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1.
Phys Chem Chem Phys ; 24(36): 22129-22143, 2022 Sep 21.
Article in English | MEDLINE | ID: covidwho-2016863

ABSTRACT

The pneumonia outbreak caused by the SARS-CoV-2 virus poses a serious threat to human health and the world economy. The development of safe and highly effective antiviral drugs is of great significance for the treatment of COVID-19. The main protease (Mpro) of SARS-CoV-2 is a key enzyme for viral replication and transcription and has no homolog in humans. Therefore, the Mpro is an ideal target for the design of drugs against COVID-19. Insights into the inhibitor-Mpro binding mechanism and conformational changes of the Mpro are essential for the design of potent drugs that target the Mpro. In this study, we analyzed the conformational changes of the Mpro that are induced by the binding of three inhibitors, YTV, YSP and YU4, using multiple replica accelerated molecular dynamics (MR-aMD) simulations, dynamic cross-correlation map (DCCM) calculations, principal component analysis (PCA), and free energy landscape (FEL) analysis. The results from DCCM calculations and PCA show that the binding of inhibitors significantly affects the kinetic behavior of the Mpro and induces a conformational rearrangement of the Mpro. The binding ability and binding mechanism of YTV, YSP and YU4 to the Mpro were investigated using the molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) method. The results indicate that substitution of the tert-butanol group by methylbenzene and trifluoromethyl groups enhances the binding ability of YSP and YU4 to the Mpro compared with YTV; moreover, massive hydrophobic interactions are detected between the inhibitors and the Mpro. Meanwhile, T25, L27, H41, M49, N142, G143, C145, M165, E166 and Q189 are identified as the key residues for inhibitor-Mpro interactions using residue-based free energy decomposition calculations, which can be employed as efficient targets in the design of drugs that inhibit the activity of the Mpro.


Subject(s)
COVID-19 , Molecular Dynamics Simulation , Antiviral Agents/chemistry , Antiviral Agents/pharmacology , Coronavirus 3C Proteases , Cysteine Endopeptidases/metabolism , Drug Repositioning/methods , Humans , Molecular Docking Simulation , Peptide Hydrolases/metabolism , Protease Inhibitors/chemistry , SARS-CoV-2 , Viral Nonstructural Proteins/metabolism , tert-Butyl Alcohol
2.
Expert Rev Clin Pharmacol ; 15(8): 945-958, 2022 Aug.
Article in English | MEDLINE | ID: covidwho-2004911

ABSTRACT

INTRODUCTION: Developing and evaluating novel compounds for treatment or prophylaxis of emerging infectious diseases is costly and time-consuming. Repurposing of already available marketed compounds is an appealing option as they already have an established safety profile. This approach could substantially reduce cost and time required to make effective treatments available to fight the COVID-19 pandemic. However, this approach is challenging since many drug candidates show efficacy in in vitro experiments, but fail to deliver effect when evaluated in clinical trials. Better approaches to evaluate in vitro data are needed, in order to prioritize drugs for repurposing. AREAS COVERED: This article evaluates potential drugs that might be of interest for repurposing in the treatment of patients with COVID-19 disease. A pharmacometric simulation-based approach was developed to evaluate in vitro activity data in combination with expected clinical drug exposure, in order to evaluate the likelihood of achieving effective concentrations in patients. EXPERT OPINION: The presented pharmacometric approach bridges in vitro activity data to clinically expected drug exposures, and could therefore be a useful compliment to other methods in order to prioritize repurposed drugs for evaluation in prospective randomized controlled clinical trials.


Subject(s)
COVID-19 , Antiviral Agents/therapeutic use , COVID-19/drug therapy , Drug Repositioning/methods , Humans , Pandemics , Prospective Studies , SARS-CoV-2
3.
Front Public Health ; 10: 902123, 2022.
Article in English | MEDLINE | ID: covidwho-1987598

