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1.
Nucleic Acids Res ; 50(D1): D27-D38, 2022 01 07.
Article in English | MEDLINE | ID: covidwho-1758797

ABSTRACT

The National Genomics Data Center (NGDC), part of the China National Center for Bioinformation (CNCB), provides a family of database resources to support global research in both academia and industry. With the explosively accumulated multi-omics data at ever-faster rates, CNCB-NGDC is constantly scaling up and updating its core database resources through big data archive, curation, integration and analysis. In the past year, efforts have been made to synthesize the growing data and knowledge, particularly in single-cell omics and precision medicine research, and a series of resources have been newly developed, updated and enhanced. Moreover, CNCB-NGDC has continued to daily update SARS-CoV-2 genome sequences, variants, haplotypes and literature. Particularly, OpenLB, an open library of bioscience, has been established by providing easy and open access to a substantial number of abstract texts from PubMed, bioRxiv and medRxiv. In addition, Database Commons is significantly updated by cataloguing a full list of global databases, and BLAST tools are newly deployed to provide online sequence search services. All these resources along with their services are publicly accessible at https://ngdc.cncb.ac.cn.


Subject(s)
Databases, Factual , Animals , China , Computational Biology , Databases, Genetic , Databases, Pharmaceutical , Dogs , Epigenome , Genome, Human , Genome, Viral , Genomics , Humans , Methylation , Neoplasms/genetics , Neoplasms/pathology , Regeneration , SARS-CoV-2/genetics , Single-Cell Analysis , Software , Synthetic Biology
2.
Comput Math Methods Med ; 2022: 9604456, 2022.
Article in English | MEDLINE | ID: covidwho-1704361

ABSTRACT

OBJECTIVE: To investigate the potential pharmacological value of extracts from honeysuckle on patients with mild coronavirus disease 2019 (COVID-19) infection. METHODS: The active components and targets of honeysuckle were screened by Traditional Chinese Medicine Database and Analysis Platform (TCMSP). SwissADME and pkCSM databases predict pharmacokinetics of ingredients. The Gene Expression Omnibus (GEO) database collected transcriptome data for mild COVID-19. Data quality control, differentially expressed gene (DEG) identification, enrichment analysis, and correlation analysis were implemented by R toolkit. CIBERSORT evaluated the infiltration of 22 immune cells. RESULTS: The seven active ingredients of honeysuckle had good oral absorption and medicinal properties. Both the active ingredient targets of honeysuckle and differentially expressed genes of mild COVID-19 were significantly enriched in immune signaling pathways. There were five overlapping immunosignature genes, among which RELA and MAP3K7 expressions were statistically significant (P < 0.05). Finally, immune cell infiltration and correlation analysis showed that RELA, MAP3K7, and natural killer (NK) cell are with highly positive correlation and highly negatively correlated with hematopoietic stem cells. CONCLUSION: Our analysis suggested that honeysuckle extract had a safe and effective protective effect against mild COVID-19 by regulating a complex molecular network. The main mechanism was related to the proportion of infiltration between NK cells and hematopoietic stem cells.


Subject(s)
COVID-19/drug therapy , Drugs, Chinese Herbal/therapeutic use , Lonicera , Phytotherapy , SARS-CoV-2 , Antiviral Agents/chemistry , Antiviral Agents/pharmacokinetics , Antiviral Agents/therapeutic use , COVID-19/genetics , COVID-19/immunology , Computational Biology , Databases, Pharmaceutical/statistics & numerical data , Drug Evaluation, Preclinical , Drugs, Chinese Herbal/chemistry , Drugs, Chinese Herbal/pharmacokinetics , Gene Expression/drug effects , Gene Ontology , Gene Regulatory Networks/drug effects , Gene Regulatory Networks/immunology , Hematopoietic Stem Cells/drug effects , Hematopoietic Stem Cells/immunology , Humans , Killer Cells, Natural/drug effects , Killer Cells, Natural/immunology , Lonicera/chemistry , Medicine, Chinese Traditional , Pandemics , SARS-CoV-2/drug effects
3.
Med Sci Monit ; 28: e934102, 2022 Jan 25.
Article in English | MEDLINE | ID: covidwho-1651076

