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What is the relevance of bioinformatics to pharmacology? Paul A. Whittaker Novartis Respiratory Research Centre, Wimblehurst Road, Horsham, West Sussex RH12 5AB, UK
Although bioinformatics achieved prominence because of its central role in genome data storage, management and analysis, its focus has shifted as the life sciences exploit these data. In pharmacology, genomic, transcriptomic and proteomic data are being used in the quest for drugs that fulfill unmet medical needs, are disease modifying or curative and are more effective and safer than current drugs. Bioinformatics is used in drug target identification and validation and in the development of biomarkers and toxicogenomic and pharmacogenomic tools to maximize the therapeutic benefit of drugs. Now that the ‘parts list’ of cellular signalling pathways is available, integrated computational and experimental programmes are being developed, with the goal of enabling in silico pharmacology by linking the genome, transcriptome and proteome to cellular pathophysiology. ‘[An] argument against the Human Genome Project was that it was trivial, it wasn’t really science. It was referred to as a fishing expedition, or a mindless collecting of facts. What they did not realize is how these databases were going to transform how we think about biology and medicine.’ This is a quote from Leroy Hood, who led the team that developed the automated DNA sequencer and made sequencing of the human and other genomes possible. He was speaking about the bitter opposition to sequencing the human genome during an interview in 2001 (http:// www.techreview.com/articles/qa0901.asp). Although the origins of the field go back to the 1960s [1,2], bioinformatics came to prominence in the 1990s as a direct result of the HumanGenomeProject.Theexponentialincreaseingenomic and genomic-related information generated during this period focused attention on how such information could be stored, managed, analysed and ultimately used to improve the life sciences, and resulted in an explosion in the number of databases containing this data [3]. Bioinformatics is the application of computing and mathematics to the management and analysis of biological datasets to aid the solution of biological problems [4]. Databases allow the storage and management of these datasets, and computational procedures (algorithms) allow the relationships between the members of these datasets to be explored [5]. This field ranges from sequence Corresponding author: Paul A. Whittaker ([email protected]).
data management and database construction to modelling and simulation of biological systems. The area in between these extremes includes the analysis of the human genome and other genomes for DNA sequences of structural and functional significance (genomics), and the analysis of complex datasets from RNA profiling (transcriptomics) [6,7] and protein profiling (proteomics) [8] experiments. Major computer manufacturers such as IBM now believe that biology will be the key driver behind the development of the next generation of supercomputers (http://www.research.ibm.com/bluegene/index.html). Just as pharmacology evolved in the 20th century from being a descriptive science to the multidisciplinary field it is today, bioinformatics is undergoing a similar evolution as integration and diversification take place simultaneously. The focus of bioinformatics has shifted from the ‘genomic era’, where the emphasis was on database construction and the analysis of DNA sequence data, to the ‘post-genomic era’, where the focus is on knowledge discovery. Hidden ‘patterns’ are ‘mined’ from large amounts of genomic, transcriptomic and proteomic data and used to form hypotheses that are investigated experimentally [9]. Consequently, bioinformatics is being integrated increasingly with bench-based science as hypotheses generated in silico are tested in vitro and in vivo in ‘wet–dry’ cycles. Craig Venter, who led the private venture to sequence the human genome, predicted ‘it is likely that pharmacology, toxicology, bioinformatics and genomics will merge into a new branch of medical science for studying and developing pharmaceuticals from molecule to bedside.’ [10] A key driver in the use of genome and genome-related data is the pharmacological sciences, with the main aims being to use this information to develop new, more effective and safer drugs. In this article, the contribution of bioinformatics to pharmacological sciences today, particularly in drug discovery and development, is examined and future directions for bioinformatics in this field are assessed. Although algorithms are an integral part of bioinformatics [11], the emphasis in this article is on webenabled databases and tools and how they can be used to aid pharmacological research. The role of bioinformatics in target discovery Of the estimated 35 000 genes in the human genome, only 43 of their encoded proteins are targeted by the top 100 best-selling drugs [12]. Although breakthrough drugs have been developed in the past few years (e.g. Pfizer’s Viagra [13] and Novartis’s Gleevec [14]), such breakthroughs are rare,
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and most new drugs that are approved by the regulatory authorities modulate protein targets for which marketed drugs already exist. Addressing this ‘innovation gap’ has resulted in the development of the new paradigm of genomics-based drug discovery (Fig. 1), with bioinformatics having a key role in exploiting genomic, transcriptomic and proteomic data to gain insights into the molecular mechanisms that underlie disease and to search for targets that will lead to drugs that: (1) have novel mechanisms of action; (2) fulfill unmet medical needs; and/or (3) are disease-modifying or curative [15]. It should be emphasized that although bioinformatics tools and resources can be used to identify putative drug targets, validating targets is still a process that requires understanding the role of the gene or protein in the disease process and is heavily dependent on laboratory-based work (Fig. 2). Mining the genome Not all targets are created equal. Certain classes of proteins are more amenable to drug development than other classes. Historically, G-protein-coupled receptors (GPCRs) have been the major target class for the pharmaceutical industry, with ion channels, nuclear hormone receptors, proteases, phosphodiesterases, kinases, phosphatases and other key enzymes making up the remaining target classes [16]. Mining the human genome sequence using bioinformatics has helped define and classify the genomic complement of the genes encoding these proteins, in addition to revealing new members that offer potential as novel drug targets [17–19]. Screening genomic databases has also clarified the (previously unknown) mechanism of action of marketed drugs [20]. Target discovery
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In parallel with the work on the human genome, microbial genomes are also being mined for antibacterial targets [21], antiviral targets [22] and genes that trigger immune responses for vaccine development [23]. The availability of the genome sequences of the malaria parasite (Plasmodium falciparum), its vector (Anopheles gambiae) and its victim (Homo sapiens) offers great potential for new antimalarial therapies [24]. The PlasmoDB database (http://PlasmoDB.org), which integrates the P. falciparum genomic sequence with other genomic and experimental data, should facilitate the recognition of areas of antigenic variability in parasite proteins in the search for vaccine therapies. Mining the transcriptome and proteome Expressed sequence tag (EST) databases [25] contain a wealth of information that can be used to identify genes based on their expression; for example, the 28 March 2003 version of dbEST (http://www.ncbi.nlm.nih.gov/dbEST/) contains . 5 million human ESTs and nearly 17 million ESTs from a wide range of organisms. Because each EST originates from a cDNA library prepared from a specific cell type, tissue or organ, the frequency of each EST can be taken as an indication of the expression pattern and expression level of a gene [26], which can be used to identify genes associated with the disease state [27] and to identify new members of pharmaceutically relevant protein families [28]. As an alternative to EST analysis, mining of gene expression profile data generated using microarray technology [29] has been widely used as a tool to examine the transcriptional state of thousands of genes simultaneously and to search for new approaches to diagnose and treat diseases [30,31]. Because of the sheer