ABSTRACT

The global spread of the SARS coronavirus 2 (SARS-CoV-2), its manifestation in human hosts as a contagious disease, and its variants have induced a pandemic resulting in the deaths of over 6,000,000 people. Extensive efforts have been devoted to drug research to cure and refrain the spread of COVID-19, but only one drug has received FDA approval yet. Traditional drug discovery is inefficient, costly, and unable to react to pandemic threats. Drug repurposing represents an effective strategy for drug discovery and reduces the time and cost compared to de novo drug discovery. In this study, a generic drug repurposing framework (SperoPredictor) has been developed which systematically integrates the various types of drugs and disease data and takes the advantage of machine learning (Random Forest, Tree Ensemble, and Gradient Boosted Trees) to repurpose potential drug candidates against any disease of interest. Drug and disease data for FDA-approved drugs (n = 2,865), containing four drug features and three disease features, were collected from chemical and biological databases and integrated with the form of drug-disease association tables. The resulting dataset was split into 70% for training, 15% for testing, and the remaining 15% for validation. The testing and validation accuracies of the models were 99.3% for Random Forest and 99.03% for Tree Ensemble. In practice, SperoPredictor identified 25 potential drug candidates against 6 human host-target proteomes identified from a systematic review of journals. Literature-based validation indicated 12 of 25 predicted drugs (48%) have been already used for COVID-19 followed by molecular docking and re-docking which indicated 4 of 13 drugs (30%) as potential candidates against COVID-19 to be pre-clinically and clinically validated. Finally, SperoPredictor results illustrated the ability of the platform to be rapidly deployed to repurpose the drugs as a rapid response to emergent situations (like COVID-19 and other pandemics).


Subject(s)
COVID-19 , Drug Repositioning , COVID-19/drug therapy , Drug Repositioning/methods , Humans , Machine Learning , Molecular Docking Simulation , SARS-CoV-2
4.
Sci Rep ; 12(1): 12920, 2022 07 28.
Article in English | MEDLINE | ID: covidwho-1960505

ABSTRACT

During the current coronavirus disease 2019 (COVID-19) pandemic, symptoms of depression are commonly documented among both symptomatic and asymptomatic quarantined COVID-19 patients. Despite that many of the FDA-approved drugs have been showed anti-SARS-CoV-2 activity in vitro and remarkable efficacy against COVID-19 in clinical trials, no pharmaceutical products have yet been declared to be fully effective for treating COVID-19. Antidepressants comprise five major drug classes for the treatment of depression, neuralgia, migraine prophylaxis, and eating disorders which are frequently reported symptoms in COVID-19 patients. Herein, the efficacy of eight frequently prescribed FDA-approved antidepressants on the inhibition of both SARS-CoV-2 and MERS-CoV was assessed. Additionally, the in vitro anti-SARS-CoV-2 and anti-MERS-CoV activities were evaluated. Furthermore, molecular docking studies have been performed for these drugs against the spike (S) and main protease (Mpro) pockets of both SARS-CoV-2 and MERS-CoV. Results showed that Amitriptyline, Imipramine, Paroxetine, and Sertraline had potential anti-viral activities. Our findings suggested that the aforementioned drugs deserve more in vitro and in vivo studies targeting COVID-19 especially for those patients suffering from depression.


Subject(s)
COVID-19 , Middle East Respiratory Syndrome Coronavirus , Antidepressive Agents/pharmacology , Antidepressive Agents/therapeutic use , Antiviral Agents/pharmacology , Antiviral Agents/therapeutic use , COVID-19/drug therapy , Drug Repositioning/methods , Humans , Molecular Docking Simulation , SARS-CoV-2
5.
Comput Biol Med ; 147: 105709, 2022 08.
Article in English | MEDLINE | ID: covidwho-1944685

