Abstract
Since coronavirus disease 2019 (COVID-19) is a serious new worldwide
public health crisis with significant morbidity and mortality,
effective therapeutic treatments are urgently needed. Drug repurposing
is an efficient and cost-effective strategy with minimum risk for
identifying novel potential treatment options by repositioning
therapies that were previously approved for other clinical outcomes.
Here, we used an integrated network-based pharmacologic and
transcriptomic approach to screen drug candidates novel for COVID-19
treatment. Network-based proximity scores were calculated to identify
the drug–disease pharmacological effect between drug–target
relationship modules and COVID-19 related genes. Gene set enrichment
analysis (GSEA) was then performed to determine whether drug candidates
influence the expression of COVID-19 related genes and examine the
sensitivity of the repurposing drug treatment to peripheral immune cell
types. Moreover, we used the complementary exposure model to recommend
potential synergistic drug combinations. We identified 18 individual
drug candidates including nicardipine, orantinib, tipifarnib and
promethazine which have not previously been proposed as possible
treatments for COVID-19. Additionally, 30 synergistic drug pairs were
ultimately recommended including fostamatinib plus tretinoin and
orantinib plus valproic acid. Differential expression genes of most
repurposing drugs were enriched significantly in B cells. The findings
may potentially accelerate the discovery and establishment of an
effective therapeutic treatment plan for COVID-19 patients.
Keywords: SARS-CoV-2, COVID-19, drug repurposing, network-based
pharmacology
1. Introduction
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused
the coronavirus disease 2019 (COVID-19) and triggered the largest
pandemic since 1918 [[38]1], which was responsible for >100 million
cases and >2 million deaths reported globally [[39]2]. However, there
are no specific antiviral drugs for SARS-CoV-2 infection so far, for
the reduction of morbidity and mortality of COVID-19, active
symptomatic support was urgently needed [[40]3].
According to recent reports [[41]4,[42]5,[43]6], the majority of
COVID-19 patients are currently given antiviral and antibiotic
treatments or combination therapy including oseltamivir, ribavirin,
lopinavir, ritonavir, and moxifloxacin. Additionally, several drugs are
under clinical trials to verify their safety and efficacy for COVID-19
treatment, such as favipiravir, remdesivir, and hydroxychloroquine
[[44]7]. However, existing therapeutic options for the treatment of
COVID-19 remain controversial. For example, remdesivir is an FDA
Emergency Use Authorization (not FDA-approval) viral RNA polymerase
inhibitor which has been widely used in COVID-19 patients [[45]8],
however, a recent randomized clinical trial demonstrated there was no
significant beneficial effect [[46]9]. Similarly, the COVID-19 WHO
SOLIDARITY trial showed that other proposed treatments such as
hydroxychloroquine, lopinavir, and interferon regimens appeared to have
little or no effect on hospitalized COVID-19 patients [[47]10].
Therefore, there is an urgent necessity to develop novel potential
candidates for COVID-19 treatment.
Traditional drug development is a time-consuming and costly process
that frequently takes 10–15 years and costs about 2–3 billion dollars
from initial lab-scale experiments through the three phases of clinical
trials and final approval for clinical usage [[48]11]. Drug
repurposing, as an effective and rapid drug discovery strategy from
existing drugs [[49]11,[50]12], is considered the most practical
approach as a rapid response to the emergent pandemic since the
candidate treatments have already previously been tested for their
safety [[51]13]. The availability of the genomic sequence of SARS-CoV-2
has rapidly accelerated the development of clinical perspectives and
recommendations. For example, David E. Gordon et al. identified 332
SARS-CoV-2 human protein-protein interactions and 69 drug candidates
including 29 FDA-approved drugs, 12 clinical trial drugs, and 28 drugs
at a preclinical stage [[52]14]. Additionally, gene set enrichment
analysis (GSEA) can be applied to identify underlying pathological
processes using gene expression of COVID-19 patients, which can
retrieve efficient drugs from patient-derived gene expression data
using drug–target gene sets [[53]15]. Therefore, the application of
GSEA for drug targets based on drug–transcriptome-responses datasets
and disease-associated gene sets can serve as an excellent screening
tool for diseases that lack a safe and reliable cellular model for in
vitro screening, such as COVID-19 [[54]16].
This study uses an integrated network-based pharmacologic and
transcriptomic approach to screen drug candidates for COVID-19
treatment. Network-based pharmacology is an effective and holistic tool
to identify drug treatments, where the drug effects are provided by the
distance between drugs and disease in the interactome [[55]17].
Additionally, several databases containing genome-wide expression
profiles of human cell lines treated with bioactive compounds have been
developed for drug discovery [[56]18]. Transcriptional profiling
studies have successfully identified potential therapies for diseases
such as breast cancer [[57]19], diabetes [[58]20], and Parkinson’s
[[59]21]. Using a network-based pharmacology approach combined with the
transcriptional profiling databases, we detected 18 single drug
candidates (e.g., dexamethasone, chloroquine, and tretinoin) and 30
synergistic drug combinations as potential therapies for COVID-19.
