COVID-19 drug repurposing: A review of computational screening methods, clinical trials, and protein interaction assays

Evaluation of infliximab-induced genotoxicity and possible action on BCL-2 and P53 genes

Although the last pandemic created an urgency for development of vaccines, there was a continuous and concerted effort to search for therapeutic medications among existing drugs with different indications. One of the medications of interest that underwent this change was infliximab (IFM). This drug is used as an anti-inflammatory, predominantly in patients with Crohn 's disease, colitis ulcerative, and rheumatoid arthritis. In addition to these patients, individuals infected with Coronavirus Disease (COVID-19) were administered this chimeric monoclonal antibody (IMF) to act as an immunomodulator for patients in the absence of comprehensive research. Consequently, the present study aimed to examine the genotoxic effects attributed to IFM treatment employing different assays in vivo using mouse Mus musculus. Therefore, IFM was found to induce genotoxic effects as evidenced by the comet assay but did not demonstrate genotoxic potential utilizing mouse bone marrow MN test. The results of evaluating the expression of the P53 and BCL-2 genes using RT-qPCR showed stimulation of expression of these genes at 24 hr followed by a decline at 48 hr. Although the comet assay provided positive results, it is noteworthy that based upon negative findings in the micronucleus test, the data did not demonstrate significant changes in the genetic material that might affect the therapeutic use of IFM. The stimulation of expression of P53 and BCL-2 genes at 24 hr followed by a decline at 48 hr suggest a transient, if any, effect on genetic material. However, there is still a need for more research to more comprehensively understand the genotoxic profile of this medication.

Monoclonal neutralizing antibodies against SARS-COV-2 S protein

Novel coronavirus pneumonia, also known as coronavirus disease 2019 (COVID-19), is caused by sub-severe acute respiratory syndrome type 2 coronavirus (SARS-CoV-2) infection. The spike (S) protein of SARS-CoV-2 binds to angiotensin-converting enzyme 2 (ACE2) receptors widely expressed on the surface of human cells leading to life-threatening respiratory infections. A serious hazard to human health is posed by the lack of particular treatment medications for this virus infection. We advocate the creation of high-affinity antibodies using the receptor binding domain (RBD) of S protein as a specific antigenic epitope to develop a drug that can precisely target therapy COVID-19 because SARS-CoV-2 infection of the host cells is dependent on S protein binding to ACE2. Finally, we obtained high-affinity antibodies 14F4HL and 14E3HL that have high affinity with RBD and well-drug-forming properties, suitable for further humanization studies. Thus, monoclonal antibodies that neutralize the S protein were identified in our study, which may provide new insights for the development of COVID-19 therapeutic drugs.

Docking and other computing tools in drug design against SARS-CoV-2

The use of computer simulation methods has become an indispensable component in identifying drugs against the SARS-CoV-2 coronavirus. There is a huge body of literature on application of molecular modelling to predict inhibitors against target proteins of SARS-CoV-2. To keep our review clear and readable, we limited ourselves primarily to works that use computational methods to find inhibitors and test the predicted compounds experimentally either in target protein assays or in cell culture with live SARS-CoV-2. Some works containing results of experimental discovery of corresponding inhibitors without using computer modelling are included as examples of a success. Also, some computational works without experimental confirmations are also included if they attract our attention either by simulation methods or by databases used. This review collects studies that use various molecular modelling methods: docking, molecular dynamics, quantum mechanics, machine learning, and others. Most of these studies are based on docking, and other methods are used mainly for post-processing to select the best compounds among those found through docking. Simulation methods are presented concisely, information is also provided on databases of organic compounds that can be useful for virtual screening, and the review itself is structured in accordance with coronavirus target proteins.

Community-based management of a five-arm randomised clinical trial in COVID-19 outpatients in South Africa: challenges and opportunities

BACKGROUND

Repeated COVID-19 waves and corresponding mitigation measures have impacted health systems globally with exceptional challenges. In response to the pandemic, researchers, regulators, and funders rapidly pivoted to COVID-19 research activities. However, many clinical drug studies were not completed, due to often complex and rapidly evolving research conditions.

METHODS

We outline our experience of planning and managing a randomised, adaptive, open-label, phase 2 clinical trial to evaluate the safety and efficacy of four repurposed drug regimens versus standard-of-care (SOC) in outpatients with 'mild to moderate' COVID-19 in Johannesburg, South Africa, in the context of a partnership with multiple stakeholders. The study was conducted between 3 September 2020 and 23 August 2021 during changing COVID-19 restrictions, significant morbidity and mortality waves, and allied supply line, economic, and political instability.

RESULTS

Our clinical study design was pragmatic, including low-risk patients who were treated open label. There was built-in flexibility, including provision for some sample size adjustment and a range of secondary efficacy outcomes. Barriers to recruitment included the timing of waves, staff shortages due to illness, late presentation of patients, COVID-19 misinformation, and political unrest. Mitigations were the use of community health workers, deployment of mobile clinical units, and simplification of screening. Trial management required a radical reorganisation of logistics and processes to accommodate COVID-19 restrictions. These included the delivery of staff training and monitoring remotely, electronic consent, patient training and support to collect samples and report data at home, and the introduction of tele-medicine. These measures were successful for data collection, safe, and well received by patients.

CONCLUSION

Completing a COVID-19 trial in outpatients during the height of the pandemic required multiple innovations in nearly every aspect of clinical trial management, a high commitment level from study staff and patients, and support from study sponsors. Our experience has generated a more robust clinical research infrastructure, building in efficiencies to clinical trial management beyond the pandemic.

A review of SARS-CoV-2 drug repurposing: databases and machine learning models

The emergence of Severe Acute Respiratory Syndrome Corona Virus 2 (SARS-CoV-2) posed a serious worldwide threat and emphasized the urgency to find efficient solutions to combat the spread of the virus. Drug repurposing has attracted more attention than traditional approaches due to its potential for a time- and cost-effective discovery of new applications for the existing FDA-approved drugs. Given the reported success of machine learning (ML) in virtual drug screening, it is warranted as a promising approach to identify potential SARS-CoV-2 inhibitors. The implementation of ML in drug repurposing requires the presence of reliable digital databases for the extraction of the data of interest. Numerous databases archive research data from studies so that it can be used for different purposes. This article reviews two aspects: the frequently used databases in ML-based drug repurposing studies for SARS-CoV-2, and the recent ML models that have been developed for the prospective prediction of potential inhibitors against the new virus. Both types of ML models, Deep Learning models and conventional ML models, are reviewed in terms of introduction, methodology, and its recent applications in the prospective predictions of SARS-CoV-2 inhibitors. Furthermore, the features and limitations of the databases are provided to guide researchers in choosing suitable databases according to their research interests.

Artificial Intelligence-based Efficacy Prediction of Phase 3 Clinical Trial for Repurposing Heart Failure Therapies

Introduction

Drug repurposing involves finding new therapeutic uses for already approved drugs, which can save costs as their pharmacokinetics and pharmacodynamics are already known. Predicting efficacy based on clinical endpoints is valuable for designing phase 3 trials and making Go/No-Go decisions, given the potential for confounding effects in phase 2.

Objectives

This study aims to predict the efficacy of the repurposed Heart Failure (HF) drugs for the Phase 3 Clinical Trial.

Methods

Our study presents a comprehensive framework for predicting drug efficacy in phase 3 trials, which combines drug-target prediction using biomedical knowledgebases with statistical analysis of real-world data. We developed a novel drug-target prediction model that uses low-dimensional representations of drug chemical structures and gene sequences, and biomedical knowledgebase. Furthermore, we conducted statistical analyses of electronic health records to assess the effectiveness of repurposed drugs in relation to clinical measurements (e.g., NT-proBNP).

