A data-driven drug repositioning framework discovered a potential therapeutic agent targeting COVID-19

Preprint (PDF Available) · March 2020with 575 Reads 
How we measure 'reads'
A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. Learn more
DOI: 10.1101/2020.03.11.986836
Cite this publication
Preprints and early-stage research may not have been peer reviewed yet.
ResearchGate Logo

This preprint is featured on the COVID-19 research community page

View COVID-19 community
Abstract
The global spread of SARS-CoV-2 requires an urgent need to find effective therapeutics for the treatment of COVID-19. We developed a data-driven drug repositioning framework, which applies both machine learning and statistical analysis approaches to systematically integrate and mine large-scale knowledge graph, literature and transcriptome data to discover the potential drug candidates against SARS-CoV-2. The retrospective study using the past SARS-CoV and MERS-CoV data demonstrated that our machine learning based method can successfully predict effective drug candidates against a specific coronavirus. Our in silico screening followed by wet-lab validation indicated that a poly-ADP-ribose polymerase 1 (PARP1) inhibitor, CVL218, currently in Phase I clinical trial, may be repurposed to treat COVID-19. Our in vitro assays revealed that CVL218 can exhibit effective inhibitory activity against SARS-CoV-2 replication without obvious cytopathic effect. In addition, we showed that CVL218 is able to suppress the CpG-induced IL-6 production in peripheral blood mononuclear cells, suggesting that it may also have anti-inflammatory effect that is highly relevant to the prevention immunopathology induced by SARS-CoV-2 infection. Further pharmacokinetic and toxicokinetic evaluation in rats and monkeys showed a high concentration of CVL218 in lung and observed no apparent signs of toxicity, indicating the appealing potential of this drug for the treatment of the pneumonia caused by SARS-CoV-2 infection. Moreover, molecular docking simulation suggested that CVL218 may bind to the N-terminal domain of nucleocapsid (N) protein of SARS-CoV-2, providing a possible model to explain its antiviral action. We also proposed several possible mechanisms to explain the antiviral activities of PARP1 inhibitors against SARS-CoV-2, based on the data present in this study and previous evidences reported in the literature. In summary, the PARP1 inhibitor CVL218 discovered by our data-driven drug repositioning framework can serve as a potential therapeutic agent for the treatment of COVID-19.
Figures - uploaded by Chunhao Yang
Author content
All content in this area was uploaded by Chunhao Yang
Content may be subject to copyright.
A data-driven drug repositioning framework discovered a
potential therapeutic agent targeting COVID-19
Yiyue Ge1,2,, Tingzhong Tian1,2,, Suling Huang3,, Fangping Wan1,, Jingxin Li2,,
Shuya Li1, Hui Yang11, Lixiang Hong1, Nian Wu1, Enming Yuan1, Lili Cheng4, Yipin
Lei11, Hantao Shu1, Xiaolong Feng6,7, Ziyuan Jiang5, Ying Chi2, Xiling Guo2, Lunbiao
Cui2, Liang Xiao10, Zeng Li10 , Chunhao Yang3, Zehong Miao3, Haidong Tang4, Ligong
Chen4, Hainian Zeng11, Dan Zhao1,* , Fengcai Zhu2,8,*, Xiaokun Shen10,*, Jianyang
Zeng1,9,*
1Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, 100084, China.
2NHC Key laboratory of Enteric Pathogenic Microbiology, Jiangsu Provincial Center for Diseases
Control and Prevention, Nanjing, Jiangsu Province, 210009, China.
3Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China.
4School of Pharmaceutical Sciences, Beijing Advanced Innovation Center for Structural Biology,
Tsinghua University, Beijing, 100084, China.
5Department of Automation, Tsinghua University, Beijing, 100084, China.
6School of Electronic Information and Communications, Huazhong University of Science and
Technology, Wuhan, Hubei Province, 430074, China.
7Institute of Pathology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and
Technology, Wuhan, Hubei Province, 430030, China.
8Center for Global Health, Nanjing Medical University, Nanjing, Jiangsu Province, 210009, China.
9MOE Key Laboratory of Bioinformatics, Tsinghua University, Beijing, 100084, China.
10Convalife (Shanghai) Co., Ltd., Shanghai, 201203, China.
11Silexon AI Technology Co., Ltd., Nanjing, Jiangsu Province, 210033, China.
These authors contributed equally to this work.
*Corresponding authors.
Abstract
The global spread of SARS-CoV-2 requires an urgent need to find effective therapeu-
tics for the treatment of COVID-19. We developed a data-driven drug repositioning
framework, which applies both machine learning and statistical analysis approaches
to systematically integrate and mine large-scale knowledge graph, literature and tran-
scriptome data to discover the potential drug candidates against SARS-CoV-2. The
retrospective study using the past SARS-CoV and MERS-CoV data demonstrated that
our machine learning based method can successfully predict effective drug candidates
Email addresses: zhaodan2018@tsinghua.edu.cn (Dan Zhao), jszfc@vip.sina.com (Fengcai
Zhu), steve.shen@convalife.com (Xiaokun Shen), zengjy321@tsinghua.edu.cn (Jianyang Zeng)
March 11, 2020
author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint (which was not peer-reviewed) is the. https://doi.org/10.1101/2020.03.11.986836doi: bioRxiv preprint
against a specific coronavirus. Our in silico screening followed by wet-lab validation
indicated that a poly-ADP-ribose polymerase 1 (PARP1) inhibitor, CVL218, currently
in Phase I clinical trial, may be repurposed to treat COVID-19. Our in vitro assays
revealed that CVL218 can exhibit effective inhibitory activity against SARS-CoV-2
replication without obvious cytopathic effect. In addition, we showed that CVL218
is able to suppress the CpG-induced IL-6 production in peripheral blood mononuclear
cells, suggesting that it may also have anti-inflammatory effect that is highly relevant to
the prevention immunopathology induced by SARS-CoV-2 infection. Further pharma-
cokinetic and toxicokinetic evaluation in rats and monkeys showed a high concentration
of CVL218 in lung and observed no apparent signs of toxicity, indicating the appealing
potential of this drug for the treatment of the pneumonia caused by SARS-CoV-2 infec-
tion. Moreover, molecular docking simulation suggested that CVL218 may bind to the
N-terminal domain of nucleocapsid (N) protein of SARS-CoV-2, providing a possible
model to explain its antiviral action. We also proposed several possible mechanisms to
explain the antiviral activities of PARP1 inhibitors against SARS-CoV-2, based on the
data present in this study and previous evidences reported in the literature. In sum-
mary, the PARP1 inhibitor CVL218 discovered by our data-driven drug repositioning
framework can serve as a potential therapeutic agent for the treatment of COVID-19.
1. Introduction
The outbreak of the pneumonia named COVID-19 caused by the novel coronavirus
SARS-CoV-2 (2019-nCoV) has infected over 110,000 people worldwide by 8th March,
2020. Apart from China, other countries or regions including South Korea, Iran, and
Europe have reported a rapid increase in the number of COVID-19 cases, implying that
this novel coronavirus has posed a global health threat. Under the current circumstance
of the absence of the specific vaccines and medicines against SARS-CoV-2, it is urgent
to discover effective therapies especially drugs to treat the resulting COVID-19 disease
2
author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint (which was not peer-reviewed) is the. https://doi.org/10.1101/2020.03.11.986836doi: bioRxiv preprint
and prevent the virus from further spreading. Considering that the development of a
new drug generally takes years, probably the best therapeutic shortcut is to apply the
drug repositioning strategy (i.e., finding the new uses of old drugs) [1,2,3] to identify
the potential antiviral effects against SARS-CoV-2 of existing drugs that have been
approved for clinical use or to enter clinical trials. Those existing drugs with potent
antiviral efficacy can be directly applied to treat COVID-19 in a short time, as their
safety has been verified in principle in clinical trials.
In this study, we applied a data-driven framework that combines both machine
learning and statistical analysis methods to systematically integrate large-scale avail-
able coronavirus-related data and identify the drug candidates against SARS-CoV-2
from a set of over 6000 drug candidates (mainly including approved, investigational
and experimental drugs). Our in silico screening process followed by experimental val-
idation revealed that a poly-ADP-ribose polymerase 1 (PARP1) inhibitor, CVL218,
currently in Phase I clinical trial, may serve as a potential drug candidate to treat
COVID-19. Our in vitro assays demonstrated that CVL218 can exhibit effective in-
hibitory activity against SARS-CoV-2 replication in a dose-dependent manner and with
no obvious cytopathic effect. In addition, we found that in human peripheral blood
mononuclear cells (PBMCs), CVL218 is able to suppress the CpG-induced production
of IL-6, which has been reported previously to be of high relevance to the viral patho-
genesis of COVID-19, especially for those intensive care unit (ICU) patients infected by
SARS-CoV-2. Further in vivo pharmacokinetic and toxicokinetic studies in rats and
monkeys showed that CVL218 was highly distributed in the lung tissue and no apparent
sign of toxicity was observed, which makes it an appealing potential drug candidate for
the treatment of the novel pneumonia caused by SARS-CoV-2 infection. Moreover, our
molecular docking study suggested that CVL218 may bind to the N-terminal domain
of nucleocapsid (N) protein of SARS-CoV-2, providing a possible mode of its antiviral
action against SARS-CoV-2. Based on the data present in this study and previous
3
author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint (which was not peer-reviewed) is the. https://doi.org/10.1101/2020.03.11.986836doi: bioRxiv preprint
known evidences reported in the literature, we also discussed several putative mech-
anisms of the anti-SARS-CoV-2 effects for CVL218 or other PARP1 inhibitors to be
involved in the treatment of COVID-19. Overall, our results indicated that the PARP1
inhibitor CVL218 identified by our drug repositioning pipeline may serve as an effective
therapeutic agent against COVID-19.
2. Results
2.1. Overview of our drug repositioning framework
The overview of our data-driven drug repositioning framework is shown in Fig-
ure 1A. We first constructed a virus related knowledge graph consisting of drug-target
interactions, protein-protein interactions and similarity networks from publically avail-
able databases (Methods). Three different types of nodes (i.e., drugs, human targets
and virus targets) within the knowledge graph were connected through edges describ-
ing their interactions, associations or similarities to establish bridges of information
aggregation and knowledge mining. We then applied a network-based knowledge min-
ing algorithm to predict an initial list of drug candidates that can be potentially used
to treat SARS-CoV-2 infection (Figure 1B and Methods). Next, we further narrowed
down the list of drug candidates with the previously reported evidences of antiviral
activities based on the text mining results from the large-scale literature texts, which
were derived through a deep learning based relation extraction method named BERE [4]
(Figure 1C and Methods), followed by a minimum of manual checking. After that, we
used the connectivity map analysis approach [5] with the gene expression profiles of
ten SARS-CoV-infected patients [6] to further refine the list of drug candidates against
SARS-CoV-2 (Figure 1D, Table 1, Table S1 and Methods). The above screening pro-
cess revealed that a poly-ADP-ribose polymerase 1 (PARP1) inhibitor PJ-34 could
potentially have the antiviral activities against SARS-CoV-2.
4
author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint (which was not peer-reviewed) is the. https://doi.org/10.1101/2020.03.11.986836doi: bioRxiv preprint
2.2. Validation of our network-based knowledge mining results
To demonstrate that our computational pipeline for drug repositioning can yield
reasonably accurate prediction results, we also validated our network-based knowledge
mining algorithm (Figure 1B) using the retrospective data of the two coronaviruses
that are closely related to SARS-CoV-2 and had been relatively well studied in the
literature, i.e., SARS-CoV and MERS-CoV. With the aid of our developed text mining
tool BERE, we found that many of the drugs that had been reported previously in the
literature to have antiviral activities against the corresponding coronavirus, were also
among the top list of our predicted results (Table 2). For example, chloroquine, an
FDA-approved drug for treating malaria [7], which was previously reported to exhibit
micromolar anti-SARS-CoV activity in vitro [8], was also repurposed for targeting the
same virus by our prediction framework. Gemcitabine, which was originally approved
for treating certain types of cancers [9], was also predicted for targeting SARS-CoV
with validation by previous in vitro studies [10]. Cyclosporine, a calcineurin inhibitor
approved as an immunomodulatory drug [11], was observed to block the replication of
SARS-CoV [12], and also successfully predicted by our approach. Among the predicted
top list for MERS-CoV, miltefosine, which was approved for treating leishmaniasis [13],
was previously identified to have anti-MERS-CoV activity [14]. Chlorpromazine and
imatinib, which were used for treating schizophrenia [15] and leukemia [16], respectively,
were also selected by our computational pipeline as anti-MERS-CoV drugs and can be
validated by previous in vitro experiments [10]. Thus, the above retrospective study
illustrated that our computational framework is able to predict effective drug candidates
against a specific coronavirus.
2.3. CVL218 exhibits in vitro inhibitory activity against SARS-CoV-2 replication
As PJ-34 is still currently in the pre-clinical trial stage (DrugBank ID: DB08348, [17]),
we selected two PARP1 inhibitors, including olaparib and mefuparib hydrochloride
(CVL218) (Figure S1), that are currently FDA-approved and at Phase I clinical trial,
5
author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint (which was not peer-reviewed) is the. https://doi.org/10.1101/2020.03.11.986836doi: bioRxiv preprint
respectively, for our initial study. We first conducted a pilot experimental test in vitro
(Methods) on the anti-SARS-CoV-2 activities of olaparib, CVL218 and several other re-
lated drugs (Figure 2A). We found that both PARP1 inhibitors olaparib and CVL218
exhibited inhibitory effects against SARS-CoV-2 replication. Nevertheless, CVL218
showed a much higher inhibition rate than olaparib. More specifically, olaparib inhib-
ited SARS-CoV-2 replication by 15.48% at a concentration of 3.2 µM, while CVL218
reached 35.16% reduction at a concentration of 3 µM.
Notably, the antiviral efficacy of CVL218 even surpassed arbidol, which is one
of the standard treatments for COVID-19 in the Diagnosis and Treatment Proto-
col for Novel Coronavirus Pneumonia (Trial Version 6) promulgated by the Chinese
government (http://www.nhc.gov.cn/yzygj/s7653p/202002/8334a8326dd94d329df
351d7da8aefc2/files/b218cfeb1bc54639af227f922bf6b817.pdf). In particular, ar-
bidol inhibited SARS-CoV-2 replication by 21.73% at 3 µM, much lower than that of
CVL218 at the same concentration (Figure 2A). In contrast, oseltamivir, zanamivir
(drugs used for preventing influenza virus infection) and baricitinib (JAK1/2 inhibitor,
which was recommended in [18] to treat COVID-19) showed no inhibitory activities
against SARS-CoV-2 at the concentration of 3 µM or 3.2 µM.
