COMPUTER AIDED DRUG DESIGN

A PROJECT REPORT SUBMITTED FOR THE FULFILLMENT OF M.SC.BIOINFORMATICS (CGPA) 4TH.

SEMESTER UNIVERSITY EXAMINATION 2012 OF S.R.T.M.UNIVERSITY, NANDED

BY

MR. SURYAWANSHI HANUMANT SHANKAR

UNDER THE GUIDENCE OF

MR.ASHISH B. GULWE

MISS.LAXMIPRIYA PADHI

SUBMITTED TO

SCHOOL OF TECHNOLOGY

SWAMI RAMANAND TEERTH MARATHWADA UNIVERSITY,NANDED

SUB CENTER LATUR (MAHARASHTRA)

APRIL 2012

CERTIFICATECERTIFICATECERTIFICATECERTIFICATE

This is certify that this report entitled ‘COMPUTER AIDED DRUG DESIGN’ submitted for the fulfillment of the partial requirement for M.Sc. Bioinformatics 4th.Semester University examination 2012 of S.R.T.M.U. Nanded is a record of independent study carried by Mr. Suryawanshi Hanumant Shankar under our supervision and guidance. This report has not be previously submitted anywhere for any examination or publication or award by the candidate.

Place: - Latur

Date: - Mr. Ashish B. Gulwe

Miss. Laxmipriya Padhi

Above statements are verified from the official record of the department.

Place: - Latur

Date: - Prof. & Head

School of Technology

ACKNOWLEDGEMENT

No task, however small work cannot be completed without proper guidance and encouragement. Before I get into thick of the thing, I would like to add few heart full words for people who gave underling support right from the stage the project was conceived. I would like to express my deep gratitude to all those behinds the scene that have helped me to transform an idea during working project.

I wish to thanks Dr. D. N. Mishra, Director,S.R.T.M.U. Sub Center,Latur for giving me permission to work in professional network and environment and also for permitted to present such study abstracts in various national seminars, conferences like IIIT Allahabad, I2IT Pune, V.B.S.Purvanchal University Jaunpur (U.P) .

Also I express thanks to

Dr.B.K.Ratha, Prof. & Head, School of Tecnnology,

Mr.Ashish B. Gulwe Asst.Prof.

Miss.Laxmipriya Padhi.

Also I thank to my M.Sc.friends namely Mr. Ram Poul, Mr.D.S. Suryawanshi, Mr. Avinash Tate, Mr.Ninad Shinde, Miss. Yogeshree Kedare & Miss.Rutuja Kedare for their kinds , assistance and encouragement to complete this project.

Last but not least, I thanks to God for transforming my limitations and impossible situations into own opportunity to see us through our parents for their motivation and moral support during the hour of need.

Place: - Latur Mr. Suryawanshi Hanumant Shankar

Date: - 24 April 2012

INDEX

Sr.No.

CHAPTER

PAGE NO

1 Drug Design 1 -12

1.1 Introduction

1.2 Ligand Based Drug Design

1.3 Structure Based Drug Design

1.4 Active Site Identification

1.5 Ligand Fragment Link

2 Computer Aided Drug Design 13 – 18

2.1 Introduction

2.2 How Drugs are Discovered?

2.3 Screening For Improvement

2.4 Mechanism Based Drug Design

2.5 Combining Techniques

3 The Basic Mechanistic Drug Design 19 – 23

3.1 Defining The Disease Process

3.2 Defining The Target

3.3 Defining The Receptor

3.4 Designing New Drugs To Effect Targets

Sr.No. CHAPTER

PAGE NO

4 Quantative Structure Activity Relationship (QSAR) 24 – 39

4.1 Introduction

4.2 Types

4.3 Applications

4.4 Parameters

4.5 Quantative Models

5 Uses Of Computer Graphics In Computer Assisted Drug Design

40 – 52

5.1 Molecular Graphics

5.2 Molecular Mechanics

5.3 Molecular Dynamics

5.4 Conformation Analysis

5.5 Quantum Mechanics

6 Important Techniques For Drug Design 54 – 65

6.1 X – Ray Crystallography

6.2 NMR Spectroscopy

7 Applications 66 – 70 8 Conclusions 71 – 72 9 References 73 – 74 10 List of National Seminars & Conferences where the

study Abstract was Presented (Poster Presentation)

75

CHAPTER 1

DRUG DESIGN

DRUG DESIGN. 1.1 Introduction

Drug design, sometimes referred to as rational drug design or more simply rational design, is

the inventive process of finding new medications based on the knowledge of a biological target.

The drug is most commonly an organic small molecule that activates or inhibits the function of

a biomolecule such as a protein, which in turn results in a therapeutic benefit to the patient. In

the most basic sense, drug design involves the design of small molecules that are

complementary in shape and charge to the biomolecular target with which they interact and

therefore will bind to it. Drug design frequently but not necessarily relies on computer

modeling techniques. This type of modeling is often referred to as computer-aided drug

design. Finally, drug design that relies on the knowledge of the three-dimensional structure of

the biomolecular target is known as structure-based drug design.

The phrase "drug design" is to some extent a misnomer. What is really meant by drug design

is ligand design (i.e., design of a small molecule that will bind tightly to its target). Although

modeling techniques for prediction of binding affinity are reasonably successful, there are many

other properties, such as bioavailability, metabolic half-life, lack of side effects, etc., that first

must be optimized before a ligand can become a safe and efficacious drug. These other

characteristics are often difficult to optimize using rational drug design techniques.

1.1.1 Background

Typically a drug target is a key molecule Involved in a

particular metabolic or signaling pathway that is specific to a disease condition orpathology or

to the infectivity or survival of a microbial pathogen. Some approaches attempt to inhibit the

functioning of the pathway in the diseased state by causing a key molecule to stop functioning.

Drugs may be designed that bind to the active region and inhibit this key molecule. Another

approach may be to enhance the normal pathway by promoting specific molecules in the normal

pathways that may have been affected in the diseased state. In addition, these drugs should

also be designed so as not to affect any other important "off-target" molecules or anti

targets that may be similar in appearance to the target molecule, since drug interactions with

off-target molecules may lead to undesirable side effects. Sequence homology is often used to

identify such risks.

Most commonly, drugs are organic small molecules produced through chemical synthesis, but

biopolymer-based drugs (also known as biologics) produced through biological processes are

becoming increasingly more common. In addition, mRNA-based gene silencing technologies

may have therapeutic applications.

Types

Flow charts of two strategies of structure-based drug design

There are two major types of drug design. The first is referred to as ligand-based drug design

and the second, structure-based drug design.

1.2 Ligand-based

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.

1.3 Structure-based

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 develop, 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. These techniques are raising much excitement to the drug design community.

1.4 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:

§ Hydrophobic atom: All carbons in hydrocarbon chains or in aromatic groups.

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

§ H-bond acceptor: Oxygen and sp2 or sp hybridized nitrogen atoms with lone electron

pair(s).

§ 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.

1.5 Ligand fragment link

Flow chart for structure-based drug design

When we want to plant “seeds” into different regions defined by the previous section, we need a

fragments database to choose fragments from. The term “fragment” is used here to describe the

building blocks used in the construction process. The rationale of this algorithm lies in the fact

that organic structures can be decomposed into basic chemical fragments. Although the

diversity of organic structures is infinite, the number of basic fragments is rather limited.