ABSTRACT

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of the contagious coronavirus disease 2019 (COVID-19) which was first identified in Wuhan, China, in December 2019. Around the world, many researchers focused their research on identifying inhibitors against the druggable SARS-CoV-2 targets. The reported genomic mutations have a direct effect on the receptor-binding domain (RBD), which interacts with host angiotensin-converting enzyme 2 (ACE-2) for viral cell entry. These mutations, some of which are variants of concern (VOC), lead to increased morbidity and mortality rates. The newest variants including B.1.617.2 (Delta), AY.1 (Delta plus), and C.37 (Lambda) were considered in this study. Thus, an exhaustive structure-based virtual screening of a ligand library (in which FDA approved drugs are also present) using the drug-likeness screening, molecular docking, ADMET profiling was performed followed by molecular dynamics (MD) simulation, and Molecular Mechanics-Poisson Boltzmann Surface Area (MM-PBSA) calculation to identify compounds or drugs can be repurposed for inhibiting the wild type, Delta, Delta plus and Lambda variants of RBD of the spike protein. Based on the virtual screening steps, two FDA approved drugs, Atovaquone (atv) and Praziquantel (prz), were selected and repurposed as the best candidates of SARS-CoV-2 RBD inhibitors. Molecular docking results display that both atv and prz contribute in different interaction with binding site residues (Gln493, Asn501 and Gly502 in the hydrogen bond formation, Phe490 and Tyr505 in the π- π stacking and Tyr449, Ser494, and Phe497 in the vdW interactions) in the wild type, Delta, Delta plus and Lambda variants of RBD of the spike protein. MD simulations revealed that among the eight studied complexes, the wild type-atv and Delta-prz complexes have the most structural stability over the simulation time. Furthermore, MM-PBSA calculation showed that in the atv containing complexes, highest binding affinity is related to the wild type-atv complex and in the prz containing complexes, it is related to the Delta-prz complex. The validation of docking results was done by comparing with experimental data (heparin in complex with wild type and Delta variants). Also, comparison of the obtained results with the result of simulation of the k22 with the studied proteins showed that atv and prz are suitable inhibitors for these proteins, especially wild type t and Delta variant, respectively. Thus, we found that atv and prz are the best candidate for inhibition of wild type and Delta variant of the spike protein. Also, atv can be an appropriate inhibitor for the Lambda variant. Obtained in silico results may help the development of new anti-COVID-19 drugs.


Subject(s)
COVID-19 , SARS-CoV-2 , Adipates , COVID-19/drug therapy , COVID-19/genetics , Drug Repositioning/methods , Humans , Molecular Docking Simulation , Molecular Dynamics Simulation , Mutation/genetics , Peptidyl-Dipeptidase A/chemistry , SARS-CoV-2/genetics , Spike Glycoprotein, Coronavirus/chemistry , Succinates
6.
OMICS ; 26(6): 339-347, 2022 06.
Article in English | MEDLINE | ID: covidwho-1878748

ABSTRACT

Drug repurposing has broad importance in planetary health for therapeutics innovation in infectious diseases as well as common or rare chronic human diseases. Drug repurposing has also proved important to develop interventions against the COVID-19 pandemic. We propose a new approach for drug repurposing involving two-stage prediction and machine learning. First, diseases are clustered by gene expression on the premise that similar patterns of altered gene expression imply critical pathways shared in different disease conditions. Next, drug efficacy is assessed by the reversibility of abnormal gene expression, and results are clustered to identify repurposing targets. To cluster similar diseases, gene expression data from 262 cases of 31 diseases and 268 controls were analyzed by Uniform Manifold Approximation and Projection for Dimension Reduction followed by k-means to optimize the number of clusters. For evaluation, we examined disease-specific gene expression data for inclusion, body myositis, polymyositis, and dermatomyositis (DM), and used LINCS L1000 characteristic direction signatures search engine (L1000CDS2) to obtain lists of small-molecule compounds that reversed the expression patterns of these specifically altered genes as candidates for drug repurposing. Finally, the functions of affected genes were analyzed by Gene Set Enrichment Analysis to examine consistency with expected drug efficacy. Consequently, we found disease-specific gene expression, and importantly, identified 20 drugs such as BMS-387032, phorbol-12-myristate-13-acetate, mitoxantrone, alvocidib, and vorinostat as candidates for repurposing. These were previously noted to be effective against two of the three diseases, and have a high probability of being effective against the other. That is, inclusion body myositis and DM. The two-stage prediction approach to drug repurposing presented here offers innovation to inform future drug discovery and clinical trials in a variety of human diseases.


