introduction to drug discovery -...

CHAPTER 1

Introduction to Drug Discovery

Introduction :

Cheminformatics or Chemo informatics is another method to reach the

destiny of drug discovery. Chemoinformatics is an interface science aimed

primarily at discovering novel chemical entities that will ultimately result in the

development of novel treatments for unmet medical needs, although these same

methods are also applied in other fields that ultimately design new molecules. The

field combines expertise from, among others, chemistry, biology, physics,

biochemistry, statistics, mathematics, and computer science. Cheminformatics

consists of several in-silico techniques which are widely used in pharmaceutical

companies in the process of drug discovery. The discovery of new medical

treatments to meet unmet medical needs is one of the most important endeavors in

humanity. The process is time consuming, expensive, and fraught with many

challenges.

Drug discovery is perhaps the area of research in which

chemoinformatics has found the greatest application. Traditional pharmaceutical

industry would require 12-14 years and costing upto $1.2 - $1.4 billion to bring a

drug from discovery to market, in this approach drugs were discovered by

synthesizing compounds in a time-consuming multi-step processes with failures

attributed to poor pharmacokinetics (39%), lack of efficacy (30%), animal toxicity

(11%), adverse effects in humans (10%) and various commercial and

miscellaneous factors. Today, the process of drug discovery has been

revolutionized with the advent of genomics, proteomics, bioinformatics and

efficient technologies like, combinatorial chemistry, cheminformatics, high

throughput screening (HTS), virtual screening, de novo design, in vitro, in silico

ADMET screening, Quantitative structure-activity relationship (QSAR) and

structure-based drug design. The process of finding a new drug against a chosen

target for a particular disease usually involves high-throughput screening (HTS),

wherein large libraries of chemicals are tested for their ability to modify the target.

The primary application of cheminformatics is in the storage of information related

to the drug molecules and the efficient presentation of such stored information

during the process of lead optimization. A range of parameters can be used to

assess the quality of a compound, or a series of compounds, as proposed in the

Lipinski's Rule of Five. Such parameters include calculated properties such as

cLogP to estimate lipophilicity, rotatable bonds to estimate molecular flexibility,

molecular weight, Hydrogen Bond Acceptors and Hydrogen Bond Donors to

estimate Pharmacophoric Properties, polar surface area and measured properties,

such as potency, in-vitro measurement of enzymatic clearance etc. Some

descriptors such as ligand efficiency (LE) and lipophilic efficiency (LiPE) combine

such parameters to assess drug likeness.

Role of Bio and Cheminformatics in Drug Discovery:

Bio and Cheminformatics tools, plays a major role in identifying target

molecule, which could be a potential drug. Not all small molecules can be drugs,

and not all proteins can be drug targets. A small molecule must have certain

properties, and a protein must contain a binding site that is complementary with

these properties.

The majority of successful drugs show their response by competing for a

binding site on a protein with and endogenous small molecule. Target

identification is one of the most important areas for in silico research. The current

dominant paradigm in pharmaceutical drug discovery seeks to find a particular

small molecule inhibitor to bind to a specific receptor, a macromolecular target.

However our ability is depending on the detection of efficient methods of

manipulation of organic small molecules and discovery of the novel biological

targets. Several approaches and databases can be applied for the discovery of drug

targets.

One important use of these databases in target discovery is to infer

relative gene expression levels, simply by counting how often a given EST

sequence appears in a given cell or tissue. Gene expression levels are important

because the phenotype is determined by the small portion of genes that are

expressed at any given time in a cell or tissue type, and changes in gene expression

can be associated with disease.

Thus, by comparing levels of gene expression in normal and disease

states, novel drug targets can be identified by in silico methods .Knowledge of

prokaryotic and viral genomes supports identification of targets for drugs against

infectious disease. Metabolic pathways and proteins associated to them and

specific to micro-organism are of particular interest.

A drug affecting such a target is less likely to interact with a human

homologue. Proteins with sequences similar across bacterial clades offer the

possibility of broad spectrum antibiotics. A drug-discovery program typically starts

with the identification of suitable drug targets and ends with the candidate drug

production and sells in the market after clinical trials.

Fig 1: Milestones of Drug Discovery.

