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1.
Eur J Clin Pharmacol ; 2022 Aug 09.
Article in English | MEDLINE | ID: covidwho-2035031

ABSTRACT

PURPOSE: The absence of specific treatments for COVID-19 leads to an intense global effort in the search for new therapeutic interventions and better clinical outcomes for patients. This review aimed to present a selection of accepted studies that reported the activity of antidepressant drugs belonging to the selective serotonin receptor inhibitor (SSRI) class for treating the novel coronavirus. METHODS: A search was performed in PubMed and SciELO databases using the following search strategies: [(coronavirus) OR (COVID) OR (SARS-CoV-2) AND (antidepressant) OR (serotonin) OR (selective serotonin receptor inhibitors)]. In the end, eleven articles were included. We also covered information obtained from ClinicalTrials.gov in our research. RESULTS: Although several clinical trials are ongoing, only a few drugs have been officially approved to treat the infection. Remdesivir, an antiviral drug, despite favorable preliminary results, has restricted the use due to the risk of toxicity and methodological flaws. Antidepressant drugs were able to reduce the risk of intubation or death related to COVID-19, decrease the need for intensive medical care, and severely inhibit viral titers by up to 99%. Among the SSRIs studied so far, fluoxetine and fluvoxamine have shown to be the most promising against SARS-CoV-2. CONCLUSION: If successful, these drugs can substantially reduce hospitalization and mortality rates, as well as allow for fully outpatient treatment for mild-to-moderate infections. Thus, repositioning SSRIs can provide benefits when faced with a rapidly evolving pandemic such as COVID-19.

2.
Flora ; 27(2):324-334, 2022.
Article in English | EMBASE | ID: covidwho-2033381

ABSTRACT

Introduction: Dipeptidyl peptidase-4 (DPP4) has been shown to be a functional receptor for MERS-CoV. An interaction between the viral spike protein and DPP4 is thought to facilitate viral entry. We aimed to find out whether sitagliptin, a member of DPP4 inhibitors, would have beneficial effects in COVID-19 patients. Materials and Methods: In this single center retrospective study, we evaluated 58 patients of whom 16 were on sitagliptin treatment. Molecular docking studies were performed to identify possible interactions between ACE2 and sitagliptin. Results: Sitagliptin use shortened the time to clinical recovery about 3.5 and fastened viral clearance more than 5 days. Resolution of all symptoms was achieved on a mean±standard error (SE) of 2.50 ± 0.40 days in sitagliptin (+) group and 5.69 ± 0.61 days in sitagliptin (-) group (Log-rank test, p< 0.001). PCR tests for SARS-CoV-2 resulted negative in mean ± SE of 7.50 ± 0.98 days in sitagliptin (+) and 13.17 ± 1.07 days in sitagliptin (-) group (Log-rank test, p= 0.003). Compared to day 0, CRP, ferritin and D-dimer levels on days three, five, and seven were significantly lower whereas lymphocyte count was higher in sitagliptin (+) group. Conclusion: Our results suggest that sitagliptin seems to have a potential to be considered for the treatment of COVID-19.

3.
Current Bioinformatics ; 17(3):217-237, 2022.
Article in English | EMBASE | ID: covidwho-2032698

ABSTRACT

Drug repositioning invovles exploring novel usages for existing drugs. It plays an important role in drug discovery, especially in the pre-clinical stages. Compared with the traditional drug discovery approaches, computational approaches can save time and reduce cost significantly. Since drug repositioning relies on existing drug-, disease-, and target-centric data, many machine learning (ML) approaches have been proposed to extract useful information from multiple data resources. Deep learning (DL) is a subset of ML and appears in drug repositioning much later than basic ML. Nevertheless, DL methods have shown great performance in predicting potential drugs in many studies. In this article, we review the commonly used basic ML and DL approaches in drug repositioning. Firstly, the related databases are introduced, while all of them are publicly available for researchers. Two types of preprocessing steps, calculating similarities and constructing networks based on those data, are discussed. Secondly, the basic ML and DL strategies are illustrated separately. Thirdly, we review the latest studies focused on the applications of basic ML and DL in identifying potential drugs through three paths: drug-disease associations, drug-drug interactions, and drug-target interactions. Finally, we discuss the limitations in current studies and suggest several directions of future work to address those limitations.