Results

We identified 24 repurposed drugs (9 with a positive effect and 15 with a non-positive) for heart failure from 266 phase 3 clinical trials. We used 25 genes related to heart failure for drug-target prediction, as well as electronic health records (EHR) from the Mayo Clinic for screening, which contained over 58,000 heart failure patients treated with various drugs and categorized by heart failure subtypes. Our proposed drug-target predictive model performed exceptionally well in all seven tests in the BETA benchmark compared to the six cutting-edge baseline methods (i.e., best performed in 266 out of 404 tasks). For the overall prediction of the 24 drugs, our model achieved an AUCROC of 82.59% and PRAUC (average precision) of 73.39%.

Conclusion

The study demonstrated exceptional results in predicting the efficacy of repurposed drugs for phase 3 clinical trials, highlighting the potential of this method to facilitate computational drug repurposing.

Insight into Tyrosine-Containing Pharmaceuticals as Potential Inhibitors of SARS-CoV-2 3CLpro and NSP16: Structural Analysis, Docking Studies, Molecular Dynamics Simulations, and Density Functional Theory Investigations

Tyrosine-containing pharmaceuticals’ (TPh) potential to inhibit SARS CoV-2 3-chymotrypsin-like proteases (3CLpro) and nonstructural protein 16 (NSP16) has been explored using docking studies, molecular dynamics simulations, and density functional theory. The TPh with FDA approval showed excellent contact with the active site pockets of 3CLpro and NSP16. Their binding affinity scores ranged from −5.8 to −4.9 kcal/mol and −6.3 to −4.8 for 3CLpro and NSP16, respectively. A 100-ns molecular dynamics simulation confirmed the stability of the carbidopa/NSP16 complex and N-acetyl tyrosine with both target enzymes. Further, the HOMO-LUMO transitions, molecular orbitals, and dipole moments of carbidopa, droxidopa, and N-acetyl tyrosine were computed using density functional theory (DFT). Considering N-acetyl tyrosine and carbidopa’s substantial inhibitory activity, it is recommended to investigate them further in order to explore their application for the treatment of COVID-19 or any other coronaviruses in the future.

Clinical efficacy of Azithromycin for COVID-19 management: A systematic meta-analysis of meta-analyses

Background

Azithromycin has been adopted as a component of the COVID-19 management protocol throughout the global healthcare settings but with a questionable if not downright unsubstantiated evidence base.

Objectives

In order to amalgamate and critically appraise the conflicting evidence around the clinical efficacy of Azithromycin (AZO) vis a vis COVID-19 management outcomes, a meta-analysis of meta-analyses was carried out to establish an evidence-based holistic status of AZO vis a vis its efficacy as a component-in-use of the COVID-19 management protocol.

Methods

A comprehensive systematic search was carried out through PubMed/Medline, Cochrane and Epistemonikos with a subsequent appraisal of abstracts and full-texts, as required. The Quality of Reporting of Meta-analyses (QUOROM) checklist and the Assessment of Multiple Systematic Reviews (AMSTAR) methodology were adopted to assess the methodological quality of the included meta-analyses. Random-effects models were developed to calculate summarized pool Odds Ratios (with 95% confidence interval) for the afore determined primary and secondary outcomes.

Results

AZO, when compared with best available therapy (BAT) including or excluding Hydroxychloroquine, exhibited statistically insignificant reduction in mortality [(n= 27,204 patients) OR= 0.77 (95% CI: 0.51-1.16) (I2= 97%)], requirement of mechanical ventilation [(n= 14,908 patients) OR= 1.4 (95% CI: 0.58-3.35) (I²= 98%)], induction of arrhythmia [(n= 9,723 patients) OR= 1.21 (95% CI: 0.63-2.32) (I²= 92%)] and QTc prolongation (a surrogate for torsadogenic effect) [(n= 6,534 patients) OR= 0.62 (95% CI: 0.23-1.73) (I²= 96%)].

Conclusion

The meta-analysis of meta-analyses portrays AZO as a pharmacological agent that does not appear to have a comparatively superior clinical efficacy than BAT when it comes to COVID-19 management. Secondary to a very real threat of anti-bacterial resistance, it is suggested that AZO be discontinued and removed from COVID-19 management protocols.

COVID‑19 vaccine hesitancy among pregnant women and their reported reasons for vaccine refusal - A prospective study in Tehran, Iran

OBJECTIVE

Evaluation of covid‑19 vaccine hesitancy among pregnant women and their reported reasons for vaccine refusal.

METHODS

This prospective study was performed in Arash women's Hospital, Tehran, Iran, between December 1, 2021 and January 1, 2022. All pregnant women who were attended to prenatal care unit were considered eligible for inclusion. A validated questionnaire was used for data gathering. Written informed consent was obtained from all participants.

RESULTS

Finally, 477 pregnant women were recruited and were divided into two groups according the status of vaccine acceptance (237 accepted and 240 women refused vaccination). The mean age of accepted participants was higher (31.65 ± 5.69 vs 30.39 ± 5.5; P = 0.01). There was a significant statistical difference between the groups regarding education level. Access to internet and social media were also significantly different between the two groups (94.8% in accepted vs 86.6% in refused group; P = 0.002). There was more rate of severe COVID-19 infection in friends or relatives of accepted group (50% vs 38%). we did not find any statistically significant differences in obstetric characteristics and the rate of obstetric complications between the two groups. The most common reasons reported by participants for vaccine refusal, was fear of vaccination side effects on the fetus (86.5%), and the less common reported reasons were husband's disagreement (9.7%), use of traditional medicine (5.6%), religious beliefs (3.7%), and information obtained from social media (2.8%). After advices from medical staff, most of these mothers (86.5%) still refused vaccination.

CONCLUSION

Based on the results of the present study,rate of COVID-19 vaccine hesitancy was about 50% and its most common reported reason was fear of probable side effects of vaccine on the fetus.

Intermodulation of gut-lung axis microbiome and the implications of biotics to combat COVID-19

The novel coronavirus disease pandemic caused by the COVID-19 virus has infected millions of people around the world with a surge in transmission and mortality rates. Although it is a respiratory viral infection that affects airway epithelial cells, a diverse set of complications, including cytokine storm, gastrointestinal disorders, neurological distress, and hyperactive immune responses have been reported. However, growing evidence indicates that the bidirectional crosstalk of the gut-lung axis can decipher the complexity of the disease. Though not much research has been focused on the gut-lung axis microbiome, there is a translocation of COVID-19 infection from the lung to the gut through the lymphatic system resulting in disruption of gut permeability and its integrity. It is believed that detailed elucidation of the gut-lung axis crosstalk and the role of microbiota can unravel the most significant insights on the discovery of diagnosis using microbiome-based-therapeutics for COVID-19. This review calls attention to relate the influence of dysbiosis caused by COVID-19 and the involvement of the gut-lung axis. It presents first of its kind details that concentrate on the momentousness of biotics in disease progression and restoration. Communicated by Ramaswamy H. Sarma.

CoviRx: A User-Friendly Interface for Systematic Down-Selection of Repurposed Drug Candidates for COVID-19

Although various vaccines are now commercially available, they have not been able to stop the spread of COVID-19 infection completely. An excellent strategy to get safe, effective, and affordable COVID-19 treatments quickly is to repurpose drugs that are already approved for other diseases. The process of developing an accurate and standardized drug repurposing dataset requires considerable resources and expertise due to numerous commercially available drugs that could be potentially used to address the SARS-CoV-2 infection. To address this bottleneck, we created the CoviRx.org platform. CoviRx is a user-friendly interface that allows analysis and filtering of large quantities of data, which is onerous to curate manually for COVID-19 drug repurposing. Through CoviRx, the curated data have been made open source to help combat the ongoing pandemic and encourage users to submit their findings on the drugs they have evaluated, in a uniform format that can be validated and checked for integrity by authenticated volunteers. This article discusses the various features of CoviRx, its design principles, and how its functionality is independent of the data it displays. Thus, in the future, this platform can be extended to include any other disease beyond COVID-19.

