Transcriptome-based drug repositioning for coronavirus disease 2019 (COVID-19)

Multi-omics characterization of RNA binding proteins reveals disease comorbidities and potential drugs in COVID-19

The COVID-19 has led to a devastating global health crisis, which emphasizes the urgent need to deepen our understanding of the molecular mechanism and identifying potential antiviral drugs. Here, we comprehensively analyzed the transcriptomic and proteomic profiles of 178 COVID-19 patients, ranging from asymptomatic to critically ill. Our analyses found that the RNA binding proteins (RBPs) were likely to be perturbed in infection. Interactome analysis revealed that RBPs interact with virus proteins and the viral interacting RBPs were likely to locate in central regions of human protein-protein interaction network. Functional enrichment analysis revealed that the viral interacting RBPs were likely to be enriched in RNA transport, apoptosis and viral genome replication-related pathways. Based on network proximity analyses of 299 human complex-disease genes and COVID-19-related RBPs in the human interactome, we revealed the significant associations between complex diseases and COVID-19. Network analysis also implicated potential antiviral drugs for treatment of COVID-19. In summary, our integrative characterization of COVID-19 patients may thus help providing evidence regarding pathophysiology and potential therapeutic strategies for COVID-19.

Scope of repurposed drugs against the potential targets of the latest variants of SARS-CoV-2

The unprecedented outbreak of the severe acute respiratory syndrome (SARS) Coronavirus-2, across the globe, triggered a worldwide uproar in the search for immediate treatment strategies. With no specific drug and not much data available, alternative approaches such as drug repurposing came to the limelight. To date, extensive research on the repositioning of drugs has led to the identification of numerous drugs against various important protein targets of the coronavirus strains, with hopes of the drugs working against the major variants of concerns (alpha, beta, gamma, delta, omicron) of the virus. Advancements in computational sciences have led to improved scope of repurposing via techniques such as structure-based approaches including molecular docking, molecular dynamic simulations and quantitative structure activity relationships, network-based approaches, and artificial intelligence-based approaches with other core machine and deep learning algorithms. This review highlights the various approaches to repurposing drugs from a computational biological perspective, with various mechanisms of action of the drugs against some of the major protein targets of SARS-CoV-2. Additionally, clinical trials data on potential COVID-19 repurposed drugs are also highlighted with stress on the major SARS-CoV-2 targets and the structural effect of variants on these targets. The interaction modelling of some important repurposed drugs has also been elucidated. Furthermore, the merits and demerits of drug repurposing are also discussed, with a focus on the scope and applications of the latest advancements in repurposing.

Landscape of Molecular Crosstalk Perturbation between Lung Cancer and COVID-19

BACKGROUND

Lung cancer patients have the worst outcomes when affected by coronavirus disease 2019 (COVID-19). The molecular mechanisms underlying the association between lung cancer and COVID-19 remain unknown. The objective of this investigation was to determine whether there is crosstalk in molecular perturbation between COVID-19 and lung cancer, and to identify a molecular signature, molecular networks and signaling pathways shared by the two diseases.

METHODS

We analyzed publicly available gene expression data from 52 severely affected COVID-19 human lung samples, 594 lung tumor samples and 54 normal disease-free lung samples. We performed network and pathways analysis to identify molecular networks and signaling pathways shared by the two diseases.

RESULTS

The investigation revealed a signature of genes associated with both diseases and signatures of genes uniquely associated with each disease, confirming crosstalk in molecular perturbation between COVID-19 and lung cancer. In addition, the analysis revealed molecular networks and signaling pathways associated with both diseases.

CONCLUSIONS

The investigation revealed crosstalk in molecular perturbation between COVID-19 and lung cancer, and molecular networks and signaling pathways associated with the two diseases. Further research on a population impacted by both diseases is recommended to elucidate molecular drivers of the association between the two diseases.

