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Introduction to Drug Target Identification

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Introduction to Drug Target Identification

Introduction to Drug Target Identification

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  • 1. Chapter - I Introduction _________________________________________________________________________ Identification and Validation of Drug Targets 1 Chapter I INTRODUCTION . . 1.1 NEW DRUGS - WHY? In the initial stages of drug therapy, scientists and medical researchers were not aware about the targets on which these antibiotics act. Only thing that fascinated them was that these newly discovered compounds exhibited reasonable antibacterial properties. The above scientific findings propelled to isolate those compounds and use them for treating bacterial diseases. Alexander Fleming’s discovery of antibiotic ‘Penicillin’ is considered as one of the historical milestones in medical research. The following are some of his words summarizing the findings (BMJ, 1955). A certain type of penicillium produces in culture a powerful antibacterial substance. The active agent is readily filterable and the name 'penicillin' has been given to filtrates of broth cultures of the mould. The action is very marked on the pyogenic cocci and the diphtheria group of bacilli. Penicillin is non-toxic to animals in enormous doses and is not irritant. It does not interfere with leucocytic function to a greater degree than does ordinary broth.
  • 2. Chapter - I Introduction _________________________________________________________________________ Identification and Validation of Drug Targets 2 "It is suggested that it may be an efficient antiseptic for application to, or injection into, areas infected with penicillin-sensitive microbes." The discovery of penicillin in 1928 gave confidence to the medical researchers that any bacterial disease could be treated. Penicillin was one of the hall mark discoveries in the field of antibiotics and in fact it managed most of the diseases of that time. Sooner its effect faded due to the inherent capability of the microbes to confer resistance (Watson, 1958). The resistance is found to be easily transmitted among the bacterial species and hence new molecules/antibiotics were always a need to combat life threatening diseases. In the 19th century penicillin was one of the most widely used antibiotics. In these days it is not common to find a person who has not received it during their life time. Almost every organism responded well to this drug. Subsequent studies carried out in 1940s explained its mode of action on cell wall. At this stage scientists and medical researchers did not have a molecular level understanding of the exact binding of this molecule, whereas the modern methods of drug discovery explain how a drug molecule binds specifically and interacts with the disease target. The growing concern of antibiotic resistance and drug efficiency demands discovery and development of new drugs to fight against the life threatening diseases. The recent technological advancements in science enabled rapid sequencing of genome of various organisms. The completion
  • 3. Chapter - I Introduction _________________________________________________________________________ Identification and Validation of Drug Targets 3 of human genome project (HGP) brought forth a paradigm shift in drug discovery process as it provided clarity on molecular level understanding of disease. With the completion of sequencing human and its various pathogenic microbes, it enabled researchers to look for novel drug targets from these genome sequences. The numbers of drug targets identified till date are 500, while the drugs currently in use are based on only 120 drug targets (Hopkins and Groom, 2002). The majority of existing antibiotics utilizes a limited number of core chemical structures and targets only a few cellular functions, such as cell wall biosynthesis, DNA replication, transcription, and translation (Moir et al., 1999). 1.2 ANTIBACTERIAL DRUG DISCOVERY - A BRIEF HISTORY The importance of new class of antibiotics will be clearly understood when we analyze the origin of antibacterial drug discovery and its prevailing status. The pharmaceutical industry owes much of its early prosperity to the discovery of antibacterial agents. Early antibacterial agents discovered were the sulfonamides, penicillin and streptomycin, and these were rapidly followed by tetracyclines, isoniazid, macrolides, glycopeptides, cephalosporins, nalidixic acid and other molecular classes. Despite its discovery in 1928, it required a consortium of five pharmaceutical companies (Abbott, Lederle, Merck, Chas. Pfizer and ER Squibb & Sons) and the US Department of Agriculture to develop and produce penicillin in the 1940s, mainly as part of the war effort during the Second World War. The cephalosporins became popular during the 1970s, with several
  • 4. Chapter - I Introduction _________________________________________________________________________ Identification and Validation of Drug Targets 4 ‘second’ and ‘third’ generation products entering the marketplace by the mid-1980s. Coincident with the growing market dominance of the third generation cephalosporins was the emergence of the pandemic of multidrug resistant Streptococcus aureus infections in US hospitals and Streptococcus pneumoniae in the community. At that time, in the early 1980s, the pharmaceutical industry began scaling back on their antibacterial drug discovery efforts with approximately half of large US and Japanese pharmaceutical companies ending or curtailing their efforts. Yet antibacterial drug discovery efforts did continue at many major European and US pharmaceutical companies through the 1990s. But since 1999 the industry has once again pulled back from anti-infective research in an even more concerted manner, with 10 of the 15 largest companies ending or curtailing their discovery efforts. While this was occurring the industry has been experiencing a series of mega-mergers leading to large scale consolidation. This consolidation alone has resulted in a major decrease in the hunt for novel antibacterial agents. The rise in the levels of antibacterial drug resistance in human pathogens is most common phenomenon. Resistance is defined as bacteria that are not inhabited by usually achievable systematic concentration of an agent with normal dosage schedule and /or fall in the minimum inhibitory concentration ranges. Drug resistance is of major concern for severely ill and hospitalized patients as therapeutic efficacy of current drugs in practice
  • 5. Chapter - I Introduction _________________________________________________________________________ Identification and Validation of Drug Targets 5 is declining. First clear proof of resistance to penicillin was reported by an accidental observation in 1958 (Ley et al., 1958). Microorganisms developing resistance towards an antibacterial substance is an inherent mechanism. Widespread occurrence of microbial resistance coupled with the declining efficiency of current antibiotics in practice demands discovery and development of novel therapeutics. Antimicrobial Availability Task Force identified six problematic pathogens, Gram negative organisms (Acinetobacter baumannii, extended spectrum β-lactamase (ESBL) producing Enterobacteriaceae, and Pseudomonas aeruginosa), Gram-positive pathogens (methicillin resistant Staphylococcus aureus (MRSA) and vancomycin resistant Enterococcus faecium) and the filamentuous fungi Aspergillus spp as a potential threat to the community. Of these organisms, MRSA is the organism that has received the most attention, largely driven by clinical need rather than by large sums of money. It is likely that interest in the other problematic pathogens will also be driven by clinical need and not by investment to increase awareness. Some experts consider two additional water-borne, non-fermenting Gram-negative pathogens, namely Stenotrophomonas maltophilia and Burkholderia cepacia, both of which are related to P. aeruginosa, to be problematic organisms. Multidrug-resistant strains are particularly problematic, conveying increased mortality, longer hospital stays, and higher hospital costs over and above the values associated with susceptible strains of these
  • 6. Chapter - I Introduction _________________________________________________________________________ Identification and Validation of Drug Targets 6 pathogens. Successful treatment requires a ‘hit hard and hit fast’ approach with an antibiotic that provides coverage of these important Gram-negative organisms, including multidrug-resistant strains. Various studies have indicated that the frequency of multidrug-resistant isolates is increasing worldwide. Considering the present need for discovery and development of novel antibiotics we are already too late. 1.3 MULTIDRUG RESISTANCE - DRIVING THE NEED FOR NEW DRUGS Increased resistance of commonly used antibiotics, a growing prevalence of infections, and the emergence of new pathogenic organisms challenge current use of antibiotic therapy (Rosamond and Allsop, 2000). Recent epidemiological studies suggest an increase in healthcare associated infections caused by gram-negative bacteria, particularly Klebsiella spp., Escherichia coli, Pseudomonas aeruginosa, and Acinetobacter spp. The rising incidence of drug resistance of these pathogens presents a challenge given the few novel antimicrobial agents under development that specifically target these organisms. Latest developments in the areas of targets involved in bacterial virulence or resistance against antibacterial agents have been reviewed previously (Schmid, 1998). Bacteria have developed a variety of resistance mechanisms coupled with the ability to mobilize the respective genetic information between bacterial strains and species (Heinemann, 1999).