Based on the above pilot experimental results, we then chose CVL218 for subsequent
experimental studies. Our further in vitro assays (Methods) showed that CVL218 exhib-
ited effective inhibitory activity against SARS-CoV-2 replication in a dose-dependent
manner, with an EC50 of 5.12 µM (Figure 2B). We also assessed the cytotoxicity of
CVL218 by the CCK8 assay (Methods), and found that CVL218 had a CC50 of 91.05
µM in Vero E6 cells. In addition, immunofluorescence microscopy (Methods) revealed
that, at 14 h post SARS-CoV-2 infection, virus nucleoprotein (NP) expression in the
CVL218-treated cells demonstrated a dose-response relationship with the treated drug
concentrations, and was significantly lower upon CVL218 treatment compared with
that in the DSMO treated cells (Figure 2C). No obvious cytopathic effect was observed
6
author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint (which was not peer-reviewed) is the. https://doi.org/10.1101/2020.03.11.986836doi: bioRxiv preprint
in the infected cells treated with CVL218 at 25 µM.
To systematically assess the inhibitory activities of CVL218 against SARS-CoV-
2, we also performed a time-of-addition assay (Methods) to determine at which stage
CVL218 inhibits viral infection. Remdesivir, which has entered the clinical trials for
the treatment of COVID-19 (https://clinicaltrials.gov/ct2/show/NCT0425765
6), was also tested in this assay for comparison. In particular, as compared to the
DMSO control group, both CVL218 and remdesivir showed potent antiviral activities
during the full-time procedure of the SARS-CoV-2 infection in Vero E6 cells (Fig-
ure 2D). The results of the time-of-addition assay indicated that CVL218 can partially
work against the viral entry and significantly inhibit the replication of virus post-entry,
while the remdesivir can only function at the post-entry stage (Figure 2D, 2E). All
together, the results of these in vitro assays indicated that CVL218 can be further
evaluated as a potential therapeutic agent for treating COVID-19.
2.4. CVL218 inhibits IL-6 production in PBMCs induced by CpG-ODN 1826
Recently it has become evident that interleukin-6 (IL-6) is one of the most impor-
tant cytokines during viral infection [19], and emerging clinical studies in humans and
animals have linked the excessive synthesis of IL-6 with the persistence of many viruses,
such as human immunodeficiency virus (HIV) [20], foot and mouth disease virus [21]
and vesicular stomatitis virus (VSV) [22]. In addition, an in vivo study in the Friend
retrovirus (FV) mouse model showed that IL-6 blockage can reduce viral loads and en-
hance virus-specific CD8+ T-cell immunity [23]. These findings supported a hypothesis
that rapid production of IL-6 might be a possible mechanism leading to the deleteri-
ous clinical manifestations in viral pathogenesis [24]. Recently published researches on
the clinical characteristics of severe patients with SARS-CoV-2 infection showed that
IL-6 was significantly elevated especially in those ICU patients, which caused excessive
activated immune response [25,26,27,28,29]. The pathological role of IL-6 in SARS-
CoV-2 infection indicated that IL-6 blockade may provide a feasible therapy for the
7
author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint (which was not peer-reviewed) is the. https://doi.org/10.1101/2020.03.11.986836doi: bioRxiv preprint
treatment of COVID-19.
To test whether CVL218 is able to regulate the IL-6 production in vitro, we stim-
ulated the IL-6 production of the peripheral blood mononuclear cells (PMBCs) by
CpG-ODN 1826, which is an effective stimulator of cytokines and chemokines. Incuba-
tion of PBMCs with 1 µM CpG-ODN 1826 for 6 h (Methods) induced IL-6 production
by 40%, when compared to untreated cells (Figure 3). In the presence of CVL218,
the stimulatory effect of CpG-ODN 1826 was counteracted. Further study showed that
CVL218 inhibited the CpG-induced IL-6 upregulation in a time- and dose-dependent
manner (Figure 3). More specifically, exposure with CVL218 at concentrations 1 µM
and 3 µM for 12 h attenuated the CpG-induced IL-6 production by 50% and 72.65%,
respectively. These results provided an in vitro evidence to support CVL218 as a poten-
tial therapeutic agent for treating pro-inflammatory response caused by SARS-CoV-2
infection.
2.5. CVL218 possesses good pharmacokinetic and toxicokinetic characteristics in ani-
mals
2.5.1. CVL218 has the highest tissue distribution in lung of rats
We further performed in vivo pharmacokinetic and toxicokinetic evaluation of CVL218
in animals (Methods). We first examined the concentrations of CVL218 over different
tissues in rats at different time points post oral administration at different doses (Fig-
ure S2 and Table S2), which was also previously reported in [30]. Among seven tissues
(i.e., lung, spleen, liver, kidney, stomach, heart and brain), we observed that lung
had the highest CVL218 concentration, which was 188-fold higher compared to that of
plasma (Table 3). The observation that lung had the highest concentration of CVL218
was in line with the fact that the SARS-CoV-2 virus has the most pathological impact
in lung with high viral loads, which suggested that CVL218 has the potential to be used
for the indications of the lung lesions caused by SARS-CoV-2 infection, if its antiviral
profile can be established in animal models and clinical trials.
8
author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint (which was not peer-reviewed) is the. https://doi.org/10.1101/2020.03.11.986836doi: bioRxiv preprint
Furthermore, we compared the pharmacokinetic data between CVL218 and arbidol,
a broad-spectrum antiviral drug commercialized in China since 2016, had been recom-
mended to treat the SARS-CoV-2-infected patients by the Chinese government. We
found that the pharmacokinetic parameters of CVL218 and arbidol were comparable,
with similar plasma concentrations and drug exposures (Table S3). Arbidol was mostly
distributed in stomach and plasma post administration in rats. In contrast, higher
distributions of CVL218 in tissues especially in lung rather than plasma compared to
those of arbidol indicated a superior pharmacokinetic profile of CVL218, which may
render it as a better potential antiviral treatment of SARS-CoV-2 infection in lung.
2.5.2. The toxicity study demonstrated a safety profile of CVL218 in rats
In rats after being orally administrated 20/60/160 mg/kg of CVL218 for 28 consec-
utive days and followed by 28 more days without drug administration (Methods), we
observed no significant difference in body weight of rats among different dosage and the
control groups (Figure 4A).
We next conducted a toxicokinetic analysis of CVL218 in rats (Methods). In par-
ticular, rats were given CVL218 20/60/160 mg/kg by oral gavage once a day for con-
secutive 28 days, followed by 28 days without CVL218 administration, to investigate
the reversibility of the toxic effects of the compound and examine whether there is
any potential delayed-onset toxicity of this drug in rats. The results showed that, the
maximum tolerable dose (MTD) and the no-observed adverse effect level (NOAEL)
were 160 mg/kg and 20 mg/kg, respectively. The exposure of female rats to CVL218
(AUC024) was 7605 h·ng/mL in day 1 and 6657 h·ng/mL in day 28, while that of male
rats (AUC024) was 9102 h·ng/mL in day 1 and 10253 h·ng/mL in day 28 (Table S4).
Based on the toxicokinetic results from the repeated dose studies, all rats survived after
a 28-day treatment period and showed no apparent signs of toxicity.
9
author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint (which was not peer-reviewed) is the. https://doi.org/10.1101/2020.03.11.986836doi: bioRxiv preprint
2.5.3. CVL218 exhibits a favorable safety profile in monkeys
Monkeys were administered CVL218 (5, 20 or 80 mg/kg) by nasogastric feeding
tubes with a consecutive daily dosing schedule for 28 days, followed by a 28-day recovery
period (Methods). Only a slight decrease of body weight was observed in the high-
dose (80 mg/kg) group, and all changes were reversed after a 28-day recovery period
(Figure 4B), demonstrating a favorable safety profile for CVL218 in monkeys. Further
examination of the toxicokinetic data of CVL218 in monkeys showed that the increase
of the exposure of CVL218 (AUC024) was approximately dose proportional, and after
consecutive 28 days of drug administration, the accumulation was not apparent. The
exposure of female monkeys to CVL218 (AUC024) was 19466 h·ng/ml in day 1 and
18774 h·ng/ml in day 28 (Table S5), while that of male monkeys (AUC024) was 16924
h·ng/ml in day 1 and 22912 h·ng/ml in day 28. The maximum tolerable dose (MTD)
of CVL218 in monkeys was 80 mg/kg, and the dose of 5 mg/kg was considered as the
no-observed adverse effect level (NOAEL).
Overall, the above in vivo data showed that CVL218 possesses good pharmacokinetic
and toxicokinetic characteristics in rats and monkeys, and its high-level distribution in
the therapeutically targeted tissue (i.e., lung) may greatly favor the treatment of SARS-
CoV-2 infection.
2.6. Molecular docking suggests the interactions between PARP1 inhibitors and the N-
terminal domain of coronavirus nucleocapsid protein
As the previous studies have reported that the PARP1 inhibitor PJ-34 can target
the N-terminal domain (NTD) of the coronavirus nucleocapsid (N) protein to reduce its
RNA binding and thus impede viral replication [31,32,33], we speculated that olaparib
and CVL218 may also interact with the N protein of SARS-CoV-2 to perform the similar
antiviral function. To test this hypothesis, we conducted molecular docking to study
the potential interactions between these two drugs and the N-NTD of SARS-CoV-2
(Methods).
10
author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint (which was not peer-reviewed) is the. https://doi.org/10.1101/2020.03.11.986836doi: bioRxiv preprint
The overall structure of HCoV-OC43 (another coronavirus phylogenetically closely
related to SARS-CoV-2) and SARS-CoV-2 share similar compositions of secondary
structure elements, including five conserved βstrands and flexible loops (Figure 5A).
In addition, their sequences covering the corresponding binding pocket regions are well
conserved (Figure 5C). Thus, their structures may provide common molecular features
in terms of interactions with small molecules at this binding pocket. Therefore, we
also used the experimentally solved structure of HCoV-OC43-N-NTD complexed with
PJ-34 as a reference to analyze our docking results.
Our docking results (more details can also be found in Supplementary Materials)
showed that both CVL218 and olaparib can bind to the N-NTD of SARS-CoV-2 around
the same binding pocket as in the experimentally solved complex structure between PJ-
34 and the corresponding protein of HCoV-OC43, though with different binding poses
(Figure 5B). Examination of docked structures indicated that CVL218 exhibits stronger
binding ability than olaparib in terms of the hydrogen bond formation. Meanwhile, the
key residues (i.e., S51, Y109 and Y111) participating in the binding with the drugs on
SARS-CoV-2-N-NTD are also highly conserved among other viruses including SARS-
CoV, HCoV-OC43, mouse hepatitis virus (MHV) and infectious bronchitis virus (IBV)
(Figure 5C), suggesting that the N-NTDs of different viruses most likely display similar
binding behaviors for PJ-34, CVL218 or other PARP1 inhibitors. Overall, our docking
results indicated that CVL218 should be more effective in binding toward the nucleo-
capsid protein of SARS-CoV-2 compared to olaparib, thus better beneficial to intervene
the nucleocapsid-dependent assembly of viral genome and thus inhibit viral replication.
3. Discussion
In this study we reported a top down data integration approach by combining both
machine learning and statistical analysis techniques, followed by web-lab experimental
validation, to identify potential drug candidates for treating SARS-CoV-2 infection. We
11
author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint (which was not peer-reviewed) is the. https://doi.org/10.1101/2020.03.11.986836doi: bioRxiv preprint
showed that the PARP1 inhibitor CVL218 discovered by our in silico drug repurposing
framework may have the therapeutic potential for the treatment of COVID-19. Al-
though we mainly conducted in vitro assays to experimentally validate the anti-SARS-
CoV-2 effects of olaparib and CVL218 due to limited time, it is natural to speculate
that other PARP1 inhibitors may also have antiviral activities against SARS-CoV-2
infection, based on our computational prediction and experimental validation results.
Based on the data present in this study and the previously known evidences re-
ported in the literature, we propose several potential mechanisms to help understand
the involvement of PARP1 inhibitors in the treatment of COVID-19 (Figure 6). First,
during the life cycle of the coronavirus, PARP1 inhibitors may inhibit the viral growth
through suppressing viral replication and impeding the binding of the nucleocapsid
protein to viral RNAs [31,34,35,36], which can also be supported by our molecular
docking results (see Section 2.6). Second, PARP1 inhibitors have been previously re-
ported to play a critical role in regulating inflammatory response by modulating the
expression of pro-inflammatory factors such as NF-κB, AP-1, IL-6 and downstream
cytokines and chemokines [37,38,39,40]. Also, it has been shown that the overacti-
vation of PARP1 promotes energy (NAD+/ATP) consumption and drives cell death,
exacerbating the inflammation response [37,38,39,41]. PARP1 inhibitors thus may
repress the NF-κB-mediated pro-inflammatory signals, and reduce energy failure and
subsequent cell death induced by necrosis, thus providing a clinical potential for attenu-
ating the cytokine storm and inflammatory response caused by SARS-CoV-2 infection.
Third, ADP-ribosylation is a conserved post-translational modification on the nucle-
ocapsid proteins across different coronavirus lineages, implying that it may have an
important regulatory role for the structure packing of viral genome. Several previous
studies have demonstrated that PARP1 is critical for viral replication [35,42,43]. For
example, PARP1 has been reported to interact with hemagglutinin (HA) of influenza A
virus (IAV) and promote its replication by triggering the degradation of host type I IFN
12
author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint (which was not peer-reviewed) is the. https://doi.org/10.1101/2020.03.11.986836doi: bioRxiv preprint
receptor [44]. In addition, the ADP-ribosylation of adenoviral core proteins displays an
antiviral defense mechanism [34]. Therefore, intervening the ADP-ribosylation medi-
ated interplay between PARP1 and viral proteins may be another important pathway
for PARP1 inhibitors to prevent SARS-CoV-2 infection. Of course, to throughly un-
derstand the anti-SARS-CoV-2 roles of PARP1 inhibitors, more experimental studies
and direct (clinical) evidences will be needed in the future.
Considering the pro-inflammatory role of PARP1, the therapeutic effects of PARP1
inhibitors in inflammatory-mediated diseases have been extensively studied over past
two decades [45,46]. PJ-34, the early generation PARP1 inhibitor, has been suggested
in previous studies to have neuroprotective effects in stroke model and protect mice
from necroptosis-associated liver injuries by repressing the IL-33 expression [47,48].