Subject(s)
COVID-19 , Drug Repositioning , COVID-19/drug therapy , COVID-19/genetics , Cluster Analysis , Drug Repositioning/methods , Gene Expression , Humans , Machine Learning , Pandemics
7.
ACS Infect Dis ; 8(6): 1191-1203, 2022 06 10.
Article in English | MEDLINE | ID: covidwho-1873405

ABSTRACT

SARS-CoV-2 is the causative viral pathogen driving the COVID-19 pandemic that prompted an immediate global response to the development of vaccines and antiviral therapeutics. For antiviral therapeutics, drug repurposing allows for rapid movement of the existing clinical candidates and therapies into human clinical trials to be tested as COVID-19 therapies. One effective antiviral treatment strategy used early in symptom onset is to prevent viral entry. SARS-CoV-2 enters ACE2-expressing cells when the receptor-binding domain of the spike protein on the surface of SARS-CoV-2 binds to ACE2 followed by cleavage at two cut sites by TMPRSS2. Therefore, a molecule capable of inhibiting the protease activity of TMPRSS2 could be a valuable antiviral therapy. Initially, we used a fluorogenic high-throughput screening assay for the biochemical screening of 6030 compounds in NCATS annotated libraries. Then, we developed an orthogonal biochemical assay that uses mass spectrometry detection of product formation to ensure that hits from the primary screen are not assay artifacts from the fluorescent detection of product formation. Finally, we assessed the hits from the biochemical screening in a cell-based SARS-CoV-2 pseudotyped particle entry assay. Of the six molecules advanced for further studies, two are approved drugs in Japan (camostat and nafamostat), two have entered clinical trials (PCI-27483 and otamixaban), while the other two molecules are peptidomimetic inhibitors of TMPRSS2 taken from the literature that have not advanced into clinical trials (compounds 92 and 114). This work demonstrates a suite of assays for the discovery and development of new inhibitors of TMPRSS2.


Subject(s)
COVID-19 , Percutaneous Coronary Intervention , Angiotensin-Converting Enzyme 2 , Antiviral Agents/pharmacology , COVID-19/drug therapy , Drug Repositioning/methods , Humans , Pandemics , SARS-CoV-2 , Serine Endopeptidases
8.
Methods ; 203: 214-225, 2022 07.
Article in English | MEDLINE | ID: covidwho-1873339

ABSTRACT

In the past 20 years, there have been several infectious disease outbreaks in humans for which the causative agent has been a zoonotic coronavirus. Novel infectious disease outbreaks, as illustrated by the current coronavirus disease 2019 (COVID-19) pandemic, demand a rapid response in terms of identifying effective treatments for seriously ill patients. The repurposing of approved drugs from other therapeutic areas is one of the most practical routes through which to approach this. Here, we present a systematic network-based drug repurposing methodology, which interrogates virus-human, human protein-protein and drug-protein interactome data. We identified 196 approved drugs that are appropriate for repurposing against COVID-19 and 102 approved drugs against a related coronavirus, severe acute respiratory syndrome (SARS-CoV). We constructed a protein-protein interaction (PPI) network based on disease signatures from COVID-19 and SARS multi-omics datasets. Analysis of this PPI network uncovered key pathways. Of the 196 drugs predicted to target COVID-19 related pathways, 44 (hypergeometric p-value: 1.98e-04) are already in COVID-19 clinical trials, demonstrating the validity of our approach. Using an artificial neural network, we provide information on the mechanism of action and therapeutic value for each of the identified drugs, to facilitate their rapid repurposing into clinical trials.


Subject(s)
COVID-19 , Drug Repositioning , Antiviral Agents/pharmacology , Antiviral Agents/therapeutic use , COVID-19/drug therapy , Drug Repositioning/methods , Humans , Pandemics , SARS-CoV-2
10.
Brief Bioinform ; 23(5)2022 09 20.
Article in English | MEDLINE | ID: covidwho-1831015