  • 7. Chapter - I Introduction _________________________________________________________________________ Identification and Validation of Drug Targets 7 Gram-negative non-fermenters exhibit resistance to essentially all commonly used antibiotics, including anti-pseudomonal penicillins and cephalosporins, aminoglycosides, tetracyclines, fluoroquinolones, trimethoprim-sulfamethoxazole, and carbapenems. Polymyxins are the remaining antibiotic drug class with fairly consistent activity against multidrug-resistant strains of P aeruginosa, Acinetobacter spp, and S. maltophilia. A variety of resistance mechanisms have been identified in P aeruginosa and other gram-negative non-fermenters, including enzyme production, over expression of efflux pumps, porin deficiencies, and target- site alterations. Multiple resistance genes frequently coexist in the same organism. Multidrug resistance in gram-negative non-fermenters makes treatment of infections caused by these pathogens both difficult and expensive. Improved antibiotic stewardship and infection-control measures will be needed to prevent or slow down the emergence and spread of multidrug-resistant, non-fermenting gram-negative bacilli in the healthcare setting, (Lautenbach and Polk, 2007; McGowan, 2006). Knowledge of the clinical and economic impact of antimicrobial resistance is useful to influence programs and behavior in healthcare facilities, to guide policy makers and funding agencies, to define the prognosis of individual patients and to stimulate interest in developing new antimicrobial agents and therapies. A recent study showed that there is an association between antimicrobial resistance in Staphylococcus aureus, Enterococci and Gram-negative bacilli and increases in mortality, morbidity,
  • 8. Chapter - I Introduction _________________________________________________________________________ Identification and Validation of Drug Targets 8 length of hospitalization and cost of healthcare. Patients with infections due to antimicrobial-resistant organisms have higher costs (US $ 6,000-30,000) than do patients with infections due to antimicrobial-susceptible organisms; the difference in cost is even greater when patients infected with antimicrobial-resistant organisms are compared with patients without infection, (Maragakis et al., 2008). Delivering healthcare with affordate cost is need of the hour as the increased healthcare care is already rising due to different factors. 1.3.1 Molecular mechanism of drug resistance Development of resistance limits usefulness of effective drugs and hence poses a major threat to the pharmaceutical industry. Over the past two decades understanding the mechanisms of drug resistance has become a central issue as its importance in medicine has assumed ever- increasing significance. The following table shows the various origin of antimicrobial resistance. Understanding the origin of resistance will aid in avoiding potential pitfalls while developing a new drug for a specific disease.
  • 9. Chapter - I Introduction _________________________________________________________________________ Identification and Validation of Drug Targets 9 Table 1 Origins of Intrinsic and Acquired Resistance S. No. Type Duration of resistance Frequency of resistance within the population Intrinsic resistance 1. Absence of target site Permanent All cells 2. Species-specific structure of target site Permanent All cells 3. High detoxication capacity, arising from: a. tissue-specific function Permanent All cells b. ontogenic variations Variable All cells c. sex-specific differences Permanent All cells d. population polymorphisms Permanent Variable e. self defence Permanent All cells f. high repair capacity Permanent All cells 4. Low drug delivery Variable Variable 5. Cell cycle effects Variable Variable 6. Adaptive change Temporary All cells 7. Stress response Temporary All cells Acquired resistance 1. Natural selection Permanent Rare 2. Constitutive adaptive change Permanent Rare 3. Constitutive stress response Permanent Rare 4. Gene transfer Required continued selection Rare 5. Gene amplification Required continued selection Rare Source: John Hayes and Roland Wolf (1990)
  • 10. Chapter - I Introduction _________________________________________________________________________ Identification and Validation of Drug Targets 10 Intrinsic drug resistance The term ‘Intrinsic resistance’ is used to describe the situation where an organism, or cell, possesses a characteristic 'feature' which allows all normal members of the species to tolerate a particular drug or chemical environment. In this case, the 'feature' responsible for resistance is an inherent, or integral, property of the species that has arisen through the processes of evolution. Mechanisms of intrinsic resistance The phenomenon of intrinsic resistance can be due to either the presence or the absence of a biochemical 'feature' (Table 2). This may, for example, be the structure of the cell envelope or membrane, the existence of a drug transport protein, the absence of a metabolic pathway, the presence of a drug-metabolizing enzyme, the structure of the drug target site and the expression of specific stress response proteins or high repair capacity. Self protection mechanism associated with intrinsic drug resistance Many organisms survive in the environment through their ability to produce chemicals which are toxic or distasteful to their predators or their competitors. As a consequence, they require their own defence against the noxious chemicals they produce. Studies on the antibiotic-producing micro organisms such as the various species of Streptomyces provide good examples of this form of intrinsic drug resistance. The mechanisms used by
  • 11. Chapter - I Introduction _________________________________________________________________________ Identification and Validation of Drug Targets 11 organisms to protect themselves against their own antibiotic products were divided into two types, firstly, resistance involving inactivation of antibiotics such as streptomycin and neomycin by the phosphotransferases and acetyltransferases and secondly, resistance resulting from modification of potential target sites within the organism (Cundliffe 1984). For example, the ribosomal RNA is protected by methylation in the erythromycin producer Streptomyces erythraeus. Chemically-induced adaptive change and intrinsic resistance Drugs and a wide variety of toxic agents (e.g. radiation, osmotic shock and heat shock) provoke many biochemical changes in cells that allow them to overcome the toxic effects of either the same or other compounds. In some circumstances this ability to resist chemical insult arises immediately following administration of the drug or, alternatively, there may be a significant time lag following exposure to the drug before the adaptive process is manifest. Physiological stress response and intrinsic resistance Environmental factors, other than drugs, can, through the ability to stress cells, elicit an adaptive response that confers resistance against chemicals. Phenomena such as heat, anoxia, viral infection, trauma, UV irradiation, pH, osmotic shock and oxidative stress stimulate a genetic reflex in all cells that is 'designed' to confer tolerance against subsequent exposure to the same physiological insult. Prokaryotes have at least four
  • 12. Chapter - I Introduction _________________________________________________________________________ Identification and Validation of Drug Targets 12 major regulations which are induced by stress, namely, the SOS response (Walker, 1985), the adaptive response to alkylating agents (Samson and Cairns, 1977; Demple et al., 1985), the oxy-R network (Christman et al., 1985; Storz et al., 1990) and the heatshock response (Lindquist, 1986; Carper et al., 1987). Acquired drug resistance The term ‘acquired resistance’ is used to describe the case where a resistant strain, or cell line, emerges from a population that was previously drug-sensitive. Three major types of genetic change can be envisaged: 1. mutations and amplifications of specific genes directly in vivo mutations and amplifications of specific genes directly involved in a protective pathway, 2. mutations in genes which regulate stress-response processes and lead to the altered expression of large numbers of proteins, and 3. gene transfer. These types of change are of course not mutually exclusive, and examination of the multiple changes that are frequently seen in resistant tumour cell lines suggests that several mechanisms can operate simultaneously.
  • 13. Chapter - I Introduction _________________________________________________________________________ Identification and Validation of Drug Targets 13 Natural selection and acquired resistance The distinction between acquired resistance through natural selection and intrinsic drug resistance lies in the frequency with which the mutated gene is observed in the 'wild type' population. Drug-mediated genetic changes and acquired resistance Herbicides, insecticides or antimicrobials are not mutagenic. However, many drugs used in cancer chemotherapy are mutagens providing the selection pressure for resistance, can significantly increase the frequency of mutations that will produce resistant cells. This is probably greatly potentiated by the inherent genetic instability of cancer cells. Such effects are exemplified by the significant increase in the frequency of DNA amplification following the exposure of tumour cells to mutagens such as monofunctional and bifunctional alkylating agents and UV. irradiation (Connors, 1984; Stark, 1986). It is technically difficult to demonstrate whether resistant cells in tumours arise from drug-mediated mutations or were present before chemotherapy was initiated.