ABSTRACT

BACKGROUND Heat-clearing and detoxifying herbs (HDHs) play an important role in the prevention and treatment of coronavirus infection. However, their mechanism of action needs further study. This study aimed to explore the anti-coronavirus basis and mechanism of HDHs. MATERIAL AND METHODS Database mining was performed on 7 HDHs. Core ingredients and targets were screened according to ADME rules combined with Neighborhood, Co-occurrence, Co-expression, and other algorithms. GO enrichment and KEGG pathway analyses were performed using the R language. Finally, high-throughput molecular docking was used for verification. RESULTS HDHs mainly acts on NOS3, EGFR, IL-6, MAPK8, PTGS2, MAPK14, NFKB1, and CASP3 through quercetin, luteolin, wogonin, indirubin alkaloids, ß-sitosterol, and isolariciresinol. These targets are mainly involved in the regulation of biological processes such as inflammation, activation of MAPK activity, and positive regulation of NF-kappaB transcription factor activity. Pathway analysis further revealed that the pathways regulated by these targets mainly include: signaling pathways related to viral and bacterial infections such as tuberculosis, influenza A, Ras signaling pathways; inflammation-related pathways such as the TLR, TNF, MAPK, and HIF-1 signaling pathways; and immune-related pathways such as NOD receptor signaling pathways. These pathways play a synergistic role in inhibiting lung inflammation and regulating immunity and antiviral activity. CONCLUSIONS HDHs play a role in the treatment of coronavirus infection by regulating the body's immunity, fighting inflammation, and antiviral activities, suggesting a molecular basis and new strategies for the treatment of COVID-19 and a foundation for the screening of new antiviral drugs.


Subject(s)
COVID-19/drug therapy , Coronavirus/drug effects , Drugs, Chinese Herbal/pharmacology , SARS-CoV-2/drug effects , Alkaloids/chemistry , Alkaloids/pharmacology , Caspase 3/drug effects , Caspase 3/genetics , Coronavirus/metabolism , Coronavirus Infections/drug therapy , Cyclooxygenase 2/drug effects , Cyclooxygenase 2/genetics , Databases, Pharmaceutical , Drugs, Chinese Herbal/chemistry , Drugs, Chinese Herbal/therapeutic use , Flavanones/chemistry , Flavanones/pharmacology , Humans , Indoles/chemistry , Indoles/pharmacology , Interleukin-6/genetics , Lignin/chemistry , Lignin/pharmacology , Luteolin/chemistry , Luteolin/pharmacology , Mitogen-Activated Protein Kinase 14/drug effects , Mitogen-Activated Protein Kinase 14/genetics , Mitogen-Activated Protein Kinase 8/drug effects , Mitogen-Activated Protein Kinase 8/genetics , Molecular Docking Simulation , NF-kappa B p50 Subunit/drug effects , NF-kappa B p50 Subunit/genetics , Naphthols/chemistry , Naphthols/pharmacology , Nitric Oxide Synthase Type III/drug effects , Nitric Oxide Synthase Type III/genetics , Protein Interaction Maps , Quercetin/chemistry , Quercetin/pharmacology , SARS-CoV-2/metabolism , Signal Transduction , Sitosterols/chemistry , Sitosterols/pharmacology , Transcriptome/drug effects , Transcriptome/genetics
4.
Molecules ; 27(3)2022 Jan 21.
Article in English | MEDLINE | ID: covidwho-1649047

ABSTRACT

Since the outbreak of SARS-CoV-2, numerous compounds against COVID-19 have been derived by computer-aided drug design (CADD) studies. They are valuable resources for the development of COVID-19 therapeutics. In this work, we reviewed these studies and analyzed 779 compounds against 16 target proteins from 181 CADD publications. We performed unified docking simulations and neck-to-neck comparison with the solved co-crystal structures. We computed their chemical features and classified these compounds, aiming to provide insights for subsequent drug design. Through detailed analyses, we recommended a batch of compounds that are worth further study. Moreover, we organized all the abundant data and constructed a freely available database, DrugDevCovid19, to facilitate the development of COVID-19 therapeutics.


Subject(s)
Antiviral Agents/chemistry , COVID-19/drug therapy , Drug Design , Antiviral Agents/therapeutic use , Databases, Pharmaceutical , Drug Development , Humans , Models, Molecular , Molecular Docking Simulation
5.
Int J Mol Sci ; 23(1)2021 Dec 27.
Article in English | MEDLINE | ID: covidwho-1580698

ABSTRACT

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.


