11footnotetext:  National Institute of Research and Development for Biological
Sciences, Bucharest, Romania 22footnotetext:  Faculty of Mathematics and
Computer Science, University of Bucharest, Bucharest, Romania 33footnotetext:
Department of Information Technologies, Åbo Akademi University, Turku, Finland
44footnotetext:  Department of Computer Science, Technical University of
Madrid, Spain 55footnotetext:  Faculty of Administration and Business,
University of Bucharest, Bucharest, Romania 66footnotetext:  Department of
Mathematics and Statistics, University of Turku, Finland 11footnotetext:
Address for correspondence<EMAIL_ADDRESS>
# Network analytics for drug repurposing in COVID-19

Nicoleta Siminea1,2 Victor Popescu3 Jose Angel Sanchez Martin4 Daniela Florea1
Georgiana Gavril1 Ana-Maria Gheorghe1 Corina Itcus1 Krishna Kanhaiya3 Octavian
Pacioglu1 Laura Ioana Popa1 Romica Trandafir1 Maria Iris Tusa1 Manuela
Sidoroff1 Mihaela Păun1,5 Eugen Czeizler3,1 Andrei Păun1,2 Ion Petre6,1,∗

###### Abstract

To better understand the potential of drug repurposing in COVID-19, we
analyzed control strategies over essential host factors for SARS-CoV-2
infection. We constructed comprehensive directed protein-protein interaction
networks integrating the top ranked host factors, drug target proteins, and
directed protein-protein interaction data. We analyzed the networks to
identify drug targets and combinations thereof that offer efficient control
over the host factors. We validated our findings against clinical studies data
and bioinformatics studies. Our method offers a new insight into the molecular
details of the disease and into potentially new therapy targets for it. Our
approach for drug repurposing is significant beyond COVID-19 and may be
applied also to other diseases.

Keywords: COVID-19, drug repurposing, network biology, network
controllability, host factors.

##  1 Introduction

The COVID-19 pandemic has caused more than 159 million infections worldwide,
with more than 3.3 million deaths (as of May 2021) [1]. Several vaccines are
available since the fall 2020 and this has helped curtail the infection rate
worldwide. Many drugs are investigated in clinical trials, and several have
been approved or recommended for use, including remdesivir, dexamethasone, and
some combinations of monoclonal antibodies. It remains of major interest to
gain a system-level understanding of the molecular details of the disease and
to translate them into effective treatment strategies for the disease. Such
data is increasingly available, for example on the human proteins that
associate with SARS-CoV-2 proteins upon infection [2] and the host genes
critical for the infection with SARS-CoV-2 [3]. Computational methods can
integrate such data into comprehensive models to help identify promising drugs
and drug combination candidates [4].

The recent results of [3] offer a dataset with a strong therapeutical
potential. Through a number of genome-scale loss-of-function screens, they
identified and ranked host factors required for the SARS-CoV-2 viral
infection, i.e., genes whose loss of function confers resistance to the
infection. They identified the relevant host factors both at a lower viral
load of MOI 0.01 and a higher one of MOI 0.3. Out of the 200 top ranked genes,
only a small fraction are drug targetable [5] (24 from the MOI 0.01 host
factors and 23 from the MOI 0.3 ones). We asked whether more of the host
factors can be targeted through drugs acting upstream of them. To investigate
this question we built two directed protein-protein interaction networks (one
for each MOI dataset) integrating interaction data upstream of the essential
SARS-CoV-2 host factors (separately for MOI 0.01 and MOI 0.3) and interaction
data downstream of all currently available drug targets from DrugBank [5]. We
analyzed the networks in the framework of control theory [6] and sought to
identify minimal combinations of drug targets that offer control (through
cascading signals in the interaction network) over the essential host factors
(Figure 1). We focused on short control paths from drug targets to host
factors to minimize the possible dissipation of the drug’s influence along the
path. Control here is understood in the sense of structural network
controllability and its results offer a systemic view on how to influence the
host factors simultaneously through available drug targets. Moreover, the
resulting drug targets (and the drugs acting on them) can be ranked with
respect to how many host factors each can control in the network,
independently of the other drug targets. Combining these results offers a
number of drugs and drug combinations that are potentially efficient at
influencing the host factors. The results of structural controllability are
qualitative: they offer therapeutically promising drugs and drug combinations,
but do not offer numerical indications on the optimal concentrations and
possible toxicities. We compared our findings with clinical trials data and
bioinformatics analyses. We found a number of drugs that have been
investigated in clinical trials, but also some new ones, not yet studied in
connection to COVID-19. Our key advance, therefore, is a new system-level
insight into the molecular details of the disease that is able to offer a
significant number of potentially efficient therapies based on drug
repurposing.

Figure 1: Network controllability for drug repurposing: study design. We
included all the approved and investigational small molecule drugs, except for
those illicit or nutraceutical. For each drug (red in the bottom-left network)
we identified their drug targets ([5]) (yellow in the bottom-left network). We
also included the top 200 host factors required for SARS-CoV-2 infection found
by [3] for viral loads MOI 0.01 and MOI 0.3, and all protein-protein directed
interactions from KEGG, OmniPath and Signor. Using the _NetControl4BioMed_
platform ([7]) we identified all the control paths of length at most 3 from
drug targets to the host factors. We ranked the drugs based on the number of
host factors they can control through any of their targets.