2. Materials and Methods
We screened novel drug combinations for COVID-19 by integrated
network-based pharmacology and transcriptome analysis based on the
following steps: (1) collection of COVID-19 related genes; (2)
collection of target-available drugs and construction of drug–target
modules; (3) calculation of network-based proximity between drug–target
modules and COVID-19 related genes; (4) filtering drugs based on gene
set enrichment analysis (GSEA); (5) network-based prediction of drug
combinations ([60]Figure 1). These steps will be detailed in the
following.
Figure 1.
[61]Figure 1
[62]Open in a new tab
Schematic illustration of the computational framework. (1) Collection
of the coronavirus disease 2019 (COVID-19) related genes from published
SARS-CoV-2 human host data and differential expression genes (DEGs)
from a single-cell study of the peripheral immune response in patients
with severe COVID-19 ([63]GSE150728). (2) Drug–target information
retrieved from DrugBank and SuperTarget. (3) Quantify the therapeutic
effect by computing the proximity between drug targets and COVID-19
related genes. (4) Gene set enrichment analysis (GSEA) to determine
whether COVID-19 related genes show significance in drug-induced gene
expression profiles. (5) Drug candidates were further prioritized for
drug combinations using the “Complementary Exposure” model.
2.1. Genes Related to COVID-19
Genes related to COVID-19 were retrieved from the latest SARS-CoV-2
human host data and a single-cell transcriptomic study of the
peripheral immune response to severe COVID-19 ([64]GSE150728).
SARS-CoV-2 protein sequences, viral genomes, literature, clinical
resources submitted to the National Center for Biotechnology
Information (NCBI) on the SARS-CoV-2 special subject have been rapidly
evolving [[65]22]. In total, 65 SARS-CoV-2 human host proteins were
selected from the coronavirus genomes of NCBI datasets and 1070
potential COVID-19 related genes were obtained from the transcriptomic
study by selecting the differential expression genes (DEGs) between
individual COVID-19 samples (n = 7) and healthy controls (n = 6) in 7
cell types, that was 409 genes from CD14+ Monocytes, 257 genes from
CD16+ Monocytes, 261 genes from Dendritic Cells, 173 genes from NK
(nature killer) cells, 180 genes from CD8+ T cells, 172 genes from CD4+
T cells and 481 genes from B cells ([66]Tables S1 and S2) [[67]23]. All
the identified proteins were mapped to the official gene symbols of
humans reported by the HUGO Gene Nomenclature Committee (HGNC).
Finally, 63 SARS-CoV-2 related genes derived from human host proteins
and 971 DEGs were retained as the COVID-19 potential related genes
after removing duplicates.
Gene Ontology (GO) enrichment analysis was performed on the potential
COVID-19 related genes to identify significant pathways. By using the R
package ClusterProfiler [[68]24], all potential COVID-19 related genes
were functionally categorized according to their biological processes,
cellular components, and molecular functions. Functional term
enrichment analysis was performed to provide insights into the
biological mechanisms underlying the COVID-19 related genes. Using this
approach, only genes involved in the significantly enriched GO terms
(p-value < 0.05) were retained for further analysis as COVID-19 related
genes in the context of networks.
2.2. Drug–Target Relationship Modules
The drug information was obtained from DrugBank and SuperTarget
[[69]25,[70]26]. Briefly, 7485 drugs with 21,335 drug–protein links
were selected from DrugBank (version 5.1.6), and 3138 drugs with 16,579
drug–protein links were retrieved from SuperTarget. After removing
drugs without targets as well as duplications, and converting all
target genes into human gene symbols, 31,139 interactions containing
3121 targets of 7811 drugs were finally identified ([71]Supplemental
Table S3). A drug–target relationship module was defined by the
drug–target interaction information, where multiple targets share one
drug.
2.3. Network-Based Proximity between Drugs and COVID-19
A network-based approach was used to analyze the correlation between
drug and disease, in which proximity scores were quantified by
calculating the closest distance between the drug–target module and
COVID-19 related genes in the context of the human protein-protein
interaction (PPI) network. The PPI data were obtained from Pathway
Commons (version 12), which contains over 5772 pathways and 2.4 million
interactions [[72]27]. Genes (nodes) with interaction (links)
constructed a network graph of PPI, while the interaction between two
nodes was undirected and unweighted. Here, a proximity score was
defined by the average shortest path length between the drug target
genes and their nearest disease proteins in the context of PPI to
quantify the therapeutic effect of drugs [[73]28,[74]29]. Given the set
of COVID-19 related genes sourced from SARS-CoV-2 proteins (S), the
group of drug target genes (T), the shortest distance between two genes
in the PPI network
[MATH:
ds,t :MATH]
where s∈S and t∈T (Equation (1)),
[MATH:
dS,T=1T∑t∈T<
/mi>mins∈Sds,t+w
:MATH]
(1)
where w is the drug influencing weight, defined as
[MATH:
w=−lnD+1 :MATH]
if the drug target is one of the COVID-19 related genes sourced from
DEGs (D is the connectivity degree of targets) and w = 0 otherwise.