Market facts towards modern drug discovery:

The majority of pharmaceutical drug discovery programs currently,

begins with a known macromolecular targets, and seek to identify a suitable small

molecule modulator (Ratti and Trist,2001;Dean,Zanders, and Bailey,2001). The

process of optimizing the lead molecule into a candidate drug is usually the longest

and most expensive stage in the drug discovery process. Following the selection of

the candidate molecule from limited chemical space of analogs of original lead

compounds, to the drug development, scientists develop large scale production

methods, and conduct the preclinical animal safety studies. Over 90% of the

compounds entering clinical trials fail to make it to market (Venkatesh and

Lipper,1999). The average cost to bring new chemical entity (NCE) to market is

estimated to be $770 million (Kettler, 1999). In this research article we are mainly

focused on to identify a lead molecule by using cheminformatics tools and docking

study using parallel computing. It will help chemists at the stage where lead

optimization takes place.

The thesis is divided in to seven chapters.

1.Introduction to Drug Discovery:

Highlights the importance of the research work and objectives of the

present investigations. Various cheminformatics tools used during the drug

discovery process are also reviewed in this chapter. The idea that effect of drug in

human body are mediated by specific interactions of the drug molecule with

biological macromolecules, (proteins or nucleic acids in most cases) led scientists

to the conclusion that individual chemicals are required for the biological activity

of the drug. This made for the beginning of the modern era in pharmacology, as

pure chemicals, instead of crude extracts, became the standard drugs. Examples of

drug compounds isolated from crude preparations are morphine, the active agent in

opium, and digoxin, a heart stimulant originating from Digitalis lanata. Organic

chemistry also led to the synthesis of many of the co-chemicals isolated from

biological sources.

Computational methodologies have become a crucial component of

many drug discovery programs, from hit identification to lead optimization and

beyond, and approaches such as ligand or structure based virtual screening

techniques are widely used in many discovery efforts. One key methodology to be

considered here docking of small molecules to protein binding sites, it was

pioneered during the early 1980s, and remains a highly active area of research.

When only the structure of a target and its active or binding site is available, high-

throughput docking is primarily used as a hit identification tool. However, similar

calculations are often also used later on during lead optimization, when

modifications to known active structures can quickly be tested in computer models

before compound synthesis. At a broader sense drug designing is classified in to

two major areas, The first is referred to as ligand-based drug design and the

second, structure-based drug design.

Ligand-based drug design (or indirect drug design) relies on knowledge

of other molecules that bind to the biological target of interest. These other

molecules may be used to derive a pharmacophore model that defines the

minimum necessary structural characteristics a molecule must possess in order to

bind to the target. In other words, a model of the biological target may be built

based on the knowledge of what binds to it, and this model in turn may be used to

design new molecular entities that interact with the target. Alternatively, a

quantitative structure-activity relationship (QSAR), in which a correlation between

calculated properties of molecules and their experimentally determined biological

activity, may be derived. These QSAR relationships in turn may be used to predict

the activity of new analogs.

Structure-based drug design (or direct drug design) relies on knowledge

of the three dimensional structure of the biological target obtained through

methods such as x-ray crystallography or NMR spectroscopy. If an experimental

structure of a target is not available, it may be possible to create a homology model

of the target based on the experimental structure of a related protein. Using the

structure of the biological target, candidate drugs that are predicted to bind with

high affinity and selectivity to the target may be designed using interactive

graphics and the intuition of a medicinal chemist. Alternatively various automated

computational procedures may be used to suggest new drug candidates. As

experimental methods such as X-ray crystallography and NMR, the amount of

information concerning 3D structures of biomolecular targets has increased

dramatically. In parallel, information about the structural dynamics and electronic

properties about ligands has also increased. This has encouraged the rapid

development of the structure-based drug design. Current methods for structure-

based drug design can be divided roughly into two categories. The first category is

about “finding” ligands for a given receptor, which is usually referred as database

searching. In this case, a large number of potential ligand molecules are screened

to find those fitting the binding pocket of the receptor. This method is usually

referred as ligand-based drug design. The key advantage of database searching is

that it saves synthetic effort to obtain new lead compounds. Another category of

structure-based drug design methods is about “building” ligands, which is usually

referred as receptor-based drug design. In this case, ligand molecules are built up

within the constraints of the binding pocket by assembling small pieces in a

stepwise manner. These pieces can be either individual atoms or molecular

fragments. The key advantage of such a method is that novel structures, not

contained in any database, can be suggested.