  • 14. Chapter - I Introduction _________________________________________________________________________ Identification and Validation of Drug Targets 14 Table 2 Examples of acquired drug resistance Example Organism Resistance to Procedure Type of resistance Bacterial drug resistance Escherichia coli Chloramphenicol, ampicillin Exposure to drug Gene transfer (+ natural selection) Bacterial drug resistance Serratia marcescens Fosfomycin Exposure to drug Gene transfer (+ natural selection) Preneoplastic hepatocyte nodules Rat Toxins, carcinogens Carcinogen exposure Carcinogen-induced stress response Persistant hepatocyte nodules Rat Toxins, carcinogens Carcinogen exposure Natural selection: altered expression of drug metabolizing enzymes Oxy RI network (adaptive response to oxidative stress) Salmonella typhimurium Peroxides, ethanol In vitro selection of cell line Constitutive overexpression of a stress response ampC, R and D genes (adaptive response to cephalosporins) Citrobacter freundii Cefuroxime, cefotaxime, cetazidime In vitro selection of cell line Constitutive overexpression of an adaptive response Ada gene (adaptive response to alkylating agents) Escherichia coli N-Methyl-N-nitrosourea N-methyl-N-nitro-N- nitrosoguanidine In vitro selection of cell line Constitutive overexpression of an adaptive response Multidrug resistance Tumour cell lines Adriamycin, vincristine, actinomycin D Stepwise exposure to increasing concentrations of cytotoxic drug Amplification of P-glycoprotein genes
  • 15. Chapter - I Introduction _________________________________________________________________________ Identification and Validation of Drug Targets 15 Example Organism Resistance to Procedure Type of resistance Alkylating agent resistance Tumour cell lines Alkylating agents Stepwise exposure to increasing concentrations of cytotoxic drug Overexpression of drug metabolizing enzymes DNA gyrase mutants Escherichia coli Nalidixic acid In vitro exposure to drug Natural selection Penicillin binding protein mutants Escherichia coli Penicillin Exposure to drug Natural selection Acetylcholinesterase mutants House flies Organophosphorus Exposure to drug Exposure to drug Natural selection Source: John Hayes and Roland Wolf (1990)
  • 16. Chapter - I Introduction _________________________________________________________________________ Identification and Validation of Drug Targets 16 1.4 CONCERNS FOR DRUG DISCOVERY AND DEVELOPMENT The process of drug development begins with the target identification and eventually leads to the development of final medication. Drug discovery and development is an expensive and laborious incremental process. The main objective of this developmental effort is to identify a molecule with desired effect to cure a specific disease. Also it should establish quality, safety and efficacy for treating the patients without any undesirable side effects (Snodin, 2002). Currently the developmental cost for bringing a new molecule to market costs around $800 million USD. It takes nearly 12 years for a drug to progress from bench to market (EMBO Reports, 2004). The drug discovery process has numerous technical bottlenecks and the molecule under research has high risk failure at any stage of the development process. In spite of the growth in drug discovery technologies, the number of drugs that has crossed the FDA approval is very less. Furthermore, no new chemical classes of active antibiotics have been successfully introduced into the clinic for over 30 years. For example, of 5000 compounds that enter pre-clinical testing approximately five compounds are tested in human trails of which only one receives the approval for therapeutic purpose. Since the development costs have increased, the number of companies venturing into R/D spending has decreased drastically. However, effective use of the new genomic technologies and
  • 17. Chapter - I Introduction _________________________________________________________________________ Identification and Validation of Drug Targets 17 available data resource accelerates the process of drug discovery and prevents potential pitfalls in the drug discovery pipeline. 1.4.1 Stages of drug discovery The cost and time taken to design develop and release new drugs to the market have continued to rise over recent times (Grabowski et al., 1990; Di Masi, 2002) and also the number of new drug approvals has declined drastically (Frantz and Smith, 2003). The pharmaceutical industry is keen on reducing the drug candidate attrition throughout the drug discovery and development process. Numerous drugs with reasonable biological activities fail at the clinical studies. Earlier testing especially through wet laboratory or in silico protocols can avoid such pitfalls in the drug development. Fig. 1: Modern Day Drug Discovery Pipeline
  • 18. Chapter - I Introduction _________________________________________________________________________ Identification and Validation of Drug Targets 18 The first step is to determine an assay for the receptor or the target. An assay is a test to assess the positive binding of a molecule (drug) to the target receptor. Usually a pharmaceutical company will first screen their entire corporate database of known compounds as the compound in the database is usually very well characterized. Also, synthetic methods will be known for this compound, and patent protection is often present. This enables the company to rapidly prototype a candidate ligand whose chemistry is well known and within the intellectual property of the company. If none of these compounds from their database match the target then they may look for a compound which will fit to their receptor. The molecule which successfully binds with the target is termed as a lead compound. The next step is to study the receptors interactions with the ligand molecule. This would involve both in silico and in vitro analysis to find the binding residues involved in the ligand-receptor association. The 3D structure of the ligand-receptor complex provides a clear perspective on the ligand- receptor interaction. 1.5 DETERMINATION OF THE CRYSTAL STRUCTURE If the receptor is water soluble, there is a chance that x-ray crystallographic analysis can be employed to determine the three- dimensional structure of the ligand bound to the receptor at the atomic level. X-ray crystallography is a very powerful tool for it allows scientists to directly visualize a snapshot of the individual atoms of the ligand as they reside within the receptor. This snapshot is referred to as a crystal
  • 19. Chapter - I Introduction _________________________________________________________________________ Identification and Validation of Drug Targets 19 structure of the ligand-receptor complex. Unfortunately, not all complexes can be analyzed in this manner. However, if a crystal structure can be determined, a strategy can then be developed based upon this characterization to improve and optimize the binding of the lead compound. From this point onward, a cycle of iterative chemical refinement and testing continues until a drug is developed that undergoes clinical trials. The techniques used to refine drugs are combinatorial chemistry and structure based drug design. 1.5.1 X-ray crystallography and drug discovery The concept of applying X-ray crystallography in drug discovery emerged more than 30 years ago as the first 3D structures of proteins were determined. A typical example for this include the synthesis of ligands of haemoglobin to decrease sickling (Beddell et al., 1976; Goodford et al., 1980), the chemical modification of insulin to increase half lives (Blundell, 1972), and the design of serine proteases inhibitors to control blood clotting. In spite of the promising results most pharmaceutical companies considered X-ray crystallography too expensive and time consuming to bring ‘in house’ and for a time most activity remained in academia. Within a decade, a radical change in drug design had begun, incorporating the knowledge of the three dimensional structures of target proteins into the design process. Although structures of the relevant drug targets were usually not available directly from X-ray crystallography, comparative models based on homologues proved useful in defining topographies of the
  • 20. Chapter - I Introduction _________________________________________________________________________ Identification and Validation of Drug Targets 20 complementary surfaces of ligands and their protein targets, and began to be exploited in lead optimization in the 1980s (Blundell et al., 1983; Blundell, 1996; Campbell, 2000). Sooner crystal structures of key 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 influenza drug Relenza was designed using the crystal structure of neuraminidase (Varghese, 1999). More than 40 drugs originating from structure-based design approaches have now entered clinical trials (Hardy and Malikayil, 2003), and seven of these had achieved regulatory approval and been marketed as drugs by mid-2003. These successes had often led the pharmaceutical segments to explore design and development of drugs applying in silico approaches. Protein structure can influence drug discovery at every stage in the design process. Classically it has been exploited in lead optimization, a process that uses structure to guide the chemical modification of a lead molecule to give an optimized fit in terms of shape, hydrogen bonds and other non-covalent interactions with the target. Protein structure can also be used in target identification and selection (the assessment of the ‘druggability’ or tractability of a target). Traditionally, this has involved homology recognition assisted by knowledge of protein structure; but now structural genomics programs are seeking to define representative structures of all protein families, allowing proposals of binding regions and
  • 21. Chapter - I Introduction _________________________________________________________________________ Identification and Validation of Drug Targets 21 molecular functions. More recently, X-ray crystallography has been used to assist the identification of hits by virtual screening and more directly in the screening of chemical fragments. The key roles of structural biology and bioinformatics in lead optimization remain as important as ever (Whittle and Blundell, 1994; Lombardino and Lowe, 2004). For protein which cannot be crystallized, it is not possible to elucidate the structure through X-ray crystallography. These structures can be predicted with high level of accuracy using protein modeling methods. The protein modeling is a widely accepted phenomenon as it produces highly reliable 3D structures and it is of high importance nowadays in the drug discovery industries. 1.5.2 Protein Modeling The process of evolution has resulted in the production of DNA sequences that encode proteins with specific functions. In the absence of a protein structure that has been determined by X-ray crystallography or nuclear magnetic resonance (NMR) spectroscopy, researchers can predict the three-dimensional structure using protein modeling. This method uses experimentally determined protein structures (templates) to predict the structure of another protein that has a similar amino acid sequence (target). Although protein modeling may not be as accurate at determining a protein's structure as experimental methods, it is still extremely helpful in proposing and testing various biological hypotheses. This technique also provides a starting point for researchers wishing to confirm a structure
