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1.
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
2.
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
3.
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
4.
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
5.
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
6.
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
7.
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
8.
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
9.
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
10.
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
11.
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
12.
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
13.
Molecules ; 26(7)2021 Mar 29.
Article in English | MEDLINE | ID: covidwho-1159212

ABSTRACT

The COVID-19 pandemic has reached over 100 million worldwide. Due to the multi-targeted nature of the virus, it is clear that drugs providing anti-COVID-19 effects need to be developed at an accelerated rate, and a combinatorial approach may stand to be more successful than a single drug therapy. Among several targets and pathways that are under investigation, the renin-angiotensin system (RAS) and specifically angiotensin-converting enzyme (ACE), and Ca2+-mediated SARS-CoV-2 cellular entry and replication are noteworthy. A combination of ACE inhibitors and calcium channel blockers (CCBs), a critical line of therapy for pulmonary hypertension, has shown therapeutic relevance in COVID-19 when investigated independently. To that end, we conducted in silico modeling using BIOiSIM, an AI-integrated mechanistic modeling platform by utilizing known preclinical in vitro and in vivo datasets to accurately simulate systemic therapy disposition and site-of-action penetration of the CCBs and ACEi compounds to tissues implicated in COVID-19 pathogenesis.


Subject(s)
Antiviral Agents/pharmacokinetics , COVID-19/drug therapy , Drug Repositioning/methods , Hypertension, Pulmonary/drug therapy , Angiotensin-Converting Enzyme Inhibitors/pharmacokinetics , Antiviral Agents/blood , Biosimilar Pharmaceuticals , COVID-19/complications , Calcium Channel Blockers/pharmacokinetics , Computer Simulation , Databases, Pharmaceutical , Drug Development/methods , Humans , Hypertension, Pulmonary/virology , Tissue Distribution
14.
Nucleic Acids Res ; 49(D1): D1113-D1121, 2021 01 08.
Article in English | MEDLINE | ID: covidwho-1139997

ABSTRACT

The recent outbreak of COVID-19 has generated an enormous amount of Big Data. To date, the COVID-19 Open Research Dataset (CORD-19), lists ∼130,000 articles from the WHO COVID-19 database, PubMed Central, medRxiv, and bioRxiv, as collected by Semantic Scholar. According to LitCovid (11 August 2020), ∼40,300 COVID19-related articles are currently listed in PubMed. It has been shown in clinical settings that the analysis of past research results and the mining of available data can provide novel opportunities for the successful application of currently approved therapeutics and their combinations for the treatment of conditions caused by a novel SARS-CoV-2 infection. As such, effective responses to the pandemic require the development of efficient applications, methods and algorithms for data navigation, text-mining, clustering, classification, analysis, and reasoning. Thus, our COVID19 Drug Repository represents a modular platform for drug data navigation and analysis, with an emphasis on COVID-19-related information currently being reported. The COVID19 Drug Repository enables users to focus on different levels of complexity, starting from general information about (FDA-) approved drugs, PubMed references, clinical trials, recipes as well as the descriptions of molecular mechanisms of drugs' action. Our COVID19 drug repository provide a most updated world-wide collection of drugs that has been repurposed for COVID19 treatments around the world.


Subject(s)
Antiviral Agents/therapeutic use , COVID-19/drug therapy , Databases, Pharmaceutical/statistics & numerical data , Drug Repositioning/statistics & numerical data , SARS-CoV-2/drug effects , COVID-19/epidemiology , COVID-19/prevention & control , COVID-19/virology , Clinical Trials as Topic/methods , Clinical Trials as Topic/statistics & numerical data , Data Mining/methods , Data Mining/statistics & numerical data , Drug Approval/statistics & numerical data , Drug Repositioning/methods , Epidemics , Humans , Machine Learning , SARS-CoV-2/physiology
15.
Int J Mol Sci ; 22(6)2021 Mar 10.
Article in English | MEDLINE | ID: covidwho-1125145

ABSTRACT

In order to treat Coronavirus Disease 2019 (COVID-19), we predicted and implemented a drug delivery system (DDS) that can provide stable drug delivery through a computational approach including a clustering algorithm and the Schrödinger software. Six carrier candidates were derived by the proposed method that could find molecules meeting the predefined conditions using the molecular structure and its functional group positional information. Then, just one compound named glycyrrhizin was selected as a candidate for drug delivery through the Schrödinger software. Using glycyrrhizin, nafamostat mesilate (NM), which is known for its efficacy, was converted into micelle nanoparticles (NPs) to improve drug stability and to effectively treat COVID-19. The spherical particle morphology was confirmed by transmission electron microscopy (TEM), and the particle size and stability of 300-400 nm were evaluated by measuring DLSand the zeta potential. The loading of NM was confirmed to be more than 90% efficient using the UV spectrum.