A review on computer-aided chemogenomics and drug repositioning for rational COVID-19 drug discovery

Application of materials capable of energy harvesting to increase the efficiency and environmental adaptability is sometimes reflected in the ability of discovery of some traces in an environment-either experimentally or computationally-to enlarge practical application window. The emergence of computational methods, particularly computer-aided drug discovery (CADD), provides ample opportunities for the rapid discovery and development of unprecedented drugs. The expensive and time-consuming process of traditional drug discovery is no longer feasible, for nowadays the identification of potential drug candidates is much easier for therapeutic targets through elaborate in silico approaches, allowing the prediction of the toxicity of drugs, such as drug repositioning (DR) and chemical genomics (chemogenomics). Coronaviruses (CoVs) are cross-species viruses that are able to spread expeditiously from the into new host species, which in turn cause epidemic diseases. In this sense, this review furnishes an outline of computational strategies and their applications in drug discovery. A special focus is placed on chemogenomics and DR as unique and emerging system-based disciplines on CoV drug and target discovery to model protein networks against a library of compounds. Furthermore, to demonstrate the special advantages of CADD methods in rapidly finding a drug for this deadly virus, numerous examples of the recent achievements grounded on molecular docking, chemogenomics, and DR are reported, analyzed, and interpreted in detail. It is believed that the outcome of this review assists developers of energy harvesting materials and systems for detection of future unexpected kinds of CoVs or other variants.

Ganoderma microsporum immunomodulatory protein acts as a multifunctional broad-spectrum antiviral against SARS-CoV-2 by interfering virus binding to the host cells and spike-mediated cell fusion

BACKGROUND

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a highly transmissible coronavirus that has caused over 6 million fatalities. SARS-CoV-2 variants with spike mutations are frequently endowed with a strong capability to escape vaccine-elicited protection. Due to this characteristic, a broad-spectrum inhibitor against SARS-CoV-2 infection is urgently demanded. Ganoderma microsporum immunomodulatory protein (GMI) was previously reported to alleviate infection of SARS-CoV-2 through ACE2 downregulation whereas the impact of GMI on virus itself was less understood. Our study aims to determine the effects of GMI on SARS-CoV-2 pseudovirus and the more detailed mechanisms of GMI inhibition against SARS-CoV-2 pseudovirus infection.

METHODS

ACE2-overexpressing HEK293T cells (HEK293T/ACE2) and SARS-CoV-2 pseudoviruses carrying spike variants were used to study the effects of GMI in vitro. Infectivity was evaluated by fluorescence microscopy and flow cytometry. Fusion rate mediated by SARS-CoV-2 spike protein was examined with split fluorescent protein /luciferase systems. The interactions of GMI with SARS-CoV-2 pseudovirus and ACE2 were investigated by immunoprecipitation and immunoblotting.

RESULTS

GMI broadly blocked SARS-CoV-2 infection in various cell lines. GMI effectively inhibited the infection of pseudotyped viruses carrying different emerged spike variants, including Delta and Omicron strains, on HEK293T/hACE2 cells. In cell-free virus infection, GMI dominantly impeded the binding of spike-bearing pseudotyped viruses to ACE2-expressing cells. In cell-to-cell fusion model, GMI could efficiently inhibit spike-mediated syncytium without the requirement of ACE2 downregulation.

CONCLUSIONS

GMI, an FDA-approved dietary ingredient, acts as a multifunctional broad-spectrum antiviral against SARS-CoV-2 and could become a promising candidate for preventing or treating SARS-CoV-2 associated diseases.

Exploring nanoselenium to tackle mutated SARS-CoV-2 for efficient COVID-19 management

Despite ongoing public health measures and increasing vaccination rates, deaths and disease severity caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its new emergent variants continue to threaten the health of people around the world. Therefore, there is an urgent need to develop novel strategies for research, diagnosis, treatment, and government policies to combat the variant strains of SARS-CoV-2. Since the state-of-the-art COVID-19 pandemic, the role of selenium in dealing with COVID-19 disease has been widely discussed due to its importance as an essential micronutrient. This review aims at providing all antiviral activities of nanoselenium (Nano-Se) ever explored using different methods in the literature. We systematically summarize the studied antiviral activities of Nano-Se required to project it as an efficient antiviral system as a function of shape, size, and synthesis method. The outcomes of this article not only introduce Nano-Se to the scientific community but also motivate scholars to adopt Nano-Se to tackle any serious virus such as mutated SARS-CoV-2 to achieve an effective antiviral activity in a desired manner.

In-silico screening and in-vitro assay show the antiviral effect of Indomethacin against SARS-CoV-2

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused the worldwide spread of coronavirus disease 19 (COVID-19), and till now, it has caused death to more than 6.2 million people. Although various vaccines and drug candidates are being tested globally with limited to moderate success, a comprehensive therapeutic cure is yet to be achieved. In this study, we applied computational drug repurposing methods complemented with the analyses of the already existing gene expression data to find better therapeutics in treatment and recovery. Primarily, we identified the most crucial proteins of SARS-CoV-2 and host human cells responsible for viral infection and host response. An in-silico screening of the existing drugs was performed against the crucial proteins for SARS-CoV-2 infection, and a few existing drugs were shortlisted. Further, we analyzed the gene expression data of SARS-CoV-2 in human lung epithelial cells and investigated the molecules that can reverse the cellular mRNA expression profiles in the diseased state. LINCS L1000 and Comparative Toxicogenomics Database (CTD) were utilized to obtain two sets of compounds that can be used to counter SARS-CoV-2 infection from the gene expression perspective. Indomethacin, a nonsteroidal anti-inflammatory drug (NSAID), and Vitamin-A were found in two sets of compounds, and in the in-silico screening of existing drugs to treat SARS-CoV-2. Our in-silico findings on Indomethacin were further successfully validated by in-vitro testing in Vero CCL-81 cells with an IC₅₀ of 12 μM. Along with these findings, we briefly discuss the possible roles of Indomethacin and Vitamin-A to counter the SARS-CoV-2 infection in humans.

The SARS-CoV-2 helicase as a target for antiviral therapy: Identification of potential small molecule inhibitors by in silico modelling

Although vaccines that provide protection against severe illness from coronavirus disease (COVID-19) have been made available, emerging variant strains of severe acute respiratory syndrome 2 coronavirus 2 (SARS-CoV-2) are of concern. A different research direction involves investigation of antiviral therapeutics. In addition to structural proteins, the SARS-CoV-2 non-structural proteins are of interest and this includes the helicase (nsp13). In this study, an initial screen of 300 ligands was performed to identify potential inhibitors of the SARS-CoV-2 nsp13 examining the nucleoside triphosphatase site (NTPase activity) as the target region. The antiviral activity of polyphenols has been previously reported in the literature and as a result, the phenolic compounds and fatty acids from the OliveNet™ library were utilised. Synthetic compounds with antimicrobial and anti-inflammatory properties were also selected. The structures of the SARS-CoV and MERS-CoV helicases, as well as the human RECQ-like DNA helicase, DHX9 helicase, PcrA helicase, hepatitis C NS3 helicase, and mouse Dna2 nuclease-helicase were used for comparison. As expected, sequence and structural homology between the various species was evident. A number of broad-spectrum and well-known inhibitors interacted with the NTPase active site highlighting the need to potentially identify more specific inhibitors for SARS-CoV-2. Acetylcysteine, clavulanic acid and homovanillic acid were identified as potential lead compounds for the SARS-CoV-2 helicase. Molecular dynamics simulations were performed with the leads bound to the SARS-CoV-2 helicase for 200 ns in triplicate, with favourable binding free energies to the NTPase site. Given their availability, further exploration of their potential inhibitory activity could be considered.