1.1 Active site identification:

Active site identification is the first step in this program. It analyzes the

protein to find the binding pocket, derives key interaction sites within the binding

pocket, and then prepares the necessary data for Ligand fragment link. The basic

inputs for this step are the 3D structure of the protein and a pre-docked ligand in

PDB format, as well as their atomic properties. Both ligand and protein atoms need

to be classified and their atomic properties should be defined, basically, into four

atomic types:

1.1.1 Hydrophobic atom: All carbons in hydrocarbon chains or in aromatic

groups.

1.1.2 H-bond donor: Oxygen and nitrogen atoms bonded to hydrogen atom(s).

1.1.3 H-bond acceptor: Oxygen and sp2 or sp hybridized nitrogen atoms with

lone electron pair(s).

1.1.4 Polar atom: Oxygen and nitrogen atoms that are neither H-bond donor

nor H-bond acceptor, sulfur, phosphorus, halogen, metal, and carbon atoms

bonded to hetero-atom(s).

The space inside the ligand binding region would be studied with

virtual probe atoms of the four types above so the chemical environment of all

spots in the ligand binding region can be known. Hence we are clear what kind of

chemical fragments can be put into their corresponding spots in the ligand binding

region of the receptor.

Structure-based drug design attempts to use the structure of proteins as a

basis for designing new ligands by applying accepted principles of molecular

recognition. The basic assumption underlying structure-based drug design is that a

good ligand molecule should bind tightly to its target. Thus, one of the most

important principles for designing or obtaining potential new ligands is to predict

the binding affinity of a certain ligand to its target and use it as a criterion for

selection.

One early method was developed by Böhm to develop a general-

purposed empirical scoring function in order to describe the binding energy. The

following “Master Equation” was derived:

where:

• desolvation – enthalpic penalty for removing the ligand from solvent

• motion – entropic penalty for reducing the degrees of freedom when a ligand

binds to its receptor

• configuration – conformational strain energy required to put

"active" conformation

• interaction – enthalpic gain for "resolvating" the ligand with its receptor

The basic idea is that the overall binding free energy can be

decomposed into independent components that are known to b

binding process. Each component reflects a certain kind of free energy alteration

during the binding process between a ligand and its target receptor. The Master

Equation is the linear combination of these components. According to Gibbs

energy equation, the relation between dissociation equilibrium constant, K

the components of free energy was built.

Various computational methods are used to estimate each of the

components of the master equation. For exampl

upon ligand binding can be used to estimate the desolvation energy. The number of

rotatable bonds frozen upon ligand binding is proportional to the motion term. The

configurational or strain energy can be estimated using

calculations. Finally the interaction energy can be estimated using methods such as

enthalpic penalty for removing the ligand from solvent

entropic penalty for reducing the degrees of freedom when a ligand

conformational strain energy required to put the ligand in its

"active" conformation

enthalpic gain for "resolvating" the ligand with its receptor


decomposed into independent components that are known to be important for the



Equation is the linear combination of these components. According to Gibbs

energy equation, the relation between dissociation equilibrium constant, K

the components of free energy was built.


components of the master equation. For example, the change in polar surface area



configurational or strain energy can be estimated using molecular mechanics


enthalpic penalty for removing the ligand from solvent

entropic penalty for reducing the degrees of freedom when a ligand

the ligand in its

enthalpic gain for "resolvating" the ligand with its receptor


e important for the



Equation is the linear combination of these components. According to Gibbs free

energy equation, the relation between dissociation equilibrium constant, Kd, and


e, the change in polar surface area



molecular mechanics


the change in non polar surface, statistically derived potentials of mean force, the

number of hydrogen bonds formed, etc. In practice, the components of the master

equation are fit to experimental data using multiple linear regression. This can be

done with a diverse training set including many types of ligands and receptors to

produce a less accurate but more general "global" model or a more restricted set of

ligands and receptors to produce a more accurate but less general "local" model.

1.2 Rational drug discovery

In contrast to traditional methods of drug discovery, which rely on trial-

and-error testing of chemical substances on cultured cells or animals, and matching

the apparent effects to treatments, rational drug design begins with a hypothesis

that modulation of a specific biological target may have therapeutic value. In order

for a biomolecule to be selected as a drug target, two essential pieces of

information are required. The first is evidence that modulation of the target will

have therapeutic value. This knowledge may come from, for example, disease

linkage studies that show an association between mutations in the biological target

and certain disease states. The second is that the target is "drugable". This means

that it is capable of binding to a small molecule and that its activity can be

modulated by the small molecule.