4.
Curr Comput Aided Drug Des ; 2022.
Article in English | PubMed | ID: covidwho-2022284

ABSTRACT

The coronavirus disease (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has become a serious global healthcare crisis, so there is an emergence of identifying efficacious therapeutic options. In a setting where there is an unavailability of definitive medication along with the constant eruption of vaccine-related controversies, the drug-repositioning approach seems to be an ideal step for the management of COVID-19 patients. Fluoroquinolones (FQs) are commonly prescribed antibiotics for the treatment of genitourinary tract and upper respiratory tract infections, including severe community-acquired pneumonia. Research over the years has postulated multifaceted implications of FQs in various pathological conditions. Previously, it has been reported that few, but not all FQs, possess strong antiviral activity with an unknown mechanism of action. Herein, an interesting perspective is discussed on repositioning possibilities of FQs for the SARS-CoV-2 infections based on the recent in silico evidential support. Noteworthy, FQs possess immunomodulatory and bactericidal activity which could be valuable for patients dealing with COVID-19 related complications. Conclusively, the current perspective could pave the way to initiate pre-clinical testing of FQs against several strains of SARS-CoV-2.

5.
Methods in Molecular Biology ; 2547:187-199, 2022.
Article in English | MEDLINE | ID: covidwho-2013831

ABSTRACT

The SARS-CoV-2 virus has been the subject of intense pharmacological research. Various pharmacotherapeutic approaches including antiviral and immunotherapy are being explored. A pandemic, however, cannot depend on the development of new drugs;the time required for conventional drug discovery and development is far too lengthy. As such, repurposing drugs is being used as a viable approach for identifying pharmacological agents for COVID-19 infections. Evaluation of repurposed drug candidates with pharmacogenomic analysis is being used to identify near-term pharmacological remedies for COVID-19.

6.
Frontiers in Pharmacology ; 13, 2022.
Article in English | EMBASE | ID: covidwho-2009896

ABSTRACT

The coronavirus disease 2019 pandemic accelerated drug/vaccine development processes, integrating scientists all over the globe to create therapeutic alternatives against this virus. In this work, we have collected information regarding proteins from SARS-CoV-2 and humans and how these proteins interact. We have also collected information from public databases on protein–drug interactions. We represent this data as networks that allow us to gain insights into protein–protein interactions between both organisms. With the collected data, we have obtained statistical metrics of the networks. This data analysis has allowed us to find relevant information on which proteins and drugs are the most relevant from the network pharmacology perspective. This method not only allows us to focus on viral proteins as the main targets for COVID-19 but also reveals that some human proteins could be also important in drug repurposing campaigns. As a result of the analysis of the SARS-CoV-2–human interactome, we have identified some old drugs, such as disulfiram, auranofin, gefitinib, suloctidil, and bromhexine as potential therapies for the treatment of COVID-19 deciphering their potential complex mechanism of action.

7.
Computers in Biology and Medicine ; : 105992, 2022.
Article in English | ScienceDirect | ID: covidwho-2003986

ABSTRACT

Drug repurposing is an approach to identify new medical indications of approved drugs. This work presents a graph neural network drug repurposing model, which we refer to as GDRnet, to efficiently screen a large database of approved drugs and predict the possible treatment for novel diseases. We pose drug repurposing as a link prediction problem in a multi-layered heterogeneous network with about 1.4 million edges capturing complex interactions between nearly 42,000 nodes representing drugs, diseases, genes, and human anatomies. GDRnet has an encoder–decoder architecture, which is trained in an end-to-end manner to generate scores for drug-disease pairs under test. We demonstrate the efficacy of the proposed model on real datasets as compared to other state-of-the-art baseline methods. For a majority of the diseases, GDRnet ranks the actual treatment drug in the top 15. Furthermore, we apply GDRnet on a coronavirus disease (COVID-19) dataset and show that many drugs from the predicted list are being studied for their efficacy against the disease.

8.
Working Paper Series National Bureau of Economic Research ; 23, 2022.
Article in English | GIM | ID: covidwho-2002487

ABSTRACT

Many medical decisions during the pandemic were made without the support of causal evidence obtained in clinical trials. We study the case of nebulized ibuprofen (NaIHS), a drug that was extensively used on COVID-19 patients in Argentina amidst wild claims about its effectiveness and without regulatory approval. We study data on 5,146 patients hospitalized in 11 health centers spread over 4 provinces, of which a total of 1,019 (19.8%) received the treatment. We find a large, negative and statistically significant correlation between NaIHS treatment and mortality using inverse probability weighting estimators. We consider several threats to identification, including the selection of "low" risks into NaIHS, spillovers affecting patients in the control group, and differences in the quality of care in centers that use NaIHS. While the negative correlation appears to be, broadly, robust, our results are best interpreted as emphasizing the benefits of running a randomized controlled trial and the challenges of incorporating information produced in other, less rigorous circumstances.