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Fig. 1. The current paradigm of drug discovery and development emphasizes the exploitation of genomic and genomic-related knowledge, with bioinformatics playing a key role in ‘unlocking’ the information contained in the DNA sequence (genome), the mRNAs (transcriptome) and the proteins (proteome) of cells. In the target discovery phase, computational procedures (algorithms) are used to ‘mine’ DNA sequence databases, gene expression data and protein profile data to: (1) identify putative drug targets; (2) aid the identification of sequence variants of genes that genetically predispose individuals to developing disease; and (3) help to establish a firm association between putative targets and the disease of interest (target validation). The availability of the three-dimensional structures of proteins gained either experimentally (structural genomics) or computationally (homology modelling) can aid the rational design of drugs in the drug discovery phase and the identification of target-specific ligands that can be used in target validation (chemogenomics). In the drug development phase, bioinformatics is being used to integrate genomic, transcriptomic, proteomic and clinical data to identify biological molecules (biomarkers) that allow predictions of patient responses to drug therapy (pharmacogenomics) and drug side-effects (toxicogenomics). Finally, bioinformatics databases and tools are being combined with experimental data on signalling pathways, with the ultimate goal of enabling in silico modelling of healthy and diseased cells, tissues and organs so that, in the future, the effects of drugs on these complex systems can be determined prospectively. http://tips.trends.com
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Fig. 2. Target validation is the process in which the case that drug modulation of a putative protein is likely to have a beneficial effect in a particular disease is constructed. It involves linking targets to biological function in healthy and diseased states. In this process, bioinformatics is used not only to provide functional information on targets based on sequence and structural homologies to proteins of known function, but also to integrate and analyse datasets obtained from experimental studies aimed at identifying: (1) which biochemical pathway(s) the target participates in; (2) what proteins the target interacts with; and (3) the biological effect of removing the target by generating knockout mice or reducing the level of the target in cultured cells by knocking down the RNA transcript for the target. Knowledge of whether polymorphic sequence variants of the gene that encodes the target protein are genetically associated with the disease or whether the protein is found at inappropriate levels in diseased cells or tissues is also valuable in target validation.
volume of data produced in these experiments, bioinformatics is essential for data management and analysis [7,32,33]. To facilitate the analysis of datasets by the scientific community, public databases of microarray data have been developed (http://www.ebi.ac.uk/ arrayexpress and http://www.ncbi.nlm.nih.gov/geo/). In terms of the proteome, a range of bioinformatics tools and databases that can be used to identify potential protein targets are accessible via the internet [8] (http:// www.expasy.org). Genetics The identification of genes that predispose individuals to the development of common chronic diseases such as asthma, diabetes and Alzheimer’s disease has been a major focus of interest by academic and industrial researchers during the past decade. The rationale behind identifying disease ‘susceptibility’ genes is to develop therapies that modulate the pathology of these diseases, rather than just the physiology [15]. Because these diseases are multifactorial (i.e. result from the interaction of environmental factors and multiple genes), the identification of susceptibility genes for common complex diseases has not enjoyed the success achieved for monogenic diseases such as cystic fibrosis, where the disease is caused by a defect in a single gene [34]. However, using bioinformatics, aspects of the gene identification process can now be carried out in silico, thus obviating months, or even years, of laborious and time-consuming bench work. Annotation of the human genome sequence (http://www.ensembl.org/) allows the http://tips.trends.com
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identification of genes in regions targeted by genetic analysis and the identification of single nucleotide polymorphisms (SNPs) (http://www.ncbi.nlm.nih.gov/SNP/ and http://snp.cshl.org/) for genetic association analyses [34]. Analyses of mouse mutants generated by genome-wide ethyl nitrosourea mutagenesis provide an alternative approach to identifying disease-relevant genes [34], and several databases have been specifically developed to facilitate this purpose (http://www.mut.har.mrc.ac.uk/, http://www.jax.org/phenome and http://www.paperglyphs. com/wmc). The role of bioinformatics in target validation Establishing a firm association between a gene or protein and the disease of interest is a key task in building up the case that drug modulation of the target is likely to have a beneficial effect in the disease. Target validation combines data from molecular biology, cell biology, bioinformatics and in vitro and in vivo experiments, with the amount of work needed for validation increasing dramatically for targets that cannot be shown to be members of existing protein families and so have no known function. Linking targets to biological function Although experimental work is the key driver in target validation (Fig. 2), bioinformatics, given a nucleotide or protein sequence, can yield a lot of useful information about a putative target. As a direct result of ongoing efforts to annotate the human genome sequence [35], there are a large number of sites that integrate knowledge about genes and proteins, and provide links to numerous information resources (e.g. http://www.ncbi.nlm.nih.gov, http://www.expasy.org, http://bioinformatics.weizmann. ac.il/cards/, http://www.genelynx.org/ and http://www.hri. co.jp/HUNT/). Functional information can also be gleaned from similarities between the putative target protein and existing protein families in human and other organisms at either the sequence or the structural level [36] (http://www.biochem.ucl.ac.uk/bsm/cath_new/Gene3D/). The identification of homologues in other species is useful for confirming the suitability of animal models for in vivo experiments and can be particularly useful in assigning function if a knockout mouse for the gene encoding the protein exists [12] (http://research.bmn.com/mkmd and http://www.paperglyphs.com/wmc). Similarly, the identification of homologous genes in the human genome with diverged function (paralogues) can be also be important in assessing probable target specificity issues. At another level, protein – protein interaction maps [37,38] (http:// cmmg.biosci.wayne.edu/finlab/interact_dbases.htm) are a valuable source of information that can be used to assign function to uncharacterized gene products with ‘guilt by association’ and are a valuable adjunct to the large volume of protein interaction data already in the scientific literature. Finally, gene profiling data can be useful in assigning biological function and, to this end, databases collating microarray (http://expression.gnf.org) and EST (http://bodymap.ims.u-tokyo.ac.jp/) data from a wide array of human and mouse tissues have been developed.
