Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

DrugBank 3.0: a comprehensive resource for Omics research on drugs

DrugBank 3.0: a comprehensive resource for Omics research on drugs Published online 8 November 2010 Nucleic Acids Research, 2011, Vol. 39, Database issue D1035–D1041 doi:10.1093/nar/gkq1126 DrugBank 3.0: a comprehensive resource for ‘Omics’ research on drugs 1 2 1 3 2 1 Craig Knox , Vivian Law , Timothy Jewison , Philip Liu , Son Ly , Alex Frolkis , 1 2 2 1 3 Allison Pon , Kelly Banco , Christine Mak , Vanessa Neveu , Yannick Djoumbou , 1 1 1,2,3,4, Roman Eisner , An Chi Guo and David S. Wishart * Department of Computing Science, University of Alberta, Edmonton, AB, Canada T6G 2E8, Faculty of Pharmacy and Pharmaceutical Sciences, University of Alberta, Edmonton, AB, Canada T6G 2N8, Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada T6G 2E8 and National Institute for Nanotechnology, 11421 Saskatchewan Drive, Edmonton, AB, Canada T6G 2M9 Received September 15, 2010; Revised October 20, 2010; Accepted October 21, 2010 ABSTRACT drug target, drug description and drug action data. DrugBank 3.0 represents the result of 2 years DrugBank (http://www.drugbank.ca) is a richly of manual annotation work aimed at making annotated database of drug and drug target infor- the database much more useful for a wide mation. It contains extensive data on the nomencla- range of ‘omics’ (i.e. pharmacogenomic, ture, ontology, chemistry, structure, function, pharmacoproteomic, pharmacometabolomic and action, pharmacology, pharmacokinetics, metabol- even pharmacoeconomic) applications. ism and pharmaceutical properties of both small molecule and large molecule (biotech) drugs. It also contains comprehensive information on the INTRODUCTION target diseases, proteins, genes and organisms on Historically most of the known information on drugs, which these drugs act. First released in 2006, drug targets and drug action has resided in books, DrugBank has become widely used by pharmacists, journals and expensive commercial databases. Over the medicinal chemists, pharmaceutical researchers, past 5 years this situation has changed quite dramatically. clinicians, educators and the general public. Since Now most drug and drug target data is freely available its last update in 2008, DrugBank has been greatly over the internet. The first on-line database to break the expanded through the addition of new drugs, new commercial ‘stranglehold’ on drug information was the targets and the inclusion of more than 40 new data Therapeutic Target Database (TTD), which was released fields per drug entry (a 40% increase in data ‘depth’). in 2002 and then updated in 2010 (1). Over the years, other These data field additions include illustrated drug-specific databases have emerged, including PDTD drug-action pathways, drug transporter data, drug (2), STITCH (3), SuperTarget (4) and the Druggable Genome database (5). These databases provide synoptic metabolite data, pharmacogenomic data, adverse data on drugs and their primary or putative drug targets. drug response data, ADMET data, pharmacokinetic Since the appearance of these drug/drug–target databases, data, computed property data and chemical classi- other kinds of drug resources have emerged including fication data. DrugBank 3.0 also offers expanded PharmGKB (6), which specializes in pharmacogenetic database links, improved search tools for drug– and pharmacogenomic data, RxList (www.rxlist.com) drug and food–drug interaction, new resources for and DailyMed (7), which provide electronic versions of querying and viewing drug pathways and hundreds the FDA’s drug-product data sheets, ChEMBL (www. of new drug entries with detailed patent, pricing and ebi.ac.uk/chembl) which provides data on drug-like com- manufacturer data. These additions have been com- pounds and BindingDB (8), which contains quantitative plemented by enhancements to the quality and drug-binding constant data. The growing appetite for quantity of existing data, particularly with regard to web-accessible drug data has also led PubChem (7), *To whom correspondence should be addressed. Tel: +780 492 0383; Fax: +780 492 1071; Email: [email protected] The Author(s) 2010. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/ by-nc/2.5), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. D1036 Nucleic Acids Research, 2011, Vol. 39, Database issue KEGG (9), ChEBI (10) and ChemSpider (11) to add drugs field additions; (iii) expanded database linkages and (iv) enhanced data querying and viewing capabilities. and drug information to their usual offerings. All of these databases are outstanding resources, but as a general rule, most of them are quite ‘lightly’ annotated with only 10–15 data fields per drug entry. GROWTH AND ENHANCEMENT OF EXISTING In contrast to most other open-access drug databases, DATA DrugBank (12) is a ‘richly’ annotated resource. The first DrugBank has grown significantly in the past 5 years with version of DrugBank (released in 2006) contained nearly perhaps the most significant changes happening between 90 data fields per drug entry, with detailed information on release 2.0 and 3.0. This progressive data content expan- the nomenclature, ontology, chemistry, structure, sion is summarized in Table 1. As can be seen from this function, action, pharmacology, pharmacokinetics, me- table, going from version 2.0 to 3.0, there has been a 40% tabolism and pharmaceutical properties of both drugs increase in the number of data fields for each drug entry. and drug targets. Much of this data was acquired Likewise there has been a 130% increase in the number of through primary literature sources, checked by experts, computed structure parameters, an 80% increase in the edited and entered manually. The richness, uniqueness number of external database links, a 67% increase in and quality of the data in DrugBank has clearly hit a the number of experimental drugs, a 46% increase in the nerve with the research community. It is widely cited number of food–drug interactions, a 42% increase in (more than 400 citations), integrated into many interna- the total number of drug targets, a 20% increase in the tional databases (more than 20) and heavily used number of possible DrugBank queries, a 13% increase in (more than 4 million page visits/year) by pharmacists, the number of FDA-approved drug targets, a 12% physicians, researchers, educators and the general public. increase in the number of biotech and nutraceutical In an effort to keep up with the growing applications drugs and a 6% increase in the number of FDA- and far-ranging requests for this particular database, an approved small molecule drugs. updated version (DrugBank 2.0) was released in 2008 (13). In addition to significantly expanding the data content Since then, the amount of easily accessible or predictable in DrugBank, a major effort has been directed at improv- knowledge on drugs has grown considerably. So too has ing the quality of DrugBank’s existing data. Hundreds of the number of requests, suggested improvements and calls drug descriptions, mechanisms of action and pharmaco- for additional kinds of data to appear in DrugBank. Based logical summaries have been either re-written or on this user feedback we have spent the past 2 years expanded. Likewise, hundreds of new drug and drug– enhancing both the quantity and quality of DrugBank’s target references were collected, checked and added. content. We have also added to or improved upon a Similarly, extensive checks have been performed on all number of DrugBank’s querying and search functions. of DrugBank’s small molecule structures to confirm that The net result is a 40% increase in the number of data they exhibit the correct chirality and stereochemistry. In fields for each drug entry, a considerable expansion particular, we developed a custom structure-checking (>50%) in the number of drug–protein and food–drug program that used direct structure comparison (via a interactions, a massive increase in the information on Mol file) of each of DrugBank’s structures against the drug metabolites, drug ADMET (absorption, distribution, corresponding structures in other databases (PubChem, metabolism, excretion, toxicology) data and the addition ChEBI, ChemSpider, etc.). Any DrugBank structure that of hundreds of colorful, interactive, hand-drawn drug- did not match with the corresponding structure in one or action pathway diagrams. With these enhancements, we more of these external databases was flagged. A total of believe DrugBank has become a much more comprehen- 340 structures were identified with potential structural sive and accessible