9.
Chem Biol Drug Des ; 2022.
Article in English | PubMed | ID: covidwho-2001616

ABSTRACT

Application of materials capable of energy harvesting to increase the efficiency and environmental adaptability is sometimes reflected in ability of discovery of some traces in an environment-either experimentally of 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. An especial focus is placed on chemogenomics and DR as unique and emerging system-based disciplines on CoVs 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.

10.
Sensors (Basel) ; 22(15)2022 Jul 26.
Article in English | MEDLINE | ID: covidwho-1994134

ABSTRACT

Transport-sharing systems are eco-friendly and the most promising services in smart urban environments, where the booming Internet of things (IoT) technologies play an important role in the smart infrastructure. Due to the imbalanced bike distribution, bikes and stalls in the docking stations could be unavailable when needed, leading to bad customer experiences. We develop a dynamic repositioning strategy for the management of bikes in this paper, which supports dispatchers to keep stations in service. Two open datasets are examined, and the exploratory data analysis presents that there is a significant difference of travel patterns between working and non-working days, where the former has an excess demand at rush hours and the latter is usually at a low demand. To evaluate the effect when the demand outstrips a station's capacity, we propose a non-linear scaling technique to transform demand patterns and perform the clustering analysis for each of five categories obtained from the sophisticated analysis of the dataset. Our repositioning strategy is developed according to the transformed demands. Compared with the previous work, numerical simulations reveal that our strategy has a better performance for high-demand stations, and thus can substantially reduce the repositioning cost, which brings benefit to bike-sharing operators for managing the city bike system.


Subject(s)
Bicycling , Induced Demand , Transportation/methods , Bicycling/classification , Bicycling/statistics & numerical data , Cities , Cluster Analysis , Humans , Induced Demand/trends , Transportation/statistics & numerical data , Travel
11.
European Journal of Tourism Research ; 32, 2022.
Article in English | Scopus | ID: covidwho-1995097

ABSTRACT

In order to propose a repositioning toolkit, this research addresses the essence of the daytime tourism milieu of the Hungarian capital Budapest’s nightly party zone and formulates the following two research questions: (1) What are the available elements of the daytime tourism milieu of Budapest’s party zone? and (2) How can this milieu enhance tourist experience for leveraging a sense of place in a future post-Covid-19 era? The data for this research were collected with the help of 85 undergraduates, who were given the task of taking 3 photos, as if they were tourists, aiming to capture the best reflection of the daytime tourism milieu of Budapest’s party zone. A database of 255 photos was analysed through visual content analysis. Additionally, each image was assigned a location of the photo, five hashtags and a short description. The descriptions of the photos were analysed by Python programming and calculations. The findings show the most important research outcomes concerning Budapest’s party zone, focusing on the daytime values of the district. The research identified the “creative milieu”’, “Jewish heritage milieu” and “gastronomic milieu” as the most important daytime profiles of the party zone. Based on the findings, the authors propose a repositioning toolkit and a strategy which, on the one hand, will develop a stronger sense of place in the case of the tourism milieu of Budapest’s party zone and, on the other hand, will position the party zone not only as a place of nightlife but also as a venue of daytime tourism. © 2022 The Author(s).