In addition, the FDA-approved PARP1 inhibitor, olaparib, has been reported to pro-
tect against the LPS (Lipopolysaccharide)-induced acute lung and kidney injuries in
a NF-κB-dependent manner in mice [49]. Numerous pre-clinical studies demonstrated
that PARP1 inhibitors play an essential role in a range of inflammatory injuries and
related diseases, especially the lung inflammatory disorders including ARDS (Acute
Respiratory Distress Syndrome), COPD (Chronic Obstructive Pulmonary Disease) and
asthma [40,46,50,51]. All these studies suggest that PARP1 inhibitors are of high
relevance to the treatment of the novel pneumonia caused by SARS-CoV-2 infection,
possibly via their roles in modulating inflammatory response.
Notably, current pathological studies have shown that the severe patients infected by
SARS-CoV-2 generally have higher plasma levels of IL-2, IL-6, IL-10, TNFα, IFN-γ[25,
27,28,29], implying a high risk of the inflammatory-associated cytokine storm after vi-
ral infection. In addition, reduction and functional exhaustion of T cells have also been
observed in COVID-19 patients [27]. Therefore, blocking the overactive inflammatory
response may be an effective strategy for the treatment of COVID-19, particularly for
those ICU patients infected by SARS-CoV-2. Recently, tocilizumab, a monoclonal anti-
13
author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint (which was not peer-reviewed) is the. https://doi.org/10.1101/2020.03.11.986836doi: bioRxiv preprint
body drug targeting IL-6, has been recommended for the treatment of COVID-19 in the
Diagnosis and Treatment Protocol for Novel Coronavirus Pneumonia (Trial Version 7)
promulgated by the Chinese government (http://www.nhc.gov.cn/yzygj/s7653p/20
2003/46c9294a7dfe4cef80dc7f5912eb1989/files/ce3e6945832a438eaae415350a8c
e964.pdf), which also highlights the vital role of anti-inflammatory response in current
therapeutics against SARS-CoV-2. Our in vitro study has showed that CVL218 can
effectively inhibit the IL-6 production induced by CpG in PBMCs (Figure 3). This
finding indicates that CVL218 may also possess the IL-6 specific anti-inflammatory
effect that is applicable to those severe patients infected by SARS-CoV-2.
PARP1 inhibitors are originally used for targeting homologous recombination repair
defects in cancers, and mainly categorized as oncology drugs. Thus, it would generally
need more safety data to justify any repurposing of PARP1 inhibitors for non-oncology
indications. Fortunately, there are numerous existing pre-clinical and clinical studies
on repurposing PARP1 inhibitors into non-oncological diseases, including the aforemen-
tioned acute diseases (e.g., acute respiratory distress syndrome (ARDS), stroke) [52]
and chronic diseases (e.g., rheumatoid arthritis and vascular diseases) [52,53]. All these
evidences indicate the possibility of repurposing PARP1 inhibitors as a safe therapeu-
tic agent to treat the current acute lung disease caused by SARS-CoV-2 infection. In
addition, our pharmacokinetic and toxicokinetic data in rats and monkeys shown in
our study indicate that CVL218 may have a relatively acceptable safety profile to be
repositioned for the antiviral purpose. Moreover, CVL218 has been approved to enter
Phase I clinical trial in 2017 by National Medical Products Administration (NMPA)
in China for cancer treatment. The preliminary data from the Phase I clinical trial
have shown that CVL218 is well tolerated in ascending dose studies at doses as high as
1000 mg QD and 500 mg BID, and no Grade II and above adverse events have been
observed, which indicates that CVL218 is also quite safe and well tolerated in human.
Our pharmacokinetic examination in rats has shown that CVL218 has the highest
14
author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint (which was not peer-reviewed) is the. https://doi.org/10.1101/2020.03.11.986836doi: bioRxiv preprint
level distribution in the lung tissue, 188-fold higher concentration compared to that
in plasma. Such a tissue specific enrichment in lung may bring an extra advantage
for CVL218 to be used for the anti-SARS-CoV-2 purpose, as lung is the therapeuti-
cally targeted tissue for COVID-19. Moreover, high level distribution in lung may also
suggest that only low dosage is needed in order to ensure the therapeutic efficacy of
CVL218 against SARS-CoV-2, which may further reduce the risk of adverse events.
Thus, CVL218 may have great potential to be repurposed as an effective therapeutic
agent to combat SARS-CoV-2 and prevent future epidemic outbreak.
15
author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint (which was not peer-reviewed) is the. https://doi.org/10.1101/2020.03.11.986836doi: bioRxiv preprint
4. Methods
4.1. Construction of the virus-related knowledge graph
The virus-related knowledge graph was constructed for predicting the coronavirus
related drugs. In total seven networks were considered in the constructed knowl-
edge graph (Figure 1B), including a human target-drug interaction network, a virus
target-drug interaction network, a human protein-protein interaction network, a virus
protein-human protein interaction network, a drug molecular similarity network, a
human protein sequence similarity network, and a virus protein sequence similarity
network. The human target-drug interaction network was derived from DrugBank
(version 5.1.0) [17]. The virus target-drug interaction network was constructed from
the integrated data from DrugBank (version 5.1.0) [17], ChEMBL (release 26) [54],
TTD (last update 11 Nov, 2019) [55], IUPHAR BPS (release 13, Nov, 2019) [56],
BindindDB [57] and GHDDI (https://ghddi-ailab.github.io/Targeting2019-nCo
V/CoV_Experiment_Data/), with a cut-off threshold of IC50/EC50 /Ki/Kd<10 µM. The
human protein-protein interaction network and the virus protein-human protein in-
teraction network were constructed from the integrated data from BioGRID (release
3.5.181) [58], HuRI [59], Instruct [60], MINT (2012 update) [61], PINA (V2.0) [62],
SignaLink (V2.0) [63] and innatedb [64]. The drug molecular similarity network was
obtained by calculating the Tanimoto similarities from Morgan fingerprints with a ra-
dius of 2 computed using the rdkit tool [65]. The protein sequence similarity networks
of both human and virus were obtained by calculating the Smith-Waterman similari-
ties of the amino acid sequences derived from UniProt [66] using a sequence alignment
software provided in [67]. Noted that we collected additional protein sequences of
SARS-CoV-2 from UniProt [66] and added them into the corresponding networks for
the final prediction. Those drugs without drug-target interactions or outside the Drug-
Bank database were removed from the corresponding networks. We then constructed
the virus-related knowledge graph by merging together all the nodes and edges of the
16
author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint (which was not peer-reviewed) is the. https://doi.org/10.1101/2020.03.11.986836doi: bioRxiv preprint
above seven networks (Figure 1B). The constructed knowledge graph G= (V, E ) is an
undirected graph, in which each node vVin the node set Vbelongs to one of the
node types (including drugs, human proteins, and virus proteins), and each edge eE
in the edge set EV×V×Rbelongs to one of the relation types from the relation
type set R(including two drug-target interactions, two protein-protein interactions and
three similarities).
4.2. The network-based knowledge mining algorithm
The initial list of drug candidates targeting SARS-CoV-2 was first screened using a
network-based knowledge mining algorithm modified from our previous work [68,69].
The goal was to capture the hidden virus-related feature information and accurately
predict the potential drug candidates from the constructed knowledge graph, which was
realized through learning a network topology-preserving embedding for each node.
More specifically, our model used a graph convolution algorithm [70] to gather and
update feature information for each node in the constructed heterogeneous knowledge
graph network from neighborhoods so that the network topology information can be
fully exploited. Suppose that we perform Titerations of graph convolution. At iteration
1tT, the message mt
vpassed to node vcan be expressed as:
mt
v=X
rRX
uNr(v),
e=(u,v,r)E
Au,v,r ReLU(Wt
rht1
u+bt
r),(1)
where av,u,r stands for the weight for edge e= (u, v, r), Au,v,r =av ,u,r
Puav,u,r ,Wt
rRd×d
and bt
rRdstand for the learnable parameters, ReLU(x) = max(0, x), and Nr(v) =
{u, u V, u 6=v, (u, v, r)E}denotes the set of adjacent nodes connected to vV
through edges of type rR.
Then the feature ht
vof node vis updated by
ht
v=ReLU(Wtconcat(ht1
v,mt
v) + ht1
v+bt)
||ReLU(Wtconcat(ht1
v,mt
v) + ht1
v+bt)||2
,(2)
17
author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint (which was not peer-reviewed) is the. https://doi.org/10.1101/2020.03.11.986836doi: bioRxiv preprint
where WtRd×dand btRdstand for the learnable parameters, and concat(·,·)
stands for the concatenation operation.
Finally, the confidence score su,v of the relation rbetween node uand node vis
derived from the learned node embeddings and the corresponding projection matrices,
that is,
su,v =ht
u
>·Gr·H>
r·ht
v,(3)
where Gr, HrRd×kstand for the edge-type specific projection matrices.
We minimized the Bayesian personalized ranking (BPR) loss [71] for drug-target
interaction reconstruction, by regarding those edges not in the edge set Eas missing
values rather than negative samples, that is,
X
rRX
u,v,w,xV,
(u,v,r)E,
(w,x,r)/E
log σ(su,v sw,x),(4)
where, su,v and sw,x stand for the confidence scores of the relation rbetween uand v
and between wand x, respectively, and σ(·) stands for the sigmoid activation function.
Intuitively, in the above loss function, the confidence scores of the node pairs (u, v) in
the edge set (i.e., (u, v, r)E) were encouraged to be higher than those of unseen pairs
(w, x) (i.e., (w, x, r)/E).
We predicted the confidence scores under the relation of virus target-drug interac-
tions for each virus target-drug pair using Equation (3). Then the confidence scores
were averaged across all the proteins of a certain virus (e.g., SARS-CoV, MERS-CoV
or SARS-CoV-2), and the corresponding p-values were obtained by z-test. For each
virus, we selected those predictions with a p-value <0.05 as drug candidates.
4.3. Automated relation extraction from large-scale literature texts
We used a deep learning based relation extraction method named BERE [4] to
extract the coronavirus related drugs from large-scale literature texts. More specifically,
18
author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint (which was not peer-reviewed) is the. https://doi.org/10.1101/2020.03.11.986836doi: bioRxiv preprint
the sentences mentioning the two entities of interest, i.e., name (or alias) of a coronavirus
or coronavirus target, or name (or alias) of a drug, were first collected using a dictionary-
based name entity recognition method (string matching). For each pair of entities
(e1, e2), there are usually more than one sentence describing the underlying relations.
Therefore, we used a bag of sentences Se1,e2, denoting the set of all the sentences
mentioning both e1and e2, to predict the relation between these two entities.
We first encoded each sentence sSe1,e2in a semantic and syntactic manner using
a hybrid deep neural network (h:sRd), including a self-attention module [72], a bi-
directional gated recurrent unit (GRU) module [73] and a Gumbel tree-GRU module [4,
74]. Each sentence representation h(s) was then scored by a sentence-level attention
module to indicate its contribution to the relation prediction, that is,
β(s) = exp (Ws·h(s))
Ps0Se1,e2exp (Ws·h(s0)),(5)
where β(s)Rstands for the weight score, and WsRd×1stands for the learnable
weight parameters. Finally, the relation was predicted by a binary classifier, based on
the weighted sum of sentence representations, that is,
re1,e2= classifierX
sSe1,e2
β(s)·h(s),(6)
where re1,e2stands for the probability of the relation of interest between entities e1and
e2mentioned by the bag of sentences Se1,e2.
The training corpus we used was curated automatically from nearly 20 million
PubMed (http://www.pubmed.gov) abstracts by a distant supervision technique [75].
In detail, the names (or aliases) of drugs or targets in sentences were first annotated
by a dictionary-based named entity recognition method (string matching), in which
the name dictionary was derived from DrugBank (version 5.1.0) [17], with ambiguous
names (e.g., common words) removed. Next, the label for each bag of sentences co-
19
author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint (which was not peer-reviewed) is the. https://doi.org/10.1101/2020.03.11.986836doi: bioRxiv preprint
mentioning a drug-target pair of interest was annotated automatically by the known
drug-target interactions in DrugBank. The unlabeled corpus that we used in this work
for text-mining the coronavirus related drugs was obtained from approximately 2.2 mil-
lion PMC full-text articles, with entities of interest annotated using the aforementioned
named entity recognition approach. A coronavirus related drug was extracted as a hit
candidate if the model found a bag of sentences describing a relation between this drug
and a target in the coronavirus of interest.
4.4. Connectivity map analysis
We used the transcriptome analysis approach to further filter the potential drug
candidates for treating the COVID-19 patients infected by SARS-CoV-2. Due to the
lack of gene expression data from the SARS-CoV-2 infected patients, we used those from
the SARS-CoV infected patients to screen the potential therapeutic drug candidates
against COVID-19. Such a strategy is reasonable as SARS-CoV and SARS-CoV-2
are two closely related and highly similar coronavirus. First, the genome of SARS-
CoV-2 is phylogenetically close to that of SARS-CoV, with about 79% of sequence
identity [76], and the M (membrane), N (nucleocapsid) and E (envelope) proteins of
these two coronaviruses have over 90% sequence similarities [77]. In addition, the
pathogenic mechanisms of SARS-CoV-2 and SARS-CoV were highly similar [78].
In particularly, we collected the gene expression profiles of the peripheral blood
mononuclear cells (PBMCs) from ten SARS-CoV infected patients (GEO:GSE1739) [6].
The raw gene expression values were first converted into logarithm scale, and then the
differential expression values (z-scores) were computed by comparing to those of healthy
persons using the same protocol as described in [5], that is,
Zinfected =Xinfected median(Xhealthy)
C·MAD(Xhealthy),(7)
MAD(Xhealthy) =median(|Xhealthy median(Xhealthy)|),(8)
20
author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint (which was not peer-reviewed) is the. https://doi.org/10.1101/2020.03.11.986836doi: bioRxiv preprint
where Zinfected stands for the z-scores of the SARS-CoV infected patients, Xinfected
and Xhealthy stand for the gene expression values in logarithm scale of the infected
and healthy persons, respectively, median(·) stands for the median operation, MAD(·)
stands for the median absolute deviation operation, and C= 1.4826 is a constant for
normalization. The p-values for all the genes with measured expression values during
the analysis were also computed based on the z-scores. The up- and down-regulated
genes were then identified using a cut-off threshold of p-value <1010. We used the
connectivity map (CMap) [5], which contains the cellular gene expression profiles under
the perturbation of 2428 well annotated reference compounds, to measure the associa-
tions of gene expression patterns between SARS-CoV infected patients and the reference
compound-perturbed cells. The connectivity map scores were computed based on the
up- and down-regulated gene sets of SARS-CoV infected patients using the web tool
(https://clue.io/query). Under the hypothesis that the gene expression pattern
resulting from the perturbation by a therapeutic compound should be negatively corre-
lated with that resulting from the coronavirus infection, we selected those compounds
that have significant negative connectivity map scores, that is, the list of drug can-
didates predicted to treat the coronavirus infected patients was obtained by selecting
the compounds with the connectivity map scores <90, which was suggested by the
original paper [5].