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Linking targets to drugs Knowledge of the three-dimensional structures of proteins help: (1) improve understanding of protein functions [39]; (2) the identification of low molecular weight compounds that selectively inhibit or activate the proteins [40]; or (3) design proteins with improved therapeutic properties [41]. Although the goal of structural genomics is to determine the three-dimensional structures of all proteins [42], the protein databank (http://www.rcsb.org/pdb/) contains only a small proportion of the protein structures from the predicted 100 000 þ proteins in the human proteome. Prediction of the secondary and tertiary structures of proteins from their primary amino acid sequence is extremely difficult and there are no generally applicable methods to achieve this. However, by combining bioinformatics with experimentally derived structural information, it is possible to build three-dimensional models based on homology [39] (http://www.biochem.ucl.ac.uk/ bsm/cath_new/Gene3D/). The finding that proteins can have similar topological folds despite having different sequences provides the basis for bioinformatics approaches to guide the design of combinatorial libraries for finding low molecular weight inhibitors of proteins [40]. The identification of target-specific ligands [43] can also be used to validate targets (chemogenomics) [44]. The role of bioinformatics in drug development In addition to impacting drug discovery, the availability of genomic, transcriptomic and proteomic information is expected to dramatically change drug development, medical practice and the prescription of drugs in the 21st century by identifying markers that will maximize the therapeutic benefit of drugs while minimizing their toxic effects [45,46]. Here, bioinformatics has a key role to play in integrating genomic, proteomic and clinical data in the areas of biomarkers, pharmacogenomics and toxicogenomics [47,48] (Fig. 3). Biomarkers Biomarkers (biological molecules associated with the pathogenic process or pharmacological responses to drug therapy) are being emphasized increasingly by pharmaceutical companies and regulatory authorities as important decision-making tools in drug development and regulatory review [49]. Such markers are useful in diagnostics, prognostics and ‘theranostics’ (the integration of therapeutics and diagnostics to facilitate correct drug selection and dosing in the clinic [50,51]). Although few validated biomarkers have been identified to date, genomic and proteomic methodologies offer increased opportunities for biomarker identification and development, with cancer biomarker discovery driving the field [52]. Bioinformatics is an integral component of biomarker identification with respect to data management, data analysis and mining of genomic and proteomic data [53 – 55] (http://clinicalproteomics.steem.com). Pharmacogenomics At present, predicting patient responses to drug therapy before clinical trials is problematic, although it is known that genetic factors play an important role in determining http://tips.trends.com
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who will respond to a drug and who will suffer an adverse reaction [56,57]. Although studies of the genetics of drug response have traditionally focused on individual genes encoding proteins involved in drug metabolism (pharmacogenetics), the scope of these studies has become more global as attention has turned to identifying and studying the entire complement of pharmacologically relevant genes in the genome (pharmacogenomics). The two approaches being used are: (1) genetically linking SNPs in genes with phenotypic response (genotype – phenotype associations [57,58]); and (2) analysing changes at the transcriptome and proteome level to gain mechanistic insights and identify genes and proteins relevant to drug response (phenotype – genotype associations) [8,59–61]. Because pharmacogenomics uses data from a range of databases, experiments, clinical data and the literature, bioinformatics has an important role in integrating this information. PharmGKB (http://www.pharmgkb. org/) and the National Cancer Institute’s Antineoplastic Drug Screen database (http://discover.nci.nih.gov) are the main public databases for integrating data and knowledge in this field. Toxicogenomics The application of transcriptomics and proteomics methodologies to toxicology has resulted in the sub-discipline of toxicogenomics in which global analyses of gene expression and protein patterns are carried out for predictive and mechanistic toxicology purposes [62 – 64]. The basic idea behind toxicogenomics is that cells or tissues exposed to drugs display specific patterns, or signatures, of expressed genes or proteins and that signatures generated using new drugs can be compared with signatures of drugs with known toxicities to identify potential toxicology issues with the new drug. Bioinformatics is important for data analysis and data mining to identify patterns in toxicogenomic data [64,65], and databases containing profiles of compounds where toxicological and pathological endpoints are well characterized have been developed (http://www.niehs.nih.gov/nct/). Compounds with similar pharmacological or toxicological effects have been shown to produce similar profiles following either in vitro or in vivo exposure [63], so expression signatures do highlight similar mechanisms of action and/or toxicological responses among the drugs being compared [66]. Examination of changes in metabolic profiles (metabonomics) [67] is also likely to have an impact on toxicology in the future [68] and to this end databases are being developed both to manage the data generated [69] and to integrate metabonomic, transcriptomic and proteomic data (e.g. the Chemical Effects in Biological Systems Knowledgebase; http:// www.niehs.nih.gov/nct/). The future: in silico pharmacology? The ‘build and test’ approach of the pharmaceutical industry fuelled by high-throughput screening and combinatorial chemistry contrasts with other industries such as aerospace, where computer simulations have largely replaced the physical evaluation of prototypes. Here, historical data are used retrospectively to develop
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Fig. 3. To maximize the therapeutic benefits of drugs and minimize their side effects, bioinformatics is being used to integrate genomic, transcriptomic and proteomic data with clinical data so that DNA sequence changes in genes or variations in gene transcript and/or protein levels can be linked with disease pathology or drug response. The aims of looking for such associations are to predict which patients will respond to a drug and which patients will have adverse reactions, and to identify potential toxicology issues with new drugs.