Computationally prioritized drugs inhibit SARS-CoV-2 infection and syncytia formation

The pharmacological arsenal against the COVID-19 pandemic is largely based on generic anti-inflammatory strategies or poorly scalable solutions. Moreover, as the ongoing vaccination campaign is rolling slower than wished, affordable and effective therapeutics are needed. To this end, there is increasing attention toward computational methods for drug repositioning and de novo drug design.

Here, multiple data-driven computational approaches are systematically integrated to perform a virtual screening and prioritize candidate drugs for the treatment of COVID-19. From the list of prioritized drugs, a subset of representative candidates to test in human cells is selected. Two compounds, 7-hydroxystaurosporine and bafetinib, show synergistic antiviral effects in vitro and strongly inhibit viral-induced syncytia formation. Moreover, since existing drug repositioning methods provide limited usable information for de novo drug design, the relevant chemical substructures of the identified drugs are extracted to provide a chemical vocabulary that may help to design new effective drugs.

Advances in the computational landscape for repurposed drugs against COVID-19

The COVID-19 pandemic has caused millions of deaths and massive societal distress worldwide. Therapeutic solutions are urgently needed, but de novo drug development remains a lengthy process. One promising alternative is computational drug repurposing, which enables the prioritization of existing compounds through fast in silico analyses. Recent efforts based on molecular docking, machine learning, and network analysis have produced actionable predictions. Some predicted drugs, targeting viral proteins and pathological host pathways are undergoing clinical trials. Here, we review this work, highlight drugs with high predicted efficacy and classify their mechanisms of action. We discuss the strengths and limitations of the published methodologies and outline possible future directions. Finally, we curate a list of COVID-19 data portals and other repositories that could be used to accelerate future research.

Strategies to identify candidate repurposable drugs: COVID-19 treatment as a case example

Drug repurposing is an invaluable strategy to identify new uses for existing drug therapies that overcome many of the time and financial costs associated with novel drug development. The COVID-19 pandemic has driven an unprecedented surge in the development and use of bioinformatic tools to identify candidate repurposable drugs. Using COVID-19 as a case study, we discuss examples of machine-learning and signature-based approaches that have been adapted to rapidly identify candidate drugs. The Library of Integrated Network-based Signatures (LINCS) and Connectivity Map (CMap) are commonly used repositories and have the advantage of being amenable to use by scientists with limited bioinformatic training. Next, we discuss how these recent advances in bioinformatic drug repurposing approaches might be adapted to identify repurposable drugs for CNS disorders. As the development of novel therapies that successfully target the cause of neuropsychiatric and neurological disorders has stalled, there is a pressing need for innovative strategies to treat these complex brain disorders. Bioinformatic approaches to identify repurposable drugs provide an exciting avenue of research that offer promise for improved treatments for CNS disorders.

Recent omics-based computational methods for COVID-19 drug discovery and repurposing

The coronavirus disease 2019 (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is the main reason for the increasing number of deaths worldwide. Although strict quarantine measures were followed in many countries, the disease situation is still intractable. Thus, it is needed to utilize all possible means to confront this pandemic. Therefore, researchers are in a race against the time to produce potential treatments to cure or reduce the increasing infections of COVID-19. Computational methods are widely proving rapid successes in biological related problems, including diagnosis and treatment of diseases. Many efforts in recent months utilized Artificial Intelligence (AI) techniques in the context of fighting the spread of COVID-19. Providing periodic reviews and discussions of recent efforts saves the time of researchers and helps to link their endeavors for a faster and efficient confrontation of the pandemic. In this review, we discuss the recent promising studies that used Omics-based data and utilized AI algorithms and other computational tools to achieve this goal. We review the established datasets and the developed methods that were basically directed to new or repurposed drugs, vaccinations and diagnosis. The tools and methods varied depending on the level of details in the available information such as structures, sequences or metabolic data.

Efficacy of naproxen in the management of patients hospitalized with COVID-19 infection: A randomized, double-blind, placebo-controlled, clinical trial

BACKGROUND AND AIMS

The current study was done to examine the efficacy of naproxen in the management of patients with COVID-19 infection.