Subject(s)
COVID-19/drug therapy , Computational Biology/methods , Drug Delivery Systems/methods , A549 Cells , Anti-Inflammatory Agents/chemistry , Anti-Inflammatory Agents/therapeutic use , Benzamidines/chemistry , Benzamidines/therapeutic use , Cell Survival/drug effects , Cluster Analysis , Computer Simulation , Databases, Pharmaceutical , Drug Carriers/chemistry , Drug Repositioning , Drug Stability , Glycyrrhizic Acid/chemistry , Glycyrrhizic Acid/therapeutic use , Guanidines/chemistry , Guanidines/therapeutic use , Humans , Hydrophobic and Hydrophilic Interactions , Micelles , Microscopy, Electron, Transmission , Molecular Structure , Nanoparticles/chemistry , Particle Size
16.
J Enzyme Inhib Med Chem ; 36(1): 727-736, 2021 Dec.
Article in English | MEDLINE | ID: covidwho-1123193

ABSTRACT

The novel coronavirus disease COVID-19, caused by the virus SARS CoV-2, has exerted a significant unprecedented economic and medical crisis, in addition to its impact on the daily life and health care systems all over the world. Regrettably, no vaccines or drugs are currently available for this new critical emerging human disease. Joining the global fight against COVID-19, in this study we aim at identifying a potential novel inhibitor for SARS COV-2 2'-O-methyltransferase (nsp16) which is one of the most attractive targets in the virus life cycle, responsible for the viral RNA protection via a cap formation process. Firstly, nsp16 enzyme bound to Sinefungin was retrieved from the protein data bank (PDB ID: 6WKQ), then, a 3D pharmacophore model was constructed to be applied to screen 48 Million drug-like compounds of the Zinc database. This resulted in only 24 compounds which were subsequently docked into the enzyme. The best four score-ordered hits from the docking outcome exhibited better scores compared to Sinefungin. Finally, three molecular dynamics (MD) simulation experiments for 150 ns were carried out as a refinement step for our proposed approach. The MD and MM-PBSA outputs revealed compound 11 as the best potential nsp16 inhibitor herein identified, as it displayed a better stability and average binding free energy for the ligand-enzyme complex compared to Sinefungin.


Subject(s)
Antiviral Agents/chemistry , Enzyme Inhibitors/chemistry , SARS-CoV-2/enzymology , Viral Nonstructural Proteins/chemistry , Adenosine/analogs & derivatives , Adenosine/chemistry , Adenosine/metabolism , Antiviral Agents/metabolism , Binding Sites , Crystallography, X-Ray , Databases, Pharmaceutical , Databases, Protein , Drug Stability , Enzyme Inhibitors/metabolism , High-Throughput Screening Assays , Humans , Kinetics , Molecular Docking Simulation , Molecular Dynamics Simulation , Protein Binding , Protein Conformation, alpha-Helical , Protein Conformation, beta-Strand , Protein Interaction Domains and Motifs , SARS-CoV-2/chemistry , Thermodynamics , Viral Nonstructural Proteins/antagonists & inhibitors
17.
Nucleic Acids Res ; 49(D1): D1152-D1159, 2021 01 08.
Article in English | MEDLINE | ID: covidwho-1117392

ABSTRACT

The current state of the COVID-19 pandemic is a global health crisis. To fight the novel coronavirus, one of the best-known ways is to block enzymes essential for virus replication. Currently, we know that the SARS-CoV-2 virus encodes about 29 proteins such as spike protein, 3C-like protease (3CLpro), RNA-dependent RNA polymerase (RdRp), Papain-like protease (PLpro), and nucleocapsid (N) protein. SARS-CoV-2 uses human angiotensin-converting enzyme 2 (ACE2) for viral entry and transmembrane serine protease family member II (TMPRSS2) for spike protein priming. Thus in order to speed up the discovery of potential drugs, we develop DockCoV2, a drug database for SARS-CoV-2. DockCoV2 focuses on predicting the binding affinity of FDA-approved and Taiwan National Health Insurance (NHI) drugs with the seven proteins mentioned above. This database contains a total of 3,109 drugs. DockCoV2 is easy to use and search against, is well cross-linked to external databases, and provides the state-of-the-art prediction results in one site. Users can download their drug-protein docking data of interest and examine additional drug-related information on DockCoV2. Furthermore, DockCoV2 provides experimental information to help users understand which drugs have already been reported to be effective against MERS or SARS-CoV. DockCoV2 is available at https://covirus.cc/drugs/.