drug information resource. It has also errors or discrepancies. Each of these was assessed and/ become significantly more useful for a wide range of or corrected manually by a team of trained chemists. In ‘omics’ (i.e. pharmacogenomic, pharmacoproteomic, many cases the DrugBank structure was correct and the pharmacometabolomic, pharmacoeconomic) applications. external database structure was found to be in error, in A more detailed description of DrugBank 3.0 follows. other cases the DrugBank structure was determined to be in error and was subsequently corrected. In addition to completing extensive data integrity checks for all of DrugBank’s chemical data, the drug WHAT’S NEW IN DRUGBANK 3.0? target information in DrugBank 3.0 has been significantly Details relating to DrugBank’s overall design, general improved. Now most of DrugBank’s approved-drug querying capabilities, curation protocols, structure depic- targets are prioritized by relevance, with each target tion conventions, quality assurance and drug selection being classified by its primary mode of action. One criteria have been described previously (12,13). These mode-of-action category lists targets known to confer have largely remained the same between release 2.0 and the desired pharmacological effects, while the other lists 3.0. Here, we shall focus primarily on describing the targets with unknown or unintended pharmacological changes and enhancements made to the database and to effects (many of which account for side effects). In the annotation processes for release 3.0. More specifically, addition to our implementation of an improved target we will describe the: (i) growth and enhancements to classification scheme, DrugBank 3.0 now formally separ- DrugBank’s existing content and quality; (ii) new data ates drug-action targets from drug transporters, drug Nucleic Acids Research, 2011, Vol. 39, Database issue D1037 Table 1. Comparison between the coverage in DrugBank 1.0, 2.0 and DrugBank 3.0 Category 1.0 2.0 3.0 No. of data fields 88 108 148 No. of search types 8 12 16 No. of drug-action pathways 0 0 223 No. of drugs with metabolizing enzyme data 0 0 762 No. of drug metabolites 0 0 811 No. of drugs with drug transporter data 0 0 516 No. of SNP-associated drug effects 0 0 113 No. of drugs with patent/pricing/manufacturer data 0 0 1208 No. of food–drug interactions 0 714 1039 No. of drug–drug interactions 0 13 242 13 795 No. of ADMET parameters (Caco-2, LogS) 0 276 890 No. of QSAR parameters per drug 5 6 14 No. of FDA-approved small molecule drugs 841 1344 1424 No. of biotech drugs 113 123 132 No. of nutraceutical drugs 61 69 82 No. of withdrawn drugs 0 57 68 No. of illicit drugs 0 188 189 No. of experimental drugs 2894 3116 5210 Total No. of experimental and FDA small molecule drugs 3796 4774 6684 Total No. of experimental and FDA drugs 3909 4897 6816 No. of names/brands/synonyms 18 304 28 447 37 171 No. of approved-drug drug targets (unique) 524 1565 1768 No. of all drug targets (unique) 2133 3037 4326 No. of approved-drug enzymes/carriers (unique) 0 0 164 No. of all drug enzymes/carriers (unique) 0 0 169 No. of external database links 12 18 31 carriers and pro-drug conversion enzymes. Note that in listing of the new data fields appearing in DrugBank 3.0. DrugBank, carriers are considered separate from trans- The five areas where most of the new data has been added porters as carriers move drugs around the body, while relate to: (i) pharmacometabolomics; (ii) transporters move drugs into and around the cell. This pharmacoproteomics; (iii) pharmacogenomics; (iv) kind of target separation should make drug-target pharmacoeconomics and (v) computed structure studies somewhat easier and substantially more features. These additions are detailed below: informative. While many visible ‘front-end’ enhancements have been Pharmacometabolomics implemented, DrugBank’s back-end has also been signifi- cantly enhanced. In particular, all of DrugBank’s data has As the field of metabolomics has grown, so too has the been converted to an easily parsed XML format. This interest in understanding drug metabolism and in should make data downloads and the development of characterizing drug metabolites. Indeed, it is now data extraction routines much simpler and far faster for recognized that drug metabolites play an important role programmers and database developers. in understanding adverse drug effects, in determining therapeutic indices and in leading to secondary or off-target therapeutic effects. In an effort to make this NEW DATA FIELD ADDITIONS kind of metabolic or metabolomic information readily available we have manually compiled detailed phase I/II Each new release of DrugBank has been characterized by metabolic fate data for over 760 FDA-approved drugs. a significant increase in the number of new data fields The data, which was compiled from hundreds of journal compared to the previous release. DrugBank 3.0 is no articles, includes links to pathways, chemical structures, exception. Going from version 2.0 to 3.0, there has been HMDB (14) entries, reaction parameters such as a substantial increase in the number of data fields (going reaction type (inducer, inhibitor and substrate), K , and from 108 to 148). Many of these data field additions were V , and reaction class information for nearly 720 drugs. the results of specific requests by DrugBank users or arose max through consultation with members of the pharmaceutical In addition to these annotations and molecular descriptors research community. DrugBank 3.0 now includes drug for drug metabolism and drug metabolites, DrugBank 3.0 also provides classical ADMET information including pathway diagrams, drug transporter information, drug drug distribution, clearance and route of elimination carrier information, drug metabolite data, drug data. These new ‘metabolic’ data fields, when combined metabolizing enzyme data, QSAR data, chemical classifi- cation data, SNP-associated drug effects (available with the structural and biological information already through the GenoBrowse link) and drug patent/pricing/ stored in DrugBank, should offer an entirely new way of manufacturer data. Table 2 provides a more complete exploiting the data in DrugBank. D1038 Nucleic Acids Research, 2011, Vol. 39, Database issue Table 2. New data fields in DrugBank 3.0 targets. It also has data on 1188 coding SNPs and 8931 non-coding SNPs from known drug metabolizing Chemical kingdom ChEMBL link enzymes. SNP information can be accessed by clicking Chemical class Drug pathway on the ‘Show SNPs’ hyperlink listed beside either the Chemical substructures Drug pathway SMPDB ID Drug manufacturers Target actions (antagonist, agonist) metabolizing enzymes or the drug target SNP field. Drug packagers Target priority These SNP summary tables include: (i) the reference Drug prices Target pharmacological effect SNP ID, with a hyperlink to dbSNP (17); (ii) the allele (known/unknown/none) variants; (iii) the validation status; (iv) the chromosome Original patent date Enzyme actions (inhibitor, inducer, substrate) Original patent number Drug metabolite structure location and reference base position; (v) the functional Last patent expiry date Drug metabolite name class (synonymous, non-synonymous, untranslated, Last patent number Drug metabolite HMDB ID intron, exon); (vi) mRNA and protein accession links (if MMCD link Drug metabolite reaction type (e.g. oxidation) applicable); (vii) the reading frame (if applicable); (viii) the ChemSpider ID and Reaction K value amino acid change (if existent); (ix) the allele frequency as link NDC ID and link Reaction V value max measured in African, European and Asian populations (if DailyMed link Metabolizing enzyme references available) and (x) the sequence of the gene fragment with Drugs.com link Metabolizing enzyme priority the SNP highlighted in a red box. The purpose of these OMIM link Drug transporter name SNP tables is to allow DrugBank users to go directly from CDPD link Drug transporter actions (substrate, inhibitor, inducer) a drug of interest to a list of potential SNPs that may TTD link Route of elimination contribute to the reaction or response seen in a given STITCH link Volume of distribution patient or in a given population. BindingDB link Clearance In addition to this drug target SNP data, Drugbank 3.0 now includes two tables that provide much more explicit information on the relationship between drug responses/ reactions and gene variant or SNP data. These tables, Pharmacoproteomics which are accessible from the GenoBrowse submenu located on DrugBank’s Browse menu bar, are called It is often said that a picture is worth a