Once a suitable target has been identified, the target is normally cloned

and expressed. The expressed target is then used to establish a screening assay. In

addition, the three-dimensional structure of the target may be determined.

The search for small molecules that bind to the target is begun by

screening libraries of potential drug compounds. This may be done by using the

screening assay (a "wet screen"). In addition, if the structure of the target is

available, a virtual screen may be performed of candidate drugs. Ideally the

candidate drug compounds should be "drug-like", that is they should possess

properties that are predicted to lead to oral bioavailability, adequate chemical and

metabolic stability, and minimal toxic effects. Several methods are available to

estimate drug likeness such as Lipinski's Rule of Five and a range of scoring

methods such as Lipophilic efficiency. Several methods for predicting drug

metabolism have been proposed in the scientific literature, and a recent example is

SPORCalc. Due to the complexity of the drug design process, two terms of interest

are still serendipity and bounded rationality. Those challenges are caused by the

large chemical space describing potential new drugs without side-effects.

1.3 Computer-aided drug design

Computer-aided drug design uses computational chemistry to discover,

enhance, or study drugs and related biologically active molecules. The most

fundamental goal is to predict whether a given molecule will bind to a target and if

so how strongly. Molecular mechanics or molecular dynamics are most often used

to predict the conformation of the small molecule and to model conformational

changes in the biological target that may occur when the small molecule binds to it.

Semi-empirical, ab initio quantum chemistry methods, or density functional theory

are often used to provide optimized parameters for the molecular mechanics

calculations and also provide an estimate of the electronic properties (electrostatic

potential, polarizability, etc.) of the drug candidate that will influence binding

affinity.

Molecular mechanics methods may also be used to provide semi-

quantitative prediction of the binding affinity. Also, knowledge-based scoring

function may be used to provide binding affinity estimates. These methods use

linear regression, machine learning, neural nets or other statistical techniques to

derive predictive binding affinity equations by fitting experimental affinities to

computationally derived interaction energies between the small molecule and the

target.

Ideally the computational method should be able to predict affinity

before a compound is synthesized and hence in theory only one compound needs to

be synthesized. The reality however is that present computational methods are

imperfect and provide at best only qualitatively accurate estimates of affinity.

Therefore in practice it still takes several iterations of design, synthesis, and testing

before an optimal molecule is discovered. On the other hand, computational

methods have accelerated discovery by reducing the number of iterations required

and in addition have often provided more novel small molecule structures.

Drug design with the help of computers may be used at any of the following stages

of drug discovery:

1. hit identification using virtual screening (structure- or ligand-based design)

2. hit-to-lead optimization of affinity and selectivity (structure-based design,

QSAR, etc.)

3. lead optimization optimization of other pharmaceutical properties while

maintaining affinity

In order to overcome the insufficient prediction of binding affinity calculated by

recent scoring functions, the protein-ligand interaction and compound 3D structure

information are used to analysis. For structure-based drug design, several post-

screening analysis focusing on protein-ligand interaction has been developed for

improving enrichment and effectively mining potential candidates:

1.3.1 Consensus scoring

• Selecting candidates by voting of multiple scoring functions

• May lose the relationship between protein-ligand structural

information and scoring criterion

1.3.2 Geometric analysis

• Comparing protein-ligand interactions by visually inspecting

individual structures

• Becoming intractable when the number of complexes to be

analyzed increasing

1.3.3 Cluster analysis

• Represent and cluster candidates according to protein-ligand 3D

information

• Needs meaningful representation of protein-ligand interactions.

Flowchart of a Usual Clustering Analysis for Structure-Based Drug Design

Fig 1.1: Clustering Analysis

1.4 Structure based drug design:

Drug discovery referred to, as ‘rational’ did not take flight until the

first structures of the targets were solved. In 1897, Ehrlich suggested a theory

called the side chain theory wherein he proposed that specific groups on the cells

combine with the toxin. Ehrlich coined these side chains as receptors. Structure-

based drug design of protein ligands has emerged as a new tool in medicinal

chemistry (Klebe, 2000). The central assumption of structure-based drug design is

an iterative one as shown in Fig.1.1 and often proceeds through multiple cycles

before an optimized lead goes into clinical trials. The first cycle includes the

cloning, purification and structure determination of the target protein or nucleic

acid by one of three principal methods: X-ray crystallography, NMR or

comparative modeling. Using computer algorithms, compounds or fragments of

compounds from a database are positioned into a selected region of the structure.