simulations that can be used prospectively to predict the behaviour of aircraft under specific conditions (http://www. boeing.com/commercial/777family/pf/pf_computing.html). Unfortunately, the information contained in genes and proteins is not enough to allow similar simulations of complex biological systems. For this to be a reality, detailed understanding of biological systems acquired through the analysis of protein interactions and signalling networks is needed. Although genomic and proteomic data provide the detailed ‘parts list’ for reconstructing signalling networks, actually doing this will require the integration of computational and experimental data (systems biology) [70,71]. Several signalling pathways have been characterized using biochemical and pharmacological approaches (http://www.biocarta.com and http://stke.sciencemag.org/) but bioinformatics databases [72,73] (http://www. signaling-gateway.org and http://transfac.gbf.de/) and tools for data acquisition (http://vision.lbl.gov/Projects/ BioSig and http://vision.lbl.gov/Projects/vsom) are needed for in silico modelling. Although the in silico biology ‘vision’ is still a way off, the approach has great potential to link the genome and proteome to cellular pathophysiology [74] (http://www.physiome.org) and to provide the basis for in silico pharmacokinetic modelling [75]. Concluding remarks As predicted by Leroy Hood and Craig Venter, the exploitation of genomic data using bioinformatics is having a major impact on the biological sciences, particularly the pharmacological sciences. In the current groundbreaking phase, bioinformatics is evolving from the sequence-orientation that characterized it in the early http://tips.trends.com
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46 Petricoin, E.F. III et al. (2002) Medical applications of microarray technologies: a regulatory science perspective. Nat. Genet. 32, 474–479 47 Altman, R.B. and Klein, T.E. (2002) Challenges for biomedical informatics and pharmacogenomics. Annu. Rev. Pharmacol. Toxicol. 42, 113–133 48 Fielden, M.R. et al. (2002) In silico approaches to mechanistic and predictive toxicology: an introduction to bioinformatics for toxicologists. Crit. Rev. Toxicol. 32, 67– 112 49 Lesko, L.J. and Atkinson, A.J. Jr (2001) Use of biomarkers and surrogate endpoints in drug development and regulatory decision making: criteria, validation, strategies. Annu. Rev. Pharmacol. Toxicol. 41, 347 – 366 50 Funkhouser, J. (2002) Reinventing pharma: the theranostic revolution. Current Drug Discovery, 17–19.(http://www.currentdrugdiscovery.com/ 2002/august.html) 51 Picard, F.J. and Bergeron, M.G. (2002) Rapid molecular theranostics in infectious diseases. Drug Discov. Today 7, 1092– 1101 52 Nicolette, C.A. and Miller, G.A. (2003) The identification of clinically relevant markers and therapeutic targets. Drug Discov. Today 8, 31–38 53 Yeatman, T.J. (2003) The future of clinical cancer management: one tumor, one chip. Am. Surg. 69, 41 – 44 54 Petricoin, E.F. et al. (2002) Clinical proteomics: translating benchside promise into bedside reality. Nat. Rev. Drug Discov. 1, 683 – 695 55 Srinivas, P.R. et al. (2002) Proteomics for cancer biomarker discovery. Clin. Chem. 48, 1160 – 1169 56 Pirmohamed, M. and Park, B.K. (2001) Genetic susceptibility to adverse drug reactions. Trends Pharmacol. Sci. 22, 298 – 305 57 Pickar, D. and Rubinow, K. (2001) Pharmacogenomics of psychiatric disorders. Trends Pharmacol. Sci. 22, 75 – 83 58 Schmidt, B. et al. (2002) Genotypic drug resistance interpretation systems – the cutting edge of antiretroviral therapy. AIDS Rev. 4, 148– 156 59 Blower, P.E. et al. (2002) Pharmacogenomic analysis: correlating molecular substructure classes with microarray gene expression data. Pharmacogenomics J. 2, 259 – 271 60 Emerson, L.R. et al. (2002) Relationship between chloroquine toxicity and iron acquisition in Saccharomyces cerevisiae. Antimicrob. Agents Chemother. 46, 787 – 796 61 Rockey, W.M. and Elcock, A.H. (2002) Progress toward virtual screening for drug side effects. Proteins 48, 664– 671 62 Amin, R.P. et al. (2002) Genomic interrogation of mechanism(s) underlying cellular responses to toxicants. Toxicology 181 – 182, 555– 563 63 Ulrich, R. and Friend, S.H. (2002) Toxicogenomics and drug discovery: will new technologies help us produce better drugs? Nat. Rev. Drug Discov. 1, 84– 88 64 Kennedy, S. (2002) The role of proteomics in toxicology: identification of biomarkers of toxicity by protein expression analysis. Biomarkers 7, 269– 290 65 Hamadeh, H.K. et al. (2001) Discovery in toxicology: mediation by gene expression array technology. J. Biochem. Mol. Toxicol. 15, 231 – 242 66 Rushmore, T.H. and Kong, A.N. (2002) Pharmacogenomics, regulation and signaling pathways of phase I and II drug metabolizing enzymes. Curr. Drug Metab. 3, 481– 490 67 Nicholson, J.K. et al. (2002) Metabonomics: a platform for studying drug toxicity and gene function. Nat. Rev. Drug Discov. 1, 153 – 161 68 Aardema, M.J. and MacGregor, J.T. (2002) Toxicology and genetic toxicology in the new era of ‘toxicogenomics’: impact of ‘-omics’ technologies. Mutat. Res. 499, 13 – 25 69 Lindon, J.C. et al. (2003) Contemporary issues in toxicology the role of metabonomics in toxicology and its evaluation by the COMET project. Toxicol. Appl. Pharmacol. 187, 137 – 146 70 Kitano, H. (2002) Computational systems biology. Nature 420, 206– 210 71 Gilman, A.G. et al. (2002) Overview of the Alliance for Cellular Signaling. Nature 420, 703 – 706 72 Li, J. et al. (2002) The Molecule Pages database. Nature 420, 716 – 717 73 Kloos, D.U. et al. (2002) The TGF-beta – Smad network: introducing bioinformatic tools. Trends Genet. 18, 96 – 103 74 Noble, D. (2003) The future: putting Humpty-Dumpty together again. Biochem. Soc. Trans. 31, 156 – 158 75 Grass, G.M. and Sinko, P.J. (2002) Physiologically-based pharmacokinetic simulation modelling. Adv. Drug Deliv. Rev. 54, 433 – 451
