. In
   MERS-CoV, “negative regulation of inclusion body assembly” was
   positively enriched in four of the seven comparisons, while
   “aggrephagy” and “regulation of nucleotide binding oligomerization
   domain containing 2 signaling pathway” were positively enriched in
   three of the seven comparisons. The SARS-CoV samples had very few
   significantly enriched annotation terms including the “signal peptidase
   complex” and “cyclin dependent protein serine threonine kinase
   activator activity” terms that were each negatively enriched in only
   one of the four SARS-CoV comparisons.

   We also identified 290 terms that were uniquely present among the
   SARS-CoV-2 comparisons, although no significant terms were identified
   in comparisons involving either the human post-mortem biopsies or the
   infected Calu-3 cells. The shared significant terms that we identified
   in the NHBE- and A549-infected cells included “response to chemokine”,
   “antimicrobial humoral response”, and “humoral immune response.”
   Enriched terms in the NHBE-infected cells alluded to the role of the
   inflammatory response, interleukin-1, interleukin-6, neuroinflammation,
   arachidonic acid binding, and T-cell activation.

Identification of affected signaling pathways

   We then wanted to calculate which intracellular signaling pathways were
   significantly modulated during viral infection, based on the DE genes
   identified above. The pathway information used for this analysis was
   obtained from public databases that manually curate the proteins that
   participate in conveying a signal from receptors on the cell membrane
   to transcription factors within the nucleus in order to respond to a
   stimulus.

   We used the results of the differential gene expression analysis as
   input to the SPIA algorithm. The robust bootstrap-based approach of
   this method identified 249 pathways that were significantly perturbed
   across at least one viral comparison. We subsequently analyzed the
   lists of significantly perturbed pathways for each comparison to
   determine those that were shared across virus taxa (e.g. influenza A,
   respiratory syncytial, MERS-CoV, SARS-CoV, and SARS-CoV-2), shared
   among coronaviruses, and unique to SARS-CoV-2. This analysis revealed a
   subset of the total number of pathways to be dysregulated across many
   of the viral taxa studied. These pathways included translation
   (affected in 12/17 comparisons and 4/5 viral taxa), cytokine-cytokine
   receptor interaction (modulated in 11/17 comparisons and 4/5 viral
   taxa), as well as rRNA processing (modulated in 10/17 comparisons and
   3/5 viral taxa). We also observed that some of these pathways were
   predicted to be activated during infection with certain viral taxa in
   specific cell types, while inhibited in others.

   We did not observe any signaling pathways that were significantly and
   consistently affected during infection by the individual coronavirus
   species examined. However, we did detect relevant pathways across
   multiple virus taxa. Interestingly, we found that the direction (i.e.
   activated or inhibited) was occasionally dependent on virus taxa or
   timepoint of infection. For example, the cytokine-cytokine receptor
   interaction pathway was predicted to be activated during infection with
   RSV, a subset of comparisons involving MERS-CoV, and many SARS-CoV-2
   infections; while the same pathway was predicted to be inhibited during
   infection with SARS-CoV.

   We predicted a total of 38 pathways that were uniquely affected during
   SARS-CoV-2 infection ( [90]Table 1). We noted that 35 of these pathways
   were only found to be significant in one SARS-CoV-2 comparison and
   included “NF-kB signaling pathway”, “Interleukin-1 signaling”,
   “IL6-mediated signaling events”, “PI3K-Akt signaling pathway”,
   “Jak-STAT signaling pathway”, “Apoptosis”, “Complement and coagulation
   cascades”, and other processes associated with either the immune system
   or infectious diseases. The remaining three pathways that were
   predicted as affected during at least two SARS-CoV-2 comparisons were
   “Cytokine signaling in Immune system”, “Tuberculosis”, and the “Innate
   immune system”. In short, these findings indicate a set of signaling
   pathways that are strongly associated with virus infection and/or
   immune activation in the host, some of which are uniquely detected
   during SARS-CoV-2 infection. The “Cytokine Signaling in Immune System”
   pathway is stored in the Reactome database and consists of interferon
   alpha/beta and gamma signaling, interleukin 1, 2, 6, and 10 signaling,
   and other components ^[91]37 . To better understand the impact of
   SARS-CoV-2 on interleukin-6 signaling, we overlaid the differential
   expression data on a representation of the signaling pathway (
   [92]Figure 2). This analysis revealed six of the eleven total proteins
   in the pathway were upregulated, while another protein was
   downregulated during SARS-CoV-2 infection.

Figure 2. Infection with SARS-CoV-2 shows differential gene expression in
Interleukin-6 signaling.

   [93]Figure 2.
   [94]Open in a new tab

   This signaling pathway is a component of the larger Cytokine Signaling
   in Immune System pathway in Reactome. Each node represents a protein in
   the network, while each edge represents a characterized interaction
   between the proteins. Higher log2 fold-change values (up-regulation)
   are represented by increasing saturation of red, while more negative
   log2-fold-change values (down-regulation) are colored with increased
   saturation of blue. White nodes indicate no measured log2-fold change.

Table 1. Intracellular signaling pathways predicted to be significantly
affected during infection with SARS-CoV-2 ([95]GSE147507).

   Pathway Name SARScov2NHBE SARScov2a549 SARScov2calu3 SARScov2Lung
   Tuberculosis U U
   Innate Immune System U U
   Cytokine Signaling in Immune system D D
   Complement and coagulation cascades U
   Complement cascade U
   NF-kappa B signaling pathway U
   Hepatitis B U
   Inflammatory bowel disease (IBD) U
   HTLV-I infection U
   Jak-STAT signaling pathway U
   MAPK signaling pathway U
   Osteoclast differentiation U
   Apoptosis U
   Measles U
   Hepatitis C U
   Toll-like receptor signaling pathway U
   Epstein-Barr virus infection U
   PI3K-Akt signaling pathway U
   Interleukin-1 signaling U
   Signaling by Interleukins U
   Nucleotide-binding domain, leucine rich repeat containing
   receptor (NLR) signaling pathways U
   nf-kb signaling pathway U
   signal transduction through il1r U
   IL23-mediated signaling events U
   Angiopoietin receptor Tie2-mediated signaling U
   IL27-mediated signaling events U
   IL12-mediated signaling events U
   IL6-mediated signaling events U
   Downstream TCR signaling U
   MyD88 dependent cascade initiated on endosome U
   TRAF6 mediated induction of NFkB and MAP kinases upon
   TLR7/8 or 9 activation U
   Translocation of ZAP-70 to Immunological synapse U
   HDR through Homologous Recombination (HRR) D
   Class A/1 (Rhodopsin-like receptors) D
   Peptide ligand-binding receptors D
   Recognition of DNA damage by PCNA-containing replication
   complex D
   Anchoring of the basal body to the plasma membrane D
   stathmin and breast cancer resistance to antimicrotubule
   agents D
   [96]Open in a new tab

   U: upregulated pathway; D: downregulated pathway

Prediction of relevant drug targets

   We next wanted to determine whether any of the significantly affected
   signaling pathways contained known drug targets that could be modulated
   to reduce infection, virus replication, and/or clinical signs and
   symptoms associated with infection by a panel of Betacoronaviruses or
   by SARS-CoV-2 alone. To do so we cross-referenced the results from our
   significant pathways analysis with the drug-target information
   accessible through an application programming interface (API) to the
   opentargets.org database, which yielded 179 potential human drug
   targets ( [97]Table 2). We ranked the predicted drugs and their
   associated targets according to how many coronaviruses shared the same
   drug target across the various datasets included in our analysis. We
   performed a separate ranking based on the data obtained solely from the
   SARS-CoV-2 studies.