These compounds are scored and ranked based on their steric and electrostatic

interactions with the target site and the best compounds are tested further with

biochemical assays. In the second cycle, structure determination of the target in

complex with a promising lead from the first cycle, one with at least micromolar

inhibition in vitro, reveals sites on the compound that can be optimized to increase

potency. Additional cycles include synthesis of the optimized lead, structure

determination of the new target: lead complex, and further optimization of the lead

compound. After several cycles of the drug design process, the optimized

compounds usually show marked improvement in binding, and often, specificity

for the target.

1.5 Evaluating a protein structure for structure based drug design:

Once a target has been identified, it is necessary to obtain accurate

structural information. There are three primary methods for structure determination

that are useful for drug-design: X-ray crystallography, NMR, and homology

modeling. High-resolution crystal structures are the most common desired source

of structural information for drug design, particularly for proteins that range in size

from a few amino acids to 998kD (Pellecchia et al., 2002). Another advantage of

crystallography is that ordered water molecules are visible in the experimental data

and are often useful in drug design. A crystal structure should be evaluated for the

resolution of the diffracted amplitudes (often simply called resolution); reliability,

or R factors; coordinate error; temperature factors; and chemical correctness.

Fig. 1.2 Steps involved in structure based drug design through in silico approach.

Typically, crystal structures determined with data extending above 2.5

A0 are acceptable for drug design purposes since they have a high data to

parameter ratio, and the placement of residues in the electron density map is

unambiguous. The R factor and R free reported for a model are measures for the

correlation between the model and experimental data. The R free value should be

below 28 % and ideally below 25 %, and the R factor should be well below 25 %

in order to use the structure in drug design. If the only structure available for a

particular target does not meet the resolution or R factor results should be judged

carefully. In principle, the drug discovery process involves three pre-clinical stages

before clinical trials, namely target selection, lead identification, and clinical

candidate selection.

Target identification

Target Validation

Lead Identification

Lead molecule optimization

Clinical trails

Due to rapid advances in structural biology and computer

technology, structurebased computer-aided drug design (CADD) using docking

techniques, virtual screening and library design, along with target/structure

focusing combinatorial chemistry, has become a powerful tool in the multi-step

process of drug discovery. As an emerging technology, CADD accelerates drug

development by making use of the accumulated information of existing drugs

and diseases, combined with inter-disciplinary inputs from other fields. This

process extensively uses mathematical models and simulation tools based on the

evaluation of potential risks from drug safety and the experimental design of

new trials (Kapetanovic, 2008). Fast expansion in this area has been made

possible by advances in software and hardware computational power and

sophistication, identification of molecular targets, and an increasing database of

publicly available target protein structures. CADD is being utilized to identify

hits (active drug candidates), select leads (most likely candidates for further

evaluation), and optimize leads i.e. transform biologically active compounds

into suitable drugs by improving their physicochemical, pharmaceutical,

ADMET/PK pharmacokinetic) properties (Pozzan, 2006). The term biological

target is frequently used in pharmaceutical research to describe the native

protein in the body whose activity is modified by a drug resulting in a desirable

therapeutic effect. In this context, the biological target is often referred to as a

drug target. The most common drug targets of currently marketed drugs include

as below (Overington et al., 2006)

• Enzymes

• ligand-gated ion channels

• voltage-gated ion channels

• G protein-coupled receptors

• nuclear hormone receptors

• Transporters

• DNA, Integrins, Miscellaneous

1.6 Species-specific genes as drug targets:

Comparative analysis of the complete genome sequences of bacterial

pathogens available in the public databases offers the first insights into drug

discovery approaches of the near future (Galperin and Koonin, 1999). An

interesting approach to the prediction of potential drug targets designated as the

differential genome display has been proposed by Huynen and co-workers

(Huynen et al., 1997). This approach relies on the fact that genome of parasitic

microorganisms are generally much smaller and code for fewer proteins than the

genomes of free-living organisms. The genes that are present in the genome of a

parasitic bacterium but absent in a closely related genome of free pathogenecity

can be considered as potential drug targets. Exhaustive comparison of H.influenzae

and E.coli gene products identified 40 H. influenzae genes that have been

exclusively found in pathogens and thus constitute potential drug targets.

Fig 1.3: Targets of currently marketed small-molecule drugs, subdivided by biochemical class (Hopkins and Groon, 2002).