Subject(s)
Antiviral Agents/chemistry , Coronavirus 3C Proteases/antagonists & inhibitors , Protease Inhibitors/chemistry , SARS-CoV-2/drug effects , Antiviral Agents/pharmacology , Coronavirus 3C Proteases/chemistry , Databases, Pharmaceutical , Enzyme Assays/methods , Inhibitory Concentration 50 , Protease Inhibitors/pharmacology , SARS-CoV-2/enzymology , Virus Replication/drug effects
6.
Sci Rep ; 11(1): 23315, 2021 12 02.
Article in English | MEDLINE | ID: covidwho-1550334

ABSTRACT

The COVID-19 pandemic has highlighted the urgent need for the identification of new antiviral drug therapies for a variety of diseases. COVID-19 is caused by infection with the human coronavirus SARS-CoV-2, while other related human coronaviruses cause diseases ranging from severe respiratory infections to the common cold. We developed a computational approach to identify new antiviral drug targets and repurpose clinically-relevant drug compounds for the treatment of a range of human coronavirus diseases. Our approach is based on graph convolutional networks (GCN) and involves multiscale host-virus interactome analysis coupled to off-target drug predictions. Cell-based experimental assessment reveals several clinically-relevant drug repurposing candidates predicted by the in silico analyses to have antiviral activity against human coronavirus infection. In particular, we identify the MET inhibitor capmatinib as having potent and broad antiviral activity against several coronaviruses in a MET-independent manner, as well as novel roles for host cell proteins such as IRAK1/4 in supporting human coronavirus infection, which can inform further drug discovery studies.


Subject(s)
Antiviral Agents/pharmacology , Coronavirus/drug effects , Coronavirus/metabolism , Drug Development/methods , Drug Repositioning/methods , Benzamides/pharmacology , COVID-19/drug therapy , Cell Line , Computer Simulation , Coronavirus/chemistry , Databases, Pharmaceutical , Drug Discovery/methods , Host-Pathogen Interactions , Humans , Imidazoles/pharmacology , Interleukin-1 Receptor-Associated Kinases/metabolism , SARS-CoV-2/chemistry , SARS-CoV-2/drug effects , SARS-CoV-2/metabolism , SARS-CoV-2/physiology , Triazines/pharmacology
7.
Nucleic Acids Res ; 50(D1): D11-D19, 2022 01 07.
Article in English | MEDLINE | ID: covidwho-1546006

ABSTRACT

The European Bioinformatics Institute (EMBL-EBI) maintains a comprehensive range of freely available and up-to-date molecular data resources, which includes over 40 resources covering every major data type in the life sciences. This year's service update for EMBL-EBI includes new resources, PGS Catalog and AlphaFold DB, and updates on existing resources, including the COVID-19 Data Platform, trRosetta and RoseTTAfold models introduced in Pfam and InterPro, and the launch of Genome Integrations with Function and Sequence by UniProt and Ensembl. Furthermore, we highlight projects through which EMBL-EBI has contributed to the development of community-driven data standards and guidelines, including the Recommended Metadata for Biological Images (REMBI), and the BioModels Reproducibility Scorecard. Training is one of EMBL-EBI's core missions and a key component of the provision of bioinformatics services to users: this year's update includes many of the improvements that have been developed to EMBL-EBI's online training offering.


Subject(s)
Computational Biology/education , Computational Biology/methods , Databases, Factual , Academies and Institutes , Artificial Intelligence , COVID-19 , Databases, Factual/economics , Databases, Factual/statistics & numerical data , Databases, Pharmaceutical , Databases, Protein , Europe , Genome, Human , Humans , Information Storage and Retrieval , RNA, Untranslated/genetics , SARS-CoV-2/genetics
8.
Nucleic Acids Res ; 50(D1): D1282-D1294, 2022 01 07.
Article in English | MEDLINE | ID: covidwho-1493886

ABSTRACT

The IUPHAR/BPS Guide to PHARMACOLOGY (GtoPdb; www.guidetopharmacology.org) is an open-access, expert-curated database of molecular interactions between ligands and their targets. We describe expansion in content over nine database releases made during the last two years, which has focussed on three main areas of infection. The COVID-19 pandemic continues to have a major impact on health worldwide. GtoPdb has sought to support the wider research community to understand the pharmacology of emerging drug targets for SARS-CoV-2 as well as potential targets in the host to block viral entry and reduce the adverse effects of infection in patients with COVID-19. We describe how the database rapidly evolved to include a new family of Coronavirus proteins. Malaria remains a global threat to half the population of the world. Our database content continues to be enhanced through our collaboration with Medicines for Malaria Venture (MMV) on the IUPHAR/MMV Guide to MALARIA PHARMACOLOGY (www.guidetomalariapharmacology.org). Antibiotic resistance is also a growing threat to global health. In response, we have extended our coverage of antibacterials in partnership with AntibioticDB.