METHODS

This randomized, double-blind, placebo-controlled, clinical trial was done on hospitalized adult patients with confirmed COVID-19 infection. Patients were randomly assigned to receive either naproxen (two capsules per day each containing 500 mg naproxen sodium) or placebo (containing starch) for five days along with the routine treatment that was nationally recommended for COVID-19 infection. Clinical symptoms of COVID-19 infection, the time to clinical improvement, blood pressure, laboratory parameters, and death due to COVID-19 infection were considered as the outcome variables in the present study.

RESULTS

Treatment with naproxen improved cough and shortness of breath in COVID-19 patients; such that, compared with placebo, naproxen intake was associated with 2.90 (95% CI: 1.10-7.66) and 2.82 (95% CI: 1.05-7.55) times more improvement in cough and shortness of breath, respectively. In addition, naproxen administration resulted in a significant increase in mean corpuscular volume (MCV) and had a preventive effect on the reduction of systolic blood pressure in COVID-19 patients.

CONCLUSION

Treatment with naproxen can improve cough and shortness of breath in COVID-19-infected patients. Further studies are required to confirm our findings.

Efficacy and safety of dihydroorotate dehydrogenase (DHODH) inhibitors "leflunomide" and "teriflunomide" in Covid-19: A narrative review

Dihydroorotate dehydrogenase (DHODH) is rate-limiting enzyme in biosynthesis of pyrimidone which catalyzes the oxidation of dihydro-orotate to orotate. Orotate is utilized in the biosynthesis of uridine-monophosphate. DHODH inhibitors have shown promise as antiviral agent against Cytomegalovirus, Ebola, Influenza, Epstein Barr and Picornavirus. Anti-SARS-CoV-2 action of DHODH inhibitors are also coming up. In this review, we have reviewed the safety and efficacy of approved DHODH inhibitors (leflunomide and teriflunomide) against COVID-19. In target-centered in silico studies, leflunomide showed favorable binding to active site of MPro and spike: ACE2 interface. In artificial-intelligence/machine-learning based studies, leflunomide was among the top 50 ligands targeting spike: ACE2 interaction. Leflunomide is also found to interact with differentially regulated pathways [identified by KEGG (Kyoto Encyclopedia of Genes and Genomes) and reactome pathway analysis of host transcriptome data] in cogena based drug-repurposing studies. Based on GSEA (gene set enrichment analysis), leflunomide was found to target pathways enriched in COVID-19. In vitro, both leflunomide (EC50 41.49 ± 8.8 μmol/L) and teriflunomide (EC50 26 μmol/L) showed SARS-CoV-2 inhibition. In clinical studies, leflunomide showed significant benefit in terms of decreasing the duration of viral shredding, duration of hospital stay and severity of infection. However, no advantage was seen while combining leflunomide and IFN alpha-2a among patients with prolonged post symptomatic viral shredding. Common adverse effects of leflunomide were hyperlipidemia, leucopenia, neutropenia and liver-function alteration. Leflunomide/teriflunomide may serve as an agent of importance to achieve faster virological clearance in COVID-19, however, findings needs to be validated in bigger sized placebo controlled studies.

Reynoutria Rhizomes as a Natural Source of SARS-CoV-2 Mpro Inhibitors-Molecular Docking and In Vitro Study