A simulated reference distance score distribution corresponding to the
drug was generated to assess the significance of the results by linking
the drug’s random target modules and disease-related genes. Referenced
drug modules were constructed by selecting random genes (denoted as R)
with the same degree of drug target sets in the network, where the
distance
[MATH:
dS,R :MATH]
indicates the relationship between a simulated drug and COVID-19. The
reference distribution was established based on 30,000 replications. A
drug with a score lower than 98% of the reference distribution scores
was considered significant [[75]28]. The network proximity was
converted to Z-score based on permutation tests (Equation (2)):
[MATH:
ZS,T=dS
,T−μdS,R<
/mrow>σdS,R<
/mfrac> :MATH]
(2)
where
[MATH:
μdS,R :MATH]
and
[MATH:
σdS,R :MATH]
are the mean and standard deviation of the permutation tests.
2.4. Biological Enrichment Analysis of COVID-19 Related Genes on the
Drug-Induced Expression Profiles
We performed GSEA as a further prioritization strategy to screen drug
candidates by examining the distribution of disease-related genes in
drug-induced gene expression profiles. GSEA was utilized to determine
whether a priori defined sets of genes showed statistically significant
enrichment in a collected gene list [[76]30], which could identify
whether drug candidates affected the expression of disease pathways. We
first collected perturbation-driven gene expression profiles from LINCS
(Library of Integrated Network-based Cellular Signatures), which
provided transcriptional responses of human cells to chemical and
genetic perturbation [[77]31]. Human myeloid leukemia mononuclear
(THP-1) cell line from blood was selected due to the important
association of peripheral blood and myelomonocytic cells with COVID-19
[[78]32,[79]33,[80]34]. The goal of GSEA was to determine whether the
COVID-19 related genes sourced from the SARS-CoV-2 related gene set was
randomly distributed throughout the drug-induced expression data set
sorted by correlation with the phenotype of interest or enriched at
either the top or bottom. Drugs with FDR (False Discovery Rate) less
than 0.25 and ES (Enrichment Scores) higher than 0 were identified as
potential drug candidates for COVID-19.
2.5. GSEA Analysis of Repurposing Drugs in Specific Cell-Types
According to the Seurat data provided by Aaron J. Wilk [[81]23], we
chose “Cell type (coarse)” as the standard to select scRNA seq data of
seven cell types, including B Cells, CD14+ Monocytes, CD16+ Monocytes,
CD4+ T Cells, CD8+ T Cells, Dendritic Cells, NK (natural killer) Cells,
and calculated differentially expressed genes between total COVID-19
samples (n = 7) and all healthy controls (n = 6). Each cell type was
divided into two groups, diseased and healthy controls, according to
whether the donor had COVID-19. Subsequently, differential gene
expression profiles between the diseased and healthy controls in
specific cell-types were calculated by using the “FindMarkers” function
in Seurat ([82]Supplemental Tables S7–S13) [[83]35]. GSEA analysis of
repurposing drug-induced THP-1 differential expression genes (logFC >
1) and specific cell-type transcriptomes were used to assess the
enrichment of sets of genes (repurposing drugs DE genes) in each cell
type (scRNA seq gene list). For each repurposing drug, a specific cell
with FDR < 0.05 and ES < 0 was identified as potential drug-sensitive
cell types for COVID-19.
2.6. Network-Based Prediction of Drug Combinations
Drug combination therapies are more beneficial rather than individual
drug since the synergistic drug pairs can target more genes and play
role in multiple complicated pathways [[84]36]. The Complementary
Exposure model has previously been demonstrated as an effective
approach to predict useful combinations [[85]37]. The model is based on
the following conditions: drug targets and disease genes overlap
topologically
[MATH: ZDA<0,ZDB<0ZDA<0,ZDB
<0 :MATH]
, and two sets of drug targets are separated topologically
[MATH:
SAB>0 :MATH]
. The Complementary Exposure model network proximity between a drug
[MATH:
A or<
mtext> B :MATH]
and a disease
[MATH: (D) :MATH]
is defined by the z-score (Equation (3)):
[MATH:
ZDA=dDA−μd
σd :MATH]
(3)
The z-score is calculated by randomly sampling both degrees of nodes
(drug targets and disease genes) with 1000 replications. The mean
distance
[MATH: μd
:MATH]
and standard deviation
[MATH: σd
:MATH]
of the reference distribution are used to convert
[MATH:
dDA :MATH]
to a normalized distance (Equation (4)), where
[MATH:
dDA :MATH]
relies on the average shortest path lengths
[MATH:
dd,a :MATH]
between disease genes
[MATH:
d,d∈
D :MATH]
and drug targets
[MATH:
a,a∈
A :MATH]
.