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What is the relevance of bioinformatics to pharmacology? Paul A. Whittaker Novartis Respiratory Research Centre, Wimblehurst Road, Horsham, West Sussex RH12 5AB, UK
Although bioinformatics achieved prominence because of its central role in genome data storage, management and analysis, its focus has shifted as the life sciences exploit these data. In pharmacology, genomic, transcriptomic and proteomic data are being used in the quest for drugs that fulfill unmet medical needs, are disease modifying or curative and are more effective and safer than current drugs. Bioinformatics is used in drug target identification and validation and in the development of biomarkers and toxicogenomic and pharmacogenomic tools to maximize the therapeutic benefit of drugs. Now that the ‘parts list’ of cellular signalling pathways is available, integrated computational and experimental programmes are being developed, with the goal of enabling in silico pharmacology by linking the genome, transcriptome and proteome to cellular pathophysiology. ‘[An] argument against the Human Genome Project was that it was trivial, it wasn’t really science. It was referred to as a fishing expedition, or a mindless collecting of facts. What they did not realize is how these databases were going to transform how we think about biology and medicine.’ This is a quote from Leroy Hood, who led the team that developed the automated DNA sequencer and made sequencing of the human and other genomes possible. He was speaking about the bitter opposition to sequencing the human genome during an interview in 2001 (http:// www.techreview.com/articles/qa0901.asp). Although the origins of the field go back to the 1960s [1,2], bioinformatics came to prominence in the 1990s as a direct result of the HumanGenomeProject.Theexponentialincreaseingenomic and genomic-related information generated during this period focused attention on how such information could be stored, managed, analysed and ultimately used to improve the life sciences, and resulted in an explosion in the number of databases containing this data [3]. Bioinformatics is the application of computing and mathematics to the management and analysis of biological datasets to aid the solution of biological problems [4]. Databases allow the storage and management of these datasets, and computational procedures (algorithms) allow the relationships between the members of these datasets to be explored [5]. This field ranges from sequence Corresponding author: Paul A. Whittaker ([email protected]).
data management and database construction to modelling and simulation of biological systems. The area in between these extremes includes the analysis of the human genome and other genomes for DNA sequences of structural and functional significance (genomics), and the analysis of complex datasets from RNA profiling (transcriptomics) [6,7] and protein profiling (proteomics) [8] experiments. Major computer manufacturers such as IBM now believe that biology will be the key driver behind the development of the next generation of supercomputers (http://www.research.ibm.com/bluegene/index.html). Just as pharmacology evolved in the 20th century from being a descriptive science to the multidisciplinary field it is today, bioinformatics is undergoing a similar evolution as integration and diversification take place simultaneously. The focus of bioinformatics has shifted from the ‘genomic era’, where the emphasis was on database construction and the analysis of DNA sequence data, to the ‘post-genomic era’, where the focus is on knowledge discovery. Hidden ‘patterns’ are ‘mined’ from large amounts of genomic, transcriptomic and proteomic data and used to form hypotheses that are investigated experimentally [9]. Consequently, bioinformatics is being integrated increasingly with bench-based science as hypotheses generated in silico are tested in vitro and in vivo in ‘wet–dry’ cycles. Craig Venter, who led the private venture to sequence the human genome, predicted ‘it is likely that pharmacology, toxicology, bioinformatics and genomics will merge into a new branch of medical science for studying and developing pharmaceuticals from molecule to bedside.’ [10] A key driver in the use of genome and genome-related data is the pharmacological sciences, with the main aims being to use this information to develop new, more effective and safer drugs. In this article, the contribution of bioinformatics to pharmacological sciences today, particularly in drug discovery and development, is examined and future directions for bioinformatics in this field are assessed. Although algorithms are an integral part of bioinformatics [11], the emphasis in this article is on webenabled databases and tools and how they can be used to aid pharmacological research. The role of bioinformatics in target discovery Of the estimated 35 000 genes in the human genome, only 43 of their encoded proteins are targeted by the top 100 best-selling drugs [12]. Although breakthrough drugs have been developed in the past few years (e.g. Pfizer’s Viagra [13] and Novartis’s Gleevec [14]), such breakthroughs are rare,
