An integrative drug repositioning framework discovered a potential therapeutic agent targeting COVID-19

Abstract

The global spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) requires an urgent need to find effective therapeutics for the treatment of coronavirus disease 2019 (COVID-19). In this study, we developed an integrative drug repositioning framework, which fully takes advantage of 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. 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 can interact with the nucleocapsid (N) protein of SARS-CoV-2 and is able to suppress the LPS-induced production of several inflammatory cytokines that are highly relevant to the prevention of immunopathology induced by SARS-CoV-2 infection.

Fig. 1

Schematic illustration of our integrative 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, 1125 investigational, and 3290 experimental drugs. The number of drug candidates after each filtering step is also shown. Knowledge graph: a network containing entities (e.g., drugs and targets) and their relations. 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 aggregation, 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.^, Then the learned sequence structures as well as the corresponding encoded word features are fed into a relation classifier to automatically extract the relations between two entities from large-scale documents in the literature. d The connectivity map (CMap) analysis module. The transcriptome profiles of the peripheral blood mononuclear cell (PBMC) or the bronchoalveolar lavage fluid (BALF) samples from the SARS-CoV-2 or 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 to filter out the anti-SARS-CoV-2 drug candidates. For transcriptome data of SARS-CoV-2 infected cells, PBMC samples were provided by three patients and three healthy volunteers, and BALF samples were collected from three patients and two healthy volunteers. For transcriptome data of SARS-CoV infected cells, PBMC samples from ten patients and four healthy volunteers were included in our analysis

Fig. 2

The in vitro anti-SARS-CoV-2 activities of the tested drugs in Vero E6 cells. a The preliminary in vitro antiviral activities of oseltamivir at 3 μM, zanamivir at 3 μM, baricitinib at 3.2 μM, olaparib at 3.2 μM, arbidol at 3 μM and 30 μM, and CVL218 at 3 μM and 30 μM, respectively, were detected in Vero E6 cells infected with SARS-CoV-2 at an MOI of 0.05. The viral yield in the cell supernatant was then quantified by qRT-PCR. Results are shown as mean ± SD over four replicates. b The concentration-dependent inhibition curve of CVL218 against SARS-CoV-2 replication and its cytotoxicity results. Viral infection and drug treatment at different concentrations were performed as mentioned above. Cytotoxicity of CVL218 to Vero E6 cells was measured by the CCK8 assays. c Visualization of virus nucleoprotein (NP) expression of the infected cells upon treatment of CVL218 at 48 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. Results are shown as mean ± SD over four replicates. e Virus NP expression in the infected cells upon the treatment of CVL218 and remdesivir was analyzed by western blot. f In vitro inhibitory activities against SARS-CoV-2 replication of favipiravir (30 μM), CVL218 (3.5 μM) and a combination of both drugs (30 μM favipiravir + 3.5 μM CVL218). The concentrations were selected according to the EC₂₅ values of individual drugs against SARS-CoV-2 in vitro. Viral infection and drug treatment were performed as mentioned above. Results are shown as mean ± SD over three replicates, and the significances were measured by p-values from t-tests. * and **** stand for p-value < 0.05 and p-value < 0.0001, respectively

Fig. 3

CVL218 binds to the nucleocapsid protein of SARS-CoV-2 (SARS-CoV-2-N). a–e Kinetic analyses of the binding between SARS-CoV-2-N and the tested drugs, including CVL218 (a), PJ-34 (b), olaparib (c), remdesivir (d), and arbidol (e), measured by a SPR-based Biacore instrument. The derived dissociation constants between SARS-CoV-2-N and the tested drugs (CVL218 and PJ-34) are also shown

Fig. 4

CVL218 attenuates the LPS-induced cytokine production in a time- and dose-dependent manner. Concentrations of four cytokines (IL-6, IL-10, IFN-γ, and TNF-α) in the LPS-induced peripheral blood mononuclear cells (PMBCs) were measured by ELISA after 6 h and 24 h of drug treatment. Dexamethasone (DEX) in 1 μg/mL was used as a positive control. a–h Concentrations of IL-6 after 6 h (a) and 12 h (b), IL-10 after 6 h (c) and 12 h (d), IFN-γ after 6 h (e) and 12 h (f), and TNF-α after 6 h (g) and 12 h (h) of drug treatment. Results are shown as mean ± SD over three replicates. The significances were measured by p-values from two-tailed t-tests between LPS+ drug and LPS only groups. *, **, ***, and **** stand for p-value < 0.05, 0.01, 0.001, 0.0001, respectively

Fig. 5

The putative mechanisms for CVL218 as a PARP1 inhibitor 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 reported in the literature to suppress viral replication and imped the binding of nucleocapsid protein to viral RNAs, thus preventing the virus infection.^– b Potential protective effects of PARP1 inhibitors in the treatment of COVID-19. The anti-inflammation effects of PARP1 inhibitors may be achieved through two possible molecular 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.^– 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.^– c The potential antiviral effects of PARP1 inhibitors through suppressing the ADP-ribosylation of viral proteins and intervening the host-pathogen interactions, thus resulting in the inhibition of viral replication^,,,