[MATH:
dDA=1‖D‖∑d∈D<
/mi>mina∈Add<
mo>,a :MATH]
(4)
The network-based separation
[MATH:
SAB :MATH]
is quantified with two drug targets module A and B by calculating the
mean shortest distances
[MATH:
dAA :MATH]
and
[MATH:
dBB :MATH]
(Equation (5)):
[MATH:
dAA=1‖A‖∑a∈A<
/mi>mina'∈Ad<
mrow>a,a' :MATH]
(5)
where
[MATH:
a′ <
mi>a'∈A
:MATH]
is the closet node to
[MATH:
a a∈
A :MATH]
within the interactome network. The mean shortest distance
[MATH:
dAB :MATH]
between their proteins is defined by the “closest” measure, where
[MATH:
da,b :MATH]
is the shortest path length between
[MATH:
a a∈
A :MATH]
and
[MATH:
b b∈
B :MATH]
in the interactome network (Equation (6)).
[MATH:
dAB=1‖A‖+<
mo>‖B‖∑a∈A<
/mi>minb<
mo>∈Bda,b+∑b∈B<
/mi>mina<
mo>∈Ada,b
:MATH]
(6)
A networked-based separation of a drug pair, A and B, can be calculated
as follows (Equation (7)):
[MATH:
SAB=dAB
−dAA
+dBB
2 :MATH]
(7)
where
[MATH:
dAB=0 :MATH]
if genes are included in both the drug A and B target modules [[86]38].
3. Results
3.1. GO Enrichment Analysis of COVID-19 Related Genes
To obtain meaningful molecular mechanisms underlying COVID-19, GO
enrichment analysis classified potential COVID-19 related genes into
enriched terms ([87]Supplemental Table S4). All 63 SARS-CoV-2 related
genes were categorized functionally into 1035 Gene Ontology terms
including biological processes, cellular components, and molecular
functions. Among the 971 COVID-19 DEGs, 860 genes were enriched in 1399
Gene Ontology terms. The COVID-19 related genes we identified were
significantly enriched in blood pressure regulation (p-value = 5.29 ×
10^−23), inflammatory response (p-value = 3.62 × 10^−09), neutrophil
activation (p-value = 6.16 × 10^−60), and response to virus (p-value =
8.68 × 10^−32) ([88]Figure 2). The results are consistent with previous
studies, indicating that the renin-angiotensin system (RAS) plays an
important role in the biological mechanisms of COVID-19
[[89]39,[90]40].
Figure 2.
Figure 2
[91]Open in a new tab
GO enrichment analysis of COVID-19 related genes. The dot plot is used
to visualize enriched terms, (a) shows the COVID-19 related genes (n =
63) enrichment visualization and category interpretation. (b) pathway
enrichment analysis visualization of single-cell DEGs (n = 860).
3.2. Network-Based Proximity Scores between Drug–Target Modules and COVID-19
Related Genes
We obtained the drug–disease proximity scores to evaluate the drug
effect on COVID-19 through a network-based calculation. Drugs with low
proximity scores are more likely to be effective against SARS-CoV-2
infection since the proximity scores reflect the distance between drug
target sets and COVID-19 related genes in the interactome networks.
Using this approach, we explored the distance of 7811 drug–target
modules and COVID-19 related genes. The distance distribution of the
drug targets to COVID-19 related genes was in the range of −2.66 to
2.79, and both real drugs and simulated drugs were widely distributed
near the point of 1.70 ([92]Figure 3). A ranked list of the potential
drugs was clearly distributed in the range of −2.66 to 0.99, suggesting
that the targets of existing drugs were closer to the COVID-19 genes
than the reference sets (simulated drugs). We selected a distance
smaller than 0.99 as the threshold to screen the potential drug
candidates for COVID-19, where the corresponding Z-score was
approximately −2.33 after converting into the proximity value. Finally,
468 drugs with proximity less than −2.33 were included in further
analyses ([93]Supplemental Table S5).
Figure 3.
Figure 3
[94]Open in a new tab
Distance distribution of all 7811 drugs and simulated reference. Peaks
suggest that the distance corresponding to most members was around this
value. The red line shows the distribution of the distance of the 7811
drugs to COVID-19 related genes. The black line illustrates the
distance distribution of the simulated reference based on 30,000
replications. The blue line shows the threshold (distance < 0.99,
Z-score < −2.33) to screen the drug candidates for COVID-19.
