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

Predicting drug-protein interactions by preserving the graph information of multi source data

Examining potential drug-target interactions (DTIs) is a pivotal component of drug discovery and repurposing. Recently, there has been a significant rise in the use of computational techniques to predict DTIs. Nevertheless, previous investigations have predominantly concentrated on assessing either the connections between nodes or the consistency of the network's topological structure in isolation. Such one-sided approaches could severely hinder the accuracy of DTI predictions. In this study, we propose a novel method called TTGCN, which combines heterogeneous graph convolutional neural networks (GCN) and graph attention networks (GAT) to address the task of DTI prediction. TTGCN employs a two-tiered feature learning strategy, utilizing GAT and residual GCN (R-GCN) to extract drug and target embeddings from the diverse network, respectively. These drug and target embeddings are then fused through a mean-pooling layer. Finally, we employ an inductive matrix completion technique to forecast DTIs while preserving the network's node connectivity and topological structure. Our approach demonstrates superior performance in terms of area under the curve and area under the precision-recall curve in experimental comparisons, highlighting its significant advantages in predicting DTIs. Furthermore, case studies provide additional evidence of its ability to identify potential DTIs.

Repurposing Drugs: An Empowering Approach to Drug Discovery and Development

Drug discovery and development is a time-consuming and costly procedure that necessitates a substantial effort. Drug repurposing has been suggested as a method for developing medicines that takes less time than developing brand new medications and will be less expensive. Also known as drug repositioning or re-profiling, this strategy has been in use from the time of serendipitous drug discoveries to the modern computer aided drug designing and use of computational chemistry. In the light of the COVID-19 pandemic too, drug repurposing emerged as a ray of hope in the dearth of available medicines. Data availability by electronic recording, libraries, and improvements in computational techniques offer a vital substrate for systemic evaluation of repurposing candidates. In the not-too-distant future, it could be possible to create a global research archive for us to access, thus accelerating the process of drug development and repurposing. This review aims to present the evolution, benefits and drawbacks including current approaches, key players and the legal and regulatory hurdles in the field of drug repurposing. The vast quantities of available data secured in multiple drug databases, assisting in drug repurposing is also discussed.

Khan R, Kumalo HM. Assessing the Potential Contribution of In Silico Studies in Discovering Drug Candidates That Interact with Various SARS-CoV-2 Receptors

The COVID-19 pandemic has spurred intense research efforts to identify effective treatments for SARS-CoV-2. In silico studies have emerged as a powerful tool in the drug discovery process, particularly in the search for drug candidates that interact with various SARS-CoV-2 receptors. These studies involve the use of computer simulations and computational algorithms to predict the potential interaction of drug candidates with target receptors. The primary receptors targeted by drug candidates include the RNA polymerase, main protease, spike protein, ACE2 receptor, and transmembrane protease serine 2 (TMPRSS2). In silico studies have identified several promising drug candidates, including Remdesivir, Favipiravir, Ribavirin, Ivermectin, Lopinavir/Ritonavir, and Camostat Mesylate, among others. The use of in silico studies offers several advantages, including the ability to screen a large number of drug candidates in a relatively short amount of time, thereby reducing the time and cost involved in traditional drug discovery methods. Additionally, in silico studies allow for the prediction of the binding affinity of the drug candidates to target receptors, providing insight into their potential efficacy. This study is aimed at assessing the useful contributions of the application of computational instruments in the discovery of receptors targeted in SARS-CoV-2. It further highlights some identified advantages and limitations of these studies, thereby revealing some complementary experimental validation to ensure the efficacy and safety of identified drug candidates.

Potential Inhibitors of SARS-CoV-2 Main Protease (Mpro) Identified from the Library of FDA-Approved Drugs Using Molecular Docking Studies

The Corona Virus Infectious Disease-2019 (COVID-19) outbreak originated at Wuhan, China, in December 2019. It has already spread rapidly and caused more than 6.5 million deaths worldwide. Its causal agent is a beta-coronavirus named SARS-CoV-2. Many efforts have already been made to develop new vaccines and drugs against these viruses, but over time, it has changed its molecular nature and evolved into more lethal variants, such as Delta and Omicron. These will lead us to target its more-conserved proteins. The sequences' BLAST and crystal structure of the main protease M^pro suggest a high sequence and structural conservation. M^pro is responsible for the proteolytic maturation of the polyprotein essential for the viral replication and transcription, which makes it an important drug target. Discovery of new drug molecules may take years before getting to the clinics. So, considering urgency, we performed molecular docking studies using FDA-approved drugs to identify molecules that could potentially bind to the substrate-binding site and inhibit SARS-CoV-2's main protease (M^pro). We used the Glide module in the Schrödinger software suite to perform molecular docking studies, followed by MM-GBSA-based energy calculations to score the hit molecules. Molecular docking and manual analysis suggest that several drugs may bind and potentially inhibit M^pro. We also performed molecular simulations studies for selected compounds to evaluate protein-drug interactions. Considering bioavailability, lesser toxicity, and route of administration, some of the top-ranked drugs, including lumefantrine (antimalarial), dipyridamole (coronary vasodilator), dihydroergotamine (used for treating migraine), hexoprenaline (anti asthmatic), riboflavin (vitamin B2), and pantethine (vitamin B5) may be taken forward for further in vitro and in vivo experiments to investigate their therapeutic potential.

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.

Repurposing drugs for treatment of SARS-CoV-2 infection: computational design insights into mechanisms of action

The COVID-19 pandemic has negatively affected human life globally. It has led to economic crises and health emergencies across the world, spreading rapidly among the human population and has caused many deaths. Currently, there are no treatments available for COVID-19 so there is an urgent need to develop therapeutic interventions that could be used against the novel coronavirus infection. In this research, we used computational drug design technologies to repurpose existing drugs as inhibitors of SARS-CoV-2 viral proteins. The Broad Institute's Drug Repurposing Hub consists of in-development/approved drugs and was computationally screened to identify potential hits which could inhibit protein targets encoded by the SARS-CoV-2 genome. By virtually screening the Broad collection, using rationally designed pharmacophore features, we identified molecules which may be repurposed against viral nucleocapsid and non-structural proteins. The pharmacophore features were generated after careful visualisation of the interactions between co-crystalised ligands and the protein binding site. The ChEMBL database was used to determine the compound's level of inhibition of SARS-CoV-2 and correlate the predicted viral protein target with whole virus in vitro data. The results from this study may help to accelerate drug development against COVID-19 and the hit compounds should be progressed through further in vitro and in vivo studies on SARS-CoV-2.