Subject(s)
Antiviral Agents/therapeutic use , COVID-19/drug therapy , Databases, Pharmaceutical/statistics & numerical data , SARS-CoV-2/drug effects , Antiviral Agents/metabolism , COVID-19/epidemiology , COVID-19/virology , Data Curation/methods , Data Mining/methods , Humans , Internet , Models, Molecular , Pandemics , Protein Binding/drug effects , Protein Domains , SARS-CoV-2/metabolism , SARS-CoV-2/physiology , Viral Proteins/chemistry , Viral Proteins/metabolism , Virus Replication/drug effects
18.
J Bioinform Comput Biol ; 19(1): 2050046, 2021 02.
Article in English | MEDLINE | ID: covidwho-1115155

ABSTRACT

Prediction of Adverse Drug Reactions (ADRs) has been an important aspect of Pharmacovigilance because of its impact in the pharma industry. The standard process of introduction of a new drug into a market involves a lot of clinical trials and tests. This is a tedious and time consuming process and also involves a lot of monetary resources. The faster approval of a drug helps the patients who are in need of the drug. The in silico prediction of Adverse Drug Reactions can help speed up the aforementioned process. The challenges involved are lack of negative data present and predicting ADR from just the chemical structure. Although many models are already available to predict ADR, most of the models use biological activities identifiers, chemical and physical properties in addition to chemical structures of the drugs. But for most of the new drugs to be tested, only chemical structures will be available. The performance of the existing models predicting ADR only using chemical structures is not efficient. Therefore, an efficient prediction of ADRs from just the chemical structure has been proposed in this paper. The proposed method involves a separate model for each ADR, making it a binary classification problem. This paper presents a novel CNN model called Drug Convolutional Neural Network (DCNN) to predict ADRs using chemical structures of the drugs. The performance is measured using the metrics such as Accuracy, Recall, Precision, Specificity, F1 score, AUROC and MCC. The results obtained by the proposed DCNN model outperform the competing models on the SIDER4.1 database in terms of all the metrics. A case study has been performed on a COVID-19 recommended drugs, where the proposed model predicted the ADRs that are well aligned with the observations made by medical professionals using conventional methods.


Subject(s)
Antiviral Agents/adverse effects , Drug-Related Side Effects and Adverse Reactions , Neural Networks, Computer , Adenosine Monophosphate/adverse effects , Adenosine Monophosphate/analogs & derivatives , Alanine/adverse effects , Alanine/analogs & derivatives , COVID-19/drug therapy , Databases, Pharmaceutical , Deep Learning , Dexamethasone/adverse effects , Humans
19.
Drug Saf ; 43(12): 1315-1322, 2020 12.
Article in English | MEDLINE | ID: covidwho-1092872

ABSTRACT

INTRODUCTION: In the stressful context of the coronavirus disease 2019 (COVID-19) pandemic, some reports have raised concerns regarding psychiatric disorders with the use of hydroxychloroquine. In this study, we reviewed all psychiatric adverse effects with hydroxychloroquine in COVID-19 patients, as well as in other indications, reported in VigiBase, the World Health Organization's (WHO) global database of individual case safety reports. METHODS: First, we analyzed all psychiatric adverse effects, including suicide, of hydroxychloroquine in COVID-19 patients reported to 16 June 2020. We also performed disproportionality analysis to investigate the risk of reporting psychiatric disorders with hydroxychloroquine compared with remdesivir, tocilizumab, or lopinavir/ritonavir prescribed in COVID-19 patients. We used reporting odds ratios (RORs) and their 95% confidence intervals (CIs) to calculate disproportionality. Second, we sought to examine the psychiatric safety profile of hydroxychloroquine in other indications (before 2020). RESULTS: Among the 1754 reports with hydroxychloroquine in COVID-19 patients, we found 56 psychiatric adverse effects. Half of these adverse effects were serious, including four completed suicides, three cases of intentional self-injury, and 12 cases of psychotic disorders with hallucinations. Compared with remdesivir, tocilizumab, or lopinavir/ritonavir, the use of hydroxychloroquine was associated with an increased risk of reporting psychiatric disorders (ROR 6.27, 95% CI 2.74-14.35). Before 2020, suicide was the main cause of death among all adverse drug reactions reported with hydroxychloroquine, followed by cardiac adverse effects (cardiomyopathy) and respiratory failure. CONCLUSIONS: This pharmacovigilance analysis suggests that COVID-19 patients exposed to hydroxychloroquine experienced serious psychiatric disorders, and, among these patients, some committed suicide. Further real-world studies are needed to quantify the psychiatric risk associated with hydroxychloroquine during the COVID-19 pandemic.


