DrugBank: A General Resource for Pharmaceutical and Pharmacological Research

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1 DOI: /mcpharmacol Molecular and Cellular Pharmacology DrugBank: A General Resource for Pharmaceutical and Pharmacological Research David S. Wishart Departments of Computing Science 1 and Biological Sciences, University of Alberta, Edmonton, AB, Canada T6G 2E8, National Institute for Nanotechnology, Saskatchewan Drive, Edmonton, AB, Canada T6G 2M9 Abstract DrugBank ( is an open-access, electronic drug database that integrates detailed drug data with comprehensive drug target and drug action information. First released in 2006 and updated on a regular basis every 6 to 12 months, DrugBank has become a very popular resource for medicinal chemists, bioinformaticians, cheminformaticians, pharmacists and physicians. Part of its appeal seems to lie in its rich data content along with the fact that all of its data is freely downloadable. Each drug entry in DrugBank contains more than 100 data fields with approximately half of the information being devoted to chemical or physico-chemical data about the drug and the other half devoted to molecular pharmacological, pharmacogenomic and biological data. DrugBank also catalogues information on food-drug interactions, drug-drug interactions, drug metabolism, drug metabolizing enzymes, adverse drug reactions and drug-target single nucleotide polymorphisms (SNPs). In addition to its rich data content, DrugBank also offers a number of powerful search utilities including chemical structure similarity searches, sequence similarity searches and advanced text queries. These search routines offer users a convenient route to explore complex pharmacological and pharmacogenomic questions in silico. This paper provides a general overview of DrugBank and describes how it can serve as a general resource for pharmacological, pharmacogenomic and pharmaceutical research. Keywords: Database; Bioinformatics; Drug; Pharmacology; Pharmacogenomics Received 10/19/09; accepted 12/4/09 Correspondence: David Wishart, Ph.D., Departments of Computing Science and Biological Sciences, University of Alberta, Edmonton, Alberta T6G 2E8, Canada. Tel , Fax david.wishart@ualberta.ca Electronic Databases in the Life Sciences Over the past 10 years, the life sciences (i.e. biology, medicine, pharmaceutical research) have evolved from being largely low-throughput, observational disciplines to primarily highthroughput, data-driven disciplines. In other words, life science is becoming a data science. Thanks to advances in DNA sequencing, medical imaging, robotic sample handling and high-throughput screening, it is possible to generate as much data in a day-long experiment as it might have taken for an entire scientific career. For instance, a single 8-hour sequencing run on a next generation DNA pyrosequencer can generate enough sequence data to fill a 1000 page book (1,2). The resulting genome sequence can now be automatically annotated in a few hours yielding an enormous volume of information that could easily occupy 10 large telephone books (3,4). Our capacity to generate gigabytes of information on a daily basis is having a profound impact on the way that scientific information is being disseminated or delivered. While most scientific data is still presented in scientific journals and most high-level scientific knowledge is still published in textbooks, it is becoming increasingly obvious that the paper publishing industry cannot keep up with the pace of scientific advancement and the quantity of data that the scientific community would like to publish. Fortunately the World Wide Web has come to the rescue. The web makes it is possible to publish and disseminate huge quantities of information quickly and inexpensively. Not only has the web helped to save scientific publishing, it has also led to the development of a new and very important kind of scientific archive: the electronic database. Electronic databases are web-accessible archives that contain scientifically important data that is either too voluminous to publish in a book or journal or in a format that is incompatible with 25