thousand words. In SNP-FX (short for SNP-associated effects) and order to simplify DrugBank’s vast collection proteomic SNP-ADR (short for SNP-associated adverse drug reac- data, DrugBank 3.0 now includes nearly 230 richly tions). SNP-FX contains data on the drug, the interacting illustrated drug-action pathways. These pathways have protein(s), the ‘causal’ SNPs or genetic variants for that been designed to display the action of drugs on protein gene/protein, the therapeutic response or effects caused by targets or protein receptors. Using the visualization frame- the SNP-drug interaction (improved or diminished work developed for SMPDB (15), each DrugBank response, changed dosing requirements, etc.) and the pathway is ‘image-mapped’, with every drug structure associated references describing these effects in more being hyperlinked to the detailed descriptions contained detail. SNP-ADR follows a similar format to SNP-FX in DrugBank or HMDB and every protein or enzyme but the clinical responses are restricted only to adverse complex being hyperlinked to the detailed descriptions drug reactions (ADR). SNP-FX contains provided by UniProt (16). DrugBank’s drug-action literature-derived data on the therapeutic effects or thera- pathways are carefully hand-drawn and frequently peutic responses for more than 60 drug-polymorphism include information on the relevant organs, organelles, combinations, while SNP-ADR contains data on adverse subcellular compartments, protein targets, protein loca- reactions compiled from more than 50 tions and drug structures that describe the pharmacology drug-polymorphsim pairings. All of the data in these or mode of action for that drug. All of DrugBank’s tables is hyperlinked to drug entries from DrugBank, pathways images may be progressively expanded by protein data from UniProt, SNP data from dbSNP and clicking on the Zoom button located at the top and bibliographic data from PubMed. bottom of the image or the magnifying-glass icons in the Highlight/Analyzer box on the right of the image. At the Pharmacoeconomics top of each image is a pathway synopsis while at the bottom of each image is a list of relevant references. Perhaps the most important ‘omics’ discipline in the Over the coming 2–3 years it is expected that another pharmaceutical world is econ-‘omics’. Indeed, the selec- 1000–1200 drug pathways will be added to DrugBank’s tion of disease targets, the money spent on research and pathway inventory. the money recovered from sales are largely determined by economic factors. To facilitate research into these issues Pharmacogenomics DrugBank 3.0 has added data on drug patent dates (from The relationship between drugs, genes and genetic variants Canada and the United States), drug manufacturers, drug (SNPs) is central to the whole field of pharmacogenomics packagers, drug prices (from different jurisdictions) and and personalized medicine. In an effort to address these drug sales (where available). Coupled with other data rapidly growing needs, DrugBank 3.0 now contains a sig- already in DrugBank (such as ATC codes, indications, nificant amount of new pharmacogenomic information, side effects, structures, chemical classes) this information including data on 26 292 coding (exon) SNPs and 73 328 should enable more detailed studies on the relationship non-coding (intron) SNPs derived from known drug between drug prices and patent dates, the connection Nucleic Acids Research, 2011, Vol. 39, Database issue D1039 between drug prices and drug sales, the relationship list of links in the ‘About’ section of DrugBank. In between drug sales and disease targets, the link between addition to these external database links, DrugBank has a drug’s price and the drug’s side effects as well as the been reciprocally linked to several major resources relationship between drug manufacturers and disease including Wikipedia, UniProt (16), BioMOBY (25), target choices. It should also enable research into histor- PubChem (7), KEGG (9), PharmGKB (6), Drugs.com ical trends in drug target or disease target choices as well and ChemSpider (11). as long-term trends in the use or exploitation of certain drug motifs or structure classes (e.g. statins, tricyclic drugs). ENHANCED QUERYING AND VIEWING CAPABILITIES Computed structure data One of the key strengths for DrugBank has been its Computed structure parameters or descriptors are fre- support for a wide range of querying and visualization quently used in quantitative structure activity tools. These include 2D and 3D structure viewers, relationship (QSAR) studies to facilitate rational drug flexible text querying systems, structure searching/ design, drug screening and medicinal chemistry. These matching, sequence searching tools, data extraction tools computed structure parameters may also be used to ra- and easy-to-use browsers. For DrugBank 3.0 we have tionalize drug activities, tissue localization, adverse reac- made a number of improvements to the existing query tions and drug metabolism. While earlier versions of tools but are also introducing four new browsing or DrugBank had provided a nominal number of computed search tools. These include PathBrowse, GenoBrowse, structure descriptors (molecular weight, pKa, LogP, ClassBrowse, ReceptorBrowse and the Interax LogS), these were often not sufficient for detailed feature Interaction Search (Figure 1). We believe all five should analyses or comprehensive in silico comparisons. make the viewing and retrieval of information in DrugBank 3.0 now provides an additional set of nine DrugBank much easier. computed property descriptors including (i) the number PathBrowse was developed to facilitate the viewing and of H-bond acceptors; (ii) the number of H-bond donors; searching of DrugBank’s drug-action pathways. Each (iii) the number of freely rotating bonds; (iv) the index of hyperlinked, interactive pathway explains the mode of refraction; (v) the predicted boiling/melting point; (vi) the action of drugs at a molecular, cellular and/or physiologic- polar surface area; (vii) the molar refractivity; (viii) the al level. PathBrowse allows users to search for drugs by polarizability and (ix) the molecular density. These DrugBank ID, name or synonyms. It also supports the properties are displayed in a summary table with links search for drug targets, metabolizing enzymes, carriers to the SDF file containing these values. While more and transporters either by their name, UniProt ID or computed structure descriptors are certainly available, gene identifier. The results are displayed as a highlighted these represent the most frequently used descriptors and list of hits. Once a pathway is selected, users can inter- should allow DrugBank users to perform much more actively explore the pathway image, with compound or detailed computed structure queries, analyses and protein hits highlighted in the pathway image. This tight comparisons. integration between DrugBank and SMPDB should allow researchers to visualize the ‘big picture’ with respect to drugs and how they act or how they are processed in the EXPANDED DATABASE LINKAGES body. The two other browsing functions (ClassBrowse and GenoBrowse) are somewhat simpler in design and Because DrugBank was designed to cover a broad functionality than PathBrowse. ClassBrowse allows users spectrum of scientific disciplines it has always been exten- to search through or sort drugs by their chemical class or sively linked to many external databases. For instance, chemical taxonomy while GenoBrowse (which has already version 2.0 of DrugBank contained up to 18 database been described) allows users to browse through or explore hyperlinks in every DrugCard entry, including links to SNP-induced drug effects or drug reactions. KEGG (9), PubChem (7), ChEBI (10), PharmGKB (6), ReceptorBrowse allows users to search or sort through PDB (18), GenBank (19), DIN, RxList, PDRhealth, the protein targets, enzymes, carriers and transporters Wikipedia, ATC, UniProt (16), Pfam (20), dbSNP (17), (along with their function and target species information) GeneCards (21), GenAtlas (22), HGNC and PubMed. that are associated with each drug in DrugBank. DrugBank 3.0 now contains an average of 31 hyperlinks DrugBank contains one of the most complete, freely per DrugCard. These new links include numerous available sources of drug–drug and food–drug interaction compound-specific, spectral, pathway and disease data- data on the Internet today. Although this information has bases such as ChemSpider (11), HMDB (14), MMCD (23), SMPDB (15) and OMIM (24). We have also added been made available in each DrugCard from version 2.0 new links to several