More than a year has passed since the world began to fight the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) responsible for the Coronavirus disease 2019 (COVID-19) pandemic, and still it spreads around the world, mutating at the same time. One of the sources of compounds with potential antiviral activity is Traditional Chinese Medicinal (TCM) plants used in China in the supportive treatment of COVID-19. Reynoutria japonica is important part of the Shu Feng Jie Du Granule/Capsule-TCM herbal formula, recommended by China Food and Drug Administration (CFDA) for treatment of patients with H1N1- and H5N9-induced acute lung injury and is also used in China to treat COVID-19, mainly combined with other remedies. In our study, 25 compounds from rhizomes of R. japonica and Reynoutria sachalinensis (related species), were docked into the binding site of SARS-CoV-2 main protease. Next, 11 of them (vanicoside A, vanicoside B, resveratrol, piceid, emodin, epicatechin, epicatechin gallate, epigallocatechin gallate, procyanidin B2, procyanidin C1, procyanidin B2 3,3'-di-O-gallate) as well as extracts and fractions from rhizomes of R. japonica and R. sachalinensis were tested in vitro using a fluorescent peptide substrate. Among the tested phytochemicals the best results were achieved for vanicoside A and vanicoside B with moderate inhibition of SARS-CoV-2 Mpro, IC50 = 23.10 µM and 43.59 µM, respectively. The butanol fractions of plants showed the strongest inhibition of SARS-CoV-2 Mpro (IC50 = 4.031 µg/mL for R. sachalinensis and IC50 = 7.877 µg/mL for R. japonica). As the main constituents of butanol fractions, besides the phenylpropanoid disaccharide esters (e.g., vanicosides), are highly polymerized procyanidins, we suppose that they could be responsible for their strong inhibitory properties. As inhibition of SARS-CoV-2 main protease could prevent the replication of the virus our research provides data that may explain the beneficial effects of R. japonica on COVID-19 and identify the most active compounds worthy of more extensive research.

A Review of Current In Silico Methods for Repositioning Drugs and Chemical Compounds

Drug repositioning is a new way of applying the existing therapeutics to new disease indications. Due to the exorbitant cost and high failure rate in developing new drugs, the continued use of existing drugs for treatment, especially anti-tumor drugs, has become a widespread practice. With the assistance of high-throughput sequencing techniques, many efficient methods have been proposed and applied in drug repositioning and individualized tumor treatment. Current computational methods for repositioning drugs and chemical compounds can be divided into four categories: (i) feature-based methods, (ii) matrix decomposition-based methods, (iii) network-based methods, and (iv) reverse transcriptome-based methods. In this article, we comprehensively review the widely used methods in the above four categories. Finally, we summarize the advantages and disadvantages of these methods and indicate future directions for more sensitive computational drug repositioning methods and individualized tumor treatment, which are critical for further experimental validation.

In silico molecular docking and in vitro antimicrobial efficacy of phytochemicals against multi-drug-resistant enteroaggregative Escherichia coli and non-typhoidal Salmonella spp

BACKGROUND

In the wake of emergence of antimicrobial resistance, bioactive phytochemical compounds are proving to be important therapeutic agents. The present study envisaged in silico molecular docking as well as in vitro antimicrobial efficacy screening of identified phytochemical ligands to the dispersin (aap) and outer membrane osmoporin (OmpC) domains of enteroaggregative Escherichia coli (EAEC) and non-typhoidal Salmonella spp. (NTS), respectively.

MATERIALS AND METHODS

The evaluation of drug-likeness, molecular properties, and bioactivity of the identified phytocompounds (thymol, carvacrol, and cinnamaldehyde) was carried out using Swiss ADME, while Protox-II and StopTox servers were used to identify its toxicity. The in silico molecular docking of the phytochemical ligands with the protein motifs of dispersin (PDB ID: 2jvu) and outer membrane osmoporin (PDB ID: 3uu2) were carried out using AutoDock v.4.20. Further, the antimicrobial efficacy of these compounds against multi-drug resistant EAEC and NTS strains was determined by estimating the minimum inhibitory concentrations and minimum bactericidal concentrations. Subsequently, these phytochemicals were subjected to their safety (sheep and human erythrocytic haemolysis) as well as stability (cationic salts, and pH) assays.

RESULTS

All the three identified phytochemicals ligands were found to be zero violators of Lipinski's rule of five and exhibited drug-likeness. The compounds tested were categorized as toxicity class-4 by Protox-II and were found to be non- cardiotoxic by StopTox. The docking studies employing 3D model of dispersin and ompC motifs with the identified phytochemical ligands exhibited good binding affinity. The identified phytochemical compounds were observed to be comparatively stable at different conditions (cationic salts, and pH); however, a concentration-dependent increase in the haemolytic assay was observed against sheep as well as human erythrocytes.