2 26 DrugBank and Pharmacological Research Figure 1. A screenshot showing the DrugBank home page with the main pull-down menus. Menu tabs with additional submenus are viewable or selectable by pointing the mouse over the down-arrow on the right of the menu name. paper publication. Electronic databases such as GenBank (5), the Protein Data Bank (6) or PubMed allow information to be continuously updated through the contributions of thousands of scientists or dozens of curators who continuously upload and deposit data into these resources. Electronic databases also allow their data to be searched, accessed or displayed in ways that simply could not be done through a paper journal or a leather-bound book. Indeed, the emergence of electronic, webaccessible databases has to be considered one of the more significant developments in the field of life sciences. Because of their utility and versatility, electronic databases are also becoming more widespread in the fields of pharmaceutical and pharmacological sciences. In particular, two types of electronic drug databases have started to emerge over the past five years: 1) clinically oriented drug databases and 2) chemically oriented drug databases. Examples of some of the better-known clinically oriented drug databases include PharmGKB (7) and RxList (8). These resources typically offer very detailed clinical information (i.e. their formulation, metabolism and indications) about selected drugs with their data content being targeted more towards pharmacists, physicians or consumers. Examples of chemically oriented drug databases include the TTD (9), KEGG (10), the Druggable Genome database (11), SuperTarget and Matador (12). These kinds of

3 DrugBank and Pharmacological Research 27 Figure 2. A screenshot showing the results of querying the DrugBank database using the Search text query tool. The input query was tricyclic. databases provide synoptic data (5-10 data fields per entry) about the nomenclature, structure and/or physical properties of small molecule drugs and, in some cases, their drug targets. As a general rule, chemically oriented drug databases tend to appeal to medicinal chemists, biochemists and molecular biologists. Unlike most other electronic drug databases, DrugBank (13) was specifically designed to serve as both a clinically oriented drug database and a chemically oriented database. Furthermore, DrugBank was structured to be much more comprehensive in terms of both the breadth and depth of its coverage. As a clinically oriented drug database, DrugBank provides detailed, up-to-date information on the pharmacology or most drugs, their adverse reactions, indications, contraindications, pharmacogenomic data, drug-drug interactions, food-drug interactions, drug metabolism and drug formulation. As a chemically oriented drug database, DrugBank provides detailed physico-chemical data on thousands of drug compounds as well as extensive molecular information on their corresponding drug targets. DrugBank is also able to provide many built-in tools for viewing, sorting, searching and extracting text, image, sequence or structure data. Because of its dual nature, and because of its rich data content, DrugBank has proven to be a particularly popular resource among medicinal chemists, bioinformaticians, cheminformaticians, pharmacists and physicians. Indeed, over the past three years

4 28 DrugBank and Pharmacological Research Figure 3. A screenshot showing how DrugBank will suggest alternate spellings of drug names if an incorrect spelling is used. In this case a user typed Asprin and DrugBank suggested Aspirin. DrugBank has been used in more than 200 published studies to facilitate in silico drug target discovery, drug design, drug docking, drug metabolism prediction, drug interaction prediction, and general pharmaceutical education. Despite its popularity with the informatics community, DrugBank (as well as other electronic drug databases) still seems to be largely unknown among the molecular pharmacology and pharmaceutical research community. The purpose of this review is to help introduce DrugBank to this particular group of researchers. It is also intended to highlight some of the more useful features that have been recently added to DrugBank and to show how this very versatile database can be used to facilitate a wide range of pharmacological and pharmaceutical research. DrugBank Content First released in 2006 (13) and then updated again in 2008 (14), DrugBank ( is a comprehensive, fully searchable electronic database that was specifically designed to link sequence, structure and mechanistic data about drug molecules with sequence, structure and mechanistic data about their drug targets. Because most drugs are chemicals and most drug targets are proteins, DrugBank was one of the first electronic databases to combine cheminformatic (i.e. chemical) tools and data with bioinformatic (i.e. molecular biological) tools and data. Indeed, the explicit linkage between drugs and drug targets is one of the main features that has made DrugBank particularly popular. Likewise the presentation of drug and drug target data in synoptic DrugCards (in anology to library cards or study flash-cards) has helped make this database particularly easy to view and navigate. As of October 2009, DrugBank contained detailed information on 1485 FDA-approved drugs corresponding to 28,447 brand names and synonyms. This collection includes 1286 synthetic small molecule drugs, 128 biotech drugs and 71 nutraceutical drugs or supplements. DrugBank also contains information on the 1678 different targets (protein, lipid or DNA molecules) and metabolizing enzymes with which these drugs interact. Additionally, the database maintains data on 188 illicit drugs (i.e. those legally banned or selectively banned in most developed nations) and 64 withdrawn drugs (those removed from the market due to safety concerns). Chemical, pharmaceutical and biological information about these classes of