4.5. Cells and virus
The African green monkey kidney Vero E6 cell line was purchased from the Cell
Resources Center of Shanghai Institute of Life Science, Chinese Academy of Sciences
(Shanghai, China) and cultured in DMEM medium (Gibco Invitrogen, no. 12430-054)
containing 10% fetal bovine serum (FBS; Gibco Invitrogen) at 37 C with 5% CO2at-
mosphere. BetaCoV/JS03/human/2020 (EPI ISL 411953), a SARS-CoV-2 virus strain,
was isolated from nasopharyngeal swab of a 40-year old female confirmed as COVID-19
case by reverse transcriptase polymerase chain reaction (RT-PCR) in December 2019.
21
author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint (which was not peer-reviewed) is the. https://doi.org/10.1101/2020.03.11.986836doi: bioRxiv preprint
The virus was propagated in Vero E6 cells, and the viral titer was determined by the
50% tissue culture infective dose (TCID50) based on microscopic observation of cyto-
pathic effects. All the in vitro SARS-CoV-2 infection experiments were performed in a
biosafety level-3 (BLS-3) laboratory in Jiangsu Provincial Center for Diseases Control
and Prevention, Jiangsu, China.
4.6. Antiviral drugs
Potential antiviral drugs, including zanamivir, oseltamivir, remdesivir, baricitinib,
olaparib and arbidol, were all provided by MCE (Medchem Express, China). The
PARP1 inhibitor mefuparib hydrochloride (CVL218) with a purity of more than 99.0%
was provided by Convalife, Shanghai, China.
4.7. Cytotoxicity test and virus infection assay
The cytotoxicity of the tested drugs on Vero E6 cells was determined by the CCK8
assays (Beyotime, China). At 48 h post addition of the tested drugs, 20 µL CCK8
was added to each well and incubated at 37 C for 1 h. Then optical density was
measured at 450 nm. The 50% cytotoxic concentration (CC50) values were calculated
using GraphPad Prism (GraphPad Software, USA). Vero E6 cells were seeded into
96-well plates with a density of 5 ×104cells/well for incubation in DMEM medium
supplemented with 10% FBS for 16 h in an incubator with 5% CO2at 37 C, for cells
to reach 80% confluent. Then, cell culture medium of each well was removed, and PBS
was used to wash the cells once, before evaluating the antiviral efficacy of the drugs.
Four duplicated wells were made for each dose of drugs, and the cells were pre-treated
with different doses of antiviral drugs diluted by the cell maintenance solution (50 µL
per well) for 1 h. For the virus control and cell control wells, cell medium containing
DMSO or only medium of 50 µL per well was added. Next, pre-treated or untreated
cells in each well were infected with the virus with multiplicity of infection (MOI) of
0.05 for 2 h. After that, the virus-drug mixture was removed and cells were further
22
author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint (which was not peer-reviewed) is the. https://doi.org/10.1101/2020.03.11.986836doi: bioRxiv preprint
cultured with fresh drug-containing medium at 37 C with 5% CO2atmosphere for 48
h. Then culture supernatant per well was harvested and inactivated at 56 C for 30
min to further extract and quantify viral RNA.
4.8. Viral RNA extraction and quantitative real-time PCR (qRT-PCR)
Viral RNA was extracted from culture supernatant using the HP RNA Isolation
Kit (Roche) according to the manufacturer’s instructions. RNA was eluted in 30 µL
RNase-free water. Reverse transcription was performed with a SARS-CoV-2 nucleic
acid detection kit (BioGerm, China) according to the manufacturer’s instructions. The
PCR reaction system was configured as follows: 6 µL of qRT-PCR reaction solution, 2
µL of qRT-PCR enzyme mixture, 2 µL of primer probe and 2.5 µL of template, and the
reaction was performed as follows: 50 C for 10 min, 95 C for 5 min, followed by 40
cycles of 95 C for 10 s, 55 C for 40 s. The values of 2CT were calculated according
to the CT value measured from the PCR instrument, to represent the relative virus
copies of the drug group to the control group. The virus replication inhibition rate (%)
was calculated as (12CT )×100%. The dose-response curves were plotted according
to viral RNA copies and the drug concentrations using GraphPad Prism (GraphPad
Software, USA).
4.9. Time-of-addition assay
To facilitate the observation of the antiviral effects of drugs against SARS-CoV-2
at different timing, relative high doses of the tested drugs (CVL218 at 20 µM and
remdesivir at 10µM) were used for the time-of-addition assay. Vero E6 cells with a
density of 5 ×104cells per well were treated with the tested drugs, or DMSO as
controls at different stages of virus infection. The cells were infected with virus at an
MOI of 0.05. The “Full-time” treatment was to evaluate the maximum antiviral effects,
with the tested drugs in the cell culture medium during the whole experiment process,
which was consistent with the descriptions in the virus infection assay. For the “Entry”
23
author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint (which was not peer-reviewed) is the. https://doi.org/10.1101/2020.03.11.986836doi: bioRxiv preprint
treatment, the tested drug was added to the cells for 1 h before virus infection, and
then cells were maintained in the drug-virus mixture for 2 h during the virus infection
process. After that, the culture medium containing both virus and the tested drug
was replaced with fresh culture medium till the end of the experiment. For the “Post-
entry” experiment, virus was first added to the cells to allow infection for 2 h before the
virus-containing supernatant was replaced with drug-containing medium until the end
of the experiment. At 14 h post infection, the viral inhibition in the cell supernatants
of the tested drug was quantified by qRT-PCR, and calculated using the DMSO group
as reference.
4.10. Indirect immunofluorescence assay
Vero E6 cells were treated with CVL218 at 5 µM, 15 µM and 25 µM, respectively,
following the same procedure of “full-time” treatment. Infected cells were fixed with
80% acetone in PBS and permeabilized with 0.5% Triton X-100, and then blocked with
5% BSA in PBS buffer containing 0.05% Tween 20 at room temperature for 30 min.
The cells were further incubated with a rabbit polyclonal antibody against a SARS-CoV
nucleocapsid protein (Cambridgebio, USA) as primary antibody at a dilution of 1:200
for 2 h, followed by incubation with the secondary Alexa 488-labeled goat anti-rabbit
antibody (Beyotime, China) at a dilution of 1:500. Nuclei were stained with DAPI
(Beyotime, China). Immunofluorescence was observed using fluorescence microscopy.
4.11. Western blot assay
NP expression in infected cells was analyzed by Western blot. Protein samples were
separated by SDS-PAGE and then transferred onto polyvinylidene difluoride mem-
branes (Millipore, USA), before being blocked with 6% Rapid Block Buff II (Sangon
Biotech, China) at room temperature for 10 min. The blot was probed with the anti-
body against the viral nucleocapsid protein (Cambridgebio, USA) and the horseradish
peroxidase-conjugated Goat Anti-Rabbit IgG (Abcam, USA) as the primary and the
24
author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint (which was not peer-reviewed) is the. https://doi.org/10.1101/2020.03.11.986836doi: bioRxiv preprint
secondary antibodies, respectively. Protein bands were detected by chemiluminescence
using an ECL kit (Sangon Biotech, China).
4.12. CpG-PDN1826 induced IL-6 production in PBMCs
Peripheral blood mononuclear cells (Yicon, China) were cultured at 37 C at con-
centration 5% CO2atmospheric on a 96-well plate in RPMI1640 cell growth medium
(Corning, Cat.10-040-CVR). For stimulation, PBMC cells were incubated with 1 µM
CpG-ODN1826 (InvivoGen, Cat. tlrl-1826). To test whether CVL218 can inhibit IL-
6 production, 1 µM and 3 µM concentrations of CVL218 were added to cell culture
medium for 6 and 12 h, respectively. The concentration of IL-6 was determined by
ELISA using a commercial kit (Dakewe Biotech, Cat. 1110602).
4.13. Pharmacokinetics and toxicity study
Sprague-Dawley rats were purchased from Shanghai Laboratory Animal Center,
China. The animals were grouped and housed in wire cages with no more than six
per cage, under good laboratory conditions (temperature 25 ±2C; relative humidity
50 ±20%) and with dark and light cycle (12 h/12 h). Only healthy animals were
used for experimental purpose. The pharmacokinetics and biodistribution study in
Sprague-Dawley rats was approved by Center for Drug Safety Evaluation and Research,
Shanghai Institute of Materia Medica, Chinese Academy of Sciences. A total of 144
Sprague-Dawley rats with each sex were used for toxicity study. Animals were ran-
domly separated into four groups (18/sex/group). CVL218 was administered at doses
of 20, 40, 60 and 160 mg/kg. For all the groups, 20 rats (10/sex/group) were randomly
selected and euthanized at day 28, and their sections of various tissues and organs were
obtained and frozen. Ten (5/sex/group) animals were euthanized after a 28-day drug
free period, and their sections of tissues and organs were obtained and frozen. Six
(3/sex/group) were euthanized after the blood-samples were obtained. For pharma-
cokinetic and toxicity evaluation, clinical symptoms, mortality and the animals’ body
25
author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint (which was not peer-reviewed) is the. https://doi.org/10.1101/2020.03.11.986836doi: bioRxiv preprint
weight were examined. Serum (0.5 mL) was collected to analyze toxicokinetics at dif-
ferent time points post drug administration. The plasma concentration-time data were
analyzed using a non-compartmental method (Phoenix, version 1.3, USA) to derive the
pharmacokinetic parameters.
4.14. Biodistribution study
Thirty Sprague-Dawley rats were randomly divided into three time point groups
(3/sex/group). At 3, 6 and 8h after CVL218 administration, animals were sacrificed,
and the brain, heart, lung, liver, spleen, stomach and kidney tissues were collected.
Tissue samples were washed in ice-cold saline, blotted with paper towel to remove
excess fluid, and weighed. Tissue samples were fluid, weighted and stored at 20 ±2
C until the determination of drug concentration by LC-MS-MS.
4.15. Toxicity study in cynomolgus monkeys
Healthy male and female cynomolgus monkeys aged 3–4 years were purchased from
Guangdong Landau Biotechnology, China. The animals were maintained in accordance
with the Guide for the Care and Use of Laboratory Animals.
Cynomolgus monkey (5/sex/group) were selected using a computerized random-
ization procedure, and administered CVL218 by nasogastric feeding at dose levels of
0 (control), 5, 20, 80 mg/kg. Individual dose volumes were adjusted weekly based on
body weight of monkeys. The monkeys were observed twice daily for viability/mortality
and for any change in behavior, reaction to treatment or ill-health. Electrocardiograms,
intraocular pressure, rectal temperature and body weight were recorded. For all the
groups, 2/3 of the animals were randomly selected and euthanized at day 28. The re-
maining animals were euthanized after a 28-day drug free period. Blood samples were
taken before and at 0.5, 1, 2, 4, 8 and 24 h post-dose on days 1 and 28 of the treat-
ment period. Pharmacokinetic evaluation was performed using a non-compartmental
method (Phoenix, version 1.3, USA) and pharmacokinetic parameters were calculated
26
author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint (which was not peer-reviewed) is the. https://doi.org/10.1101/2020.03.11.986836doi: bioRxiv preprint
for individual monkeys.
4.16. Statistical analysis
All data represent the means ±standard deviations (SDs) of n values, where n cor-
responds to the number of data points used. The figures were prepared using GraphPad
Prism (GraphPad Software, USA). The statistical significance was calculated by SPSS
(ver.12), and two values were considered significantly different if the p-value is <0.05.
4.17. Molecular docking
The docking program AutoDock4.2 [79] was used to model the molecular interactions
between PARP1 inhibitors CVL218 and olaparib to the N-terminal domain of the N
protein of SARS-CoV-2 (SARS-CoV-2-N-NTD). The structure of SARS-CoV-2-N-NTD
used for molecular docking was built from homology modeling [80]. The AutoGrid
program was used to generate a grid map with 60×60×60 points spaced equally at
0.375 ˚
A for evaluating the binding energies between the protein and the ligands.
27
author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint (which was not peer-reviewed) is the. https://doi.org/10.1101/2020.03.11.986836doi: bioRxiv preprint
Acknowledgement
This work was supported in part by the National Natural Science Foundation of
China (61872216, 81630103, 31900862), Jiangsu Provincial Emergency Project on Pre-
vention and Control of COVID-19 Epidemic (BE2020601), the Nation Science and Tech-
nology Major Projects for Major New Drugs Innovation and Development (2018ZX09711003-
004-0022019ZX09301010), Pudong New Area Science and Technology Development
Foundation (PKX2019-S08), and the Turing AI Institute of Nanjing, and the Zhong-
guancun Haihua Institute for Frontier Information Technology. The authors thank Dr.
Feixiong Cheng for email communications on the connectivity map analysis.
28
author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint (which was not peer-reviewed) is the. https://doi.org/10.1101/2020.03.11.986836doi: bioRxiv preprint
29
author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint (which was not peer-reviewed) is the. https://doi.org/10.1101/2020.03.11.986836doi: bioRxiv preprint
Figure 1 (preceding page): Schematic illustration of our drug repositioning pipeline
for discovering the potential drugs to treat the COVID-19 disease. (A). The overview
of our drug screening pipeline. The initial drug set for screening contains 6255 drug
candidates, mainly including 1786 approved drugs, 1125 investigational drugs and 3290
experimental drugs. The number of drug candidates after each filtering step is also
shown. (B). The network-based knowledge mining module. Seven individual networks
containing three types of nodes (i.e., drugs, human targets and virus targets) and the
corresponding edges describing their interactions, associations or similarities are first
constructed based on the known chemical structures, protein sequences and relations
derived from publically available databases. Then a deep learning based method, which
learns and updates the feature representation of each node through information aggre-
gation, is used to predict the potential drug candidates against a specific coronavirus.
(C). The automated relation extraction module. The structure of each sentence from
the literature texts is first learned from the encoded word features using the Gumbel
tree gated recurrent unit technique [4,74]. Then the learned sequence structures as
well as the corresponding encoded word features are fed into a relation classifier to au-
tomatically extract the relations between two entities from large-scale documents in the
literature. (D). The connectivity map (CMap) analysis module. The transcriptome pro-
files of the Peripheral Blood Mononuclear Cell (PBMC) samples from the SARS-CoV
infected patients and healthy persons are compared to derive the query gene expression
signatures, which are then correlated to the drug-perturbed cellular expression profiles
in the connectivity map [5] to filter out the anti-SARS-CoV drug candidates.
30
author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint (which was not peer-reviewed) is the. https://doi.org/10.1101/2020.03.11.986836doi: bioRxiv preprint
31
author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint (which was not peer-reviewed) is the. https://doi.org/10.1101/2020.03.11.986836doi: bioRxiv preprint
Figure 2 (preceding page): The in vitro anti-SARS-CoV-2 activities of the tested drugs.