##  2 Results

###  2.1 COVID-19–specific directed protein-protein interaction networks

We constructed directed protein-protein interaction (PPI) networks around the
host factors identified in [3] to be required for SARS-CoV-2 infection. We
constructed two different interaction networks, one for each set of top-ranked
host factors reported by [3], one obtained at a low (0.01) multiplicity of
infection (MOI), the other at a high (0.3) MOI. From each experiment we
included the top 200 ranked genes, whose loss-of-function mutations led to
enrichment in their pools. We also included all drug targets from DrugBank
[5]. For the interaction data we used KEGG [8], OmniPath [9] and SIGNOR [10].
We only included the directed protein-protein interactions found in these
databases. A description of how the networks were generated is in the
supplementary information, Section 6.1 (Figure 4 for an illustration and Table
2 for an overview of their topological properties). They were well connected
(a single connected component in one, two in the other), compact (diameter
equal to 10), and rich in interactions (over 20,000 interactions). Each
network included more than 1000 drug targets and about one third of the top
200 host factors (70 for MOI 0.01 and 62 for MOI 0.3; the others were further
from the drug targets in our networks).

We analyzed the topology of our networks using several centrality measures:
degree (total degree, in-degree, and out-degree) centrality, closeness-,
betweenness-, and harmonic-centrality [11]. We identified the top $3$ ranked
proteins in each network, based on each of these centrality measures, with the
results collected in the supplementary information, Table 3. The top-ranked
proteins were virtually identical for both networks, with only one difference
in the proteins with the highest out-degree: GSK3B was ranked third in the MOI
0.01 network, while CDK1 was similarly ranked in the MOI 0.3 network.

The proteins with the highest total- and out- degree were PRKCA, SRC and
GSK3B, out of which SRC and GSK3B have been shown to be potential targets for
COVID-19 ([12]). Another protein of interest is TP53, which was top 2-ranked
in all the other centrality measures. TP53 is known to be active in down-
regulating the SARS-CoV replication ([13]). Other highly ranked central
proteins include AKT1, shown to be of significance to SARS-CoV-2 ([14]), CDK1,
identified as potential therapeutic option in for COVID-19 ([15]), and CTNNB1,
linked to co-morbidities associated with COVID-19 severity ([16]).

###  2.2 Network-based identification of repurposable drug targets

We performed target controllability analysis on the interaction networks,
considering as control targets the MOI-specific top 200 host factors of [3].
We used as preferred control inputs the drug-targetable genes from DrugBank
[5]. In each analysis we identified the drug(s) whose targets control the most
host factors. The analysis is based on a stochastic search for paths from drug
targets to host factors and so, repeated analyses identified multiple results
on the same network. For each network, we repeated the analysis until three
consecutive runs identified no new drugs. The analyses were run on the
_NetControl4BioMed_ platform ([7]).

We found that the host factors can be controlled though 35 drug-targets for
the MOI 0.01 network and through 15 for the MOI 0.3 network (see Figure 9 in
the supplementary information), of which 10 are common for both networks:
ACTB, AKT1, ATM, ATP6AP1, CSNK2A1, CDK2, EGF, MAPK14, MTOR and TP53. Of these
10, two are essential host factors for the SARS-CoV-2 infection [3]: ACTB (in
the MOI 0.3 experiment) and ATP6AP1 (in both MOI experiments). Additionally,
ATP6AP1 is also known to interact directly with SARS-CoV-2 [2]. We identified
$116$ drugs from the analysis of the MOI 0.01 network acting on these targets
and $55$ drugs from the analysis of the MOI 0.3 network, with $41$ common to
both of them (see Figure 5 in the supplementary information for the different
categories of drugs).

We found that not all host factors can be controlled by repurposed drugs: only
44 MOI 0.01 host factors and only 28 MOI 0.3 host factors were included in the
control results found by our analysis (see Figure 9 in the supplementary
information). In the MOI 0.01 analysis the gene that turned out to be the
easiest to control is GALT: it can be controlled by more than 10 drug-targets
(Figure 2). By inhibiting GALT, the galactose levels may increase ([17]), and
SARS-CoV-2 does not replicate in the presence of galactose ([18]). On the
other hand, the well-known ACE2 is one of the hardest to target: it can be
controlled by only one drug-target, namely AGT.

Figure 2: A-B: Drug-targets and the number of host factors they can control
(A: MOI 0.01, B: MOI 0.3). C-D: Host factors for the SARS-CoV-2 infection and
the number of drug-target genes that can be used to control them (C: MOI 0.01,
D: MOI 0.3).

Our analysis also identified the control paths from the drug targets to the
host factors. The control paths were identical for the MOI 0.01 and the MOI
0.3 data in several cases: ATP6AP1 (which is a host factor on both lists, as
well as a drug target), RAB7A (controllable by ATP6AP1 through the direct
interaction ATP6AP1 $\rightarrow$ RAB7A), UVRAG (controllable by MTOR through
MTOR $\rightarrow$ UVRAG), SRSF6 (controllable by EGF through EGF
$\rightarrow$ CLK1 $\rightarrow$ SRPK1 $\rightarrow$ SRSF6), and ATP6V1A
(controllable by both CSNK2A1 and MTOR through CSNK2A1 $\rightarrow$ YY1
$\rightarrow$ ATP6V1A, MTOR $\rightarrow$ YY1 $\rightarrow$ ATP6V1A), ACTR3
(controllable by ACTB through ACTB $\rightarrow$ CTTN $\rightarrow$ ACTR3).
Three of these interact with SARS-CoV-2: ATP6AP1 with nsp6, ATP6V1A with M,
and RAB7A with nsp7 [2], while several others are relevant for other viruses
(UVRAG, SRSF6, ATP6AP1, RAB7A, ATP6V1A, ACTR3).