ABSTRACT

BACKGROUND: Coronavirus disease 2019 (COVID-19) has spurred a boom in uncovering repurposable existing drugs. Drug repurposing is a strategy for identifying new uses for approved or investigational drugs that are outside the scope of the original medical indication. MOTIVATION: Current works of drug repurposing for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are mostly limited to only focusing on chemical medicines, analysis of single drug targeting single SARS-CoV-2 protein, one-size-fits-all strategy using the same treatment (same drug) for different infected stages of SARS-CoV-2. To dilute these issues, we initially set the research focusing on herbal medicines. We then proposed a heterogeneous graph embedding method to signaled candidate repurposing herbs for each SARS-CoV-2 protein, and employed the variational graph convolutional network approach to recommend the precision herb combinations as the potential candidate treatments against the specific infected stage. METHOD: We initially employed the virtual screening method to construct the 'Herb-Compound' and 'Compound-Protein' docking graph based on 480 herbal medicines, 12,735 associated chemical compounds and 24 SARS-CoV-2 proteins. Sequentially, the 'Herb-Compound-Protein' heterogeneous network was constructed by means of the metapath-based embedding approach. We then proposed the heterogeneous-information-network-based graph embedding method to generate the candidate ranking lists of herbs that target structural, nonstructural and accessory SARS-CoV-2 proteins, individually. To obtain precision synthetic effective treatments forvarious COVID-19 infected stages, we employed the variational graph convolutional network method to generate candidate herb combinations as the recommended therapeutic therapies. RESULTS: There were 24 ranking lists, each containing top-10 herbs, targeting 24 SARS-CoV-2 proteins correspondingly, and 20 herb combinations were generated as the candidate-specific treatment to target the four infected stages. The code and supplementary materials are freely available at https://github.com/fanyang-AI/TCM-COVID19.


Subject(s)
COVID-19 , COVID-19/drug therapy , Drug Combinations , Drug Repositioning/methods , Drugs, Investigational , Humans , SARS-CoV-2
11.
Drug Discov Today ; 27(7): 1983-1993, 2022 07.
Article in English | MEDLINE | ID: covidwho-1773250

ABSTRACT

Drug repurposing is an appealing method to address the Coronavirus 2019 (COVID-19) pandemic because of the low cost and efficiency. We analyzed our in-house database of approved drug screens and compared their activity profiles with results from a severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) cytopathic effect (CPE) assay. The activity profiles of the human ether-à-go-go-related gene (hERG), phospholipidosis (PLD), and many cytotoxicity screens were found significantly correlated with anti-SARS-CoV-2 activity. hERG inhibition is a nonspecific off-target effect that has contributed to promiscuous drug interactions, whereas drug-induced PLD is an undesirable effect linked to hERG blockers. Thus, this study identifies preferred drug candidates as well as chemical structures that should be avoided because of their potential to induce toxicity. Lastly, we highlight the hERG liability of anti-SARS-CoV-2 drugs currently enrolled in clinical trials.


Subject(s)
COVID-19 , SARS-CoV-2 , Antiviral Agents/adverse effects , COVID-19/drug therapy , Drug Repositioning/methods , Humans , Pandemics
12.
Br J Pharmacol ; 179(13): 3250-3267, 2022 07.
Article in English | MEDLINE | ID: covidwho-1764898

ABSTRACT

Vaccines have reduced the transmission and severity of COVID-19, but there remains a paucity of efficacious treatment for drug-resistant strains and more susceptible individuals, particularly those who mount a suboptimal vaccine response, either due to underlying health conditions or concomitant therapies. Repurposing existing drugs is a timely, safe and scientifically robust method for treating pandemics, such as COVID-19. Here, we review the pharmacology and scientific rationale for repurposing niclosamide, an anti-helminth already in human use as a treatment for COVID-19. In addition, its potent antiviral activity, niclosamide has shown pleiotropic anti-inflammatory, antibacterial, bronchodilatory and anticancer effects in numerous preclinical and early clinical studies. The advantages and rationale for nebulized and intranasal formulations of niclosamide, which target the site of the primary infection in COVID-19, are reviewed. Finally, we give an overview of ongoing clinical trials investigating niclosamide as a promising candidate against SARS-CoV-2.


Subject(s)
COVID-19 , Antiviral Agents/pharmacology , Antiviral Agents/therapeutic use , COVID-19/drug therapy , Drug Repositioning/methods , Humans , Niclosamide/pharmacology , Niclosamide/therapeutic use , Pandemics , SARS-CoV-2
13.
Mol Inform ; 41(9): e2200001, 2022 09.
Article in English | MEDLINE | ID: covidwho-1763266

ABSTRACT

Identification of disease-drug associations is an effective strategy for drug repurposing, especially in searching old drugs for newly emerged diseases like COVID-19. In this study, we put forward a network-based method named NEDNBI to predict disease-drug associations based on a gene-disease-drug tripartite network, which could be applied in drug repurposing. The novelty of our method lies in the fact that no negative data are required, and new disease could be added into the disease-drug network with gene as the bridge. The comprehensive evaluation results showed that the proposed method had good performance, with AUC value 0.948±0.009 for 10-fold cross validation. In a case study, 8 of the 20 predicted old drugs have been tested clinically for the treatment of COVID-19, which illustrated the usefulness of our method in drug repurposing. The source code and data of the method are available at https://github.com/Qli97/NEDNBI.