dedicated drug and pharmaceutical onwards, the data has not been easily searchable. The databases [DailyMed (7), Drugs.com, the National Drug ‘Interax’ Interaction Search was developed to allow Code identifier database and the Canadian Drug Product facile searching of drug and food interactions. Unlike Database] as well as a number of drug target databases, existing interaction search tools, Interax takes the such as the Therapeutic Target Database (TTD), STITCH process one step further by including transporter, target (4), BindingDB (8) and ChEMBL. These DrugCard and enzyme information in the search results. Several dif- hyperlinks are also complemented with a comprehensive ferent search types are supported by Interax. For instance, D1040 Nucleic Acids Research, 2011, Vol. 39, Database issue Figure 1. A screenshot montage of some of DrugBank 3.0’s new browser views (GenoBrowse, Interax, ClassBrowse, PathBrowse). standard drug–drug or food–drug interaction searches can functionality provides a unique method of searching and be performed, whereby a user inputs a list of drugs, exploring drug–drug interactions and should be of interest to pharmacists, pharmaceutical researchers, and the presses the ‘submit’ button and a list of drug and food general public. interactions are produced. Users can also input two lists of drugs and Interax will identify any interactions between the lists. Additionally, any interactions that may be target, CONCLUSION enzyme, carrier or transporter related (e.g. two drugs bind the same target) will be flagged with symbols representing DrugBank 3.0 contains a significant number of enhance- a target interaction, enzyme interaction, carrier interaction ments over its predecessor (DrugBank 2.0). As highlighted or transporter interaction. This comprehensive search throughout this article, numerous improvements have Nucleic Acids Research, 2011, Vol. 39, Database issue D1041 7. Wang,Y., Xiao,J., Suzek,T.O., Zhang,J., Wang,J. and Bryant,S.H. been made in the quantity, quality, depth and organiza- (2009) PubChem: a public information system for analyzing tion of the information provided. These include the bioactivities of small molecules. Nucleic Acids Res., 37(Database addition of new drugs, new targets, new data fields, new issue), W623–W633. links and new tools. DrugBank 3.0 now contains 8. Liu,T., Lin,Y., Wen,X., Jorissen,R.N. and Bilson,M.K. (2007) illustrated drug-action pathways, drug transporter data, BindingDB: a web-accessible database of experimentally determined protein-ligand binding affinities. Nucleic Acids Res., drug metabolite data, pharmacogenomic data, adverse 35(Database issue), D198–D201. drug response data, ADMET data, pharmacokinetic 9. Kanehisa,M., Goto,S., Furumichi,M., Tanabe,M. and data, extensive computed property data and chemical clas- Hirakawa,M. (2010) KEGG for representation and analysis of sification data. DrugBank 3.0 also offers expanded molecular networks involving diseases and drugs. database links, improved search tools for drug–drug and Nucleic Acids Res., 38(Database issue), D355–D360. 10. Brooksbank,C., Cameron,G. and Thornton,J. (2005) The food–drug interaction, new tools for searching and European Bioinformatics Institute’s data resources: towards viewing drug pathways and hundreds of new drug systems biology. Nucleic Acids Res., 33(Database issue), D46–D53. entries with detailed patent, pricing and manufacturer 11. Williams,A.J. (2008) Public chemical compound databases. Curr. data. These additions have been complemented by Opin. Drug Discov. Devel., 11, 393–404. enhancements to the quality and quantity of existing 12. Wishart,D.S., Knox,C., Guo,A.C., Shrivastava,S., Hassanali,M., Stothard,P., Chang,Z. and Woolsey,J. (2006) DrugBank: a data, particularly with regard to drug target, drug descrip- comprehensive resource for in silico drug discovery and tion and drug action data. With these enhancements exploration. Nucleic Acids Res., 34(Database issue), D668–D672. DrugBank 3.0 should be much more useful for a wider 13. Wishart,D.S., Knox,C., Guo,A.C., Cheng,D., Shrivastava,S., range of ‘omics’ applications. It is hoped that with more Tzur,D., Gautam,B. and Hassanali,M. (2008) DrugBank: a user feedback, DrugBank will continue to develop to fit knowledgebase for drugs, drug actions and drug targets. Nucleic Acids Res., 36(Database issue), D901–D906. the needs of its users and provide an increasingly useful, 14. Wishart,D.S., Knox,C., Guo,A.C., Eisner,R., Young,N., information-rich drug resource. Gautam,B., Hau,D.D., Psychogios,N., Bouatra,S., Mandal,R. et al. (2009) HMDB: a knowledgebase for the human meabolome. Nucleic Acids Res., 37(Database issue), D603–D610. ACKNOWLEDGEMENTS 15. Frolkis,A., Knox,C., Lim,E., Jewison,T., Law,V., Hau,D.D., Liu,P., Gautam,B., Ly,S., Guo,A.C. et al. (2010) SMPDB: the The authors are indebted to the many users of DrugBank small molecule pathway database. Nucleic Acids Res., 38(Database who have provided valuable feedback and suggestions. issue), D480–D487. 16. UniProt Consortium. (2009) The Universal Protein Resource (UniProt) 2009. Nucleic Acids Res., 37(Database issue), FUNDING D169–D174. 17. Sherry,S.T., Ward,M.H., Kholodov,M., Baker,J., Phan,L., Canadian Institutes of Health Research (CIHR); Genome Smigielski,E.M. and Sirotkin,K. (2001) dbSNP: the NCBI Alberta; Genome Canada; GenomeQuest Inc. Funding database of genetic variation. Nucleic Acids Res., 29, 308–311. for open access charge: CIHR. 18. Kouranov,A., Xie,L., de la Cruz,J., Chen,L., Westbrook,J., Bourne,P.E. and Berman,H.M. (2006) The RCSB PDB Conflict of interest statement. None declared. information portal for structural genomics. Nucleic Acids Res., 34(Database issue), D302–D305. 19. Benson,D.A., Karsch-Mizrachi,I., Lipman,D.J., Ostell,J. and Sayers,E.W. (2010) GenBank. Nucleic Acids Res., 38(Database REFERENCES issue), D46–D51. 1. Zhu,F., Han,G., Kumar,P., Liu,X., Ma,X., Wei,X., Huang,L., 20. Finn,R.D., Mistry,J., Tate,J., Coggill,P., Heger,A., Guo,Y., Han,L., Zheng,C. et al. (2010) Update of TTD: Polliington,J.E., Gavin,O.L., Gunasekaran,P., Ceric,G., therapeutic target database. Nucleic Acids Res., 38(Database Froslund,K. et al. (2010) The Pfam protein families database. issue), D787–D791. Nucleic Acids Res., 38(Database issue), D211–D222. 2. Gao,Z., Li,H., Zhang,H., Liu,X., Kang,L., Luo,X., Zhu,W., 21. Safran,M., Dalah,I., Alexander,J., Rosen,N., Iny Stein,T., Chen,K., Wang,X. and Jiang,H. (2008) PDTD: a web-accessible Shmoish,M., Nativ,N., Bahir,I., Doniger,T., Krug,H. et al. (2010) protein database for drug target identification. GeneCards Version 3: the human gene integrator. Database, doi: BMC Bioinformatics, 9, 104. 10.1093/baq020; [7 August 2010, epub ahead of print]. 3. Kuhn,M., Szklarczyk,D., Franceschini,A., Campillos,M., von 22. Roux-Rouquie,M., Chavet,M.L., Munnich,A. and Frezal,J. (1999) Mering,C., Jensen,L.J., Beyer,A. and Bork,P. (2010) STITCH 2: Human genes involvedin chromatin remodeling in transcription an interaction network database for small molecules and proteins. initiation and associated diseases: an overview using the Nucleic Acids Res., 38(Database issue), D552–D556. GENATLAS database. Mol. Genet. Metab., 62, 261–277. 4. Gunther,S., Kun,M., Dunkel,M., Camillos,M., Senger,C., 23. Markley,J.L., Anderson,M.E., Cui,Q., Eghbalnia,H.R., Lewis,I.A., Petsalaki,E., Ahmed,J., Urdiales,E.G., Gewiess,A., Jensen,L.J. Hegeman,A.D., Li,J., Schulte,C.F., Sussman,M.R., Westler,W.M. et al. (2008) SuperTarget and Matador: resources for exploring et al. (2007) New bioinformatics resources for metabolomics. Pac. drug-target relationships. Nucleic Acids Res., 36(Database issue), Symp. Biocomput., 12, 157–168. D919–D922. 24. Hamosh,A., Scott,A.F., Amberger,J.S., Bocchini,C.A. and 5. Russ,A.P. and Lampel,S. (2005) The druggable genome: an McKusick,V.A. (2005) Online Mendelian Inheritance in Man update. Drug Discov. Today, 10, 1607–1610. (OMIM), a knowledgebase of human genes and genetic disorders. 6. Hernandez-Boussard,T., Whirl-Carrillo,M., Hebert,J.M., Gong,L., Nucleic Acids Res., 33(Database issue), D514–D517. Owen,R., Gong,M., Gor,W., Liu,F., Truong,C., Whaley,R. et al. 25. Kawas,E., Senger,M. and Wilkinson,M.D. (2006) BioMoby (2008) The pharmacogenetics and pharmacogenomics knowledge extensions to the Taverna workflow management and enactment base: accentuating the knowledge. Nucleic Acids Res., 36(Database software. BMC Bioinformatics, 7, 523. issue), D913–D918. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Nucleic Acids Research Oxford University Press

DrugBank 3.0: a comprehensive resource for Omics research on drugs

Download PDF
7 pages

Loading next page...