A ranking of the theoretical efficacy of a drug target can be done on the
basis of how many host factors was found to control in our analyses (Figure
2A-B). Also, a ranking of how easily controllable a host factor is can be done
on the basis of how many drug targets can control it (Figure 2C-D). For the
MOI 0.01 host factors, the top-ranked drug target was MAPK8, which controls 7
host factors. Interestingly, MAPK8 was not identified as a control input in
our analyses for the MOI 0.3 list of host factors. For MAPK8 we found two
drugs acting on it: minocycline and tamoxifen. The top-ranked drug targets
occurring in both sets of results is MTOR, controlling 5 host factors from
each list, and ATM, controlling 4 host factors from each list. There are
several drugs that act on MTOR: everolimus, pimecrolimus, ridaforolimus,
rimiducid, SF1126, sirolimus, temsirolimus and XL765. For the 0.3 MOI list of
host factors, the top-ranked drug target, in addition to MTOR, is BCL2, that
regulates cell death and attenuates inflammation ([17]). The following drugs
act on this target: apoptone, dexibuprofen, docetaxel, eribulin, ibuprofen,
isosorbide, navitoclax, obatoclax, paclitaxel, paclitaxel docosahexaenoic
acid, rasagiline and venetoclax.

###  2.3 Potentially repurposable drugs

Using data from DrugBank ([5]) we found 130 drugs acting on the drug targets
resulting from the two MOI datasets and their corresponding control analyses,
including 41 that act on the 10 drug-targets common to the two analyses. The
results are listed in the supplementary information (Table 2). Of these drugs,
72 are approved for at least one condition according to [5]. Most are drugs
used in oncology, but we have also found direct inhibitors of thrombin, anti-
inflammatory, estrogens, and mood-stabilizers. In terms of their cellular
location of action, most drug targets are located in the surrounding of
cytoplasmic vesicles, and in the cell-substrate junction between the cell and
extracellular matrix (see Figure 8 in the supplementary information).

#### Antineoplastic and immunomodulating agents.

Most drugs identified in our analyses are antineoplastic agents and, within
this group, most of them are protein kinase inhibitors. Some of the drugs we
identified are not yet approved, and therefore do not have ATC codes (e.g.
alvocidib, gensitein, pelitinib, seliciclib). The approved drugs are
summarized in Table 1, grouped by their targets in our networks. Some of these
drugs were included in this list through some of their secondary targets (e.g.
brigatinib on INSR, and dasatinib on MAPK14), and other drugs are included
through several of their targets, potentially indicating increased efficacy
(e.g. bosutinib and brigatinib).

Table 1: The top-ranked antineoplastic and immunomodulating agents identified in our analyses and their targets. Control inputs | Drugs targeting the control inputs
---|---
ABL1 | bosutinib, brigatinib, dasatinib, imatinib, nilotinib, ponatinib, regorafenib
BRAF | dabrafenib, encorafenib, regorafenib, ripretinib, sorafenib, vemurafenib
CDK2 | bosutinib
EGFR | afatinib, brigatinib, dacomitinib, erlotinib, gefitinib, icotinib, lapatinib,
neratinib, olmutinib, osimertinib, vandetanib, zanubrutinib
INSR | brigatinib
JAK2 | entrectinib, fedratinib, ruxolitinib, zanubrutinib
MAPK14 | dasatinib
MTOR | everolimus, ridaforolimus, temsirolimus
PRKCA | midostaurin

Some of these inhibitors have been investigated in connection with several
viruses, including SARS-CoV-2. The EGFR inhibitors, which are principally used
in non-small cell lung cancers or breast cancers, may act on SARS-CoV-2 virus
replication [19], while JAK2 inhibitors could act on SARS-CoV-2 cytokine storm
because IL-6 and GM-CSF, which are stimulated in this infection, depend on
JAK2 signaling [20]. The mTOR pathway can be targeted by many viruses (eg IAV,
MERS) to promote their replication. Its inhibition was shown to lead to a
decrease in SARS-CoV-2 virus production [14].

Other antineoplastic agents found in our analyses, docetaxel, paclitaxel,
eribulin, venetoclax, are not protein kinase inhibitors. They were identified
in the network corresponding to the MOI 0.3 experiment.

We also obtained selective immunosuppressants such as: baricitinib, sirolimus,
tofacitinib, and a calcineurin inhibitor, voclosporin. Sirolimus, a drug used
to prevent organ rejection in renal transplants and suggested for COVID-19
also in [21], was identified in both network analyses, due to its inhibitory
effect on MTOR, while baricitinib and tofacitinib were identified only in the
MOI 0.01 network analysis, due to their inhibitory action on JAK2. Both are
used in rheumatoid arthritis, and baricitinib has FDA approval for use in
COVID-19. We obtained voclosporin only for the MOI 0.3 network because of its
inhibitory effect on PPP3R1 subunit of calcineurin, which leads to blocking
the transcription of early inflammatory cytokines ([5]).

#### Antithrombotic agents

There is a known link between COVID-19 and coagulopathy: in many severe cases,
disseminated intravascular coagulation is observed [22]. In COVID-19, there is
an activation of the coagulation cascade which leads to an increase in the
amounts of fibrin that are deposited in various organs [23], which is
associated with higher mortality [24]. Our analysis revealed several compounds
in use as antithrombotic agents (argatroban, bivalirudin, dabigatran etexilate
and ximelagatran), all based on their inhibitory effect on prothrombin. We
also obtained investigational drugs (flovagatran, gabexate, nafamostat), and
an F2 agonist, in other words an antihemorrhagic (kappadione). Kappadione is a
vitamin K derivative, and vitamin K has an important role in activating both
pro- and anti-clotting factors in the liver, and extra-hepatic vitamin K
deficiency has been observed in COVID-19 patients [25].

One drug present on our list that cannot be used in patients with COVID-19 is
proflavine because it has only topical use as a disinfectant, and it is toxic
and carcinogenic in mammals [5]. It ends up being included in our results
through its targeted action on F2, that was found to control several host
factors.