12.
Embase; 2022.
Preprint in English | EMBASE | ID: ppcovidwho-342140

ABSTRACT

Importance Drug repurposing requires distinguishing established drug class targets from novel molecule-specific mechanisms and rapidly derisking their therapeutic potential in a time-critical manner, particularly in a pandemic scenario. In response to the challenge to rapidly identify treatment options for COVID-19, several studies reported that statins, as a drug class, reduce mortality in these patients. However, it is unknown if different statins exhibit consistent function or may have varying therapeutic benefit. Objectives To test if different statins differ in their ability to exert protective effects based on molecular computational predictions and electronic medical record analysis. Main Outcomes and Measures A Bayesian network tool was used to predict drugs that shift the host transcriptomic response to SARS-CoV-2 infection towards a healthy state. Drugs were predicted using 14 RNA-sequencing datasets from 72 autopsy tissues and 465 COVID-19 patient samples or from cultured human cells and organoids infected with SARS-CoV-2, with a total of 2,436 drugs investigated. Top drug predictions included statins, which were then assessed using electronic medical records containing over 4,000 COVID-19 patients on statins to determine mortality risk in patients prescribed specific statins versus untreated matched controls. The same drugs were tested in Vero E6 cells infected with SARS-CoV-2 and human endothelial cells infected with a related OC43 coronavirus. Results Simvastatin was among the most highly predicted compounds (14/14 datasets) and five other statins, including atorvastatin, were predicted to be active in > 50% of analyses. Analysis of the clinical database revealed that reduced mortality risk was only observed in COVID-19 patients prescribed a subset of statins, including simvastatin and atorvastatin. In vitro testing of SARS-CoV-2 infected cells revealed simvastatin to be a potent direct inhibitor whereas most other statins were less effective. Simvastatin also inhibited OC43 infection and reduced cytokine production in endothelial cells. Conclusions and Relevance Different statins may differ in their ability to sustain the lives of COVID-19 patients despite having a shared drug target and lipid-modifying mechanism of action. These findings highlight the value of target-agnostic drug prediction coupled with patient databases to identify and clinically evaluate non-obvious mechanisms and derisk and accelerate drug repurposing opportunities.

13.
Pharma Times ; 54(4-5):17-22, 2022.
Article in English | EMBASE | ID: covidwho-1980810

ABSTRACT

The advancement of Artificial intelligence (AI) is found to have dual appearances as it can create the betterment of society and can threaten employment. AI is the automating process which has led to innovation in various educational methods as well as automated business procedures. Major disease areas that use AI tools include cancer, neurology and cardiology. The emergent idea of adopting AI in the drug development process has shifted from hype to hope. AI chains the decision-making processes for prevailing drugs & expanded treatments for other conditions, as well as accelerates the clinical trials procedure by finding the right patients from a number of data sources. Integrating AI into the healthcare ecosystem allows for a multitude of benefits, including automating tasks and analysing big patient data sets to deliver better healthcare faster, and at a lower cost. Machine learning, deep learning and Artificial Intelligence can be utilised to revolutionise the drug development process. At present, the main concern of the Pharmaceutical industry is drug development programmes because of increased R&D costs and reduced efficiency. In this review, we will discuss the applications and role of AI and the possible ways it can advance the effectiveness of the drug development process.

14.
Journal of Health and Translational Medicine ; 25(1):145-153, 2022.
Article in English | EMBASE | ID: covidwho-1979857

ABSTRACT

Viral diseases are the most devastating health concern worldwide. Outbreaks of coronavirus (CoVs)-related acute respiratory diseases are responsible for the massive health/socio-economic breakdown in the last two decades including the Severe Acute Respiratory Syndrome (SARS) and Middle East Respiratory Syndrome (MERS), the third reported spillover SARS-CoV-2 from an animal coronavirus to humans. After the H1N1 pandemic influenza (2009), SARS-CoV-2 (novel-beta coronavirus) causing COVID-19 has stretched across 215 countries in 5 major continents with 200,523,190 confirmed cases (4 August 2021;https://www.worldometers.info/coronavirus/). COVID-19 patients had cough, fever, dyspnea, headache, and respiratory failure, as well as shock, acute respiratory distress syndrome, and sepsis in severe instances. Independent of two preceding epidemics, SARS (2002) and MERS (2012), a knowledge gap about the emerging medical manifestations as well as complications of SARS-CoV-2 (2019-2020) infections in humans must be filled, with a focus on immunological complications and computational genomics for forecasting/preparedness for a similar outbreak in the future. This paper aims to address aspects of this gap.