3.3. GSEA Analysis of COVID-19 Related Genes in Drug-Induced Signatures
To further estimate the drug candidate’s efficacy on the disease and
explore the underlying signaling pathways, we performed GSEA to examine
their impact on the transcriptome of THP-1 cells. Since drugs were not
fully matched between DrugBank and LINCS, some drugs were removed
during the matching progress. In the total of 7811 drugs included in
DrugBank and 377 from LINCS (THP-1 cell line), 112 drugs were matched
by common name and 101 were matched by InChI Key (International
Chemical Identifier Key). After removing overlaps, 131 drugs were
included in both DrugBank and LINCS, 27 of which had low proximity
scores (Z < −2.33) and were obtained for further GSEA.
We identified 18 drugs (FDR < 0.25 and ES > 0, [95]Table 1) as
potential therapeutic candidates since they significantly affected the
expression of COVID-19 related genes in the mononuclear cells
([96]Supplemental Table S6). These candidates included anti-viral
agents (curcumin, dexamethasone, chloroquine), anti-diabetic agents
(glibenclamide), analgesics (resveratrol), anti-convulsant (valproic
acid), anti-cholesteremic agents (simvastatin), anti-carcinogenic
agents (phenethyl isothiocyanate), anti-neoplastic agents (tretinoin),
immunosuppressive agents (fostamatinib, atorvastatin, cyclosporine),
anti-estrogen (tamoxifen), anti-hypertensive (nicardipine, nifedipine),
anti-allergic agents (promethazine), and anti-cancer agents (orantinib,
tipifarnib).
Table 1.
Eighteen repurposable candidates for COVID-19.
DrugBank ID Z-Score Drug Name Structure Pharmacodynamics Reported
Studies of COVID-19
(PMID)
DB12010 −8.75 Fostamatinib graphic file with name
pharmaceutics-13-00545-i001.jpg immunosuppressive agents 32637960
DB12695 −6.64 Phenethyl-isothiocyanate graphic file with name
pharmaceutics-13-00545-i002.jpg anti-carcinogenic agents 33131530
DB01069 −5.65 Promethazine graphic file with name
pharmaceutics-13-00545-i003.jpg anti-allergic agents NA ^1
DB00641 −5.49 Simvastatin graphic file with name
pharmaceutics-13-00545-i004.jpg anti-cholesteremic agents 32626922
DB00675 −4.75 Tamoxifen graphic file with name
pharmaceutics-13-00545-i005.jpg anti-estrogen 32663742
DB01076 −4.74 Atorvastatin graphic file with name
pharmaceutics-13-00545-i006.jpg immunosuppressive agents 32664990
32817953
DB11672 −3.65 Curcumin graphic file with name
pharmaceutics-13-00545-i007.jpg antiviral agents 32430996
32442323
DB00755 −3.37 Tretinoin graphic file with name
pharmaceutics-13-00545-i008.jpg anti-neoplastic agents 32707573
DB01234 −3.21 Dexamethasone graphic file with name
pharmaceutics-13-00545-i009.jpg antiviral agents 327065533
2620554
DB00608 −3.14 Chloroquine graphic file with name
pharmaceutics-13-00545-i010.jpg antiviral agents 32145363
32147496
DB00313 −2.90 Valproic acid graphic file with name
pharmaceutics-13-00545-i011.jpg anti-convulsant 32498007
DB01016 −2.82 Glibenclamide graphic file with name
pharmaceutics-13-00545-i012.jpg antiviral agents 32787684
DB00622 −2.75 Nicardipine graphic file with name
pharmaceutics-13-00545-i013.jpg anti-hypertensive NA
DB01115 −2.68 Nifedipine graphic file with name
pharmaceutics-13-00545-i014.jpg anti-hypertensive 32226695
32411566
DB00091 −2.65 Cyclosporine graphic file with name
pharmaceutics-13-00545-i015.jpg immunosuppressive agents 32376422
32487139
DB02709 −5.63 Resveratrol graphic file with name
pharmaceutics-13-00545-i016.jpg analgesics 32412158
32764275
DB12072 −2.54 Orantinib graphic file with name
pharmaceutics-13-00545-i017.jpg anti-cancer agents NA
DB04960 −2.40 Tipifarnib graphic file with name
pharmaceutics-13-00545-i018.jpg anti-cancer agents NA
[97]Open in a new tab
^1 NA: Not previously been reported as potential treatments for
COVID-19.
3.4. Repurposing Drugs Sensitivity in Specific Cell Type
Differential expression analyses in 7 cell types between COVID-19
patients (n = 7) and controls (n = 6) were performed based on the
scRNA-seq data ([98]Tables S7–S13). According to the GSEA analysis, the
DE genes of most repurposing drugs were enriched significantly in B
cells ([99]Table 2, [100]Table S14). CD14+ Monocytes Cells and
Dendritic Cells also showed sensitivity to the repurposing drug
treatment. None repurposing drug DE genes were significantly enriched
for the Single-cell gene expression spectrum of NK Cells, CD8+ T Cells,
CD4+ T Cells.