1.7 DNA as drug targets:

Nucleic acids are the repository of genetic information. DNA itself has

been shown to be the receptor for many drugs used in cancer and other diseases.

These work through a variety of mechanisms including chemical modification

and cross linking of DNA (cisplatin) or cleavage of the DNA (bleomycin). Much

work either by intercalation of a polyaromatic ring system into the double stranded

helix (actinomycin D, ethidium) or by binding to the major and minor grooves of

DNA (e.g., netropsin) has been reported (Haq, 2002). DNA has been shown to be

the target for chemotherapy with efforts to design sequence specific reagents for

gene therapy.

1.8 RNA as drug target:

Recent advances in the determination of RNA structure and function

have led to new opportunities that will have a significant impact on the

pharmaceutical industry. RNA, which, among other functions, serves as a

messenger between DNA and proteins, was thought to be an entirely flexible

molecule without significant structural complexity. However, recent studies have

revealed a surprising intricacy in RNA structure. This observation unlocks

opportunities for the pharmaceutical industry to target RNA with small molecules.

Perhaps more importantly, drugs that bind to RNA might produce effects that

cannot be achieved by drugs that bind to proteins (Ecker and Griffey, 1999). Proof

of the principle has already been provided by success of several classes of drugs

obtained from natural sources that bind to RNA or RNA-protein complexes.

1.9 Membranes as drug targets:

Membranes are significant structural elements, both in defining the

boundaries of a cell as well as providing interior compartments within the cell

associated with particular functions. Cell membranes themselves can also act as

targets for molecular recognition. An understanding of the structural and dynamic

functions of the membranes (e.g., plasma membranes and intercellular

membranes) may add to a more rational design of drug molecules with improved

permeation characteristics or specific membrane effects. Many general anesthetics

are believed to work by their physical effects when dissolved in membranes.

Several classes of antibiotics like gramicidin A, antifungals like alamethicin and

toxins such as mellitin found in bee venoms have direct effects on planar lipid

bilayers, causing transmembrane pores.

1.10 Proteins as drug targets:

Proteins continue to assume significant attention from the

pharmaceutical and biotechnology industries as a valuable source of potential drug

targets. Proteins provide the critical link between genes and disease, and as such

are the key to the understanding of basic biological processes including disease

pathology, diagnosis, and treatment. Researchers have discovered many potential

therapeutic targets, and there are currently more than 700 products in various

phases of development. However, translating the study of proteins into validated

drug targets poses substantial challenges. Genome sequences instruct cells on

how and when to make proteins. The proteins in turn are the active players in the

cell. Proteins form the machinery of cells, allow cells to communicate, and can

control growth or death of an organism. Because of their role in cells, most of the

drug targets are proteins. Drugs work by binding specifically to a protein.

Extensive knowledge about the function of a protein can guide the selection of

targets for pharmaceutical chemists. Studying the complex domain of 200,000-

300,000 distinct and interactive proteins poses substantial challenges.

Most target proteins for drug evelopment participate in key regulatory

steps in the human body or in an infectious organism. As such, they tend to be

present in few copies only and often within specific cells. Their isolation and

purification using traditional preparative biochemical means and in quantities

required for routine assays has been a formidable challenge. This situation has

been radically changed by the ability to clone and express proteins. Thus many key

target proteins are now becoming available in sufficient amounts to make them

amenable not only to biological assays but also to NMR studies in solution and to

crystallization for X-ray analysis. The number of protein structures solved using X-

ray or NMR has begun to rise sharply and more than 60,230 proteinthree-

dimensional structures have been deposited in the Protein Data Bank till date (Feb,

2013) (www. rcsb.org/pdb/) shown in

Fig 1.4. PDB Total structures (Yearly Growth of Total Structures)

Various classes of proteins can be categorized as potential drug targets.

Small molecules such as drugs, insecticides or herbicides usually exert their effects

by binding to protein targets. In the past, many of these molecules were found

empirically with little or no knowledge of the mechanism of action involved. In

many cases, the targets that are modified by these substances were identified in

retrospect. Interestingly, the majority of drugs currently in use modulate either

enzymes or receptors, most of them G-protein-coupled receptors.