 
/lp/oxford-university-press/drugbank-3-0-a-comprehensive-resource-for-omics-research-on-drugs-pgjuGEIGaI

References (27)

Publisher
Oxford University Press
Copyright
The Author(s) 2010. Published by Oxford University Press.
ISSN
0305-1048
eISSN
1362-4962
DOI
10.1093/nar/gkq1126
pmid
21059682
Publisher site
See Article on Publisher Site

Abstract

Published online 8 November 2010 Nucleic Acids Research, 2011, Vol. 39, Database issue D1035–D1041 doi:10.1093/nar/gkq1126 DrugBank 3.0: a comprehensive resource for ‘Omics’ research on drugs 1 2 1 3 2 1 Craig Knox , Vivian Law , Timothy Jewison , Philip Liu , Son Ly , Alex Frolkis , 1 2 2 1 3 Allison Pon , Kelly Banco , Christine Mak , Vanessa Neveu , Yannick Djoumbou , 1 1 1,2,3,4, Roman Eisner , An Chi Guo and David S. Wishart * Department of Computing Science, University of Alberta, Edmonton, AB, Canada T6G 2E8, Faculty of Pharmacy and Pharmaceutical Sciences, University of Alberta, Edmonton, AB, Canada T6G 2N8, Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada T6G 2E8 and National Institute for Nanotechnology, 11421 Saskatchewan Drive, Edmonton, AB, Canada T6G 2M9 Received September 15, 2010; Revised October 20, 2010; Accepted October 21, 2010 ABSTRACT drug target, drug description and drug action data. DrugBank 3.0 represents the result of 2 years DrugBank (http://www.drugbank.ca) is a richly of manual annotation work aimed at making annotated database of drug and drug target infor- the database much more useful for a wide mation. It contains extensive data on the nomencla- range of ‘omics’ (i.e. pharmacogenomic, ture, ontology, chemistry, structure, function, pharmacoproteomic, pharmacometabolomic and action, pharmacology, pharmacokinetics, metabol- even pharmacoeconomic) applications. ism and pharmaceutical properties of both small molecule and large molecule (biotech) drugs. It also contains comprehensive information on the INTRODUCTION target diseases, proteins, genes and organisms on Historically most of the known information on drugs, which these drugs act. First released in 2006, drug targets and drug action has resided in books, DrugBank has become widely used by pharmacists, journals and expensive commercial databases. Over the medicinal chemists, pharmaceutical researchers, past 5 years this situation has changed quite dramatically. clinicians, educators and the general public. Since Now most drug and drug target data is freely available its last update in 2008, DrugBank has been greatly over the internet. The first on-line database to break the expanded through the addition of new drugs, new commercial ‘stranglehold’ on drug information was the targets and the inclusion of more than 40 new data Therapeutic Target Database (TTD), which was released fields per drug entry (a 40% increase in data ‘depth’). in 2002 and then updated in 2010 (1). Over the years, other These data field additions include illustrated drug-specific databases have emerged, including PDTD drug-action pathways, drug transporter data, drug (2), STITCH (3), SuperTarget (4) and the Druggable Genome database (5). These databases provide synoptic metabolite data, pharmacogenomic data, adverse data on drugs and their primary or putative drug targets. drug response data, ADMET data, pharmacokinetic Since the appearance of these drug/drug–target databases, data, computed property data and chemical classi- other kinds of drug resources have emerged including fication data. DrugBank 3.0 also offers expanded PharmGKB (6), which specializes in pharmacogenetic database links, improved search tools for drug– and pharmacogenomic data, RxList (www.rxlist.com) drug and food–drug interaction, new resources for and DailyMed (7), which provide electronic versions of querying and viewing drug pathways and hundreds the FDA’s drug-product data sheets, ChEMBL (www. of new drug entries with detailed patent, pricing and ebi.ac.uk/chembl) which provides data on drug-like com- manufacturer data. These additions have been com- pounds and BindingDB (8), which contains quantitative plemented by enhancements to the quality and drug-binding constant data. The growing appetite for quantity of existing data, particularly with regard to web-accessible drug data has also led PubChem (7), *To whom correspondence should be addressed. Tel: +780 492 0383; Fax: +780 492 1071; Email: [email protected] The Author(s) 2010. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/ by-nc/2.5), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. D1036 Nucleic Acids Research, 2011, Vol. 39, Database issue KEGG (9), ChEBI (10) and ChemSpider (11) to add drugs field additions; (iii) expanded database linkages and (iv) enhanced data querying and viewing capabilities. and drug information to their usual offerings. All of these databases are outstanding resources, but as a general rule, most of them are quite ‘lightly’ annotated with only 10–15 data fields per drug entry. GROWTH AND ENHANCEMENT OF EXISTING In contrast to most other open-access drug databases, DATA DrugBank (12) is a ‘richly’ annotated resource. The first DrugBank has grown significantly in the past 5 years with version of DrugBank (released in 2006) contained nearly perhaps the most significant changes happening between 90 data fields per drug entry, with detailed information on release 2.0 and 3.0. This progressive data content expan- the nomenclature, ontology, chemistry, structure, sion is summarized in Table 1. As can be seen from this function, action, pharmacology, pharmacokinetics, me- table, going from version 2.0 to 3.0, there has been a 40% tabolism and pharmaceutical properties of both drugs increase in the number of data fields for each drug entry. and drug targets. Much of this data was acquired Likewise there has been a 130% increase in the number of through primary literature sources, checked by experts, computed structure parameters, an 80% increase in the edited and entered manually. The richness, uniqueness number of external database links, a 67% increase in and quality of the data in DrugBank has clearly hit a the number of experimental drugs, a 46% increase in the nerve with the research community. It is widely cited number of food–drug interactions, a 42% increase in (more than 400 citations), integrated into many interna- the total number of drug targets, a 20% increase in the tional databases (more than 20) and heavily used number of possible DrugBank queries, a 13% increase in (more than 4 million page visits/year) by pharmacists, the number of FDA-approved drug targets, a 12% physicians, researchers, educators and the general public. increase in the number of biotech and nutraceutical In an effort to keep up with the growing applications drugs and a 6% increase in the number of FDA- and far-ranging requests for this particular database, an approved small molecule drugs. updated version (DrugBank 2.0) was released in 2008 (13). In addition to significantly expanding the data content Since then, the amount of easily accessible or predictable in DrugBank, a major effort has been directed at improv- knowledge on drugs has grown considerably. So too has ing the quality of DrugBank’s existing data. Hundreds of the number of requests, suggested improvements and calls drug descriptions, mechanisms of action and pharmaco- for additional kinds of data to appear in DrugBank. Based logical summaries have been either re-written or on this user feedback we have spent the past 2 years expanded. Likewise, hundreds of new drug and drug– enhancing both the quantity and quality of DrugBank’s target references were collected, checked and added. content. We have also added to or improved upon a Similarly, extensive checks have been performed on all number of DrugBank’s querying and search functions. of DrugBank’s small molecule structures to confirm that The net result is a 40% increase in the number of data they exhibit the correct chirality and stereochemistry. In fields for each drug entry, a considerable expansion particular, we developed a custom structure-checking (>50%) in the number of drug–protein and food–drug program that used direct structure comparison (via a interactions, a massive increase in the information on Mol file) of each of DrugBank’s structures against the drug metabolites, drug ADMET (absorption, distribution, corresponding structures in other databases (PubChem, metabolism, excretion, toxicology) data and the addition ChEBI, ChemSpider, etc.). Any DrugBank structure that of hundreds of colorful, interactive, hand-drawn drug- did not match with the corresponding structure in one or action pathway diagrams. With these enhancements, we more of these external databases was