5 DrugBank and Pharmacological Research 29 Figure 4. A screenshot of a DrugBank DrugCard for the drug Protriptyline. drugs is extremely important, not only in understanding their adverse reactions, but also in being able to predict whether a new drug entity may have unexpected chemical or functional similarities to a dangerous or highly addictive drug. To facilitate structural and functional comparisons to experimental or investigational drugs, DrugBank also maintains a collection of 3243 experimental or unapproved drugs (or druglike) compounds, which is primarily derived from the PDB s Ligand Expo database. Each DrugCard (a specific drug entry) in DrugBank contains nearly 110 data fields. A listing of these data fields is provided in Table 1. Approximately one half of the data fields are concerned with chemical or physico-chemical data and the other half are devoted to molecular pharmacological, pharmacogenomic and biological data. A number of the physico-chemical parameters (such as 2D coordinates, 3D coordinates, LogP and molecular weight) are calculated or predicted, while other parameters, such as melting points, LogS and Caco-2 permeability values were obtained from experimental values reported in the literature. A total of 194 experimental LogS values and 82 experimental Caco-2 permeability values are currently in DrugBank. These values are particularly useful for computational ADMET (Absorption, Distribution, Metabolism, Excretion, Toxicty) prediction. Additionally 714 food-drug interactions and 13,242 drug-drug interactions have been compiled and entered into DrugBank. This interaction information is obviously particularly useful for pharmacists, physicians and patients. The

6 30 DrugBank and Pharmacological Research Table 1. A Summary of the Main DrugBank Data Fields. Database Version Water Solubility (Pred) Organisms Affected DrugCard Creation Date State Phase 1 Metabolizing Enz. DrugCard Update Date LogP (Exp) Phase 1 Enzyme Sequences DrugBank Accession # LogP (Pred) Phase 1 Enzyme SProt IDs Old DrugBank Accession # LogS (Exp) Phase 1 Enzyme GeneName Common or Generic Name Caco2 Permeability (Exp) Drug Targets (Names) Drug Type pka/isoelectric Point Drug Target (1) Name Drug Description Mass Spectrum Drug Target Synonyms Drug Synonyms MOL File (Image) Drug Target Gene Name Drug Brand Names MOL File (Text) Drug Target Prot. Seq. Drug Brand Mixtures SDF File (Text) Drug Target # Residues Chemical or IUPAC Name PDB File (Text) Drug Target MW Chemical Structure PDB File (Image) Drug Target pi CAS Registry # PDB File (Exp) Drug Target GO Class InChi Identifier PDB File (Exp-Image) Drug Target Functions KEGG Drug ID Isomeric SMILES Drug Target Reaction KEGG Compound ID Canonical SMILES Drug Target Pfam Function PubChem Compound ID Drug Category Drug Target Signal Seqs PubChem Substance ID ATC Codes Drug Target Transmemb. ChEBI ID AHFS Codes Drug Target Essentiality PharmGKB ID Indication Drug Target GenBank ID HET (PDB) ID Pharmacology Drug Target SwissProt ID GenBank ID (if Biotech) Mechanims of Action Drug Target SProt Name Drug ID Number (DIN) Absoprtion Drug Target PDB ID RxList Link Toxicity Drug Target PDB (Text) PDRhealth Link Biotransformation Drug Target PDB (Image) Wikipedia Link Half Life Drug Target Cell Location FDA Label Dosage Forms Drug Target Gene Sequence MSDS Link Patient Information Drug Target GenBank ID Synthesis Reference Contraindications Drug Target GeneCard ID Average Molecular Weight Interactions Drug Target HGNC ID Monoisotopic Weight Drug-Drug Interactions Drug Target Locus Melting Point Food-Drug Interactions Drug Target SNPs Water Solubility (Exp) Drug References Drug Target References entire DrugBank database, including text, sequence, structure and image data occupies nearly 16 Gigabytes of data. DrugBank updates are typically released every 6 months. The updates include information on newly approved drugs, corrections or updates on old drugs/drug targets, the addition of new data fields and enhancements or improvements to DrugBank