(A). The in vitro inhibition rates of multiple tested drugs on SARS-CoV-2 replication
at individual concentrations. (B). The concentration-dependent inhibition curve of
CVL218 against SARS-CoV-2 replication and its cytotoxicity results. (C). Visualization
of virus nucleoprotein (NP) expression of the infected cells upon treatment of CVL218
at 14 h post the SARS-CoV-2 infection using fluorescence microscopy. (D). Time-of-
addition results on the inhibition of CVL218 and remdesivir against SARS-CoV-2 in
vitro. The viral inhibitory activities of CVL218 and remdesivir were measured at “full-
time”, “entry”, and “post-entry” stages, respectively. (E). Virus NP expression in the
infected cells upon the treatment of CVL218 and remdesivir was analyzed by Western
blot.
32
author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint (which was not peer-reviewed) is the. https://doi.org/10.1101/2020.03.11.986836doi: bioRxiv preprint
Figure 3: CVL218 attenuates the CpG-induced IL-6 production in a time- and dose-
dependent manner.
33
author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint (which was not peer-reviewed) is the. https://doi.org/10.1101/2020.03.11.986836doi: bioRxiv preprint
Figure 4: Effects of CVL218 on body weight in rats (A) and monkeys (B). Rats and
monkeys were orally administered 20/60/160 mg/kg and 5/20/80 mg/kg of CVL218,
respectively, for 28 consecutive days and then followed by 28 more days without drug
administration.
34
author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint (which was not peer-reviewed) is the. https://doi.org/10.1101/2020.03.11.986836doi: bioRxiv preprint
35
author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint (which was not peer-reviewed) is the. https://doi.org/10.1101/2020.03.11.986836doi: bioRxiv preprint
Figure 5 (preceding page): The modeled structure of the N-terminal domain of nu-
cleocapsid protein (N-NTD) of SARS-CoV-2 complexed with PARP1 inhibitors. (A).
Overall complex structures of HCoV-OC43-N-NTD (i.e., the N-NTD of HCoV-OC43)
bound to PJ-34 (PDB ID: 4kxj), and SARS-CoV-2-N-NTD (i.e., the N-NTD of SARS-
CoV-2) bound to CVL218 and olaparib (both modeled by AutoDock4.2) in ribbon
view. The structure of SARS-CoV-2-N-NTD that we used for docking simulation was
derived from homology modeling [80], with residues ranging from 47 to 177 (GenBank:
QHD43423). (B). Detailed molecular interactions between the coronavirus N-NTDs
and PARP1 inhibitors. Left panel: The experimentally solved complex structure of
HCoV-OC43-N-NTD (cyan ribbon) bound to PJ-34 (yellow sticks). Middle panel: The
modeled complex structure of SARS-CoV-2-N-NTD (purple ribbon) bound to CVL218
(yellow sticks). Right panel: The modeled complex structure of SARS-CoV-2-N-NTD
(purple ribbon) bound to olaparib (yellow sticks). The key residues interacting with
the inhibitors are shown as green sticks. The hydrogen bonds are denoted as pink
dashes. (C). Multiple sequence alignment (performed using MUSCLE [81]) of the N-
NTDs among SARS-CoV-2, SARS-CoV, HCoV-OC43, mouse hepatitis virus (MHV)
and infectious bronchitis virus (IBV). The virus names are listed on the left with avail-
able PDB codes shown in the parentheses. The sequence of SARS-CoV-2-N-NTD was
obtained from GenBank (QHD43423). Secondary structure elements of HCoV-OC43-N
are depicted above the sequence alignment. Asterisks indicate the key residues inter-
acting with the inhibitors. The residues conserved among all five viruses are shaded
in red, the residues with the percentage of conservation larger than 50% are shaded in
green, and the similar residues are shaded in yellow.
36
author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint (which was not peer-reviewed) is the. https://doi.org/10.1101/2020.03.11.986836doi: bioRxiv preprint
37
author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint (which was not peer-reviewed) is the. https://doi.org/10.1101/2020.03.11.986836doi: bioRxiv preprint
Figure 6 (preceding page): The putative mechanisms for PARP1 inhibitors to combat
the COVID-19 disease, derived based on the data present in this study and the known
antiviral activities of PARP1 inhibitors previously reported in the literature. (A).
Schematic diagram showing the possible antiviral mechanisms of PARP1 inhibitors in
the life cycle of coronavirus in human cells. PARP1 inhibitors have been previously re-
ported in the literature to suppress viral replication and imped the binding of nucleocap-
sid protein to viral RNAs, thus preventing the virus infection [31,34,35,36]. (B). Po-
tential protective effects of PARP1 inhibitors in the treatment of COVID-19. The anti-
inflammation effects of PARP1 inhibitors may be achieved through two possible molec-
ular pathways. The first one is to modulate the expression of pro-inflammation factors
such as NF-κB, AP-1, IL-6 and downstream cytokines and chemokines [37,38,39,40].
The second possible pathway is to prevent the overactivation of PARP1 and thus avoid
the depletion of NAD+and ATP, and the consequent cellular energy failure and cell
death caused by necrosis [37,38,39,40]. (C). The potential antiviral effects of PARP1
inhibitors through suppressing the ADP-ribosylation of viral proteins and interven-
ing the host-pathogen interactions, thus resulting in the inhibition of viral replica-
tion [34,35,42,43].
38
author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint (which was not peer-reviewed) is the. https://doi.org/10.1101/2020.03.11.986836doi: bioRxiv preprint
Table 1: The top list of drug candidates identified by the connectivity map analysis
using the gene expression profiles of the peripheral blood mononuclear cell (PBMC)
samples of ten SARS-CoV-infected patients [6]. The connectivity map score [5] of
90.0 was used as the cut-off threshold to determine the top list, i.e., only those drug
candidates with the connectivity scores of the query ranked to the top 10% of the
reference perturbations were selected. Two PARP1 inhibitors (i.e., veriparib and PJ-
34) were chosen into the top list (shown in bold).
Connectivity Map Score Compound BRD ID Name Description
98.94 BRD-K87142802 Veliparib PARP inhibitor
95.37 BRD-A35338386 NECA Adenosine receptor agonist
95.26 BRD-K11853856 PJ-34 PARP inhibitor
92.96 BRD-A53952395 Prilocaine Local anesthetic
91.8 BRD-K32977963 Eugenol Androgen receptor antagonist
91.56 BRD-A09495397 Bicuculline GABA receptor antagonist
91.32 BRD-K82164249 Andarine Androgen receptor modulator
39
author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint (which was not peer-reviewed) is the. https://doi.org/10.1101/2020.03.11.986836doi: bioRxiv preprint
Table 2: Selected examples of the predicted drug candidates against SARS-CoV or
MERS-CoV that can be validated by the literature evidences in a retrospective study.
The drug candidates were first predicted using our network based knowledge mining al-
gorithm with a cut-off threshold of p-value <0.05. Then the identified drug candidates
were validated using an automated relation extraction method from the large-scale
literature texts, followed by a minimum of manual checking.
Drug name Virus Original targetsaOriginal indicationsbReferencesc
Chloroquine SARS-
CoV
Fe(II)-protoporphyrin IX (Plasmodium
falciparum)
Malaria [8,82]
Gemcitabine SARS-
CoV
Ribonucleoside-diphosphate reductase
large subunit (Human)
Cancer [10,82]
Cyclosporine SARS-
CoV
Calcineurin subunit B type 2 (Human) Prophylaxis of organ rejection, severe
active rheumatoid arthritis (RA)
[12]
Indomethacin SARS-
CoV
Prostaglandin G/H synthase 1 and 2
(Human)
Symptomatic management of rheuma-
toid arthritis
[83]
Curcumin SARS-
CoV
Peroxisome proliferator-activated re-
ceptor gamma (Human)
Various pro-inflammatory diseases [84][85]
PJ-34 SARS-
CoV
Poly-ADP-ribose polymerase 1 (Hu-
man)
Experimental allergic encephalomyeli-
tis [86]
[87]
Hesperetin SARS-
CoV
Sterol O-acyltransferase 1 (Human) Lowering cholesterol [88][89]
Miltefosine MERS-
CoV
P-glycoprotein 1 (Human) Mucosal, cutaneous, visceral leishmani-
asis
[14]
Chlorpromazine MERS-
CoV
Dopamine D2 and D1 receptors, 5-
hydroxytryptamine receptor 1A and
2A, Alpha-1A and -1B adrenergic re-
ceptors, Histamine H1 receptor (Hu-
man)
Schizophrenia and other psychotic dis-
orders
[10,82]
Imatinib MERS-
CoV
BCR-ABL fusion kinase (Human) Leukemia [82,90]
a. The parenthesis indicates the organism of the target(s).
b. Drug indications stand for the official indications approved by the FDA, obtained
from DrugBank [17], unless other references are stated.
c. References stand for the supporting literatures.
40
author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint (which was not peer-reviewed) is the. https://doi.org/10.1101/2020.03.11.986836doi: bioRxiv preprint
Table 3: Comparison of the tissue to plasma concentration ratios between CVL218
and arbidol in rats. The concentrations of CVL218 over different tissues of rats were
measured at the 180 min time point following 20 mg/kg oral administration. The
concentrations of arbidol over different tissues of rats at the 15 min time point following
54 mg/kg oral administration were obtained from the literature [91,92]. Means and
standard deviations are shown.
Tissue Tissue to plasma concentration ratio
CVL218 Arbidol
Lung 188.364 ±28.467 0.553 ±0.392
Spleen 54.897 ±6.250 0.110 ±0.060
Liver 46.780 ±5.215 0.204 ±0.062
Kidney 41.307 ±5.391 0.055 ±0.040
Stomach 22.133 ±7.130 4.920 ±2.159
Heart 16.514 ±1.348 0.028 ±0.015
Brain 9.728 ±1.130 0.011 ±0.002
41
author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint (which was not peer-reviewed) is the. https://doi.org/10.1101/2020.03.11.986836doi: bioRxiv preprint
Supplementary Materials
Detailed docking results on the interactions between PARP1 inhibitors and the N-terminal
domain of the nucleocapsid protein of SARS-CoV-2
We noticed that the binding surface of HCoV-OC43-N-NTD with PJ-34 consists of
the N-terminal loop, β2 and β3 strands (Figure 5B left panel). The oxygen and nitrogen
atoms on the 6-phenanthridinone of PJ-34 form three hydrogen bonds with S64 (3.1
˚
A), Y126 (3.0 ˚
A) and F66 (water-mediated) of HCoV-OC43-N-NTD. In addition to
the hydrogen network, the aromatic ring of phenanthridinone on PJ-34 participates in
π-stacking with H104 on β2 strand and Y124 on β3 strand. Compared to CoV-OC43-
N-NTD, the binding pocket of SARS-CoV-2-N-NTD encompassed by β2 strand, β3
strand and loops is approximately similar in structural compositions, but more spacious,
which may facilitate to bind with larger molecules. As shown in the Figure 5B (middle
panel), CVL218 can be reliably docked inside the pocket of SARS-CoV-2-N-NTD with
key residues including Y111 on β2 strand, R92 on β3 strand, as well as S51 and E118
on loops. Among them, Y111 forms a bifurcated hydrogen bond to the oxygen atom
of the benzofuran ring and the nitrogen atom of the amide group on CVL218 with a
distance of 2.6 ˚
A and 3.1 ˚
A, respectively. In addition, R92 further stabilizes the CVL218
molecule by forming a hydrogen bond with the oxygen atom of the amide group on the
benzofuran ring. The nitrogen atom of the amine close to the benzene ring of CVL218
also forms a hydrogen bond of distance 2.8 ˚
A with the side chain of E118. In addition to
the hydrogen network which plays an essential role in CVL218 binding, the hydrophobic
interactions involved by T49, Y109, and Y112 of SARS-CoV-2-N-NTD also contribute
to the molecular interaction.
The binding surface of olaparib on SARS-CoV-2-N-NTD is similar but not exactly
the same to that of CVL218. The two carbonyl groups of olaparib form hydrogen
bonds with residues S51 and R149 of SARS-CoV-2-N-NTD with distances 3.0 ˚
A and
3.3 ˚
A, respectively. In addition, the residues around the binding surface (i.e., R88, R92,
42
author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint (which was not peer-reviewed) is the. https://doi.org/10.1101/2020.03.11.986836doi: bioRxiv preprint
Y109, Y111, R149, P151) participate in the interaction with olaparib via hydrophobic
interactions.
43
author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint (which was not peer-reviewed) is the. https://doi.org/10.1101/2020.03.11.986836doi: bioRxiv preprint
Figure S1: Structures of PARP1 inhibitors mentioned in this study.
44
author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint (which was not peer-reviewed) is the. https://doi.org/10.1101/2020.03.11.986836doi: bioRxiv preprint
Figure S2: Tissue distribution characteristics of CVL218 in rats, with the highest
concentration in lung. The concentrations of CVL218 in different tissues were measured
at the 3/6/8 h time points after 20 mg/kg oral administration to rats. With the
extension of administration time, the concentration of CVL218 in each organ decreased
in a time-dependent manner.
45
author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint (which was not peer-reviewed) is the. https://doi.org/10.1101/2020.03.11.986836doi: bioRxiv preprint
Table S1: The top list of perturbational classes identified by the connectivity map
analysis using the gene expression profiles of the peripheral blood mononuclear cell
(PBMC) samples of ten SARS-CoV-infected patients [6].
Connectivity map score Perturbational classes
40.84 PARP inhibitor
37.31 RNA Polymerase Enzymes LOF
37.27 DNA synthesis inhibitor
36.55 GABA receptor antagonist
29.62 MDM inhibitor
46
author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint (which was not peer-reviewed) is the. https://doi.org/10.1101/2020.03.11.986836doi: bioRxiv preprint
Table S2: Comparison of the tissue distributions of CVL218 and arbidol in rats,
following 20 mg/kg and 54 mg/kg oral administrations, respectively.
Drugs Dose Time Lung Spleen Liver Kidney StomachHeart Brain
(mg/kg) (min)
CVL218 20
180 69318±10476 20202±2300 17215±1919 15201±1984 8145±2624 6077±496 3580±416
360 18858±2365 6358±1058 2187±859 3903±594 1871±813 1390±292 998±220
480 4183±847 1475±324 213±88 993±327 569±293 275±80 317±55
Arbidola54
5933±837 48±35 104±82 79±54 8210±5410 72±47 101±67
15 2603±1848 519±281 963±290 259±190 23180±10170 132±69 50±10
360 833±397 143±51 262±175 58±21 52750±3059 41±28 31±21
a. The concentrations of arbidol in different tissues of rats at 5/15/360 min time
points with 54 mg/kg oral administration were obtained from [92].