  • 22. Chapter - I Introduction _________________________________________________________________________ Identification and Validation of Drug Targets 22 through X-ray crystallography and NMR spectroscopy. Because the different genome projects are producing more sequences and because novel protein folds and families are being determined, protein modeling will become an increasingly important tool for scientists working to understand normal and disease-related processes in living organisms. 1.5.2.1 The Four Steps of Protein Modeling (Lorenza, 2009)  Identify the proteins with known three-dimensional structures that are related to the target sequence  Align the related three-dimensional structures with the target sequence and determine those structures that will be used as templates  Construct a model for the target sequence based on its alignment with the template structure(s)  Evaluate the model against a variety of criteria to determine if it is satisfactory
  • 23. Chapter - I Introduction _________________________________________________________________________ Identification and Validation of Drug Targets 23 Fig. 2: Protein modeling steps 1.5.2.2 Comparative or homology protein structure modeling Homology or comparative protein structure modeling constructs a three-dimensional model of a given protein sequence based on its similarity to one or more known structures. The first class of protein structure prediction methods, including threading and comparative modeling, rely on detectable similarity spanning most of the modeled sequence and at least one known structure. The second class of methods, de novo or ab initio methods, predict the structure from sequence alone, without relying on similarity at the fold level between the modeled sequence and any of the known structures. Despite progress in ab initio protein structure prediction, comparative modeling remains the most reliable method to predict the 3D
  • 24. Chapter - I Introduction _________________________________________________________________________ Identification and Validation of Drug Targets 24 structure of a protein with an accuracy that can be comparable to a low- resolution, experimentally determined structure. 1.6 PROTEIN MODELING AND DRUG DISCOVERY Advances in bioinformatics and protein modeling algorithms, in addition to the enormous increase in experimental protein structure information, have aided in the generation of databases that comprise homology models of a significant portion of known genomic protein sequences. Currently, 3D structure information can be generated for up to 56% of all known proteins. However, there is considerable controversy concerning the real value of homology models for drug design. Despite the numerous uncertainties that are associated with homology modeling, recent research has shown that this can be used to significant advantage in the identification and validation of drug targets, as well as for the identification and optimization of lead compounds. Homology model-based drug design has been applied to epidermal growth factor receptor tyrosine kinase protein (Ghosh et al., 2001), Bruton’s tyrosine kinase (Mahajan et al., 1999), Janus kinase 3 (Sudbeck et al., 1996) and human aurora 1 and 2 kinases (Vankayalapati et al., 2003). Traditionally, the crucial impasse in the industry’s search for new drug targets was the availability of biological data. Now with the advent of human genomic sequence, bioinformatics offers several approaches for the prediction of structure and function of proteins on the basis of sequence
  • 25. Chapter - I Introduction _________________________________________________________________________ Identification and Validation of Drug Targets 25 and structural similarities. The protein sequence>structure>function relationship is well established and reveals that the structural details at atomic level help understand molecular function of proteins. Impressive technological advances in areas such as structural characterization of biomacromolecules, computer sciences and molecular biology have made rational drug design feasible and present a holistic approach. The protein modeling being a computational approach generates the 3D structure of a receptor with high accuracy in a short duration. Also it is possible to study the various binding pockets of the receptor (protein) and ligand by molecular docking. These structures are of high importance for screening the new chemical entities by in silico methods. 1.6.1 Multidomain Protein Targets One of the great internal contradictions of drug discovery in practice is that most regulatory proteins in man, the obvious targets for new drugs, are complex proteins that are often multidomain and very usually components of multiprotein systems. A domain represents a complete functional unit. A protein may have one or more domains. Most of the focus in the pharmaceutical industry is on the active sites of monomeric proteins. Many proteins in the higher eukaryotes are large and contain multiple domains. A typical example is the DNA protein kinase (DNA-PK), a key molecule in non-homologous end joining, which signals the assembly of the multiprotein system involved in the repair of double strand breaks (Smider
  • 26. Chapter - I Introduction _________________________________________________________________________ Identification and Validation of Drug Targets 26 et al., 1994; Taccioli et al., 1994). This protein is composed of a large catalytic subunit and a regulating heterodimer Ku70 and Ku80. DOMINANT, a program has been written to deconvolute protein structures into their constituent domains in order that domains and domain boundaries can be classified (Brewerton, 2004). For an input protein structure, DOMINANT checks the existing domain database using a structure comparison procedure to identify any recurrent domains, and then uses a procedure to identify domains from the spatial separation of secondary structures to deconvolute the remaining structure. Programs like DOMINANT will be helpful in identifying multi domain protein and further assessing them for druggability. 1.7 IN SILICO - ITS ORIGIN AND REVOLUTION The term ‘in silico’ is a modern word usually used to mean experimentation performed by computer and is related to the more commonly known biological terms in vivo and in vitro. The history of the ‘in silico’ term is poorly defined, with several researchers claiming their role in its origination. However, some of the earliest published examples of the word include the use by Sieburg (1990) and Danchin et al. (1991). Informatics is a real aid to discovery when analyzing biological functions. We could reiterate this for drug discovery, which is a hugely complex information handling and interpretation exercise. With so much information to process, we need to be able to discover the shortcuts or the
  • 27. Chapter - I Introduction _________________________________________________________________________ Identification and Validation of Drug Targets 27 rules that will point us as quickly as possible to the targets and molecules that are likely to proceed to the clinic then onto the market. It has also been suggested that if we are to build on the advances of the human genome, we need to integrate computational and experimental data, with the aim of initiating in silico pharmacology linking all data types. This could change the way the pharmaceutical industry discovers drugs using data to enable simulations; however, there may still be significant gaps in our knowledge beyond genes and proteins (Whittaker, 2003). Structure-based methods are broadly used for drug discovery but these are just a beginning, for example in neuropharmacology, it is expected that ligand-receptor interaction kinetic models will need to be integrated with network approaches to understand fully neurological disorders, in general this could be applied more widely to pharmacology (Aradi and Erdi, 2006). Basically, there are two outcomes when bioactive compounds and biological systems interact (Testa and Kramer, 2006). Note that ‘biological system’ is defined here very broadly and includes functional proteins (for example, receptors), monocellular organisms and cells isolated from multicellular organisms, isolated tissues and organs, multicellular organisms and even populations of individuals, be they unicellular or multicellular. As for the interactions between a drug and a biological system, they may be simplified to ‘what the compound does to the biosystem’ and ‘what the biosystem does to the compound.’ A drug that acts on a biological system can elicit a pharmacological and/or toxic response, in other words a pharmacodynamic (PD) event. With the
  • 28. Chapter - I Introduction _________________________________________________________________________ Identification and Validation of Drug Targets 28 computational methods decision making and virtually simulating every facet of drug discovery and development is a reality (Swaan and Ekins, 2005) 1.7.1 In silico drug discovery Applying computational methods and techniques in the drug discovery and development process is more appreciated and it is gaining popularity among the pharmaceutical companies. In silico application reduces the time and resource requirements of chemical synthesis and biological testing. The utilities of computational application in drug discovery include hit identification, lead identification and optimizing lead. Before the introduction of genomic sciences, the drug discovery processes have been guided mostly by chemistry and pharmacology. With the completion of human genome project coupled with the molecular level understanding of the diseases, biology is the major driving force of this discovery process. 1.7.1.1 Chemo genomics approach Chemogenomics approach aims at studying the effect of wide array of small molecule ligands on a wide array of macro molecular targets. Human genome has approximately 3000 druggable targets of which only 800 proteins are currently investigated by pharmaceutical companies. Chemo genomic approach attempts to match these potential targets with the ligand space. It depends on these components like compound library, representative biological system and reliable output (Gene/protein
  • 29. Chapter - I Introduction _________________________________________________________________________ Identification and Validation of Drug Targets 29 expression data). This approach considers the fact that compounds sharing some chemical similarity also share targets and targets sharing similar ligands should share similar patterns or binding sites. 1.7.2 Virtual Screening and In silico Drug Targets Assessment of 617 approved oral drugs in two-dimensional (2D) molecular property space (molecular weight versus cLogP) showed that many of them had cLogP 45 and MW 4500. In spite of this, their associated targets were potentially druggable but had yet to realize their potential (Paolini et al., 2006). A recent analysis using 48 molecular 2D descriptors followed by principal component analysis of over 12,000 anticancer molecules representing cancer medicinal chemistry space, showed that they populated a different space broader than hit-like space and orally available drug-like space. This would indicate that in order to find molecules for anticancer targets in commercially available databases, different rules are required other than those widely used for drug-likeness, as they may unfortunately filter out possible clinical candidates (Lloyd et al., 2006). A representative of this inverse docking approach is INVDOCK, which was recently applied for identifying potential adverse reactions using a database of 147 proteins related to toxicities (DART). This method has been recently demonstrated with 11 marketed anti-HIV drugs resulting in reasonable accuracy against the DNA polymerase beta and DNA