Using artificial intelligence technology to fight COVID-19: a review

In late December 2019, a new type of coronavirus was discovered, which was later named severe acute respiratory syndrome coronavirus 2(SARS-CoV-2). Since its discovery, the virus has spread globally, with 2,975,875 deaths as of 15 April 2021, and has had a huge impact on our health systems and economy. How to suppress the continued spread of new coronary pneumonia is the main task of many scientists and researchers. The introduction of artificial intelligence technology has provided a huge contribution to the suppression of the new coronavirus. This article discusses the main application of artificial intelligence technology in the suppression of coronavirus from three major aspects of identification, prediction, and development through a large amount of literature research, and puts forward the current main challenges and possible development directions. The results show that it is an effective measure to combine artificial intelligence technology with a variety of new technologies to predict and identify COVID-19 patients.

VDA-RWLRLS: An anti-SARS-CoV-2 drug prioritizing framework combining an unbalanced bi-random walk and Laplacian regularized least squares

BACKGROUND

A new coronavirus disease named COVID-19, caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), is rapidly spreading worldwide. However, there is currently no effective drug to fight COVID-19.

METHODS

In this study, we developed a Virus-Drug Association (VDA) identification framework (VDA-RWLRLS) combining unbalanced bi-Random Walk, Laplacian Regularized Least Squares, molecular docking, and molecular dynamics simulation to find clues for the treatment of COVID-19. First, virus similarity and drug similarity are computed based on genomic sequences, chemical structures, and Gaussian association profiles. Second, an unbalanced bi-random walk is implemented on the virus network and the drug network, respectively. Third, the results of the random walks are taken as the input of Laplacian regularized least squares to compute the association score for each virus-drug pair. Fourth, the final associations are characterized by integrating the predictions from the virus network and the drug network. Finally, molecular docking and molecular dynamics simulation are implemented to measure the potential of screened anti-COVID-19 drugs and further validate the predicted results.

RESULTS

In comparison with six state-of-the-art association prediction models (NGRHMDA, SMiR-NBI, LRLSHMDA, VDA-KATZ, VDA-RWR, and VDA-BiRW), VDA-RWLRLS demonstrates superior VDA prediction performance. It obtains the best AUCs of 0.885 8, 0.835 5, and 0.862 5 on the three VDA datasets. Molecular docking and dynamics simulations demonstrated that remdesivir and ribavirin may be potential anti-COVID-19 drugs.

CONCLUSIONS

Integrating unbalanced bi-random walks, Laplacian regularized least squares, molecular docking, and molecular dynamics simulation, this work initially screened a few anti-SARS-CoV-2 drugs and may contribute to preventing COVID-19 transmission.

Pharmacoepidemiological research of COVID-19 in the Russian Federation EGIDA-2020

Aim. An analysis of coronavirus infection in Russia and evaluation of different AVT regimens effectiveness. Materials and methods. The study involved a retrospective analysis of 1082 patient records with laboratory-confirmed COVID-19 in 17 regions of Russia. The number of men and women was equal, mean age 48.718.1 (median 50). Patients with moderate COVID-19 (85%) versus mild COVID-19 (15%) were characterized by higher age (median 54 vs 21 years; p0.001), higher body mass index (27.8 vs 23.4; p0.001), prevalence of chronic diseases (75.3% vs 8.5%; p0.001), including circulatory system diseases (37.8%). Moderate COVID-19 characterized higher intoxication (10.86.1 vs 4.22.7 days; p0.001) and catarrhal symptoms duration (10.25.4 vs 6.14.1 days; p0.001). Results. During hospitalization 92% of the patients received AVT, 77% antibiotics, and 16% corticosteroids. Umifenovir therapy resulted in a significant reduction of intoxication (8.75.5 vs 11.75.5 days; p0.001) and catarrhal symptoms duration (8.85.1 vs 12.04.9 days; p0.001) compared to the group without AVT. The usage of INF reduced intoxication symptoms compared with the group without AVT (8.97.5 vs 11.75.5; p0.05). Therapy with hydroxychloroquine, imidazolylethanamide pentandioic acid, and lopinavir + ritonavir combination did not affect the course of COVID-19. Most of adverse reactions were related to antibiotics. Conclusion. Umifenovir therapy and inclusion of interferon in AVT regimens was associated improvement in the clinical manifestation of the disease among patients.

Therapeutically effective covalent spike protein inhibitors in treatment of SARS-CoV-2

COVID-19 [coronavirus disease 2019] has resulted in over 204,644,849 confirmed cases and over 4,323,139 deaths throughout the world as of 12 August 2021, a total of 4,428,168,759 vaccine doses have been administered. The lack of potentially effective drugs against the virus is making the situation worse and dangerous. Numerous forces are working on finding an effective treatment against the virus but it is believed that a de novo drug would take several months even if huge financial support is provided. The only solution left with is drug repurposing that would not only provide effective therapy with the already used clinical drugs, but also save time and cost of the de novo drug discovery. The initiation of the COVID-19 infection starts with the attachment of spike glycoprotein of SARS-CoV-2 to the host receptor. Hence, the inhibition of the binding of the virus to the host membrane and the entry of the viral particle into the host cell are one of the main therapeutic targets. This paper not only summarizes the structure and the mechanism of spike protein, but the main focus is on the potential covalent spike protein inhibitors.

Comprehensive Consensus Analysis of SARS-CoV-2 Drug Repurposing Campaigns

The current COVID-19 pandemic has elicited extensive repurposing efforts (both small and large scale) to rapidly identify COVID-19 treatments among approved drugs. Herein, we provide a literature review of large-scale SARS-CoV-2 antiviral drug repurposing efforts and highlight a marked lack of consistent potency reporting. This variability indicates the importance of standardizing best practices-including the use of relevant cell lines, viral isolates, and validated screening protocols. We further surveyed available biochemical and virtual screening studies against SARS-CoV-2 targets (Spike, ACE2, RdRp, PLpro, and Mpro) and discuss repurposing candidates exhibiting consistent activity across diverse, triaging assays and predictive models. Moreover, we examine repurposed drugs and their efficacy against COVID-19 and the outcomes of representative repurposed drugs in clinical trials. Finally, we propose a drug repurposing pipeline to encourage the implementation of standard methods to fast-track the discovery of candidates and to ensure reproducible results.