flagged. A total of believe DrugBank has become a much more comprehen- 340 structures were identified with potential structural sive and accessible drug information resource. It has also errors or discrepancies. Each of these was assessed and/ become significantly more useful for a wide range of or corrected manually by a team of trained chemists. In ‘omics’ (i.e. pharmacogenomic, pharmacoproteomic, many cases the DrugBank structure was correct and the pharmacometabolomic, pharmacoeconomic) applications. external database structure was found to be in error, in A more detailed description of DrugBank 3.0 follows. other cases the DrugBank structure was determined to be in error and was subsequently corrected. In addition to completing extensive data integrity checks for all of DrugBank’s chemical data, the drug WHAT’S NEW IN DRUGBANK 3.0? target information in DrugBank 3.0 has been significantly Details relating to DrugBank’s overall design, general improved. Now most of DrugBank’s approved-drug querying capabilities, curation protocols, structure depic- targets are prioritized by relevance, with each target tion conventions, quality assurance and drug selection being classified by its primary mode of action. One criteria have been described previously (12,13). These mode-of-action category lists targets known to confer have largely remained the same between release 2.0 and the desired pharmacological effects, while the other lists 3.0. Here, we shall focus primarily on describing the targets with unknown or unintended pharmacological changes and enhancements made to the database and to effects (many of which account for side effects). In the annotation processes for release 3.0. More specifically, addition to our implementation of an improved target we will describe the: (i) growth and enhancements to classification scheme, DrugBank 3.0 now formally separ- DrugBank’s existing content and quality; (ii) new data ates drug-action targets from drug transporters, drug Nucleic Acids Research, 2011, Vol. 39, Database issue D1037 Table 1. Comparison between the coverage in DrugBank 1.0, 2.0 and DrugBank 3.0 Category 1.0 2.0 3.0 No. of data fields 88 108 148 No. of search types 8 12 16 No. of drug-action pathways 0 0 223 No. of drugs with metabolizing enzyme data 0 0 762 No. of drug metabolites 0 0 811 No. of drugs with drug transporter data 0 0 516 No. of SNP-associated drug effects 0 0 113 No. of drugs with patent/pricing/manufacturer data 0 0 1208 No. of food–drug interactions 0 714 1039 No. of drug–drug interactions 0 13 242 13 795 No. of ADMET parameters (Caco-2, LogS) 0 276 890 No. of QSAR parameters per drug 5 6 14 No. of FDA-approved small molecule drugs 841 1344 1424 No. of biotech drugs 113 123 132 No. of nutraceutical drugs 61 69 82 No. of withdrawn drugs 0 57 68 No. of illicit drugs 0 188 189 No. of experimental drugs 2894 3116 5210 Total No. of experimental and FDA small molecule drugs 3796 4774 6684 Total No. of experimental and FDA drugs 3909 4897 6816 No. of names/brands/synonyms 18 304 28 447 37 171 No. of approved-drug drug targets (unique) 524 1565 1768 No. of all drug targets (unique) 2133 3037 4326 No. of approved-drug enzymes/carriers (unique) 0 0 164 No. of all drug enzymes/carriers (unique) 0 0 169 No. of external database links 12 18 31 carriers and pro-drug conversion enzymes. Note that in listing of the new data fields appearing in DrugBank 3.0. DrugBank, carriers are considered separate from trans- The five areas where most of the new data has been added porters as carriers move drugs around the body, while relate to: (i) pharmacometabolomics; (ii) transporters move drugs into and around the cell. This pharmacoproteomics; (iii) pharmacogenomics; (iv) kind of target separation should make drug-target pharmacoeconomics and (v) computed structure studies somewhat easier and substantially more features. These additions are detailed below: informative. While many visible ‘front-end’ enhancements have been Pharmacometabolomics implemented, DrugBank’s back-end has also been signifi- cantly enhanced. In particular, all of DrugBank’s data has As the field of metabolomics has grown, so too has the been converted to an easily parsed XML format. This interest in understanding drug metabolism and in should make data downloads and the development of characterizing drug metabolites. Indeed, it is now data extraction routines much simpler and far faster for recognized that drug metabolites play an important role programmers and database developers. in understanding adverse drug effects, in determining therapeutic indices and in leading to secondary or off-target therapeutic effects. In an effort to make this NEW DATA FIELD ADDITIONS kind of metabolic or metabolomic information readily available we have manually compiled detailed phase I/II Each new release of DrugBank has been characterized by metabolic fate data for over 760 FDA-approved drugs. a significant increase in the number of new data fields The data, which was compiled from hundreds of journal compared to the previous release. DrugBank 3.0 is no articles, includes links to pathways, chemical structures, exception. Going from version 2.0 to 3.0, there has been HMDB (14) entries, reaction parameters such as a substantial increase in the number of data fields (going reaction type (inducer, inhibitor and substrate), K , and from 108 to 148). Many of these data field additions were V , and reaction class information for nearly 720 drugs. the results of specific requests by DrugBank users or arose max through consultation with members of the pharmaceutical In addition to these annotations and molecular descriptors research community. DrugBank 3.0 now includes drug for drug metabolism and drug metabolites, DrugBank 3.0 also provides classical ADMET information including pathway diagrams, drug transporter information, drug drug distribution, clearance and route of elimination carrier information, drug metabolite data, drug data. These new ‘metabolic’ data fields, when combined metabolizing enzyme data, QSAR data, chemical classifi- cation data, SNP-associated drug effects (available with the structural and biological information already through the GenoBrowse link) and drug patent/pricing/ stored in DrugBank, should offer an entirely new way of manufacturer data. Table 2 provides a more complete exploiting the data in DrugBank. D1038 Nucleic Acids Research, 2011, Vol. 39, Database issue Table 2. New data fields in DrugBank 3.0 targets. It also has data on 1188 coding SNPs and 8931 non-coding SNPs from known drug metabolizing Chemical kingdom ChEMBL link enzymes. SNP information can be accessed by clicking Chemical class Drug pathway on the ‘Show SNPs’ hyperlink listed beside either the Chemical substructures Drug pathway SMPDB ID Drug manufacturers Target actions (antagonist, agonist) metabolizing enzymes or the drug target SNP field. Drug packagers Target priority These SNP summary tables include: (i) the reference Drug prices Target pharmacological effect SNP ID, with a hyperlink to dbSNP (17); (ii) the allele (known/unknown/none) variants; (iii) the validation status; (iv) the chromosome Original patent date Enzyme actions (inhibitor, inducer, substrate) Original patent number Drug metabolite structure location and reference base position; (v) the functional Last patent expiry date Drug metabolite name class (synonymous, non-synonymous, untranslated, Last patent number Drug metabolite HMDB ID intron, exon); (vi) mRNA and protein accession links (if MMCD link Drug metabolite reaction type (e.g. oxidation) applicable); (vii) the reading frame (if applicable); (viii) the ChemSpider ID and Reaction K value amino acid change (if existent); (ix) the allele frequency as link NDC ID and link Reaction V value max measured in African, European and Asian populations (if DailyMed link Metabolizing enzyme references available) and (x) the sequence of the gene fragment with Drugs.com link Metabolizing enzyme priority the SNP highlighted in a red box. The purpose of these OMIM link Drug transporter name SNP tables is to allow DrugBank users to go directly from CDPD link Drug transporter actions (substrate, inhibitor, inducer) a drug of interest to a list of potential SNPs that may TTD link Route of elimination contribute to the reaction or response seen in a given STITCH link Volume of distribution patient or in a given population. BindingDB link Clearance In addition to this drug target SNP data, Drugbank 3.0 now includes two tables that provide much more explicit information on the relationship between drug responses/ reactions and gene variant or SNP data. These tables, Pharmacoproteomics which are accessible