Literature Review


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Literature Review

  1. 1. Chapter - III Review of Literature _________________________________________________________________________ Identification and Validation of Drug Targets 60 Chapter III REVIEW OF LITERATURE . . 3.1 MOLECULAR DRUG TARGETS An assessment of the number of molecular targets that represent an opportunity for therapeutic intervention is crucial to the development of post-genomic research strategies within the pharmaceutical industry. Now that we know the size of the human genome, it is interesting to consider just how many molecular targets this opportunity represents. It is vital that the position that we understand the properties that is required for a good drug, and therefore must be able to understand what makes a good drug target (Hopkins and Groom, 2002). Drug research has contributed more to the progress of medicine during the past century than any other scientific factor. The advent of molecular biology and, in particular, of genomic sciences is having a deep impact on drug discovery. Genome sciences, combined with bioinformatic tools, allow us to dissect the genetic basis of multifactorial diseases and to determine the most suitable points of attack for future medicines, thereby increasing the number of treatment options. The dramatic increase in the complexity of drug research is enforcing changes in the institutional basis of this interdisciplinary endeavor. The biotech industry is establishing itself as the discovery arm of the pharmaceutical industry. In bridging the gap
  2. 2. Chapter - III Review of Literature _________________________________________________________________________ Identification and Validation of Drug Targets 61 between academia and large pharmaceutical companies, the biotech firms have been effective instruments of technology transfer, (Drews J. 2000). 3.2 TARGET PREDICTION METHODS AND STRATEGIES Therefore, scientists interested in discovering antibiotics must extract useful information from genomes through comparative, functional, or structural genomics in order to simplify drug target selection. The advent of bacterial whole-genome sequences and establishment of useful genomic analyses comes at a crucial time for antibiotic development. The information gained from genome sequencing projects has already had a major impact on both basic microbiology and its industrial applications, and has rapidly changed the way research is conducted in this field. No less remarkable, however, is the versatility shown by microorganisms in overcoming the effects of antibiotics. Chemical modification of existing antibiotics and development of inhibitors of resistance genes, will have a significant impact on antibacterial therapy in the immediate future. However, it is evident that this field requires additional targets, innovative assay development strategies and new chemical entities (Schmid, 1998). While the introduction of innovative chemistry will probably be triggered by combinatorial chemistry (Myers, 1997) and novel resources for natural products (Nisbet and Moore, 1997), there are high expectations for microbial genomics in accelerating target discovery and assay development.
  3. 3. Chapter - III Review of Literature _________________________________________________________________________ Identification and Validation of Drug Targets 62 3.3 TARGET SELECTION METHODS Computational techniques for the identification of potential drug targets based on genomic data have been reviewed recently (Schmid, 1998; Galperin, and Koonin, 1999, Moir et al., 1999; Freiberg,. 2001). In a few studies, in silico analysis was successfully combined with experimental techniques (Arigoni et al., 1998; Freiberg et al., 1998; Chalker et al., 2001). For example, Arigoni and coauthors used comparative genome analysis to identify previously uncharacterized genes as potential broad-spectrum targets by emphasizing genes which are;  Broadly conserved in various bacteria, including pathogens,  Not conserved in humans; and  Likely to encode soluble proteins. The essentiality of selected genes was further assessed by directed knockouts in E. coli and Bacillus subtilis (Arigoni et al., 1998). Most in silico target identification techniques are focused on formal comparative sequence analysis, without attempting to assess conservation of the overall biological context in various pathogens and the human host. Comparative analysis of pathways and biological subsystems may significantly improve our ability to select potential targets. Antimicrobial drugs should be essential to the pathogen, have a unique function in the pathogen, be present only in the pathogen, and be able to be inhibited by a small molecule.
  4. 4. Chapter - III Review of Literature _________________________________________________________________________ Identification and Validation of Drug Targets 63 The target should be essential, in that it is a part of a crucial cycle in the cell, and its elimination should lead to the pathogen’s death. The target should be unique: no other pathway should be able to supplement the function of the target and overcome the presence of the inhibitor. If the macromolecule satisfies all the outlined criteria to be a drug target but functions in healthy human cells as well as in a pathogen, specificity can often be engineered into the inhibitor by exploiting structural or biochemical differences between the pathogenic and human forms. Finally, the target molecule should be capable of inhibition by binding of a small molecule. Enzymes are often excellent drug targets because compounds are designed to fit within the active site pocket. 3.3.1 Essential gene selection Traditionally, the search for novel genes required for bacterial survival or virulence was based on several genetic methods involving random mutagenesis of a bacterial genome followed by screening for the relevant phenotype (Berg and Berg, 1996). Previously, the most successful strategy for finding novel genes essential for viability was the isolation of conditionally lethal mutants. A conditionally lethal mutation causes the gene product to function normally under one set of environmental conditions (the permissive condition - e.g. growth at 37.8 C for Escherichia coli), but fail to function under another set of conditions (the non-permissive condition – e.g. growth at 42.8 C for E. coli). The genes that can mutate to
  5. 5. Chapter - III Review of Literature _________________________________________________________________________ Identification and Validation of Drug Targets 64 conditional lethality (i.e. mutations that are lethal to a bacterium under non- permissive conditions) are generally genes that are essential for viability. However, such temperature-sensitive mutants do have their limitations (Schmid, 1998). It has been estimated that approximately one-third of proteins are difficult to mutate into a thermolabile form. In addition, genes have been identified that are required for viability at high temperatures, and this will lead to false-positive assignment of some genes as being essential under all growth conditions (Schmid, 1989). Despite the widespread success of using conditional mutants to identify essential genes, it can be assumed that a significant number of essential genes still remain undiscovered by this technology. Until recently, transposon mutagenesis (i.e. the inactivation of genes by insertion of a transposable genetic element) of bacterial genomes was not a powerful approach for the identification of essential genes, because the frequency and randomness of insertions was too low. A breakthrough in using transposon mutagenesis to map genes required for cellular viability was achieved by combining in vitro transposition with natural transformation of certain bacterial species (Akerley et al., 1998). 3.3.2 Structural genomics based target selection Despite the exponential growth of sequence information from a large diversity of organisms, each newly sequenced bacterial genome continues
  6. 6. Chapter - III Review of Literature _________________________________________________________________________ Identification and Validation of Drug Targets 65 to reveal up to 50% of genes without significant similarity to protein of characterized function (Stover et al., 2000). By focusing a structural genomics project on such anonymous proteins, our goal is twofold. On one hand, we expect a sizable fraction of these proteins to exhibit a recognizable 3-D structure similarity with previously characterized protein families. Structure determination is thus used as a technique of functional genomics, allowing common functional attributes to be recognized beyond the twilight zone of sequence similarity. On the other hand, focusing a structural genomics effort on anonymous proteins should also enhance the probability of discovering original folds that are highly valuable byproducts for the academic community. 3.3.3 Phylogeny based target selection Scientists can use phylogenetic groups that are based on the specific folds shared by organisms. These fold and sequence families in bacterial pathogens can be useful antibiotic targets (Gerstein, 2000). One can find a fold common to an entire phylogenetic group in order to target all of the organisms with a broad-spectrum antibiotic. Alternatively, one can find a fold that is unique to one particular pathogen for an effective narrow- spectrum antibiotic target.