Table 2. Comparison of predicted human drug targets across multiple datasets.

   GEO Series Identifier
   [98]GSE122876 [99]GSE139516 [100]GSE56192 [101]GSE147507
   Human
   Target
   Symbol MERS-inf MERS6 MERS24 MERS
   high24 MERS
   low24 MERS
   low48 MERS
   high48 SARS
   low24 SARS
   high24 SARS
   low48 SARS
   high48 SARScov2
   NHBE SARScov2
   A549 SARScov2
   Calu3 SARScov2
   Lung
   IFNA1 1 0 1 0 1 0 1 1 1 0 1 1 1 1 1
   IFNA4 1 0 1 0 1 0 1 1 1 0 1 1 1 1 1
   IFNA5 1 0 1 0 1 0 1 1 1 0 1 1 1 1 1
   IFNA6 1 0 1 0 1 0 1 1 1 0 1 1 1 1 1
   IFNA7 1 0 1 0 1 0 1 1 1 0 1 1 1 1 1
   IFNA8 1 0 1 0 1 0 1 1 1 0 1 1 1 1 1
   IFNA10 1 0 1 0 1 0 1 1 1 0 1 1 1 1 1
   IFNA13 1 0 1 0 1 0 1 1 1 0 1 1 1 1 1
   IFNA14 1 0 1 0 1 0 1 1 1 0 1 1 1 1 1
   IL1B 1 0 1 0 1 0 1 1 1 0 1 1 1 1 1
   IFNA16 1 0 1 0 1 0 1 1 1 0 1 1 1 1 1
   IFNA17 1 0 1 0 1 0 1 1 1 0 1 1 1 1 1
   IFNA21 1 0 1 0 1 0 1 1 1 0 1 1 1 1 1
   IFNAR1 1 0 1 0 1 0 1 1 1 0 1 1 1 1 1
   IFNAR2 1 0 1 0 1 0 1 1 1 0 1 1 1 1 1
   IL1A 1 0 1 0 1 0 1 1 1 0 1 1 1 1 1
   IL12B 1 0 1 0 1 0 1 1 1 0 1 1 1 1 1
   TNFSF13B 1 0 1 0 1 0 1 1 1 0 1 1 0 1 1
   CCR4 1 0 1 0 1 0 1 1 1 0 1 1 0 1 1
   CCR5 1 0 1 0 1 0 1 1 1 0 1 1 0 1 1
   CSF3R 1 0 1 0 1 0 1 1 1 0 1 1 0 1 1
   IFNLR1 1 0 1 0 1 0 1 1 1 0 1 1 0 1 1
   EPOR 1 0 1 0 1 0 1 1 1 0 1 1 0 1 1
   IL17RA 1 0 1 0 1 0 1 1 1 0 1 1 0 1 1
   GHR 1 0 1 0 1 0 1 1 1 0 1 1 0 1 1
   IL1R1 1 0 1 0 1 0 1 1 1 0 1 1 0 1 1
   CXCR4 1 0 1 0 1 0 1 1 1 0 1 1 0 1 1
   IL2RA 1 0 1 0 1 0 1 1 1 0 1 1 0 1 1
   IL2RB 1 0 1 0 1 0 1 1 1 0 1 1 0 1 1
   IL3RA 1 0 1 0 1 0 1 1 1 0 1 1 0 1 1
   IL4R 1 0 1 0 1 0 1 1 1 0 1 1 0 1 1
   IL5 1 0 1 0 1 0 1 1 1 0 1 1 0 1 1
   IL5RA 1 0 1 0 1 0 1 1 1 0 1 1 0 1 1
   IL6R 1 0 1 0 1 0 1 1 1 0 1 1 0 1 1
   MPL 1 0 1 0 1 0 1 1 1 0 1 1 0 1 1
   IL20 1 0 1 0 1 0 1 1 1 0 1 1 0 1 1
   TNFRSF4 1 0 1 0 1 0 1 1 1 0 1 1 0 1 1
   TNFRSF8 1 0 1 0 1 0 1 1 1 0 1 1 0 1 1
   CALM1 0 0 1 0 0 1 1 1 0 1 0 0 0 1 1
   HCK 0 0 1 0 1 0 0 0 1 0 1 0 0 1 1
   LYN 0 0 1 0 1 0 0 0 1 0 1 0 0 1 1
   ROCK1 0 0 1 0 1 0 0 0 1 0 1 0 0 1 1
   ROCK2 0 0 1 0 1 0 0 0 1 0 1 0 0 1 1
   PDE1A 0 0 1 0 0 1 1 1 0 1 0 0 0 0 0
   PDE1C 0 0 1 0 0 1 1 1 0 1 0 0 0 0 0
   PDE1B 0 0 1 0 0 1 1 1 0 1 0 0 0 0 0
   GSK3B 0 0 0 0 1 0 0 0 1 0 1 0 0 1 1
   FGR 0 0 0 0 1 0 0 0 1 0 1 0 0 1 1
   GSK3A 0 0 0 0 1 0 0 0 1 0 1 0 0 1 1
   ITK 0 0 0 0 1 0 0 0 1 0 1 0 0 1 1
   PIK3CD 0 0 0 0 1 0 0 0 1 0 1 0 0 1 1
   PIK3CG 0 0 0 0 1 0 0 0 1 0 1 0 0 1 1
   PRKCZ 0 0 0 0 1 0 0 0 1 0 1 0 0 1 1
   SRC 0 0 0 0 1 0 0 0 1 0 1 0 0 1 1
   F2R 0 0 1 0 0 0 0 0 0 0 0 1 0 1 0
   MAPK11 0 0 1 0 0 0 0 0 0 0 0 0 0 1 1
   JAK1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1
   CDK6 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0
   TUBA1B 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0
   FDPS 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0
   HDAC1 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0
   HDAC2 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0
   TUBA3E 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0
   F2 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0
   TUBA4A 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0
   TUBA3C 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0
   TUBA1A 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0
   TLR7 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0
   PRKCA 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0
   TYK2 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0
   TUBA1C 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0
   CHRM1 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0
   CHRM2 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0
   EGFR 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0
   ERBB2 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0
   ERBB3 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0
   BDKRB2 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0
   PDGFRB 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0
   TLR9 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1
   VDR 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1
   ABL1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
   TUBB3 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
   TUBB4A 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
   TUBB4B 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
   RBX1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
   TUBB8 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
   TUBB2B 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
   MYH7B 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
   TUBB2A 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
   TUBB1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
   TUBB6 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
   CYSLTR1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
   CHRM3 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
   CHRM5 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
   ADORA2A 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
   ADORA2B 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
   ADRA1D 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
   ADRA1B 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
   ADRA1A 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
   ADRB1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
   ADRB2 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
   ADRB3 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
   DRD1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
   DRD5 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
   AGTR1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
   EDNRA 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
   F5 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
   EDNRB 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
   F8 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
   PTGS2 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
   F9 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
   BTK 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
   F10 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
   FGB 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
   KLKB1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
   HRH1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
   SERPINC1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
   HRH2 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
   PLAT 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
   HTR2A 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
   PLG 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
   HTR2B 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
   HTR2C 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
   HTR4 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
   HTR5A 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
   HTR6 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
   HTR7 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
   LHCGR 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
   OXTR 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
   CASP12 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
   RRM2 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
   AVPR1A 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
   AVPR1B 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
   PTGER1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
   PTGER3 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
   PTGFR 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
   TACR2 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
   TACR1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
   TBXA2R 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
   PRKCG 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
   EPHA2 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
   FGFR3 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
   FGFR4 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
   FLT1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
   FLT3 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
   FLT4 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
   INSR 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
   KDR 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
   PGF 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
   VEGFA 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
   VEGFB 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
   VEGFC 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
   ITGB3 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
   LCK 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
   PPARG 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
   CALCR 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
   PARP2 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
   PARP3 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
   PARP1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
   PPARA 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
   RXRA 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
   CD80 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
   CD86 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
   ITGAL 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
   COL1A1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
   COL1A2 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
   COL4A2 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
   COL4A4 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
   COL4A5 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
   COL4A6 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
   COL6A1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
   COL6A2 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
   COL6A3 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
   COL6A6 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
   LAMB4 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
   COL6A5 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
   ITGA2B 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
   ITGA4 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
   ITGB7 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
   [102]Open in a new tab