CONCLUSIONS

In silico molecular docking studies provided useful insights to understand the interaction of phytochemical ligands with protein motifs of pathogen and should be used routinely before the wet screening of any phytochemicals for their antibacterial, stability, and safety aspects.

Controversies’ clarification regarding ribavirin efficacy in measles and coronaviruses: Comprehensive therapeutic approach strictly tailored to COVID-19 disease stages

BACKGROUND

Ribavirin is a broad-spectrum nucleoside antiviral drug with multimodal mechanisms of action, which supports its longevity and quality as a clinical resource. It has been widely administered for measles and coronavirus infections. Despite the large amount of data concerning the use of ribavirin alone or in combination for measles, severe acute respiratory syndrome, Middle East respiratory syndrome, and coronavirus disease 2019 (COVID-19) outbreaks, the conclusions of these studies have been contradictory. Underlying reasons for these discrepancies include possible study design inaccuracies and failures and misinterpretations of data, and these potential confounds should be addressed.

AIM

To determine the confounding factors of ribavirin treatment studies and propose a therapeutic scheme for COVID-19.

METHODS

PubMed database was searched over a period of five decades utilizing the terms “ribavirin” alone or combined with other compounds in measles, severe acute respiratory syndrome, Middle East respiratory syndrome, and COVID-19 infections. The literature search was performed and described according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Articles were considered eligible when they reported on ribavirin dose regimens and/or specified outcomes concerning its efficacy and/or possible adverse-effects. In vitro and animal studies were also retrieved. A chapter on ribavirin’s pharmacology was included as well.

RESULTS

In addition to the difficulties and pressures of an emerging pandemic, there is the burden of designing and conducting well-organized, double-blind, randomized controlled trials. Many studies have succumbed to specific pitfalls, one of which was identified in naturally ribavirin-resistant Vero cell lines in in vitro studies. Other pitfalls include study design inconsistent with the well-established clinical course of disease; inappropriate pharmacology of applied treatments; and the misinterpretation of study results with misconceived generalizations. A comprehensive treatment for COVID-19 is proposed, documented by thorough, long-term investigation of ribavirin regimens in coronavirus infections.

CONCLUSION

A comprehensive treatment strictly tailored to distinct disease stages was proposed based upon studies on ribavirin and coronavirus infections.

A COVID-19 Drug Repurposing Strategy through Quantitative Homological Similarities Using a Topological Data Analysis-Based Framework

Since its emergence in March 2020, the SARS-CoV-2 global pandemic has produced more than 116 million cases and 2.5 million deaths worldwide. Despite the enormous efforts carried out by the scientific community, no effective treatments have been developed to date. We applied a novel computational pipeline aimed to accelerate the process of identifying drug repurposing candidates which allows us to compare three-dimensional protein structures. Its use in conjunction with two in silico validation strategies (molecular docking and transcriptomic analyses) allowed us to identify a set of potential drug repurposing candidates targeting three viral proteins (3CL viral protease, NSP15 endoribonuclease, and NSP12 RNA-dependent RNA polymerase), which included rutin, dexamethasone, and vemurafenib. This is the first time that a topological data analysis (TDA)-based strategy has been used to compare a massive number of protein structures with the final objective of performing drug repurposing to treat SARS-CoV-2 infection.

Temporal dynamics of the host molecular responses underlying severe COVID-19 progression and disease resolution

BACKGROUND

The coronavirus disease-19 (COVID-19) pandemic has cost lives and economic hardships globally. Various studies have found a number of different factors, such as hyperinflammation and exhausted/suppressed T cell responses to the etiological SARS coronavirus-2 (SARS-CoV-2), being associated with severe COVID-19. However, sieving the causative from associative factors of respiratory dysfunction has remained rudimentary.

METHODS

We postulated that the host responses causative of respiratory dysfunction would track most closely with disease progression and resolution and thus be differentiated from other factors that are statistically associated with but not causative of severe COVID-19. To track the temporal dynamics of the host responses involved, we examined the changes in gene expression in whole blood of 6 severe and 4 non-severe COVID-19 patients across 15 different timepoints spanning the nadir of respiratory function.