#### Estrogens

Estrogens being included in our list is consistent with COVID-19 mortality
being higher in the elderly, but also in men compared to women [26]. One cause
of these differences may be estradiol, several of which are included in our
results. Estradiol regulates several pathways in the immune system [27]. Our
analysis revealed BECN1 as being relevant, a gene that plays a role in
autophagy and may also play a role in antiviral host defense [5]. Using these
drugs carries the risk of thromboembolism, even if they may increase the
expression/activity of ACE2 in the adipose tissue and in the kidney [28]. Some
studies recommend estradiol for further investigation: 68,466 cases were
analysed in [29] with the results indicating that estradiol decreased COVID-19
fatality. However, estradiol has multiple functions in the body, so its
potential adoption in COVID-19 should be cautiously verified further.

#### Other compounds

There are several natural compounds in our list such as resveratrol, caffeine,
genistein, ellagic acid, alvocidib, quercetin, emodin. Of these, alvocidib,
quercetin, resveratrol and genistein contains flavonoids. There are some
antiallergic, anti-inflammatory, antiviral, antioxidant activities reported
for some flavonoids [30], but also antithrombogenic effects because they can
increase fibrinolysis [31].

We also obtained lithium-based mood stabilizers as GSK3B inhibitors (lithium
carbonate and lithium citrate). Targeting GSK3B is also tideglusib, a drug
investigated for use in Alzheimer’s disease.

We also obtained some investigational compounds that act on MAPK14: KC706,
talmapimod, PH-797804, VX-702. Inhibition of p38/MAPK signaling is beneficial
in SARS, DENV and IAV, and in the case of SARS-CoV-2 it suppresses cytokine
production and affects viral replication [32]. PH-797804 was also discussed in
[33] for potential use to limit lung damage.

Isoprenaline is a heart stimulant, used especially in emergencies, in heart
shock, but can also be used by inhalation in asthma and chronic bronchitis. In
our results it came up through its inhibitory action on MAPK1.

Our analysis identified some compounds that should not be recommended for use
in practice. Antibiotics are usually not recommended in a viral infection. We
obtained minocycline, a drug from the class of tetracycline that is known to
be preferred to macrolides because its representatives are less toxic. Another
compound that is difficult to use is cefazolin, a cephalosporin that acts on
the Il2 target. Invalid compounds are also those that act as a topical cream
(for example, ingenol mebutate) or as eye drops (for example, netarsudil).
Sucralfate is another false positive because it does not act systemically, but
could help to prevent further transmission from the stomach to the intestine.

###  2.4 Drug combinations

We used the results of the target controllability analyses to identify
potential drug combinations (supplementary information, Table 3). For each
analysis, we identified the combinations of two and three drugs whose drug
targets control together the highest number of host factors. We only
considered the combinations of drugs whose sets of drug targets don’t fully
overlap (i.e., each drug has at least one specific drug target not shared with
the other drugs).

In the case of the MOI 0.01 network, we found 23 unique combinations of two
drugs with a maximum number of controlled host factors. Some of them included
aspirin and one of the drugs acting on MTOR (everolimus, pimecrolimus,
ridaforolimus, rimiducid, SF1126, sirolimus, temsirolimus, XL765). Other
combinations of aspirin are those with dasatinib, alvocidib, and phenethyl
isothiocyanate. Other drug combinations are centered on alvocidib, a drug
investigated for use in non-small lung cancer. Its combinations are with
enzastaurin, minocycline, perifosine, tamoxifen, phenethyl isothiocyanate and
quercetin. Enzastaurin and perifosine are AKT1 inhibitors and may be
associated with CDK4/6 inhibitors (two of the alvocidib targets).

In the case of the MOI 0.3 network, we found 42 combinations of two drugs with
a maximum number of controlled host factors. Dasatinib and ellagic acid are
used in multidrug-resistant tumors ([34]), while dasatinib plus quercetin has
been shown to be useful in relieving intestinal senescence and inflammation
([35]). Other combinations obtained are those with bosutinib and any of the
drugs that act on BCL2. Our analysis also gave BCL2 inhibitors associated with
SF1126 or sirolimus (both MTOR inhibitors), and this combination of BCL2 with
MTOR inhibitors is used in resistant acute lymphoblastic leukemia ([36]). The
other drug combinations include tesevatinib with MTOR inhibitors or caffeine.

For the MOI 0.01 network, we found 95 three-drug combinations with a maximum
number of controlled host factors, almost all centered on alvocidib. The fact
that alvocidib acts on several targets, quite different from other drugs,
explains why we got it in a large number of combinations. For the MOI 0.3
network, we found 69 three-drug combinations with a maximum number of
controlled host factors, almost all centered on BCL2 inhibitors.

###  2.5 Experimental and clinical validation

##### Validation using the NCATS OpenData|COVID-19 Portal.

Our first approach to validate the results was to search for them on the
OpenData Portal [37]. This data platform collects validation data on potential
COVID-19 drugs in terms of viral entry, viral replication, in vitro
infectivity, life virus infectivity and human cell toxicity. The platform
lists both complete and incomplete results, e.g., untested drugs, or drugs
with parts of the test results not yet available. We only focused on the
results on SARS-CoV-2, and excluded the analyzes made on MERS and SARS. We
also excluded human cell toxicology tests because we considered these types of
tests more as a selection measure between two drugs with similar effects and
not as a validation of activity because a drug can be inactive and can be very
toxic or, conversely, can be well tolerated. So, we included the Spike-ACE2
protein-protein interaction assay and counter-assay, ACE2, TMPRSS2 and 3CLpro
enzymatic activity tests and SARS-CoV-2 cytophatic effect and counter assay.