47
author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint (which was not peer-reviewed) is the. https://doi.org/10.1101/2020.03.11.986836doi: bioRxiv preprint
Table S3: Comparisons of pharmacokinetic parameters in rats between CVL218 and
arbidol following 20/40 mg/kg and 18/54 mg/kg oral administrations, respectively.
Drugs Dose Gender Tmax Cmax AUC0tAUC0−∞ MRT0 − ∞ t1/2
(mg/kg) (h) (ng/mL) (ng·h/mL) (ng·h/mL) (h) (h)
CVL218
20
male 4.0 (4.0˜
4.0) 234 ±35 1070 ±176 1111 ±192 3.91 ±0.19 1.19 ±0.09
female 3.0 (2.0˜
3.0) 502 ±80 2196 ±228 2222 ±241 3.16 ±0.41 1.1 ±0.16
total 3.5 (2.0˜
4.0) 368 ±157 1633 ±643 1666 ±639 3.54 ±0.50 1.15 ±0.13
40
male 3.0 (2.0˜
4.0) 510 ±259 2802 ±967 2830 ±983 4.51 ±0.18 1.3 ±0.33
female 2.0 (2.0˜
3.0) 940 ±117 5220 ±1113 5242 ±1115 4.05 ±0.43 1.29 ±0.21
total 2.5 (2.0˜
4.0) 725 ±296 4011 ±1620 4036 ±1620 4.28 ±0.39 1.3 ±0.24
Arbidola18 male 0.28 ±0.11 1002 ±298 1956 ±895 2224 ±1058 -3.6 ±1.2
54 male 0.18 ±0.06 4711 ±2361 6790 ±2749 7558 ±2877 -3.3 ±0.7
a. The pharmacokinetic data of arbidol were obtained from [91].
48
author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint (which was not peer-reviewed) is the. https://doi.org/10.1101/2020.03.11.986836doi: bioRxiv preprint
Table S4: Toxicokinetic parameters of CVL218 in rats in a four-week toxicity study.
Toxicological parameters
Day 1 Day 28
Group Gender Dose Tmax Cmax AUC024 Tmax Cmax AUC024
(mg/kg) (h) (ng/mL) (h·ng/mL) (h) (ng/mL) (h·ng/mL)
1
M20
Mean 3.00 261 2373 3.00 147 1004
SD 0.00 124 2000 0.00 61.0 431
N 3 3 3 3 3 3
F20
Mean 3.00 314 1674 3.00 147 797
SD 0.00 56.2 382 0.00 35.7 197
N 3 3 3 3 3 3
2
M60
Mean 5.00 513 6784 3.00 611 5610
SD 0.00 119 1592 0.00 114 1343
N 3 3 3 3 3 3
F60
Mean 5.30 708 9092 2.30 453 4090
SD 2.50 137 549 1.20 115 312
N 3 3 3 3 3 3
3
M 160
Mean 6.00 659 9102 5.30 824 10253
SD 1.70 77.1 1776 2.50 268 3008
N 3 3 3 3 3 3
F160
Mean 2.30 614 7605 3.00 629 6657
SD 1.20 122 1056 2.00 213 4592
N 3 3 3 3 3 3
49
author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint (which was not peer-reviewed) is the. https://doi.org/10.1101/2020.03.11.986836doi: bioRxiv preprint
Table S5: Toxicokinetic parameters of CVL218 in monkeys in a four-week toxicity
study.
Toxicological parameters
Day 1 Day 28
Group Gender Dose Tmax Cmax AUC024 Tmax Cmax AUC024
(mg/kg) (h) (ng/mL) (h·ng/mL) (h) (ng/mL) (h·ng/mL)
1
M5
Mean 2.2 119 528 2.8 48 215
SD 0.4 31.7 138.2 1.3 12.5 62.3
N 5 5 5 5 5 5
F5
Mean 4 76 451 3.8 36 172
SD 1.4 38.8 239.8 1.6 20.3 77.7
N 5 5 5 5 5 5
2
M20
Mean 4 440 4838 5 239 2111
SD 2.5 162.4 2086.4 0.0 91.1 1186.8
N 5 5 5 5 5 5
F20
Mean 4.6 479 4963 4.6 322 2779
SD 0.9 100.5 1189.5 0.9 125.4 1458
N 5 5 5 5 5 5
3
M 80
Mean 3.4 1372 16924 6.8 1582 22912
SD 0.9 617.4 8831.1 1.6 416.6 8859.6
N 5 5 5 5 5 5
F80
Mean 5.2 1389 19466 5.6 1403 18774
SD 1.8 387.5 5535.4 2.5 489.6 6179.1
N 5 5 5 5 5 5
50
author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint (which was not peer-reviewed) is the. https://doi.org/10.1101/2020.03.11.986836doi: bioRxiv preprint
References
[1] T. T. Ashburn, K. B. Thor, Drug repositioning: identifying and developing new
uses for existing drugs, Nature reviews drug discovery 3 (8) (2004) 673–683.
[2] S. Pushpakom, F. Iorio, P. A. Eyers, K. J. Escott, S. Hopper, A. Wells, A. Doig,
T. Guilliams, J. Latimer, C. McNamee, et al., Drug repurposing: progress, chal-
lenges and recommendations, Nature reviews drug discovery 18 (1) (2019) 41–58.
[3] Y. Zhou, Y. Hou, J. Shen, Y. Huang, W. Martin, F. Cheng, Network-based drug
repurposing for human coronavirus, medRxiv.
[4] L. Hong, J. Lin, J. Tao, J. Zeng, BERE: An accurate distantly supervised biomed-
ical entity relation extraction network, arXiv preprint arXiv:1906.06916.
[5] A. Subramanian, R. Narayan, S. M. Corsello, D. D. Peck, T. E. Natoli, X. Lu,
J. Gould, J. F. Davis, A. A. Tubelli, J. K. Asiedu, et al., A next generation
connectivity map: L1000 platform and the first 1,000,000 profiles, Cell 171 (6)
(2017) 1437–1452.
[6] R. Reghunathan, M. Jayapal, L.-Y. Hsu, H.-H. Chng, D. Tai, B. P. Leung, A. J.
Melendez, Expression profile of immune response genes in patients with severe
acute respiratory syndrome, BMC immunology 6 (1) (2005) 2.
[7] P. Goel, V. Gerriets, Chloroquine, in: StatPearls [Internet], StatPearls Publishing,
2019.
[8] E. Keyaerts, L. Vijgen, P. Maes, J. Neyts, M. Van Ranst, In vitro inhibition of
severe acute respiratory syndrome coronavirus by chloroquine, Biochemical and
biophysical research communications 323 (1) (2004) 264–268.
[9] V. Oldfield, K. Wellington, Gemcitabine, American journal of cancer 4 (5) (2005)
337–344.
51
author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint (which was not peer-reviewed) is the. https://doi.org/10.1101/2020.03.11.986836doi: bioRxiv preprint
[10] J. Dyall, C. M. Coleman, B. J. Hart, T. Venkataraman, M. R. Holbrook, J. Kin-
drachuk, R. F. Johnson, G. G. Olinger, P. B. Jahrling, M. Laidlaw, et al., Re-
purposing of clinically developed drugs for treatment of Middle East respiratory
syndrome coronavirus infection, Antimicrobial agents and chemotherapy 58 (8)
(2014) 4885–4893.
[11] P. Forsythe, S. Paterson, Ciclosporin 10 years on: indications and efficacy, The
veterinary record 174 (Suppl 2) (2014) 13.
[12] S. Pfefferle, J. Sch¨opf, M. K¨ogl, C. C. Friedel, M. A. M¨uller, J. Carbajo-
Lozoya, T. Stellberger, E. von DallArmi, P. Herzog, S. Kallies, et al., The SARS-
coronavirus-host interactome: identification of cyclophilins as target for pan-
coronavirus inhibitors, PLoS pathogens 7 (10).
[13] J. Berman, Miltefosine, an FDA-approved drug for the ‘orphan disease’, leishma-
niasis, Expert opinion on orphan drugs 3 (6) (2015) 727–735.
[14] J. Kindrachuk, B. Ork, B. J. Hart, S. Mazur, M. R. Holbrook, M. B. Frieman,
D. Traynor, R. F. Johnson, J. Dyall, J. H. Kuhn, et al., Antiviral potential of
ERK/MAPK and PI3K/AKT/mTOR signaling modulation for Middle East respi-
ratory syndrome coronavirus infection as identified by temporal kinome analysis,
Antimicrobial agents and chemotherapy 59 (2) (2015) 1088–1099.
[15] C. E. Adams, J. Rathbone, B. Thornley, M. Clarke, J. Borrill, K. Wahlbeck, A. G.
Awad, Chlorpromazine for schizophrenia: a Cochrane systematic review of 50 years
of randomised controlled trials, BMC medicine 3 (1) (2005) 15.
[16] M. H. Cohen, G. Williams, J. R. Johnson, J. Duan, J. Gobburu, A. Rahman,
K. Benson, J. Leighton, S. K. Kim, R. Wood, et al., Approval summary for ima-
tinib mesylate capsules in the treatment of chronic myelogenous leukemia, Clinical
cancer research 8 (5) (2002) 935–942.
52
author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint (which was not peer-reviewed) is the. https://doi.org/10.1101/2020.03.11.986836doi: bioRxiv preprint
[17] D. S. Wishart, Y. D. Feunang, A. C. Guo, E. J. Lo, A. Marcu, J. R. Grant,
T. Sajed, D. Johnson, C. Li, Z. Sayeeda, et al., DrugBank 5.0: a major update to
the DrugBank database for 2018, Nucleic acids research 46 (D1) (2018) D1074–
D1082.
[18] P. Richardson, I. Griffin, C. Tucker, D. Smith, O. Oechsle, A. Phelan, J. Stebbing,
Baricitinib as potential treatment for 2019-nCoV acute respiratory disease, The
lancet.
[19] L. Velazquez-Salinas, A. Verdugo-Rodriguez, L. L. Rodriguez, M. V. Borca, The
role of interleukin 6 during viral infections, Frontiers in microbiology 10 (2019)
1057.
[20] M. M. McFarland-Mancini, H. M. Funk, A. M. Paluch, M. Zhou, P. V. Giridhar,
C. A. Mercer, S. C. Kozma, A. F. Drew, Differences in wound healing in mice with
deficiency of IL-6 versus IL-6 receptor, The journal of immunology 184 (12) (2010)
7219–7228.
[21] G. A. Palumbo, C. Scisciani, N. Pediconi, L. Lupacchini, D. Alfalate, F. Guerrieri,
L. Calvo, D. Salerno, S. Di Cocco, M. Levrero, et al., IL6 inhibits HBV transcrip-
tion by targeting the epigenetic control of the nuclear cccdna minichromosome,
PLoS one 10 (11).
[22] L. Velazquez-Salinas, S. J. Pauszek, C. Stenfeldt, E. S. OHearn, J. M. Pacheco,
M. V. Borca, A. Verdugo-Rodriguez, J. Arzt, L. L. Rodriguez, Increased virulence
of an epidemic strain of vesicular stomatitis virus is associated with interference
of the innate response in pigs, Frontiers in microbiology 9 (2018) 1891.
[23] W. Wu, K. K. Dietze, K. Gibbert, K. S. Lang, M. Trilling, H. Yan, J. Wu, D. Yang,
M. Lu, M. Roggendorf, et al., TLR ligand induced IL-6 counter-regulates the anti-
53
author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint (which was not peer-reviewed) is the. https://doi.org/10.1101/2020.03.11.986836doi: bioRxiv preprint
viral CD8+ T cell response during an acute retrovirus infection, Scientific reports
5 (2015) 10501.
[24] J. Zheng, Y. Shi, L. Xiong, W. Zhang, Y. Li, P. G. Gibson, J. L. Simpson, C. Zhang,
J. Lu, J. Sai, et al., The expression of IL-6, tNF-α, and MCP-1 in respiratory viral
infection in acute exacerbations of chronic obstructive pulmonary disease, Journal
of immunology research 2017.
[25] Y. Zhou, B. Fu, X. Zheng, D. Wang, C. Zhao, Y. Qi, R. Sun, Z. Tian, X. Xu,
H. Wei, Aberrant pathogenic GM-CSF+ T cells and inflammatory CD14+ CD16+
monocytes in severe pulmonary syndrome patients of a new coronavirus, bioRxiv.
[26] J.-j. Zhang, X. Dong, Y.-Y. Cao, Y.-d. Yuan, Y.-b. Yang, Y.-q. Yan, C. A. Akdis,
Y.-d. Gao, Clinical characteristics of 140 patients infected by SARS-CoV-2 in
Wuhan, China, Allergy.
[27] B. Diao, C. Wang, Y. Tan, X. Chen, Y. Liu, L. Ning, L. Chen, M. Li, Y. Liu,
G. Wang, et al., Reduction and functional exhaustion of T cells in patients with
coronavirus disease 2019 (COVID-19), medRxiv.
[28] C. Huang, Y. Wang, X. Li, L. Ren, J. Zhao, Y. Hu, L. Zhang, G. Fan, J. Xu,
X. Gu, et al., Clinical features of patients infected with 2019 novel coronavirus in
Wuhan, China, The lancet 395 (10223) (2020) 497–506.
[29] B. Li, F. Feng, G. Yang, A. Liu, N. Yang, Q. Jiang, H. Zhang, T. Wang, P. Li,
Y. Mao, et al., Immunoglobulin G/M and cytokines detections in continuous sera
from patients with novel coronaviruses (2019-nCoV) infection, Available at SSRN
3543609.
[30] J.-X. He, M. Wang, X.-J. Huan, C.-H. Chen, S.-S. Song, Y.-Q. Wang, X.-M. Liao,
C. Tan, Q. He, L.-J. Tong, et al., Novel PARP1/2 inhibitor mefuparib hydrochlo-
54
author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint (which was not peer-reviewed) is the. https://doi.org/10.1101/2020.03.11.986836doi: bioRxiv preprint
ride elicits potent in vitro and in vivo anticancer activity, characteristic of high
tissue distribution, Oncotarget 8 (3) (2017) 4156.
[31] S.-Y. Lin, C.-L. Liu, Y.-M. Chang, J. Zhao, S. Perlman, M.-H. Hou, Structural
basis for the identification of the N-terminal domain of coronavirus nucleocapsid
protein as an antiviral target, Journal of medicinal chemistry 57 (6) (2014) 2247–
2257.
[32] C.-k. Chang, S. Jeyachandran, N.-J. Hu, C.-L. Liu, S.-Y. Lin, Y.-S. Wang, Y.-M.