s interface or search utilities. These updates, as well as nearly all of the database content, are freely available in downloadable flat files. This accessibility has greatly facilitated a number of large-scale in silico studies where DrugBank has been used to help in the prediction of drug targets (15), on the molecular characterization of pathological pathways (16) and on the global characterization of geneexpression and drug-response data (17). Navigating Through DrugBank DrugBank is not simply a data repository. It is also equipped with many software tools and a variety of customized features for viewing, sorting, querying and extracting drug or drug target data. These querying tools include a number of higher-level database searching functions such as a local BLAST (18) sequence search (SeqSearch) that supports both single and multiple protein sequence queries (for drug target searching), a boolean text search (TextSearch) for sophisticated text searching and querying, a chemical structure search utility (ChemQuery) for structure matching and structure-based querying as well as a relational data extraction tool (Data Extractor) for performing complex queries. The BLAST search (SeqSearch) is particularly useful for drug discovery applications as it can potentially allow users to quickly and simply identify drug leads from newly sequenced pathogens. Specifically, a new sequence, a group of sequences or even an entire proteome can be searched against

7 DrugBank and Pharmacological Research 31 Figure 5. A screenshot showing how the KEGG hyperlink in a DrugBank DrugCard links to the KEGG database. DrugBank s database of known drug target sequences by pasting the FASTA formatted sequence (or sequences) into the SeqSearch query box and pressing the submit button. A significant hit can reveal the name(s) or chemical structure(s) of potential drug leads that may act on that query protein (or proteome). The structure similarity search tool (ChemQuery) can be used in a similar manner to SeqSearch. For instance, users may sketch a chemical structure or paste a SMILES string (SMILES strings are simple text-string representations or shorthand for describing chemical structures see ref. 19) of a possible drug lead or a drug that appears to be causing an adverse reaction into the ChemQuery window. After submitting the query, the database launches a structure similarity search that looks for common substructures from the query compound that match DrugBank s database of known drug or drug-like compounds. High scoring hits are presented in a tabular format with hyperlinks to the corresponding DrugCards. The ChemQuery tool allows users to quickly determine whether their compound of interest acts on the desired protein target or whether the compound of interest may unexpected interact with unintended protein targets. DrugBank also provides a number of general browsing tools for exploring the database as well as several specialized browsing tools, such as PharmaBrowse and GenoBrowse for more specific tasks. For instance, PharmaBrowse is designed to address the needs of pharmacists, physicians and medicinal chemists who tend to think of drugs in clusters of indications or drug classes. This particular browsing tool provides navigation hyperlinks to more than 70 drug classes, which in turn list the FDA-approved drugs associated with the drugs. Each drug name is then linked to its

8 32 DrugBank and Pharmacological Research Figure 6. A screenshot of a drug structure (Protriptyline) as displayed by pressing the View 2D Structure button on the Protriptyline DrugCard. respective DrugCard. GenoBrowse, on the other hand, is specifically designed to address the needs of pharmacogeneticists or those specialists interested in the relationships between single nucleotide polymorphisms (SNPs) and drug activity. This browsing tool provides navigation hyperlinks to more than 60 different drugs, which in turn list the target genes, SNPs and the physiological effects associated with these drugs. A more detailed description of the GenoBrowse tool is given later. An Illustrated Tour of DrugBank While it is impossible to cover all of DrugBank s content and features of these in this short review, a brief illustrated tour of the database may help the reader understand a little bit more about the database and its capabilities. As with any online tool, DrugBank has a home page with a hyperlinked blue menu bar located near the top of the page with the 6 clickable titles Home, Browse, Search, About, Download, Contact Us (Figure 1). The Browse, Search and About menu tabs contain additional submenus that are accessible/viewable by dragging the mouse pointer over the main menu name. Under Browse, there are three submenus for DrugBank Browse, PharmaBrowse and GenoBrowse. Under Search there are four submenus for ChemQuery, TextQuery, SeqSearch and Data Extractor. Under About, submenus for Details, Citing DrugBank, Release Notes, What s New, Statistics, Source Data, Data Field Explanations and Other Databases are provided. Clicking any of the menu or submenu names will launch a new window with the appropriate function or selected view. DrugBank s