Subject(s)
COVID-19 , Drug Repositioning , COVID-19/drug therapy , Drug Repositioning/methods , Humans , Software
14.
ChemMedChem ; 17(10): e202200092, 2022 05 18.
Article in English | MEDLINE | ID: covidwho-1748765

ABSTRACT

A focused drug repurposing approach is described where an FDA-approved drug is rationally selected for biological testing based on structural similarities to a fragment compound found to bind a target protein by an NMR screen. The approach is demonstrated by first screening a curated fragment library using 19 F NMR to discover a quality binder to ACE2, the human receptor required for entry and infection by the SARS-CoV-2 virus. Based on this binder, a highly related scaffold was derived and used as a "smart scaffold" or template in a computer-aided finger-print search of a library of FDA-approved or marketed drugs. The most interesting structural match involved the drug vortioxetine which was then experimentally shown by NMR spectroscopy to bind directly to human ACE2. Also, an ELISA assay showed that the drug inhibits the interaction of human ACE2 to the SARS-CoV-2 receptor-binding-domain (RBD). Moreover, our cell-culture infectivity assay confirmed that vortioxetine is active against SARS-CoV-2 and inhibits viral replication. Thus, the use of "smart scaffolds" based on binders from fragment screens may have general utility for identifying candidates of FDA-approved or marketed drugs as a rapid repurposing strategy. Similar approaches can be envisioned for other fields involving small-molecule chemical applications.


Subject(s)
Antiviral Agents , Drug Repositioning , SARS-CoV-2 , Angiotensin-Converting Enzyme 2/antagonists & inhibitors , Antiviral Agents/pharmacology , COVID-19/drug therapy , Drug Repositioning/methods , Humans , Protein Binding , SARS-CoV-2/drug effects , Spike Glycoprotein, Coronavirus/antagonists & inhibitors , Vortioxetine
15.
Biochem Soc Trans ; 50(2): 747-758, 2022 04 29.
Article in English | MEDLINE | ID: covidwho-1740494

ABSTRACT

Over the last decade, for the first time, substantial efforts have been directed at the development of dedicated in silico platforms for drug repurposing, including initiatives targeting cancers and conditions as diverse as cryptosporidiosis, dengue, dental caries, diabetes, herpes, lupus, malaria, tuberculosis and Covid-19 related respiratory disease. This review outlines some of the exciting advances in the specific applications of in silico approaches to the challenge of drug repurposing and focuses particularly on where these efforts have resulted in the development of generic platform technologies of broad value to researchers involved in programmatic drug repurposing work. Recent advances in molecular docking methodologies and validation approaches, and their combination with machine learning or deep learning approaches are continually enhancing the precision of repurposing efforts. The meaningful integration of better understanding of molecular mechanisms with molecular pathway data and knowledge of disease networks is widening the scope for discovery of repurposing opportunities. The power of Artificial Intelligence is being gainfully exploited to advance progress in an integrated science that extends from the sub-atomic to the whole system level. There are many promising emerging developments but there are remaining challenges to be overcome in the successful integration of the new advances in useful platforms. In conclusion, the essential component requirements for development of powerful and well optimised drug repurposing screening platforms are discussed.