from the GenoBrowse submenu located on DrugBank’s Browse menu bar, are called It is often said that a picture is worth a thousand words. In SNP-FX (short for SNP-associated effects) and order to simplify DrugBank’s vast collection proteomic SNP-ADR (short for SNP-associated adverse drug reac- data, DrugBank 3.0 now includes nearly 230 richly tions). SNP-FX contains data on the drug, the interacting illustrated drug-action pathways. These pathways have protein(s), the ‘causal’ SNPs or genetic variants for that been designed to display the action of drugs on protein gene/protein, the therapeutic response or effects caused by targets or protein receptors. Using the visualization frame- the SNP-drug interaction (improved or diminished work developed for SMPDB (15), each DrugBank response, changed dosing requirements, etc.) and the pathway is ‘image-mapped’, with every drug structure associated references describing these effects in more being hyperlinked to the detailed descriptions contained detail. SNP-ADR follows a similar format to SNP-FX in DrugBank or HMDB and every protein or enzyme but the clinical responses are restricted only to adverse complex being hyperlinked to the detailed descriptions drug reactions (ADR). SNP-FX contains provided by UniProt (16). DrugBank’s drug-action literature-derived data on the therapeutic effects or thera- pathways are carefully hand-drawn and frequently peutic responses for more than 60 drug-polymorphism include information on the relevant organs, organelles, combinations, while SNP-ADR contains data on adverse subcellular compartments, protein targets, protein loca- reactions compiled from more than 50 tions and drug structures that describe the pharmacology drug-polymorphsim pairings. All of the data in these or mode of action for that drug. All of DrugBank’s tables is hyperlinked to drug entries from DrugBank, pathways images may be progressively expanded by protein data from UniProt, SNP data from dbSNP and clicking on the Zoom button located at the top and bibliographic data from PubMed. bottom of the image or the magnifying-glass icons in the Highlight/Analyzer box on the right of the image. At the Pharmacoeconomics top of each image is a pathway synopsis while at the bottom of each image is a list of relevant references. Perhaps the most important ‘omics’ discipline in the Over the coming 2–3 years it is expected that another pharmaceutical world is econ-‘omics’. Indeed, the selec- 1000–1200 drug pathways will be added to DrugBank’s tion of disease targets, the money spent on research and pathway inventory. the money recovered from sales are largely determined by economic factors. To facilitate research into these issues Pharmacogenomics DrugBank 3.0 has added data on drug patent dates (from The relationship between drugs, genes and genetic variants Canada and the United States), drug manufacturers, drug (SNPs) is central to the whole field of pharmacogenomics packagers, drug prices (from different jurisdictions) and and personalized medicine. In an effort to address these drug sales (where available). Coupled with other data rapidly growing needs, DrugBank 3.0 now contains a sig- already in DrugBank (such as ATC codes, indications, nificant amount of new pharmacogenomic information, side effects, structures, chemical classes) this information including data on 26 292 coding (exon) SNPs and 73 328 should enable more detailed studies on the relationship non-coding (intron) SNPs derived from known drug between drug prices and patent dates, the connection Nucleic Acids Research, 2011, Vol. 39, Database issue D1039 between drug prices and drug sales, the relationship list of links in the ‘About’ section of DrugBank. In between drug sales and disease targets, the link between addition to these external database links, DrugBank has a drug’s price and the drug’s side effects as well as the been reciprocally linked to several major resources relationship between drug manufacturers and disease including Wikipedia, UniProt (16), BioMOBY (25), target choices. It should also enable research into histor- PubChem (7), KEGG (9), PharmGKB (6), Drugs.com ical trends in drug target or disease target choices as well and ChemSpider (11). as long-term trends in the use or exploitation of certain drug motifs or structure classes (e.g. statins, tricyclic drugs). ENHANCED QUERYING AND VIEWING CAPABILITIES Computed structure data One of the key strengths for DrugBank has been its Computed structure parameters or descriptors are fre- support for a wide range of querying and visualization quently used in quantitative structure activity tools. These include 2D and 3D structure viewers, relationship (QSAR) studies to facilitate rational drug flexible text querying systems, structure searching/ design, drug screening and medicinal chemistry. These matching, sequence searching tools, data extraction tools computed structure parameters may also be used to ra- and easy-to-use browsers. For DrugBank 3.0 we have tionalize drug activities, tissue localization, adverse reac- made a number of improvements to the existing query tions and drug metabolism. While earlier versions of tools but are also introducing four new browsing or DrugBank had provided a nominal number of computed search tools. These include PathBrowse, GenoBrowse, structure descriptors (molecular weight, pKa, LogP, ClassBrowse, ReceptorBrowse and the Interax LogS), these were often not sufficient for detailed feature Interaction Search (Figure 1). We believe all five should analyses or comprehensive in silico comparisons. make the viewing and retrieval of information in DrugBank 3.0 now provides an additional set of nine DrugBank much easier. computed property descriptors including (i) the number PathBrowse was developed to facilitate the viewing and of H-bond acceptors; (ii) the number of H-bond donors; searching of DrugBank’s drug-action pathways. Each (iii) the number of freely rotating bonds; (iv) the index of hyperlinked, interactive pathway explains the mode of refraction; (v) the predicted boiling/melting point; (vi) the action of drugs at a molecular, cellular and/or physiologic- polar surface area; (vii) the molar refractivity; (viii) the al level. PathBrowse allows users to search for drugs by polarizability and (ix) the molecular density. These DrugBank ID, name or synonyms. It also supports the properties are displayed in a summary table with links search for drug targets, metabolizing enzymes, carriers to the SDF file containing these values. While more and transporters either by their name, UniProt ID or computed structure descriptors are certainly available, gene identifier. The results are displayed as a highlighted these represent the most frequently used descriptors and list of hits. Once a pathway is selected, users can inter- should allow DrugBank users to perform much more actively explore the pathway image, with compound or detailed computed structure queries, analyses and protein hits highlighted in the pathway image. This tight comparisons. integration between DrugBank and SMPDB should allow researchers to visualize the ‘big picture’ with respect to drugs and how they act or how they are processed in the EXPANDED DATABASE LINKAGES body. The two other browsing functions (ClassBrowse and GenoBrowse) are somewhat simpler in design and Because DrugBank was designed to cover a broad functionality than PathBrowse. ClassBrowse allows users spectrum of scientific disciplines it has always been exten- to search through or sort drugs by their chemical class or sively linked to many external databases. For instance, chemical taxonomy while GenoBrowse (which has already version 2.0 of DrugBank contained up to 18 database been described) allows users to browse through or explore hyperlinks in every DrugCard entry, including links to SNP-induced drug effects or drug reactions. KEGG (9), PubChem (7), ChEBI (10), PharmGKB (6), ReceptorBrowse allows users to search or sort through PDB (18), GenBank (19), DIN, RxList, PDRhealth, the protein targets, enzymes, carriers and transporters Wikipedia, ATC, UniProt (16), Pfam (20), dbSNP (17), (along with their function and target species information) GeneCards (21), GenAtlas (22), HGNC and PubMed. that are associated with each drug in DrugBank. DrugBank 3.0 now contains an average of 31 hyperlinks DrugBank contains one of the most complete, freely per DrugCard. These new links include numerous available sources of drug–drug and food–drug interaction compound-specific, spectral, pathway and disease data- data on the Internet today. Although this information has bases such as ChemSpider (11), HMDB (14), MMCD (23), SMPDB (15) and