  7. 7. Chapter - III Review of Literature _________________________________________________________________________ Identification and Validation of Drug Targets 66 3.3.4 Structure based target selection Structural methods are the ideal for selection of drug targets. However, structural databases are not complete since quality protein- crystals are difficult to form and hinders X-ray crystallography (Holm and Sander, 1993). However, nuclear magnetic resonance can determine 3D structure determination. Also, computational modeling is approaching accurate functional predictions based on alignment of amino acid sequences (Grigoriev and Kim, 1999). 3.3.5 Sequence motif based: Target selection Motif analysis is another strategy to identify potential antibiotic targets among genes with unknown functions. Many databases, including PROSITE database, can search for motifs in a sequence (Hood, 1999). The motifs may show the approximate biochemical function of the gene. Next, gene fusion is a new computational method to infer protein interactions from genome sequences. Proteins that interact with each other tend to have homologs in other organisms that are joined into a single protein chain. This method would give additional functional information for target proteins. 3.3.6 Gene expression based target selection Finally, drug targets can be characterized further by using gene expression profiles: DNA microarrays, large-scale protein interaction
  8. 8. Chapter - III Review of Literature _________________________________________________________________________ Identification and Validation of Drug Targets 67 mapping, and proteomics (Hood, 1999). Genes that are functionally related are assumed to have similar gene expression profile patterns. Protein synthesis patterns are also useful to analyze the antimicrobial effect certain drugs would have on particular necessary or important proteins (Frosch and Reidl, 1998). 3.3.7 Finding antimicrobial drug targets using genetic foot printing Identification of unexplored cellular functions as potential targets is a prerequisite for development of novel antibiotic chemotypes. Choosing an optimal target function is a crucial step in the long and expensive process of drug development and requires the best possible understanding of related biological processes in bacterial pathogens and their hosts. Extensive programs utilizing genomic information to search for novel antimicrobial targets have been launched recently in industry and academia (Timberlake and Gavrias, 1999; Benton et al., 2000, 2005; Palmer, 2000; Nilsen, 2001). Complete genome sequences of multiple bacterial species, including many major pathogens, have become available in the last few years, with many more such projects under way (Bernal et al., 2001). The abundance of genomic data has enabled the development of novel postgenomic experimental and computational techniques aimed at drug target discovery. Experimental approaches to genomewide identification of genes essential for cell viability in several microbial species have been reviewed recently (Schmid, 1998; Moir et al., 1999; Judson and Mekalanos,
  9. 9. Chapter - III Review of Literature _________________________________________________________________________ Identification and Validation of Drug Targets 68 2000; Loferer et al., 2000; Rosamond and Allsop, 2000; Chalker et al., 2001; Hamer et al., 2001). These techniques are based on either systematic gene inactivation by directed mutagenesis on a whole-genome scale or high-throughput random transposon mutagenesis. The major advantage of the latter technique is the parallel analysis of thousands of genes under multiple growth conditions. A transposon-based approach termed “genetic foot-printing” was originally described for Saccharomyces cerevisiae (Smith et al., 1995, 1996). Genetic foot-printing is a three-step process:  Random transposon mutagenesis of a large number of cells,  Competitive outgrowth of the mutagenized population under various selective conditions, and  Analysis of individual mutants surviving in the population using direct sequencing or various hybridization and PCR-based techniques. Various modifications of genetic foot printing have been recently applied to several microorganisms, including Mycoplasma genitalium and Mycoplasma pneumoniae (Hutchison et al., 1999); Pseudomonas aeruginosa (Wong and Mekalanos, 2000), Helicobacter pylori (Jenks et al., 2001), and Escherichia coli (Badarinarayana, 2001; Hare et al., 2001). Another version of this method, termed genomic analysis and mapping by in vitro transposition, has been developed for Haemophilus influenzae
  10. 10. Chapter - III Review of Literature _________________________________________________________________________ Identification and Validation of Drug Targets 69 (Akerley et al., 1998; Reich et al., 1999) and Streptococcus pneumoniae (Akerley et al., 1998). Direct application of this technique is usually limited to microbial species with natural competence, since transposon mutagenesis is performed in vitro on isolated DNA fragments, and mutations are introduced into the genome by transformation with linear DNA fragments followed by gene conversion. By targeting a specific genomic region, this approach increases insertion density, improving resolution of genetic foot-printing. An elegant extension of this method beyond naturally competent species was described for P. aeruginosa (Wong and Mekalanos, 2000). 3.3.8 Target selection using comparative genomics Bacterial genome sequencing has triggered a complementary approach to target discovery that is directed rather than random, consisting of comparative genomics combined with bacterial genetics (Arigoni et al., 1998). Extensive genomic information gained from many evolutionarily distant bacterial species has made the automated comparison of bacterial genomes a powerful tool for categorizing genes and their respective products (Mushegian and Koonin, 1996; Tatusov et al., 1997). Focused lists of target candidates are generated by comparative genomics that are then rapidly validated using bacterial genetics. The gene categories generated by this approach enable such a preselection of target candidates on a whole-genome scale; in other words, targets can be
  11. 11. Chapter - III Review of Literature _________________________________________________________________________ Identification and Validation of Drug Targets 70 defined according to the required characteristics for a given antibacterial treatment. For example, genes that have orthologs in many evolutionarily distant organisms are target candidates for broadspectrum applications. Similarly, genes can be selected that are present only in a small subset of the bacterial species sequenced to date, thereby representing possible targets for narrow-spectrum antibacterial compounds. This target category is of particular importance for the treatment of chronic infections, as narrow-spectrum drugs would reduce both the spread of drug resistance and the side effects caused by destruction of the commensal bacterial flora, both of which are major disadvantages of long-term treatment with broad- spectrum antibiotics. Targets can also be selected according to their putative functions, although it should be noted that this approach, in particular, is highly dependent on accurate functional annotation of genomes and experimental validation is still a necessity (Arigoni et al., 1998). The comparative analysis of the genomes of Chlamydia trachomatis and Chlamydia pneumoniae has generated testable hypotheses of genes that might be responsible for the differences in tropism and pathologies between these two organisms (Kalman et al., 1999). In parallel to analyzing differences in closely related genomes by sequencing, DNA-array technologies have enabled the possibility of comparing genomes by hybridization. As exemplified by the comparative