  • 30. Chapter - I Introduction _________________________________________________________________________ Identification and Validation of Drug Targets 30 topoisomerase I (Ji et al., 2006). The public availability of data on drugs and drug-like molecules may make the analyses described above possible for scientists outside the private sector. For example, chemical repositories such as DrugBank (http://redpoll.pharmacy.ualberta.ca/drugbank/) (Wishart et al., 2006), PubChem (http://pubchem.ncbi.nlm.nih.gov/), KiDB (http://kidb.bioc.cwru.edu/) (Roth et al., 2004; Strachan et al., 2006) and others consist of a wealth of target and small molecule data that can be mined and used for computational pharmacology approaches. Nuclear receptors: Nuclear receptors constitute a family of ligand- activated transcription factors of paramount importance for the pharmaceutical industry since many of its members are often considered as double-edged swords (Shi, 2006). On the one hand, because of their important regulatory role in a variety of biological processes, mutations in nuclear receptors are associated with many common human diseases such as cancer, diabetes and osteoporosis and thus, they are also considered highly relevant therapeutic targets. On the other hand, nuclear receptors act also as regulators of some the CYP enzymes responsible for the metabolism of pharmaceutically relevant molecules, as well as transporters that can mediate drug efflux, and thus they are also regarded as potential therapeutic antitargets. Examples of the use of target-based virtual screening to identify novel small molecule modulators of nuclear receptors have been recently reported. Using the available structure of the oestrogen receptor subtype a
  • 31. Chapter - I Introduction _________________________________________________________________________ Identification and Validation of Drug Targets 31 (ERa) in its antagonist conformation, a homology model of the retinoic acid receptor a (RARa) was constructed. Using this homology model, virtual screening of a compound library lead to the identification of two novel RARa antagonists in the micromolar range. The same approach was later applied to discover 14 novel and diverse micromolar antagonists of the thyroid hormone receptor (Schapira et al., 2000). By means of a procedure designed particularly to select compounds fitting onto the LxxLL peptide- binding surface of the oestrogen receptor, novel ERa antagonists were identified (Shao et al., 2004). The discovery of three low micromolar hits for ERb displaying over 100-fold binding selectivity with respect to ERa was also recently reported using database screening (Zhao and Brinton, 2005). A final example reports the identification and optimization of a novel family of peroxisome proliferator-activated receptors-g partial agonists based upon pyrazol-4-ylbenzenesulfonamide after employing structure-based virtual screening, with good selectivity profile against the other subtypes of the same nuclear receptor group (Lu et al., 2006). Antibacterials Twenty deoxythymidine monophosphate analogues were used along with docking to generate a pharmacophore for Mycobacterium tuberculosis thymidine monophosphosphate kinase inhibitors with the Catalyst software. A final model was used to screen a large database spiked with known inhibitors. In addition, the model was used to rapidly screen half a million
  • 32. Chapter - I Introduction _________________________________________________________________________ Identification and Validation of Drug Targets 32 compounds in an effort to discover new inhibitors (Gopalakrishnan et al., 2005). Antivirals Neuroamidase is a major surface protein in influenza virus. A structure-based approach was used to generate Catalyst pharmacophores and these in turn were used for a database search and aided the discovery of known inhibitors. The hit lists were also very selective (Steindl and Langer, 2004). Utilizing this screening to design antivirals could help in managing the major epidemics and pandemics. Usually during an outbreak of a pandemic there is very less chance for surveillance as the discovery process takes time. Screening for compounds with activity will lead to rapid identification and to start an appropriate control measure. Human rhinovirus 3C protease is an antirhinitis target. A structure- based pharmacophore was developed initially around AG 7088 but this proved too restrictive. A second pharmacophore was developed from seven peptidic inhibitors using the Catalyst HIPHOP method. This hypothesis was useful in searching the world drug index database to retrieve compounds with known antiviral activity and several novel compounds were selected from other databases with good fits to the pharmacophore, indicative that they would be worth testing although these ultimate testing validation data were not presented (Steindl et al., 2005b).
  • 33. Chapter - I Introduction _________________________________________________________________________ Identification and Validation of Drug Targets 33 Human rhinovirus coat protein is another target for antirhinitis. A pharmacophore was generated from the structure and shape of a known inhibitor and tested for its ability to find known inhibitors in a database. Ultimately, after screening the Maybridge database, 10 compounds were suggested that were then docked and scored. Six compounds were tested and found to inhibit viral growth. However, the majority of them was found to be cytotoxic or had poor solubility (Steindl et al., 2005a). The Ligand Scout approach was tested on the rhinovirus serotype 16 and was able to find known inhibitors in the PDB (Wolber and Langer, 2005). The SARS coronavirus 3C-like proteinase has been addressed as a potential drug design target. A homology model was built and chemical databases were docked into it. A pharmacophore model and drug-like rules were used to narrow the hit list. Forty compounds were tested and three were found with micromolar activity, the best being calmidazolium at 61 mM (Liu et al., 2005), perhaps a starting point for further optimization. A pharmacophore has also been developed to predict the hepatitis C virus RNA-dependent RNA polymerase inhibition of diketo acid derivatives. A Catalyst HypoGen model was derived with 40 molecules with activities over three log orders to result in a five-feature pharmacophore model. This was in turn tested with 19 compounds from the same data set as well as nine diketo acid derivatives, for which the predicted and experimental data were in good agreement (Di Santo et al., 2005).
  • 34. Chapter - I Introduction _________________________________________________________________________ Identification and Validation of Drug Targets 34 1.7.3 Protein-protein interactions Protein-protein interactions are key components of cellular signalling cascades, the selective interruption of which would represent a sought after therapeutic mechanism to modulate various diseases (Tesmer, 2006). However, such pharmacological targets have been difficult for in silico methods to derive small molecule inhibitors owing to generally quite shallow binding sites. The G-protein Gbg complex can regulate a number of signalling proteins via protein-protein interactions. The search for small molecules to interfere with the Gbg-protein-protein interaction has been targeted using FlexX docking and consensus scoring of 1990 molecules from the NCI diversity set database (Bonacci et al., 2006). After testing 85 compounds as inhibitors of the Gb1g2-SIRK peptide, nine compounds were identified with IC50 values from 100 nM to 60 mM. Further substructure searching was used to identify similar compounds to one of the most potent inhibitors to build a SAR. These efforts may eventually lead to more potent lead compounds. A structure-based catalyst pharmacophore was developed for acetylcholine esterase, which was subsequently used to search a natural product database. The strategy identified scopoletin and scopolin as hits and were later shown to have moderate in vivo activity (Rollinger et al., 2004). The same database was also screened against cyclooxygenase (COX)-1 and (COX)-2 structure-based pharmacophores, leading to the identification of known COX inhibitors. These represent examples where a
  • 35. Chapter - I Introduction _________________________________________________________________________ Identification and Validation of Drug Targets 35 combination of ethnopharmacological and computational approaches may aid drug discovery (Rollinger et al., 2005). Homology models for the human 12-LOX and 15-LOX have also been used with the flexible ligand docking programme Glide (Schrodinger Inc.) to perform virtual screening of 50 000 compounds. Out of 20 compounds tested, 8 had inhibitory activity and several were in the low micromolar range (Kenyon et al., 2006). 1.7.4 Kinases The kinases represent an attractive family of over 500 targets for the pharmaceutical industry, with several drugs approved recently. Kinase space has been mapped using selectivity data for small molecules to create a chemogenomic dendrogram for 43 kinases that showed the highly homologous kinases to be inhibited similarly by small molecules (Vieth et al., 2004). Drug-metabolizing enzymes and transporters: Mathematical models describing quantitative structure-metabolism relationships were pioneered by (Hansch et al., 1968) using small sets of similar molecules and a few molecular descriptors. Later, Lewis and co-workers provided many QSAR and homology models for the individual human CYPs (Lewis, 2000). As more sophisticated computational modelling tools became available, there is a steep growth in the number of available models (De Groot and Ekins, 2002; De Graaf et al., 2005; De Groot, 2006) and the size of the data sets they encompass. Some more recent methods are also
  • 36. Chapter - I Introduction _________________________________________________________________________ Identification and Validation of Drug Targets 36 incorporating water molecules into the binding sites when docking molecules into these enzymes and these may be important as hydrogen bond mediators with the binding site amino acids (Lill et al., 2006). Docking methods can also be useful for suggesting novel metabolites for drugs. A recent example used a homology model of CYP2D6 and docked metoclopramide as well as 19 other drugs to show a good correlation between IC50 and docking score r2¼0.61 (Yu et al., 2006). A novel aromatic N-hydroxy metabolite was suggested as the major metabolite and confirmed in vitro. Now that several crystal structures of the mammalian CYPs are available, they have been found to compare quite favourably to the prior computational models (Rowland et al., 2006). However, for some enzymes like CYP3A4, where there is both ligand and protein promiscuity, there may be difficulty in making reliable predictions with some computational approaches such as docking with the available crystal structures (Ekroos and Sjogren, 2006). Hence, multiple pharmacophores or models may be necessary for this and other enzymes (Ekins et al., 1999), as it has been indicated by others more recently (Mao et al., 2006). Sulfotransferases, a second class of conjugating enzymes, have been crystallized (Dajani et al., 1999; Gamage et al., 2003) and a QSAR method has also been used to predict substrate affinity to SULT1A3 The computational modelling of drug transporters has been thoroughly reviewed by numerous groups (Zhang et al., 2002a, b; Chang and Swaan, 2005).