Before the first fragment, i.e. the seed, is put into the binding pocket, and other fragments can

be added one by one, it is useful to identify potential problems. First, the possibility for the

fragment combinations is huge. A small perturbation of the previous fragment conformation

would cause great difference in the following construction process. At the same time, in order to

find the lowest binding energy on the Potential energy surface (PES) between planted

fragments and receptor pocket, the scoring function calculation would be done for every step of

conformation change of the fragments derived from every type of possible fragments

combination.

Since this requires a large amount of computation, one may think using other possible

strategies to let the program works more efficiently. When a ligand is inserted into the pocket

site of a receptor, conformation favor for these groups on the ligand that can bind tightly with

receptor should be taken priority. Therefore it allows us to put several seeds at the same time

into the regions that have significant interactions with the seeds and adjust their favorite

conformation first, and then connect those seeds into a continuous ligand in a manner that

make the rest part of the ligand having the lowest energy. The conformations of the pre-placed

seeds ensuring the binding affinity decide the manner that ligand would be grown. This strategy

reduces calculation burden for the fragment construction efficiently. On the other hand, it

reduces the possibility of the combination of fragments, which reduces the number of possible

ligands that can be derived from the program. These two strategies above are well used in most

structure-based drug design programs. They are described as “Grow” and “Link”. The two

strategies are always combined in order to make the construction result more reliable.

1.5.1 Scoring method

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 remov

§ motion – entropic penalty for reducing the degrees of freedom when a ligand binds to its

receptor

§ configuration – conformational strain energy required to put the l

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 be important for the bindi

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 free energy equation, the relation between dissociation equilibrium constant, K

components of free energy was built.

Various computational methods are used to estimate each of the components of the master

equation. For example, the change in polar surface area upon

estimate the desolation energy. The number of rotatable bonds frozen upon ligand binding is

proportional to the motion term. The configurationally or strain energy can be estimated

using molecular mechanics calculations. Finally the interaction energy can be estimated using

methods such as the change in non polar surface, statistically derived

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 di

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.

1.5.2 Rational drug discovery

In contrast to traditional methods of

chemical substances on cultured cells

treatments, rational drug design begins with a hypothesis that modulation of a specific biological

penalty for removing the ligand from solvent

penalty for reducing the degrees of freedom when a ligand binds to its

conformational strain energy required to put the ligand in its "active"

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 be important for the 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

e relation between dissociation equilibrium constant, K

components of free energy was built.

Various computational methods are used to estimate each of the components of the master

equation. For example, the change in polar surface area upon ligand binding can be used to

estimate the desolation energy. The number of rotatable bonds frozen upon ligand binding is

proportional to the motion term. The configurationally or strain energy can be estimated

calculations. Finally the interaction energy can be estimated using

methods such as 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 di

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.

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

cultured cells or animals, and matching the apparent effects to

design begins with a hypothesis that modulation of a specific biological

penalty for reducing the degrees of freedom when a ligand binds to its

igand in its "active"

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

ng 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

e relation between dissociation equilibrium constant, Kd, and the

Various computational methods are used to estimate each of the components of the master

ligand binding can be used to

estimate the desolation energy. The number of rotatable bonds frozen upon ligand binding is

proportional to the motion term. The configurationally or strain energy can be estimated

calculations. Finally the interaction energy can be estimated using

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

and-error testing of

, and matching the apparent effects to

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 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.

1.5.3 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

Flowchart of a Usual Clustering Analysis for Structure

:

§ Consensus scoring

§ Selecting candidates by voting of multiple scoring functions

§ May lose the relationship between protein

criterion

§ Geometric analysis

§ Comparing protein-ligand interactions by visually inspecting individual structures

§ Becoming intractable when the number of complexes to be analyzed increasing

§ Cluster analysis

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

Selecting candidates by voting of multiple scoring functions

May lose the relationship between protein-ligand structural information and scorin

ligand interactions by visually inspecting individual structures

Becoming intractable when the number of complexes to be analyzed increasing

Based Drug Design

ligand structural information and scoring

ligand interactions by visually inspecting individual structures

Becoming intractable when the number of complexes to be analyzed increasing

§ Represent and cluster candidates according to protein-ligand 3D information

§ Needs meaningful representation of protein-ligand interactions.

1.5.4 Examples

A particular example of rational drug design involves the use of three-dimensional information

about biomolecules obtained from such techniques as X-ray crystallography and NMR

spectroscopy. Computer-aided drug design in particular becomes much more tractable when

there's a high-resolution structure of a target protein bound to a potent ligand. This approach to

drug discovery is sometimes referred to as structure-based drug design. The first unequivocal

example of the application of structure-based drug design leading to an approved drug is the

carbonic anhydrase inhibitor dorzolamide, which was approved in 1995.

Another important case study in rational drug design is imatinib, a tyrosine kinase inhibitor

designed specifically for the bcr-abl fusion protein that is characteristic for Philadelphia

chromosome-positive leukemias (chronic myelogenous leukemia and occasionally acute

lymphocytic leukemia). Imatinib is substantially different from previous drugs for cancer, as most

agents of chemotherapy simply target rapidly dividing cells, not differentiating between cancer

cells and other tissues.

Additional examples include:

§ Many of the atypical antipsychotics

§ Cimetidine, the prototypical H2-receptor antagonist from which the later members of the

class were developed

§ Selective COX-2 inhibitor NSAIDs

§ Dorzolamide, a carbonic anhydrase inhibitor used to treat glaucoma

§ Enfuvirtide, a peptide HIV entry inhibitor

§ Nonbenzodiazepines like zolpidem and zopiclone

§ Probenecid

§ SSRIs (selective serotonin reuptake inhibitors), a class ofantidepressants

§ Zanamivir, an antiviral drug

§ Isentress, HIV Integrase inhibitor

§ Case studies

§ 5-HT3 antagonists

§ Acetylcholine receptor agonists

§ Angiotensin receptor blockers

§ Bcr-Abl tyrosine kinase inhibitors

§ Cannabinoid receptor antagonists

§ CCR5 receptor antagonists

§ Cyclooxygenase 2 inhibitors

§ Dipeptidyl peptidase-4 inhibitors

§ HIV protease inhibitors

§ NK1 receptor antagonists

§ Non-nucleoside reverse transcriptase inhibitors

§ Proton pump inibitors

§ Triptans

§ TRPV1 antagonists

§ Renin inhibitors

§ c-Met inhibitors

CHAPTER - 2

INTRODUCTION TO

COMPUTER-AIDED DRUG DESIGN

INTRODUCTION TO

COMPUTER-AIDED DRUG DESIGN

2.1 INTRODUCTION

Although the phrase computer-aided drug design may seem to imply that drug

discovery lies in the hands of the computational scientists who are able to manipulate

molecules on their computer screens, the drug design process is actually a complex and

interactive one, involving scientists from many disciplines working together to provide many

types of information. The modern computational and experimental techniques that have

been developed in recent years can be used together to provide structural information

about the biologically active molecules that are involved in disease processes and in

modulating disease processes.

2.2 HOW DRUGS ARE DISCOVERED

Occasionally new drugs are found by accident. More frequently they are developed as part

of an organized effort to discover new ways to treat specific diseases. The discovery of new

pharmaceutical agents has gone through an evolution over the years and has been adding

new technologies to this increasingly complex process1.

2.3 Screening for new drugs

The traditional way to discover new drugs has been to screen a large number of

synthetic chemical compounds or natural products for desirable effects. Although this

approach for the development of new pharmaceutical agents has been successful in the

past, it is not an ideal one for a number of reasons.