Subject(s)
Anti-Bacterial Agents/pharmacology , Antimalarials/pharmacology , Antiviral Agents/pharmacology , COVID-19/drug therapy , Anti-Bacterial Agents/chemistry , COVID-19/etiology , Data Curation , Databases, Pharmaceutical , Humans , Ligands , Malaria/drug therapy , Malaria/metabolism , User-Computer Interface , Viral Proteins/chemistry , Viral Proteins/metabolism
9.
Sci Rep ; 11(1): 19839, 2021 10 06.
Article in English | MEDLINE | ID: covidwho-1454816

ABSTRACT

Computational drug repositioning aims at ranking and selecting existing drugs for novel diseases or novel use in old diseases. In silico drug screening has the potential for speeding up considerably the shortlisting of promising candidates in response to outbreaks of diseases such as COVID-19 for which no satisfactory cure has yet been found. We describe DrugMerge as a methodology for preclinical computational drug repositioning based on merging multiple drug rankings obtained with an ensemble of disease active subnetworks. DrugMerge uses differential transcriptomic data on drugs and diseases in the context of a large gene co-expression network. Experiments with four benchmark diseases demonstrate that our method detects in first position drugs in clinical use for the specified disease, in all four cases. Application of DrugMerge to COVID-19 found rankings with many drugs currently in clinical trials for COVID-19 in top positions, thus showing that DrugMerge can mimic human expert judgment.


Subject(s)
Antineoplastic Agents/pharmacology , COVID-19/drug therapy , Drug Repositioning/methods , Neoplasms/drug therapy , Antiviral Agents , COVID-19/genetics , COVID-19/metabolism , COVID-19/virology , Computational Biology/methods , Databases, Genetic , Databases, Pharmaceutical , Gene Regulatory Networks , Humans , Neoplasms/genetics , Neoplasms/metabolism , Neoplasms/virology , SARS-CoV-2/isolation & purification
10.
Sci Rep ; 11(1): 19426, 2021 09 30.
Article in English | MEDLINE | ID: covidwho-1447322

ABSTRACT

The COVID-19 pandemic poses a huge problem of public health that requires the implementation of all available means to contrast it, and drugs are one of them. In this context, we observed an unmet need of depicting the continuously evolving scenario of the ongoing drug clinical trials through an easy-to-use, freely accessible online tool. Starting from this consideration, we developed COVIDrugNet ( http://compmedchem.unibo.it/covidrugnet ), a web application that allows users to capture a holistic view and keep up to date on how the clinical drug research is responding to the SARS-CoV-2 infection. Here, we describe the web app and show through some examples how one can explore the whole landscape of medicines in clinical trial for the treatment of COVID-19 and try to probe the consistency of the current approaches with the available biological and pharmacological evidence. We conclude that careful analyses of the COVID-19 drug-target system based on COVIDrugNet can help to understand the biological implications of the proposed drug options, and eventually improve the search for more effective therapies.


Subject(s)
COVID-19/drug therapy , Computational Biology/methods , Clinical Trials as Topic , Computational Biology/instrumentation , Databases, Pharmaceutical , Drug Repositioning , Humans , Internet , Viral Proteins/metabolism
11.
Database (Oxford) ; 20212021 03 31.
Article in English | MEDLINE | ID: covidwho-1387844

ABSTRACT

Understanding the underlying molecular and structural similarities between seemingly heterogeneous sets of drugs can aid in identifying drug repurposing opportunities and assist in the discovery of novel properties of preclinical small molecules. A wealth of information about drug and small molecule structure, targets, indications and side effects; induced gene expression signatures; and other attributes are publicly available through web-based tools, databases and repositories. By processing, abstracting and aggregating information from these resources into drug set libraries, knowledge about novel properties of drugs and small molecules can be systematically imputed with machine learning. In addition, drug set libraries can be used as the underlying database for drug set enrichment analysis. Here, we present Drugmonizome, a database with a search engine for querying annotated sets of drugs and small molecules for performing drug set enrichment analysis. Utilizing the data within Drugmonizome, we also developed Drugmonizome-ML. Drugmonizome-ML enables users to construct customized machine learning pipelines using the drug set libraries from Drugmonizome. To demonstrate the utility of Drugmonizome, drug sets from 12 independent SARS-CoV-2 in vitro screens were subjected to consensus enrichment analysis. Despite the low overlap among these 12 independent in vitro screens, we identified common biological processes critical for blocking viral replication. To demonstrate Drugmonizome-ML, we constructed a machine learning pipeline to predict whether approved and preclinical drugs may induce peripheral neuropathy as a potential side effect. Overall, the Drugmonizome and Drugmonizome-ML resources provide rich and diverse knowledge about drugs and small molecules for direct systems pharmacology applications. Database URL: https://maayanlab.cloud/drugmonizome/.