Chang, M.-H. Hou, Structure-based virtual screening and experimental validation
of the discovery of inhibitors targeted towards the human coronavirus nucleocapsid
protein, Molecular biosystems 12 (1) (2016) 59–66.
[33] A. Zumla, J. F. Chan, E. I. Azhar, D. S. Hui, K.-Y. Yuen, Coronavirusesdrug
discovery and therapeutic options, Nature reviews drug discovery 15 (5) (2016)
327.
[34] C. V. D´ery, G. de Murcia, D. Lamarre, N. Morin, G. G. Poirier, J. Weber, Possi-
ble role of ADP-ribosylation of adenovirus core proteins in virus infection, Virus
research 4 (4) (1986) 313–329.
[35] L. Liu, Z. Lear, D. J. Hughes, W. Wu, E.-m. Zhou, A. Whitehouse, H. Chen,
J. A. Hiscox, Resolution of the cellular proteome of the nucleocapsid protein from
a highly pathogenic isolate of porcine reproductive and respiratory syndrome virus
identifies PARP-1 as a cellular target whose interaction is critical for virus biology,
Veterinary microbiology 176 (1-2) (2015) 109–119.
[36] I. Tempera, Z. Deng, C. Atanasiu, C.-J. Chen, M. D’Erme, P. M. Lieberman, Reg-
ulation of Epstein-Barr virus OriP replication by poly (ADP-ribose) polymerase
1, Journal of virology 84 (10) (2010) 4988–4997.
55
author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint (which was not peer-reviewed) is the. https://doi.org/10.1101/2020.03.11.986836doi: bioRxiv preprint
[37] C. B. Larmonier, K. W. Shehab, D. Laubitz, D. R. Jamwal, F. K. Ghishan, P. R.
Kiela, Transcriptional reprogramming and resistance to colonic mucosal injury
in poly (ADP-ribose) polymerase 1 (PARP1)-deficient mice, Journal of biological
chemistry 291 (17) (2016) 8918–8930.
[38] G. Li, P. Cunin, D. D. Di Wu, Y. Yang, Y. Okada, R. M. Plenge, P. A. Nigrovic,
The rheumatoid arthritis risk variant CCR6DNP regulates CCR6 via PARP-1,
PLoS genetics 12 (9).
[39] M. Scalia, C. Satriano, R. Greca, A. M. G. Stella, E. Rizzarelli, V. Spina-Purrello,
PARP-1 inhibitors DPQ and PJ-34 negatively modulate proinflammatory commit-
ment of human glioblastoma cells, Neurochemical research 38 (1) (2013) 50–58.
[40] V. Schreiber, F. Dantzer, J.-C. Ame, G. De Murcia, Poly (ADP-ribose): novel
functions for an old molecule, Nature reviews molecular cell biology 7 (7) (2006)
517–528.
[41] J. Markovi´c, N. Grdovi´c, S. Dini´c, T. Karan-Djuraˇsevi´c, A. Uskokovi´c,
J. Arambaˇsi´c, M. Mihailovi´c, S. Pavlovi´c, G. Poznanovi´c, M. Vidakovi´c, PARP-
1 and YY1 are important novel regulators of CXCL12 gene transcription in rat
pancreatic beta cells, PLoS one 8 (3).
[42] E. Bortz, L. Westera, J. Maamary, J. Steel, R. A. Albrecht, B. Manicassamy,
G. Chase, L. Mart´ınez-Sobrido, M. Schwemmle, A. Garc´ıa-Sastre, Host-and strain-
specific regulation of influenza virus polymerase activity by interacting cellular
proteins, MBio 2 (4) (2011) e00151–11.
[43] S. L. Grady, J. Hwang, L. Vastag, J. D. Rabinowitz, T. Shenk, Herpes simplex virus
1 infection activates poly (ADP-ribose) polymerase and triggers the degradation of
poly (ADP-ribose) glycohydrolase, Journal of virology 86 (15) (2012) 8259–8268.
56
author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint (which was not peer-reviewed) is the. https://doi.org/10.1101/2020.03.11.986836doi: bioRxiv preprint
[44] C. Xia, J. J. Wolf, C. Sun, M. Xu, C. J. Studstill, J. Chen, H. Ngo, H. Zhu,
B. Hahm, PARP1 enhances influenza A virus propagation by facilitating degrada-
tion of host type I interferon receptor, Journal of virology.
[45] N. J. Curtin, C. Szabo, Therapeutic applications of PARP inhibitors: anticancer
therapy and beyond, Molecular aspects of medicine 34 (6) (2013) 1217–1256.
[46] P. Jagtap, C. Szab´o, Poly (ADP-ribose) polymerase and the therapeutic effects of
its inhibitors, Nature reviews drug discovery 4 (5) (2005) 421–440.
[47] G. E. Abdelkarim, K. Gertz, C. Harms, J. Katchanov, U. Dirnagl, C. Szabo,
M. Endres, Protective effects of PJ34, a novel, potent inhibitor of poly (ADP-
ribose) polymerase (PARP) in in vitro and in vivo models of stroke, International
journal of molecular medicine 7 (3) (2001) 255–260.
[48] M. I. Arshad, C. Piquet-Pellorce, A. Filliol, A. LHelgoualch, C. Lucas-Clerc,
S. Jouan-Lanhouet, M.-T. Dimanche-Boitrel, M. Samson, The chemical inhibitors
of cellular death, PJ34 and Necrostatin-1, down-regulate IL-33 expression in liver,
Journal of molecular medicine 93 (8) (2015) 867–878.
[49] K. Kapoor, E. Singla, B. Sahu, A. S. Naura, PARP inhibitor, olaparib ameliorates
acute lung and kidney injury upon intratracheal administration of LPS in mice,
Molecular and cellular biochemistry 400 (1-2) (2015) 153–162.
[50] S. Pazzaglia, C. Pioli, Multifaceted role of PARP-1 in DNA repair and inflamma-
tion: Pathological and therapeutic implications in cancer and non-cancer diseases,
Cells 9 (1) (2020) 41.
[51] G. S. Sethi, V. Dharwal, A. S. Naura, Poly (ADP-ribose) polymerase-1 in lung
inflammatory disorders: a review, Frontiers in immunology 8 (2017) 1172.
[52] N. A. Berger, V. C. Besson, A. H. Boulares, A. B¨urkle, A. Chiarugi, R. S. Clark,
N. J. Curtin, S. Cuzzocrea, T. M. Dawson, V. L. Dawson, et al., Opportunities
57
author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint (which was not peer-reviewed) is the. https://doi.org/10.1101/2020.03.11.986836doi: bioRxiv preprint
for the repurposing of PARP inhibitors for the therapy of non-oncological diseases,
British journal of pharmacology 175 (2) (2018) 192–222.
[53] S. Garc´ıa, C. Conde, The role of poly (ADP-ribose) polymerase-1 in rheumatoid
arthritis, Mediators of inflammation 2015.
[54] D. Mendez, A. Gaulton, A. P. Bento, J. Chambers, M. De Veij, E. F´elix, M. P.
Magari˜nos, J. F. Mosquera, P. Mutowo, M. Nowotka, et al., ChEMBL: towards
direct deposition of bioassay data, Nucleic acids research 47 (D1) (2019) D930–
D940.
[55] Y. Wang, S. Zhang, F. Li, Y. Zhou, Y. Zhang, Z. Wang, R. Zhang, J. Zhu, Y. Ren,
Y. Tan, et al., Therapeutic target database 2020: enriched resource for facilitating
research and early development of targeted therapeutics, Nucleic acids research
48 (D1) (2020) D1031–D1041.
[56] A. J. Pawson, J. L. Sharman, H. E. Benson, E. Faccenda, S. P. Alexander, O. P.
Buneman, A. P. Davenport, J. C. McGrath, J. A. Peters, C. Southan, et al., The
IUPHAR/BPS Guide to PHARMACOLOGY: an expert-driven knowledgebase of
drug targets and their ligands, Nucleic acids research 42 (D1) (2014) D1098–D1106.
[57] M. K. Gilson, T. Liu, M. Baitaluk, G. Nicola, L. Hwang, J. Chong, BindingDB
in 2015: a public database for medicinal chemistry, computational chemistry and
systems pharmacology, Nucleic acids research 44 (D1) (2016) D1045–D1053.
[58] R. Oughtred, C. Stark, B.-J. Breitkreutz, J. Rust, L. Boucher, C. Chang, N. Kolas,
L. ODonnell, G. Leung, R. McAdam, et al., The BioGRID interaction database:
2019 update, Nucleic acids research 47 (D1) (2019) D529–D541.
[59] D. Figeys, Mapping the human protein interactome, Cell research 18 (7) (2008)
716–724.
58
author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint (which was not peer-reviewed) is the. https://doi.org/10.1101/2020.03.11.986836doi: bioRxiv preprint
[60] M. J. Meyer, J. Das, X. Wang, H. Yu, INstruct: a database of high-quality 3D
structurally resolved protein interactome networks, Bioinformatics 29 (12) (2013)
1577–1579.
[61] L. Licata, L. Briganti, D. Peluso, L. Perfetto, M. Iannuccelli, E. Galeota, F. Sacco,
A. Palma, A. P. Nardozza, E. Santonico, et al., MINT, the molecular interaction
database: 2012 update, Nucleic acids research 40 (D1) (2012) D857–D861.
[62] M. J. Cowley, M. Pinese, K. S. Kassahn, N. Waddell, J. V. Pearson, S. M. Grim-
mond, A. V. Biankin, S. Hautaniemi, J. Wu, PINA v2. 0: mining interactome
modules, Nucleic acids research 40 (D1) (2012) D862–D865.
[63] A. Ebrahim, J. A. Lerman, B. O. Palsson, D. R. Hyduke, COBRApy: COnstraints-
based reconstruction and analysis for python, BMC systems biology 7 (1) (2013)
74.
[64] K. Breuer, A. K. Foroushani, M. R. Laird, C. Chen, A. Sribnaia, R. Lo, G. L.
Winsor, R. E. Hancock, F. S. Brinkman, D. J. Lynn, InnateDB: systems biology of
innate immunity and beyondrecent updates and continuing curation, Nucleic acids
research 41 (D1) (2013) D1228–D1233.
[65] G. Landrum, et al., RDKit: Open-source cheminformatics.
[66] UniProt: the universal protein knowledgebase, Nucleic acids research 45 (D1)
(2017) D158–D169.
[67] M. Zhao, W.-P. Lee, E. P. Garrison, G. T. Marth, SSW library: an SIMD Smith-
Waterman C/C++ library for use in genomic applications, PloS one 8 (12).
[68] Y. Luo, X. Zhao, J. Zhou, J. Yang, Y. Zhang, W. Kuang, J. Peng, L. Chen, J. Zeng,
A network integration approach for drug-target interaction prediction and compu-
tational drug repositioning from heterogeneous information, Nature communica-
tions 8 (1) (2017) 1–13.
59
author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint (which was not peer-reviewed) is the. https://doi.org/10.1101/2020.03.11.986836doi: bioRxiv preprint
[69] F. Wan, L. Hong, A. Xiao, T. Jiang, J. Zeng, NeoDTI: neural integration of neigh-
bor information from a heterogeneous network for discovering new drug–target
interactions, Bioinformatics 35 (1) (2019) 104–111.
[70] J. Gilmer, S. S. Schoenholz, P. F. Riley, O. Vinyals, G. E. Dahl, Neural message
passing for quantum chemistry, in: Proceedings of the 34th international conference
on machine learning-volume 70, JMLR. org, 2017, pp. 1263–1272.
[71] S. Rendle, C. Freudenthaler, Z. Gantner, L. Schmidt-Thieme, BPR: Bayesian per-
sonalized ranking from implicit feedback, arXiv preprint arXiv:1205.2618.
[72] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser,
I. Polosukhin, Attention is all you need, in: Advances in neural information pro-
cessing systems, 2017, pp. 5998–6008.
[73] K. Cho, B. Van Merri¨enboer, D. Bahdanau, Y. Bengio, On the properties
of neural machine translation: Encoder-decoder approaches, arXiv preprint
arXiv:1409.1259.
[74] E. Jang, S. Gu, B. Poole, Categorical reparameterization with gumbel-softmax,
arXiv preprint arXiv:1611.01144.
[75] S. Riedel, L. Yao, A. McCallum, Modeling relations and their mentions without
labeled text, in: Joint european conference on machine learning and knowledge
discovery in databases, Springer, 2010, pp. 148–163.
[76] R. Lu, X. Zhao, J. Li, P. Niu, B. Yang, H. Wu, W. Wang, H. Song, B. Huang,
N. Zhu, et al., Genomic characterisation and epidemiology of 2019 novel coron-
avirus: implications for virus origins and receptor binding, The lancet 395 (10224)
(2020) 565–574.
60
author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint (which was not peer-reviewed) is the. https://doi.org/10.1101/2020.03.11.986836doi: bioRxiv preprint
[77] S. F. Ahmed, A. A. Quadeer, M. R. McKay, Preliminary identification of potential
vaccine targets for the COVID-19 coronavirus (SARS-CoV-2) based on SARS-CoV
immunological studies, Viruses 12 (3) (2020) 254.
[78] J. Xu, S. Zhao, T. Teng, A. E. Abdalla, W. Zhu, L. Xie, Y. Wang, X. Guo,
Systematic comparison of two animal-to-human transmitted human coronaviruses:
SARS-CoV-2 and SARS-CoV, Viruses 12 (2) (2020) 244.
[79] G. M. Morris, R. Huey, W. Lindstrom, M. F. Sanner, R. K. Belew, D. S. Goodsell,
A. J. Olson, AutoDock4 and AutoDockTools4: Automated docking with selective
receptor flexibility, Journal of computational chemistry 30 (16) (2009) 2785–2791.
[80] C. Zhang, W. Zheng, X. Huang, E. W. Bell, X. Zhou, Y. Zhang, Protein structure
and sequence re-analysis of 2019-nCoV genome does not indicate snakes as its
intermediate host or the unique similarity between its spike protein insertions and
HIV-1, arXiv preprint arXiv:2002.03173.
[81] R. C. Edgar, MUSCLE: multiple sequence alignment with high accuracy and high
throughput, Nucleic acids research 32 (5) (2004) 1792–1797.
[82] J. Dyall, R. Gross, J. Kindrachuk, R. F. Johnson, G. G. Olinger, L. E. Hensley,
M. B. Frieman, P. B. Jahrling, Middle East respiratory syndrome and severe acute
respiratory syndrome: current therapeutic options and potential targets for novel
therapies, Drugs 77 (18) (2017) 1935–1966.
[83] K. Bhardwaj, How prepared are we to control severe acute respiratory syndrome
in future, Am. J. Virol 2 (2013) 8–19.