   **KEY: 1 = target was present in comparison, 0 = target was NOT present
   in comparison

   After reviewing the results of this analysis, we identified 38
   potential human protein targets to counteract MERS-CoV, SARS-CoV,
   and/or SARS-CoV-2. Specifically, 38 targets were predicted to be
   relevant in at least 10 of the 15 coronavirus comparisons ( [103]Table
   3). Seventeen of these targets were identified across 11 comparisons
   and included interferon (IFN)-A, IFN A receptors, IL1A and IL12B.
   Twenty-one additional targets were identified across 10 comparisons and
   included members of the tumor necrosis factor (TNF) superfamily, CCR4,
   CCR5, GHR, CXCR4, IL2R, IL4R, IL5, IL5R, IL6R, MPL, and IL20. We also
   predicted seven targets that were relevant specifically to SARS-CoV-2
   including JAK1, FDPS, TLR7, TLR9, PRKCA, TYK2, and VDR.

Table 3. Potential drug candidates for repurposing as antiviral prophylactics
or therapeutics.

   Target (Gene
   Symbol) # Comparisons with
   Target Detected Name of Therapeutic Is Small
   Molecule Is
   Antibody Is
   Protein
   IFNA1 11 SIFALIMUMAB, RONTALIZUMAB X, X
   IFNA10 11 SIFALIMUMAB, RONTALIZUMAB X, X
   IFNA13 11 SIFALIMUMAB, RONTALIZUMAB X, X
   IFNA14 11 SIFALIMUMAB, RONTALIZUMAB X, X
   IFNA16 11 SIFALIMUMAB, RONTALIZUMAB X, X
   IFNA17 11 SIFALIMUMAB, RONTALIZUMAB X, X
   IFNA21 11 SIFALIMUMAB, RONTALIZUMAB X, X
   IFNA4 11 SIFALIMUMAB, RONTALIZUMAB X, X
   IFNA5 11 SIFALIMUMAB, RONTALIZUMAB X, X
   IFNA6 11 SIFALIMUMAB, RONTALIZUMAB X, X
   IFNA7 11 SIFALIMUMAB, RONTALIZUMAB X, X
   IFNA8 11 SIFALIMUMAB, RONTALIZUMAB X, X
   IFNAR1 11 PEGINTERFERON BETA-1A, PEGINTERFERON ALFA-2A,
   INTERFERON BETA-1A, INTERFERON BETA-1B, X, X, X, X
   IFNAR2 11 PEGINTERFERON BETA-1A, PEGINTERFERON ALFA-2A,
   INTERFERON BETA-1A, INTERFERON BETA-1B, X, X, X, X
   IL12B 11 CANAKINUMAB, USTEKINUMAB, BRIAKINUMAB X, X, X
   IL1A 11 BERMEKIMAB X
   IL1B 11 CANAKINUMAB X
   CCR4 10 MOGAMULIZUMAB X
   CCR5 10 MARAVIROC X
   CSF3R 10 FILGRASTIM, PEGFILGRASTIM X, X
   CXCR4 10 PLERIXAFOR X
   EPOR 10 DARBEPOETIN ALFA, PEGINESATIDE X, X
   GHR 10 SOMATROPIN, PEGVISOMANT X, X
   IFNLR1 10 PEGINTERFERON LAMBDA-1A X, X
   IL17RA 10 BRODALUMAB X
   IL1R1 10 ANAKINRA X
   IL20 10 FLETIKUMAB X
   IL2RA 10 DACLIZUMAB, DENILEUKIN DIFTITOX X, ,X
   IL2RB 10 ALDESLEUKIN, DENILEUKIN DIFTITOX X, X
   IL3RA 10 TAGRAXOFUSP X
   IL4R 10 DUPILUMAB X
   IL5 10 MEPOLIZUMAB, RESLIZUMAB X, X
   IL5RA 10 BENRALIZUMAB X
   IL6R 10 TOCILIZUMAB X
   MPL 10 ELTROMBOPAG X
   TNFRSF4 10 MEDI-6469 X
   TNFRSF8 10 BRENTUXIMAB VEDOTIN X
   TNFSF13B 10 BELIMUMAB X
   [104]Open in a new tab

   X: indicates the type of each therapeutic. Drug types against the same
   target are indicated with commas.