Subject(s)
COVID-19/drug therapy , Databases, Pharmaceutical , SARS-CoV-2/drug effects , Antiviral Agents/chemistry , Antiviral Agents/pharmacology , COVID-19/virology , Drug Discovery , Drug Evaluation, Preclinical , Drug Repositioning , Drug-Related Side Effects and Adverse Reactions , Humans , In Vitro Techniques , Machine Learning , Peripheral Nervous System Diseases/chemically induced , SARS-CoV-2/physiology , Small Molecule Libraries , User-Computer Interface , Virus Replication/drug effects
12.
IEEE/ACM Trans Comput Biol Bioinform ; 18(4): 1290-1298, 2021.
Article in English | MEDLINE | ID: covidwho-1349906

ABSTRACT

An outbreak of COVID-19 that began in late 2019 was caused by a novel coronavirus(SARS-CoV-2). It has become a global pandemic. As of June 9, 2020, it has infected nearly 7 million people and killed more than 400,000, but there is no specific drug. Therefore, there is an urgent need to find or develop more drugs to suppress the virus. Here, we propose a new nonlinear end-to-end model called LUNAR. It uses graph convolutional neural networks to automatically learn the neighborhood information of complex heterogeneous relational networks and combines the attention mechanism to reflect the importance of the sum of different types of neighborhood information to obtain the representation characteristics of each node. Finally, through the topology reconstruction process, the feature representations of drugs and targets are forcibly extracted to match the observed network as much as possible. Through this reconstruction process, we obtain the strength of the relationship between different nodes and predict drug candidates that may affect the treatment of COVID-19 based on the known targets of COVID-19. These selected candidate drugs can be used as a reference for experimental scientists and accelerate the speed of drug development. LUNAR can well integrate various topological structure information in heterogeneous networks, and skillfully combine attention mechanisms to reflect the importance of neighborhood information of different types of nodes, improving the interpretability of the model. The area under the curve(AUC) of the model is 0.949 and the accurate recall curve (AUPR) is 0.866 using 10-fold cross-validation. These two performance indexes show that the model has superior predictive performance. Besides, some of the drugs screened out by our model have appeared in some clinical studies to further illustrate the effectiveness of the model.


Subject(s)
Antiviral Agents/pharmacology , COVID-19/drug therapy , COVID-19/virology , Drug Evaluation, Preclinical/methods , Neural Networks, Computer , SARS-CoV-2/drug effects , COVID-19/epidemiology , Computational Biology , Databases, Pharmaceutical/statistics & numerical data , Drug Development/methods , Drug Development/statistics & numerical data , Drug Evaluation, Preclinical/statistics & numerical data , Drug Repositioning/methods , Drug Repositioning/statistics & numerical data , Host Microbial Interactions/drug effects , Humans , Nonlinear Dynamics , Pandemics
13.
Brief Bioinform ; 22(2): 1508-1510, 2021 03 22.
Article in English | MEDLINE | ID: covidwho-1343639

ABSTRACT

The outbreak and pandemic of SARS-CoV-2 in 2019 has caused a severe public health burden and will challenge global health for the future. The discovery and mechanistic investigation of drugs against Coronavirus disease 2019 (COVID-19) is in deadly demand. The paper published by Li and colleagues proposed the hypothesis that vitamin C combined with glycyrrhizic acid in treating COVID-19 and its mechanistic investigation was performed by a database-based network pharmacology. In this letter, we present critical comments on the limitations and insufficiencies involved, from both the perspective of network pharmacology and current evidence on COVID-19.