Table 2.
GSEA analysis of drug-induced different expression (DE) genes in scRNA
profiles.
Drug Name B Cells CD14+ Monocytes Cells CD16+ Monocytes Cells Dendritic
Cells NK Cells CD4+ T Cells CD8+ T Cells
Chloroquine NA ^1 NA NA NA NA NA NA
Nicardipine Significant ^2 Significant NA NA NA NA NA
Simvastatin NA NA NA NA NA NA NA
Tamoxifen Significant Significant NA Significant NA NA NA
Promethazine NA NA NA NA NA NA NA
Nifedipine Significant NA NA NA NA NA NA
Resveratrol Significant NA NA Significant NA NA NA
Tipifarnib Significant Significant NA Significant NA NA NA
Orantinib NA NA NA NA NA NA NA
Tretinoin Significant Significant Significant Significant NA NA NA
Atorvastatin Significant NA NA NA NA NA NA
Dexamethasone Significant Significant Significant Significant NA NA NA
Curcumin NA NA NA NA NA NA NA
Fostamatinib Significant Significant NA Significant NA NA NA
Valproic-acid Significant NA NA NA NA NA NA
Glibenclamide Significant Significant NA NA NA NA NA
Phenethyl Isothiocyanate Significant NA NA NA NA NA NA
Cyclosporin Significant NA NA NA NA NA NA
[101]Open in a new tab
^1 Significant: Drug-induced DE genes statistically significant
enrichment in scRNA profile; ^2 NA: Drug-induced DE genes statistically
no significant enrichment in scRNA profile.
3.5. Identification of Synergistic Drug Combinations
Based on the Complementary Exposure model, we identified 153 drug
combinations based on the 18 potential therapeutic candidates for
COVID-19. Among these combinations, 123 drug pairs were excluded due to
close drug–target modules
[MATH:
SAB<0 :MATH]
, while 30 drug combination conformed to the Complementary Exposure
Model and may therefore be effective in the treatment of COVID-19
([102]Table 3).
Table 3.
All predicted possible combinations for COVID-19.
Drug A Drug B Drug A Common. Name Drug B Common.Name
[MATH: SAB
:MATH]
[MATH: ZDA
:MATH]
[MATH: ZDB
:MATH]
DB01069 DB12072 Promethazine Orantinib 0.76 −2.58 −2.53
DB12072 DB00313 Orantinib Valproic acid 0.67 −2.53 −2.99
DB12072 DB00755 Orantinib Tretinoin 0.66 −2.53 −2.44
DB00755 DB12010 Tretinoin Fostamatinib 0.66 −2.44 −3.68
DB00622 DB12072 Nicardipine Orantinib 0.60 −2.81 −2.53
DB01115 DB12072 Nifedipine Orantinib 0.57 −2.71 −2.53
DB12072 DB01234 Orantinib Dexamethasone 0.54 −2.53 −3.40
DB01069 DB04960 Promethazine Tipifarnib 0.49 −2.58 −2.35
DB12695 DB00091 Phenethyl Isothiocyanate Cyclosporine 0.43 −3.22 −2.67
DB04960 DB12695 Tipifarnib Phenethyl Isothiocyanate 0.43 −2.35 −3.22
DB00675 DB12072 Tamoxifen Orantinib 0.42 −3.40 −2.53
DB01069 DB12010 Promethazine Fostamatinib 0.42 −2.58 −3.68
DB12072 DB01016 Orantinib Glyburide 0.40 −2.53 −2.90
DB00641 DB12072 Simvastatin Orantinib 0.39 −4.37 −2.53
DB12072 DB00091 Orantinib Cyclosporine 0.37 −2.53 −2.67
DB02709 DB12072 Resveratrol Orantinib 0.37 −3.91 −2.53
DB12072 DB01076 Orantinib Atorvastatin 0.37 −2.53 −4.23
DB01069 DB01076 Promethazine Atorvastatin 0.37 −2.58 −4.23
DB01069 DB12695 Promethazine Phenethyl Isothiocyanate 0.34 −2.58 −3.22
DB00608 DB12072 Chloroquine Orantinib 0.34 −3.31 −2.53
DB01069 DB02709 Promethazine Resveratrol 0.33 −2.58 −3.91
DB12072 DB12695 Orantinib Phenethyl Isothiocyanate 0.30 −2.53 −3.22
DB01069 DB11672 Promethazine Curcumin 0.26 −2.58 −2.81
DB01016 DB12695 Glyburide Phenethyl Isothiocyanate 0.18 −2.90 −3.22
DB12010 DB12695 Fostamatinib Phenethyl Isothiocyanate 0.17 −3.68 −3.22
DB00622 DB12695 Nicardipine Phenethyl Isothiocyanate 0.16 −2.81 −3.22
DB04960 DB00755 Tipifarnib Tretinoin 0.14 −2.35 −2.44
DB01076 DB12695 Atorvastatin Phenethyl Isothiocyanate 0.11 −4.23 −3.22
DB11672 DB12695 Curcumin Phenethyl Isothiocyanate 0.06 −2.81 −3.22
DB00608 DB11672 Chloroquine Curcumin 0.04 −3.31 −2.81
[103]Open in a new tab
One notable potential drug combination was fostamatinib
[MATH: F :MATH]
plus tretinoin
[MATH: T :MATH]
. Fostamatinib
[MATH:
ZDF=−3.68 :MATH]
and tretinoin
[MATH:
ZDT=−2.44 :MATH]
targets were both overlapped with the COVID-19 disease module,
indicating that the drug combination might have a therapeutic effect on
the disease. At the same time, the targets of fostamatinib and
tretinoin were independent with network-based separation
[MATH:
SFT>0 :MATH]
, and therefore fit the Complementary Exposure pattern ([104]Figure
4a). We also used the Sankey diagram to represent the interactions
among drug–target-disease ([105]Figure 4b). Apart from the drug
directly targeting COVID-19 related genes, un-targetable drug–disease
effects were present due to the drug–target interaction with COVID-19
related genes in the PPI as reflected by the proximity scores.