The biggest drawback to the screening process is the requirement for an appropriate

screening procedure. Although drugs are ultimately developed in the clinic, it is usually

inappropriate to put chemicals of unknown efficacy directly into humans. Consequently,

other systems have to be developed. Normally a battery of screens is used to select

potential new drug candidates, with activity in initial, rough screens feeding compounds into

later, more sophisticated screens. Initial screens are often in vitro tests for some

fundamental activity, such as the ability to kill bacteria in solution. Ultimately, however,

more applicable in vivo screens are needed. This second level of screening is normally

carried out using animal model systems for the disease.

Screens have inherent limitations2. Primary screens are used for large number of

chemicals to choose which compounds should be further tested with more sophisticated

tests. If the primary screen does not select for an appropriate activity, however, an active

structure will appear to be inactive and will not be discovered. Secondary screening in

animal model systems has additional problems, such as

1. The animal model may not accurately reflect the human disease

2. The chemical may be extensively metabolized to a different compound in the animal

before it reaches its target

3. The chemical may not be absorbed or distributed as it is in humans.

In each of these cases, the active structure potentially will not be identified.

Another serious problem with the screening process is that, because of its random nature,

it is inherently repetitious and time-consuming just to find a chemical with the desired

activity.

Furthermore, chemical compounds discovered by this approach commonly do not have

optimal structures for modulating the biological process. This in turn may require

administration of larger quantities of the drug and increase the risk of unwanted side

effects. The major advantage of screening is the larger amount of information that is not

needed to carry out the process. One does not need to know the structure of the drug being

sought. Nor does one need to know the structure of the target upon which the drug will act.

Most importantly, one does not need to know about the underlying mechanism of the

disease process itself.

2. Modifications for improvements

Once an active (lead) compound has been identified and its chemical structure

determined, it is usually possible to improve on this activity and/or to reduce side effects by

making modifications to the basic chemical structure. Modifications to improve performance

are often carried out using chemical or bio fermentative means to make changes in the lead

structure or its intermediates. Alternatively, for some natural products, the gene itself may

be engineered so that the producer organism synthesizes the modified compound directly.

The process of developing drugs via modification of active lead compounds requires

the structure of the compound to be known. One still does not need to know the structure of

the target on which the drug works. Likewise, no information about the underlying disease

process is required

As with screening, the process of modification is often based on a primarily trial-and-error

approach. Because more information is known, however, this process can be carried out

with much greater probability of success than a purely random process. A prime example of

the power of this approach is in the anti-infective area where modifications of the original

first generation cephalosporin’s have led to second and now third generation offspring with

substantially improved characteristics3.

The limitations of this process are inherent to the fact th

compound as the basis for further drug design. Improvements are likely however, no major

breakthrough in developing new chemical entities (NCEs) is probable. Further, if the original

lead compound fails to generate a desirable

finding a new lead molecule.

2.4 Mechanism-based drug design

As still more information becomes available about the biological basis of a disease, it is

possible to begin to design drugs using a

When the disease process is understood at the molecular level and the target molecule(s)

The limitations of this process are inherent to the fact that one is using a single lead

compound as the basis for further drug design. Improvements are likely however, no major

breakthrough in developing new chemical entities (NCEs) is probable. Further, if the original

lead compound fails to generate a desirable drug, one must start the process over again by

based drug design

As still more information becomes available about the biological basis of a disease, it is

possible to begin to design drugs using a mechanistic approach to the disease process.

When the disease process is understood at the molecular level and the target molecule(s)

at one is using a single lead

compound as the basis for further drug design. Improvements are likely however, no major

breakthrough in developing new chemical entities (NCEs) is probable. Further, if the original

drug, one must start the process over again by

As still more information becomes available about the biological basis of a disease, it is

mechanistic approach to the disease process.

When the disease process is understood at the molecular level and the target molecule(s)

are defined, drugs can be designed specifically to interact with the target molecule in such a

way as to disrupt the disease1-6.

Clearly a mechanistic approach to drug design requires a great deal of knowledge.

Furthermore, processing this knowledge in such a way that a scientist can use the

knowledge to develop a new drug is a formidable task. The major breakthroughs in drug

design in the future are most likely to come via the use of this approach7. Because of the

massive amount of information that must be harnessed to develop drugs by this technique,

it is in this area where computer-aided drug design will have its greatest impact

2.5 Combining technique

The various techniques for finding new drugs, it is important to remember that drug

discovery is both a cumulative and a reiterative process8. Potential drugs developed by

modifying a lead structure are certain to be sent through selective screening processes to

confirm activity and select for the best candidate to go on for further development. Likewise,

drugs developed mechanistically will likely be both screened and later modified in order to

produce the best candidate drug.

Furthermore, every new chemical entity that affects the disease process whether

found by accident, screening, modification, or mechanistic design provides useful

information for developing still better compounds. This is true whether the chemical has

positive or negative effects on the disease process9. Each new chemical increases the data

base of information about the disease-target-drug interaction. This in turn is the basis for

rational drug design10.

CHAPTER - 3

THE BASICS OF MECHANISTIC DRUG DESIGN

THE BASICS OF MECHANISTIC DRUG DESIGN

Most diseases affecting man have been identified by their clinical manifestations. Thus we

are familiar with medical conditions such as hypertension, cancer, infections, etc. Modern

biological techniques now have enabled researchers to study such diseases at the

molecular level and to identify the processes or molecules responsible for producing the

clinical effects.

3.1 Defining the disease process

The first step in the mechanistic design of drugs to treat diseases is to determine the

biochemical basis of the disease process. Ideally, one would know the various steps

involved in the physiological pathway that carries out the normal function. In addition, one

would know the exact step(s) in the pathway that are altered in the diseased state.

Knowledge about the regulation of the pathway is also important. Finally, one would know

the three-dimensional structures of the molecules involved in the process.

3.2 Defining the target

There are potentially many ways in which biochemical pathways could become abnormal

and result in disease. Therefore, knowledge of the molecular basis of the disease is

important in order to select a target at which to disrupt the process. Target for mechanistic

drug design usually fall into three categories: enzymes, receptors and nucleic acids.

3.2.1 Enzymes as targets:

Enzymes are frequently the target of choice for disruption of a disease. If a disease

is the result of the overproduction of a certain compound, then one or more of the enzymes

involved in its synthesis can often be inhibited, resulting in a disease in production of the

compound and disruption of the disease process. This is the theoretical basis behind the

design of both the angiotensin-converting enzyme inhibitors and the rennin inhibitors.

Inhibition of either of these enzymes, which are in the same biochemical pathway,

decreases the production of angiotensin II and consequently reduces blood pressure. In

other instances specific enzymes may be required for pathogenic micro organisms or

cancerous cells to live and grow, thereby causing disease. Inhibition of such enzymes

would prevent the growth of these microbes or cells and hence reverse the disease. Such is

the case with the enzyme dihydrofolate reductase.

Enzymes are usually the targets of choice because they are relatively small,

aqueous-soluble proteins that often can be isolated for study. When enough of the enzyme

is difficult to obtain from its natural source, genetic engineering techniques are frequently

utilized to provide material for conducting X-ray crystallography, NMR spectroscopy and

enzyme kinetics. Ultimately the data obtained by these techniques allow one to determine

the Three-dimensional structures of the enzyme molecule in its active conformation. These

structures provide a starting point for the design of new effectors molecules by computer

graphics and molecular modeling techniques.