Subject(s)
Ascorbic Acid/therapeutic use , COVID-19/drug therapy , Databases, Pharmaceutical , Drug Repositioning , Glycyrrhizic Acid/therapeutic use , Ascorbic Acid/administration & dosage , COVID-19/virology , Glycyrrhizic Acid/administration & dosage , Humans , SARS-CoV-2/isolation & purification
14.
Elife ; 102021 08 03.
Article in English | MEDLINE | ID: covidwho-1339710

ABSTRACT

The discovery of a drug requires over a decade of intensive research and financial investments - and still has a high risk of failure. To reduce this burden, we developed the NICEdrug.ch resource, which incorporates 250,000 bioactive molecules, and studied their enzymatic metabolic targets, fate, and toxicity. NICEdrug.ch includes a unique fingerprint that identifies reactive similarities between drug-drug and drug-metabolite pairs. We validated the application, scope, and performance of NICEdrug.ch over similar methods in the field on golden standard datasets describing drugs and metabolites sharing reactivity, drug toxicities, and drug targets. We use NICEdrug.ch to evaluate inhibition and toxicity by the anticancer drug 5-fluorouracil, and suggest avenues to alleviate its side effects. We propose shikimate 3-phosphate for targeting liver-stage malaria with minimal impact on the human host cell. Finally, NICEdrug.ch suggests over 1300 candidate drugs and food molecules to target COVID-19 and explains their inhibitory mechanism for further experimental screening. The NICEdrug.ch database is accessible online to systematically identify the reactivity of small molecules and druggable enzymes with practical applications in lead discovery and drug repurposing.


Subject(s)
Drug Design , Drug Discovery/methods , Drug Repositioning , Pharmaceutical Preparations/metabolism , Animals , Antimetabolites, Antineoplastic/chemistry , Antimetabolites, Antineoplastic/metabolism , Antiviral Agents/chemistry , Antiviral Agents/pharmacology , COVID-19/drug therapy , Databases, Pharmaceutical , Drug-Related Side Effects and Adverse Reactions/etiology , Drug-Related Side Effects and Adverse Reactions/metabolism , Fluorouracil/chemistry , Fluorouracil/metabolism , Humans , Pharmaceutical Preparations/chemistry , Workflow
15.
Int J Mol Sci ; 22(14)2021 Jul 19.
Article in English | MEDLINE | ID: covidwho-1323269

ABSTRACT

In the last year, the COVID-19 pandemic has highly affected the lifestyle of the world population, encouraging the scientific community towards a great effort on studying the infection molecular mechanisms. Several vaccine formulations are nowadays available and helping to reach immunity. Nevertheless, there is a growing interest towards the development of novel anti-covid drugs. In this scenario, the main protease (Mpro) represents an appealing target, being the enzyme responsible for the cleavage of polypeptides during the viral genome transcription. With the aim of sharing new insights for the design of novel Mpro inhibitors, our research group developed a machine learning approach using the support vector machine (SVM) classification. Starting from a dataset of two million commercially available compounds, the model was able to classify two hundred novel chemo-types as potentially active against the viral protease. The compounds labelled as actives by SVM were next evaluated through consensus docking studies on two PDB structures and their binding mode was compared to well-known protease inhibitors. The best five compounds selected by consensus docking were then submitted to molecular dynamics to deepen binding interactions stability. Of note, the compounds selected via SVM retrieved all the most important interactions known in the literature.


Subject(s)
COVID-19/drug therapy , Coronavirus Protease Inhibitors/pharmacology , Drug Evaluation, Preclinical/methods , SARS-CoV-2/drug effects , Support Vector Machine , Antiviral Agents/pharmacology , COVID-19/virology , Coronavirus Protease Inhibitors/metabolism , Databases, Pharmaceutical , Humans , Molecular Docking Simulation , Molecular Dynamics Simulation , Pandemics , SARS-CoV-2/enzymology , Small Molecule Libraries , Supervised Machine Learning , Viral Nonstructural Proteins/metabolism , Viral Proteases/metabolism
16.
CPT Pharmacometrics Syst Pharmacol ; 10(9): 973-982, 2021 09.
Article in English | MEDLINE | ID: covidwho-1293320

ABSTRACT

A critical step to evaluate the potential in vivo antiviral activity of a drug is to connect the in vivo exposure to its in vitro antiviral activity. The Anti-SARS-CoV-2 Repurposing Drug Database is a database that includes both in vitro anti-SARS-CoV-2 activity and in vivo pharmacokinetic data to facilitate the extrapolation from in vitro antiviral activity to potential in vivo antiviral activity for a large set of drugs/compounds. In addition to serving as a data source for in vitro anti-SARS-CoV-2 activity and in vivo pharmacokinetic information, the database is also a calculation tool that can be used to compare the in vitro antiviral activity with in vivo drug exposure to identify potential anti-SARS-CoV-2 drugs. Continuous development and expansion are feasible with the public availability of this database.