9 DrugBank and Pharmacological Research 33 blue menu bar, which appears near the top of every DrugBank webpage, allows users to easily navigate to the different browsing and search utilities in the database. Below this menu bar is a text box with the phrase Search. This text search utility, which the most commonly used search feature in DrugBank, is also displayed near the top of nearly every DrugBank webpage. Essentially all text (or sequence) queries in DrugBank are typically done by typing or pasting text into the text-boxes and activated by pressing a Search or Submit button. For instance, if one typed tricylic into the Search text box, then clicked on the All Text Fields check box and then pressed the Search button, a 4-column table should appear within a few seconds consisting of a list of almost all known tricyclic antidepressant drugs as well as other tricyclic molecules (Figure 2). The first column displays the DrugBank accession number (which is hyperlinked), the second column displays the drug s common name, the third column displays the chemical formula and the fourth column displays the molecular weight. Directly below this data, the query word is also highlighted in the selected DrugCard field(s) from which it was retrieved. Because the spelling of many drug names, chemical compound names and protein names is often difficult or non-intuitive, DrugBank also supports an intelligent text search where alternative spellings to mis-spelled or incompletely entered names are automatically provided. Figure 3 shows an example of a user entering the word Asprin (a common misspelling the drug Aspirin ) and DrugBank suggesting the correct spelling for this drug. Clicking on the hyperlinked drug name or on a DrugCard button will launch a new table containing a more detailed description of the drug of interest (Figure 4). Each DrugCard contains two columns, with one column corresponding to data field names or titles (given on the left side with a blue background) and the other corresponding to drugspecific descriptors (given on the right, with a white background). The data fields follow a very specific order with drug nomenclature being given first followed by physical property data, then chemical structural data, pharmacological data and finally drug target or target protein data. If a drug has more than one biomolecular target, the protein and genetic data fields are repeated for each protein target. In addition to providing comprehensive numeric, sequence and textual data, each DrugCard also contains hyperlinks to other databases, abstracts, digital images, and interactive applets for viewing molecular structures. These hyperlinks can be activated by simply clicking the appropriate hyperlinked word, image or button. For instance clicking on the KEGG Compound ID hyperlink will launch a new window to the KEGG web page for the drug of interest (see Figure 5). To view a 2D image of a drug of interest one simply scrolls down to the data field called MOL File Image and clicks on the hyperlinked button called View 2D Structure. This launches ChemAxon s MarvinView Structure Viewing Java applet. After a few seconds an image of the drug should appear in the applet window (Figure 6). This structure display applet allows the user to interactively view, rotate, edit, export or zoom into the structure. The editing/exporting functions can be activated by double clicking on the structure image itself (this launches a separate, editable view) or by clicking on the right mouse button (which activates control menus superimposed over the image). A 3D structure of a drug of interest can also be viewed by clicking on the hyperlinked button View 3D Structure contained in the PDB File Calculated Image field. Right-clicking the mouse or double clicking the image will create a similar list of editing and viewing functions as seen for the View 2D Structure. Scrolling further down any given DrugCard will reveal many other data fields containing detailed pharmaceutical, pharmacological and clinical data (Drug Category, Indication, Pharmacology, Mechanism of Action, Contraindications, Absorption, Toxicity, Half Life, Interactions, etc.) as well as hyperlinks to different online Drug References (RxList, PDRhealth, Wikipedia). Scrolling even further