Toward a Coronavirus Knowledge Graph

This study builds a coronavirus knowledge graph (KG) by merging two information sources. The first source is Analytical Graph (AG), which integrates more than 20 different public datasets related to drug discovery. The second source is CORD-19, a collection of published scientific articles related to COVID-19. We combined both chemo genomic entities in AG with entities extracted from CORD-19 to expand knowledge in the COVID-19 domain. Before populating KG with those entities, we perform entity disambiguation on CORD-19 collections using Wikidata. Our newly built KG contains at least 21,700 genes, 2500 diseases, 94,000 phenotypes, and other biological entities (e.g., compound, species, and cell lines). We define 27 relationship types and use them to label each edge in our KG. This research presents two cases to evaluate the KG’s usability: analyzing a subgraph (ego-centered network) from the angiotensin-converting enzyme (ACE) and revealing paths between biological entities (hydroxychloroquine and IL-6 receptor; chloroquine and STAT1). The ego-centered network captured information related to COVID-19. We also found significant COVID-19-related information in top-ranked paths with a depth of three based on our path evaluation.

Comprehensive Survey of Using Machine Learning in the COVID-19 Pandemic

Since December 2019, the global health population has faced the rapid spreading of coronavirus disease (COVID-19). With the incremental acceleration of the number of infected cases, the World Health Organization (WHO) has reported COVID-19 as an epidemic that puts a heavy burden on healthcare sectors in almost every country. The potential of artificial intelligence (AI) in this context is difficult to ignore. AI companies have been racing to develop innovative tools that contribute to arm the world against this pandemic and minimize the disruption that it may cause. The main objective of this study is to survey the decisive role of AI as a technology used to fight against the COVID-19 pandemic. Five significant applications of AI for COVID-19 were found, including (1) COVID-19 diagnosis using various data types (e.g., images, sound, and text); (2) estimation of the possible future spread of the disease based on the current confirmed cases; (3) association between COVID-19 infection and patient characteristics; (4) vaccine development and drug interaction; and (5) development of supporting applications. This study also introduces a comparison between current COVID-19 datasets. Based on the limitations of the current literature, this review highlights the open research challenges that could inspire the future application of AI in COVID-19.

DeepR2cov: deep representation learning on heterogeneous drug networks to discover anti-inflammatory agents for COVID-19

Recent studies have demonstrated that the excessive inflammatory response is an important factor of death in coronavirus disease 2019 (COVID-19) patients. In this study, we propose a deep representation on heterogeneous drug networks, termed DeepR2cov, to discover potential agents for treating the excessive inflammatory response in COVID-19 patients. This work explores the multi-hub characteristic of a heterogeneous drug network integrating eight unique networks. Inspired by the multi-hub characteristic, we design 3 billion special meta paths to train a deep representation model for learning low-dimensional vectors that integrate long-range structure dependency and complex semantic relation among network nodes. Based on the representation vectors and transcriptomics data, we predict 22 drugs that bind to tumor necrosis factor-α or interleukin-6, whose therapeutic associations with the inflammation storm in COVID-19 patients, and molecular binding model are further validated via data from PubMed publications, ongoing clinical trials and a docking program. In addition, the results on five biomedical applications suggest that DeepR2cov significantly outperforms five existing representation approaches. In summary, DeepR2cov is a powerful network representation approach and holds the potential to accelerate treatment of the inflammatory responses in COVID-19 patients. The source code and data can be downloaded from https://github.com/pengsl-lab/DeepR2cov.git.

Overview of current state of research on the application of artificial intelligence techniques for COVID-19

BACKGROUND

Until now, there are still a limited number of resources available to predict and diagnose COVID-19 disease. The design of novel drug-drug interaction for COVID-19 patients is an open area of research. Also, the development of the COVID-19 rapid testing kits is still a challenging task.

METHODOLOGY

This review focuses on two prime challenges caused by urgent needs to effectively address the challenges of the COVID-19 pandemic, i.e., the development of COVID-19 classification tools and drug discovery models for COVID-19 infected patients with the help of artificial intelligence (AI) based techniques such as machine learning and deep learning models.

RESULTS

In this paper, various AI-based techniques are studied and evaluated by the means of applying these techniques for the prediction and diagnosis of COVID-19 disease. This study provides recommendations for future research and facilitates knowledge collection and formation on the application of the AI techniques for dealing with the COVID-19 epidemic and its consequences.

CONCLUSIONS

The AI techniques can be an effective tool to tackle the epidemic caused by COVID-19. These may be utilized in four main fields such as prediction, diagnosis, drug design, and analyzing social implications for COVID-19 infected patients.

A Hybrid Model for COVID-19 Monitoring and Prediction

COVID-19 is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and has a case-fatality rate of 2–3%, with higher rates among elderly patients and patients with comorbidities. Radiologically, COVID-19 is characterised by multifocal ground-glass opacities, even for patients with mild disease. Clinically, patients with COVID-19 present respiratory symptoms, which are very similar to other respiratory virus infections. Our knowledge regarding the SARS-CoV-2 virus is still very limited. These facts make it vitally important to establish mechanisms that allow to model and predict the evolution of the virus and to analyze the spread of cases under different circumstances. The objective of this article is to present a model developed for the evolution of COVID in the city of Manizales, capital of the Department of Caldas, Colombia, focusing on the methodology used to allow its application to other cases, as well as on the monitoring tools developed for this purpose. This methodology is based on a hybrid model which combines the population dynamics of the SIR model of differential equations with extrapolations based on recurrent neural networks. This combination provides self-explanatory results in terms of a coefficient that fluctuates with the restraint measures, which may be further refined by expert rules that capture the expected changes in such measures.

Repurposing novel therapeutic candidate drugs for coronavirus disease-19 based on protein-protein interaction network analysis

BACKGROUND

The coronavirus disease-19 (COVID-19) emerged in Wuhan, China and rapidly spread worldwide. Researchers are trying to find a way to treat this disease as soon as possible. The present study aimed to identify the genes involved in COVID-19 and find a new drug target therapy. Currently, there are no effective drugs targeting SARS-CoV-2, and meanwhile, drug discovery approaches are time-consuming and costly. To address this challenge, this study utilized a network-based drug repurposing strategy to rapidly identify potential drugs targeting SARS-CoV-2. To this end, seven potential drugs were proposed for COVID-19 treatment using protein-protein interaction (PPI) network analysis. First, 524 proteins in humans that have interaction with the SARS-CoV-2 virus were collected, and then the PPI network was reconstructed for these collected proteins. Next, the target miRNAs of the mentioned module genes were separately obtained from the miRWalk 2.0 database because of the important role of miRNAs in biological processes and were reported as an important clue for future analysis. Finally, the list of the drugs targeting module genes was obtained from the DGIDb database, and the drug-gene network was separately reconstructed for the obtained protein modules.