  • 37. Chapter - I Introduction _________________________________________________________________________ Identification and Validation of Drug Targets 37 Various transporter models have also been applied to database searching to discover substrates and inhibitors (Langer et al., 2004; Pleban et al., 2005; Chang et al., 2006b) and increase the efficiency of in vitro screening or enrichment over random screening. Receptors: There are more than 20 different families of receptors that are present in the plasma membrane, altogether representing over 1000 proteins of the receptorome (Strachan et al., 2006). Receptors have been widely used as drug targets and they have a wide array of potential ligands. However, it should be noted that to date we have only characterized and found agonists and antagonists for a small percentage of the receptorome. 1.8 DRUG TARGETS Wikipedia defines drug target as "A biological target is a biopolymer such as a protein or nucleic acid whose activity can be modified by an external stimulus". It has been estimated that current drug therapies are directed at less than 500 targets. With unprecedented growth in medical sciences and technology only approximately 500 drug targets had been reported till 2000. Considering that the human genome contains some 30,000 genes, it is possible that its study could lead to at least 3,000 to 5,000 potential new targets for therapy. Currently, predominant candidates include G protein- coupled receptor families and other receptors and related molecules, a wide range of enzymes including proteases, kinases and phosphatases,
  • 38. Chapter - I Introduction _________________________________________________________________________ Identification and Validation of Drug Targets 38 hormones, growth factors, chemokines, soluble receptors and related molecules, and many others. Exactly the same principles are being applied to the search for agents to interfere with key biochemical pathways in pathogens, based on information which is being obtained from the pathogen genome project (WHO Reports, 2002). 1.8.1 Characteristics of an ideal drug target (Pathogenic Organisms) The genome data must be analyzed by in vitro and in silico means to nail down drug targets for developing new drugs. The following are the characteristic features of an ideal target. The criteria for the ideal target should fulfill the following four consideration. Essentiality: The target should be essential for the growth, replication and survival of the organism. Selectivity: The target should not have clear orthologs in the human host. This aspect is referred to as selectivity. Spectrum: The target should be conserved in a number of pathogens, providing adequate spectrum for any potential inhibitors. Functionality: Functionality of the target has to be determined to detect the inhibitors of the target. 1.8.2 Identifying Drug Targets Virulence genes as drug targets The complete genome data sets also spur early identification of virulence genes. These genes can be identified either by in vitro expression
  • 39. Chapter - I Introduction _________________________________________________________________________ Identification and Validation of Drug Targets 39 technology or by DNA micro arrays. Extensive analysis coupled with the comparison of pathogenic and non-pathogenic microbes will reveal the pathogenic islands which encodes the virulent factors. Most often, these islands differ from the rest of the genome in certain parameters like GC content, codon usage and gene density. The protein encodes from these pathogenic islands are thrust areas for alternative targets. Species specific genes as drug targets Peer Bork and his coworkers devised an interesting approach for the prediction of potential drug targets. They designate this approach as “Differential genome display”. The approach relied on the fact that pathogenic organism codes for fewer proteins than free living organisms; and those proteins which is present in pathogen and absent in free living organisms are considered potential drug targets. Effective drug targets are selected based on several important criteria: they must be necessary to bacterial survival or growth, highly conserved in either a broad- or narrow- range of pathogens, absent or very different in humans, and understood biochemically (Rosamond and Allsop, 2000). Microbial genomics and drug discovery Sequencing technique enabled rapid sequencing and it is still assisted by the computational tools to perform automated annotation of these freshly sequenced genome data. Researchers quickly mine these
  • 40. Chapter - I Introduction _________________________________________________________________________ Identification and Validation of Drug Targets 40 data sets for exploring novel targets for both antimicrobial and vaccine development. Unique enzyme and drug targets Since most of the known antibacterials act as inhibitors of bacterial enzymes, all bacteria-specific enzymes can be considered potential drug targets. These enzymes can be identified as potential drug targets. These enzymes can be identified in organisms based on genome substraction methods and comprehensive analysis of these resistant proteins for confirmation. Much more easier and efficient identification is possible by a similar approach called “Pathway substraction” This approach quickly identifies enzyme pathways that are specific for bacteria and based on which drug targets can be easily identified. A typical example is isoprenoid biosynthesis in lower organisms and higher organisms. Since both these group uses a completely different enzyme system for the biosynthesis of this isoprenoid, the enzymes of the pathway are obvious drug targets for drug design. This has also led to the discovery of fosmidomycin which binds to the one of the enzyme target in this pathway. The ubiquitin regulatory pathway, in which ubiquitin is conjugated and deconjugated with substrate proteins, represents a source of many potential targets for modulation of cancer and other diseases (Wong et al., 2003).
  • 41. Chapter - I Introduction _________________________________________________________________________ Identification and Validation of Drug Targets 41 Membrane transporters as drug targets Comparative analysis of bacterial genome showed that most of the pathogenic microbes do not have well developed biosynthetic capabilities when compared to the free living or its related non-pathogenic forms. Hence most of the organisms depend on the host completely for their essential nutrients. A metabolic pathway analysis will reveal substrates that cannot be produced by their bacterial forms and hence needs to be transported. This eventually leads to identify bacterial transport protein which could be an affirmative drug target. 1.9 TARGET PREDICTION METHODS AND STRATEGIES - AN OVERVIEW 1.9.1 Protein interaction network strategy for drug target identification Proteins are the principal targets of drug discovery. Knowing what proteins are expressed and how is therefore the first step to generating value from the knowledge of the human genome. High-throughput proteomics, identifying potentially hundreds to thousands of protein expression changes in model systems following perturbation by drug treatment or disease, lends itself particularly well to target identification in drug discovery. Protein-protein interaction is the basis of drug target identification. Protein interaction maps can reveal novel pathways and functional complexes, allowing ‘guilt by association’ annotation of uncharacterized proteins. Once the pathways are mapped, these need to be analyzed and validated functionally in a biological model. It is possible
  • 42. Chapter - I Introduction _________________________________________________________________________ Identification and Validation of Drug Targets 42 that other proteins operating in the same pathway as a known drug target could also represent appropriate drug targets. Recent analyses of network properties of protein-protein interactions and of metabolic maps have provided some insights into the structure of these networks. So identifying protein-protein interactions can provide insights into the function of important genes, elucidate relevant pathways, and facilitate the identification of potential drug targets. Powerful bioinformatics software enables rapid interpretation of protein-protein interactions, accelerating functional assignment and drug target discovery. No matter whether the number of actual drug targets is correct or not, the available data strongly suggest that the present number of known and well-validated drug targets is still relatively small. Bioinformatics is making practical contributions in identifying large number of potential drug targets, however, target validation efforts are required to link them to the aetiology of known diseases and/or to demonstrate that the novel targets have relevant therapeutic potential. The biochemical pathways put a drug target into context: one can chart those in which a target is seen, and thus make educated guesses about the effects that blocking the target are likely to have. Further, more complete knowledge of biological pathways should be used to gain clues for potential target proteins. Despite the promising results obtained in the different tests carried out by this strategy, there are several potential problems in applications to drug target identification and validation. First, it is yet unclear if the currently available genomic
  • 43. Chapter - I Introduction _________________________________________________________________________ Identification and Validation of Drug Targets 43 databases, coupled with newly developed computational algorithms, can offer sufficient information for automated in silico drug target identification. For improving the biological accuracy of estimated gene networks, other biological information such as sequence information on promoter regions and protein-protein interactions should be integrated. Secondly, as real biological processes are often condition specific, and gene expression data tend to be noisy and often plagued by outliers, it is important to take “conditions” or “environments” into account. The problem of capturing long- run network behavior for large-size networks is difficult owing to the exponential increase of the state spaces. Thirdly, an increasing population of bioinformatics tools and the lack of an integrated and systematized interface for their selection and utilization is becoming widely acknowledged. Last and perhaps more important, understanding how a target protein works in the context of cellular pathways is rudimentary and linking diseases in humans to biochemical pathways studied in cells is also difficult, gene network identification is a really hard problem and modeling a larger protein complex will be an important challenge. The identification and validation of drug targets depends critically on knowledge of the biochemical pathways in which potential target molecules operate within cells. This requires a restructuring of the classical linear progression from gene identification, functional elucidation, target validation and screen development. One of the major goals of pharmaceutical bioinformatics is to
  • 44. Chapter - I Introduction _________________________________________________________________________ Identification and Validation of Drug Targets 44 develop computational tools for systematic in silico molecular target identification. One of the most important challenges for drug development, however, is to rapidly identify target proteins most appropriate to further development. Bioinformatics technology in the past decade has given birth to the new paradigm of a biology-driven process. There are many exciting developments to come in the field of target identification. Gene network technology creates cell and organ-level computer models able to simulate the clinical performance of drugs and drug candidates. By predicting how and why specific compounds impact human biology, gene networks technique may provide a glimpse of the signals and interactions within regulatory pathways of the cell. In fact, it is now possible to think of the whole pharmaceutical process as a computational approach, with confirmatory experiments at each decision-point. 1.10 METHODS FOR DRUG TARGET IDENTIFICATION The identification of disease relevant phenotypes follows the identification of novel drug targets that modulate or inhibit these responses. This can be broadly classified into three approaches  Mechanism- driven approach  Physiological approach  Gene driven approach