FINDINGS

We found that neutrophil activation but not type I interferon signaling transcripts tracked most closely with disease progression and resolution. Moreover, transcripts encoding for protein phosphorylation, particularly the serine-threonine kinases, many of which have known T cell proliferation and activation functions, were increased after and may thus contribute to the upswing of respiratory function. Notably, these associative genes were targeted by dexamethasone, but not methylprednisolone, which is consistent with efficacy outcomes in clinical trials.

INTERPRETATION

Our findings suggest neutrophil activation as a critical factor of respiratory dysfunction in COVID-19. Drugs that target this pathway could be potentially repurposed for the treatment of severe COVID-19.

FUNDING

This study was sponsored in part by a generous gift from The Hour Glass. EEO and JGL are funded by the National Medical Research Council of Singapore, through the Clinician Scientist Awards awarded by the National Research Foundation of Singapore.

Identification of candidate repurposable drugs to combat COVID-19 using a signature-based approach

The COVID-19 pandemic caused by the novel SARS-CoV-2 is more contagious than other coronaviruses and has higher rates of mortality than influenza. Identification of effective therapeutics is a crucial tool to treat those infected with SARS-CoV-2 and limit the spread of this novel disease globally. We deployed a bioinformatics workflow to identify candidate drugs for the treatment of COVID-19. Using an "omics" repository, the Library of Integrated Network-Based Cellular Signatures (LINCS), we simultaneously probed transcriptomic signatures of putative COVID-19 drugs and publicly available SARS-CoV-2 infected cell lines to identify novel therapeutics. We identified a shortlist of 20 candidate drugs: 8 are already under trial for the treatment of COVID-19, the remaining 12 have antiviral properties and 6 have antiviral efficacy against coronaviruses specifically, in vitro. All candidate drugs are either FDA approved or are under investigation. Our candidate drug findings are discordant with (i.e., reverse) SARS-CoV-2 transcriptome signatures generated in vitro, and a subset are also identified in transcriptome signatures generated from COVID-19 patient samples, like the MEK inhibitor selumetinib. Overall, our findings provide additional support for drugs that are already being explored as therapeutic agents for the treatment of COVID-19 and identify promising novel targets that are worthy of further investigation.

Computational analysis to repurpose drugs for COVID-19 based on transcriptional response of host cells to SARS-CoV-2

BACKGROUND

The Coronavirus Disease 2019 (COVID-19) pandemic has infected over 10 million people globally with a relatively high mortality rate. There are many therapeutics undergoing clinical trials, but there is no effective vaccine or therapy for treatment thus far. After affected by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), molecular signaling pathways of host cells play critical roles during the life cycle of SARS-CoV-2. Thus, it is significant to identify the involved molecular signaling pathways within the host cells. Drugs targeting these molecular signaling pathways could be potentially effective for COVID-19 treatment.

METHODS

In this study, we developed a novel integrative analysis approach to identify the related molecular signaling pathways within host cells, and repurposed drugs as potentially effective treatments for COVID-19, based on the transcriptional response of host cells.

RESULTS

We identified activated signaling pathways associated with the infection caused SARS-CoV-2 in human lung epithelial cells through integrative analysis. Then, the activated gene ontologies (GOs) and super GOs were identified. Signaling pathways and GOs such as MAPK, JNK, STAT, ERK, JAK-STAT, IRF7-NFkB signaling, and MYD88/CXCR6 immune signaling were particularly activated. Based on the identified signaling pathways and GOs, a set of potentially effective drugs were repurposed by integrating the drug-target and reverse gene expression data resources. In addition to many drugs being evaluated in clinical trials, the dexamethasone was top-ranked in the prediction, which was the first reported drug to be able to significantly reduce the death rate of COVID-19 patients receiving respiratory support.

CONCLUSIONS

The integrative genomics data analysis and results can be helpful to understand the associated molecular signaling pathways within host cells, and facilitate the discovery of effective drugs for COVID-19 treatment.