We divided our results into the five categories used in the portal. The first
category (in green in the Supplementary Table 1) is that of active drugs in at
least one of the following 5 tests: Spike-ACE2 protein-protein interaction,
SARS-CoV-2 cytophatic effect and enzymatic activity of ACE2, TMPRSS2 and
3CLpro. We also set the condition that, if it is active in a test, it should
be inactive in the corresponding counter-test. The second category (in yellow
in the Supplementary Table 1) contains active drugs in a counter assay,
regardless of its status in the corresponding assay. The third category (in
gray in the Supplementary Table 1) contains drugs that are inactive in all
tests. The fourth (in orange in the Supplementary Table 1) includes drugs that
haven’t been tested at all. Finally, the fifth category (in cream in the
Supplementary Table 1) contains partially tested drugs that have been found
inactive in the tests performed. We illustrated the distribution of our
results among these categories in Figure 3. The largest proportion is that of
the drugs active in counter-assays.

We found the following results in the “active drug” category: afatinib,
baricitinib, docetaxel, entrectinib, enzastaurin, erlotinib, fasudil,
gabexate, imatinib, minocycline, nafamostat, navitoclax, ponatinib,
proflavine, R-1487, ruxolitinib, sirolimus, suramin and venetoclax.

Figure 3: Drug classification based on their activity according to NCATS
OpenData|COVID-19 Portal [37]. A: drugs identified in either of the two MOI
networks, B: drugs identified in both MOI networks, C: drugs identified in the
MOI 0.01 network, D: drugs identified in the MOI 0.3 network. Color code:
green – active, orange – not tested, cream – inactive, but not tested in all
assays, gray – inactive, yellow – active in the counter assay.

We also found several drugs active both in the assay, as well as in the
counter-assay for the Spike-ACE2 protein-protein interaction, and several
drugs in the counter assay for cytopathic effect (Figure 6 in the
supplementary information).

We also investigated the results on the enzymatic activity of ACE2, TMPRSS2
and 3CLpro (Figure 7 in the supplementary information) and found two drugs
that act on ACE2 and TMPRSS2, in other words on viral entry, namely
ruxolitinib and fasudil. Ruxolitinib was obtained in our study on the MOI 0.01
network as a JAK2 inhibitor. It has been previously recommended for
investigation in COVID-19 ([38]) and it has been included in clinical trials
with little success ([39]). Fasudil was also obtained in the MOI 0.01 network,
targeting ROCK1 and PRKACA. It may help in the associated acute lung injury
and acute respiratory distress syndrome ([40]). It could also up-regulate ACE2
expression and down-regulate ACE expression ([41]). In addition to its
protective effect against lung damage, it possesses antifibrotic properties
and the ability to up-regulate ACE2 ([42]). We also found (in the MOI 0.3
network) a drug that acts on the enzymatic activity of ACE2 and 3CLpro:
venetoclax, a BCL2 inhibitor.

##### In vitro validation.

About a quarter of the drugs we identified were already reported in the
experimental literature. One of them is nafamostat, which inhibited mediated
entry into host cells with an efficiency approximately 15 times higher than
camostat mesylate ([43]), and also blocked infection of Calu-3 cells with an
effective concentration (EC) 50 around 10 nM, while a significantly higher
dose (EC50 around 30 $\mu$M) was required for VeroE6/TMPRSS2 ([44]). It was
the most potent drug for human lung cells (IC50 = 0.0022 $\mu$M), of the 24
drugs selected ([45]). In a small series of 11 cases, nafamostat mesylate
therapy in combination with favipiravir could block the entry and replication
of the virus as well as inhibit hypercoagulopathy ([46]).

Other drugs were tested on several cell types and were found to be active. For
example, nilotinib inhibits SARS-CoV-2 in Vero-E6 cells and Calu-3 cells
([47]). Another drug is obatoclax, which can inhibit SARS-Cov-2 replication in
vitro, in human nasal epithelial cells ([48]), and in Vero-E6 cells ([49]).
Tamoxifen showed a 100-fold reduction in viral load in human cells in vitro
([3]), but also showed inhibitory activity in Vero E6 cells ([50]).

Several of the drugs we identified have been reported to potentially act on
the SARS-CoV-2 replication: doramapimod ([51]), lapatinib ([51]), ponatinib
([52]). Imatinib has been shown to be an inhibitor of SARS-CoV-2 entry on a
lung organoid model using human pluripotent stem cells ([53]).

There are also drugs with conflicting results based on the type of cells used,
such as bosutinib which is active in Huh 7, active in Vero E6, but not as
potent, and inactive in Cacao-2 E6 and in iAEC2 [54].

BRAF inhibitors (dabrafenib, regorafenib and sorafenib) as well as baricitinib
lead to increased virus growth ([55]). Regorafenib may play a role in the
receptor mediated host response to SARS-CoV-2 [56], dabrafenib may inhibit
SARS-CoV-2 infection [57] and sorafenib could prevent in vitro replication
[19]. According to [56], sorafenib has antiviral activities, but the cytotoxic
and antiviral IC50 values are close. Baricitinib prevented progression to a
severe form by modulating the patient’s immune landscape in a 20-case study
[58] and its administration resulted in an improvement in respiratory function
in a study of 62 patients who received baricitinib and coticosteroids compared
with 50 patients who received only corticosteroids [59]. In another study [60]
in rhesus macaques, baricitinib suppressed the production of proinflammatory
cytokines, mentained innate antiviral responses and SARS-CoV-2 T cells, and
limited the recruitment of neutrophils to the lungs and neutrophil cell death
(NETosis).

##### Clinical trials.

We checked which of the drugs identified in our analyses have been included in
clinical trials for COVID-19. We looked for them first in the dedicated
COVID-19 section of [5]. We then verified ClinicalTrails.gov [61], but also
IRTC [62] to identify drugs that weren’t listed yet on DrugBank. Of the 130
drugs we identified in our analyses, 23 have been investigated in clinical
trials. We included in the supplementary material, for each of the validated
drugs, a selection of clinical trials dedicated to them. Of the 23 drugs
included in clinical trials, 14 were obtained only on the MOI 0.01 network, 8
on both networks and one, ibuprofen, only on MOI 0.3 network.