  • 45. Chapter - I Introduction _________________________________________________________________________ Identification and Validation of Drug Targets 45 1.10.1 Mechanism driven-Determining novel drug targets from network structures With the development of bioinformatics, a number of computational techniques have been used to search for novel drug targets from the information contained in genomics. The network-based strategy for drug target identification attempts to reconstruct endogenous metabolic, regulatory and signaling networks with which potential drug targets interact. Once having these information provided by gene networks or protein networks, the interaction relationships between potential drug targets could be explicitly revealed, so it could be easily determined which one of these potential drug targets is most proper, or the scope of selecting candidate drug targets could be narrowed down to a great extent , for example, if a potential drug target participates in many biological pathways of the pathogen, the inhibition of this target may interfere with many activities associated with those pathways, and therefore, may be a good candidate for drug target. It involves acquiring a molecular level understanding of the function of drug targets. On the molecular level, function is manifested in the behavior of complex networks. It is necessary to know the cellular context of the drug target and the impact of its inhibition or activation on multiple signaling pathways. Graphical models are often used to describe genetic networks. Generally, a gene network could be presented in a directed graph, in which nodes indicate genes and edges represent regulations
  • 46. Chapter - I Introduction _________________________________________________________________________ Identification and Validation of Drug Targets 46 between genes (e.g. activation or suppression). Analyzing the network structures of large-scale interrogation of cellular processes holds promise for the identification of essential mediators of signal transduction pathways and potential drug targets. In order to find proper candidate target genes, one needs biological knowledge of the pathways underlying the disease process. So the study of biochemical pathways is the focus of numerous researchers. However, owing to the complexity of pathway structures, many potential drug targets turned out worthless because the pathways in which they participate were more complex than expected. A promising strategy is to examine the functionality of different genes in the network and observe the connectivity of different functional domains. Some researchers have implemented this gene network-based strategy for drug target identification. First, using the gene expression data obtained from expression experiments of several dose and time responses to the drug, those genes affected by the drug (drug-affected genes) could be identified by fold- change analysis or virtual gene technique. Because there is no guarantee that genes most affected by the drug are the genes that were "drugged" by the drug agent, nor is there any guarantee that the drugged target represents the most biologically available and advantageous molecular target for intervention with new drugs, they further searched the most proper drug target genes upstream of the drug-affected genes in a regulatory network. Using gene expression profiles obtained from 120 gene disruptions, they employed a method based on Bayesian network model to
  • 47. Chapter - I Introduction _________________________________________________________________________ Identification and Validation of Drug Targets 47 construct a gene network. Then, by exploring the gene network, they found the “druggable genes”, namely drug targets regulating the drug-affected genes most strongly, and a novel drug target gene was identified and validated. 1.10.2 Gene driven-Gene network strategy for drug target identification The molecular interactions of genes and gene products underlie fundamental questions of biology. Genetic interactions are central to the understanding of molecular structure and function, cellular metabolism, and response of organisms to their environments. If such interaction patterns can be measured for various kinds of tissues and the corresponding data can be interpreted, potential benefits are obvious for the identification of candidate drug targets. It has already been demonstrated that it is possible to infer a predictive model of a genetic network by time-series gene expression data or steady-state gene expression data of gene knockout. Using the inferred model, useful predictions can be made by mathematical analysis and computer simulations. Recently several computational methods have been proposed to reconstruct gene networks, such as Boolean networks, differential equation models and Bayesian networks. These quantitative approaches can be applied to natural gene networks and used to generate a more comprehensive understanding of cellular regulation, discover the underlying gene regulatory mechanisms and reveal the interactions between drugs and the drug targets in cells.
  • 48. Chapter - I Introduction _________________________________________________________________________ Identification and Validation of Drug Targets 48 1.10.3 Physiological approach- Protein interaction network strategy for drug target identification Proteins are the principal targets of drug discovery. Knowing what proteins are expressed and how is therefore the first step to generating value from the knowledge of the human genome. Proteomics has unique and significant advantages as an important complement to a genomics approach. High-throughput proteomics, identifying potentially hundreds to thousands of protein expression changes in model systems following perturbation by drug treatment or disease, lends itself particularly well to target identification in drug discovery. Protein-protein interaction is the basis of drug target identification. Protein interaction maps can reveal novel pathways and functional complexes, allowing ‘guilt by association’ annotation of uncharacterized proteins. Once the pathways are mapped, these need to be analyzed and validated functionally in a biological model. It is possible that other proteins operating in the same pathway as a known drug target could also represent appropriate drug targets. Recent analyses of network properties of protein-protein interactions and of metabolic maps have provided some insights into the structure of these networks. So identifying protein-protein interactions can provide insights into the function of important genes, elucidate relevant pathways, and facilitate the identification of potential drug targets. Powerful bioinformatics software enables rapid interpretation of protein-protein interactions, accelerating functional assignment and drug target discovery.
  • 49. Chapter - I Introduction _________________________________________________________________________ Identification and Validation of Drug Targets 49 No matter whether the number of actual drug targets is correct or not, the available data strongly suggest that the present number of known and well-validated drug targets is still relatively small. Bioinformatics is making practical contributions in identifying large number of potential drug targets; however, target validation efforts are required to link them to the aetiology of known diseases and/or to demonstrate that the novel targets have relevant therapeutic potential. The biochemical pathways put a drug target into context: one can chart those in which a target is seen, and thus make educated guesses about the effects that blocking the target are likely to have. Further, more complete knowledge of biological pathways should be used to gain clues for potential target proteins. Despite the promising results obtained in the different tests carried out by this strategy, there are several potential problems in applications to drug target identification and validation. First, it is yet unclear if the currently available genomic databases, coupled with newly developed computational algorithms, can offer sufficient information for automated in silico drug target identification. For improving the biological accuracy of estimated gene networks, other biological information such as sequence information on promoter regions and protein-protein interactions should be integrated. Secondly, as real biological processes are often condition specific, and gene expression data tend to be noisy and often plagued by outliers, it is important to take “conditions” or “environments” into account. The problem of capturing long- run network behavior for large-size networks is difficult owing to the
  • 50. Chapter - I Introduction _________________________________________________________________________ Identification and Validation of Drug Targets 50 exponential increase of the state spaces. Thirdly, an increasing population of bioinformatics tools and the lack of an integrated and systematized interface for their selection and utilization is becoming widely acknowledged. Last and perhaps more important, understanding how a target protein works in the context of cellular pathways is rudimentary and linking diseases in humans to biochemical pathways studied in cells is also difficult, gene network identification is a really hard problem and modeling a larger protein complex will be an important challenge. The identification and validation of drug targets depends critically on knowledge of the biochemical pathways in which potential target molecules operate within cells. This requires a restructuring of the classical linear progression from gene identification, functional elucidation, target validation and screen development. One of the major goals of pharmaceutical bioinformatics is to develop computational tools for systematic in silico molecular target identification. The advent of genomics offers means to expand the range of targets, the choice of potential drug targets thrown up by genomics data is overwhelming. One of the most important challenges for drug development, however, is to rapidly identify target proteins most appropriate to further development. Genomics and proteomics technologies have created a paradigm shift in the drug discovery process. Bioinformatics technology in the past decade has given birth to the new paradigm of a biology-driven process. There are many exciting developments to come in the field of
  • 51. Chapter - I Introduction _________________________________________________________________________ Identification and Validation of Drug Targets 51 target identification. Gene network technology creates cell and organ-level computer models able to simulate the clinical performance of drugs and drug candidates. By predicting how and why specific compounds impact human biology, gene networks technique may provide a glimpse of the signals and interactions within regulatory pathways of the cell. In fact, it is now possible to think of the whole pharmaceutical process as a computational approach, with confirmatory experiments at each decision- point. There are several directions for future research. First, in the near future, data produced about cellular processes at molecular level will accumulate with an accelerating rate as a result of genomics studies. In this regard, it is essential to develop approaches for inferring gene networks from microarray data and other biological data effectively. The development of systematic approaches to finding genes for effective therapeutic intervention requires new models and powerful tools for understanding complex genetic networks. Secondly, owing to the reason that integrating the information from different types of networks may lead to the notion of functional networks and functional modules, to find these modules, we should consider the general question of the potential effect of individual genes on the global dynamical network behavior both from the view of random gene perturbation as well as intervention. It should be emphasized that although computational tools and resources can be used to identify putative drug targets, validating targets is still a process that
  • 52. Chapter - I Introduction _________________________________________________________________________ Identification and Validation of Drug Targets 52 requires understanding the role of the gene or protein in the disease process and is heavily dependent on laboratory based work. The new integrative technological developments in Systems biology, coupled with a number of ‘omic’ techniques, may lead to a breakthrough for the identification and validation of important drug targets in the future. The application of information technology in biological and chemical sciences has become a critical part of the molecular modelling, drug designing, database designing. Proteins and nucleic acids that play key roles in disease processes have been explored as therapeutic targets for drug development (Drews, 2000). Knowledge of these therapeutically relevant proteins and nucleic acids has facilitated modern drug discovery by providing platforms for drug screening against a preselected target. It has also contributed to the study of the molecular mechanism of drug actions, discovery of new therapeutic targets and development of drug design tools. Information about non-target proteins and natural small molecules involved in these pathways is also useful for facilitating the search of new therapeutic targets and for understanding how therapeutic targets interact with other molecules to perform specific tasks. Number of web-based resources of therapeutically targeted proteins and nucleic acids are available, which provide useful information about the targets of drugs and investigational agents. Antibiotics are among the most frequently prescribed medications in modern medicine. Antibiotics cure disease by killing or injuring bacteria.