RESULTS

Based on the network analysis of the PPI network, seven clusters of proteins were specified as the complexes of proteins which are more associated with the SARS-CoV-2 virus. Moreover, seven therapeutic candidate drugs were identified to control gene regulation in COVID-19. PACLITAXEL, as the most potent therapeutic candidate drug and previously mentioned as a therapy for COVID-19, had four gene targets in two different modules. The other six candidate drugs, namely, BORTEZOMIB, CARBOPLATIN, CRIZOTINIB, CYTARABINE, DAUNORUBICIN, and VORINOSTAT, some of which were previously discovered to be efficient against COVID-19, had three gene targets in different modules. Eventually, CARBOPLATIN, CRIZOTINIB, and CYTARABINE drugs were found as novel potential drugs to be investigated as a therapy for COVID-19.

CONCLUSIONS

Our computational strategy for predicting repurposable candidate drugs against COVID-19 provides efficacious and rapid results for therapeutic purposes. However, further experimental analysis and testing such as clinical applicability, toxicity, and experimental validations are required to reach a more accurate and improved treatment. Our proposed complexes of proteins and associated miRNAs, along with discovered candidate drugs might be a starting point for further analysis by other researchers in this urgency of the COVID-19 pandemic.

Drug repurposing for COVID-19 via knowledge graph completion

OBJECTIVE

To discover candidate drugs to repurpose for COVID-19 using literature-derived knowledge and knowledge graph completion methods.

METHODS

We propose a novel, integrative, and neural network-based literature-based discovery (LBD) approach to identify drug candidates from PubMed and other COVID-19-focused research literature. Our approach relies on semantic triples extracted using SemRep (via SemMedDB). We identified an informative and accurate subset of semantic triples using filtering rules and an accuracy classifier developed on a BERT variant. We used this subset to construct a knowledge graph, and applied five state-of-the-art, neural knowledge graph completion algorithms (i.e., TransE, RotatE, DistMult, ComplEx, and STELP) to predict drug repurposing candidates. The models were trained and assessed using a time slicing approach and the predicted drugs were compared with a list of drugs reported in the literature and evaluated in clinical trials. These models were complemented by a discovery pattern-based approach.

RESULTS

Accuracy classifier based on PubMedBERT achieved the best performance (F1 = 0.854) in identifying accurate semantic predications. Among five knowledge graph completion models, TransE outperformed others (MR = 0.923, Hits@1 = 0.417). Some known drugs linked to COVID-19 in the literature were identified, as well as others that have not yet been studied. Discovery patterns enabled identification of additional candidate drugs and generation of plausible hypotheses regarding the links between the candidate drugs and COVID-19. Among them, five highly ranked and novel drugs (i.e., paclitaxel, SB 203580, alpha 2-antiplasmin, metoclopramide, and oxymatrine) and the mechanistic explanations for their potential use are further discussed.

CONCLUSION

We showed that a LBD approach can be feasible not only for discovering drug candidates for COVID-19, but also for generating mechanistic explanations. Our approach can be generalized to other diseases as well as to other clinical questions. Source code and data are available at https://github.com/kilicogluh/lbd-covid.

De novo design and bioactivity prediction of SARS-CoV-2 main protease inhibitors using recurrent neural network-based transfer learning

The global pandemic of coronavirus disease (COVID-19) caused by SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) created a rush to discover drug candidates. Despite the efforts, so far no vaccine or drug has been approved for treatment. Artificial intelligence offers solutions that could accelerate the discovery and optimization of new antivirals, especially in the current scenario dominated by the scarcity of compounds active against SARS-CoV-2. The main protease (Mpro) of SARS-CoV-2 is an attractive target for drug discovery due to the absence in humans and the essential role in viral replication. In this work, we developed a deep learning platform for de novo design of putative inhibitors of SARS-CoV-2 main protease (Mpro). Our methodology consists of 3 main steps: (1) training and validation of general chemistry-based generative model; (2) fine-tuning of the generative model for the chemical space of SARS-CoV- Mpro inhibitors and (3) training of a classifier for bioactivity prediction using transfer learning. The fine-tuned chemical model generated > 90% valid, diverse and novel (not present on the training set) structures. The generated molecules showed a good overlap with Mpro chemical space, displaying similar physicochemical properties and chemical structures. In addition, novel scaffolds were also generated, showing the potential to explore new chemical series. The classification model outperformed the baseline area under the precision-recall curve, showing it can be used for prediction. In addition, the model also outperformed the freely available model Chemprop on an external test set of fragments screened against SARS-CoV-2 Mpro, showing its potential to identify putative antivirals to tackle the COVID-19 pandemic. Finally, among the top-20 predicted hits, we identified nine hits via molecular docking displaying binding poses and interactions similar to experimentally validated inhibitors.

A Revisit to the Research Updates of Drugs, Vaccines, and Bioinformatics Approaches in Combating COVID-19 Pandemic

A new strain of coronavirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) responsible for the coronavirus disease 2019 (COVID-19) pandemic was first detected in the city of Wuhan in Hubei province, China in late December 2019. To date, more than 1 million deaths and nearly 57 million confirmed cases have been recorded across 220 countries due to COVID-19, which is the greatest threat to global public health in our time. Although SARS-CoV-2 is genetically similar to other coronaviruses, i.e., SARS and Middle East respiratory syndrome coronavirus (MERS-CoV), no confirmed therapeutics are yet available against COVID-19, and governments, scientists, and pharmaceutical companies worldwide are working together in search for effective drugs and vaccines. Repurposing of relevant therapies, developing vaccines, and using bioinformatics to identify potential drug targets are strongly in focus to combat COVID-19. This review deals with the pathogenesis of COVID-19 and its clinical symptoms in humans including the most recent updates on candidate drugs and vaccines. Potential drugs (remdesivir, hydroxychloroquine, azithromycin, dexamethasone) and vaccines [mRNA-1273; measles, mumps and rubella (MMR), bacille Calmette-Guérin (BCG)] in human clinical trials are discussed with their composition, dosage, mode of action, and possible release dates according to the trial register of US National Library of Medicines (clinicaltrials.gov), European Union (clinicaltrialsregister.eu), and Chinese Clinical Trial Registry (chictr.org.cn) website. Moreover, recent reports on in silico approaches like molecular docking, molecular dynamics simulations, network-based identification, and homology modeling are included, toward repurposing strategies for the use of already approved drugs against newly emerged pathogens. Limitations of effectiveness, side effects, and safety issues of each approach are also highlighted. This review should be useful for the researchers working to find out an effective strategy for defeating SARS-CoV-2.