Subject(s)
Antiviral Agents/pharmacology , Databases, Pharmaceutical , SARS-CoV-2/drug effects , Antiviral Agents/pharmacokinetics , Drug Repositioning/methods , Humans
17.
Nat Commun ; 12(1): 3309, 2021 06 03.
Article in English | MEDLINE | ID: covidwho-1260940

ABSTRACT

The ongoing pandemic caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), necessitates strategies to identify prophylactic and therapeutic drug candidates for rapid clinical deployment. Here, we describe a screening pipeline for the discovery of efficacious SARS-CoV-2 inhibitors. We screen a best-in-class drug repurposing library, ReFRAME, against two high-throughput, high-content imaging infection assays: one using HeLa cells expressing SARS-CoV-2 receptor ACE2 and the other using lung epithelial Calu-3 cells. From nearly 12,000 compounds, we identify 49 (in HeLa-ACE2) and 41 (in Calu-3) compounds capable of selectively inhibiting SARS-CoV-2 replication. Notably, most screen hits are cell-line specific, likely due to different virus entry mechanisms or host cell-specific sensitivities to modulators. Among these promising hits, the antivirals nelfinavir and the parent of prodrug MK-4482 possess desirable in vitro activity, pharmacokinetic and human safety profiles, and both reduce SARS-CoV-2 replication in an orthogonal human differentiated primary cell model. Furthermore, MK-4482 effectively blocks SARS-CoV-2 infection in a hamster model. Overall, we identify direct-acting antivirals as the most promising compounds for drug repurposing, additional compounds that may have value in combination therapies, and tool compounds for identification of viral host cell targets.


Subject(s)
Antiviral Agents/pharmacology , COVID-19/drug therapy , Drug Repositioning/methods , Pandemics , SARS-CoV-2 , Animals , COVID-19/prevention & control , COVID-19/virology , Cell Line , Cytidine/administration & dosage , Cytidine/analogs & derivatives , Cytidine/pharmacology , Databases, Pharmaceutical , Drug Discovery/methods , Drug Evaluation, Preclinical/methods , HeLa Cells , High-Throughput Screening Assays/methods , Humans , Hydroxylamines/administration & dosage , Hydroxylamines/pharmacology , Mesocricetus , Nelfinavir/pharmacology , SARS-CoV-2/drug effects , SARS-CoV-2/physiology , Virus Replication/drug effects
18.
Proc Natl Acad Sci U S A ; 118(19)2021 05 11.
Article in English | MEDLINE | ID: covidwho-1205472

ABSTRACT

The COVID-19 pandemic has highlighted the need to quickly and reliably prioritize clinically approved compounds for their potential effectiveness for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections. Here, we deployed algorithms relying on artificial intelligence, network diffusion, and network proximity, tasking each of them to rank 6,340 drugs for their expected efficacy against SARS-CoV-2. To test the predictions, we used as ground truth 918 drugs experimentally screened in VeroE6 cells, as well as the list of drugs in clinical trials that capture the medical community's assessment of drugs with potential COVID-19 efficacy. We find that no single predictive algorithm offers consistently reliable outcomes across all datasets and metrics. This outcome prompted us to develop a multimodal technology that fuses the predictions of all algorithms, finding that a consensus among the different predictive methods consistently exceeds the performance of the best individual pipelines. We screened in human cells the top-ranked drugs, obtaining a 62% success rate, in contrast to the 0.8% hit rate of nonguided screenings. Of the six drugs that reduced viral infection, four could be directly repurposed to treat COVID-19, proposing novel treatments for COVID-19. We also found that 76 of the 77 drugs that successfully reduced viral infection do not bind the proteins targeted by SARS-CoV-2, indicating that these network drugs rely on network-based mechanisms that cannot be identified using docking-based strategies. These advances offer a methodological pathway to identify repurposable drugs for future pathogens and neglected diseases underserved by the costs and extended timeline of de novo drug development.