down one will eventually see a field labeled Phase 1 Metabolizing Enzyme followed shortly thereafter by a field separator titled Drug Target 1. This enzymatic and drug target data marks the beginning of the molecular biological data for this drug. As can be seen from Table 1, the biological data about most drug targets and drug metabolizing enzymes is quite extensive. For drug targets, this includes information on the names, sequences (DNA and protein), physical properties, function(s) of the protein as well as any reactions or pathways that it is known to participate in. It also includes multiple database links, chromosomal/locus information and SNP data. For drug metabolizing enzymes DrugBank provides information on their names, sequences and associated SNPs. This molecular

10 34 DrugBank and Pharmacological Research Figure 7. A screenshot of the drug-action pathway for rosuvastatin. biological information is presented both in terms of protein information and genetic information. The protein-based information is particularly useful to protein chemists, structural biologists and medicinal chemists, while the genetic information is most useful to pharmacogeneticists and pharmacogenomics specialists. DrugBank For Pharmacological Research In addition to its general utility as a general drug resource for pharmaceutical sciences, DrugBank also contains several tables, pathway illustrations, data fields or data types that are particularly useful for pharmacological research. As of October 2009, DrugBank now includes links to 168 richly illustrated drug-action pathways (Figure 7). Each pathway is image-mapped, meaning that specific images are hyperlinked to another web page that can be accessed by clicking on the image of interest. For instance, each drug structure is hyperlinked to the detailed descriptions contained in DrugBank and each protein or enzyme complex is hyperlinked to the detailed descriptions provided by UniProt (20). DrugBank s drug-action pathways are carefully hand-drawn and frequently include information on the relevant organs, organelles, subcellular compartments, protein targets, protein locations and drug structures that describe the pharmacology or mode of action for that drug. All of DrugBank s pathways images may be progressively expanded by clicking on the Zoom button located at the top and bottom of the image or the magnifying-glass icons in the Highlight/Analyzer box on the right of the image. A pathway legend link is also available above the Zoom button. At the top of each image is pathway synopsis while at the bottom of each image is a list of relevant references. On the right of each pathway image is a grey-green Highlight/Analyzer tool with a list of the key metabolites/drugs and enzymes/proteins found in the pathway. Checking on selected items when in the Highlight mode will cause the corresponding drug or protein in the pathway image to be highlighted with a red box. Entering concentration or relative expression values (arbitrary units) beside drug or protein names, when in the Analyzer mode, will cause the corresponding drugs or proteins to be highlighted with differing shades of green or red to illustrate increased or

11 DrugBank and Pharmacological Research 35 decreased concentrations. While only about 1/8 of all drugs in DrugBank currently have pathway diagrams, it is expected that by 2011, most of the drugs in the database will have accompanying illustrations. In addition to these drug pathway images, DrugBank also includes synoptic descriptions of nearly every FDA-approved drug s Pharmacology as well as its Mechanism of Action, Contraindications, Toxicity, Phase I Metabolizing Enzymes (name, protein sequence and SNPs), and associated Drug Targets (names, protein sequence, DNA sequence, chromosome location, locus number and SNPs). The information contained in DrugBank s Pharmacology, Mechanism of Action, Contraindications and Toxicity fields often includes details about any known adverse reactions. This may include descriptions of known phase I or phase II enzyme interactions, alternate metabolic routes or the existence of secondary drug targets. Secondary drug targets represent proteins (or other macromolecules) that are different than the primary target for which the drug was initially designed or targeted towards. Some drugs may have five or more targets, of which only one might be relevant to treating the disease. DrugBank uses a relatively liberal interpretation of drug targets in order to help identify these secondary drug targets. That is, the database curators define a drug target as any macromolecule identified in the literature that binds, transports or transforms a drug. The binding or transformation of a drug by