Stenoparib, an Inhibitor of Cellular Poly(ADP-Ribose) Polymerase, Blocks Replication of the SARS-CoV-2 and HCoV-NL63 Human Coronaviruses In Vitro

By late 2020, the coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), had caused tens of millions of infections and over 1 million deaths worldwide. A protective vaccine and more effective therapeutics are urgently needed. We evaluated a new poly(ADP-ribose) polymerase (PARP) inhibitor, stenoparib, that recently advanced to phase II clinical trials for treatment of ovarian cancer, for activity against human respiratory coronaviruses, including SARS-CoV-2, in vitro Stenoparib exhibits dose-dependent suppression of SARS-CoV-2 multiplication and spread in Vero E6 monkey kidney and Calu-3 human lung adenocarcinoma cells. Stenoparib was also strongly inhibitory to the human seasonal respiratory coronavirus HCoV-NL63. Compared to remdesivir, which inhibits viral replication downstream of cell entry, stenoparib impedes entry and postentry processes, as determined by time-of-addition (TOA) experiments. Moreover, a 10 μM dosage of stenoparib-below the approximated 25.5 μM half-maximally effective concentration (EC50)-combined with 0.5 μM remdesivir suppressed coronavirus growth by more than 90%, indicating a potentially synergistic effect for this drug combination. Stenoparib as a stand-alone or as part of combinatorial therapy with remdesivir should be a valuable addition to the arsenal against COVID-19.IMPORTANCE New therapeutics are urgently needed in the fight against COVID-19. Repurposing drugs that are either already approved for human use or are in advanced stages of the approval process can facilitate more rapid advances toward this goal. The PARP inhibitor stenoparib may be such a drug, as it is currently in phase II clinical trials for the treatment of ovarian cancer and its safety and dosage in humans have already been established. Our results indicate that stenoparib possesses strong antiviral activity against SARS-CoV-2 and other coronaviruses in vitro. This activity appears to be based on multiple modes of action, where both pre-entry and postentry viral replication processes are impeded. This may provide a therapeutic advantage over many current options that have a narrower target range. Moreover, our results suggest that stenoparib and remdesivir in combination may be especially potent against coronavirus infection.

Applications of artificial intelligence in battling against covid-19: A literature review

Colloquially known as coronavirus, the Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2), that causes CoronaVirus Disease 2019 (COVID-19), has become a matter of grave concern for every country around the world. The rapid growth of the pandemic has wreaked havoc and prompted the need for immediate reactions to curb the effects. To manage the problems, many research in a variety of area of science have started studying the issue. Artificial Intelligence is among the area of science that has found great applications in tackling the problem in many aspects. Here, we perform an overview on the applications of AI in a variety of fields including diagnosis of the disease via different types of tests and symptoms, monitoring patients, identifying severity of a patient, processing covid-19 related imaging tests, epidemiology, pharmaceutical studies, etc. The aim of this paper is to perform a comprehensive survey on the applications of AI in battling against the difficulties the outbreak has caused. Thus we cover every way that AI approaches have been employed and to cover all the research until the writing of this paper. We try organize the works in a way that overall picture is comprehensible. Such a picture, although full of details, is very helpful in understand where AI sits in current pandemonium. We also tried to conclude the paper with ideas on how the problems can be tackled in a better way and provide some suggestions for future works.

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.

[Umifenovir and coronavirus infections: a review of research results and clinical practice]

Coronaviruses are known to cause acute respiratory infections. Antiviral therapy, including for COVID-19, is based on clinical practice, experimental data and trial results. The purpose of this review is to: provide and systematize actual preclinical data, clinical trials results and clinical practice for antiviral agent umifenovir (Arbidol). Databases Scopus, Web of Science, RSCI and medRxiv were used for publication searching from 2004. A meta-analysis of clinical trials results was performed. Umifenovir is antiviral agent, it belongs to fusion inhibitors, interacts with SARS-CoV-2 spike protein. Umifenovir the impede the trimerization of spike glycoprotein and inhibit host cell adhesion, at the level of the coronaviruses S-protein of interaction with ACE2 receptor. Preclinical studies in vitro and on animals show umifenovir activity against a number of coronaviruses, including SARS-CoV, MERS-CoV, SARS-CoV-2, and others. Umifenovir, in combination with other antiviral drugs, symptomatic or traditional medicine, was used in China to treat patients with COVID-19, resulting in reduced mortality, virus elimination, the frequency of more severe course and complications in middle severity. However, antiviral therapy for the treatment of severe patients, with ARDS, did not lead to improved outcomes. In comparative clinical studies, umifenovir showed similar effectiveness with other antiviral drugs, and lower frequency of adverse reactions. Therapy with umifenovir, led to an increase percentage of patients with negative results of PCR tests on days 714 (I2=69.8%, RR 0.48, 95% CI 0.190.76; p=0.001). The efficacy and safety of antivirals against SARS-CoV-2 still requires clinical investigation. Moderate forms of COVID-19 could be effectively treated by antivirals, but severe forms of COVID-19, characterized by pulmonary immunopathology, require different approaches to treatment.

Artificial Intelligence in the Fight Against COVID-19: Scoping Review

BACKGROUND

In December 2019, COVID-19 broke out in Wuhan, China, leading to national and international disruptions in health care, business, education, transportation, and nearly every aspect of our daily lives. Artificial intelligence (AI) has been leveraged amid the COVID-19 pandemic; however, little is known about its use for supporting public health efforts.

OBJECTIVE

This scoping review aims to explore how AI technology is being used during the COVID-19 pandemic, as reported in the literature. Thus, it is the first review that describes and summarizes features of the identified AI techniques and data sets used for their development and validation.