   Our analysis predicted twelve existing drugs that are predicted to be
   useful as repurposed therapeutics against 73.3% of all coronavirus taxa
   evaluated in this work and 100% of the SARS-CoV-2 comparisons included
   in our analysis ( [105]Table 4). Eight of these twelve drugs are used
   to treat common autoimmune disorders such as systemic lupus
   erythematosus (SLE), Crohn’s disease, and multiple sclerosis (MS). Five
   of the twelve drugs have been associated or used as antiviral measures
   largely against hepatitis C virus. Another 27 drugs were predicted to
   have potential therapeutic activity against 66.7% of all coronavirus
   infections and 30 were predicted as potential therapeutics against 75%
   of SARS-CoV-2 infections.

Table 4. Comparison of therapeutic drugs and small molecules predicted to be
repurposed against SARS-CoV-2.

   [106]GSE122876 [107]GSE139516 [108]GSE56192 [109]GSE147507 Coronavirus
   Summary SARS-CoV-2 Summary
   Drug Name MERS inf MERS6 MERS24 MERS
   high24 MERS
   low24 MERS
   low48 MERS
   high48 SARS
   low24 SARS
   high24 SARS
   low48 SARS
   high48 SARScov2
   NHBE SARScov2
   A549 SARScov2
   Calu3 SARScov2
   Lung Flu
   A549 RSV
   A549 # Positive
   Comparisons # Corona % Corona Rank
   (All
   Corona) #
   SARS-
   CoV-2 %
   SARS-
   CoV-2 Rank
   (SARS-
   CoV-2)
   SIFALIMUMAB 1 0 1 0 1 0 1 1 1 0 1 1 1 1 1 0 1 12 10 66.6666667 1 3 75 1
   RONTALIZUMAB 1 0 1 0 1 0 1 1 1 0 1 1 1 1 1 0 1 12 10 66.6666667 1 3 75
   1
   PEGINTERFERON ALFA-2A 1 0 1 0 1 0 1 1 1 0 1 1 1 1 1 0 1 12 10
   66.6666667 1 3 75 1
   PEGINTERFERON ALFA-2B 1 0 1 0 1 0 1 1 1 0 1 1 1 1 1 0 1 12 10
   66.6666667 1 3 75 1
   PEGINTERFERON BETA-1A 1 0 1 0 1 0 1 1 1 0 1 1 1 1 1 0 1 12 10
   66.6666667 1 3 75 1
   INTERFERON BETA-1A 1 0 1 0 1 0 1 1 1 0 1 1 1 1 1 0 1 12 10 66.6666667 1
   3 75 1
   INTERFERON BETA-1B 1 0 1 0 1 0 1 1 1 0 1 1 1 1 1 0 1 12 10 66.6666667 1
   3 75 1
   INTERFERON ALFA-2B 1 0 1 0 1 0 1 1 1 0 1 1 1 1 1 0 1 12 10 66.6666667 1
   3 75 1
   BERMEKIMAB 1 0 1 0 1 0 1 1 1 0 1 1 1 1 1 0 1 12 10 66.6666667 1 3 75 1
   CANAKINUMAB 1 0 1 0 1 0 1 1 1 0 1 1 1 1 1 0 1 12 10 66.6666667 1 3 75 1
   USTEKINUMAB 1 0 1 0 1 0 1 1 1 0 1 1 1 1 1 0 1 12 10 66.6666667 1 3 75 1
   BRIAKINUMAB 1 0 1 0 1 0 1 1 1 0 1 1 1 1 1 0 1 12 10 66.6666667 1 3 75 1
   BELIMUMAB 1 0 1 0 1 0 1 1 1 0 1 1 0 1 1 0 1 11 10 66.6666667 1 3 75 1
   MOGAMULIZUMAB 1 0 1 0 1 0 1 1 1 0 1 1 0 1 1 0 1 11 10 66.6666667 1 3 75
   1
   MARAVIROC 1 0 1 0 1 0 1 1 1 0 1 1 0 1 1 0 1 11 10 66.6666667 1 3 75 1
   FILGRASTIM 1 0 1 0 1 0 1 1 1 0 1 1 0 1 1 0 1 11 10 66.6666667 1 3 75 1
   PEGFILGRASTIM 1 0 1 0 1 0 1 1 1 0 1 1 0 1 1 0 1 11 10 66.6666667 1 3 75
   1
   PEGINTERFERON LAMBDA-1A 1 0 1 0 1 0 1 1 1 0 1 1 0 1 1 0 1 11 10
   66.6666667 1 3 75 1
   DARBEPOETIN ALFA 1 0 1 0 1 0 1 1 1 0 1 1 0 1 1 0 1 11 10 66.6666667 1 3
   75 1
   PEGINESATIDE 1 0 1 0 1 0 1 1 1 0 1 1 0 1 1 0 1 11 10 66.6666667 1 3 75
   1
   BRODALUMAB 1 0 1 0 1 0 1 1 1 0 1 1 0 1 1 0 1 11 10 66.6666667 1 3 75 1
   SOMATROPIN 1 0 1 0 1 0 1 1 1 0 1 1 0 1 1 0 1 11 10 66.6666667 1 3 75 1
   PEGVISOMANT 1 0 1 0 1 0 1 1 1 0 1 1 0 1 1 0 1 11 10 66.6666667 1 3 75 1
   ANAKINRA 1 0 1 0 1 0 1 1 1 0 1 1 0 1 1 0 1 11 10 66.6666667 1 3 75 1
   DENILEUKIN DIFTITOX 1 0 1 0 1 0 1 1 1 0 1 1 0 1 1 0 1 11 10 66.6666667
   1 3 75 1
   DACLIZUMAB 1 0 1 0 1 0 1 1 1 0 1 1 0 1 1 0 1 11 10 66.6666667 1 3 75 1
   ALDESLEUKIN 1 0 1 0 1 0 1 1 1 0 1 1 0 1 1 0 1 11 10 66.6666667 1 3 75 1
   TAGRAXOFUSP 1 0 1 0 1 0 1 1 1 0 1 1 0 1 1 0 1 11 10 66.6666667 1 3 75 1
   DUPILUMAB 1 0 1 0 1 0 1 1 1 0 1 1 0 1 1 0 1 11 10 66.6666667 1 3 75 1
   MEPOLIZUMAB 1 0 1 0 1 0 1 1 1 0 1 1 0 1 1 0 1 11 10 66.6666667 1 3 75 1
   RESLIZUMAB 1 0 1 0 1 0 1 1 1 0 1 1 0 1 1 0 1 11 10 66.6666667 1 3 75 1
   BENRALIZUMAB 1 0 1 0 1 0 1 1 1 0 1 1 0 1 1 0 1 11 10 66.6666667 1 3 75
   1
   TOCILIZUMAB 1 0 1 0 1 0 1 1 1 0 1 1 0 1 1 0 1 11 10 66.6666667 1 3 75 1
   SARILUMAB 1 0 1 0 1 0 1 1 1 0 1 1 0 1 1 0 1 11 10 66.6666667 1 3 75 1
   ELTROMBOPAG 1 0 1 0 1 0 1 1 1 0 1 1 0 1 1 0 1 11 10 66.6666667 1 3 75 1
   FLETIKUMAB 1 0 1 0 1 0 1 1 1 0 1 1 0 1 1 0 1 11 10 66.6666667 1 3 75 1
   MEDI-6469 1 0 1 0 1 0 1 1 1 0 1 1 0 1 1 0 1 11 10 66.6666667 1 3 75 1
   PLERIXAFOR 1 0 1 0 1 0 1 1 1 0 1 1 0 1 1 0 1 11 10 66.6666667 1 3 75 1
   BRENTUXIMAB VEDOTIN 1 0 1 0 1 0 1 1 1 0 1 1 0 1 1 0 1 11 10 66.6666667
   1 3 75 1
   MIDOSTAURIN 0 0 0 0 1 0 0 0 1 0 1 0 1 1 1 0 1 7 6 40 41 3 75 1
   BARICITINIB 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 1 4 3 20 51 3 75 1
   HYDROXYCHLOROQUINE 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 1 4 3 20 51 3 75 1
   BENZIODARONE 0 0 1 0 0 1 1 1 0 1 0 0 0 1 1 0 0 7 7 46.6666667 40 2 50
   43
   DASATINIB 0 0 1 0 1 0 0 0 1 0 1 0 0 1 1 0 0 6 6 40 41 2 50 43
   NETARSUDIL 0 0 1 0 1 0 0 0 1 0 1 0 0 1 1 0 0 6 6 40 41 2 50 43
   LITHIUM CARBONATE 0 0 0 0 1 0 0 0 1 0 1 0 0 1 1 0 0 5 5 33.3333333 44 2
   50 43
   PAZOPANIB 0 0 0 0 1 0 0 0 1 0 1 0 0 1 1 0 0 5 5 33.3333333 44 2 50 43
   IDELALISIB 0 0 0 0 1 0 0 0 1 0 1 0 0 1 1 0 0 5 5 33.3333333 44 2 50 43
   DUVELISIB 0 0 0 0 1 0 0 0 1 0 1 0 0 1 1 0 0 5 5 33.3333333 44 2 50 43
   REGORAFENIB 0 0 1 0 0 0 0 0 0 0 1 0 0 1 1 0 0 4 4 26.6666667 50 2 50 43
   VORAPAXAR 0 0 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 3 2 13.3333333 53 1 25 58
   IMIQUIMOD 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 1 3 2 13.3333333 53 2 50 43
   TOFACITINIB 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 1 3 2 13.3333333 53 2 50 43
   PARICALCITOL 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 2 2 13.3333333 53 2 50
   43
   CALCIPOTRIENE 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 2 2 13.3333333 53 2 50
   43
   CALCITRIOL 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 2 2 13.3333333 53 2 50 43
   CHOLECALCIFEROL 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 2 2 13.3333333 53 2
   50 43
   ERGOCALCIFEROL 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 2 2 13.3333333 53 2 50
   43
   ABEMACICLIB 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 2 2 13.3333333 53 1 25 58
   VORINOSTAT 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 2 2 13.3333333 53 1 25 58
   PANOBINOSTAT 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 2 2 13.3333333 53 1 25
   58
   ROMIDEPSIN 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 2 2 13.3333333 53 1 25 58
   VINFLUNINE 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 2 2 13.3333333 53 1 25 58
   TRASTUZUMAB EMTANSINE 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 2 2 13.3333333
   53 1 25 58
   PACLITAXEL POLIGLUMEX 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 2 2 13.3333333
   53 1 25 58
   BIVALIRUDIN 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 2 1 6.66666667 81 0 0 129
   DABIGATRAN ETEXILATE 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 2 1 6.66666667
   81 0 0 129
   ARGATROBAN 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 2 1 6.66666667 81 0 0 129
   DESIRUDIN 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 2 1 6.66666667 81 0 0 129
   GLYCOPYRRONIUM 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 2 2 13.3333333 53 1 25
   58
   ATROPINE 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 2 2 13.3333333 53 1 25 58
   BENZTROPINE 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 2 2 13.3333333 53 1 25 58
   TERODILINE 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 2 2 13.3333333 53 1 25 58
   TROSPIUM 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 2 2 13.3333333 53 1 25 58
   SOLIFENACIN 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 2 2 13.3333333 53 1 25 58
   OXYBUTYNIN 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 2 2 13.3333333 53 1 25 58
   DARIFENACIN 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 2 2 13.3333333 53 1 25 58
   TOLTERODINE 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 2 2 13.3333333 53 1 25 58
