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DrugBank (www.drugbank.ca) is a web-enabled database containing comprehensive molecular information about drugs, their mechanisms, their interactions and their targets. First described in 2006, DrugBank has continued to evolve over the past 12 years in response to marked improvements to web standards and changing needs for drug research and development. This year's update, DrugBank 5.0, represents the most significant upgrade to the database in more than 10 years. In many cases, existing data content has grown by 100% or more over the last update. For instance, the total number of investigational drugs in the database has grown by almost 300%, the number of drug-drug interactions has grown by nearly 600% and the number of SNP-associated drug effects has grown more than 3000%. Significant improvements have been made to the quantity, quality and consistency of drug indications, drug binding data as well as drug-drug and drug-food interactions. A great deal of brand new data have also been added to DrugBank 5.0. This includes information on the influence of hundreds of drugs on metabolite levels (pharmacometabolomics), gene expression levels (pharmacotranscriptomics) and protein expression levels (pharmacoprotoemics). New data have also been added on the status of hundreds of new drug clinical trials and existing drug repurposing trials. Many other important improvements in the content, interface and performance of the DrugBank website have been made and these should greatly enhance its ease of use, utility and potential applications in many areas of pharmacological research, pharmaceutical science and drug education.
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The DrugBank database is a comprehensive, freely accessible, online database containing information on drugs and drug targets created and maintained by the University of Alberta and The Metabolomics Innovation Centre located in Alberta, Canada.[1] As both a bioinformatics and a cheminformatics resource, DrugBank combines detailed drug (i.e. chemical, pharmacological and pharmaceutical) data with comprehensive drug target (i.e. sequence, structure, and pathway) information.[1][2] DrugBank has used content from Wikipedia;[3] Wikipedia also often links to Drugbank, posing potential circular reporting issues.[3]
The DrugBank Online website is available to the public as a free-to-access resource. However, use and re-distribution of content from DrugBank Online or the underlying DrugBank Data, in whole or part, and for any purpose requires a license. Academic users can apply for a free license for certain use cases while all other users require a paid license.
The latest release of the database (version 5.0) contains 9591 drug entries including 2037 FDA-approved small molecule drugs, 241 FDA-approved biotech (protein/peptide) drugs, 96 nutraceuticals and over 6000 experimental drugs.[4] Additionally, 4270 non-redundant protein (i.e. drug target/enzyme/transporter/carrier) sequences are linked to these drug entries. Each DrugCard entry (Fig. 1) contains more than 200 data fields with half of the information being devoted to drug/chemical data and the other half devoted to drug target or protein data.[4]
The first version of DrugBank was released in 2006.[1] This early release contained relatively modest information about 841 FDA-approved small molecule drugs and 113 biotech drugs. It also included information on 2133 drug targets. The second version of DrugBank was released in 2009.[2] This greatly expanded and improved version of the database included 1344 approved small molecule drugs and 123 biotech drugs as well as 3037 unique drug targets. Version 2.0 also included, for the first time, withdrawn drugs and illicit drugs, extensive food-drug and drug-drug interactions as well as ADMET (absorption, distribution, metabolism, excretion and toxicity) parameters. Version 3.0 was released in 2011.[8] This version contained 1424 approved small molecule drugs and 132 biotech drugs as well as >4000 unique drug targets. Version 3.0 also included drug transporter data, drug pathway data, drug pricing, patent and manufacturing data as well as data on >5000 experimental drugs. Version 4.0 was released in 2014.[4] This version included 1558 FDA-approved small molecule drugs, 155 biotech drugs and 4200 unique drug targets. Version 4.0 also incorporated extensive information on drug metabolites (structures and reactions), drug taxonomy, drug spectra, drug binding constants and drug synthesis information. Table 1 provides a more complete statistical summary of the history of DrugBank's development.
All data in DrugBank is derived from public non-proprietary sources. Nearly every data item is fully traceable and explicitly referenced to the original source. DrugBank data is available through a public web interface.[9]
Description
Drug entries include FDA-approved small molecule and biotech drugs, nutraceuticals and experimental drugs as well as non-redundant protein sequences linked to the drugs.
This section lists source terminology specific data examples and sample database queries using Structured Query Language (SQL) to obtain the data. For more information about RxNorm data and files, see the RxNorm Technical Documentation.