15.
Journal of Internal Medicine of Taiwan ; 33(2):110-127, 2022.
Article in English | EMBASE | ID: covidwho-1979601

ABSTRACT

The Coronavirus disease 2019 (COVID-19), which is caused by the severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2), is a previously unrecognized viral illness with high infectivity that has sparked a global crisis. Poorly controlled diabetes was demonstrated to be a crucial risk factor for poor COVID-19 outcomes. COVID-19 infections are associated with severe metabolic dysfunctions, new-onset diabetes, and increased thrombotic events against the backdrop of aberrant endothelial function. The current body of evidence suggests that when hyperglycemia interacts with other risk factors, it might modify immune and inflammatory responses such that individuals become susceptible to severe COVID-19 infection and worse outcomes including higher mortality. Apart from their glucose-lowering actions, the pleiotropic effects of antidiabetic medications can inhibit viral action, attenuate endothelial dysfunction, ameliorate oxidant effects, and modulate inflammatory and immune responses during COVID-19 infections. These actions make antidiabetic medications feasible candidates for drug repurposing to combat the SARS-CoV-2-induced tsunami in diabetic COVID-19 patients. This review discusses the association between diabetes and COVID-19, pathophysiology of the disease in diabetes, and therapeutic potential of antidiabetic medications for diabetic patients during the current COVID-19 pandemic. Given the short history of human infection with SARS-CoV-2, the information provided by recent studies is limited. Hence, further investigations of the optimal management of patients with diabetes who are affected by COVID-19 are warranted.

16.
American Journal of Cancer Research ; 12(7):3333-3346, 2022.
Article in English | EMBASE | ID: covidwho-1976011

ABSTRACT

Disulfiram is an FDA-approved drug that has been used to treat alcoholism and has demonstrated a wide range of anti-cancer, anti-bacterial, and anti-viral effects. In the global COVID-19 pandemic, there is an urgent need for effective therapeutics and vaccine development. According to recent studies, disulfiram can act as a potent SARS-CoV-2 replication inhibitor by targeting multiple SARS-CoV-2 non-structural proteins to inhibit viral polyprotein cleavage and RNA replication. Currently, disulfiram is under evaluation in phase II clinical trials to treat COVID-19. With more and more variants of the SARS-CoV-2 worldwide, it becomes critical to know whether disulfiram can also inhibit viral entry into host cells for various variants and replication inhibition. Here, molecular and cellular biology assays demonstrated that disulfiram could interrupt viral spike protein binding with its receptor ACE2. By using the viral pseudo-particles (Vpps) of SARS-CoV-2, disulfiram also showed the potent activity to block viral entry in a cell-based assay against Vpps of different SARS-CoV-2 variants. We further established a live virus model system to support the anti-viral entry activity of disulfiram with the SARS-CoV-2 virus. Molecular docking revealed how disulfiram hindered the binding between the ACE2 and wild-type or mutated spike proteins. Thus, our results indicate that disulfiram has the capability to block viral entry activity of different SARS-CoV-2 variants. Together with its known anti-replication of SARS-CoV-2, disulfiram may serve as an effective therapy against different SARS-CoV-2 variants.

17.
Letters in Drug Design and Discovery ; 19(7):637-653, 2022.
Article in English | EMBASE | ID: covidwho-1968944

ABSTRACT

Background: Since the end of 2019, the etiologic agent SAR-CoV-2 responsible for one of the most significant epidemics in history has caused severe global economic, social, and health damages. The drug repurposing approach and application of Structure-based Drug Discovery (SBDD) using in silico techniques are increasingly frequent, leading to the identification of several molecules that may represent promising potential. Methods: In this context, here we use in silico methods of virtual screening (VS), pharmacophore modeling (PM), and fragment-based drug design (FBDD), in addition to molecular dynamics (MD), molecular mechanics/Poisson-Boltzmann surface area (MM-PBSA) calculations, and covalent docking (CD) for the identification of potential treatments against SARS-CoV-2. We initially validated the docking protocol followed by VS in 1,613 FDA-approved drugs obtained from the ZINC database. Thus, we identified 15 top hits, of which three of them were selected for further simulations. In parallel, for the compounds with a fit score value ≤ of 30, we performed the FBDD protocol, where we designed 12 compounds. Results: By applying a PM protocol in the ZINC database, we identified three promising drug candidates. Then, the 9 top hits were evaluated in simulations of MD, MM-PBSA, and CD. Subsequently, MD showed that all identified hits showed stability at the active site without significant changes in the pro-tein's structural integrity, as evidenced by the RMSD, RMSF, Rg, SASA graphics. They also showed interactions with the catalytic dyad (His41 and Cys145) and other essential residues for activity (Glu166 and Gln189) and high affinity for MM-PBSA, with possible covalent inhibition mechanism. Conclusion: Finally, our protocol helped identify potential compounds wherein ZINC896717 (Zafir-lukast), ZINC1546066 (Erlotinib), and ZINC1554274 (Rilpivirine) were more promising and could be explored in vitro, in vivo, and clinical trials to prove their potential as antiviral agents.