An open label randomized clinical trial of Indomethacin for mild and moderate hospitalised Covid-19 patients

Indomethacin, a non-steroidal anti-inflammatory drug (NSAID), has been presented as a broad-spectrum antiviral agent. This randomised clinical trial in a hospital setting evaluated the efficacy and safety of this drug in RT-PCR-positive coronavirus disease 2019 (COVID-19) patients. A total of 210 RT-PCR-positive COVID-19 patients who provided consent were allotted to the control or case arm, based on block randomisation. The control arm received standard of care comprising paracetamol, ivermectin, and other adjuvant therapies. The patients in the case arm received indomethacin instead of paracetamol, with other medications retained. The primary endpoint was the development of hypoxia/desaturation with SpO₂ ≤ 93, while time to become afebrile and time for cough and myalgia resolution were the secondary endpoints. The results of 210 patients were available, with 103 and 107 patients in the indomethacin and paracetamol arms, respectively. We monitored patient profiles along with everyday clinical parameters. In addition, blood chemistry at the time of admission and discharge was assessed. As no one in either of the arms required high-flow oxygen, desaturation with a SpO₂ level of 93 and below was the vital goal. In the indomethacin group, none of the 103 patients developed desaturation. On the other hand, 20 of the 107 patients in the paracetamol arm developed desaturation. Patients who received indomethacin also experienced more rapid symptomatic relief than those in the paracetamol arm, with most symptoms disappearing in half the time. In addition, 56 out of 107 in the paracetamol arm had fever on the seventh day, while no patient in the indomethacin group had fever. Neither arm reported any adverse event. The fourteenth-day follow-up revealed that the paracetamol arm patients had faced several discomforts; indomethacin arm patients mostly complained only of tiredness. Indomethacin is a safe and effective drug for treating patients with mild and moderate covid-19.

A Comprehensive Investigation Regarding the Differentiation of the Procurable COVID-19 Vaccines

COVID-19 caused by coronavirus SARS-CoV-2 became a serious threat to humankind for the past couple of years. The development of vaccine and its immediate application might be the only to escape from the grasp of this demoniac pandemic. Approximately 343 clinical trials on COVID-19 vaccines are ongoing currently, and almost all countries are motivating ongoing researches at warp speed for the development of vaccines against COVID-19. This review explores the progress in the development of the vaccines, their current status of ongoing clinical research, mechanisms, and regulatory approvals. Many pharmaceutical companies are already in the endgame for manufacturing various vaccines of which some are already being marketed across the globe, while others are yet to get approval for marketing. The primary aim of this review is to compare regulatory accepted vaccines in terms of their composition, doses, regulatory status, and efficacy. The study is conducted by grouping into approved and unapproved vaccines for marketing. Different routes of administration of vaccines along with the efficacy of the routes are also presented in the review. A wide range of database and clinical trial data is reviewed for sorting out the information on different vaccines. Unfortunately, many mutations (alpha, beta, gamma, delta, kappa, omicron etc.) of SARS-CoV-2 have attacked people in very short time, which is the great challenge for investigational vaccines. Moreover, some vaccines like Pfizer's BNT162, Oxford's ChAdOx1, Moderna's mRNA-1273, and Bharat Biotech's Covaxin have got regulatory approval in some countries for its distribution which may prove to stand tall against the pandemic.

COVID-19 Drug Repurposing: A Network-Based Framework for Exploring Biomedical Literature and Clinical Trials for Possible Treatments

Background: With the Coronavirus becoming a new reality of our world, global efforts continue to seek answers to many questions regarding the spread, variants, vaccinations, and medications. Particularly, with the emergence of several strains (e.g., Delta, Omicron), vaccines will need further development to offer complete protection against the new variants. It is critical to identify antiviral treatments while the development of vaccines continues. In this regard, the repurposing of already FDA-approved drugs remains a major effort. In this paper, we investigate the hypothesis that a combination of FDA-approved drugs may be considered as a candidate for COVID-19 treatment if (1) there exists an evidence in the COVID-19 biomedical literature that suggests such a combination, and (2) there is match in the clinical trials space that validates this drug combination. Methods: We present a computational framework that is designed for detecting drug combinations, using the following components (a) a Text-mining module: to extract drug names from the abstract section of the biomedical publications and the intervention/treatment sections of clinical trial records. (b) a network model constructed from the drug names and their associations, (c) a clique similarity algorithm to identify candidate drug treatments. Result and Conclusions: Our framework has identified treatments in the form of two, three, or four drug combinations (e.g., hydroxychloroquine, doxycycline, and azithromycin). The identifications of the various treatment candidates provided sufficient evidence that supports the trustworthiness of our hypothesis.

Mitoxantrone dihydrochloride, an FDA approved drug, binds with SARS-CoV-2 NSP1 C-terminal

One of the major virulence factors of SARS-CoV-2, NSP1, is a vital drug target due to its role in host immune evasion through multiple pathways. NSP1 protein is associated with inhibiting host mRNA translation by binding to the small subunit of ribosome through its C-terminal region. Previously, we have shown the structural dynamics of the NSP1 C-terminal region (NSP1-CTR) in different physiological environments. So, it would be very interesting to investigate the druggable compounds that could bind with NSP1-CTR. Here, in this article, we have performed different spectroscopic technique-based binding assays of an anticancer drug mitoxantrone dihydrochloride (MTX) against the NSP1-CTR. We have also performed molecular dynamics simulations of the docked complex with two different force fields up to one microsecond. Overall, our results have suggested good binding between NSP1-CTR and MTX and may have implications in developing therapeutic strategies targeting the NSP1 protein of SARS-CoV-2.

SARS-CoV-2 NSP1 is believed to be a potential drug target. Its C-terminal region which associates with ribosomal subunit has shown good binding with an anticancer drug, Mitoxantrone.

Systematic virtual screening in search of SARS CoV-2 inhibitors against spike glycoprotein: pharmacophore screening, molecular docking, ADMET analysis and MD simulations

In the absence of efficient anti-viral medications, the coronavirus disease 2019 (COVID-19), stemming from severe acute respiratory syndrome coronavirus-2 (SARS CoV-2), has spawned a worldwide catastrophe and global emergency. Amidst several anti-viral targets of COVID-19, spike glycoprotein has been recognized as an essential target for the viral entry into the host cell. In the search of effective SARS CoV-2 inhibitors acting against spike glycoprotein, the virtual screening of 175,851 ligands from the 2020.1 Asinex BioDesign library has been performed using in silico tools like SiteMap analysis, pharmacophore-based screening, molecular docking using different levels of precision, such as high throughput virtual screening, standard precision and extra precision, followed by absorption, distribution, metabolism, excretion and toxicity analysis, and molecular dynamics (MD) simulation. Following a molecular docking study, seventeen molecules (with a docking score of less than - 6.0) were identified having the substantial interactions with the catalytic amino acid and nucleic acid residues of spike glycoprotein at the binding site. In investigations using MD simulations for 10 ns, the hit molecules (1 and 2) showed adequate compactness and uniqueness, as well as satisfactory stability. These computational research findings have offered a key starting point in the field of design and development of novel SARS CoV-2 entry inhibitors with appropriate drug likeliness.