   GEFITINIB 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 2 2 13.3333333 53 1 25 58
   ERLOTINIB 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 2 2 13.3333333 53 1 25 58
   VANDETANIB 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 2 2 13.3333333 53 1 25 58
   NINTEDANIB 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 2 2 13.3333333 53 1 25 58
   ICATIBANT 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 2 1 6.66666667 81 0 0 129
   RISEDRONIC ACID 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 2 1 6.66666667 81 1
   25 58
   ALENDRONIC ACID 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 2 1 6.66666667 81 1
   25 58
   ZOLEDRONIC ACID 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 2 1 6.66666667 81 1
   25 58
   PAMIDRONIC ACID 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 2 1 6.66666667 81 1
   25 58
   DROTRECOGIN ALFA (ACTIVATED) 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0
   235 0 0 129
   EMICIZUMAB 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 235 0 0 129
   EDOXABAN 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 235 0 0 129
   RIVAROXABAN 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 235 0 0 129
   APIXABAN 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 235 0 0 129
   FIBRINOLYSIN, HUMAN 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 235 0 0 129
   APROTININ 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 235 0 0 129
   ECALLANTIDE 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 235 0 0 129
   LANADELUMAB 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 235 0 0 129
   HEPARIN SODIUM 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 235 0 0 129
   ENOXAPARIN SODIUM 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 235 0 0 129
   TINZAPARIN SODIUM 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 235 0 0 129
   AMINOCAPROIC ACID 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 235 0 0 129
   TRANEXAMIC ACID 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 235 0 0 129
   UROKINASE 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 235 0 0 129
   ALTEPLASE 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 235 0 0 129
   ACETAMINOPHEN 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 6.66666667 81 1 25
   58
   ACECLOFENAC 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 6.66666667 81 1 25 58
   ASPIRIN 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 6.66666667 81 1 25 58
   IBRUTINIB 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 6.66666667 81 1 25 58
   EMRICASAN 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 6.66666667 81 1 25 58
   Small molecule 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 6.66666667 81 1 25
   58
   Protein 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 6.66666667 81 1 25 58
   ERDAFITINIB 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 6.66666667 81 1 25 58
   SUNITINIB 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 6.66666667 81 1 25 58
   LENVATINIB 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 6.66666667 81 1 25 58
   SORAFENIB 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 6.66666667 81 1 25 58
   AXITINIB 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 6.66666667 81 1 25 58
   INSULIN GLARGINE 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 6.66666667 81 1
   25 58
   INSULIN SUSP ISOPHANE BEEF 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1
   6.66666667 81 1 25 58
   INSULIN GLULISINE 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 6.66666667 81 1
   25 58
   INSULIN LISPRO 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 6.66666667 81 1 25
   58
   INSULIN DETEMIR 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 6.66666667 81 1
   25 58
   INSULIN PORK 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 6.66666667 81 1 25
   58
   ANLOTINIB 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 6.66666667 81 1 25 58
   CABOZANTINIB 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 6.66666667 81 1 25
   58
   AFLIBERCEPT 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 6.66666667 81 1 25 58
   RANIBIZUMAB 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 6.66666667 81 1 25 58
   CONBERCEPT 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 6.66666667 81 1 25 58
   TIROFIBAN 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 6.66666667 81 1 25 58
   PIOGLITAZONE 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 6.66666667 81 1 25
   58
   MESALAMINE 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 6.66666667 81 1 25 58
   ROSIGLITAZONE 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 6.66666667 81 1 25
   58
   PRAMLINTIDE 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 6.66666667 81 1 25 58
   CALCITONIN SALMON RECOMBINANT 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1
   6.66666667 81 1 25 58
   CALCITONIN SALMON 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 6.66666667 81 1
   25 58
   CALCITONIN 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 6.66666667 81 1 25 58
   TALAZOPARIB 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 6.66666667 81 1 25 58
   NIRAPARIB 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 6.66666667 81 1 25 58
   OLAPARIB 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 6.66666667 81 1 25 58
   RUCAPARIB 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 6.66666667 81 1 25 58
   VELIPARIB 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 6.66666667 81 1 25 58
   FENOFIBRATE 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 6.66666667 81 1 25 58
   FENOFIBRIC ACID 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 6.66666667 81 1
   25 58
   ACITRETIN 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 6.66666667 81 1 25 58
   ABATACEPT 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 6.66666667 81 1 25 58
   BELATACEPT 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 6.66666667 81 1 25 58
   EFALIZUMAB 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 6.66666667 81 1 25 58
   COLLAGENASE CLOSTRIDIUM HISTOLYTICUM 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
   1 1 6.66666667 81 1 25 58
   OCRIPLASMIN 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 6.66666667 81 1 25 58
   VEDOLIZUMAB 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 6.66666667 81 1 25 58
   NATALIZUMAB 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 6.66666667 81 1 25 58
   PENTOXIFYLLINE 0 0 1 0 0 1 1 1 0 1 0 0 0 0 0 0 0 5 5 33.3333333 44 0 0
   129
   DIPYRIDAMOLE 0 0 1 0 0 1 1 1 0 1 0 0 0 0 0 0 0 5 5 33.3333333 44 0 0
   129
   NILOTINIB 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 1 6.66666667 81 0 0 129
   IMATINIB 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 1 6.66666667 81 0 0 129
   LENALIDOMIDE 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 1 6.66666667 81 0 0
   129
   THALIDOMIDE 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 1 6.66666667 81 0 0 129
   HYDROXYUREA 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 1 6.66666667 81 0 0 129
   DOCETAXEL 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 6.66666667 81 0 0 129
   CABAZITAXEL 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 6.66666667 81 0 0 129
   PACLITAXEL 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 6.66666667 81 0 0 129
   COLCHICINE 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 6.66666667 81 0 0 129
   VINCRISTINE 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 6.66666667 81 0 0 129
   IXABEPILONE 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 6.66666667 81 0 0 129
   VINORELBINE 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 6.66666667 81 0 0 129
   VINBLASTINE 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 6.66666667 81 0 0 129
   OMECAMTIV MECARBIL 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 6.66666667 81