  • 53. Chapter - I Introduction _________________________________________________________________________ Identification and Validation of Drug Targets 53 The first antibiotic was penicillin, discovered accidentally from a mold culture. Today, over 100 different antibiotics are available to doctors to cure minor discomforts as well as life-threatening infections. Antibiotics are substances that are produced by molds or bacteria and that kill or inhibit the growth of other microorganisms. In 1929, Alexander Flemming, a British scientist who was working with Staphylococcus, a bacterium that most of us have encountered as it causes wound infections, discovered the first antibiotic. One day, when he, by mistake, contaminated his bacterial plate with a mold, he noticed that the Staphylococcus colonies growing near the contaminating mold looked strange, as if they were dissolving. He realized that this mold secreted a substance that killed the bacteria. Since the discovery of this antibiotic many other antibiotics have been discovered and have made it possible to cure diseases caused by bacteria such as pneumonia, tuberculosis, and meningitis, saving the lives of millions of people around the world. Antibiotics specifically attack bacteria without harming cells belonging to the organism that produced them. Antibiotics such as penicillin kill bacteria by inhibiting them from making cell walls that are needed for their survival. Without their cell wall the contents of the cells leak out and the cell is destroyed. Human and animal cells do not require a cell wall in order to survive, thus these antibiotics do not damage them. The current increase in the number of microbes resistant to antibacterial or antifungal agents represents a potential crisis in human and
  • 54. Chapter - I Introduction _________________________________________________________________________ Identification and Validation of Drug Targets 54 veterinary medicine. Some believe that we are entering a post antibiotic era where most antibiotics no longer will be efficacious. Therefore, it is important that new antibiotics be developed. Since bacteria can exchange DNA with other bacteria (even with distant genera), bacteria can acquire resistance genes from resistant organisms. However, because of the potential for cross-resistance, new targets for the discovery of antibiotics are needed particularly where resistance does not currently exist. Two major classes of targets can be considered: essential genes and virulence- based genes. Bioinformatics has become indispensable to all fields of life sciences. The rapid progress of genome projects has brought a vast accumulation of molecular biological information in the past decade. Millions of nucleic acid sequences with billions of bases have been deposited in EMBL, GenBank and DDBJ. Hundreds of specialist databases have been derived from the above primary sequence databases. In the year 2000, people saw the completion of the genome projects of the fruit fly and the Arabidopsis thaliana. People also witness the completion of the draft of the Human Genome Project in the same year. Biology is entering the post genome era in the new century. A number of approaches for new vaccine development exist, including sub-unit protein and DNA vaccines; recombinant vaccines; auxotrophic organisms to deliver genes and so on. Testing such candidates is tiresome and expensive. Bioinformatics enables us to reduce substantially the number of such candidates to test. Scanning of bacterial
  • 55. Chapter - I Introduction _________________________________________________________________________ Identification and Validation of Drug Targets 55 genomes to identify essential genes is of biological interest, for understanding the basic functions required for life, and of practical interest, for the identification of novel targets for new antimicrobial therapies. The recent availability of the human genome sequence represents a major step in drug discovery. Knowledge of the human proteome will provide unprecedented opportunities for studies of human gene function. Often clues will be provided by sequence similarity with proteins of known function in model organisms. Such initial observations must then be followed up by detailed studies to establish the actual function of these molecules in humans. The spread of antibiotic resistance in bacteria has intensified the need for novel approaches to antimicrobial drug discovery. In recent years, we have seen an explosion in the amount of biological information that is available. Various databases are doubling in size every 15 months and we now have the complete genome sequences of more than 100 organisms. It appears that the ability to generate vast quantities of data has surpassed the ability to use this data meaningfully. The pharmaceutical industry has embraced genomics as a source of drug targets. It also recognises that the field of bioinformatics is crucial for validating these potential drug targets and for determining which ones are the most suitable for entering the drug development pipeline. Researchers have a continued need for enhanced and expanded genomic and proteomic databases and tools to allow for more rapid, accurate, and predictive target selection and validation. Genomics and
  • 56. Chapter - I Introduction _________________________________________________________________________ Identification and Validation of Drug Targets 56 proteomics are now being leveraged into the next phase of the drug discovery process, which is finding the best drug molecules. Comparative and functional genomic data can provide fundamental scientific knowledge with applications in medicine, industry, agriculture and environmental biomonitoring. These approaches depend on bioinformatics and methods. The growing use of technologies, such as DNA microarrays and BACs, in the field of bacterial genomics, has immense potential with respect to beneficial applications. Recently, there has been a change in the way that medicines are being developed due to our increased understanding of molecular biology. In the past, new synthetic organic molecules were tested in animals or in whole organ preparations. This has been replaced with a molecular target approach in which in-vitro screening of compounds against purified, recombinant proteins or genetically modified cell lines is carried out with a high throughput. This change has come about as a consequence of better and ever improving knowledge of the molecular basis of disease. The availability of whole genomes of many pathogenic bacteria allows one to speed up the process of drug target selection by finding novel genes in new and old functional categories previously mentioned. The analysis of open reading frames of bacterial sequences makes all genes and gene products as possible drug targets (Smith, 1996). Scientist must therefore isolate the genes that are essential to cell survival or growth, which would be most effective as antibiotic targets. Traditionally, new genes that were
  • 57. Chapter - I Introduction _________________________________________________________________________ Identification and Validation of Drug Targets 57 necessary to bacterial survival or virulence were discovered through random mutagenesis and phenotyping of the bacterial genome (Hood, 1999). However, scientists can now use automated comparisons of bacterial genomes to categorize genes and the proteins encoded. Primary sequence comparison programs, like BLAST or PSI-BLAST, can determine gene functions by sequence homology. Sequence homology is also used to determine clusters of orthologous groups (COGs). COGs are groups of genes shared by evolutionarily distant organisms. These orthologous families of genes are prime candidates for broad-spectrum antimicrobial agents. 1.11 OUR APPROACH In this current research, we have designed an approach to identify drug targets from bacterial genome. The figure-3 represents the steps involved in prediction and validation of drug targets in microbial genome. The target is predicted by comparing the bacterial genome with essential genes and then comparing these predicted essential genes with the human genes/protein to identify non homologues drug target. Previously subtractive genomics approach was used (Sakharkar et al., 2004; Anirban Dutta et al., 2006) to identify potential drug targets in Pseudomonas aeruginosa and Helicobacter pylori. In the present approach the complete sequence of identification is
  • 58. Chapter - I Introduction _________________________________________________________________________ Identification and Validation of Drug Targets 58 automated so that the user can submit the input and get the output as target sequences. Fig. 3: Approach - Target prediction and validation The obtained target sequences were analyzed for its functional role using sequence analysis tools (BLAST and Pfam). The validation of these drug targets were done by comparing these against the approved and proposed genes/proteins from the Drugbank database. The predicted targets from the selected pathogenic organism’s gene name, protein product, Enzyme Commission Number, function, functional information were collected and populated in a web based database to act as a base for drug discovery process. ______

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