3.2.2 RECEPTORS AS TARGETS:

Sometimes a disease can be modulated by blocking the action of an effectors

at its cellular receptor. A classic example of this is the well-known inhibition of the gastric

histamine-2 receptor by the drug cimetidine which decreases acid secretion in the stomach

and reduces ulcer formation. Unlike enzymes, which often circulate in the body and can be

isolated and studied outside their biological environment, cellular receptors consist of

proteins imbedded in a surface membrane. Consequently these targets are difficult to

isolate and thus it is difficult to determine their structures. Nonetheless, molecular biological

techniques are beginning to produce these macromolecules in larger amounts. Structural

information will soon be available for many of them, using the same experimental

techniques used for determine enzymes structures.

Receptors that are easily isolated are the most amenable to rational design of

effectors. An illustrative use of this concept is in the three-dimensional structural

determination of rhinoviruses, which then can serve as a receptor-type target for the design

of antiviral drugs.

3.2.3 Nucleic acids as targets:

Diseases can also potentially be blocked by preventing the synthesis of undesirable

proteins at the nucleic acid level. This strategy has frequently been employed in the

antimicrobial and antitumor areas, where DNA blocking drugs are used to prevent the

synthesis of critical proteins. Since the microorganisms or tumor cells cannot grow and/or

replicate, the disease process is effectively blocked.

Examples include the use of the DNA intercalating drug adriamycin to treat certain

forms of cancer.

3.3 DEFINING THE RECEPTOR

Effector molecules are compounds that can occupy an active site of a target

molecule. As used in this context, they can be substrates, natural effectors that regulate the

target I positive or negative ways or drugs. Effector molecules and their targets interact with

each other via a lo0ck and key type of mechanism, in which the target enzyme or receptor

is the lock and the effector is the key. Implicit in this concept is that the two fit together in a

physically complementary fashion. Therefore, it should be possible to determine the shape

of the mutual contact surface of either by knowing the three-dimensional conformation of

the active portion of one.

In reality the relationship between the effector and target is more complex. The

natural effect or molecule fit into the effective site of enzyme or the binding site of the

receptor in a manner that maximizes the complementarity’s of the two molecules. In

addition, this complementarity not only recognized as a function of shape, that also includes

the interaction of charged regions, hydrogen bonding hydrophilic interactions, etc. Because

of the interactions between effector and its target are so complex , the best information for

designing drugs is obtained when one can determine the three-dimensional structure of

both the target and effector molecules. However, since effector molecules are often much

smaller and are more readily available than their targets, they are ususally more amenable

to structural analyses. Again the information obtained from experimental techniques

provides the spatial coordinates that are utilized in the computerized analyses of effectors

structure.

3.4 DESIGNING NEW DRUGS TO EFFECT TARGETS

To make a good drug, a compound should exhibit a number of useful

characteristics. In addition to producing the desired effect, it should be sufficiently potent

that large amounts do not have to be administered. It should have low toxicity and minimal

side effects. Drugs that have to be given for chronic conditions should have considerable

residence time in the body(half life) so that continuous administration is not needed. Oral

administration of the drug is the preferred route in order to encourage patient compliance.

In the normal condition, natural effectors interact with their targets to carry out a

needed physiological function. The natural effectors for a target thus often represent an

optimal structure for the complex formed. These natural molecules are not often used as

drugs, however, for a number of reasons. The body generally has the ability to produce

these effectors, whenever they are needed to modulate a physiological process. Once they

have fulfilled their functions, they are rapidly removed via., metabolic and elimination

mechanisms. Natural effectors also generally are not orally active. The metabolic instability

built into the molecule to facilitate natural inactivation. Often allows it to be degraded by

enzymes in the gastrointestinal tract. Even when natural effector survives this process, they

typically do not have the properties necessary to pass through the gastrointestinal mucosa.

Additionally, endogenous effectors frequently interact with similar targets in a variety of

systems. Thus, they tend to cause substantial unrelated side effects under conditions of

high-level or long-term administration.

On the other hand, natural effectors molecules are often used as the starting point

for the development of new drugs, since they generally have selectivity and potency for the

desired target. By careful manipulation of the native structure, one can frequently retain the

binding characteristics of the effector. While designing in other desirable characteristics.

Examples of drug design with natural effectors as the starting point include the use of the

structure of luteinizing hormone-releasing hormone in the design of LHRH receptor agonists

such as the anticancer drug Leuprolide and the use of the structure of the Enkephalins in

the design of opioid receptors agonists as potential analgesics.

There are other sources for complimentary structures for enzyme and receptor

targets, which can also be used as a starting point, or to provide additional structural

information, for designing new drugs. If the natural effector is unavailable, similar effectors

from a different host may be used.

Example, the structure equine angiotensinogen was used in the development of

early human rennin inhibitors. Natural products, particularly those obtained from microbes,

often provide novel structures that are potent effectors.

For example, Pepstatin, a natural product produced by an actinomycete, is a

potent inhibitor of aspartic proteinases and therefore was useful in the design of rennin

inhibitors.

CHAPTER - 4

QUANTITATIVE STRUCTURE ACTIVITY RELATIONSHIP

(QSAR)

QUANTITATIVE STRUCTURE ACTIVITY RELATIONSHIP (QSAR)

4.1 Introduction

Quantitative structure–activity relationship

chemical and biological sciences and engineering. Like other regression models, QSAR models

relate measurements on a set of "predictor" variables to the behavior of the

In QSAR modeling, the predictors consist of properties of chemicals; the QSAR response

variable is the biological activity

relationship between chemical structures

Second QSAR models predict

include quantitative structure–property relationships

For example, biological activity can be expressed quantitatively as the concentration of a

substance required to give a certain biological response. Add

properties or structures are expressed by numbers, one can form a mathematical relationship,

or quantitative structure-activity relationship, between the two. The mathematical expression

can then be used to predict the biolo

A QSAR has the form of a mathematical model

§

The error includes model error

observations even on a correct model.

4.1.1 SAR and the SAR paradox

The basic assumption for all molecule basedactivities. This principle is also calledproblem is therefore how to define aactivity, e.g. reaction ability, biotransformationdepend on another difference. A good example was given in thePatanie/LaVoie.[1]

In general, one is more interested in finding stronga finite number of chemical davoid overfitted hypotheses and dstructural/molecular data.

The SAR paradox refers to the fact that it is not the case that all similar molecules have similar activities.

QUANTITATIVE STRUCTURE ACTIVITY RELATIONSHIP (QSAR)

activity relationship models are regression models used in the

chemical and biological sciences and engineering. Like other regression models, QSAR models

relate measurements on a set of "predictor" variables to the behavior of the response variable

In QSAR modeling, the predictors consist of properties of chemicals; the QSAR response

biological activity of the chemicals. QSAR models first summarize a supposed

chemical structures and biological activity in a data-set of chemicals.

predict the activities of new chemicals. Related terms

property relationships (QSPR).

For example, biological activity can be expressed quantitatively as the concentration of a

substance required to give a certain biological response. Additionally, when physicochemical

properties or structures are expressed by numbers, one can form a mathematical relationship,

activity relationship, between the two. The mathematical expression

can then be used to predict the biological response of other chemical structures.

mathematical model:

(bias) and observational variability, that is, the variability in

on a correct model.

SAR and the SAR paradox

The basic assumption for all molecule based hypotheses is that similar molecules have similar activities. This principle is also called Structure–Activity Relationship (SAR). The underlying problem is therefore how to define a small difference on a molecular level, since

biotransformation ability, solubility, target activity, and so on, might depend on another difference. A good example was given in the bioisosterism

In general, one is more interested in finding strong trends. Created hypothesesnumber of chemical data. Thus, theinduction principle should be respected to

hypotheses and deriving overfitted and useless interpretations on

The SAR paradox refers to the fact that it is not the case that all similar molecules have similar

models used in the

chemical and biological sciences and engineering. Like other regression models, QSAR models

response variable.