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and most new drugs that are approved by the regulatory authorities modulate protein targets for which marketed drugs already exist. Addressing this ‘innovation gap’ has resulted in the development of the new paradigm of genomics-based drug discovery (Fig. 1), with bioinformatics having a key role in exploiting genomic, transcriptomic and proteomic data to gain insights into the molecular mechanisms that underlie disease and to search for targets that will lead to drugs that: (1) have novel mechanisms of action; (2) fulfill unmet medical needs; and/or (3) are disease-modifying or curative [15]. It should be emphasized that although bioinformatics tools and resources can be used to identify putative drug targets, validating targets is still a process that requires understanding the role of the gene or protein in the disease process and is heavily dependent on laboratory-based work (Fig. 2). Mining the genome Not all targets are created equal. Certain classes of proteins are more amenable to drug development than other classes. Historically, G-protein-coupled receptors (GPCRs) have been the major target class for the pharmaceutical industry, with ion channels, nuclear hormone receptors, proteases, phosphodiesterases, kinases, phosphatases and other key enzymes making up the remaining target classes [16]. Mining the human genome sequence using bioinformatics has helped define and classify the genomic complement of the genes encoding these proteins, in addition to revealing new members that offer potential as novel drug targets [17–19]. Screening genomic databases has also clarified the (previously unknown) mechanism of action of marketed drugs [20]. Target discovery
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In parallel with the work on the human genome, microbial genomes are also being mined for antibacterial targets [21], antiviral targets [22] and genes that trigger immune responses for vaccine development [23]. The availability of the genome sequences of the malaria parasite (Plasmodium falciparum), its vector (Anopheles gambiae) and its victim (Homo sapiens) offers great potential for new antimalarial therapies [24]. The PlasmoDB database (http://PlasmoDB.org), which integrates the P. falciparum genomic sequence with other genomic and experimental data, should facilitate the recognition of areas of antigenic variability in parasite proteins in the search for vaccine therapies. Mining the transcriptome and proteome Expressed sequence tag (EST) databases [25] contain a wealth of information that can be used to identify genes based on their expression; for example, the 28 March 2003 version of dbEST (http://www.ncbi.nlm.nih.gov/dbEST/) contains . 5 million human ESTs and nearly 17 million ESTs from a wide range of organisms. Because each EST originates from a cDNA library prepared from a specific cell type, tissue or organ, the frequency of each EST can be taken as an indication of the expression pattern and expression level of a gene [26], which can be used to identify genes associated with the disease state [27] and to identify new members of pharmaceutically relevant protein families [28]. As an alternative to EST analysis, mining of gene expression profile data generated using microarray technology [29] has been widely used as a tool to examine the transcriptional state of thousands of genes simultaneously and to search for new approaches to diagnose and treat diseases [30,31]. Because of the sheer
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Fig. 1. The current paradigm of drug discovery and development emphasizes the exploitation of genomic and genomic-related knowledge, with bioinformatics playing a key role in ‘unlocking’ the information contained in the DNA sequence (genome), the mRNAs (transcriptome) and the proteins (proteome) of cells. In the target discovery phase, computational procedures (algorithms) are used to ‘mine’ DNA sequence databases, gene expression data and protein profile data to: (1) identify putative drug targets; (2) aid the identification of sequence variants of genes that genetically predispose individuals to developing disease; and (3) help to establish a firm association between putative targets and the disease of interest (target validation). The availability of the three-dimensional structures of proteins gained either experimentally (structural genomics) or computationally (homology modelling) can aid the rational design of drugs in the drug discovery phase and the identification of target-specific ligands that can be used in target validation (chemogenomics). In the drug development phase, bioinformatics is being used to integrate genomic, transcriptomic, proteomic and clinical data to identify biological molecules (biomarkers) that allow predictions of patient responses to drug therapy (pharmacogenomics) and drug side-effects (toxicogenomics). Finally, bioinformatics databases and tools are being combined with experimental data on signalling pathways, with the ultimate goal of enabling in silico modelling of healthy and diseased cells, tissues and organs so that, in the future, the effects of drugs on these complex systems can be determined prospectively. http://tips.trends.com
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Fig. 2. Target validation is the process in which the case that drug modulation of a putative protein is likely to have a beneficial effect in a particular disease is constructed. It involves linking targets to biological function in healthy and diseased states. In this process, bioinformatics is used not only to provide functional information on targets based on sequence and structural homologies to proteins of known function, but also to integrate and analyse datasets obtained from experimental studies aimed at identifying: (1) which biochemical pathway(s) the target participates in; (2) what proteins the target interacts with; and (3) the biological effect of removing the target by generating knockout mice or reducing the level of the target in cultured cells by knocking down the RNA transcript for the target. Knowledge of whether polymorphic sequence variants of the gene that encodes the target protein are genetically associated with the disease or whether the protein is found at inappropriate levels in diseased cells or tissues is also valuable in target validation.