  12. 12. Chapter - III Review of Literature _________________________________________________________________________ Identification and Validation of Drug Targets 71 hybridization analysis of Mycobacterium tuberculosis and Mycobacterium bovis by Bacille Calmette-Guérin (BCG), genomic regions that are different between pathogenic and non-pathogenic variants of a species can be rapidly identified (Behr et al., 1999). These regions contain genes that are likely to be of relevance for the development of antibiotics and/or vaccines. In addition to categorizing genes solely by sequence based comparisons (e.g. BLAST - basic linear alignment search tool) (Altschul et al., 1990), the coding sequences elucidated in new sequencing projects can be compared with reference databases of cellular pathways created mainly using the biological information known about E. coli and Bacillus subtilis (Karp et al., 1999). Using this method, the metabolic capabilities of a newly sequenced organism can be assessed, and putatively essential pathways and missing components of pathways identified. 3.3.9 Genes of unknown function as drug targets About 25-40% of the genes in a bacterial genome usually do not find matches with known genes (Smith, 1996). This is mainly because the earlier functions of most of the genes were not identified, also currently fast automated annotations methods enable rapid identification of most of the gene sequences and its functions. Furthermore, sequence homology is based on the assumption that similar sequences will share similar functions - a presupposition that does not hold true in many cases where similar sequences are structurally and functionally diverse.
  13. 13. Chapter - III Review of Literature _________________________________________________________________________ Identification and Validation of Drug Targets 72 One of the most intriguing results from the bacterial sequencing projects completed so far is that a significant fraction of the genes have unknown function (Hinton, 1997); in other words, these genes have been identified solely by sequencing and have not been previously characterized by any genetic or biochemical approach (Blattner et al., 1997). Even in well studied model organisms such as E. coli, <38% of all genes surprisingly fall into this category (Blattner et al., 1997; Hinton, 1997). Given the drawbacks listed earlier of random screens for temperature sensitive mutations, it can be speculated that, despite the extent of genetic studies on E. coli and B. subtilis, many targets still remain undiscovered among the genes of unknown function. Indeed, in a pilot study that investigated 26 FUN (Function unknown) genes that are broadly conserved among diverse bacterial species (including currently the smallest bacterial genome of Mycoplasma genitalium), six novel genes essential for the growth of E. coli and B. subtilis were identified, Arigoni et al., 1998. One of these genes was earlier eliminated from a postulated minimal gene set required for life based on its annotation as a host-interacting protein (Mushegian and Koonin, 1996). 3.3.10 Proteins as drug targets Proteins continue to assume significant attention from the pharmaceutical and biotechnology industries as a valuable source of potential drug targets (Deisenhofer and Smith, 2001). Proteins provide the
  14. 14. Chapter - III Review of Literature _________________________________________________________________________ Identification and Validation of Drug Targets 73 critical link between genes and disease, and as such are the key to the understanding of basic biological processes including disease pathology, diagnosis, and treatment. Researchers have discovered many potential therapeutic targets, and there are currently more than 700 products in various phases of development. However, translating the study of proteins into validated drug targets poses substantial challenges. Genome sequences instruct cells on how and when to make proteins. The proteins in turn are the active players in the cell. Proteins form the machinery of cells, allow cells to communicate, and can control growth or death of an organism. Because of their role in cells, most of the drug targets are proteins. Drugs work by binding specifically to a protein. Extensive knowledge about the function of a protein can guide the selection of targets for pharmaceutical chemists. Studying the complex domain of 200,000-300,000 distinct and interactive proteins poses substantial challenges. Most target proteins for drug development participate in key regulatory steps in the human body or in an infectious organism. As such, they tend to be present in few copies only and often within specific cells. Their isolation and purification using traditional preparative biochemical means and in quantities required for routine assays has been a formidable challenge. This situation has been radically changed by the ability to clone and express proteins. Thus many key target proteins are now becoming available in sufficient amounts to make them amenable not only to
  15. 15. Chapter - III Review of Literature _________________________________________________________________________ Identification and Validation of Drug Targets 74 biological assays but also to NMR studies in solution and to crystallization for X-ray analysis. The number of protein structures solved using X-ray or NMR has begun to rise sharply and more than 40,000 protein three-dimensional structures have been deposited in the Protein Data Bank till date (December 2006). Various classes of proteins can be categorized as potential drug targets. Small molecules such as drugs, insecticides or herbicides usually exert their effects by binding to protein targets. In the past, many of these molecules were found empirically with little or no knowledge of the mechanism of action involved. In many cases, the targets that are modified by these substances were identified in retrospect. Interestingly, the majority of drugs currently in use modulate either enzymes or receptors, most of them G-protein-coupled receptors. Protein structures are a rich source of information about membership of families and super-families. It is such divergently evolved proteins that need to be recognized as they are most likely to exhibit similar structure and function. A classical example is the recognition of HIV proteinase as a distant member of the pepsin/renin super-family and the subsequent modeling of its three-dimensional structure and the design of inhibitors (Blundell, 1988). In general, putative relatives are identified, the sequences aligned, and the three-dimensional structures are modelled. This is usually helpful in