Additionally, take promethazine
[MATH: P :MATH]
and nicardipine
[MATH: N :MATH]
as a counterexample. Promethazine (
[MATH:
ZDP=−2.58 :MATH]
) and nicardipine
[MATH:
ZDN=−2.81 :MATH]
targets fell into the Overlapping Exposure with the COVID-19 disease
module. Although promethazine and nicardipine showed effective
treatment on the disease, overlapping drug pair
[MATH:
SPN<0 :MATH]
was not a synergistic drug pair due to adverse effects such as
overlapping drug toxicity ([106]Figure 4c). Additionally, sharing
targets of promethazine and nicardipine meant the drug pair had limits
in treatment from different therapeutic pathways ([107]Figure 4d).
Figure 4.
Figure 4
[108]Open in a new tab
Network-based stratification of hypertensive drug combinations. (a) A
network-based separation of a drug pair, fostamatinib (F), and
tretinoin (T). For
[MATH:
ZDF<0 :MATH]
and
[MATH:
ZDT<0 :MATH]
, the drug–target module of fostamatinib (F) and tretinoin (T) was
overlapped with the disease module (D). For
[MATH:
SFT>0 :MATH]
, the two sets of drug targets are separated topologically.
Fostamatinib and tretinoin targets both separately hit the COVID-19
module, which was captured by the Complementary Exposure pattern. The
disease module in orange (D) included disease-related genes (nodes) and
their undirected and unweighted interactions (links), while the drug
module (F or T) in blue (green) included drug–targets (nodes) and their
undirected and unweighted interactions (links). (b) Sankey diagram
visualizes drug pairs’ mechanism hypothesis: drugs are on the left, and
COVID-19 related genes are right. Links show drugs that were mapped
onto COVID-19 related genes through drug–target associations and human
protein-protein interaction. (c) Nicardipine (N) and Promethazine (P)
drug–target modules overlapped the network. For
[MATH:
SPN<0 :MATH]
, the two sets of drug targets were Overlapping Exposure, which meant
more adverse effects and less efficacy compared to the Complementary
Exposure pattern. (d) Sankey diagram showed how drug–targets of
Nicardipine and Promethazine overlapped and interacted with related
genes.
4. Discussion
This study used a network-based drug repurposing combined with a
transcriptomics strategy to identify potential drug candidates and drug
pairs for COVID-19 treatment. The joint analysis of the proximity of
drug–target relationship modules, SARS-CoV-2 genomics, transcriptomics,
and synergistic drug effects could overcome the limitations of
analyzing data from only network distance or transcriptome and improve
drug candidate prediction. We proposed 18 drugs and 30 drug
combinations including broad-spectrum antiviral agents, receptor
antagonists, channel blockers, and renin-angiotensin system agents.
Some medications such as dexamethasone, chloroquine, curcumin
[[109]41], glyburide [[110]42], tretinoin [[111]43,[112]44],
cyclosporine [[113]45,[114]46], valproic acid [[115]47], fostamatinib
[[116]48,[117]49], atorvastatin [[118]50,[119]51,[120]52], and
phenethyl-isothiocyanate [[121]53] have recently received major
attention for the treatment of COVID-19 and have been validated by
previous studies, supporting the reliability of our findings.
Nicardipine, promethazine, orantinib, and tipifarnib have not
previously been reported as potential treatments for COVID-19.
Therefore, we will discuss these novel drug candidates in the
following.