Therapeutics for COVID-19 and post COVID-19 complications: An update

Since its inception in late December 2020 in China, novel coronavirus has affected the global socio-economic aspect. Currently, the world is seeking safe and effective treatment measures against COVID-19 to eradicate it. Many established drug molecules are tested against SARS-CoV-2 as a part of drug repurposing where some are proved effective for symptomatic relief while some are ineffective. Drug repurposing is a practical strategy for rapidly developing antiviral agents. Many drugs are presently being repurposed utilizing basic understanding of disease pathogenesis and drug pharmacodynamics, as well as computational methods. In the present situation, drug repurposing could be viewed as a new treatment option for COVID-19. Several new drug molecules and biologics are engineered against SARS-CoV-2 and are under different stages of clinical development. A few biologics drug products are approved by USFDA for emergency use in the covid management. Due to continuous mutation, many of the approved vaccines are not much efficacious to render the individual immune against opportunistic infection of SARS-CoV-2 mutants. Hence, there is a strong need for the cogent therapeutic agent for covid management. In this review, a consolidated summary of the therapeutic developments against SARS-CoV-2 are depicted along with an overview of effective management of post COVID-19 complications.

Structural biology of SARS-CoV-2: open the door for novel therapies

Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) is the causative agent of the pandemic disease COVID-19, which is so far without efficacious treatment. The discovery of therapy reagents for treating COVID-19 are urgently needed, and the structures of the potential drug-target proteins in the viral life cycle are particularly important. SARS-CoV-2, a member of the Orthocoronavirinae subfamily containing the largest RNA genome, encodes 29 proteins including nonstructural, structural and accessory proteins which are involved in viral adsorption, entry and uncoating, nucleic acid replication and transcription, assembly and release, etc. These proteins individually act as a partner of the replication machinery or involved in forming the complexes with host cellular factors to participate in the essential physiological activities. This review summarizes the representative structures and typically potential therapy agents that target SARS-CoV-2 or some critical proteins for viral pathogenesis, providing insights into the mechanisms underlying viral infection, prevention of infection, and treatment. Indeed, these studies open the door for COVID therapies, leading to ways to prevent and treat COVID-19, especially, treatment of the disease caused by the viral variants are imperative.

Bridging-BPs: a novel approach to predict potential drug-target interactions based on a bridging heterogeneous graph and BPs2vec

Predicting drug-target interactions (DTIs) is a convenient strategy for drug discovery. Although various computational methods have been put forward in recent years, DTIs prediction is still a challenging task. In this paper, based on indirect prior information (we term them as mediators), we proposed a new model, called Bridging-BPs (bridging paths), for DTIs prediction. Specifically, we regarded linkage process between mediators and DTs (drugs and proteins) as 'bridging' and source (drug)-mediators-destination (protein) as bridging paths. By integrating various bridging paths, we constructed a bridging heterogeneous graph for DTIs. After that, an improved graph-embedding algorithm-BPs2vec-was designed to capture deep topological features underlying the bridging graph, thereby obtaining the low-dimensional node vector representations. Then, the vector representations were fed into a Random Forest classifier to train and score the probability, outputting the final classification results for potential DTIs. Under 5-fold cross validation, our method obtained AUPR of 88.97% and AUC of 88.63%, suggesting that Bridging-BPs could effectively mine the link relationships hidden in indirect prior information and it significantly improved the accuracy and robustness of DTIs prediction without direct prior information. Finally, we confirmed the practical prediction ability of Bridging-BPs by case studies.

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.

A Review of the Current Landscape of SARS-CoV-2 Main Protease Inhibitors: Have We Hit the Bullseye Yet?

In this review, we collected 1765 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) M-pro inhibitors from the bibliography and other sources, such as the COVID Moonshot project and the ChEMBL database. This set of inhibitors includes only those compounds whose inhibitory capacity, mainly expressed as the half-maximal inhibitory concentration (IC50) value, against M-pro from SARS-CoV-2 has been determined. Several covalent warheads are used to treat covalent and non-covalent inhibitors separately. Chemical space, the variation of the IC50 inhibitory activity when measured by different methods or laboratories, and the influence of 1,4-dithiothreitol (DTT) are discussed. When available, we have collected the values of inhibition of viral replication measured with a cellular antiviral assay and expressed as half maximal effective concentration (EC50) values, and their possible relationship to inhibitory potency against M-pro is analyzed. Finally, the most potent covalent and non-covalent inhibitors that simultaneously inhibit the SARS-CoV-2 M-pro and the virus replication in vitro are discussed.

Artificial intelligence unifies knowledge and actions in drug repositioning

Drug repositioning aims to reuse existing drugs, shelved drugs, or drug candidates that failed clinical trials for other medical indications. Its attraction is sprung from the reduction in risk associated with safety testing of new medications and the time to get a known drug into the clinics. Artificial Intelligence (AI) has been recently pursued to speed up drug repositioning and discovery. The essence of AI in drug repositioning is to unify the knowledge and actions, i.e. incorporating real-world and experimental data to map out the best way forward to identify effective therapeutics against a disease. In this review, we share positive expectations for the evolution of AI and drug repositioning and summarize the role of AI in several methods of drug repositioning.

Applications of artificial intelligence in COVID-19 pandemic: A comprehensive review

During the current global public health emergency caused by novel coronavirus disease 19 (COVID-19), researchers and medical experts started working day and night to search for new technologies to mitigate the COVID-19 pandemic. Recent studies have shown that artificial intelligence (AI) has been successfully employed in the health sector for various healthcare procedures. This study comprehensively reviewed the research and development on state-of-the-art applications of artificial intelligence for combating the COVID-19 pandemic. In the process of literature retrieval, the relevant literature from citation databases including ScienceDirect, Google Scholar, and Preprints from arXiv, medRxiv, and bioRxiv was selected. Recent advances in the field of AI-based technologies are critically reviewed and summarized. Various challenges associated with the use of these technologies are highlighted and based on updated studies and critical analysis, research gaps and future recommendations are identified and discussed. The comparison between various machine learning (ML) and deep learning (DL) methods, the dominant AI-based technique, mostly used ML and DL methods for COVID-19 detection, diagnosis, screening, classification, drug repurposing, prediction, and forecasting, and insights about where the current research is heading are highlighted. Recent research and development in the field of artificial intelligence has greatly improved the COVID-19 screening, diagnostics, and prediction and results in better scale-up, timely response, most reliable, and efficient outcomes, and sometimes outperforms humans in certain healthcare tasks. This review article will help researchers, healthcare institutes and organizations, government officials, and policymakers with new insights into how AI can control the COVID-19 pandemic and drive more research and studies for mitigating the COVID-19 outbreak.

Drug repurposing strategies and key challenges for COVID-19 management

COVID-19 is a clinical outcome of viral infection emerged due to strain of beta coronavirus which attacks the type-2 pneumocytes in alveoli via angiotensin-converting enzyme 2 (ACE2) receptors. There is no satisfactory drug developed against 'SARS-CoV2', highlighting an immediate necessity chemotherapeutic repurposing plan COVID-19. Drug repurposing is a method of selection of approved therapeutics for new use and is considered to be the most effective drug finding strategy since it includes less time and cost to obtain treatment compared to the de novo drug acquisition process. Several drugs such as hydroxychloroquine, remdesivir, teicoplanin, darunavir, ritonavir, nitazoxanide, chloroquine, tocilizumab and favipiravir (FPV) showed their activity against 'SARS-CoV2' in vitro. This review has emphasized on repurposing of drugs, and biologics used in clinical set up for targeting COVID-19 and to evaluate their pharmacokinetics, pharmacodynamics and safety with their future aspect. The key benefit of drug repurposing is the wealth of information related to its safety, and easy accessibility. Altogether repurposing approach allows access to regulatory approval as well as reducing sophisticated safety studies.