  16. 16. Chapter - III Review of Literature _________________________________________________________________________ Identification and Validation of Drug Targets 75 proposing binding sites and molecular functions if key residues are conserved. Combined algorithms have been reported; for example, GenTHREADER uses the sequence comparison method to generate the sequence-structure alignment and then evaluates the alignment using threading potentials 3.3.11 Molecular drug targets and structure based drug design Discussion of the use of structural biology in drug discovery began over 35 years ago, with the advent of knowledge of the 3D structures of globins, enzymes and polypeptide hormones. Early ideas in circulation were the use of 3D structures to guide the synthesis of ligands of haemoglobin to decrease sickling or to improve storage of blood (Goodford, et al., 1980), the chemical modification of insulins to increase half-lives in circulation (Blundell et al., 1972) and the design of inhibitors of serine proteases to control blood clotting (Davie et al., 1991). An early and bold venture was the UK Wellcome Foundation programme focussing on haemoglobin structures established in 1975. (Beddell et al., 1976) However, X-ray crystallography was expensive and time consuming. It was not feasible to bring this technique ‘in-house’ into industrial laboratories, and initially the pharmaceutical industry did not embrace it with any real enthusiasm. In time, knowledge of the 3D structures of target proteins found its way into thinking about drug design. Although, in the early days, structures
  17. 17. Chapter - III Review of Literature _________________________________________________________________________ Identification and Validation of Drug Targets 76 of the relevant drug targets were usually not available directly from X-ray crystallography, comparative models based on homologues began to be exploited in lead optimization in the 1980s. (Blundell et al., 1996). An example was the use of aspartic protease structures to model renin, a target for antihypertensives (Sibanda et al., 1983). It was recognized that 3D structures were useful in defining topographies of the complementary surfaces of ligands and their protein targets, and could be exploited to optimize potency and selectivity (Campbell, 2000). Eventually, crystal structures of real drug targets became available; AIDS drugs, such as Agenerase and Viracept, were developed using the crystal structure of HIV protease (Lapatto et al., 1989) and the flu drug Relenza was designed using the crystal structure of neuraminidase (Varghese, 1999). There are now several drugs on the market that originated from this structure-based design approach (Hardy and Malikayil, 2003); list >40 compounds that have been discovered with the aid of structure-guided methods and that have entered clinical trials. The structure-based design methods used to optimize these leads into drugs are now often applied much earlier in the drug discovery process. Protein structure is used in target identification and selection (the assessment of the ‘druggability’ or tractability of a target), in the identification of hits by virtual screening and in the screening of fragments. Additionally, the key role of structural biology during lead optimization to
  18. 18. Chapter - III Review of Literature _________________________________________________________________________ Identification and Validation of Drug Targets 77 engineer increased affinity and selectivity into leads remains as important as ever. 3.3.12 Common drug targets The introduction of genomics, proteomics and metabolomics has paved the way for biology-driven process, leading to plethora of drug targets. The list of potential drug targets encoded in a genome includes most natural choice of virulent genes and species-specific genes. Other options include targeting RNA, enzymes of the intermediary metabolism, systems for DNA replication, translation apparatus or repair and membrane proteins. 3.3.13 Species-specific genes as drug targets Comparative analysis of the complete genome sequences of bacterial pathogens available in the public databases offers the first insights into drug discovery approaches of the near future (Galperin and Koonin, 1999). An interesting approach to the prediction of potential drug targets designated as the differential genome display has been proposed by Bork and co-workers (Huynen et al., 1997). This approach relies on the fact that genome of parasitic microorganisms are generally much smaller and code for fewer proteins than the genomes of free-living organisms. The genes that are present in the genome of a parasitic bacterium, but absent in a closely related genome of free living bacterium, are therefore likely to be
  19. 19. Chapter - III Review of Literature _________________________________________________________________________ Identification and Validation of Drug Targets 78 important for pathogenecity and can be considered as potential drug targets. Exhaustive comparison of H. influenzae and E. coli gene products identified 40 H. influenzae genes that have been exclusively found in pathogens and thus constitute potential drug targets. 3.3.14 Nucleic acid as drug targets Nucleic acids are the repository of genetic information. DNA itself has been shown to be the receptor for many drugs used in cancer and other diseases. These work through a variety of mechanisms including chemical modification and cross linking of DNA (cisplatin) or cleavage of the DNA (bleomycin). Much work either by intercalation of a polyaromatic ring system into the double stranded helix (actinomycin-D, ethidium) or by binding to the major and minor grooves of DNA (e.g., netropsin) (Haq, 2002) has been reported. DNA has been shown to be the target for chemotherapy with efforts to design sequence-specific reagents for gene therapy. 3.3.15 RNA as drug target Recent advances in the determination of RNA structure and function have led to new opportunities that will have a significant impact on the pharmaceutical industry. RNA, which, among other functions, serves as a messenger between DNA and proteins, was thought to be an entirely flexible molecule without significant structural complexity.
  20. 20. Chapter - III Review of Literature _________________________________________________________________________ Identification and Validation of Drug Targets 79 However, recent studies have revealed a surprising intricacy in RNA structure. This observation unlocks opportunities for the pharmaceutical industry to target RNA with small molecules. Perhaps more importantly, drugs that bind to RNA might produce effects that cannot be achieved by drugs that bind to proteins (Ecker and Griffey, 1999). Proof of the principle has already been provided by success of several classes of drugs obtained from natural sources that bind to RNA or RNA-protein complexes. 3.3.16 Membranes as drug targets Membranes are significant structural elements, both in defining the boundaries of a cell as well as providing interior compartments within the cell associated with particular functions. Cell membranes themselves can also act as targets for molecular recognition. An understanding of the structural and dynamic functions of the membranes (e.g., plasma membranes and intercellular membranes) may add to a more rational design of drug molecules with improved permeation characteristics or specific membrane effects. Many general anesthetics are believed to work by their physical effects when dissolved in membranes. Several classes of antibiotics like gramicidin A, antifungal like alamethicin and toxins such as mellitin found in bee venoms have direct effects on planar lipid bilayers, causing transmembrane pores.