1.10.1 Enzymes :

The macromolecule responsible for the catalysis of biochemical reactions

are an obvious target when a disease state is associated with production of a

biologically active species. Enzymes are a classic target for therapeutic

intervention and numerous well-studied examples exist. Traditional medicinal

chemistry enzyme targets include kinases, phosphodiesterases, proteases and

phosphotases. Some of the examples of drug targeted against enzymes are listed in

Table 1.1

1.10.2 G-proteins

G-protein–coupled receptors are a super family of seven transmembrane

spanning proteins that are activated by a wide range of extracellular ligands and are

expressed in virtually all tissues. Signaling through receptors regulate a wide

variety of physiological processes such as neurotransmission, chemotaxis,

inflammation, cell proliferation, cardiac and smooth muscle contraction as well as

visual and chemosensory perception.

In view of their widespread distribution and importance in health

and disease, it is not surprising that GPCRs are the most successful class of target

proteins for drug discovery research (Cacace et al., 2003). The sequencing of

human genome has led to the prediction of as many as 1000 GPCRs, of which 400

are nonchemosensory receptors and can therefore be considered as potential drug-

targets (Venter et al., 2001). It has been estimated that up to 50 % of all marketed

drugs directly target this family of receptors (Drews, 2000), some of which are

listed in Table 1.2.

The goal in developing drugs against the targets listed is often to

modulate the function of the human protein while the goal in developing drugs

against pathogenic organisms is total inhibition, leading to the death of the

pathogen. Antimicrobial drugs should be essential to the pathogen, have a unique

function in the pathogen, be present only in the pathogen, and be able to be

inhibited by a small molecule. The target should be essential, in that it is a part of a

crucial cycle in the cell, and its elimination should lead to the pathogen’s death.

Tab 1

Few currently marketed drugs that target GPCRs

GPCR Indication(s) Drug(s)

Histamine Allergies, ulcers Cimetidine, Ranitidine,

Terfenadine

β -adrenergic Hypertension, asthma Atenolol, Albuterol,

Salmeterol

α -adrenergic Benign

prostatichypertrophy

Terazosin, Doxazosin

Dopamine Psychosis, Parkinson’s Aripiprazole, Ropinerole

Serotonin Migraine, anxiety Zolmitriptan, Clozapine,

Buspirone

Opoid Pain Butarphanol

Angiotensin Hypertension Losartan, Eprosartan

Muscarinic acetylcoline Alzheimer’s disease Bethanechol,Dicyclomine

Leukotriene Asthma Pranlukast

The target should be unique; no other pathway should be able to

supplement the function of the target and overcome the presence of the inhibitor. If

the macromolecule satisfies all the outlined criteria to be a drug target but

functions in healthy human cells as well as in a pathogen, specificity can often be

engineered into the inhibitor by exploiting structural or biochemical differences

between the pathogenic and human forms. Finally, the target molecule should be

capable of inhibition by binding of a small molecule. Enzymes are often excellent

drug targets because compounds are designed to fit within the active site pocket.

Structures determined by nuclear magnetic resonance, using a

concentrated protein or nucleic acid in solution are also valuable sources for drug

design (Pellecchia et al., 2002). Since the target is in solution it is sometimes

possible to interpret the dynamics of the target from the data. If no experimentally

determined structure is available, a homology model can be used for drug design

(Enyedy et al., 2001). To evaluate a homology model, SWISS MODEL outputs a

confidence factor per residue that reflects the amount of structural information

used to create that portion of the model. Using the structural information obtained

through the above techniques, the structure is then prepared for drug design

programs.

1.11 Present state of the art: Computer-aided drug design:

Given the vast size of organic chemical space drug discovery cannot be

reduced to a simple “synthesize and test” drudgery (Kuntz, 1992). There is an

urgent need to identify and/ or design drug-like molecules from the vast expanse of

what could be synthesized. In silico methods have the potential to reduce both time

and cost in developing suggestions on drug/ lead-like molecules. Computational

tools have the advantage for delivering new lead candidate more quickly and at

lower cost. Drug discovery in the 21st century is expected to be different in at least

two distinct ways: development of individualized medicine departing from

genomic information and extensive use of in silico simulations to facilitate target

identification, structure prediction and lead/drug discovery.

The expectations from computational methods for reliable and

expeditious protocols for developing suggestions on potential leads are

continuously on the increase. Several conceptual and methodological concerns

remain before an automation of drug design in silico could be contemplated.