Pandemic strategies with computational and structural biology against COVID-19: A retrospective

The emergence of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), which is the etiologic agent of the coronavirus disease 2019 (COVID-19) pandemic, has dominated all aspects of life since of 2020. Research studies on the virus and exploration of therapeutic and preventive strategies has been moving at rapid rates to control the pandemic. In the field of bioinformatics or computational and structural biology, recent research strategies have used multiple disciplines to compile large datasets to uncover statistical correlations and significance, visualize and model proteins, perform molecular dynamics simulations, and employ the help of artificial intelligence and machine learning to harness computational processing power to further the research on COVID-19, including drug screening, drug design, vaccine development, prognosis prediction, and outbreak prediction. These recent developments should help us better understand the viral disease and develop the much-needed therapies and strategies for the management of COVID-19.

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.

The Drug Repurposing for COVID-19 Clinical Trials Provide Very Effective Therapeutic Combinations: Lessons Learned From Major Clinical Studies

SARS-CoV-2 has spread across the globe in no time. In the beginning, people suffered due to the absence of efficacious drugs required to treat severely ill patients. Nevertheless, still, there are no established therapeutic molecules against the SARS-CoV-2. Therefore, repurposing of the drugs started against SARS-CoV-2, due to which several drugs were approved for the treatment of COVID-19 patients. This paper reviewed the treatment regime for COVID-19 through drug repurposing from December 8, 2019 (the day when WHO recognized COVID-19 as a pandemic) until today. We have reviewed all the clinical trials from RECOVERY trials, ACTT-1 and ACTT-2 study group, and other major clinical trial platforms published in highly reputed journals such as NEJM, Lancet, etc. In addition to single-molecule therapy, several combination therapies were also evaluated to understand the treatment of COVID-19 from these significant clinical trials. To date, several lessons have been learned on the therapeutic outcomes for COVID-19. The paper also outlines the experiences gained during the repurposing of therapeutic molecules (hydroxychloroquine, ritonavir/ lopinavir, favipiravir, remdesivir, ivermectin, dexamethasone, camostatmesylate, and heparin), immunotherapeutic molecules (tocilizumab, mavrilimumab, baricitinib, and interferons), combination therapy, and convalescent plasma therapy to treat COVID-19 patients. We summarized that anti-viral therapeutic (remdesivir) and immunotherapeutic (tocilizumab, dexamethasone, and baricitinib) therapy showed some beneficial outcomes. Until March 2021, 4952 clinical trials have been registered in ClinicalTrials.gov toward the drug and vaccine development for COVID-19. More than 100 countries have participated in contributing to these clinical trials. Other than the registered clinical trials (medium to large-size), several small-size clinical trials have also been conducted from time to time to evaluate the treatment of COVID-19. Four molecules showed beneficial therapeutic to treat COVID-19 patients. The short-term repurposing of the existing drug may provide a successful outcome for COVID-19 patients. Therefore, more clinical trials can be initiated using potential anti-viral molecules by evaluating in different phases of clinical trials.

Haste makes waste: A critical review of docking-based virtual screening in drug repurposing for SARS-CoV-2 main protease (M-pro) inhibition

This review makes a critical evaluation of 61 peer‐reviewed manuscripts that use a docking step in a virtual screening (VS) protocol to predict SARS‐CoV‐2 M‐pro (M‐pro) inhibitors in approved or investigational drugs. Various manuscripts predict different compounds, even when they use a similar initial dataset and methodology, and most of them do not validate their methodology or results. In addition, a set of known 150 SARS‐CoV‐2 M‐pro inhibitors extracted from the literature and a second set of 81 M‐pro inhibitors and 113 inactive compounds obtained from the COVID Moonshot project were used to evaluate the reliability of using docking scores as feasible predictors of the potency of a SARS‐CoV‐2 M‐pro inhibitor. Using two SARS‐CoV‐2 M‐pro structures and five protein‐ligand docking programs, we proved that the correlation between the pIC₅₀ and docking scores is not good. Neither was any correlation found between the pIC₅₀ and the ∆G calculated with an MM‐GBSA method. When a group of experimentally known inactive compounds was added, neither the docking scores or the ∆G were able to distinguish between compounds with or without M‐pro experimental inhibitory activity. Performances improved when covalent and noncovalent inhibitors were treated separately, but were not good enough to fully support using a docking score as a cutoff value for selecting new putative M‐pro inhibitors or predicting the relative bioactivity of a compound by comparison with a reference compound. The two sets of known SARS‐CoV‐2 M‐pro inhibitors presented here could be used for validating future VS protocols which aim to predict M‐pro inhibitors.

Beyond Vaccines: Clinical Status of Prospective COVID-19 Therapeutics

Since November 2019 the SARS-CoV-2 pandemic has caused nearly 200 million infection and more than 4 million deaths globally (Updated information from the World Health Organization, as on 2nd Aug 2021). Within only one year into the pandemic, several vaccines were designed and reached approval for the immunization of the world population. The remarkable protective effects of the manufactured vaccines are demonstrated in countries with high vaccination rates, such as Israel and UK. However, limited production capacities, poor distribution infrastructures and political hesitations still hamper the availability of vaccines in many countries. In addition, due to the emergency of SARS-CoV-2 variants with immune escape properties towards the vaccines the global numbers of new infections as well as patients developing severe COVID-19, remains high. New studies reported that about 8% of infected individuals develop long term symptoms with strong personal restrictions on private as well as professional level, which contributes to the long socioeconomic problems caused by this pandemic. Until today, emergency use-approved treatment options for COVID-19 are limited to the antiviral Remdesivir, a nucleoside analogue targeting the viral polymerase, the glucocorticosteroide Dexamethasone as well as neutralizing antibodies. The therapeutic benefits of these treatments are under ongoing debate and clinical studies assessing the efficiency of these treatments are still underway. To identify new therapeutic treatments for COVID-19, now and by the post-pandemic era, diverse experimental approaches are under scientific evaluation in companies and scientific research teams all over the world. To accelerate clinical translation of promising candidates, repurposing approaches of known approved drugs are specifically fostered but also novel technologies are being developed and are under investigation. This review summarizes the recent developments from the lab bench as well as the clinical status of emerging therapeutic candidates and discusses possible therapeutic entry points for the treatment strategies with regard to the biology of SARS-CoV-2 and the clinical course of COVID-19.

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.

Acceptance and willingness to pay for COVID-19 vaccines among pregnant women in Vietnam

Objectives

The aim of this study was to assess acceptance of COVID‐19 vaccination and the willingness to pay (WTP) for it, and investigate associated factors among pregnant women in Vietnam.

Methods

Cross‐sectional survey of pregnant women in two obstetric hospitals in Hanoi and Ca Mau provinces, Vietnam. Data on acceptance and WTP for COVID‐19, demographic characteristics, maternal characteristics, and risk perceptions toward COVID‐19 were collected. Multivariate logistic and linear regression models were performed to identify factors associated with the acceptance and WTP for the vaccine.

Results

Of 651 pregnant women, 60.4% accepted to receive the vaccine, and 82.6% of the total pregnant women were willing to pay for a COVID‐19 vaccine with the mean amount of WTP of USD 15.2 (SD ± 27.4). The most common reason for refusing vaccination was “Worry about the safety of the vaccine” (66.9%) in Hanoi and “The preventive effect of COVID‐19 is low” (45.2%) in Ca Mau. A higher income, having children, self‐perceived risk of COVID‐19 infection, and perceived risk to friends were associated with a higher likelihood of acceptance and WTP for the vaccine.