In QSAR modeling, the predictors consist of properties of chemicals; the QSAR response-

micals. QSAR models first summarize a supposed

set of chemicals.

ctivities of new chemicals. Related terms

For example, biological activity can be expressed quantitatively as the concentration of a

itionally, when physicochemical

properties or structures are expressed by numbers, one can form a mathematical relationship,

activity relationship, between the two. The mathematical expression

gical response of other chemical structures.

) and observational variability, that is, the variability in

is that similar molecules have similar ). The underlying

difference on a molecular level, since each kind of , target activity, and so on, might

sosterism review of

hypotheses usually rely on should be respected to

eriving overfitted and useless interpretations on

The SAR paradox refers to the fact that it is not the case that all similar molecules have similar

4.2 Types

a) Fragment based (group contribution)

It has been shown that the logP of compound can be determined by the sum of its fragments. Fragmentary logP values have been determined statistically. This method gives mixed results and is generally not trusted to have accuracy of more than ±0.1 units.

Group or Fragment based QSAR is also known as GQSAR. GQSAR allows flexibility to study various molecular fragments of interest in relation to the variation in biological response. The molecular fragments could be substituents at various substitution sites in congeneric set of molecules or could be on the basis of pre-defined chemical rules in case of non-congeneric set. GQSAR also considers cross-terms fragment descriptors, which could be helpful in identification of key fragment interactions in determining variation of activity. Lead discovery using Fragnomics is an emerging paradigm. In this context FB-QSAR proves to be a promising strategy for fragment library design and in fragment-to-lead identification endeavours.

b) 3D-QSAR

3D-QSAR refers to the application of force field calculations requiring three-dimensional structures, e.g. based on protein crystallography or molecule superimposition. It uses computed potentials, e.g. the Lennard-Jones potential, rather than experimental constants and is concerned with the overall molecule rather than a single substituent. It examines the steric fields (shape of the molecule) and the electrostatic fields based on the applied energy function.

The created data space is then usually reduced by a following feature extraction (see also dimensionality reduction). The following learning method can be any of the already mentioned machine learning methods, e.g. support vector machines. An alternative approach usesmultiple-instance learning by encoding molecules as sets of data instances, each of which represents a possible molecular conformation. A label or response is assigned to each set corresponding to the activity of the molecule, which is assumed to be determined by at least one instance in the set (i.e. some conformation of the molecule).

On June 18th 2011 the CoMFA patent has dropped any restriction on the use of GRID and PLS technologies and the RCMD team (www.rcmd.it) has opened a 3D QSAR web server (www.3d-qsar.com).

c) Modeling

In the literature it can be often found that chemists have a preference for partial least squares (PLS) methods, since it applies the feature extraction and induction in one step.

Data mining approach

For the coding usually a relatively large number of features or molecular descriptors are calculated, which can lack structural interpretation ability. In combination with the later applied learning method or as preprocessing step occurs a feature selection problem.

A typical data mining based prediction uses e.g. support vector machines, decision trees, neural networks for inducing a predictive learning model.

Molecule mining approaches, a special case of structured data mining approaches, apply a similarity matrix based prediction or an automatic fragmentation scheme into molecular substructures. Furthermore there exist also approaches using maximum common sub graph searches or graph kernels.

Evaluation of the quality of QSAR models

QSAR modeling produces predictive models derived from application of statistical tools correlating biological activity (including desirable therapeutic effect and undesirable side effects) of chemicals (drugs/toxicants/environmental pollutants) with descriptors representative of molecular structure and/or properties. QSARs are being applied in many disciplines for example risk assessment, toxicity prediction, and regulatory decisions in addition to drug discovery and lead optimization. Obtaining a good quality QSAR model depends on many factors, such as the quality of biological data, the choice of descriptors and statistical methods. Any QSAR modeling should ultimately lead to statistically robust models capable of making accurate and reliable predictions of biological activities of new compounds.

For validation of QSAR models usually four strategies are adopted: internal validation or cross-validation;

1. validation by dividing the data set into training and test compounds;

2. true external validation by application of model on external data and

3. Data randomization or Y-scrambling.

The success of any QSAR model depends on accuracy of the input data, selection of appropriate descriptors and statistical tools, and most importantly validation of the developed model. Validation is the process by which the reliability and relevance of a procedure are established for a specific purpose. Leave one-out cross-validation generally leads to an overestimation of predictive capacity, and even with external validation, no one can be sure whether the selection of training and test sets was manipulated to maximize the predictive capacity of the model being published. Different aspects of validation of QSAR models that need attention includes methods of selection of training set compounds, setting training set size and impact of variable selection for training set models for determining the quality of

prediction. Development of novel validation parameters for judging quality of QSAR models is also important

4.3 Application

a) Chemical

One of the first historical QSAR applications was to predict boiling points. It is well known for instance that within a particular family of chemical compounds, especially of organic chemistry, that there are strong correlations between structure and observed properties. A simple example is the relationship between the number of carbons in alkanes and their boiling points. There is a clear trend in the increase of boiling point with an increase in the number carbons and this serves as a means for predicting the boiling points of higher alkanes. A still very interesting application is the Hammett equation, Taft equation and pKa prediction methods.

b) Biological

The biological activity of molecules is usually measured in assays to establish the level of inhibition of particular signal transduction or metabolic pathways. Chemicals can also be biologically active by being toxic. Drug discovery often involves the use of QSAR to identify chemical structures that could have good inhibitory effects on specific targets and have low toxicity (non-specific activity). Of special interest is the prediction of partition coefficient log P, which is an important measure used in identifying "drug likeness" according to Lipinski's Rule of Five.

While many quantitative structure activity relationship analyses involve the interactions of a family of molecules with an enzyme or receptor binding site, QSAR can also be used to study the interactions between the structural domains of proteins. Protein-protein interactions can be quantitatively analyzed for structural variations resulted from site-directed mutagenesis.

It is part of the machine learning method to reduce the risk for a SAR paradox, especially taking into account that only a finite amount of data is available (see also MVUE). In general all QSAR problems can be divided into a coding and learning.

(Q)SAR models have been used for the risk management of chemicals risk. QSARS are suggested by regulatory authorities; in the European Union, QSARs are suggested by the REACH regulation, where "REACH" abbreviates "Registration, Evaluation, Authorisation and Restriction of Chemicals".

The chemical descriptor space whose convex hull is generated by a particular training set of chemicals is called the training set's applicability domain. Prediction of properties of novel chemicals that are located outside the applicability domain uses extrapolation, and so is less reliable (on average) than prediction within the applicability domain. The assessment of the reliability of QSAR predictions remains a research topic.

In 1968 Crum-Brown and Fraser published an equation which is considered to the first general formulation of QSARs. In their investigation on different alkaloids they recognized that alkylation of the basic nitrogen atom produced different biological effects of the resulting quaternary ammonium compound, when compared to the basic amines11. Therefore they assumed that biological activity must be the function of the chemical structure.

BA=f[C]

Richet discovered that toxicity of organic compounds inversely follows their water

solubility. Such relationship shows that changing the biological activity (∆BA) corresponds

to the change in the chemical and physiological properties ∆C.

∆BA=f (∆C)

All the QSAR equation corresponds to equation2, because only the difference in BA

are quantitatively correlates with changes in lipophilicity and/or other physiochemical

properties of the compound under investigation.