Subject(s)
COVID-19/drug therapy , Drug Repositioning/methods , Systems Biology/methods , Animals , Antiviral Agents/administration & dosage , Antiviral Agents/pharmacology , Antiviral Agents/therapeutic use , Chlorocebus aethiops , Databases, Pharmaceutical , Humans , Neural Networks, Computer , Protein Binding , Vero Cells , Viral Proteins/metabolism
19.
Eur Rev Med Pharmacol Sci ; 25(7): 3122-3131, 2021 04.
Article in English | MEDLINE | ID: covidwho-1194853

ABSTRACT

OBJECTIVE: Transcriptome data related to severe acute respiratory syndrome-related coronavirus 2 (a novel coronavirus discovered in 2019, SARS-CoV-2) in GEO database were downloaded. Based on the data, influence of SARS-CoV-2 on human cells was analyzed and potential therapeutic compounds against the SARS-CoV-2 were screened. MATERIALS AND METHODS: R package "DESeq2" was used for differential gene analysis on the data of cells infected or non-infected with SARS-CoV-2. The "ClusterProfiler" package was used for GO functional annotation and KEGG pathway enrichment analysis of the differentially expressed genes (DEGs). A protein-protein interaction (PPI) network of the DEGs was constructed through STRING website, and the key subset in the PPI network was identified after visualization by Cytoscape software. Connectivity Map (CMap) database was used to screen known compounds that caused genomic change reverse to that caused by SARS-CoV-2. RESULTS: By intersecting DEGs in two datasets, a total of 145 DEGs were screened out, among which 136 genes were upregulated and 9 genes were downregulated in SARS-CoV-2-infected cells. Functional enrichment analyses revealed that these genes were mainly associated with the pathways involved in viral infection, inflammatory response, and immunity. The CMap research found that there were three compounds with a median_tau_score less than -90, namely triptolide, tivozanib and daunorubicin. CONCLUSIONS: SARS-CoV-2 can cause abnormal changes in a large number of molecules and related signaling pathways in human cells, among which IL-17 and TNF signaling pathways may play a key role in pathogenic process of SARS-CoV-2. Here, three compounds that may be effective for the treatment of SARS-CoV-2 were screened, which would provide new options for improving treatment of patients infected with SARS-CoV-2.


Subject(s)
COVID-19/drug therapy , COVID-19/genetics , Drug Discovery , Gene Expression Profiling , Databases, Genetic , Databases, Pharmaceutical , Daunorubicin , Diterpenes , Down-Regulation , Epoxy Compounds , Gene Ontology , Gene Regulatory Networks , Humans , Molecular Targeted Therapy , Phenanthrenes , Phenylurea Compounds , Protein Interaction Maps , Quinolines , SARS-CoV-2 , Signal Transduction/genetics , Up-Regulation
20.
Molecules ; 26(6)2021 Mar 11.
Article in English | MEDLINE | ID: covidwho-1190434

ABSTRACT

Considering the urgency of the COVID-19 pandemic, we developed a receptor-based pharmacophore model for identifying FDA-approved drugs and hits from natural products. The COVID-19 main protease (Mpro) was selected for the development of the pharmacophore model. The model consisted of a hydrogen bond acceptor, donor, and hydrophobic features. These features demonstrated good corroboration with a previously reported model that was used to validate the present model, showing an RMSD value of 0.32. The virtual screening was carried out using the ZINC database. A set of 208,000 hits was extracted and filtered using the ligand pharmacophore mapping, applying the lead-like properties. Lipinski's filter and the fit value filter were used to minimize hits to the top 2000. Simultaneous docking was carried out for 200 hits for natural drugs belonging to the FDA-approved drug database. The top 28 hits from these experiments, with promising predicted pharmacodynamic and pharmacokinetic properties, are reported here. To optimize these hits as Mpro inhibitors and potential treatment options for COVID-19, bench work investigations are needed.


Subject(s)
Antiviral Agents/chemistry , Antiviral Agents/pharmacology , Biological Products/chemistry , Biological Products/pharmacology , COVID-19/drug therapy , Receptors, Drug/metabolism , Binding Sites , Coronavirus 3C Proteases/antagonists & inhibitors , Coronavirus 3C Proteases/chemistry , Coronavirus 3C Proteases/metabolism , Databases, Pharmaceutical , Drug Discovery , Humans , Hydrogen Bonding , Hydrophobic and Hydrophilic Interactions , Ligands , Molecular Docking Simulation , Molecular Dynamics Simulation , Protein Binding , Quantitative Structure-Activity Relationship
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