1. The document discusses the history of antibiotic discovery and the growing problem of antimicrobial resistance driving the need for new drugs. It highlights key discoveries like penicillin but notes resistance grew quickly. 2. It then covers the molecular mechanisms that enable intrinsic and acquired drug resistance in bacteria. Intrinsic resistance includes inherent cellular defenses while acquired resistance develops through genetic mutations or transfer. 3. The rise of multidrug-resistant bacterial pathogens is a major concern as it reduces the effectiveness of current antibiotic treatments. New drug targets and antibiotics are urgently needed to address drug-resistant infections.

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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.

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"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

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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

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‘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

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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

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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).

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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,

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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.

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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)

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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

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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

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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.

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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.

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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

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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)

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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(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

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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).

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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).

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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

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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

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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).

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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,

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Identification and Validation of Drug Targets
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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

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Identification and Validation of Drug Targets
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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

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Identification and Validation of Drug Targets
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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).

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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

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Identification and Validation of Drug Targets
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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

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Identification and Validation of Drug Targets
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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

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Identification and Validation of Drug Targets
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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

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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

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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

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Identification and Validation of Drug Targets
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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.

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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.

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Identification and Validation of Drug Targets
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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

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Identification and Validation of Drug Targets
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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

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Identification and Validation of Drug Targets
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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

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Identification and Validation of Drug Targets
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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.

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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

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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

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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

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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

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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

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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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