QSAR involves the derivation of mathematical formula which relates the biological

activities of a group of compounds to their measurable physiochemical parameters. These

parameters have major influence on the drug’s activity. QSAR derived equation take the

general form

Biological activity=function {parameters}

Biological activity of a drug is a function of chemical features (i.e., lipophilicity,

electronic and steric) of the substituents and skeleton of the molecule. For example

lipophilicity is the main factor governing transport, distribution and metabolism of drug in

biological system. Similarly electronic and steric features influence the metabolism and

pharmacodynamic process of the drug.

4.4 PARAMETERS

The various parameters used in QSAR studies are

1. Lipophilic parameters: Partition coefficient, chromatographic parameters and π-

substitution constant.

2. Polarizability parameters: Molar refractivity, Molar volume, Parachor

3. Electronic parameters: Hammett constant, Field and resonance parameters, parameters

derived from spectroscopic data, Charge transfer constant, Dipole moment, Quantum

chemical parameter.

4. Steric parameters: Taft’s steric constant, Vanderwaal’s radii.

5. Miscellaneous parameters: Molecular weight, Geometric parameters, Conformational

entropies, Connectivity indices, other topological parameters.

4.4.1 LIPOPHILIC PARAMETERS

Lipophilicity is defined by the partitioning of a compound between an aqueous and

a non-aqueous phase. Two parameters are commonly used to represent lipophilicity,

namely the partition coefficient (p) and lipophilic substitution constant (π). The former

parameter refers to whole molecule, while the latter is related to substituted groups.

4.4.2 PARTITION COEFFICIENT

A drug has to pass through a number of biological membranes in order to reach its

site of action. Partition coefficient is generally given as

P= [C]org

[C]aqu

It is a ratio of concentration of substance in organic and aqueous phase of a two

compartment system under equilibrium conditions.

P= [C]org

[C]aqu (1-α)

α = degree of ionization.

The nature of the relationship between P and drug activity depends on the range of P

values obtained in the compounds used.

Log1/c=K1 logP+K2

Where

K1 and K2 are constants.

4.4.3 Chromatographic parameters

When the solubility of a solute is considerably greater in one phase

than the other, partition coefficient becomes difficult to determine experimentally.

Chromatographic parameters obtained from reversed phase thin layer chromatography are

occasionally used as substituent for partition coefficient. Silica gel plate, being coated with

hydrophobic phases, is eluted with aqueous/organic solvent system of increasing water

content. The Rf values are converted into Rm value, which are the true measure of

lipophilicity from the following equation.

Rm = log (1/ Rf-1)

Rm value has been used as a substitute for partition coefficient in QSAR investigations. The

determination of Rm values offers many important advantages, as compared to the measure

of logP values.

• Compounds need not be pure.

• Only trace of materials needed.

• A wide range of hydrophilic and lipophilic congeners can be investigated.

• The measurement of practically insoluble analogs possesses no problem.

• No quantitative method for concentration determination needed.

• Several compounds can be estimated simultaneously.

The main disadvantages are

• Lack of precision and reproducibility.

• Use of different organic solvent system renders the derivation of π and f related

scales are impossible.

4.4.4 POLARIZABILITY PARAMETERS

Molar refractivity

The molar refractivity is a measure of both the volume of a compound and how

easily it is polarized.

MR= (n2-1)M

(n2+2)d

Where

N is the refraction index

M is the molecular weight and

d is the density.

The term Mw/d defines a volume, while the term (n2-1) / (n2+1) provide a correction factor

by defining how easily the substituent can be polarized. This is particularly significant if the

substituent has a π electron or lone pair of electrons.

The significance of molar refractivity terms in QSAR equation of some ligand-enzyme

interaction could be interpreted with the help of 3D structure. These investigation shows

that substituent modeled by MR bind in polar areas, while substituents modeled by π, bind

in hydrophobic space. The positive sign of MR in QSAR equation explains that the

substituent binds to polar surface, while a negative sign or nonlinear relationship indicates

steric hindrance at the binding site.

Parachor

The parachor [p] is molar volume V which has been corrected for forces of

intermolecular attraction by multiplying the fourth root of surface tension γ .

[p] = Vγ1/4 = M γ1/4

D

Where

M is molecular weight

D is the density

4.4.5 ELECTRONIC PARAMETERS

The distribution of electron in a drug molecule has a considerable influence on the

distribution and activity of the drug. In general, non-polar and polar drug in their unionized

form are more readily transported through membranes than polar drugs and drugs in their

ionized form. If the drug reaches the target site, the distributed electron will control the type

of bond that it forms with the target site, which in turn affects its biological activity.

The Hammett constant (σ)

The distribution of electrons within a molecule depends on the nature of the

electron withdrawing and donating group found in the structure. Hammett used this concept

to calculate what now known as Hammett constant.

Hammett constant is defined as

σx= log KBX

KB

i.e., σx= log KBX- log KB

And so as pKa = -logKa

σx = p KB-pKBX

Where,

KB and KBX are the equilibrium constants for benzoic acid and mono substituted benzoic

acid respectively.

Hammett substitution constant (σ) is a measure of the electron withdrawing or

electron donating ability of a substituent. A negative value of σx indicates that the

substituent is acting as an electron donor and the positive value indicates that it is acting as

electron withdrawing group. Hammett constant takes into account both resonance and

inductive effect. Hammett constant suffer from the disadvantage that they only apply to

substituents directly attached to benzene ring.

Taft’s substituent constant

Taft’s substituent constant (σ*) are a measure of the polar effects of substituent in

aliphatic compound when the group in question does not form part of a conjugated system.

They are based on the hydrolysis of ester and calculated from the following equation

σ* = 1/2.48 [log (k/ko)B - log(k/ ko)A]

Where

k represents the rate constants for the hydrolysis of the substituted compound

ko those of methyl derivative.

The bracketed term with subscript B represent basic hydrolysis and A as acid hydrolysis

respectively. In Taft’s substituent constant only methyl group is the standard for which the

constant is zero. However, that can be compared with other constant by writing the methyl

group in the form CH2 – H and identifying it as the group for H. Taft’s and inductive

substituent constants are related as

σ*= 2.51σ i

4.4.6 STERIC SUBSTITUTION CONSTANT

For a drug to interact with an enzyme or to receptor, it has to approach to the

binding site. The bulk, size and shape of the drug may influence on this process. A steric

substitution constant is a measure of the bulkiness of the group it represents and its effect

on the closeness of constant between the drug and the receptor site.

Verloop steric parameter

Verloop steric parameter is called as sterimol parameter, which involves a

computer programme to calculate the steric substituent values from standard bond angles,

Vander Waals radii, bond length and possible conformation for substituents. It can be used

to measure any substituents.

For example the Verloop steric parameters for carboxylic acid group are demonstrated. L is

the length of the substituent while B1- B4 are the radii of the group.

Charton’s steric constants

The principal problem with Vander Waal’s radii and Taft’s Es value is the limited

number of groups to which these constants have been allocated. Charton introduced a

corrected Vander Waal’s radius U in which the minimum Vander Waal’s radius of the

substituent group (rv(min) ) is corrected for the corresponding radius for hydrogen (rvH), as

defined by equation. They were shown to be a good measure of steric effect by correlation

with Es values.