Computational methods are needed to exploit the structural information to

understand specific molecular recognition events and to elucidate the function of

the target macromolecule (Fig. 1.4). This information should ultimately lead to the

design of small molecule ligands for the target, which will block/activate its

normal function and thereby act as improved drugs.

As structural genomics, bioinformatics, and computational power

continue to explode with new advances, further successes in structure-based drug

design are likely to follow. Each year, new targets are being identified; structures

of those targets are being determined at an amazing rate, and capability to capture a

quantitative picture of the interactions between macromolecules and ligands is

accelerating.

1.12 Success of computer-assisted molecular design:

The greatest success of computer-aided structure-based drug design to

date is the HIV-1 protease inhibitors that have been approved by the United States

Food and Drug Administration and reached the market (Wlodawer and Vondrasek,

1998). An example of the success story is that of SAR work carried out on

antibacterial agent, Norfloxacin showed 6-fluro derivative of norfloxacin being

500 fold more potent over nalidixic acid. Other examples of drugs that were

developed using computer –assisted drug design include Captopril

(antihypertensive), Crixican (anti- HIV) (Greer et al., 1994), Teveten

(antihypertensive) (Keenan et al., 1993), Aricept(for Alzheimers disease).

1.13 Utility of Homology Models in the Drug Discovery Process:

Advances in bioinformatics and protein modeling algorithms, in addition

to the enormous increase in experimental protein structure information, have aided

in 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 approach 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 modelbased drug design has been

applied to epidermal growth factor receptor tyrosine (Kawakami et al., 1996),

Trusopt (for Glaucoma) (Geer et al., 1994) and Zomig (for migraine) (Glen et al.,

1995).

Fig 1.4. Holistic approach in an in silico method of drug discovery

Drug target

identification In silico

generation of

candidates

Molecular

modeling

targets with

candidates

Filters to

asses

drug

likeness

Lead like

compounds Affinity

based

virtual

screening

Further

optimizatio

n

Leads

kinase protein (Ghosh et al., 2001), Bruton’s tyrosine kinase (Mahajan

et al.,1999), Janus kinase 3 (sudbeck et al., 1999) and human aurora 1 and 2

kinases (Venkayalapati et al., 2003). In the thesis, focus is on the application of

homology model of hHH2R of GPCR family used to design new drug molecule.

Thus, it can be said that pharmaceutical and biotechnology research has undergone

great change. 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 and structural

similarities.

The protein sequence to structure and to 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.

Drug discovery is perhaps the area of research in which

chemoinformatics has found the greatest scope. Drug discovery generally follows a

set of common stages as depicted below.

Fig 1.6: An illustration of the typical workflow of a drug discovery endeavor

First, a biological target is identified that is screened against many

thousands of molecules in parallel. The results from these screens are referred to as

hits. A number of the hits will be followed up as leads with various profiling

analyses to determine whether any of these molecules are suitable for the target of

interest. The leads can then be converted to candidates by optimizing on the

biological activity and other objectives of interest, such as the number of synthetic

steps. Once a suitable candidate has been designed, the candidate enters preclinical

development. Chemoinformatics is involved from initially designing screening

libraries, to determining hits to take forward to lead optimization and the

determination of candidates by application of a variety of tools. The lifecycle of

drug discovery can take many years and hundreds of millions of dollars to achieve.

Recent estimates are leaning toward an average drug discovery that lasts at least a

decade (10–15 years) and costs almost a billion US dollars ($897 million) in

bringing a single drug to market, with many avenues necessarily being explored

throughout the process [DiMasi et al. 2003]. However these figures are only

estimates, and there is no doubt that the discovery of new medical treatments is a

very complex process and the field of chemoinformatics is assisting in allowing us

to explore new areas that we have so far been unable to access.

In this research article the tool that has been majorly used is

Discovery Studio 1.5 from Accelrys. Discovery Studio facilitates modeling and

simulation solutions for protein modeling and computational chemistry. Discovery

Studio provides the most advanced software solutions for life science researchers

available today. From project conception to lead optimization, Discovery Studio

includes a diverse collection of sophisticated software applications to take your

research to the next level, all conveniently packaged into a single, easy-to use

Linux- or Windows-based environment. Because Discovery Studio is built upon

Pipeline Pilot™, Accelrys' scientific operating platform, any software that you

need can be integrated into the research environment, whether it's software from

Accelrys, in-house developers, or other vendors.

Fig 1.7: Discovery Studio facilitates modeling and simulation solutions for protein modeling and computational chemistry.

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