   0 0 129
   MONTELUKAST 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 6.66666667 81 0 0 129
   ZAFIRLUKAST 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 6.66666667 81 0 0 129
   IPRATROPIUM 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 6.66666667 81 0 0 129
   TROPICAMIDE 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 6.66666667 81 0 0 129
   TIOTROPIUM 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 6.66666667 81 0 0 129
   UMECLIDINIUM 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 6.66666667 81 0 0
   129
   FESOTERODINE 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 6.66666667 81 0 0
   129
   CAFFEINE 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 6.66666667 81 0 0 129
   THEOPHYLLINE 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 6.66666667 81 0 0
   129
   ADENOSINE 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 6.66666667 81 0 0 129
   CARVEDILOL 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 6.66666667 81 0 0 129
   NOREPINEPHRINE 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 6.66666667 81 0 0
   129
   EPINEPHRINE 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 6.66666667 81 0 0 129
   NAPHAZOLINE 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 6.66666667 81 0 0 129
   TAMSULOSIN 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 6.66666667 81 0 0 129
   DOXAZOSIN 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 6.66666667 81 0 0 129
   ERGOTAMINE 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 6.66666667 81 0 0 129
   ALFUZOSIN 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 6.66666667 81 0 0 129
   METARAMINOL 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 6.66666667 81 0 0 129
   PRAZOSIN 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 6.66666667 81 0 0 129
   SERTINDOLE 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 6.66666667 81 0 0 129
   ATENOLOL 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 6.66666667 81 0 0 129
   METOPROLOL 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 6.66666667 81 0 0 129
   BISOPROLOL 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 6.66666667 81 0 0 129
   SOTALOL 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 6.66666667 81 0 0 129
   ALBUTEROL 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 6.66666667 81 0 0 129
   TERBUTALINE 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 6.66666667 81 0 0 129
   FORMOTEROL 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 6.66666667 81 0 0 129
   LABETALOL 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 6.66666667 81 0 0 129
   PROPRANOLOL 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 6.66666667 81 0 0 129
   LEVOSALBUTAMOL 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 6.66666667 81 0 0
   129
   DROXIDOPA 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 6.66666667 81 0 0 129
   METHYLERGONOVINE 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 6.66666667 81 0
   0 129
   PIMOZIDE 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 6.66666667 81 0 0 129
   ERGOLOID 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 6.66666667 81 0 0 129
   AMOXAPINE 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 6.66666667 81 0 0 129
   VALSARTAN 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 6.66666667 81 0 0 129
   LOSARTAN 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 6.66666667 81 0 0 129
   TELMISARTAN 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 6.66666667 81 0 0 129
   BOSENTAN 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 6.66666667 81 0 0 129
   MACITENTAN 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 6.66666667 81 0 0 129
   DIPHENHYDRAMINE 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 6.66666667 81 0 0
   129
   CETIRIZINE 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 6.66666667 81 0 0 129
   FEXOFENADINE 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 6.66666667 81 0 0
   129
   EPINASTINE 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 6.66666667 81 0 0 129
   OLOPATADINE 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 6.66666667 81 0 0 129
   DIBENZEPIN 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 6.66666667 81 0 0 129
   AZELASTINE 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 6.66666667 81 0 0 129
   TRIPROLIDINE 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 6.66666667 81 0 0
   129
   DESLORATADINE 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 6.66666667 81 0 0
   129
   RANITIDINE 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 6.66666667 81 0 0 129
   FAMOTIDINE 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 6.66666667 81 0 0 129
   NIZATIDINE 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 6.66666667 81 0 0 129
   CIMETIDINE 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 6.66666667 81 0 0 129
   TOLAZOLINE 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 6.66666667 81 0 0 129
   HALOPERIDOL 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 6.66666667 81 0 0 129
   RISPERIDONE 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 6.66666667 81 0 0 129
   CLOZAPINE 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 6.66666667 81 0 0 129
   OLANZAPINE 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 6.66666667 81 0 0 129
   QUETIAPINE 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 6.66666667 81 0 0 129
   AMISULPRIDE 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 6.66666667 81 0 0 129
   METHYSERGIDE 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 6.66666667 81 0 0
   129
   DEXFENFLURAMINE 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 6.66666667 81 0 0
   129
   MIRTAZAPINE 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 6.66666667 81 0 0 129
   METOCLOPRAMIDE 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 6.66666667 81 0 0
   129
   PRUCALOPRIDE 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 6.66666667 81 0 0
   129
   LUTROPIN ALFA 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 6.66666667 81 0 0
   129
   MENOTROPINS 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 6.66666667 81 0 0 129
   GONADOTROPIN, CHORIONIC 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1
   6.66666667 81 0 0 129
   OXYTOCIN 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 6.66666667 81 0 0 129
   CARBETOCIN 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 6.66666667 81 0 0 129
   ATOSIBAN 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 6.66666667 81 0 0 129
   DESMOPRESSIN 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 6.66666667 81 0 0
   129
   CONIVAPTAN 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 6.66666667 81 0 0 129
   VASOPRESSIN 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 6.66666667 81 0 0 129
   ALPROSTADIL 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 6.66666667 81 0 0 129
   MISOPROSTOL 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 6.66666667 81 0 0 129
   LATANOPROST 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 6.66666667 81 0 0 129
   TRAVOPROST 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 6.66666667 81 0 0 129
   BIMATOPROST 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 6.66666667 81 0 0 129
   IBODUTANT 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 6.66666667 81 0 0 129
   FOSAPREPITANT 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 6.66666667 81 0 0
   129
   APREPITANT 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 6.66666667 81 0 0 129
   NETUPITANT 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 6.66666667 81 0 0 129
   FOSNETUPITANT 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 6.66666667 81 0 0
   129
   SERATRODAST 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 6.66666667 81 0 0 129
   [110]Open in a new tab