Ibuprofen was obtained due to its effect on BCL2. It is currently included in
two clinical trials (NCT04382768, NCT04334629) to be evaluated for its ability
to reduce the severity and progression of lung injury.

The followings drugs were obtained only on the MOI 0.01 network: argatroban,
baricitinib, bivalirudin, estradiol, estradiol cypionate, imatinib, lidocaine,
lithium carbonate, nafamostat, ruxolitinib, suramin, tamoxifen, tofacitinib,
zanubrutinib. Argatroban and bivalirudin may help in COVID-19 due to their
action on coagulation, especially when heparin resistance is involved [63],
[64]. Argatroban is included in NCT04406389 clinical trail together with
enoxaparin, fondapariniux and unfractioned heparin to establish if they can
reduce mortality, while bivalirudin is included in NCT04445935, to see if it
could prevent clotting better than low molecular weight heparin and support
the dissolution of existing clots. Nafamostat, another F2 inhibitor, could be
used due to its antiviral, anti-inflammatory and anti-coagulation properties
(NCT04418128). According to clinical studies, estradiol may control the
inflammmation (IRCT20150716023235N15), while lidocaine may impact the gas
exchange and inflammation in acute respiratory distress syndrome
(NCT04609865). Tofacitinib, a selective immunosuppressant used in rheumatoid
arthritis that is also being investigated for its use in preventing transplant
rejection [5], is included in clinical studies to find out if it is suitable
to moderate or severe cases (NCT04469114, NCT04415151). Imatinib may have some
antiviral properties due to lysosomal alkalization (NCT04394416), while
zanubrutinib may lower elevated levels of pro-inflammatory cytokines [65],
[66], and it is investigated as it could increase the respiratory failure-free
survival rate (NCT04382586). Lithium carbonate could also have an effect on
pro-inflammatory cytokines [67], and it is included in several clinical
trials, such as: CTRI/2020/06/026193, IRCT20081019001369N5,
IRCT20130812014333N147. Tamoxifen is administred in two clinical studies, but
only in combination with other drugs (NCT04389580, NCT04568096).

The drugs obtained on both networks are: acetylsalicylic acid, arsenic
trioxide, dasatinib, genistein, minocycline, quercetin, resveratrol and
sirolimus. Aspirin has been investigated in COVID-19 clinical trials for its
use in coagulation problems (NCT04363840, NCT04498273), but may inhibit virus
replication and reduce lung injury (NCT04365309). It inhibits the platelet
aggregation due to its action on COX-1. However, we did not identify it
through its effect on COX-1, but through its action on TP53, on RPS6KA3, on
PRKAA1, and on MAPK1. The only drug target obtained for both MOI network
analyses is TP53. The action on TP53 could be related to the aspirin-induced
inhibition of adipogenesis [68]; obesity can aggravate COVID-19.

Other clinical trials have mentioned possible effects for the followings
drugs: genistein (could be efficient in pulmonary fibrosis in patients
discharged from hospital (NCT04482595)), quercetin (may help because it has
antiviral activities in SARS infection and antioxidant properties
(NCT04377789)), resveratrol (could have anti-fibrotic effects (NCT04799743)),
sirolimus (may help in reducing lung injury/acute respiratory distress
syndrome (NCT04482712)), dasatinib (may help reduce the strong inflammation
(NCT04830735)).

##### Computational validation.

More than half of the drugs identified by our analyses were also found through
other computational approaches. We discuss them in the supplementary
information, Section 6.3.

##  3 Discussion

The recent study of [3] on the survivability of SARS-CoV-2 infected cells
identified many host factors that are essential for the SARS-CoV-2 infection.
This offers a new guide to therapeutic targeting of COVID-19. Yet, out of
their top 200 ranked genes in each of the two viral load experiments only 23
(24, resp.) are drug targetable [5]. We investigated whether they can be
instead targeted through short network signaling pathways and found 40 drug
targetable proteins that can control these genes. We identified 130 drugs that
target these proteins and can be significant in COVID-19. Some of these
include drugs recommended to be used, drugs that have been evaluated in
clinical trials and various in-vitro assays. The network-based approach offers
a wider spectrum of options by identifying drug targetable nodes at a short
distance upstream of the SARS-CoV-2 infection host factors, able to influence
them in the sense of network controllability. Moreover, we also identified in
this way possible mechanisms of action for drugs, offering a mechanistic
understandings of drugs through the changes they may induce in the protein-
protein interactions.

Many of the drugs we identified act on the immune system (including the FDA-
approved COVID-19 drug baricitinib), and on the coagulation cascade (e.g.
nafamostat). Antivirals were not found by our analyses because for many of
them (e.g. favipiravir, remdesivir, umifenovir) their human targets are
unknown. Our list of drugs also includes false positives, such as drugs that
are applied topically and should not be swallowed (e.g. ingenol mebutate), but
also possible harmful substances in this disease (kappadione). They are found
by our algorithms because they do influence the host factors, in addition to
their other effects. Our results could be further improved by leveraging data
on the quantitative strength of various interactions, on the result of their
concurrent activation/inhibition signals, and on the specificity of their
mechanism in the context of the disease. Such data would be needed for all the
protein-protein interactions in our network, not just for a selected subset of
them. A quantitative version of network controllability theory remains to be
developed to scale to become applicable to such data.