Lessons from the COVID-19 pandemic for advancing computational drug repurposing strategies

Responding quickly to unknown pathogens is crucial to stop uncontrolled spread of diseases that lead to epidemics, such as the novel coronavirus, and to keep protective measures at a level that causes as little social and economic harm as possible. This can be achieved through computational approaches that significantly speed up drug discovery. A powerful approach is to restrict the search to existing drugs through drug repurposing, which can vastly accelerate the usually long approval process. In this Review, we examine a representative set of currently used computational approaches to identify repurposable drugs for COVID-19, as well as their underlying data resources. Furthermore, we compare drug candidates predicted by computational methods to drugs being assessed by clinical trials. Finally, we discuss lessons learned from the reviewed research efforts, including how to successfully connect computational approaches with experimental studies, and propose a unified drug repurposing strategy for better preparedness in the case of future outbreaks.

Comparative transcriptome analysis of SARS-CoV, MERS-CoV, and SARS-CoV-2 to identify potential pathways for drug repurposing

The ongoing COVID-19 pandemic caused by the coronavirus, SARS-CoV-2, has already caused in excess of 1.25 million deaths worldwide, and the number is increasing. Knowledge of the host transcriptional response against this virus and how the pathways are activated or suppressed compared to other human coronaviruses (SARS-CoV, MERS-CoV) that caused outbreaks previously can help in the identification of potential drugs for the treatment of COVID-19. Hence, we used time point meta-analysis to investigate available SARS-CoV and MERS-CoV in-vitro transcriptome datasets in order to identify the significant genes and pathways that are dysregulated at each time point. The subsequent over-representation analysis (ORA) revealed that several pathways are significantly dysregulated at each time point after both SARS-CoV and MERS-CoV infection. We also performed gene set enrichment analyses of SARS-CoV and MERS-CoV with that of SARS-CoV-2 at the same time point and cell line, the results of which revealed that common pathways are activated and suppressed in all three coronaviruses. Furthermore, an analysis of an in-vivo transcriptomic dataset of COVID-19 patients showed that similar pathways are enriched to those identified in the earlier analyses. Based on these findings, a drug repurposing analysis was performed to identify potential drug candidates for combating COVID-19.

Target-Centered Drug Repurposing Predictions of Human Angiotensin-Converting Enzyme 2 (ACE2) and Transmembrane Protease Serine Subtype 2 (TMPRSS2) Interacting Approved Drugs for Coronavirus Disease 2019 (COVID-19) Treatment through a Drug-Target Interaction Deep Learning Model

Previously, our group predicted commercially available Food and Drug Administration (FDA) approved drugs that can inhibit each step of the replication of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) using a deep learning-based drug-target interaction model called Molecule Transformer-Drug Target Interaction (MT-DTI). Unfortunately, additional clinically significant treatment options since the approval of remdesivir are scarce. To overcome the current coronavirus disease 2019 (COVID-19) more efficiently, a treatment strategy that controls not only SARS-CoV-2 replication but also the host entry step should be considered. In this study, we used MT-DTI to predict FDA approved drugs that may have strong affinities for the angiotensin-converting enzyme 2 (ACE2) receptor and the transmembrane protease serine 2 (TMPRSS2) which are essential for viral entry to the host cell. Of the 460 drugs with Kd of less than 100 nM for the ACE2 receptor, 17 drugs overlapped with drugs that inhibit the interaction of ACE2 and SARS-CoV-2 spike reported in the NCATS OpenData portal. Among them, enalaprilat, an ACE inhibitor, showed a Kd value of 1.5 nM against the ACE2. Furthermore, three of the top 30 drugs with strong affinity prediction for the TMPRSS2 are anti-hepatitis C virus (HCV) drugs, including ombitasvir, daclatasvir, and paritaprevir. Notably, of the top 30 drugs, AT1R blocker eprosartan and neuropsychiatric drug lisuride showed similar gene expression profiles to potential TMPRSS2 inhibitors. Collectively, we suggest that drugs predicted to have strong inhibitory potencies to ACE2 and TMPRSS2 through the DTI model should be considered as potential drug repurposing candidates for COVID-19.