   **KEY: 1 = Drug predicted to be relevant in comparison; 0 = drug NOT
   predicted to be relevant in comparison

   We then analyzed the drugs that were identified as having targets in
   pathways affected by SARS-CoV-2 to determine which were predicted to
   reverse the effects of the viral infection on the affected pathway. Of
   the 42 drugs that targeted SARS-CoV-2 related pathways, 27 were
   predicted to reverse viral effects on these pathways. We then performed
   a literature search to determine if any of these drugs had been
   previously used to treat COVID-19 or had been identified as potential
   therapeutics by other research groups. We found six of the 12
   therapeutics that we predicted to be useful against SARS-CoV-2 had
   already shown positive results in clinical tests including canakinumab,
   anakinra, tocilizumab, sarilumab, and baricitinib. These results give
   further support to the validity of our computational workflow.

Discussion

   The computational workflow that we describe in this work predicts human
   therapeutic targets from signaling pathways and gene expression that
   are significantly affected during infection. We applied this workflow
   within the context of a meta-analysis that consisted of multiple public
   transcriptomic datasets of Betacoronaviruses. We then validated our
   results by comparing our predictions against recently published studies
   reporting therapeutics for SARS-CoV-2. Specifically, our downstream
   analyses enable us to calculate significant signaling pathways from DE
   genes using the SPIA algorithm as well as to predict potential
   therapeutics and their respective targets. Our analysis revealed
   thousands of DE genes, 580 enriched functional terms, as well as 249
   significant pathways, including 38 pathways that were specifically
   affected during infection with SARS-CoV-2. It is important to point out
   that this workflow focuses on identifying human drug targets for two
   reasons: 1) to aid in the repurposing of existing drugs against
   emerging pathogens, and 2) to reduce the likelihood that a pathogen
   will develop resistance against the therapeutic(s) since they interact
   with a human protein that is much less likely to mutate than a viral
   protein.

   Our approach differs from prior meta-analyses by focusing on a
   consistent, robust ARMOR-based RNA-seq preprocessing workflow for all
   datasets as well as a downstream pathway perturbation analysis.
   Previous studies have used a variety of approaches to predict possible
   therapeutics ^[111]42– [112]46 , but none have combined the various
   aspects that are described in this work. The SPIA algorithm we used in
   this study has been shown to provide robust statistical results of
   perturbed pathways while not simply enriching for DE genes ^[113]34 .
   It also enabled us to identify protein components in signaling pathways
   that could be reversed to reduce the adverse signs and symptoms that
   occur during infection, which differs from simply attempting to target
   DE genes. This approach drastically differs from some attempts to
   directly target DE genes without accounting for how a treatment may
   affect the cellular protein-protein interaction network. In this
   analysis we have compared significant DE genes and pathways identified
   in multiple different studies that used different MOIs, timepoints, and
   cell types. We found many DE genes and pathways that were affected
   across a variety of samples which increases our confidence in our
   results as those genes and pathways appear to be affected more by the
   virus itself than by other variables such as cell type, MOI, or
   timepoint.