METHODS

A scoping review was conducted following the guidelines of PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews). We searched the most commonly used electronic databases (eg, MEDLINE, EMBASE, and PsycInfo) between April 10 and 12, 2020. These terms were selected based on the target intervention (ie, AI) and the target disease (ie, COVID-19). Two reviewers independently conducted study selection and data extraction. A narrative approach was used to synthesize the extracted data.

RESULTS

We considered 82 studies out of the 435 retrieved studies. The most common use of AI was diagnosing COVID-19 cases based on various indicators. AI was also employed in drug and vaccine discovery or repurposing and for assessing their safety. Further, the included studies used AI for forecasting the epidemic development of COVID-19 and predicting its potential hosts and reservoirs. Researchers used AI for patient outcome-related tasks such as assessing the severity of COVID-19, predicting mortality risk, its associated factors, and the length of hospital stay. AI was used for infodemiology to raise awareness to use water, sanitation, and hygiene. The most prominent AI technique used was convolutional neural network, followed by support vector machine.

CONCLUSIONS

The included studies showed that AI has the potential to fight against COVID-19. However, many of the proposed methods are not yet clinically accepted. Thus, the most rewarding research will be on methods promising value beyond COVID-19. More efforts are needed for developing standardized reporting protocols or guidelines for studies on AI.

Development of a machine learning algorithm to predict intubation among hospitalized patients with COVID-19

PURPOSE

The purpose of this study is to develop a machine learning algorithm to predict future intubation among patients diagnosed or suspected with COVID-19.

MATERIALS AND METHODS

This is a retrospective cohort study of patients diagnosed or under investigation for COVID-19. A machine learning algorithm was trained to predict future presence of intubation based on prior vitals, laboratory, and demographic data. Model performance was compared to ROX index, a validated prognostic tool for prediction of mechanical ventilation.

RESULTS

4087 patients admitted to five hospitals between February 2020 and April 2020 were included. 11.03% of patients were intubated. The machine learning model outperformed the ROX-index, demonstrating an area under the receiver characteristic curve (AUC) of 0.84 and 0.64, and area under the precision-recall curve (AUPRC) of 0.30 and 0.13, respectively. In the Kaplan-Meier analysis, patients alerted by the model were more likely to require intubation during their admission (p < 0.0001).

CONCLUSION

In patients diagnosed or under investigation for COVID-19, machine learning can be used to predict future risk of intubation based on clinical data which are routinely collected and available in clinical setting. Such an approach may facilitate identification of high-risk patients to assist in clinical care.

KG-COVID-19: A Framework to Produce Customized Knowledge Graphs for COVID-19 Response

Integrated, up-to-date data about SARS-CoV-2 and COVID-19 is crucial for the ongoing response to the COVID-19 pandemic by the biomedical research community. While rich biological knowledge exists for SARS-CoV-2 and related viruses (SARS-CoV, MERS-CoV), integrating this knowledge is difficult and time-consuming, since much of it is in siloed databases or in textual format. Furthermore, the data required by the research community vary drastically for different tasks; the optimal data for a machine learning task, for example, is much different from the data used to populate a browsable user interface for clinicians. To address these challenges, we created KG-COVID-19, a flexible framework that ingests and integrates heterogeneous biomedical data to produce knowledge graphs (KGs), and applied it to create a KG for COVID-19 response. This KG framework also can be applied to other problems in which siloed biomedical data must be quickly integrated for different research applications, including future pandemics.

Current status of COVID-19 pandemic; characteristics, diagnosis, prevention, and treatment

Humans have always been encountered to big infectious diseases outbreak throughout the history. In December 2019, novel coronavirus (COVID-19) was first noticed as an agent causing insidious pneumonia in Wuhan, China. COVID-19 was spread rapidly from Wuhan to the rest of the world. Until late June 2020, it infected more than 10,000,000 people and caused more than 500,000 deaths in almost all of countries in the world, creating a global crisis worse than all previous epidemics and pandemics. In the current review, we gathered and summarized the results of various studies on characteristics, diagnosis, treatment, and prevention of this pandemic crisis.

Immunotherapy of COVID-19 with poly (ADP-ribose) polymerase inhibitors: starting with nicotinamide

COVID-19 induces a proinflammatory environment that is stronger in patients requiring intensive care. The cytokine components of this environment may determine efficacy or otherwise of glucocorticoid therapy. The immunity modulators, the aryl hydrocarbon receptor (AhR) and the nuclear NAD+-consuming enzyme poly (ADP-ribose) polymerase 1 (PARP 1) may play a critical role in COVID-19 pathophysiology. The AhR is overexpressed in coronaviruses, including COVID-19 and, as it regulates PARP gene expression, the latter is likely to be activated in COVID-19. PARP 1 activation leads to cell death mainly by depletion of NAD+ and adenosine triphosphate (ATP), especially when availability of these energy mediators is compromised. PARP expression is enhanced in other lung conditions: the pneumovirus respiratory syncytial virus (RSV) and chronic obstructive pulmonary disease (COPD). I propose that PARP 1 activation is the terminal point in a sequence of events culminating in patient mortality and should be the focus of COVID-19 immunotherapy. Potent PARP 1 inhibitors are undergoing trials in cancer, but a readily available inhibitor, nicotinamide (NAM), which possesses a highly desirable biochemical and activity profile, merits exploration. It conserves NAD+ and prevents ATP depletion by PARP 1 and Sirtuin 1 (silent mating type information regulation 2 homologue 1) inhibition, enhances NAD+ synthesis, and hence that of NADP+ which is a stronger PARP inhibitor, reverses lung injury caused by ischaemia/reperfusion, inhibits proinflammatory cytokines and is effective against HIV infection. These properties qualify NAM for therapeutic use initially in conjunction with standard clinical care or combined with other agents, and subsequently as an adjunct to stronger PARP 1 inhibitors or other drugs.

Combining hydroxychloroquine and minocycline: potential role in moderate to severe COVID-19 infection

INTRODUCTION

Patients with moderate to severe COVID-19 infection require specific drugs to prevent the morbidity and mortality. Hydroxychloroquine (HCQ) has shown some promise in the management of COVID 19. Minocycline, because of its anticytokine and other useful properties can be an ideal candidate for combining with HCQ.

AREAS COVERED

Here we review the need and mechanisms and reasons for combining HCQ and minocycline moderate to severe COVID-19 infection. We also reviewed the advantages, potential safety concerns and precautions to be taken, while combining HCQ and minocycline.