U= rv(min) - rvH = rv(min) – 1.20

4.4.7 OTHER PARAMETERS

Molecular weight was used by Lein to improve the fit of parabolic Hansch

equation. A more appropriate use of MW was demonstrated in QSAR study of multidrug

resistance of tumor cells, where the MW term stands for the dependence of biological

activities on diffusion rate constant. The relationship between MW and volume implies that

3√MW corresponding to linear dimension of size should be better than log MW.

Indicator variables sometimes known as dummy variables or de-nova constant are used in

linear multiple regression analysis to account for certain features, which can not be

described by continuous variables. It is used to account for other structural features like

intra molecular hydrogen bonding, hydrogen donor and acceptor properties, ortho effects,

cis/trans isomers, different parent skeleton, different test models etc.

QUANTITATIVE MODELS

To draw the QSAR equation with these parameters, it is simple to draw a QSAR

model with such property. But biological activity of most of the drug is related to

combination of physiochemical properties. Various methods are used to draw the QSAR

model. One among these models is Hansch analysis.

Hansch analysis (The extra thermodynamic approach)

This is the most popular mathematical approach to QSAR introduced by Corwin

Hansch. It is based on the fact that the drug action could be divided into two stages.

• Transport of drug to its site of action.

• The binding of drug to the target site.

Each of these stages depends on the chemical and physical properties of the drug and its

target site. In Hansch analysis these properties are described by the parameters which

correlate the biological activity. The most commonly used physiochemical parameters foe

Hansch analysis are log p, π, σ and steric parameters as practically all the parameters

used in Hansch analysis are “linear free energy approach” or “extra thermodynamic

approach”.

If the hydrophobic values are limited to a small range then the equation will be linear as

follows.

log (1/c) = k1 log p + k2 σ + k3 E3 + k4

Where

k1, k2 and k3 are constant obtained by least square procedure, c is the molar

concentration that produce certain biological action.

The molecules which are too hydrophilic or too lipophilic will not be able to cross the

lipophilic or hydrophilic barriers respectively. Therefore the p value are spread over a large

range, then the equation will be parabolic and given as

log (1/c) = -k (logp)2 + k2logp + k3σ+ k4Es + k5

The constant k1 - k5 are obtained by least square method. Not all the parameters are

necessarily significant in a QSAR model for biological activity. To derive an extra

thermodynamic equation following rules are formulated by Hansch:

i. Selection of independent variables. A wide range of different parameter like log

p, π, σ, MR, steric parameters etc should be tried. The parameters selected for the best

equation should be essentials independent i.e., the intercorrelation coefficient should be

larger than 0.6-0.7.

ii. All the reasonable parameters must be validated by appropriate statistical

procedure i.e., either by stepwise regression analysis or cross validation. The best equation

is normally one with lower standard deviation and higher F value.

iii. If all the equations are equal then one should accept the simplest one.

iv. Number of terms or variables should be atleast 5 or 6 data point per variable to

avoid chance correlations.

v. It is important to have a model which is consistent with known physical-organic

and bio-medical chemistry of the process under consideration.

Applications of Hansch analysis

Hansch equation may be used to predict the activity of an yet un synthesized

analogue. This enables the medicinal chemist to make a synthesis of analogue which is

worthy. However this prediction should only be regarded as valid, if they are made within

the range of parameter values used to establish the Hansch equation. Hansch analysis may

also be used to give an indication of the importance of the influence of parameters on the

mechanism by which a drug acts.

Example

The adrenergic blocking activity of series of analogue of β-Halo aryl amine was

observed. It was found that only π and σ values only related to the activity and not the

steric factor, from the following Hansch equation

Log1/c = 1.78π – 0.12σ + 1.674.

The smaller the value of coefficient of σ relative to that of π in the above equation shows

that electronic effect do not play an important role in the action of drug.

The accuracy of Hansch equation depends on

i. The number of analogues (n) used. The greater the number, the higher the

probability of obtaining an accurate Hansch equation.

ii. The accuracy of biological data used in the derivation of the equation.

iii. The choice of parameters.

CHAPTER - 5

USES OF COMPUTER GRAPHICS IN

COMPUTER-ASSISTED DRUG DESIGN

USES OF COMPUTER GRAPHICS IN COMPUTER-ASSISTED DRUG DESIGN

INTRODUCTION

Computers are essential tool in modern mechanical chemistry and are important

in both drug discovery and development. The development of this powerful desktop

enabled the chemist to predict the structure and the value of the properties of known,

unknown, stable and unstable molecular species using mathematical equation. Solving this

equation gives required data. Graphical package convert the data for the structure of a

chemical species into a variety of visual formats. Consequently, in medicinal chemistry, it is

now possible to visualize the three dimensional shape of both the ligands and their target

sites. In addition, sophisticated computational chemistry packages also allow the medicinal

chemists to evaluate the interaction between a compound and its target site before

synthesizing that compound. This means that, medicinal chemists need only synthesize

and test the compounds that considerably increase the potency that is, it increase the

chance of discovering a potent drug. It also significantly reduces the cost of development.

MOLECULAR MODELING

Molecular modeling is a general term that covers a wide range of molecular

graphics and computational chemistry techniques used to build, display, manipulate,

simulate and analyze molecular structure and to calculate properties of these structures.

Molecular modeling is used in several different researches and therefore the term does not

have a rigid definition. To a chemical physicist, molecular modeling imply performing a high

quality quantum mechanical calculation using a super computer on the structure to a

medicinal chemists, molecular modeling mean displaying and modifying a candidate drug

molecule on the desktop computer. Molecular modeling techniques can be divided into

molecular graphics and computation chemistry.

5.1 Molecular graphics (Computer graphic displays)

Molecular graphics is the core of a modeling system, providing for the

visualization of molecular structure and its properties. In molecular modeling, the data

produced are converted into visual image on the computer screen by graphic packages.

These images may be displayed in a variety of styles like fill, CPK (Corey-Pauling-Koltum),

stick, ball and stick, mesh and ribbon and colour scheme with visual aids. Ribbon

presentation is used for larger molecules like nucleic acid and protein.

Visualization of molecular properties is an extremely important aspect of molecular

modeling. The properties might be calculated using a computational chemistry program and

visualized as 3D contours along with the associated structure. The most common

computational methods are based on either molecular or quantum mechanics. Both these

approaches produce equation for the total energy of the structure. In this equation the

position of the atom in the structures are represented by either Cartesian or polar co-

ordinates. Once the energy equation is established, the computer computes a set of co-

ordinates which corresponds to minimum total energy value for the system. This set of co-

ordinate is converted into the required visual display by the graphic packages. The program

usually indicates the three dimensional nature of the molecule and it can be viewed from

different angles and allows the structure to be fitted to its target site. In addition, it is also

possible by molecular dynamics, to show how the shape of structure might vary with time

by visualizing the natural vibration of the molecule.

5.2 Molecular mechanics

Molecular mechanics is the more popular of the methods used to obtain molecular models

as it is simple to use and requires considerably less computing time to produce a model. In

this technique the energy of structure is calculated. The equation used in molecular

mechanics follow the laws of classical physics and applies them to molecular nuclei without

consideration of the electrons. The molecular mechanics method is based on the

assumption that the position of the nuclei of the atom forming the structure is determined by

the force of attraction and repulsion operating in that structure. It assumes that the total

potential energy [Etotal ] of a molecule is given by the sum of all the energies of the attractive

and repulsive forces between the atoms in the structure. Molecules are treated as a series

of sphere (the atoms) connected by spring (the bond) using this model: Etotal is expressed

mathematically by equation known as force fields given by:

E total = Σ Estretching + Σ Ebend + Σ Etorsion + Σ Evdw + Σ Ecoulombic

Estretching

Estretching is the bond stretching energy. The value of the Estretching bond energy for

pair of atoms joined by a single bond can be estimated by considering the bond to be a

mechanical spring that obeys Hooke’s law. If r is the stretched length of the bond and r0 is

the ideal bond length, then

Estretching = ½ K (r- r0)2

Where,

K is the force constant in other word a measure of the strength of the bond.