  21. 21. Chapter - III Review of Literature _________________________________________________________________________ Identification and Validation of Drug Targets 80 3.3.17 GPCR as drug targets G protein-coupled receptors (GPCRs) are membrane embedded proteins, responsible for communication between the cell and its environment (Horn et al., 1998). As a consequence, many major diseases, such as hypertension, cardiac dysfunction, depression, anxiety, obesity, inflammation, and pain, involve malfunction of these receptors (Wilson and Bergsma, 2000), making them among the most important drug targets for pharmacological intervention (Rattner et al., 1999; Sautel and Milligan, 2000; Schoneberg et al., 2000). Thus, whereas GPCRs are only a small subset of the human genome, they are the targets for ~50% of all recently launched drugs (Klabunde and Hessler, 2002). G protein-coupled receptors (GPCRs), form one of the major groups of receptors in eukaryotes; they possess seven transmembrane α-helical domains, as confirmed by analysis of the crystal structure of Rhodopsin (Palczewski et al., 2000). The study of GPCRs, and the way that they are activated by their ligands, is of great importance in current research aiming at the design of new drugs. The importance of GPCRs in pharmaceutical industry, is reflected in the fact, that an estimated 50% of current prescription drugs target GPCRs (Drews, 2000; Hopkins and Groom, 2002; Ma and Zemmel, 2002). Characteristically, the human genome possesses approximately 700–800 GPCRs (Wise et al., 2004). Traditional medicinal chemistry enzyme targets include kinases, phosphodiesterases, proteases
  22. 22. Chapter - III Review of Literature _________________________________________________________________________ Identification and Validation of Drug Targets 81 and phosphotases. In view of their widespread distribution and importance in health and disease, it is not surprising that GPCRs are the most successful class of target proteins for drug discovery research. 3.3.18 Surrogate markers One approach towards the development of a generic assay is the identification of surrogate markers. Within the context of antibacterial drug discovery, surrogate markers are defined as genes that are deregulated as a specific response towards the inactivation of a given essential target. Eventually, inactivation of the respective target should occur through the action of a small molecule. However, for the identification of surrogate markers, target inactivation can be achieved through in vivo expression of small-peptide inhibitors (i.e. surrogate ligands) or by shifting a conditional mutant towards non-permissive conditions. Besides isolating temperature- sensitive alleles of the respective gene, conditional mutants can be generated by positioning a complementary copy of the essential gene under the control of a tightly regulated promoter (Arigoni et al., 1998). The availability of such conditional mutants enables the analysis of the phenotypic consequences for a bacterial cell caused by the inactivation of an essential gene. Samples harvested before and after depletion of the target of interest can be compared to investigate its molecular effect on the bacterial cell. Characterization of these types of responses using genomic technologies such as RNA expression profiling or proteome analysis
  23. 23. Chapter - III Review of Literature _________________________________________________________________________ Identification and Validation of Drug Targets 82 enables the identification of genes/proteins that are deregulated as a consequence of target inhibition. Hence, such deregulated genes/proteins are indicative of the biological activity of a given target and are defined as surrogate markers. One crucial issue regarding this approach is whether such target- or pathway-specific responses can be detected or whether generic stress responses will dominate across most mutants. Analyses by the current authors of several conditional mutants showed that specific deregulation of protein expression can be observed together with common responses. Identification of surrogate markers leads to an assay for compound screening Genes that are identified as surrogate markers can be linked to a reporter gene and its deregulation can then be assayed as a measure of target/pathway inhibition. Such a target-specific whole cell assay will combine the advantage of selection for cell permeable compounds inherent in killing assays with the target-specificity and sensitivity achieved by in vitro assays. Hence, the important additional advantage is that no detailed functional information concerning the target is required. 3.3.19 Surrogate ligands Another generic assay development strategy is the identification of surrogate ligands for a given target. Surrogate ligands are short peptides that bind with high affinity (low micromolar to nanomolar) to a target protein
  24. 24. Chapter - III Review of Literature _________________________________________________________________________ Identification and Validation of Drug Targets 83 and thereby inhibit its function. Based on such surrogate ligands, in vitro assays can be designed whereby compound libraries are screened for small molecules that competitively displace the peptide and thus occupy the peptide’s binding site on the target protein. There are several approaches for isolating surrogate ligands from random peptide libraries, the most prominent being phage display technology (Paige, 1999; Wrighton, 1996). Here, a library of random peptides is displayed on the surface of filamentous phages. Binding peptides are isolated by a procedure called ‘biopanning’, where the purified target protein is immobilized on a solid support and phages carrying binding peptides are isolated from the phage library by repeated cycles of adhesion and washing. Sequencing the appropriate segment of the DNA of each captured phage provides the primary sequence of peptides that binds to the target. Isolation of high- affinity binding peptides using phage display has been described for several different protein classes, as will now be summarized. Agonists that activate the cytokine erythropoietin (EPO) receptor were isolated from random phage display peptide libraries (Wrighton, 1996). These agonists were represented by a 14-amino acid consensus sequence of cyclic peptides containing a disulphide bond. The amino acid sequences of these peptides were not found in the primary sequence of EPO. Furthermore, the signalling pathways activated by these peptides
  25. 25. Chapter - III Review of Literature _________________________________________________________________________ Identification and Validation of Drug Targets 84 appeared to be identical to those induced by the natural ligand. Another example of isolating small-peptide ligands for a protein of therapeutic relevance was the nuclear hormone receptor for oestrogen (Paige, 1999). There is great potential for the isolation of binding peptides that serve as lead structures for many functionally diverse proteins, thus qualifying this strategy for assay development of targets where functional information is lacking. However, these experimental strategies aim to isolate surrogate ligands in vitro. In many instances, it is desirable to validate that the respective peptide(s) have a significant effect on the target protein in vivo. Thioredoxin, isolated from E. coli, serves as a structural framework for presenting peptides in vivo (Colas et al., 1996). Using the yeast two-hybrid approach, a peptide aptamer was isolated that binds, and competitively inhibits, the cyclin-dependent kinase 2 (Cdk2)26. Expression of this peptide in human cells slows their progression through the G1 phase of the cell cycle. Furthermore, expression of inhibitory peptide aptamers directed against the essential cyclin-dependent kinases, DmCdk1 and DmCdk2 in Drosophila, caused adult eye defects typical of those caused by cell cycle inhibition (Kolonin and Finley, 1998). These findings demonstrate that in vivo validation of surrogate peptide ligands is possible even in complex systems such as cultured mammalian cells or Drosophila. For bacterial targets that are essential for viability, it