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The viral infection affects all forms of life including birds, mammals and humans causing severe effects on the healthcare system and economy. Most of the viral infections are contagious and spread through air, water, food or exchange of body fluids. The viral infection can lead to outbreak, or can become epidemic or pandemic based on the spreading mechanism and strength of virus-host cell interaction. There are many factors that dictate the severity of a viral infection such as intrinsic pathogenicity, mortality rate, basic reproduction number1. It appears like the ongoing pandemic due to SARS-CoV-2 is the one of the most adverse infections observed during recent decades. Even though the mortality rate appears to be quite lower for this compared to previous outbreaks due to SARS and MERS viruses, the severity, disruption to the healthcare and damage to the economy have become very high due to its more aggressive human-to-human spread2. After its first report in December, 2019 within 6 months time, it has spread to almost all countries, infecting more than 44 M people. Moreover, it has been lethal to more than 1.1 M people. It is a challenging time for all researchers in medicine and pharmacology to develop a vaccine, small molecular drug or an epitope to circumvent the current case of outbreak3. There exist standard protocols to rationally develop such medicine from scratch for any such infections starting from gene mining which involves targets discovery and identification of lead drug-like compounds from structure based design. Another approach is through the high throughput experimental screening4 of compounds from chemical space which is rather shooting for something in the darkness. Our aim here is to present a rational approach involving computational screening for identification of lead drug-like compounds for Covid-19 associated coronavirus5.
The genomics data encode the biomolecular machineries necessary for the life processes of any organism including pathogens and can be used to obtain information regarding potential targets relevant for therapy or diagnostics6. Therefore, in order to design drugs against viral pathogens, we need to start with the data mining of viral genome. More than 10,000 genomics data are already deposited for SARS-CoV-2 in GISAID, an open source online platform ( )7. It is a wealth of information which can be used to find the routes followed by the virus to spread the infection8. The SARS-CoV-2 genome is made of less than 30000 nucleotides and contains genes for 29 different proteins9. The ORF1ab alone encodes as many as 16 non-structural proteins10. Some of the key proteins encoded by this gene are PLpro (NSP2), 3CLpro (NSP5), RdRp (NSP12), and helicase (NSP13) which play a vital role in the replication and transcription11,12. The ORF2-10 encodes various structural proteins such as membrane protein (M), envelope protein (E), spike protein (S), nucleocapsid protein (N) and other auxiliary proteins12,13. The M, E and S make the viral coat while the RNA gene is packaged within the N protein11,12. Further, the spike protein is involved in the host cell recognition and in particular binds to Angiotensin-converting enzyme 2 (ACE2) mammalian receptor14. Based on their involvement in different biological processes, many SARS-CoV-2 proteins (e.g. spike protein, PLpro, 3CLpro, RdRp, and helicase etc) can be considered as potential targets for therapy. More specifically, for therapeutic purpose, it is essential to target enzymes involved in viral replication as well as transcription. In addition, we need to target those catalytic sites involved in the key enzymatic reaction. In SARS-CoV-2, the main role of 3CLpro and PLpro proteases is to cleave the polyprotein into smaller functional units to facilitate replication/transcription process and thus are potential targets for the therapeutics15.
In this study, we have identified several compounds from the Drugbank that are predicted to bind to individual target proteins with high affinity (Tables 1, 2 and 3). In addition, we also identified compounds that potentially interact with two or more viral proteins (Table 4). In the context of SARS-CoV-2 viral therapeutics32,47, binding of a drug molecule to a single or multiple targets can be of significance depending upon the different stages of the viral infection. For instance, during the host cell recognition only the spike protein of SARS-CoV-2 plays a key role and can be targeted. However, once the infection occurs, other proteins associated with transcription and replication processes are expressed. In this case, it is advantageous to use a drug which targets multiple proteins or cocktail of drugs with each drug having significant binding affinity towards a specific target. Due to the complexity involved in the development of infection, it is desirable to target multiple targets with many low affinity ligands instead of targeting a single target with high affinity ligand47. This also has an advantage that even when a specific target mutates rapidly, the other targets can be inhibited by the drug cocktails which eventually makes the treatment effective. Such combination of drugs subscription is already in practice for viral infections48. In case of HIV treatment, a combination of drugs belonging to categories such as nucleoside reverse transcriptase inhibitor, non-nucleoside reverse transcriptase inhibitor, protease inhibitor and integrase inhibitor has been successfully tested49. For example, a FDA-approved drug Combivir is a mixture of AZT and 3TC and targets enzymes which appear in the early and later stage of HIV replication48. 152ee80cbc
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