* Nicardipine
With a similar structure to nifedipine (Z = −2.68), nicardipine (Z =
−2.75) was initially developed to regulate high blood pressure as a
dihydropyridine calcium channel blocker [[122]54]. Nifedipine is
indicated to potentially be effective in the treatment regimens of
elderly patients with hypertension hospitalized with COVID-19
[[123]55,[124]56]. Therefore, nicardipine might play a similar role
with nifedipine in the adjuvant treatment of COVID-19 patients.
* Promethazine
Promethazine (Z = −5.65) antagonizes various receptors including
dopaminergic, histamine, and cholinergic receptors, and is commonly
used for indications such as allergic conditions, motion sickness,
sedation, nausea, and vomiting [[125]57]. The proximity score of
promethazine was significantly low partly by targeting genes including
CALM1, KCNS1, LPAR4, LPAR6, P2RY12, P2PY8, and P2RX5, which were DEGs
between T cell subsets of COVID-19 samples and healthy controls.
Characteristics of the bronchoalveolar immune genes have been explored
as potential mechanisms underlying pathogenesis in COVID-19 [[126]58].
These findings implied that promethazine might be effective for
COVID-19 by regulating the immune cell microenvironment.
* Orantinib and Tipifarnib
Orantinib (Z = −2.54) showed preliminary efficacy and safety in
advanced hepatocellular carcinoma [[127]59]. Tipifarnib (Z = −2.40) was
studied in the treatment of acute myeloid leukemia (AML) and other
types of cancer [[128]60]. Although orantinib and tipifarnib are both
not yet approved by the FDA, anticancer drugs identified by our study
such as phenethyl isothiocyanate have been reported to be an effective
treatment strategy to treat COVID-19 [[129]53]. Drug repurposing
against COVID-19 focused on anticancer agents was previously predicted
to be effective and it was speculated that drugs interfering with
specific cancer cell pathways may be effective in reducing viral
replication [[130]61]. Therefore, the anticancer drugs orantinib and
tipifarnib might also be potential candidates for the treatment of
COVID-19.
In contrast with our results, tamoxifen (Z = −4.75) was reported to
increase the COVID-19 risk due to its anti-estrogen and P-glycoprotein
inhibitory effects [[131]62]. Data from previous experiments suggested
that estrogen could regulate the expression of angiotensin-converting
enzyme 2 (ACE2) [[132]63], which was reported to be the critical
natural cellular receptor for SARS-CoV-2 and was an important factor
for infection. However, a recent study discussed the uncertain effects
of RAS blockers on ACE2 levels and activity in humans and proposed an
alternative hypothesis that ACE2 might more likely be beneficial than
harmful in patients with lung injury [[133]64]. The controversies of
ACE2 system inhibition attempt to explain the relationship between the
virus and the RAS [[134]65], but existing research is too limited to
support or refute these hypotheses. Our research suggested that
tamoxifen may influence cytokine storm syndrome by regulating
cytokine-mediated signaling pathways (ES = 0.67, P = 0.14), which is a
severe clinical symptom of COVID-19 [[135]66,[136]67]. Several studies
have indicated that tamoxifen could reduce cytokines to normal levels
and it has been demonstrated to be beneficial for inflammation in rats
[[137]68,[138]69]. Overall, we recommend that tamoxifen may protect
against cytokine storms and alleviate ARDS in COVID-19 patients as well
as reduce the incidence of critical illness and mortality.
There are some limitations to our strategy. First, the proximity
calculation regards proteins interaction as nodes and links, which may
not completely capture important information about the interaction
types. Second, the LINCS and DrugBank databases are only partly
matched, and therefore many important drug candidates may be ignored.
Additionally, some of the potentially interesting drugs, such as
alemtuzumab (Z = −3.27), were not able to be included in the final
screening. Third, although THP-1 cells might be a useful tool in the
research of monocyte and macrophage-related mechanisms [[139]70],
heterogeneity still exists in the gene expression profile of the
mononuclear cells of COVID-19 patients and THP-1 cells. Additionally,
considering that the impaired function of heart, brain, lung, and liver
were complications of COVID-19 [[140]71], more types of
infection-related cell lines could be taken into account to fully
investigate drugs and treatment outcome on COVID-19.
In conclusion, our effective drug repurposing strategy combined
network-based pharmacology and transcriptomes methods to identify 18
potential COVID-19 drugs, and recommend 30 drug combinations. Although
several candidate repurposing drugs were previously reported to have
the anti-COVID-19 effect, four drugs such as nicardipine, promethazine,
orantinib, and tipifarnib were recommended for the first time in
COVID-19 treatment. Additionally, based on our repurposing drug
sensitivity analysis, DE genes of most repurposing drugs were enriched
significantly in B cells. Our analysis contributed to guide and
accelerate research in COVID-19 drug development, and this method would
be kindly applicable for drug repurposing research in future complex
diseases. However, the identified drug candidates still require future
experimental validation and large-scale clinical trials before their
use in COVID-19 management.
Acknowledgments