volume of data produced in these experiments, bioinformatics is essential for data management and analysis [7,32,33]. To facilitate the analysis of datasets by the scientific community, public databases of microarray data have been developed (http://www.ebi.ac.uk/ arrayexpress and http://www.ncbi.nlm.nih.gov/geo/). In terms of the proteome, a range of bioinformatics tools and databases that can be used to identify potential protein targets are accessible via the internet [8] (http:// www.expasy.org). Genetics The identification of genes that predispose individuals to the development of common chronic diseases such as asthma, diabetes and Alzheimer’s disease has been a major focus of interest by academic and industrial researchers during the past decade. The rationale behind identifying disease ‘susceptibility’ genes is to develop therapies that modulate the pathology of these diseases, rather than just the physiology [15]. Because these diseases are multifactorial (i.e. result from the interaction of environmental factors and multiple genes), the identification of susceptibility genes for common complex diseases has not enjoyed the success achieved for monogenic diseases such as cystic fibrosis, where the disease is caused by a defect in a single gene [34]. However, using bioinformatics, aspects of the gene identification process can now be carried out in silico, thus obviating months, or even years, of laborious and time-consuming bench work. Annotation of the human genome sequence (http://www.ensembl.org/) allows the http://tips.trends.com
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identification of genes in regions targeted by genetic analysis and the identification of single nucleotide polymorphisms (SNPs) (http://www.ncbi.nlm.nih.gov/SNP/ and http://snp.cshl.org/) for genetic association analyses [34]. Analyses of mouse mutants generated by genome-wide ethyl nitrosourea mutagenesis provide an alternative approach to identifying disease-relevant genes [34], and several databases have been specifically developed to facilitate this purpose (http://www.mut.har.mrc.ac.uk/, http://www.jax.org/phenome and http://www.paperglyphs. com/wmc). The role of bioinformatics in target validation Establishing a firm association between a gene or protein and the disease of interest is a key task in building up the case that drug modulation of the target is likely to have a beneficial effect in the disease. Target validation combines data from molecular biology, cell biology, bioinformatics and in vitro and in vivo experiments, with the amount of work needed for validation increasing dramatically for targets that cannot be shown to be members of existing protein families and so have no known function. Linking targets to biological function Although experimental work is the key driver in target validation (Fig. 2), bioinformatics, given a nucleotide or protein sequence, can yield a lot of useful information about a putative target. As a direct result of ongoing efforts to annotate the human genome sequence [35], there are a large number of sites that integrate knowledge about genes and proteins, and provide links to numerous information resources (e.g. http://www.ncbi.nlm.nih.gov, http://www.expasy.org, http://bioinformatics.weizmann. ac.il/cards/, http://www.genelynx.org/ and http://www.hri. co.jp/HUNT/). Functional information can also be gleaned from similarities between the putative target protein and existing protein families in human and other organisms at either the sequence or the structural level [36] (http://www.biochem.ucl.ac.uk/bsm/cath_new/Gene3D/). The identification of homologues in other species is useful for confirming the suitability of animal models for in vivo experiments and can be particularly useful in assigning function if a knockout mouse for the gene encoding the protein exists [12] (http://research.bmn.com/mkmd and http://www.paperglyphs.com/wmc). Similarly, the identification of homologous genes in the human genome with diverged function (paralogues) can be also be important in assessing probable target specificity issues. At another level, protein – protein interaction maps [37,38] (http:// cmmg.biosci.wayne.edu/finlab/interact_dbases.htm) are a valuable source of information that can be used to assign function to uncharacterized gene products with ‘guilt by association’ and are a valuable adjunct to the large volume of protein interaction data already in the scientific literature. Finally, gene profiling data can be useful in assigning biological function and, to this end, databases collating microarray (http://expression.gnf.org) and EST (http://bodymap.ims.u-tokyo.ac.jp/) data from a wide array of human and mouse tissues have been developed.
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Linking targets to drugs Knowledge of the three-dimensional structures of proteins help: (1) improve understanding of protein functions [39]; (2) the identification of low molecular weight compounds that selectively inhibit or activate the proteins [40]; or (3) design proteins with improved therapeutic properties [41]. Although the goal of structural genomics is to determine the three-dimensional structures of all proteins [42], the protein databank (http://www.rcsb.org/pdb/) contains only a small proportion of the protein structures from the predicted 100 000 þ proteins in the human proteome. Prediction of the secondary and tertiary structures of proteins from their primary amino acid sequence is extremely difficult and there are no generally applicable methods to achieve this. However, by combining bioinformatics with experimentally derived structural information, it is possible to build three-dimensional models based on homology [39] (http://www.biochem.ucl.ac.uk/ bsm/cath_new/Gene3D/). The finding that proteins can have similar topological folds despite having different sequences provides the basis for bioinformatics approaches to guide the design of combinatorial libraries for finding low molecular weight inhibitors of proteins [40]. The identification of target-specific ligands [43] can also be used to validate targets (chemogenomics) [44]. The role of bioinformatics in drug development In addition to impacting drug discovery, the availability of genomic, transcriptomic and proteomic information is expected to dramatically change drug development, medical practice and the prescription of drugs in the 21st century by identifying markers that will maximize the therapeutic benefit of drugs while minimizing their toxic effects [45,46]. Here, bioinformatics has a key role to play in integrating genomic, proteomic and clinical data in the areas of biomarkers, pharmacogenomics and toxicogenomics [47,48] (Fig. 3). Biomarkers Biomarkers (biological molecules associated with the pathogenic process or pharmacological responses to drug therapy) are being emphasized increasingly by pharmaceutical companies and regulatory authorities as important decision-making tools in drug development and regulatory review [49]. Such markers are useful in diagnostics, prognostics and ‘theranostics’ (the integration of therapeutics and diagnostics to facilitate correct drug selection and dosing in the clinic [50,51]). Although few validated biomarkers have been identified to date, genomic and proteomic methodologies offer increased opportunities for biomarker identification and development, with cancer biomarker discovery driving the field [52]. Bioinformatics is an integral component of biomarker identification with respect to data management, data analysis and mining of genomic and proteomic data [53 – 55] (http://clinicalproteomics.steem.com). Pharmacogenomics At present, predicting patient responses to drug therapy before clinical trials is problematic, although it is known that genetic factors play an important role in determining http://tips.trends.com