a secondary drug target or an off-target protein is one of the most common causes for unwanted side effects or adverse drug reactions (ADRs) (21). On the other hand, there are a few cases where a secondary drug target can lead to synergestic effects that may enhance the potency of a drug (22). By providing a fairly comprehensive listing of secondary drug targets (along with their SNP information and other genetic data), DrugBank is potentially able to provide additional insight into the underlying causes of a patient s response to a given drug. DrugBank and Pharmacogenomics A growing trend in pharmacological research has been to explore the connections between genes, gene expression and gene mutations with drug activity (i.e. pharmacogenomics). DrugBank s rich content of drug-target sequence and drug-target SNP data potentially makes it an ideal resource for pharmacogenomics researchers. In particular, DrugBank contains detailed summary tables about all of the SNPs for each of the drug targets or drug metabolizing enzymes that have been characterized by various SNP typing efforts, such as the SNP Consortium (23) and HapMap (24). Currently DrugBank contains information on 26,292 coding (exon) SNPs and 73,328 non-coding (intron) SNPs derived from known drug targets. It also has data on 1188 coding SNPs and 8931 non-coding SNPs from known drug metabolizing enzymes. An in-house program, called SNP-Updater, is run prior to each semi-annual DrugBank release to assemble and format DrugBank s SNP data. SNP-Updater compiles mapping, validation and population frequency data from dbsnp (25) and HapMap corresponding to every intron and exon for each of the human drug targets and metabolizing enzymes in DrugBank. The SNP-Updater program then reformats the data into more easily viewed synoptic tables. By clicking on the Show SNPs hyperlink listed beside either the metabolizing enzymes or the drug target SNP field, the SNP summary table can be viewed. These tables include: 1) the reference SNP ID (with a hyperlink to dbsnp); 2) the allele variants; 3) the validation status; 4) the chromosome location and reference base position; 5) the functional class (synonymous, non-synonymous, untranslated, intron, exon); 6) mrna and protein accession links (if applicable); 7) the reading frame (if applicable); 8) the amino acid change (if existent); 9) the allele frequency as measured in African, European and Asian populations (if available) and 10) the sequence of the gene fragment with the SNP highlighted in a red box. The purpose of these SNP tables is to allow one to go directly from a drug of interest to a list of potential SNPs that may contribute to the reaction or response seen in a given patient or in a given population. In particular, these SNP lists may serve as a hypothesis generators that allow SNP or gene characterization studies to be somewhat more focused or targeted. At the same time, DrugBank s SNP tables may also be used to interpret the results of SNP chip or AmpliChip CYP450 microarray tests (26) for those patients showing an unusual response to a given drug. By comparing the experimentally obtained SNP results to those listed in DrugBank for that drug (and its drug targets) it may be possible to ascertain which polymorphism for which drug target or drug metabolizing enzyme may be contributing to an unusual drug response. Obviously these database-derived SNP suggestions may require additional experimental validation to prove their causal association.

12 36 DrugBank and Pharmacological Research Figure 8. A screenshot of DrugBank s SNP-ADR table showing the information contained on drugs, genes, SNPs and adverse drug reactions. As part of its July 1, 2008 release, Drugbank added two tables that provide much more explicit information on the relationship between drug responses/reactions and gene variant or SNP data. The two tables, which are accessible from the GenoBrowse submenu located on DrugBank s Browse menu bar, are called SNP-FX (short for SNP-associated effects) and SNP-ADR (short for SNP-associated adverse drug reactions). SNP-FX contains data on the drug, the interacting protein(s), the causal SNPs or genetic variants for that gene/protein, the therapeutic response or effects caused by the SNP-drug interaction (improved or diminished response, changed dosing requirements, etc.) and the associated references describing these effects in more detail. SNP-ADR follows a similar format to SNP-FX but the clinical responses are restricted only to adverse drug reactions (ADR). SNP-FX contains literature-derived data on the therapeutic effects or therapeutic responses for more than 70 drug-polymorphism combinations, while SNP-ADR contains data on adverse reactions