OMIM (24). We have also added been made available in each DrugCard from version 2.0 new links to several dedicated drug and pharmaceutical onwards, the data has not been easily searchable. The databases [DailyMed (7), Drugs.com, the National Drug ‘Interax’ Interaction Search was developed to allow Code identifier database and the Canadian Drug Product facile searching of drug and food interactions. Unlike Database] as well as a number of drug target databases, existing interaction search tools, Interax takes the such as the Therapeutic Target Database (TTD), STITCH process one step further by including transporter, target (4), BindingDB (8) and ChEMBL. These DrugCard and enzyme information in the search results. Several dif- hyperlinks are also complemented with a comprehensive ferent search types are supported by Interax. For instance, D1040 Nucleic Acids Research, 2011, Vol. 39, Database issue Figure 1. A screenshot montage of some of DrugBank 3.0’s new browser views (GenoBrowse, Interax, ClassBrowse, PathBrowse). standard drug–drug or food–drug interaction searches can functionality provides a unique method of searching and be performed, whereby a user inputs a list of drugs, exploring drug–drug interactions and should be of interest to pharmacists, pharmaceutical researchers, and the presses the ‘submit’ button and a list of drug and food general public. interactions are produced. Users can also input two lists of drugs and Interax will identify any interactions between the lists. Additionally, any interactions that may be target, CONCLUSION enzyme, carrier or transporter related (e.g. two drugs bind the same target) will be flagged with symbols representing DrugBank 3.0 contains a significant number of enhance- a target interaction, enzyme interaction, carrier interaction ments over its predecessor (DrugBank 2.0). As highlighted or transporter interaction. This comprehensive search throughout this article, numerous improvements have Nucleic Acids Research, 2011, Vol. 39, Database issue D1041 7. Wang,Y., Xiao,J., Suzek,T.O., Zhang,J., Wang,J. and Bryant,S.H. been made in the quantity, quality, depth and organiza- (2009) PubChem: a public information system for analyzing tion of the information provided. These include the bioactivities of small molecules. Nucleic Acids Res., 37(Database addition of new drugs, new targets, new data fields, new issue), W623–W633. links and new tools. DrugBank 3.0 now contains 8. Liu,T., Lin,Y., Wen,X., Jorissen,R.N. and Bilson,M.K. (2007) illustrated drug-action pathways, drug transporter data, BindingDB: a web-accessible database of experimentally determined protein-ligand binding affinities. Nucleic Acids Res., drug metabolite data, pharmacogenomic data, adverse 35(Database issue), D198–D201. drug response data, ADMET data, pharmacokinetic 9. Kanehisa,M., Goto,S., Furumichi,M., Tanabe,M. and data, extensive computed property data and chemical clas- Hirakawa,M. (2010) KEGG for representation and analysis of sification data. DrugBank 3.0 also offers expanded molecular networks involving diseases and drugs. database links, improved search tools for drug–drug and Nucleic Acids Res., 38(Database issue), D355–D360. 10. Brooksbank,C., Cameron,G. and Thornton,J. (2005) The food–drug interaction, new tools for searching and European Bioinformatics Institute’s data resources: towards viewing drug pathways and hundreds of new drug systems biology. Nucleic Acids Res., 33(Database issue), D46–D53. entries with detailed patent, pricing and manufacturer 11. Williams,A.J. (2008) Public chemical compound databases. Curr. data. These additions have been complemented by Opin. Drug Discov. Devel., 11, 393–404. enhancements to the quality and quantity of existing 12. Wishart,D.S., Knox,C., Guo,A.C., Shrivastava,S., Hassanali,M., Stothard,P., Chang,Z. and Woolsey,J. (2006) DrugBank: a data, particularly with regard to drug target, drug descrip- comprehensive resource for in silico drug discovery and tion and drug action data. With these enhancements exploration. Nucleic Acids Res., 34(Database issue), D668–D672. DrugBank 3.0 should be much more useful for a wider 13. Wishart,D.S., Knox,C., Guo,A.C., Cheng,D., Shrivastava,S., range of ‘omics’ applications. It is hoped that with more Tzur,D., Gautam,B. and Hassanali,M. (2008) DrugBank: a user feedback, DrugBank will continue to develop to fit knowledgebase for drugs, drug actions and drug targets. Nucleic Acids Res., 36(Database issue), D901–D906. the needs of its users and provide an increasingly useful, 14. Wishart,D.S., Knox,C., Guo,A.C., Eisner,R., Young,N., information-rich drug resource. Gautam,B., Hau,D.D., Psychogios,N., Bouatra,S., Mandal,R. et al. (2009) HMDB: a knowledgebase for the human meabolome. Nucleic Acids Res., 37(Database issue), D603–D610. ACKNOWLEDGEMENTS 15. Frolkis,A., Knox,C., Lim,E., Jewison,T., Law,V., Hau,D.D., Liu,P., Gautam,B., Ly,S., Guo,A.C. et al. (2010) SMPDB: the The authors are indebted to the many users of DrugBank small molecule pathway database. Nucleic Acids Res., 38(Database who have provided valuable feedback and suggestions. issue), D480–D487. 16. UniProt Consortium. (2009) The Universal Protein Resource (UniProt) 2009. Nucleic Acids Res., 37(Database issue), FUNDING D169–D174. 17. Sherry,S.T., Ward,M.H., Kholodov,M., Baker,J., Phan,L., Canadian Institutes of Health Research (CIHR); Genome Smigielski,E.M. and Sirotkin,K. (2001) dbSNP: the NCBI Alberta; Genome Canada; GenomeQuest Inc. Funding database of genetic variation. Nucleic Acids Res., 29, 308–311. for open access charge: CIHR. 18. Kouranov,A., Xie,L., de la Cruz,J., Chen,L., Westbrook,J., Bourne,P.E. and Berman,H.M. (2006) The RCSB PDB Conflict of interest statement. None declared. information portal for structural genomics. Nucleic Acids Res., 34(Database issue), D302–D305. 19. Benson,D.A., Karsch-Mizrachi,I., Lipman,D.J., Ostell,J. and Sayers,E.W. (2010) GenBank. Nucleic Acids Res., 38(Database REFERENCES issue), D46–D51. 1. Zhu,F., Han,G., Kumar,P., Liu,X., Ma,X., Wei,X., Huang,L., 20. Finn,R.D., Mistry,J., Tate,J., Coggill,P., Heger,A., Guo,Y., Han,L., Zheng,C. et al. (2010) Update of TTD: Polliington,J.E., Gavin,O.L., Gunasekaran,P., Ceric,G., therapeutic target database. Nucleic Acids Res., 38(Database Froslund,K. et al. (2010) The Pfam protein families database. issue), D787–D791. Nucleic Acids Res., 38(Database issue), D211–D222. 2. Gao,Z., Li,H., Zhang,H., Liu,X., Kang,L., Luo,X., Zhu,W., 21. Safran,M., Dalah,I., Alexander,J., Rosen,N., Iny Stein,T., Chen,K., Wang,X. and Jiang,H. (2008) PDTD: a web-accessible Shmoish,M., Nativ,N., Bahir,I., Doniger,T., Krug,H. et al. (2010) protein database for drug target identification. GeneCards Version 3: the human gene integrator. Database, doi: BMC Bioinformatics, 9, 104. 10.1093/baq020; [7 August 2010, epub ahead of print]. 3. Kuhn,M., Szklarczyk,D., Franceschini,A., Campillos,M., von 22. Roux-Rouquie,M., Chavet,M.L., Munnich,A. and Frezal,J. (1999) Mering,C., Jensen,L.J., Beyer,A. and Bork,P. (2010) STITCH 2: Human genes involvedin chromatin remodeling in transcription an interaction network database for small molecules and proteins. initiation and associated diseases: an overview using the Nucleic Acids Res., 38(Database issue), D552–D556. GENATLAS database. Mol. Genet. Metab., 62, 261–277. 4. Gunther,S., Kun,M., Dunkel,M., Camillos,M., Senger,C., 23. Markley,J.L., Anderson,M.E., Cui,Q., Eghbalnia,H.R., Lewis,I.A., Petsalaki,E., Ahmed,J., Urdiales,E.G., Gewiess,A., Jensen,L.J. Hegeman,A.D., Li,J., Schulte,C.F., Sussman,M.R., Westler,W.M. et al. (2008) SuperTarget and Matador: resources for exploring et al. (2007) New bioinformatics resources for metabolomics. Pac. drug-target relationships. Nucleic Acids Res., 36(Database issue), Symp. Biocomput., 12, 157–168. D919–D922. 24. Hamosh,A., Scott,A.F., Amberger,J.S., Bocchini,C.A. and 5. Russ,A.P. and Lampel,S. (2005) The druggable genome: an McKusick,V.A. (2005) Online Mendelian Inheritance in Man update. Drug Discov. Today, 10, 1607–1610. (OMIM), a knowledgebase of human genes and genetic disorders. 6. Hernandez-Boussard,T., Whirl-Carrillo,M., Hebert,J.M., Gong,L., Nucleic Acids Res., 33(Database issue), D514–D517. Owen,R., Gong,M., Gor,W., Liu,F., Truong,C., Whaley,R. et al. 25. Kawas,E., Senger,M. and Wilkinson,M.D. (2006) BioMoby (2008) The pharmacogenetics and pharmacogenomics knowledge extensions to the Taverna workflow management and enactment base: accentuating the knowledge. Nucleic Acids Res., 36(Database software. BMC Bioinformatics, 7, 523. issue), D913–D918.

Journal

Nucleic Acids Research – Oxford University Press

Published: Jan 8, 2011

There are no references for this article.