Subject(s)
COVID-19 , Dental Caries , Artificial Intelligence , COVID-19/drug therapy , Drug Discovery/methods , Drug Repositioning/methods , Humans , Molecular Docking Simulation
16.
Cell Mol Biol (Noisy-le-grand) ; 67(5): 387-398, 2022 Feb 04.
Article in English | MEDLINE | ID: covidwho-1699112

ABSTRACT

Despite the accelerated emerging of vaccines, development against the severe acute respiratory syndrome coronavirus 2 (SARS CoV-2) drugs discovery is still in demand. Repurposing the existing drugs is an ideal time/cost-effective strategy to tackle the clinical impact of SARS CoV-2. Thereby, the present study is a promising strategy that proposes the repurposing of approved drugs against pivotal proteins that are responsible for the viral propagation of SARS-CoV-2 virus Angiotensin-converting enzyme-2 (ACE2; 2AJF), 3CL-protease: main protease (6LU7), Papain-like protease (6W9C), Receptor Binding Domain of Spike protein (6VW1), Transmembrane protease serine 2 (TMPRSS-2; 5AFW) and Furin (5MIM) by in silico methods. Molecular docking results were analyzed based on the binding energy and active site interactions accomplished with pharmacokinetic analysis. It was observed that both anisomycin and oleandomycin bind to all selected target proteins with good binding energy, achieving the most favorable interactions. Considering the results of binding affinity, pharmacokinetics and toxicity of anisomycin and oleandomycin, it is proposed that they can act as potential drugs against the SARS CoV-2 infection. Further clinical testing of the reported drugs is essential for their use in the treatment of SARS CoV-2 infection.


Subject(s)
COVID-19 , SARS-CoV-2 , Anisomycin , Antiviral Agents/chemistry , Antiviral Agents/pharmacology , COVID-19/drug therapy , Drug Repositioning/methods , Humans , Molecular Docking Simulation , Oleandomycin
17.
Viruses ; 14(2)2022 02 11.
Article in English | MEDLINE | ID: covidwho-1687050

ABSTRACT

Despite the development of specific therapies against severe acute respiratory coronavirus 2 (SARS-CoV-2), the continuous investigation of the mechanism of action of clinically approved drugs could provide new information on the druggable steps of virus-host interaction. For example, chloroquine (CQ)/hydroxychloroquine (HCQ) lacks in vitro activity against SARS-CoV-2 in TMPRSS2-expressing cells, such as human pneumocyte cell line Calu-3, and likewise, failed to show clinical benefit in the Solidarity and Recovery clinical trials. Another antimalarial drug, mefloquine, which is not a 4-aminoquinoline like CQ/HCQ, has emerged as a potential anti-SARS-CoV-2 antiviral in vitro and has also been previously repurposed for respiratory diseases. Here, we investigated the anti-SARS-CoV-2 mechanism of action of mefloquine in cells relevant for the physiopathology of COVID-19, such as Calu-3 cells (that recapitulate type II pneumocytes) and monocytes. Molecular pathways modulated by mefloquine were assessed by differential expression analysis, and confirmed by biological assays. A PBPK model was developed to assess mefloquine's optimal doses for achieving therapeutic concentrations. Mefloquine inhibited SARS-CoV-2 replication in Calu-3, with an EC50 of 1.2 µM and EC90 of 5.3 µM. It reduced SARS-CoV-2 RNA levels in monocytes and prevented virus-induced enhancement of IL-6 and TNF-α. Mefloquine reduced SARS-CoV-2 entry and synergized with Remdesivir. Mefloquine's pharmacological parameters are consistent with its plasma exposure in humans and its tissue-to-plasma predicted coefficient points suggesting that mefloquine may accumulate in the lungs. Altogether, our data indicate that mefloquine's chemical structure could represent an orally available host-acting agent to inhibit virus entry.


Subject(s)
Alveolar Epithelial Cells/drug effects , Antiviral Agents/pharmacology , Chloroquine/pharmacology , Mefloquine/pharmacology , SARS-CoV-2/drug effects , Adenosine Monophosphate/analogs & derivatives , Adenosine Monophosphate/pharmacology , Alanine/analogs & derivatives , Alanine/pharmacology , Alveolar Epithelial Cells/virology , COVID-19/drug therapy , Cell Line , Drug Repositioning/methods , Humans , Serine Endopeptidases/genetics , Virus Internalization/drug effects
18.
Comb Chem High Throughput Screen ; 25(12): 2089-2102, 2022.
Article in English | MEDLINE | ID: covidwho-1686280