   Although our approach was dependent on identifying DE genes using a
   consistent preprocessing workflow, the focus of our analysis was to
   identify relevant functions, pathways, and potential drug targets from
   the DE genes. These processed data could then be used to better
   understand the underlying biological mechanisms of pathogenesis, but to
   better identify host-based therapeutic targets. A subset of the
   enriched annotations identified by the Camera algorithm have been
   reported to be relevant during infection with SARS-CoV-2 in clinical
   studies including “response to chemokine” ^[114]47 , “humoral immune
   response” ^[115]48– [116]50 , “chronic inflammatory response” ^[117]51,
   [118]52 , “toll like receptor binding” ^[119]53, [120]54 ,
   “interleukin-6 production” ^[121]55 and citrate metabolism. A separate
   study of COVID-19 identified Bradykinin as potentially playing a role
   in pathogenesis ^[122]56 . Interestingly, the annotations for this gene
   and its receptor include several of the enriched terms we identified
   such as arachidonic acid, inflammation, and G-protein coupled receptor
   activity. We interpret these separate studies to validate the findings
   of our functional enrichment analysis and anticipate that future
   studies will shed additional insight into the underlying mechanism(s)
   of viral pathogenesis.

   Many of the signaling pathway components that we identified in this
   study have also been reported previously. One prior proteomics study
   reported applying translation inhibitors to Caco-2 cells infected with
   SARS-CoV-2 reduced virus replication ^[123]57 , which supports our
   pathway perturbation results. Other studies have reported that the
   ORF6, ORF8, and nucleocapsid proteins of SARS-CoV-2 are antagonists of
   type-I interferon and NF-kB in HEK-293 T cells ^[124]58 or the
   induction of apoptosis during viral infection ^[125]59 . In contrast,
   our meta-analysis predicted the type-I interferon pathway to be
   activated, suggesting either that this response could be dependent on
   the cell type, or that a potential redundant mechanism in the host cell
   can still turn on this pathway even if certain components are
   down-regulated. Our analysis predicted that noncanonical NF-kB
   signaling was inhibited during MERS and SARS infection, while being
   activated during SARS-CoV-2 infection in Calu-3 cells. While it is
   possible that this difference is due to a cell-specific response from
   the studies included in our meta-analysis, a NF-kB inhibitor applied to
   Vero E6 cells infected with SARS-CoV-2 has been shown to reduce
   cytopathic effects and virus plaques ^[126]60 . This result suggests
   that NF-kB signaling may be active and contribute to the inflammatory
   signs and symptoms observed during virus infection, which agrees with
   our results. The Toll-like receptor and JAK-STAT pathways were
   previously found to be relevant to SARS-CoV-2 infection in A549 cells,
   which we also identified in infected Calu-3 cells ^[127]42 . Citrate
   metabolism was also identified as an important pathway by our analysis
   and has been supported elsewhere ^[128]61 .

   Although the US FDA has only issued emergency use authorization for
   therapeutic treatment for severe cases of COVID-19, a multitude of
   studies have reported results from attempting to treat patients with a
   variety of existing FDA approved therapeutics ^[129]62– [130]69 . We
   found that 27 of our 42 predicted therapeutics are predicted to
   “reverse” the effect on the pathways relevant to the viral infections
   being compared. Twelve of the 27 drugs that were predicted to be
   potential therapeutics against SARS-CoV-2 and are used to combat
   autoimmune or inflammatory diseases such as MS, SLE, and rheumatoid
   arthritis while others have been used in cancer treatments and against
   viral infections such as hepatitis C and human immunodeficiency virus.
   Six of these 12 drugs have been used to treat COVID-19 in patients
   including canakinumab, anakinra, tocilizumab, sarilumab, baricitinib,
   and hydroxychloroquine ^[131]70 . Two other drugs, maraviroc and
   brodalumab, have been identified as potential treatments via cell
   cultures and computer models ^[132]71– [133]74 . Others on the list
   such as benralizumab have been identified through anecdotal data as
   biologics that potentially exert a prophylactic effect for SARS-CoV-2,
   when they are taken at the time of infection ^[134]75 . Baricitinib is
   of particular interest as the US FDA has issued emergency use
   authorization for its use in conjunction with remdesivir in the
   treatment of COVID-19 patients over the age of two that have been
   hospitalized and require supplemental oxygen, invasive mechanical
   ventilation, or extracorporeal membrane oxygenation ^[135]76 .
   Baricitinib has also been shown to be effective against COVID-19 when
   combined with corticosteroids ^[136]77 . A small study involving
   Tocilizumab has also shown it can be useful in improving the outcome of
   patients with severe COVID-19 ^[137]78 .

Conclusions

   In conclusion, we developed and applied an important bioinformatics
   workflow, that combines existing tools with custom scripts, to predict
   potential human therapeutic targets. This workflow was then validated
   through a meta-analysis of publicly available transcriptomics data. The
   multiple Betacoronavirus and SARS-CoV-2 datasets revealed significant
   genes, annotations, signaling pathways, and human proteins that could
   be targeted by therapeutics during infection with various
   Betacoronaviruses. It is important to recognize that many of the
   predictions made by our workflow have been supported by experimental
   and clinical work on this virus, which suggests that our approach could
   enable the rapid identification of relevant therapeutics against future
   emerging pathogens.

Abbreviations

   SARS-CoV-2: Severe acute respiratory syndrome coronavirus-2

   COVID-19: Coronavirus disease-2019

   GO: Gene ontology

   SARS / SARS-CoV: Severe acute respiratory syndrome coronavirus

   MERS / MERS-CoV: Middle east respiratory syndrome coronavirus

   ACE2: Angiotensin-converting enzyme 2

   FDA: Food and Drug Administration

   GEO: Gene expression omnibus

   NCBI: National center for biotechnology information

   ARMOR: Automated Reproducible MOdular Workflow for Preprocessing and
   Differential Analysis of RNA-seq Data

   SPIA: Signaling pathway impact analysis

   DE: Differentially expressed

   FDR: False-discovery rate

Data availability

Underlying data

   GEO: Transcriptomic analysis of MERS-CoV infected Calu-3 cell with or
   without AM580 treatment, Accession number [138]GSE122876:
   [139]https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE122876

   GEO: Transcriptomic analysis of the Novel Middle East Respiratory
   Syndrome Coronavirus (Human, MRC5 cells, Accession number
   [140]GSE56192:
   [141]https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE56192

   GEO: Transcriptional response to SARS-CoV-2 infection, Accession number
   [142]GSE147507:
   [143]https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE147507

   GEO: Transcriptomic Analysis Of circRNAs/miRNAs/mRNAs upon Middle East
   Respiratory Syndrome Coronavirus (MERS-CoV) infection, Accession number
   [144]GSE139516:
   [145]https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE139516

   Code for this workflow can be found on GitHub:
   [146]https://github.com/bpickett/Pathway2Targets.

   Archived code as at time of publication:
   [147]http://doi.org/10.5281/zenodo.4706197 ^[148]79 .

   Code is available under the terms of the [149]Creative Commons Zero "No
   rights reserved" data waiver (CC0 1.0 Public domain dedication).

Acknowledgments