EXPERT OPINION

Combining HCQ and minocycline offers many advantages in the management of moderate to severe COVID-19 infection. Both drugs are cheaper, widely available and long-term safety data and contraindications are well known. We do not recommend this combination for prophylaxis or use in asymptomatic or mild disease patients as this can lead to unnecessary safety concerns. Additive antimicrobial and anticytokine effects of both drugs may reduce the morbidity and mortality among patients with COVID-19 and may act as a cheaper alternative to the costlier drugs, however, thorough clinical research is warranted. We call upon public and private healthcare bodies to come up with large well-designed clinical studies for generating evidence-based recommendations.

Coronavirus in Continuous Flux: From SARS-CoV to SARS-CoV-2

The world is currently experiencing a global pandemic caused by a novel coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes severe respiratory disease similar to SARS. Previous studies have suggested that SARS-CoV-2 shares 79% and 96% sequence identity to SARS-CoV and to bat coronavirus RaTG13, respectively, at the whole-genome level. Furthermore, a series of studies have shown that SARS-CoV-2 induces clusters of severe respiratory illnesses (i.e., pneumonia, acute lung injury, acute respiratory distress syndrome) resembling SARS-CoV. Moreover, the pathological syndrome may, in part, be caused by cytokine storms and dysregulated immune responses. Thus, in this work the recent literature surrounding the biology, clinical manifestations, and immunology of SARS-CoV-2 is summarized, with the aim of aiding prevention, diagnosis, and treatment for SARS-CoV-2 infection.

Drug Repositioning: New Approaches and Future Prospects for Life-Debilitating Diseases and the COVID-19 Pandemic Outbreak

Traditionally, drug discovery utilises a de novo design approach, which requires high cost and many years of drug development before it reaches the market. Novel drug development does not always account for orphan diseases, which have low demand and hence low-profit margins for drug developers. Recently, drug repositioning has gained recognition as an alternative approach that explores new avenues for pre-existing commercially approved or rejected drugs to treat diseases aside from the intended ones. Drug repositioning results in lower overall developmental expenses and risk assessments, as the efficacy and safety of the original drug have already been well accessed and approved by regulatory authorities. The greatest advantage of drug repositioning is that it breathes new life into the novel, rare, orphan, and resistant diseases, such as Cushing's syndrome, HIV infection, and pandemic outbreaks such as COVID-19. Repositioning existing drugs such as Hydroxychloroquine, Remdesivir, Ivermectin and Baricitinib shows good potential for COVID-19 treatment. This can crucially aid in resolving outbreaks in urgent times of need. This review discusses the past success in drug repositioning, the current technological advancement in the field, drug repositioning for personalised medicine and the ongoing research on newly emerging drugs under consideration for the COVID-19 treatment.

DeepH-DTA: Deep Learning for Predicting Drug-Target Interactions: A Case Study of COVID-19 Drug Repurposing

The rapid spread of novel coronavirus pneumonia (COVID-19) has led to a dramatically increased mortality rate worldwide. Despite many efforts, the rapid development of an effective vaccine for this novel virus will take considerable time and relies on the identification of drug-target (DT) interactions utilizing commercially available medication to identify potential inhibitors. Motivated by this, we propose a new framework, called DeepH-DTA, for predicting DT binding affinities for heterogeneous drugs. We propose a heterogeneous graph attention (HGAT) model to learn topological information of compound molecules and bidirectional ConvLSTM layers for modeling spatio-sequential information in simplified molecular-input line-entry system (SMILES) sequences of drug data. For protein sequences, we propose a squeezed-excited dense convolutional network for learning hidden representations within amino acid sequences; while utilizing advanced embedding techniques for encoding both kinds of input sequences. The performance of DeepH-DTA is evaluated through extensive experiments against cutting-edge approaches utilising two public datasets (Davis, and KIBA) which comprise eclectic samples of the kinase protein family and the pertinent inhibitors. DeepH-DTA attains the highest Concordance Index (CI) of 0.924 and 0.927 and also achieved a mean square error (MSE) of 0.195 and 0.111 on the Davis and KIBA datasets respectively. Moreover, a study using FDA-approved drugs from the Drug Bank database is performed using DeepH-DTA to predict the affinity scores of drugs against SARS-CoV-2 amino acid sequences, and the results show that that the model can predict some of the SARS-Cov-2 inhibitors that have been recently approved in many clinical studies.

KG-COVID-19: a framework to produce customized knowledge graphs for COVID-19 response

Integrated, up-to-date data about SARS-CoV-2 and coronavirus disease 2019 (COVID-19) is crucial for the ongoing response to the COVID-19 pandemic by the biomedical research community. While rich biological knowledge exists for SARS-CoV-2 and related viruses (SARS-CoV, MERS-CoV), integrating this knowledge is difficult and time consuming, since much of it is in siloed databases or in textual format. Furthermore, the data required by the research community varies drastically for different tasks - the optimal data for a machine learning task, for example, is much different from the data used to populate a browsable user interface for clinicians. To address these challenges, we created KG-COVID-19, a flexible framework that ingests and integrates biomedical data to produce knowledge graphs (KGs) for COVID-19 response. This KG framework can also be applied to other problems in which siloed biomedical data must be quickly integrated for different research applications, including future pandemics.

BIGGER PICTURE

An effective response to the COVID-19 pandemic relies on integration of many different types of data available about SARS-CoV-2 and related viruses. KG-COVID-19 is a framework for producing knowledge graphs that can be customized for downstream applications including machine learning tasks, hypothesis-based querying, and browsable user interface to enable researchers to explore COVID-19 data and discover relationships.

Turning the Tide: Natural Products and Natural-Product-Inspired Chemicals as Potential Counters to SARS-CoV-2 Infection

The novel and highly pathogenic severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes coronavirus disease 2019 (COVID-19), has become a continued focus of global attention due to the serious threat it poses to public health. There are no specific drugs available to combat SARS-CoV-2 infection. Natural products (carolacton, homoharringtonine, emetine, and cepharanthine) and natural product-inspired small molecules (ivermectin, GS-5734, EIDD-2801, and ebselen) are potential anti-SARS-CoV-2 agents that have attracted significant attention due to their broad-spectrum antiviral activities. Here, we review the research on potential landmark anti-SARS-CoV-2 agents, systematically discussing the importance of natural products and natural-product-inspired small molecules in the research and development of safe and effective antiviral agents.