ABSTRACT

BACKGROUND: As COVID-19 pandemic continues to affect people's lives, the government of India gave emergency use approval to the ayurvedic antimalarial drug Ayush-64 in April 2021 to treat asymptomatic COVID-19 positive and mild COVID-19 positive patients. OBJECTIVE: This study aims to explore the therapeutic potential of Ayush-64 to treat COVID-19 and provide a new approach for repurposing Ayurvedic drugs. METHODS: The bioactives present in Ayush-64 were found along with their targets, and a plantbioactive- target network was created. A protein-protein interaction network of the common targets of Ayush-64 and COVID-19 was constructed and analyzed to find the key targets of Ayush-64 associated with the disease. Gene ontology and pathway enrichment analysis were performed to find COVID-19 related biological processes and pathways involved by the key targets. The key bioactives were docked with SARS-CoV-2 main protease 3CL, native Human Angiotensin-converting Enzyme ACE2, Spike protein S1, and RNA-dependent RNA polymerase RdRp. RESULTS: From the 336 targets for Ayush-64, we found 38 key targets. Functional enrichment analysis of the key targets resulted in 121 gene ontology terms and 38 pathways. When molecular docking was performed with four receptors, thirteen bioactives showed good binding affinity comparable to that of the eight drugs presently used to treat COVID-19. CONCLUSION: Network pharmacological analysis and molecular docking study of Ayush-64 revealed that it can be recommended to treat COVID-19. Further in vitro and in vivo studies are needed to confirm the results. The study demonstrated a new approach for repurposing Ayurvedic drugs.


Subject(s)
Antimalarials , COVID-19 , Angiotensin-Converting Enzyme 2 , Angiotensins , COVID-19/drug therapy , Drug Repositioning/methods , Humans , Molecular Docking Simulation , Network Pharmacology , Pandemics , Plant Extracts , RNA-Dependent RNA Polymerase , SARS-CoV-2 , Spike Glycoprotein, Coronavirus/chemistry
19.
Molecules ; 27(3)2022 Jan 27.
Article in English | MEDLINE | ID: covidwho-1674735

ABSTRACT

Viral infections pose a persistent threat to human health. The relentless epidemic of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has become a global health problem, with millions of infections and fatalities so far. Traditional approaches such as random screening and optimization of lead compounds by organic synthesis have become extremely resource- and time-consuming. Various modern innovative methods or integrated paradigms are now being applied to drug discovery for significant resistance in order to simplify the drug process. This review provides an overview of newly emerging antiviral strategies, including proteolysis targeting chimera (PROTAC), ribonuclease targeting chimera (RIBOTAC), targeted covalent inhibitors, topology-matching design and antiviral drug delivery system. This article is dedicated to Prof. Dr. Erik De Clercq, an internationally renowned expert in the antiviral drug research field, on the occasion of his 80th anniversary.


Subject(s)
Antiviral Agents/pharmacology , Antiviral Agents/therapeutic use , Drug Discovery/methods , Drug Design/methods , Drug Design/trends , Drug Discovery/trends , Drug Repositioning/methods , Drug Repositioning/trends , Humans , Virus Diseases/drug therapy
20.
Interdiscip Sci ; 14(1): 15-21, 2022 Mar.
Article in English | MEDLINE | ID: covidwho-1641016

ABSTRACT

The coronavirus disease (COVID-19) has led to an rush to repurpose existing drugs, although the underlying evidence base is of variable quality. Drug repurposing is a technique by taking advantage of existing known drugs or drug combinations to be explored in an unexpected medical scenario. Drug repurposing, hence, plays a vital role in accelerating the pre-clinical process of designing novel drugs by saving time and cost compared to the traditional de novo drug discovery processes. Since drug repurposing depends on massive observed data from existing drugs and diseases, the tremendous growth of publicly available large-scale machine learning methods supplies the state-of-the-art application of data science to signaling disease, medicine, therapeutics, and identifying targets with the least error. In this article, we introduce guidelines on strategies and options of utilizing machine learning approaches for accelerating drug repurposing. We discuss how to employ machine learning methods in studying precision medicine, and as an instance, how machine learning approaches can accelerate COVID-19 drug repurposing by developing Chinese traditional medicine therapy. This article provides a strong reasonableness for employing machine learning methods for drug repurposing, including during fighting for COVID-19 pandemic.


Subject(s)
COVID-19 , Drug Repositioning , COVID-19/drug therapy , Drug Repositioning/methods , Humans , Machine Learning , Pandemics , SARS-CoV-2
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