18.
Fundamental and Clinical Pharmacology ; 36:84-85, 2022.
Article in English | EMBASE | ID: covidwho-1968113

ABSTRACT

Introduction: During the first wave of Covid-19, various treatments have been tested off label, including HCQ, despite its significant adverse effects and the absence of proof of effectiveness against this virus. A recent meta-analysis showed an increase of mortality rate related to HCQ use. We aimed to estimate the number of deaths caused by HCQ worldwide. Material and methods: The HCQ exposure according to countries was estimated from meta-analysis using published cohorts (Pubmed) identified thanks to a systematic review. We retrieved the mortality rate of HCQ from meta-analysis of randomized trials (MetaEvidence tool) as well as the number of hospitalized patients in the selected countries between the beginning of the pandemic and mid-July 2020 using various online databases. Results: The included studies were conducted in Turkey (k = 3), Brazil (k = 1), Belgium (k = 1), France (k = 2), United Kingdom (k = 1), Spain (k = 10), Italy (k = 13), and United States of America (k = 19). The prescription HCQ rates vary greatly from one country to another (median 52%, range 6-97). The number of hospitalisations related to the first wave of COVID-19 ranged from 3,082 (Brazil) to 888,037 (USA). In Belgium, HCQ induced 151 deaths. In Turkey, the median was 111 HCQ-induced deaths (range 109-150). In France, the HCQ-related deaths varied between 98 and 256. In Italy and Spain, the medians of HCQ-induced deaths were, respectively, 1,549 (range 996-1,758) and 1,435 (range 760-1,704). In UK, HCQ induced 415 deaths. The highest number of HCQ-induced deaths is found in the USA with a median of 5645 (range 1,194-9,059). Overall, using median estimates, HCQ totalized 9564 deaths in countries with available data. Discussion/Conclusion: HCQ was associated with at least 9,564 deaths during first wave of COVID-19 pandemic. These results were underestimated in regards of lacking data in mostly countries. These findings illustrate the risk of drug repurposing from chronic diseases to deadly conditions, with a low-level evidence.

19.
Comput Chem Eng ; : 107947, 2022 Aug 04.
Article in English | MEDLINE | ID: covidwho-1966455

ABSTRACT

Given that the usual process of developing a new vaccine or drug for COVID-19 demands significant time and funds, drug repositioning has emerged as a promising therapeutic strategy. We propose a method named DRPADC to predict novel drug-disease associations effectively from the original sparse drug-disease association adjacency matrix. Specifically, DRPADC processes the original association matrix with the WKNKN algorithm to reduce its sparsity. Furthermore, multiple types of similarity information are fused by a CKA-MKL algorithm. Finally, a compressed sensing algorithm is used to predict the potential drug-disease (virus) association scores. Experimental results show that DRPADC has superior performance than several competitive methods in terms of AUC values and case studies. DRPADC achieved the AUC value of 0.941, 0.955 and 0.876 in Fdataset, Cdataset and HDVD dataset, respectively. In addition, the conducted case studies of COVID-19 show that DRPADC can predict drug candidates accurately.

20.
Viruses ; 14(6)2022 06 20.
Article in English | MEDLINE | ID: covidwho-1964111

ABSTRACT

Molnupiravir is a ß-d-N4-hydroxycytidine-5'-isopropyl ester (NHC) compound that exerts antiviral activity against various RNA viruses such as influenza, SARS, and Ebola viruses. Thus, the repurposing of Molnupiravir has gained significant attention for combatting infection with SARS-CoV-2, the etiological agent of COVID-19. Recently, Molnupiravir was granted authorization for the treatment of mild-to-moderate COVID-19 in adults. Findings from in vitro experiments, in vivo studies and clinical trials reveal that Molnupiravir is effective against SARS-CoV-2 by inducing viral RNA mutagenesis, thereby giving rise to mutated complementary RNA strands that generate non-functional viruses. To date, the data collectively suggest that Molnupiravir possesses promising antiviral activity as well as favorable prophylactic efficacy, attributed to its effective mutagenic property of disrupting viral replication. This review discusses the mechanisms of action of Molnupiravir and highlights its clinical utility by disabling SARS-CoV-2 replication, thereby ameliorating COVID-19 severity. Despite relatively few short-term adverse effects thus far, further detailed clinical studies and long-term pharmacovigilance are needed in view of its mutagenic effects.


Subject(s)
COVID-19 , Antiviral Agents/pharmacology , Antiviral Agents/therapeutic use , COVID-19/drug therapy , Cytidine/analogs & derivatives , Humans , Hydroxylamines , SARS-CoV-2
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