The network-based approach explored in this study can be applicable to other
diseases and can be especially fruitful in drug repurposing for rare diseases.
The key part of our approach is identifying a set of targets, whose control
may be therapeutically beneficial. In this study we used as control targets
the host factors required for SARS-CoV-2 infections. The network
controllability analysis yields a set of input nodes that can control these
targets. Moreover, the input nodes can be selected to a large extent to be
drug targetable with currently available drugs, bringing this method within
the drug repurposing realm. Each of the input nodes identified by the analysis
(and the drugs targeting them), as well as various combinations of them, can
be used to influence some of the control targets. This yields a rich set of
predictions that could inform the setup of new drug repurposing clinical
trials.

##  4 Materials and methods

###  4.1 Data

The signaling data was extracted from the KEGG [8], OmniPath [9] and SIGNOR
[10] databases. Only the directed interactions were considered. An interaction
can appear in multiple databases. The interactions were matched based on the
UniProt identifiers of their proteins, which are provided by default by all of
the three databases.

We considered as targets of our study the $200$ highest ranked host factors in
the cell survivability experiments of [3] at 0.01 MOI and 0.3 MOI that were
shown to be required for SARS-CoV-2 infection. These proteins were targeted
through short multi-step signaling paths originating in drug targets. The drug
targets were collected from the DrugBank [5] database. We selected the drug-
targets of the approved and investigational small molecule drugs, except for
those illicit or nutraceutical. We also discarded a number of specific $19$
approved and/or investigational drugs that have more than $50$ targets, most
of which are not targeted by any other drug.

###  4.2 Network generation

For each of the two MOI experiments of [3] we identified all proteins that are
upstream of the top 200 highest ranked host factors, at a distance of maximum
two interactions. We also identified all proteins that are downstream of the
drug-target proteins, at a distance of maximum two interactions. The network
generation is discussed in all details in the supplementary information,
Section 6.1.

###  4.3 Network analysis

We used the NetControl4BioMed web application ([7]) to apply the target
controllability analysis on the two generated networks using the greedy
algorithm described in [69]. The analysis setup is the same in both cases: we
use the set of host factors in each network as controllability targets and we
aim on identifying a minimal set of proteins that control the host factors,
while maximizing the number of drug-target proteins among them. We are
limiting the analysis to using control paths of maximum length $3$ (i.e. there
are at most three interactions between a drug-target and a host factor that it
controls), to minimize the potential dissipation of a drug’s effects along the
pathway. The analyses are stochastic, which means that the same input can lead
to different control solutions.

For each control analysis, we define the control score of a drug-target as the
number of host factors it controls in that analysis. We then define the
efficiency score of a drug as the maximum control score of any of its drug
targets. Thus, for each analysis, we identify the set of drugs with the
maximum efficiency score. The actual value of the maximum score may vary
between analyses. We then define the efficiency score of a drug as the maximum
control score of its set of drug targets within any of the stochastic analyses
we ran. Thus, in each analysis, we identify the drugs with the maximum
efficiency score. The actual value of the maximum score may vary between
analyses.

We run repeatedly the network controllability analysis for each network until
no new top ranked drug is found within three consecutive runs.

##  5 Data Availability

The drug-target data set used in this article, as well as the generated
networks, are available online in the GitHub repository [70].

## Acknowledgments

This work was partially supported by the Romanian Ministry of Education and
Research, CCCDI – UEFISCDI, project number PNIII-P2-2.1-PED-2019-2391, within
PNCDI III.

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##  6 Supplementary information

###  6.1 The COVID-19 directed protein-protein interaction networks

We constructed two directed protein-protein interaction (PPI) networks, one
for each of the datasets of [3] with the essential host factors required for
the SARS-CoV-2 infection at MOI 0.01 and MOI 0.3, resp. We narrowed the
interaction networks to include only the proteins upstream of the essential
proteins of [3] and downstream of the drug-targets from DrugBank [5], at a
distance of at most two interactions from either of these sets. We added
between these proteins all interactions we found in KEGG, OmniPath and SIGNOR.
Any essential protein or drug target that remained isolated in the network was
removed. This way, we narrowed the search for possible influences from drug-
targets to essential proteins to those acting along very short distances in
the networks. The networks are illustrated in Figure 4 and their topological
properties are in Table 2. The results of the centrality analysese of the
networks are in Table 3.

We then calculated the intersection of the two sets of protein identifiers,
obtaining all proteins that are on a signaling path from a drug target to an
essential gene, at a downstream distance from the drug targets at most 2 and
at an upstream distance from the essential genes at most 2. We obtained the
networks by considering all the directed interactions between these proteins,
the drug target proteins, and the essential proteins. The same method was
applied to both MOI datasets, the difference being in the sets of host
factors. This obviously also led to differences in the intermediate proteins
from the drug targets to the host factors.

Figure 4: Networks: A – MOI 0.01, B – MOI 0.3. Color code: light blue – control proteins, green – control and essential proteins, dark blue – host factors required for SARS-CoV-2 infection, red – drug-target proteins, purple – drug-target proteins that are also host factors, yellow – remaining proteins Table 2: Statistics of the two protein-protein directed interaction networks. N: number of nodes; E: number of edges; T: number of host factors in the network; DT: number of drug-targets; AD: network average degree; D: network diameter; CC: number of connected components. MOI | N | E | T | DT | AD | D | CC
---|---|---|---|---|---|---|---
0.01 | 2491 | 23746 | 70 | 1010 | 17.305 | 10 | 2
0.3 | 2532 | 24105 | 62 | 1008 | 17.296 | 10 | 1
Table 3: Top ranked proteins in our networks, based on their centrality measures Total degree | In-degree | Out-degree | Closeness | Betweenness | Harmonic
---|---|---|---|---|---
SRC | TP53 | PRKCA | TP53 | SRC | TP53
GSK3B | PTK2 | SRC | CTNNB1 | TP53 | CTNNB1
PRKCA | CTNNB1 | GSK3B/CDK1 | SRC | AKT1 | SRC

###  6.2 Target network controllability

Network controllability is about being able to induce arbitrary changes to the
variables of a network through suitable changes applied to a (small) set of
input nodes. It is a method applicable to linear networks. Such a network with
$n\geq 2$ nodes can be represented as an $n$-node directed (weighted) graph,
and also as a matrix $A\in\mathbb{R}^{n\times n}$ describing the weights of
each possible interaction between the nodes. A directed network is said to be
controllable from a set of input nodes if for any desired initial and final
numerical configurations of the nodes in the network, there exists a suitable
set of input functions applicable to the input nodes that induce the desired
change of configuration [71]. The methodology accommodates also the
possibility of imposing such a desired change of configuration to a target
subset of nodes in the network [72]. The target network controllability
problem asks to find a minimum set of input nodes controlling the target
subset.