  26. 26. Chapter - III Review of Literature _________________________________________________________________________ Identification and Validation of Drug Targets 85 can be easily tested whether the peptide ligand binds to a functionally relevant site on the protein (i.e. the respective peptides have to be lethal or inhibit growth on induction of expression in E. coli). 3.4 DRUG TARGET PREDICTION - IN VITRO, IN VIVO AND IN SILICO 3.4.1 Genomic Studies and Identification of Diseased Gene With the wealth of available genomic information, it is possible now to test hundreds of thousands of DNA markers in human, mouse, and other species for association with disease. The variations in DNA impact complex physiologic processes flows through transcriptional and other molecular networks. As it is possible to monitor these changes at transcript levels, combining them with the DNA variation, transcription, and phenotypic data will lead towards identification of the associations between DNA variation and disease. Now it’s possible to assess more than 1,000,000 polymorphisms or the expression of more than 25,000 genes in each participant of a clinical study at reasonable costs. However, there are difficulties in identifying the most likely disease-related genes. The application of statistical techniques in biology always contributed to substantial results from the times of Mendel. The currently evolving statistical procedures that operate on networks will be critical to extracting information related to complex phenotypes like disease. The challenge is adopting novel integrative genomics approaches for dissecting disease traits. This will significantly enhance the identification of key drivers of
  27. 27. Chapter - III Review of Literature _________________________________________________________________________ Identification and Validation of Drug Targets 86 disease beyond what could be achieved by genetic association studies alone. Numerous studies have been undertaken adopting these technologies to identify polymorphisms in genes that associate with diseases like age-related macular degeneration (Edwards et al., 2005; Haines, 2005; Klein, 2005), diabetes (Grant, 2006; Sladek, 2007), and obesity (Herbert, 2006). The susceptibility gene (ApoE) for the disease Alzheimer’s was identified nearly 15 years ago (Peacock et al., 1993). The advent of DNA microarrays techniques has radically changed the way we study genes. This enables us to have a perspective of their role in everything from the regulation of normal cellular processes to complex diseases like obesity and diabetes. Practically, microarrays allow researchers to screen thousands of genes for differences in expression at various experimental conditions (Schadt and Lum, 2006). Performing these experiments and comparing them with a normal and diseased condition will unravel the genes associated with the disease. Using these technologies, gene transcripts associated with complex disease phenotypes (Karp et al., 2000; Schadt et al., 2003) and disease subtypes are identified (Van’t Veer et al., 2002; Mootha et al., 2003; Schadt et al., 2003). 3.4.2 Pharmacogenetics for Improving Drug discovery and Development Pharmacogenetics is defined as the use of biological markers like DNA, RNA or protein to predict the efficacy of a drug and the likelihood of the occurrence of an adverse event in individual patients (Roses, 2000).
  28. 28. Chapter - III Review of Literature _________________________________________________________________________ Identification and Validation of Drug Targets 87 The variable response to drugs results from the variation in the human genome. Driven by the advancements in molecular biology, pharmaco- genetics has evolved within the past 40 years from a niche discipline to a major driving force of clinical pharmacology. More obviously, pharmacogenetics has changed the practices and requirements in preclinical and clinical drug research; large clinical trials without a pharmacogenomic add-on appear to have become the minority. The science of pharmacogenetics originated from the analysis of a few rare and sometimes serendipitously found extreme reactions (phenotypes) observed in some humans; these phenotypes were either inherited diseases or abnormal reactions to drugs or other environmental factors. In 2008, we have witnessed about 12 million single nucleotide polymorphisms in the human genome and a large amount of other types of genomic variation. The impact of inherited chemical individuality in metabolic enzymes has been well known for more than 100 years. 3.4.3 Pharmacokinetics for Improving Drug discovery and Development For a maximum ROI, pharmaceutical industry should improve success rates and reduce candidate attrition. Early identification of potential drug attrition candidates leads to overall cost reduction (Di Masi, 2002). To accomplish this, all potential stages and causes of attrition that have led to drug development failure in the past has to be clearly identified. Published survey on the causes of failure in drug development points to inappropriate
  29. 29. Chapter - III Review of Literature _________________________________________________________________________ Identification and Validation of Drug Targets 88 pharmacokinetics parameters (Prentis et al., 1998). Hence pharmacokinetic factors are very crucial for the assessment of safety during early drug development. Drug discovery employing pharmacogentic principles provides treatments customized for individuals or specific subpopulations with minimized adverse effects (Ozdemir et al., 2001). If the target protein is polymorphic it leads to varied drug responses. Polymorphisms results due to single nucleotide polymorphisms (SNPs), gene deletions and gene duplications. Recently, pharmaceutical companies are focused on screening compounds that are substrates solely for a polymorphic enzyme to avoid the wider inter-subject variability in exposure, and hence safety and efficacy (Rodrigues and Rushmore, 2002). 3.4.4 Virtual screening Target-based virtual screening methods depend on the availability of structural information of the target, that being either determined experimentally or derived computationally by means of homology modeling techniques (Shoichet, 2004; Klebe, 2006). These methods aim at providing, on one hand, a good approximation of the expected conformation and orientation of the ligand into the protein cavity (docking) and, on the other hand, a reasonable estimation of its binding affinity (scoring). Despite its appealing concept, docking and scoring ligands in target sites is still a challenging process after more than 20 years of research in the field
  30. 30. Chapter - III Review of Literature _________________________________________________________________________ Identification and Validation of Drug Targets 89 (Kitchen et al., 2004) and the performance of different implementations has been found to vary widely depending on the given target (Cummings et al., 2005). To alleviate this situation, the use of multiple active site corrections has been suggested to remedy the ligand dependent biases in scoring functions and the use of multiple scoring functions (consensus scoring) has been also recommended to improve the enrichment of true positives in virtual screening (Charifson et al., 1999). Also, as the number of protein– ligand complexes available continues to grow, docking methods are beginning to incorporate all the information derived from the conformation adopted by protein-bound ligands as a knowledge-based strategy to correct some of the limitations of current scoring functions and actively guide the orientation of the ligands into the protein cavity. In spite of all these limitations, target-based virtual screening has gained a reputation in successfully identifying and generating novel bioactive compounds. As an example, the use of a knowledge-based potential (SMoG) in protein-ligand docking, resulted in the identification of new picomolar ligands for the human carbonic anhydrase-II (Grzybowski et al., 2002). Docking methods have also resulted in the discovery of novel inhibitors for several kinase targets, including cyclin-dependent kinases, epidermal growth factor receptor kinase and vascular endothelial growth factor receptor 2 kinase among others (Muegge and Enyedy, 2004). Finally, the application of docking methods to targets for which experimentally