Conclusions

Implementing COVID‐19 vaccination and resource mobilisation among pregnant women in Vietnam is feasible, although communication programmes to improve risk perception and awareness about vaccine should be developed for facilitating acceptance of the vaccine.

Strategies to target SARS-CoV-2 entry and infection using dual mechanisms of inhibition by acidification inhibitors

Many viruses utilize the host endo-lysosomal network for infection. Tracing the endocytic itinerary of SARS-CoV-2 can provide insights into viral trafficking and aid in designing new therapeutic strategies. Here, we demonstrate that the receptor binding domain (RBD) of SARS-CoV-2 spike protein is internalized via the pH-dependent CLIC/GEEC (CG) endocytic pathway in human gastric-adenocarcinoma (AGS) cells expressing undetectable levels of ACE2. Ectopic expression of ACE2 (AGS-ACE2) results in RBD traffic via both CG and clathrin-mediated endocytosis. Endosomal acidification inhibitors like BafilomycinA1 and NH4Cl, which inhibit the CG pathway, reduce the uptake of RBD and impede Spike-pseudoviral infection in both AGS and AGS-ACE2 cells. The inhibition by BafilomycinA1 was found to be distinct from Chloroquine which neither affects RBD uptake nor alters endosomal pH, yet attenuates Spike-pseudovirus entry. By screening a subset of FDA-approved inhibitors for functionality similar to BafilomycinA1, we identified Niclosamide as a SARS-CoV-2 entry inhibitor. Further validation using a clinical isolate of SARS-CoV-2 in AGS-ACE2 and Vero cells confirmed its antiviral effect. We propose that Niclosamide, and other drugs which neutralize endosomal pH as well as inhibit the endocytic uptake, could provide broader applicability in subverting infection of viruses entering host cells via a pH-dependent endocytic pathway.

Fusion of intelligent learning for COVID-19: A state-of-the-art review and analysis on real medical data

The unprecedented surge of a novel coronavirus in the month of December 2019, named as COVID-19 by the World Health organization has caused a serious impact on the health and socioeconomic activities of the public all over the world. Since its origin, the number of infected and deceased cases has been growing exponentially in almost all the affected countries of the world. The rapid spread of the novel coronavirus across the world results in the scarcity of medical resources and overburdened hospitals. As a result, the researchers and technocrats are continuously working across the world for the inculcation of efficient strategies which may assist the government and healthcare system in controlling and managing the spread of the COVID-19 pandemic. Therefore, this study provides an extensive review of the ongoing strategies such as diagnosis, prediction, drug and vaccine development and preventive measures used in combating the COVID-19 along with technologies used and limitations. Moreover, this review also provides a comparative analysis of the distinct type of data, emerging technologies, approaches used in diagnosis and prediction of COVID-19, statistics of contact tracing apps, vaccine production platforms used in the COVID-19 pandemic. Finally, the study highlights some challenges and pitfalls observed in the systematic review which may assist the researchers to develop more efficient strategies used in controlling and managing the spread of COVID-19.

Existing Drugs Considered as Promising in COVID-19 Therapy

COVID-19 is a respiratory disease caused by newly discovered severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The disease at first was identified in the city of Wuhan, China in December 2019. Being a human infectious disease, it causes high fever, cough, breathing problems. In some cases it can be fatal, especially in people with comorbidities like heart or kidney problems and diabetes. The current COVID-19 treatment is based on symptomatic therapy, so finding an appropriate drug against COVID-19 remains an immediate and crucial target for the global scientific community. Two main processes are thought to be responsible for the COVID-19 pathogenesis. In the early stages of infection, disease is determined mainly by virus replication. In the later stages of infection, by an excessive immune/inflammatory response, leading to tissue damage. Therefore, the main treatment options are antiviral and immunomodulatory/anti-inflammatory agents. Many clinical trials have been conducted concerning the use of various drugs in COVID-19 therapy, and many are still ongoing. The majority of trials examine drug reposition (repurposing), which seems to be a good and effective option. Many drugs have been repurposed in COVID-19 therapy including remdesivir, favipiravir, tocilizumab and baricitinib. The aim of this review is to highlight (based on existing and accessible clinical evidence on ongoing trials) the current and available promising drugs for COVID-19 and outline their characteristics.

Systematic review on role of structure based drug design (SBDD) in the identification of anti-viral leads against SARS-Cov-2

The outbreak of existing public health distress is threatening the entire world with emergence and rapid spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The novel coronavirus disease 2019 (COVID-19) is mild in most people. However, in some elderly people with co-morbid conditions, it may progress to pneumonia, acute respiratory distress syndrome (ARDS) and multi organ dysfunction leading to death. COVID-19 has caused global panic in the healthcare sector and has become one of the biggest threats to the global economy. Drug discovery researchers are expected to contribute rapidly than ever before. The complete genome sequence of coronavirus had been reported barely a month after the identification of first patient. Potential drug targets to combat and treat the coronavirus infection have also been explored. The iterative structure-based drug design (SBDD) approach could significantly contribute towards the discovery of new drug like molecules for the treatment of COVID-19. The existing antivirals and experiences gained from SARS and MERS outbreaks may pave way for identification of potential drug molecules using the approach. SBDD has gained momentum as the essential tool for faster and costeffective lead discovery of antivirals in the past. The discovery of FDA approved human immunodeficiency virus type 1 (HIV-1) inhibitors represent the foremost success of SBDD. This systematic review provides an overview of the novel coronavirus, its pathology of replication, role of structure based drug design, available drug targets and recent advances in in-silico drug discovery for the prevention of COVID-19. SARSCoV- 2 main protease, RNA dependent RNA polymerase (RdRp) and spike (S) protein are the potential targets, which are currently explored for the drug development.

Design of advanced siRNA therapeutics for the treatment of COVID-19

COVID-19 is a newly emerged viral disease that is currently affecting the whole globe. A variety of therapeutic approaches are underway to block the SARS-CoV-2 virus. Among these methods, siRNAs could be a safe and specific option, as they have been tested against other viruses. siRNAs are a class of inhibitor RNAs that act promisingly as mRNA expression blockers and they can be designed to interfere with viral mRNA to block virus replication. In order to do this, we designed and evaluated the efficacy of six highly specific siRNAs, which target essential viral mRNAs with no predicted human genome off-targets. We observed a significant reduction in the copy number viral mRNAs after treatment with the siRNAs, and are expected to inhibit virus replication. We propose siRNAs as a potential co-therapy for acute SARS-CoV-2 infection.

Resources and computational strategies to advance small molecule SARS-CoV-2 discovery: Lessons from the pandemic and preparing for future health crises

There is an urgent need to identify new therapies that prevent SARS-CoV-2 infection and improve the outcome of COVID-19 patients. This pandemic has thus spurred intensive research in most scientific areas and in a short period of time, several vaccines have been developed. But, while the race to find vaccines for COVID-19 has dominated the headlines, other types of therapeutic agents are being developed. In this mini-review, we report several databases and online tools that could assist the discovery of anti-SARS-CoV-2 small chemical compounds and peptides. We then give examples of studies that combined in silico and in vitro screening, either for drug repositioning purposes or to search for novel bioactive compounds. Finally, we question the overall lack of discussion and plan observed in academic research in many countries during this crisis and suggest that there is room for improvement.

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.