[84] S. C. Gupta, S. Patchva, B. B. Aggarwal, Therapeutic roles of curcumin: lessons
learned from clinical trials, The AAPS journal 15 (1) (2013) 195–218.
[85] V. Mac´ıas-Villamizar, R. Gonz´alez-Ascanio, Plantas de Santa Marta con posible
actividad biol´ogica antimicrobiana, Duazary 16 (2) (2019) 414–439.
61
author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint (which was not peer-reviewed) is the. https://doi.org/10.1101/2020.03.11.986836doi: bioRxiv preprint
[86] G. S. Scott, R. B. Kean, T. Mikheeva, M. J. Fabis, J. G. Mabley, C. Szabo, D. C.
Hooper, The therapeutic effects of PJ34 [N-(6-oxo-5, 6-dihydrophenanthridin-
2-yl)-N, N-dimethylacetamide. HCl], a selective inhibitor of poly (ADP-ribose)
polymerase, in experimental allergic encephalomyelitis are associated with im-
munomodulation, Journal of pharmacology and experimental therapeutics 310 (3)
(2004) 1053–1061.
[87] S. J. Nicolino Jr, I. Chayut, Ambient audio transformation using transformation
audio, uS Patent 8,280,068 (Oct. 2 2012).
[88] H. K. Kim, T.-S. Jeong, M.-K. Lee, Y. B. Park, M.-S. Choi, Lipid-lowering efficacy
of hesperetin metabolites in high-cholesterol fed rats, Clinica chimica acta 327 (1-2)
(2003) 129–137.
[89] C.-W. Lin, F.-J. Tsai, C.-H. Tsai, C.-C. Lai, L. Wan, T.-Y. Ho, C.-C. Hsieh, P.-
D. L. Chao, Anti-SARS coronavirus 3C-like protease effects of isatis indigotica root
and plant-derived phenolic compounds, Antiviral research 68 (1) (2005) 36–42.
[90] J. S. Shin, E. Jung, M. Kim, R. S. Baric, Y. Y. Go, Saracatinib inhibits middle
east respiratory syndrome-coronavirus replication in vitro, Viruses 10 (6) (2018)
283.
[91] X. LIU, Q.-g. ZHOU, H. LI, B.-c. CAI, X.-h. CHEN, K.-s. BI, Pharmacokinetics
of arbidol hydrochloride in rats, Chinese pharmacological bulletin 28 (12) (2012)
1747–1750.
[92] X. LIU, K. PEI, X.-h. CHEN, K.-s. BI, Distribution and excretion of arbidol hy-
drochloride in rats, Chinese journal of new drugs 22 (7) (2013) 829–833.
62
author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint (which was not peer-reviewed) is the. https://doi.org/10.1101/2020.03.11.986836doi: bioRxiv preprint
  • ... Research statement Poly-ADP-ribose polymerase 1 (PARP1) inhibition (Ge et al. 2020) Montelukast, deoxyrhapontin, polydatin, chalcone, disulfiram, carmofur, shikonin, ebselen, tideglusib, PX-12, TDZD-8, cyclosporin A, and cinanserin (Jin et al. 2020) are the other proposed agents against COVID-19 that are not included in the text and this table ACE2, angiotensin-converting enzyme 2; dGTP, deoxyguanosine triphosphate a based on www.medscape.com, April 2020; www.uptodate.com, ...
    Article
    Severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) is the reason for this ongoing pandemic infection diseases termed coronavirus disease 2019 (COVID-19) that has emerged since early December 2019 in Wuhan City, Hubei Province, China. In this century, it is the worst threat to international health and the economy. After 4 months of COVID-19 outbreak, there is no certain and approved medicine against it. In this public health emergency, it makes sense to investigate the possible effects of old drugs and find drug repositioning that is efficient, economical, and riskless process. Old drugs that may be effective are from different pharmacological categories, antimalarials, anthelmintics, anti-protozoal, anti-HIVs, anti-influenza, anti-hepacivirus, antineoplastics, neutralizing antibodies, immunoglobulins, and interferons. In vitro, in vivo, or preliminary trials of these drugs in the treatment of COVID-19 have been encouraging, leading to new research projects and trials to find the best drug/s. In this review, we discuss the possible mechanisms of these drugs against COVID-19. Also, it should be mentioned that in this manuscript, we discuss preliminary rationales; however, clinical trial evidence is needed to prove them. COVID-19 therapy must be based on expert clinical experience and published literature and guidelines from major health organizations. Moreover, herein, we describe current evidence that may be changed in the future.
  • Article
    Full-text available
    As the infection of 2019-nCoV coronavirus is quickly developing into a global pneumonia epidemic, careful analysis of its transmission and cellular mechanisms is sorely needed. In this report, we first analyzed two recent studies which concluded that snakes are the intermediate hosts of 2019-nCoV and that the 2019-nCoV spike protein insertions shared a unique similarity to HIV-1. The re-implementation of the analyses, built on larger-scale datasets using state-of-the-art bioinformatics methods and databases, present however clear evidences rebutting these conclusions. Next, using metagenomic samples from Manis javanica we assembled a draft genome of the 2019-nCoV-like coronavirus, which shows 73% coverage and 91% sequence identity to the 2019-nCoV genome. In particular, the alignments of the spike surface glycoprotein receptor binding domain revealed 4-fold more variations in the bat coronavirus RaTG13 than those in the Manis coronavirus compared to 2019-nCoV, suggesting the pangolin as a missing link in the transmission of 2019-nCoV from bats to human.
  • Article
    Full-text available
    The beginning of 2020 has seen the emergence of COVID-19 outbreak caused by a novel coronavirus, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). There is an imminent need to better understand this new virus and to develop ways to control its spread. In this study, we sought to gain insights for vaccine design against SARS-CoV-2 by considering the high genetic similarity between SARS-CoV-2 and SARS-CoV, which caused the outbreak in 2003, and leveraging existing immunological studies of SARS-CoV. By screening the experimentally-determined SARS-CoV-derived B cell and T cell epitopes in the immunogenic structural proteins of SARS-CoV, we identified a set of B cell and T cell epitopes derived from the spike (S) and nucleocapsid (N) proteins that map identically to SARS-CoV-2 proteins. As no mutation has been observed in these identified epitopes among the 120 available SARS-CoV-2 sequences (as of 21 February 2020), immune targeting of these epitopes may potentially offer protection against this novel virus. For the T cell epitopes, we performed a population coverage analysis of the associated MHC alleles and proposed a set of epitopes that is estimated to provide broad coverage globally, as well as in China. Our findings provide a screened set of epitopes that can help guide experimental efforts towards the development of vaccines against SARS-CoV-2.
  • Article
    Full-text available
    After the outbreak of the severe acute respiratory syndrome (SARS) in the world in 2003, human coronaviruses (HCoVs) have been reported as pathogens that cause severe symptoms in respiratory tract infections. Recently, a new emerged HCoV isolated from the respiratory epithelium of unexplained pneumonia patients in the Wuhan seafood market caused a major disease outbreak and has been named the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This virus causes acute lung symptoms, leading to a condition that has been named as “coronavirus disease 2019” (COVID-19). The emergence of SARS-CoV-2 and of SARS-CoV caused widespread fear and concern and has threatened global health security. There are some similarities and differences in the epidemiology and clinical features between these two viruses and diseases that are caused by these viruses. The goal of this work is to systematically review and compare between SARS-CoV and SARS-CoV-2 in the context of their virus incubation, originations, diagnosis and treatment methods, genomic and proteomic sequences, and pathogenic mechanisms.
  • Article
    Background: In late December, 2019, patients presenting with viral pneumonia due to an unidentified microbial agent were reported in Wuhan, China. A novel coronavirus was subsequently identified as the causative pathogen, provisionally named 2019 novel coronavirus (2019-nCoV). As of Jan 26, 2020, more than 2000 cases of 2019-nCoV infection have been confirmed, most of which involved people living in or visiting Wuhan, and human-to-human transmission has been confirmed. Methods: We did next-generation sequencing of samples from bronchoalveolar lavage fluid and cultured isolates from nine inpatients, eight of whom had visited the Huanan seafood market in Wuhan. Complete and partial 2019-nCoV genome sequences were obtained from these individuals. Viral contigs were connected using Sanger sequencing to obtain the full-length genomes, with the terminal regions determined by rapid amplification of cDNA ends. Phylogenetic analysis of these 2019-nCoV genomes and those of other coronaviruses was used to determine the evolutionary history of the virus and help infer its likely origin. Homology modelling was done to explore the likely receptor-binding properties of the virus. Findings: The ten genome sequences of 2019-nCoV obtained from the nine patients were extremely similar, exhibiting more than 99·98% sequence identity. Notably, 2019-nCoV was closely related (with 88% identity) to two bat-derived severe acute respiratory syndrome (SARS)-like coronaviruses, bat-SL-CoVZC45 and bat-SL-CoVZXC21, collected in 2018 in Zhoushan, eastern China, but were more distant from SARS-CoV (about 79%) and MERS-CoV (about 50%). Phylogenetic analysis revealed that 2019-nCoV fell within the subgenus Sarbecovirus of the genus Betacoronavirus, with a relatively long branch length to its closest relatives bat-SL-CoVZC45 and bat-SL-CoVZXC21, and was genetically distinct from SARS-CoV. Notably, homology modelling revealed that 2019-nCoV had a similar receptor-binding domain structure to that of SARS-CoV, despite amino acid variation at some key residues. Interpretation: 2019-nCoV is sufficiently divergent from SARS-CoV to be considered a new human-infecting betacoronavirus. Although our phylogenetic analysis suggests that bats might be the original host of this virus, an animal sold at the seafood market in Wuhan might represent an intermediate host facilitating the emergence of the virus in humans. Importantly, structural analysis suggests that 2019-nCoV might be able to bind to the angiotensin-converting enzyme 2 receptor in humans. The future evolution, adaptation, and spread of this virus warrant urgent investigation. Funding: National Key Research and Development Program of China, National Major Project for Control and Prevention of Infectious Disease in China, Chinese Academy of Sciences, Shandong First Medical University.
  • Article
    Influenza A virus (IAV) utilizes multiple strategies to confront or evade host type I interferon (IFN)-mediated antiviral responses in order to enhance its own propagation within the host. One such strategy is to induce the degradation of type I IFN receptor 1 (IFNAR1) by utilizing viral hemagglutinin (HA). However, the molecular mechanism behind this process is poorly understood. Here, we report that a cellular protein, poly (ADP-ribose) polymerase 1 (PARP1), plays a critical role in mediating IAV HA induced degradation of IFNAR1. We identified PARP1 as an interacting partner for IAV HA through mass spectrometry analysis. This interaction was confirmed by co-immunoprecipitation analyses. Furthermore, confocal fluorescence microscopy showed altered localization of endogenous PARP1 upon transient IAV HA expression or during IAV infection. Knockdown or inhibition of PARP1 rescued IFNAR1 levels upon IAV infection or HA expression, exemplifying the importance of PARP1 for IAV-induced reduction of IFNAR1. Notably, PARP1 was crucial for the robust replication of IAV, which was associated with regulation of the type I IFN receptor signaling pathway. These results indicate that PARP1 promotes IAV replication by controlling viral HA-induced degradation of host type I IFN receptor. Altogether, these findings provide novel insight into interactions between influenza virus and the host innate immune response and reveal a new function for PARP1 during influenza virus infection. IMPORTANCE Influenza A virus (IAV) infections cause seasonal and pandemic influenza, which pose a devastating global health concern. Despite the availability of antivirals against influenza, new IAV strains continue to persist by overcoming the therapeutics. Therefore, much emphasis in the field is placed on identifying new therapeutic targets that can more effectively control influenza. IAV utilizes several tactics to evade host innate immunity, which include the evasion of antiviral type I interferon (IFN) responses. Degradation of type I IFN receptor (IFNAR) is one known method of subversion, but the molecular mechanism for IFNAR downregulation during IAV infection remains unclear. Here we have found that a host protein poly (ADP-ribose) polymerase 1 (PARP1) facilitates IFNAR degradation and accelerates IAV replication. The findings reveal a novel cellular target for the potential development of antivirals against influenza, as well as expand our base of knowledge regarding interactions between influenza and the host innate immunity.
  • Article
    Full-text available
    PARP-1 (poly(ADP-ribose)-polymerase 1), mainly known for its protective role in DNA repair, also regulates inflammatory processes. Notably, defects in DNA repair and chronic inflammation may both predispose to cancer development. On the other hand, inhibition of DNA repair and inflammatory responses can be beneficial in cancer therapy and PARP inhibitors are currently used for their lethal effects on tumor cells. Furthermore, excess of PARP-1 activity has been associated with many tumors and inflammation-related clinical conditions, including asthma, sepsis, arthritis, atherosclerosis, and neurodegenerative diseases, to name a few. Activation and inhibition of PARP represent, therefore, a double-edged sword that can be exploited for therapeutic purposes. In our review, we will discuss recent findings highlighting the composite multifaceted role of PARP-1 in cancer and inflammation-related diseases.
  • Article
    Motivation: Accurately predicting drug-target interactions (DTIs) in silico can guide the drug discovery process and thus facilitate drug development. Computational approaches for DTI prediction that adopt the systems biology perspective generally exploit the rationale that the properties of drugs and targets can be characterized by their functional roles in biological networks. Results: Inspired by recent advance of information passing and aggregation techniques that generalize the convolution neural networks to mine large-scale graph data and greatly improve the performance of many network-related prediction tasks, we develop a new nonlinear end-to-end learning model, called NeoDTI, that integrates diverse information from heterogeneous network data and automatically learns topology-preserving representations of drugs and targets to facilitate DTI prediction. The substantial prediction performance improvement over other state-of-the-art DTI prediction methods as well as several novel predicted DTIs with evidence supports from previous studies have demonstrated the superior predictive power of NeoDTI. In addition, NeoDTI is robust against a wide range of choices of hyperparameters and is ready to integrate more drug and target related information (e.g. compound-protein binding affinity data). All these results suggest that NeoDTI can offer a powerful and robust tool for drug development and drug repositioning. Availability and implementation: The source code and data used in NeoDTI are available at: https://github.com/FangpingWan/NeoDTI. Supplementary information: Supplementary data are available at Bioinformatics online.
  • Article
    Full-text available
    ChEMBL is a large, open-access bioactivity database (https://www.ebi.ac.uk/chembl), previously described in the 2012, 2014 and 2017 Nucleic Acids Research Database Issues. In the last two years, several important improvements have been made to the database and are described here. These include more robust capture and representation of assay details; a new data deposition system, allowing updating of data sets and deposition of supplementary data; and a completely redesigned web interface, with enhanced search and filtering capabilities.