  31. 31. Chapter - III Review of Literature _________________________________________________________________________ Identification and Validation of Drug Targets 90 determined structures are not available yet has gained considerable attention in recent years, particularly for the many targets of therapeutic relevance belonging to the super-family of G-protein-coupled receptors (GPCRs) (Bissantz et al., 2003). In these cases, structural information is generated computationally by modelling the structure of the target of interest on the basis of a template structure of a related target, including often information on ligands as restraints (Evers et al., 2003). Such strategies have resulted in the successful identification of novel antagonists for the neurokinin-1 and the a1A-adrenergic receptors (Evers and Klebe, 2004; Evers and Klabunde, 2005). One of the earliest developed initiatives is the computer system PASS (Poroikov et al., 2000), which is based on the analysis of structure- activity relationships for a training set of compounds consisting of about 35, 000 biologically active compounds extracted from the literature. The system provides a prediction of the activity spectra of substances for more than 500 biological activities. 3.4.5 Present state of the art: Computer-aided drug design Given the vast size of organic chemical space (Kuntz, 1992), drug discovery cannot be reduced to a simple “synthesize and test” drudgery. There is an urgent need to identify and/or design drug-like molecules (Walters et al., 1998) from the vast expanse of what could be synthesized. In silico methods have the potential to reduce both time and cost in
  32. 32. Chapter - III Review of Literature _________________________________________________________________________ Identification and Validation of Drug Targets 91 developing suggestions on drug/lead-like molecules. Computational tools have the advantage for delivering new lead candidate more quickly and at lower cost. Drug discovery in the 21st century is expected to be different in at least two distinct ways: development of individualized medicine departing from genomic information and extensive use of in silico simulations to facilitate target identification, structure prediction and lead/drug discovery. The expectations from computational methods for reliable and expeditious protocols for developing suggestions on potential leads are continuously on the increase. Several conceptual and methodological concerns remain before an automation of drug design in silico could be contemplated. Computational methods are needed to exploit the structural information to understand specific molecular recognition events and to elucidate the function of the target macromolecule. This information should ultimately lead to the design of small molecule ligands for the target, which will block/activate its normal function and thereby act as improved drugs. As structural genomics, bioinformatics, and computational power continue to explode with new advances, further successes in structure-based drug design are likely to follow. Every year, new targets are being identified; structures of those targets are being determined at an amazing rate, and capability to capture a quantitative picture of the interactions between macromolecules and ligands is accelerating.
  33. 33. Chapter - III Review of Literature _________________________________________________________________________ Identification and Validation of Drug Targets 92 3.4.6 Genomics and drug development So far, bacterial genomics has had a major impact on identification and validation of targets and on assay development technologies for high- throughput screening. However, genomic technologies will also be crucial to subsequent stages of drug development such as lead optimization, toxicology and clinical studies. One technology with particularly high potential in these areas is the determination of cellular gene expression patterns using DNA arrays (Maier et al., 1997). The global changes in gene expression of a given cell as a response to the effect of a compound can be viewed as a reflection of the mechanism by which a compound acts on the cell. In other words, compounds with similar effects on the cell’s physiology could produce related changes in gene expression patterns. Using high density oligonucleotide expression arrays representing nearly all the yeast genes, novel kinase inhibitors isolated from combinatorial chemical libraries were characterized by investigating their effect on yeast gene expression on a genome-wide scale (Gray et al., 1998). Hannes Loferer et al. (2000) experimented two inhibitors with an identical in vitro inhibitory spectrum (Cdc28p and Pho85p) were compared with a structurally related compound that showed no inhibitory in vitro activity. Results showed 3% of the genes showed a greater than twofold change in transcript level when treated with each of the kinase inhibitors,
  34. 34. Chapter - III Review of Literature _________________________________________________________________________ Identification and Validation of Drug Targets 93 whereas only 0.03% were affected following treatment with the control compound. Part of the set of genes affected by the inhibitors were loci involved in cell cycle progression and phosphate metabolism, consistent with the spectrum of kinases that were inhibited in vitro. Very few of the genes induced by the inhibitors were affected by the control compound, suggesting that many of the drug-sensing mechanisms might respond to signals associated with the function rather than the structure of the drug (Gray et al., 1998). Pharmaceutical and biotechnological companies are screening large numbers of chemical libraries for compounds with antibacterial activity. Downstream development of these primary hits into leads would be greatly facilitated if efficient technologies for mode-of-action studies were available. Recently, the effect of the anti-tuberculosis drug isoniazid on global gene expression profiles was investigated. One of the key findings was the induction of a set of genes encoding the pathway known to be affected by the drug (synthesis of the outer lipid envelope of mycobacteria) (Wilson, 1999). Also it is evident that its possible to correlate changes in gene expression patterns with a drug’s mode of action. However, for analyzing entirely new chemical entities only described by their bactericidal or bacteristatic activity, extensive databases of the effects of reference compounds (of known mode of action) and conditional mutants on transcript
  35. 35. Chapter - III Review of Literature _________________________________________________________________________ Identification and Validation of Drug Targets 94 profiles will have to be generated. The degree of resolution to which the mechanism of action of a currently undescribed compound can be pinpointed by gene expression profiling remains to be determined. Another application of the same principle is the assessment of the risk of developing early hits from drug screening. For example, characterization using gene expression profiling of an anti-arteriosclerotic compound that, in cell culture, drastically reduced levels of low-density lipoprotein, revealed that the effect of this compound on gene expression strongly resembled that of a completely different class of compounds that was already shown to be toxic (Service, 1998). Thus, resources were not wasted on unsuitable drug candidates. Such approaches are also being discussed in the field of general toxicology and have already been defined as the subdiscipline of toxicogenomics (Nuwaysir et al., 1999). Together with advances in the human genome project and genomic technologies, the development of anti-infective drugs in the future will become more efficient and targeted by defining patient sub-populations according to their suitability for a given treatment. This era of genetic susceptibility to infectious disease will streamline clinical trials by using more focused patient populations. For example, a small deletion in the gene for the interferon g(IFNg) receptor (IFNGR1) was found to be associated with dominant susceptibility to infections caused by BCG that is