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who will respond to a drug and who will suffer an adverse reaction [56,57]. Although studies of the genetics of drug response have traditionally focused on individual genes encoding proteins involved in drug metabolism (pharmacogenetics), the scope of these studies has become more global as attention has turned to identifying and studying the entire complement of pharmacologically relevant genes in the genome (pharmacogenomics). The two approaches being used are: (1) genetically linking SNPs in genes with phenotypic response (genotype – phenotype associations [57,58]); and (2) analysing changes at the transcriptome and proteome level to gain mechanistic insights and identify genes and proteins relevant to drug response (phenotype – genotype associations) [8,59–61]. Because pharmacogenomics uses data from a range of databases, experiments, clinical data and the literature, bioinformatics has an important role in integrating this information. PharmGKB (http://www.pharmgkb. org/) and the National Cancer Institute’s Antineoplastic Drug Screen database (http://discover.nci.nih.gov) are the main public databases for integrating data and knowledge in this field. Toxicogenomics The application of transcriptomics and proteomics methodologies to toxicology has resulted in the sub-discipline of toxicogenomics in which global analyses of gene expression and protein patterns are carried out for predictive and mechanistic toxicology purposes [62 – 64]. The basic idea behind toxicogenomics is that cells or tissues exposed to drugs display specific patterns, or signatures, of expressed genes or proteins and that signatures generated using new drugs can be compared with signatures of drugs with known toxicities to identify potential toxicology issues with the new drug. Bioinformatics is important for data analysis and data mining to identify patterns in toxicogenomic data [64,65], and databases containing profiles of compounds where toxicological and pathological endpoints are well characterized have been developed (http://www.niehs.nih.gov/nct/). Compounds with similar pharmacological or toxicological effects have been shown to produce similar profiles following either in vitro or in vivo exposure [63], so expression signatures do highlight similar mechanisms of action and/or toxicological responses among the drugs being compared [66]. Examination of changes in metabolic profiles (metabonomics) [67] is also likely to have an impact on toxicology in the future [68] and to this end databases are being developed both to manage the data generated [69] and to integrate metabonomic, transcriptomic and proteomic data (e.g. the Chemical Effects in Biological Systems Knowledgebase; http:// www.niehs.nih.gov/nct/). The future: in silico pharmacology? The ‘build and test’ approach of the pharmaceutical industry fuelled by high-throughput screening and combinatorial chemistry contrasts with other industries such as aerospace, where computer simulations have largely replaced the physical evaluation of prototypes. Here, historical data are used retrospectively to develop
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Fig. 3. To maximize the therapeutic benefits of drugs and minimize their side effects, bioinformatics is being used to integrate genomic, transcriptomic and proteomic data with clinical data so that DNA sequence changes in genes or variations in gene transcript and/or protein levels can be linked with disease pathology or drug response. The aims of looking for such associations are to predict which patients will respond to a drug and which patients will have adverse reactions, and to identify potential toxicology issues with new drugs.
simulations that can be used prospectively to predict the behaviour of aircraft under specific conditions (http://www. boeing.com/commercial/777family/pf/pf_computing.html). Unfortunately, the information contained in genes and proteins is not enough to allow similar simulations of complex biological systems. For this to be a reality, detailed understanding of biological systems acquired through the analysis of protein interactions and signalling networks is needed. Although genomic and proteomic data provide the detailed ‘parts list’ for reconstructing signalling networks, actually doing this will require the integration of computational and experimental data (systems biology) [70,71]. Several signalling pathways have been characterized using biochemical and pharmacological approaches (http://www.biocarta.com and http://stke.sciencemag.org/) but bioinformatics databases [72,73] (http://www. signaling-gateway.org and http://transfac.gbf.de/) and tools for data acquisition (http://vision.lbl.gov/Projects/ BioSig and http://vision.lbl.gov/Projects/vsom) are needed for in silico modelling. Although the in silico biology ‘vision’ is still a way off, the approach has great potential to link the genome and proteome to cellular pathophysiology [74] (http://www.physiome.org) and to provide the basis for in silico pharmacokinetic modelling [75]. Concluding remarks As predicted by Leroy Hood and Craig Venter, the exploitation of genomic data using bioinformatics is having a major impact on the biological sciences, particularly the pharmacological sciences. In the current groundbreaking phase, bioinformatics is evolving from the sequence-orientation that characterized it in the early http://tips.trends.com
days to a discipline that faces many ways. Integration of bioinformatics with the structure-oriented world of proteins and small-molecule drugs and with the pathwayoriented world of systems biology is driving this evolution, with the ultimate goal of the efficient discovery and development of drugs that are safer and more effective. References 1 Dayhoff, M.O. (1969) Computer analysis of protein evolution. Sci. Am. 221, 86 – 95 2 Rybak, B. (1978) A program for teaching bio-informatics. Bioscience Communications 4, 158 – 159 3 Baxevanis, A.D. (2003) The Molecular Biology Database Collection: 2003 update. Nucleic Acids Res. 31, 1 – 12 4 Luscombe, N.M. et al. (2001) What is bioinformatics? A proposed definition and overview of the field. Methods Inf. Med. 40, 346– 358 5 Westhead, D.R. and Parish, J.H. (2002) Bioinformatics, BIOS Scientific Publishers 6 Scheel, J. et al. (2002) Yellow pages to the transcriptome. Pharmacogenomics 3, 791 – 807 7 Slonim, D.K. (2002) From patterns to pathways: gene expression data analysis comes of age. Nat. Genet. 32, 502 – 508 8 Marshall, T. and Williams, K.M. (2002) Proteomics and its impact upon biomedical science. Br. J. Biomed. Sci. 59, 47 – 64 9 Weinstein, J.N. (2002) ‘Omic’ and hypothesis-driven research in the molecular pharmacology of cancer. Curr. Opin. Pharmacol. 2, 361 – 365 10 Broder, S. and Venter, J.C. (2000) Sequencing the entire genomes of free-living organisms: the foundation of pharmacology in the new millennium. Annu. Rev. Pharmacol. Toxicol. 40, 97 – 132 11 Orengo, C.A. et al. (2003) Bioinformatics: Genes, Proteins and Computers, BIOS Scientific Publishers 12 Zambrowicz, B.P. and Sands, A.T. (2003) Knockouts model the 100 best-selling drugs – will they model the next 100? Nat. Rev. Drug Discov. 2, 38– 51 13 Corbin, J.D. et al. (2002) Phosphodiesterase type 5 as a pharmacologic target in erectile dysfunction. Urology 60, 4 – 11 14 Druker, B.J. (2002) Imatinib and chronic myeloid leukemia: validating
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