A comprehensive review on drug repositioning against coronavirus disease 2019 (COVID19)

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.

Human Leukocyte Antigen Susceptibility Map for Severe Acute Respiratory Syndrome Coronavirus 2

Genetic variability across the three major histocompatibility complex (MHC) class I genes (human leukocyte antigen A [HLA-A], -B, and -C genes) may affect susceptibility to and severity of the disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus responsible for coronavirus disease 2019 (COVID-19). We performed a comprehensive in silico analysis of viral peptide-MHC class I binding affinity across 145 HLA-A, -B, and -C genotypes for all SARS-CoV-2 peptides. We further explored the potential for cross-protective immunity conferred by prior exposure to four common human coronaviruses. The SARS-CoV-2 proteome was successfully sampled and was represented by a diversity of HLA alleles. However, we found that HLA-B*46:01 had the fewest predicted binding peptides for SARS-CoV-2, suggesting that individuals with this allele may be particularly vulnerable to COVID-19, as they were previously shown to be for SARS (M. Lin, H.-T. Tseng, J. A. Trejaut, H.-L. Lee, et al., BMC Med Genet 4:9, 2003, https://bmcmedgenet.biomedcentral.com/articles/10.1186/1471-2350-4-9). Conversely, we found that HLA-B*15:03 showed the greatest capacity to present highly conserved SARS-CoV-2 peptides that are shared among common human coronaviruses, suggesting that it could enable cross-protective T-cell-based immunity. Finally, we reported global distributions of HLA types with potential epidemiological ramifications in the setting of the current pandemic.IMPORTANCE Individual genetic variation may help to explain different immune responses to a virus across a population. In particular, understanding how variation in HLA may affect the course of COVID-19 could help identify individuals at higher risk from the disease. HLA typing can be fast and inexpensive. Pairing HLA typing with COVID-19 testing where feasible could improve assessment of severity of viral disease in the population. Following the development of a vaccine against SARS-CoV-2, the virus that causes COVID-19, individuals with high-risk HLA types could be prioritized for vaccination.

Host transcriptome-guided drug repurposing for COVID-19 treatment: a meta-analysis based approach

Background

Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been declared a pandemic by the World Health Organization, and the identification of effective therapeutic strategy is a need of the hour to combat SARS-CoV-2 infection. In this scenario, the drug repurposing approach is widely used for the rapid identification of potential drugs against SARS-CoV-2, considering viral and host factors.

Methods

We adopted a host transcriptome-based drug repurposing strategy utilizing the publicly available high throughput gene expression data on SARS-CoV-2 and other respiratory infection viruses. Based on the consistency in expression status of host factors in different cell types and previous evidence reported in the literature, pro-viral factors of SARS-CoV-2 identified and subject to drug repurposing analysis based on DrugBank and Connectivity Map (CMap) using the web tool, CLUE.

Results

The upregulated pro-viral factors such as TYMP, PTGS2, C1S, CFB, IFI44, XAF1, CXCL2, and CXCL3 were identified in early infection models of SARS-CoV-2. By further analysis of the drug-perturbed expression profiles in the connectivity map, 27 drugs that can reverse the expression of pro-viral factors were identified, and importantly, twelve of them reported to have anti-viral activity. The direct inhibition of the PTGS2 gene product can be considered as another therapeutic strategy for SARS-CoV-2 infection and could suggest six approved PTGS2 inhibitor drugs for the treatment of COVID-19. The computational study could propose candidate repurposable drugs against COVID-19, and further experimental studies are required for validation.

Artificial Intelligence in the Fight Against COVID-19: Scoping Review (Preprint).. [DOI: 10.2196/preprints.20756] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

BACKGROUND

In December 2019, COVID-19 broke out in Wuhan, China, leading to national and international disruptions in health care, business, education, transportation, and nearly every aspect of our daily lives. Artificial intelligence (AI) has been leveraged amid the COVID-19 pandemic; however, little is known about its use for supporting public health efforts.

OBJECTIVE

This scoping review aims to explore how AI technology is being used during the COVID-19 pandemic, as reported in the literature. Thus, it is the first review that describes and summarizes features of the identified AI techniques and data sets used for their development and validation.

METHODS

A scoping review was conducted following the guidelines of PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews). We searched the most commonly used electronic databases (eg, MEDLINE, EMBASE, and PsycInfo) between April 10 and 12, 2020. These terms were selected based on the target intervention (ie, AI) and the target disease (ie, COVID-19). Two reviewers independently conducted study selection and data extraction. A narrative approach was used to synthesize the extracted data.

RESULTS

We considered 82 studies out of the 435 retrieved studies. The most common use of AI was diagnosing COVID-19 cases based on various indicators. AI was also employed in drug and vaccine discovery or repurposing and for assessing their safety. Further, the included studies used AI for forecasting the epidemic development of COVID-19 and predicting its potential hosts and reservoirs. Researchers used AI for patient outcome–related tasks such as assessing the severity of COVID-19, predicting mortality risk, its associated factors, and the length of hospital stay. AI was used for infodemiology to raise awareness to use water, sanitation, and hygiene. The most prominent AI technique used was convolutional neural network, followed by support vector machine.

CONCLUSIONS

The included studies showed that AI has the potential to fight against COVID-19. However, many of the proposed methods are not yet clinically accepted. Thus, the most rewarding research will be on methods promising value beyond COVID-19. More efforts are needed for developing standardized reporting protocols or guidelines for studies on AI.

COVID-19: Advances in diagnostic tools, treatment strategies, and vaccine development

An unprecedented worldwide spread of the SARS-CoV-2 has imposed severe challenges on healthcare facilities and medical infrastructure. The global research community faces urgent calls for the development of rapid diagnostic tools, effective treatment protocols, and most importantly, vaccines against the pathogen. Pooling together expertise across broad domains to innovate effective solutions is the need of the hour. With these requirements in mind, in this review, we provide detailed critical accounts on the leading efforts at developing diagnostics tools, therapeutic agents, and vaccine candidates. Importantly, we furnish the reader with a multidisciplinary perspective on how conventional methods like serology and RT-PCR, as well as cutting-edge technologies like CRISPR/Cas and artificial intelligence/machine learning, are being employed to inform and guide such investigations. We expect this narrative to serve a broad audience of both active and aspiring researchers in the field of biomedical sciences and engineering and help inspire radical new approaches towards effective detection, treatment, and prevention of this global pandemic.