  36. 36. Chapter - III Review of Literature _________________________________________________________________________ Identification and Validation of Drug Targets 95 normally avirulent and used as a tuberculosis vaccine (Jouanguy et al., 1999). The increasing resistance of bacterial pathogens to present-day antibiotics and the lack of a robust pipeline of innovative antimicrobial substances demand innovative and more efficient approaches towards the development of anti-infective drugs. Bacterial genomics has so far greatly increased the rate with which novel targets are identified and validated. Furthermore, the chances that ‘second generation’ genomic technologies will accelerate target identification and generic assay development are high. Genomics can also help to streamline later stages of the development of antimicrobials, such as lead optimization, toxicology and clinical trials. However, a concerted innovative application of genomic technologies and chemistries are required to decrease the lag-period between lead identification and marketing of a new drug. 3.4.7 Genomics and assay development Traditionally, screening for novel antimicrobial compounds in industry has been performed by testing large libraries of natural products for their ability to kill bacteria. Many of the antibiotics used today were discovered this way. This classical approach is again enjoying increased interest because of improved chemical diversity through combinatorial chemistry (Myers, 1997) and the exploitation of unexplored resources of natural products (Nisbet and Moore, 1997). However, this approach has
  37. 37. Chapter - III Review of Literature _________________________________________________________________________ Identification and Validation of Drug Targets 96 disadvantages such as low sensitivity and the fact that the targets of the respective compounds are unknown. The latter point has also created interest in applying genomic technologies to investigate the mode of action of antibacterial compounds. In an alternative approach, individual proteins involved in well-studied essential pathways are purified and biochemical in vitro assays set up to screen compound libraries for inhibitors. Empirically, many of the primary hits of these screens will have to be modified to enable penetration into the bacterial cell. Furthermore, the biological function (i.e. biochemical activity) of the respective target protein needs to be known to enable this type of assay development. As already outlined, the latest developments in whole genome genetic analysis (e.g. GAMBIT or the B. subtilis functional analysis program) will lead to the identification of most genes essential for growth and survival of several bacterial species within the near future. However, detailed functional information that will enable classical assay development will only be available for a minority of new targets. Thus, to use this extensive target information efficiently, innovative assay development strategies are required that are applicable to a broad functional variety of antibacterial targets. 3.4.8 Protein-protein interaction technologies It can be anticipated that many validated drug targets from microbial genomics will be identified in the near future for which detailed functional
  38. 38. Chapter - III Review of Literature _________________________________________________________________________ Identification and Validation of Drug Targets 97 information is lacking. Although existing technologies enable assay development based on such targets, large scale functional analysis helps to prioritize targets for screening programs by positioning them in the context of cellular pathways. One historically very successful method of gaining functional information concerning proteins is the identification of protein- protein interactions using the yeast two-hybrid system (Colas et al., 1996). This technology utilizes the fact that the DNA binding and the trans- activating domains of the yeast transcriptional activator Gal4 can be separated, rendering the protein inactive. Activity of the separated domains can be regained by physical proximity. Hence, protein fusions to these domains are generated and physical interaction of the respective protein fusion partners can be identified on the basis of transcriptional activation of the reporter genes. In the standard molecular biology laboratory, performing a yeast two- hybrid screen is very labour-intensive, because of the high frequency of false-positive hits that must be identified for every screen. The two largest problems in laboratory-scale yeast two-hybrid screens are the appearance of auto activating protein domains fused to the DNA-binding domain vector and the effect of ‘sticky’ proteins that bind non-specifically to many other proteins (Bartel et al., 1993). Thus, solutions for efficiently eliminating these problematic clones are essential if large-scale analyses of protein–protein interactions are to be
  39. 39. Chapter - III Review of Literature _________________________________________________________________________ Identification and Validation of Drug Targets 98 feasible. Genome Pharmaceuticals Corporation (GPC) has developed an efficient procedure for parallel identification and elimination of autoactivators and sticky proteins. In this method, called PathCode™, replicas of putatively interaction- positive yeast clones (as identified by positive selection) are arrayed on nylon filters and allowed to grow on three different selective media in parallel. One medium selects for the presence of both plasmids and assays for protein interaction using the lacZ reporter. The remaining two media each counterselect against one of the two interaction plasmids and positively select for the other. The selection (for and against) is reversed between the two media. Clones that are lacZ- positive on any of the counterselective media are false-positive hits. Large numbers of clones are analyzed using digital image analysis procedures based on the software package BiochipExplorer™ (GPC). This technology opens up the possibility of analyzing large numbers of ‘baits’ or even performing library versus- library screens for entire bacterial genomes. Assay formats can be developed that enable screening for small molecules that inhibit and/or disrupt such protein–protein interactions (‘reverse two-hybrid screening’) (Huang and Schreiber, 1997). Thus, the investigation of protein–protein interactions using the yeast two-hybrid system not only helps to gain functional information, but might directly be the basis for drug screening. However, it remains to be elucidated how
  40. 40. Chapter - III Review of Literature _________________________________________________________________________ Identification and Validation of Drug Targets 99 many of the antibacterial drug targets will be involved in protein–protein interactions that can be disrupted by small molecules. 3.5 NOVEL GENOMICS APPROACH Availability of genome sequences of pathogens has provided a tremendous amount of information that can be useful in drug target and vaccine target identification. One of the recently adopted strategies is based on a subtractive genomics approach, in which the subtraction dataset between the host and pathogen genome provides information for a set of genes that are likely to be essential to the pathogen but absent in the host. This approach has been used successfully in recent times to identify essential genes in Pseudomonas aeruginosa. The same methodology was used to analyse the whole genome sequence of the human gastric pathogen Helicobacter pylori. The analysis revealed that out of the 1590 coding sequences of the pathogen, 40 represent essential genes that have no human homolog. Further analyses of these 40 genes by the protein sequence databases lists some 10 genes whose products are possibly exposed on the pathogen surface. This preliminary work reported here identifies a small subset of the Helicobacter proteome that might be investigated further for identifying potential drug and vaccine targets in this pathogen. The completion of the human genome project has revolutionised the field of drug-discovery against threatening human pathogens (Miesel et al.,
  41. 41. Chapter - III Review of Literature _________________________________________________________________________ Identification and Validation of Drug Targets 100 2003). The strategies for drug design and development are progressively shifting from the genetic approach to the genomic approach (Galperin and Koonin, 1999). The search for potential drug targets has increasingly relied on genomic approaches. Subtractive genomics has been successfully used by authors to locate novel drug targets in Pseudomonas aeruginosa (Sakharkar et al., 2004). _____