Formally, we have a dynamical system defined by the system of differential
equations $dx(t)/{dt}=Ax(t)$, where $x:\mathbb{R}\rightarrow\mathbb{R}^{n}$
and $x(t)$ represents the configuration of the $n$ nodes at time $t$. Let $V$
define the $n$-set of nodes of the network. The set of input functions acting
on some size-$m$ subset $I\subseteq V$ of the network can be seen as an
$m$-dimensional input vector $u$ of real functions,
$u:\mathbb{R}\rightarrow\mathbb{R}^{m}$. Thus, the dynamical system under the
influence of these functions is formally described by the system

$dx(t)/{dt}=Ax(t)+B_{I}u(t),$ (1)

where $B_{I}\in\mathbb{R}^{n\times m}$ is the characteristic matrix associated
to set $I$.

Within this theoretical setting, the target controllability problem for a
dynamical system defined by matrix $A\in\mathbb{R}^{n\times n}$ and target set
$T\subseteq V$, $|T|=l$, is defined as follows: find the smallest $m\leq n$
and input set (a.k.a controller) $I\subseteq V$, $|I|=m$, such that for any
$x(0)\in\mathbb{R}^{n}$ and any $\alpha\in\mathbb{R}^{l}$, there is an input
vector $u:\mathbb{R}\rightarrow\mathbb{R}^{m}$ such that the solution
$\tilde{x}$ of (1) eventually coincides with $\alpha$ on its $T$-components,
i.e., $C_{T}\tilde{x}(\tau)=\alpha$, for some $\tau\geq 0$, where $C_{T}$ is
the characteristic matrix of the target set $T$. Although the problem is known
to be computationally hard [73], some efficient approximation algorithms were
provided in [73, 74].

Motivated by bio-medical applications of network controllability, a new layer
of optimization has been added to the above formulation. Namely, the input-
constrained target controllability problem [75, 69] asks to minimize the size
of the controller for a given target $T$ within a network, by maximizing in
the same time the use of so-called preferred input nodes within the
controller, i.e., elements of a previously defined subset $P\subseteq V$ of
preferred input nodes. Indeed such a subset $P$ can be associated in the bio-
medical setting to those proteins/genes known to be the targets of available
drugs. Thus, it is useful not only to minimize the controller of a targeting
subset of focused nodes, but in doing so to rely as much as possible to the
use of already available drugs. It is this particular setting of the
controllability problem that has been used as the underlying methodological
approach in this research.

###  6.3 Computational validation

More than half of the drugs we found were also reported by other computational
studies. Some of these drugs could have an inhibitory effect on the main
protease (e.g. dabigatran etexilate [76], [77], dasatinib [78], ellagic acid
[79], [80], radotinib [81]), on the papain-like protease (caffeine [82],
phenformin [83], ximelagatran [84]), or on the RNA-dependent RNA polymerase
(e.g. docetaxel [83], eribulin [85], nilotinib [86], pelitinib [87]), as well
as on the interaction between the spike and ACE2 (e.g. dexibuprofen [88],
midostaurin [89], paclitaxel [90], ponatinib [91], regorafenib [92]), or on
another viral important point (e.g. nilotinib on nsp13 [93], isoprenaline on
nsp9 [94], docetaxel on nsp14 [95], enzastaurin on nsp15 [96], entrectinib on
nsp16 [97]). There are also drugs that can act on host and virus interactions
(e.g. erlotinib [98], XL019 [99]) or only on host genes (e.g. lidocaine [100],
kappadione [101], [102], phenethyl isothiocyanate [101]). As we can see, a
drug can act on several points. For some of the drugs in our results we found
some proposed theories on their potential mode of action (e.g. bryostatin
[66], emodin [103], [104], [105] or ripretinib [106]). Others can be deduced
based on their targets, identified in other studies (e.g. flovagatran,
rimiducid, rindopepimut).

###  6.4 Supplementary figures

Figure 5: Drug categories. A: The overlap between all approved or
investigational drugs, B: The overlap between drug-targets in both networks,
C: The overlap between approved and investigational drugs, for networks
intersection and for set differences  Figure 6: The overlap between drugs
active in the assay and in the counter-assay. A: spike-ACE2 PPI in assay and
counter-assay, B: sytophatic effect in assay and counter-assay. Color code:
green for the drugs active in the assay, yellow for the drugs active in the
counter-assay. Figure 7: The effect on enzymatic activity: A \- Euler diagram
on enzymatic activity, B \- 3CLpro enzymatic activity, C \- TMPRSS2 enzymatic
activity, D \- ACE2 enzymatic activity. Figure 8: Localization at the cell
level [107], [108]. A: GO Cellular Component 2018, B: Manhattan plot. Figure
9: Correspondence between drug-target and SARS-CoV-2 host factors: A MOI 0.01,
B MOI 0.3. On the horizontal axis: the top 200 SARS-CoV-2 host factors ([3])
included in the interaction network. On the vertical axis: the drug targets
included in the interaction network. A black rectangle marks a drug target
identified by our analysis to control the corresponding host factor.