13 DrugBank and Pharmacological Research 37 compiled from more than 50 drug-polymorphsim pairings. All of the data in these tables is hyperlinked to drug entries from DrugBank, protein data from UniProt, SNP data from dbsnp and bibliographic data from PubMed. A screen shot of the SNP-ADR table is shown in Figure 8. As can be seen from the figure, these tables provide consolidated, detailed and easily accessed information that clearly identifies those SNPs that are known to affect a given drug s efficacy, toxicity or metabolism. For instance, in the case of 5- fluoruracil (a common anti-cancer drug) it can be seen that polymorphisms in no less than five different proteins are responsible for a range of hematological adverse reactions. Because of the relatively small number of SNP-drug associations that have been compiled so far, these tables do not have the extensive searching and sorting tools found in many of DrugBank s other tables or views. However, with the number of reported SNP-drug interactions rapidly growing and with the interest of pharmacogenomics and SNP-typing increasing, it can be expected that upcoming releases of DrugBank will contain additional database linkages, improved search tools and much more data on those SNPs, copy number variants and mutations that have been convincingly proven to affect a given drug s efficacy, toxicity or metabolism. Conclusions This overview was intended to provide readers with a brief introduction to DrugBank and to highlight some of the newer or more unique features in this very versatile electronic drug database. In describing DrugBank s contents and software tools, a number of potential pharmaceutical, pharmacological or pharmacogenomic applications have also been suggested. These include the use of BLAST sequence queries to discover potentially druggable targets for newly sequenced pathogens, the use of drug similarity searches to identify possible mechanisms or possible targets for newly synthesized or newly isolated compounds and the use of DrugBank s SNP viewers and browsers to explore the connection between an individual s genotype and an adverse drug reaction. Beyond these applications, DrugBank can also be used in such diverse applications as the construction of text mining dictionaries (27), in the characterization of pathological pathways (16) and in the global characterization of gene-expression and drugresponse data (17). DrugBank s rich data content, its open accessibility along with its extensive search, query and retrieval functions have all been designed to facilitate research and to offer users a convenient route to explore complex pharmacological and pharmacogenomic questions in silico. Acknowledgement The author would like to acknowledge the continuing efforts of the DrugBank annotation team as well as the financial support of GenomeQuest Inc. and the Canadian Institutes for Health Research (CIHR) for keeping DrugBank freely accessible. Conflicts of Interest No potential conflicts of interest to disclose. References 1. Moore MJ, Dhingra A, Soltis PS, et al. Rapid and accurate pyrosequencing of angiosperm plastid genomes. BMC Plant Biol 2006;6: Mao C, Evans C, Jensen RV, Sobral BW. Identification of new genes in Sinorhizobium meliloti using the Genome Sequencer FLX system. BMC Microbiol 2008;8: Stothard P, Wishart DS. Automated bacterial genome analysis and annotation. Curr Opin Microbiol 2006;9: Van Domselaar GH, Stothard P, Shrivastava S, et al. BASys: a web server for automated bacterial genome annotation. Nucleic Acids Res 2005;33 (Web Server issue):w Benson DA, Karsch-Mizrachi I, Lipman DJ, Ostell J, Sayers EW. GenBank. Nucleic Acids Res 2009;37 (Database issue):d Kouranov A, Xie L, de la Cruz J, et al. The RCSB PDB information portal for structural genomics. Nucleic Acids Res 2006;34 (Database issue):d Hodge AE, Altman RB, Klein TE. The PharmGKB: integration, aggregation, and annotation of pharmacogenomic data and knowledge. Clin Pharmacol Ther 2007;81: Hatfield CL, May SK, Markoff JS. Quality of consumer drug information provided by four Web sites. Am J Health Syst Pharm 1999;56: Chen X, Ji ZL, Chen YZ. TTD: Therapeutic Target Database. Nucleic Acids Res 2002;30: Okuda S, Yamada T, Hamajima M, et al. KEGG Atlas mapping for global analysis of metabolic pathways. Nucleic Acids Res 2008;36(Web Server issue):w Russ AP, Lampel S. The druggable genome: an update. Drug Discov Today 2005;10: Günther S, Kuhn M, Dunkel M, et al. SuperTarget and Matador: resources for exploring drug-target relationships. Nucleic Acids Res 2008;36 (Database issue):d Wishart DS, Knox C, Guo AC, et al. DrugBank: a comprehensive resource for in silico drug discovery and

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