Network bioinformatics analysis provides insight into drug repurposing for COVID-19

The COVID-19 disease caused by the SARS-CoV-2 virus is a health crisis worldwide. While developing novel drugs and vaccines is long, repurposing existing drugs against COVID-19 can yield treatments with known preclinical, pharmacokinetic, pharmacodynamic, and toxicity profiles, which can rapidly enter clinical trials. In this study, we present a novel network-based drug repurposing platform to identify candidates for the treatment of COVID-19. At the time of the initial outbreak, knowledge about SARS-CoV-2 was lacking, but based on its similarity with other viruses, we sought to identify repurposing candidates to be tested rapidly at the clinical or preclinical levels. We first analyzed the genome sequence of SARS-CoV-2 and confirmed SARS as the closest virus by genome similarity, followed by MERS and other human coronaviruses. Using text mining and database searches, we obtained 34 COVID-19-related genes to seed the construction of a molecular network where our module detection and drug prioritization algorithms identified 24 disease-related human pathways, five modules, and 78 drugs to repurpose. Based on clinical knowledge, we re-prioritized 30 potentially repurposable drugs against COVID-19 (including pseudoephedrine, andrographolide, chloroquine, abacavir, and thalidomide). Our work shows how in silico repurposing analyses can yield testable candidates to accelerate the response to novel disease outbreaks.

The impact of structural bioinformatics tools and resources on SARS-CoV-2 research and therapeutic strategies

SARS-CoV-2 is the causative agent of COVID-19, the ongoing global pandemic. It has posed a worldwide challenge to human health as no effective treatment is currently available to combat the disease. Its severity has led to unprecedented collaborative initiatives for therapeutic solutions against COVID-19. Studies resorting to structure-based drug design for COVID-19 are plethoric and show good promise. Structural biology provides key insights into 3D structures, critical residues/mutations in SARS-CoV-2 proteins, implicated in infectivity, molecular recognition and susceptibility to a broad range of host species. The detailed understanding of viral proteins and their complexes with host receptors and candidate epitope/lead compounds is the key to developing a structure-guided therapeutic design. Since the discovery of SARS-CoV-2, several structures of its proteins have been determined experimentally at an unprecedented speed and deposited in the Protein Data Bank. Further, specialized structural bioinformatics tools and resources have been developed for theoretical models, data on protein dynamics from computer simulations, impact of variants/mutations and molecular therapeutics. Here, we provide an overview of ongoing efforts on developing structural bioinformatics tools and resources for COVID-19 research. We also discuss the impact of these resources and structure-based studies, to understand various aspects of SARS-CoV-2 infection and therapeutic development. These include (i) understanding differences between SARS-CoV-2 and SARS-CoV, leading to increased infectivity of SARS-CoV-2, (ii) deciphering key residues in the SARS-CoV-2 involved in receptor-antibody recognition, (iii) analysis of variants in host proteins that affect host susceptibility to infection and (iv) analyses facilitating structure-based drug and vaccine design against SARS-CoV-2.

Repurposing of CNS drugs to treat COVID-19 infection: targeting the sigma-1 receptor

The novel coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The escalating number of SARS-CoV-2-infected individuals has conferred the viral spread with the status of global pandemic. However, there are no prophylactic or therapeutic drugs available on the market to treat COVID-19, although several drugs have been approved. Recently, two articles using the comparative viral-human protein-protein interaction map revealed that the sigma-1 receptor in the endoplasmic reticulum plays an important role in SARS-CoV-2 replication in cells. Knockout and knockdown of SIGMAR1 (sigma-1 receptor, encoded by SIGMAR1) caused robust reductions in SARS-CoV-2 replication, which indicates that the sigma-1 receptor is a key therapeutic target for SARS-CoV-2 replication. Interestingly, a recent clinical trial demonstrated that treatment with the antidepressant fluvoxamine, which has a high affinity at the sigma-1 receptor, could prevent clinical deterioration in adult outpatients infected with SARS-CoV-2. In this review, we discuss the brief history of the sigma-1 receptor and its role in SARS-CoV-2 replication in cells. Here, we propose repurposing of traditional central nervous system (CNS) drugs that have a high affinity at the sigma-1 receptor (i.e., fluvoxamine, donepezil, ifenprodil) for the treatment of SARS-CoV-2-infected patients. Finally, we discussed the potential of other CNS candidates such as cutamesine and arketamine.

The Interface of Therapeutics and Genomics in Cardiovascular Medicine

Pharmacogenomics has a burgeoning role in cardiovascular medicine, from warfarin dosing to antiplatelet choice, with recent developments in sequencing bringing the promise of personalised medicine ever closer to the bedside. Further scientific evidence, real-world clinical trials, and economic modelling are needed to fully realise this potential. Additionally, tools such as polygenic risk scores, and results from Mendelian randomisation analyses, are only in the early stages of clinical translation and merit further investigation. Genetically targeted rational drug design has a strong evidence base and, due to the nature of genetic data, academia, direct-to-consumer companies, healthcare systems, and industry may meet in an unprecedented manner. Data sharing navigation may prove problematic. The present manuscript addresses these issues and concludes a need for further guidance to be provided to prescribers by professional bodies to aid in the consideration of such complexities and guide translation of scientific knowledge to personalised clinical action, thereby striving to improve patient care. Additionally, technologic infrastructure equipped to handle such large complex data must be adapted to pharmacogenomics and made user friendly for prescribers and patients alike.

Repurposing drugs for the management of COVID-19

Introduction: The coronavirus disease 2019 (COVID-19) caused by SARS-CoV-2 represents a serious health issue worldwide, with more than 61 million cases and more than 1.4 million deaths since the beginning of the epidemic near the end of 2019. The scientific community strongly responded to this emergency situation with massive research efforts, mostly focused on diagnosis and clinical investigation of therapeutic solutions. In this scenario, drug repurposing played a crucial role in accelerating advanced clinical testing and shortening the time to access the regulatory review.Areas covered: This review covers the main and most successful drug repurposing approaches from a design, clinical, and regulatory standpoint. Available patents on repurposed drugs are also discussed.Expert opinion: Drug repurposing proved highly successful in response to the current pandemic, with remdesivir becoming the first specific antiviral drug approved for the treatment of COVID-19. In parallel, a number of drugs such as corticosteroids and low molecular weight heparin (LMWH) are used to treat hospitalized COVID-19 patients, while clinical testing of additional therapeutic options is ongoing. It is reasonably expected that these research efforts will deliver optimized and specific therapeutic tools that will increase the preparedness of health systems to possible future epidemics.

A connectivity map-based drug repurposing study and integrative analysis of transcriptomic profiling of SARS-CoV-2 infection

AIMS

The recent outbreak of COVID-19 has become a global health concern. There are currently no effective treatment strategies and vaccines for the treatment or prevention of this fatal disease. The current study aims to determine promising treatment options for the COVID-19 through a computational drug repurposing approach.

MATERIALS AND METHODS

In this study, we focus on differentially expressed genes (DEGs), detected in SARS-CoV-2 infected cell lines including "the primary human lung epithelial cell line NHBE" and "the transformed lung alveolar cell line A549". Next, the identified DEGs are used in the connectivity map (CMap) analysis to identify similarly acting therapeutic candidates. Furthermore, to interpret lists of DEGs, pathway enrichment and protein network analysis are performed. Genes are categorized into easily interpretable pathways based on their biological functions, and overrepresentation of each pathway is tested in comparison to what is expected randomly.

KEY FINDINGS

The results suggest the effectiveness of lansoprazole, folic acid, sulfamonomethoxine, tolnaftate, diclofenamide, halcinonide, saquinavir, metronidazole, ebselen, lidocaine and benzocaine, histone deacetylase (HDAC) inhibitors, heat shock protein 90 (HSP90) inhibitors, and many other clinically approved drugs as potent drugs against COVID-19 outbreak.

SIGNIFICANCE

Making new drugs remain a lengthy process, so the drug repurposing approach provides an insight into the therapeutics that might be helpful in this pandemic. In this study, pathway enrichment and protein network analysis are also performed, and the effectiveness of some drugs obtained from the CMap analysis has been investigated according to previous researches.