Subject(s)
Antiviral Agents/adverse effects , COVID-19/drug therapy , Hallucinations/chemically induced , Hydroxychloroquine/adverse effects , Psychoses, Substance-Induced/etiology , Self-Injurious Behavior/chemically induced , Suicide/statistics & numerical data , Adenosine Monophosphate/adverse effects , Adenosine Monophosphate/analogs & derivatives , Adult , Aged , Aged, 80 and over , Alanine/adverse effects , Alanine/analogs & derivatives , Antibodies, Monoclonal, Humanized/adverse effects , Databases, Pharmaceutical , Drug Combinations , Female , Hallucinations/epidemiology , Humans , Lopinavir/adverse effects , Male , Mental Disorders/chemically induced , Mental Disorders/epidemiology , Middle Aged , Psychoses, Substance-Induced/epidemiology , Ritonavir/adverse effects , Self-Injurious Behavior/epidemiology , Suicide, Attempted/statistics & numerical data , Young Adult
20.
Drug Saf ; 43(12): 1309-1314, 2020 12.
Article in English | MEDLINE | ID: covidwho-1092869

ABSTRACT

INTRODUCTION: In late 2019, a new coronavirus-severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-was discovered in Wuhan, China, and the World Health Organization later declared coronavirus disease 2019 (COVID-19) a pandemic. Numerous drugs have been repurposed and investigated for therapeutic effectiveness in the disease, including those from "Solidarity," an international clinical trial (azithromycin, chloroquine, hydroxychloroquine, the fixed combination lopinavir/ritonavir, and remdesivir). OBJECTIVE: Our objective was to evaluate adverse drug reaction (ADR) reporting for drugs when used in the treatment of COVID-19 compared with use for other indications, specifically focussing on sex differences. METHOD: We extracted reports on COVID-19-specific treatments from the global ADR database, VigiBase, using an algorithm developed to identify reports that listed COVID-19 as the indication. The Solidarity trial drugs were included, as were any drugs reported ≥ 100 times. We performed a descriptive comparison of reports for the same drugs used in non-COVID-19 indications. The data lock point date was 7 June 2020. RESULTS: In total, 2573 reports were identified for drugs used in the treatment of COVID-19. In order of frequency, the most reported ADRs were electrocardiogram QT-prolonged, diarrhoea, nausea, hepatitis, and vomiting in males and diarrhoea, electrocardiogram QT-prolonged, nausea, vomiting, and upper abdominal pain in females. Other hepatic and kidney-related events were included in the top ten ADRs in males, whereas no hepatic or renal terms were reported for females. COVID-19-related reporting patterns differed from non-pandemic reporting for these drugs. CONCLUSION: Review of a global database of suspected ADR reports revealed sex differences in the reporting patterns for drugs used in the treatment of COVID-19. Patterns of ADR sex differences need further elucidation.


Subject(s)
Antiviral Agents/adverse effects , COVID-19/drug therapy , Drug-Related Side Effects and Adverse Reactions/epidemiology , Abdominal Pain/chemically induced , Abdominal Pain/epidemiology , Adenosine Monophosphate/adverse effects , Adenosine Monophosphate/analogs & derivatives , Alanine/adverse effects , Alanine/analogs & derivatives , Antibodies, Monoclonal, Humanized/adverse effects , Azithromycin/adverse effects , Chemical and Drug Induced Liver Injury/epidemiology , Chemical and Drug Induced Liver Injury/etiology , Chloroquine/adverse effects , Databases, Pharmaceutical , Diarrhea/chemically induced , Diarrhea/epidemiology , Drug Combinations , Drug Eruptions/epidemiology , Drug Eruptions/etiology , Drug Repositioning , Female , Humans , Hydroxychloroquine/adverse effects , Long QT Syndrome/chemically induced , Long QT Syndrome/epidemiology , Lopinavir/adverse effects , Male , Nausea/chemically induced , Nausea/epidemiology , Oseltamivir/adverse effects , Ritonavir/adverse effects , Sex Distribution , Sex Factors , Vomiting/chemically induced , Vomiting/epidemiology
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