If a molecule consist of three atoms, (a-b-c), then

Estretching = Ea-b + Eb-c

= ½ K(a-b) [r(a-b)- r0(a-b)]2 + ½ K(b-c) [r(b-c)- r0(b-c)]

2

Ebend

Ebend is bond energy due to the changes in bond angle and estimated as

Ebend = ½ (K0(θ-θ0)2

Where.

θ0 is the ideal bond length i.e., the minimum energy position of the 3 atoms.

Etorsion

Etorsion is the bond energy due to changes in the conformation of the bond and given by

Etorsion = 1/2 Kø (1+cos (m (ø+ø offset))

Where Kø is the energy barrier to the rotation about the torsion about the torsion angleø, m

is the periodicity of the rotation and øoffset is the ideal torsion angle relative to staggered

arrangement of two atoms.

Evdw

Evdw is the total energy contribution due to the Vander Waal’s force and it is calculated

from the Lennard-Jone6-12 potential equation.

Evdw = ε[(rmin)12 – 2(rmin)

6]

r r

The (rmin)6 term in this equation represents attractive force, while (rmin)

12 term represents

r r

the short range of repulsive forces between the atoms. The rmin is the distance between

two atoms i and j when the energy at a minimum ε and r is the actual distance between the

atoms.

Ecoulombic

Ecoulombic is the electrostatic attractive and repulsive forces operating in the molecule

between the atoms carrying a partial or full charge.

Ecoulombic = qi qj

Drij

Where

qi and qj are the point charges on atoms i and j.

rij is the distance between the charges and

D is the dielectric constant of the medium surrounding the charges.

The values of the parameters r, r0, k . . . . etc used in the expression for the energy term in

the above equation is either obtained/calculated from experimental observations. The

experimental values are derived from variety of spectroscopic techniques. Thermodynamic

data measurement and crystal structure measurement for inter atomic distances.

The best fit parameters are obtained by looking with known parameter values and

stored in the data base of the molecular modeling computer program.

Creating a molecular model using molecular mechanics

Molecular modeling can be created by any of these methods.

• Commercial force field computer program

• Assembling model

Commercial force field computer program

Commercial packages usually have several different force fields within the same

package and it is necessary to pick the most appropriate one for the structure being

modeled.

Assembling model

Molecular models are created by assembling a model from structural fragments held

in the database of the molecular modeling program. Initially, these fragments are put

together in a reasonably sensible manner to give a structure that does not allow for steric

hindrance. It is necessary to check that, the computer has selected atoms for the structure

whose configuration corresponds to the type of bond required in structure. For example, if

the atom in the structure is double bonded, then the computer has selected a form of atom

that is double bonded. These checks are carried out by matching a code for the atoms on

the screen against the code given in the manual for the program and replacing atom where

necessary.

An outline of the steps involved using INSIGHT II to produce a stick model of the structure

of paracetmol.

STEP 1

The selection of the structure fragments from the database of the INSIGHT II program. The

molecule with the relevant functional group and/or structure is selected.

The INSIGHT II models of these structures.

STEP 2

The fragments are linked together. Fragments are joined to each other by removing

hydrogen atoms at the points at which the fragments are to be linked. The bonding state of

each atom is checked and if necessary adjusted.

STEP 3

A representation of the change in the value of Etotal demonstrating how the computation

could stop at a local(x) rather than the true (global) minimum value. The use of molecular

dynamics gives the structure kinetic energy which allows it to overcome energy barriers,

such as Y, to reach the global minimum energy structure of the molecule.

Once the structure is created energy minimization should be carried out. This is because

the construction process may have resulted in un favourable bond lengths, bond angle or

torsion angle. The energy minimization process is carried out by a molecular mechanics

program, calculates the energy of the starting molecule, then varies the bond lengths, bond

angle and torsion angle to create a new structure in whatever software program used. The

program will interpret the most stable structure and will stop at that stage when the force

field reaches the nearest local minimum energy value. This final structure may be around

the screen and expanded or reduced in size. It can also be rotated about the x and y axis to

view different elevation of the model.

The molecular mechanic method requires less computing time than the quantum

mechanical approach and may be used for large molecules containing more than a

thousand atoms. Energy calculation has a range of application in molecular modeling.

• They can be used in the conformational analysis to evaluate the relative stability

of different conformers and to predict the equilibrium geometry of a structure.

• They can also be used to evaluate the energy of two or more interacting

molecules, such as when docking a substrate the enzyme active site.

It is not useful for computing properties such as electron density. The accuracy of the

structure obtained will depend on the quality and appropriateness of the parameters used in

the force field. Molecular mechanical calculations are normally based on isolated structures

at zero Kelvin and not normally take into account the effect of the environment on the

structure.

5.3 Molecular dynamics

Molecular mechanics calculations are made at zero Kelvin, that is on structure that

are frozen in time and so do not show the natural motion in the structure. Molecular

dynamics programs allow the modular to show the dynamic nature of the molecule by

stimulating the natural motion of the atom in a structure.

Starting with the molecular mechanics energy description of the structure as

described above, the force acting as the atom can be evaluated. Since the masses of the

atom are known, Newton’s second law of motion (force=mass*acceleration) may be used to

compute the acceleration and thus the velocities of the atoms. The acceleration and

velocities may be used to calculate new position for the atom over a short time step thus

moving each atom to a new position in the space. The velocities of the atoms are related

directly to the temperature at which the stimulation is run. Higher temperature stimulations

are used to search conformational shape, since more energy is available to climb and cross

barriers. These variations are displayed on the monitor in as a moving picture. The

appearance of this picture will depend on the force field selected for the structure and the

time interval and temperature used for the integration of the Newtonian equation. Molecular

dynamics can be used to find minimal energy structure and conformational analysis.

5.4 Conformational analysis

Using molecular mechanics (MM2), it is possible to generate a variety or different

conformations by using a molecular dynamics program which ‘heats’ the molecule to 800-

900K. Of course, this does not mean that the inside of your computer is about to melt. It

means that the program allows the structure to undergo bond stretching and bond rotation

as if it was being heated. As a result, energy barriers between different conformations are

overcome, allowing the crossing of energy saddles. In the process, the molecule is ‘heated’

at a high T(900K) for a certain period, then ‘cooled’ to 300K for another period to give a final

structure. The process can be repeated automatically as many times a wished to give as

many different structures as required. Each of these structures can then be recovered,

energy minimized and its steric energy measured. By carrying out this procedure, it is

usually possible to identify distinct conformations, some of which might be more stable than

the initial conformation.

Example

The 2D drawing of butane was imported into Chem3D and energy minimized.

Because of the way molecule was represented, energy minimization stopped at the first

local energy minimum it found, which was the gauche conformation having a steric energy

of 3.038Kcal/mol. The molecular dynamic program was run to generate other conformations

and successfully produced the fully staggered trans conformation which, after optimization,

had a steric energy of 2.175Kcal/mol, showing that the latter was more stable by about

1Kcal/mol.

In

fact, this particular problem could be solved more efficiently by the stepwise rotation of

bonds described below. Molecular dynamic is more useful for creating different

conformations of molecule which are not conductive to stepwise bond rotation (cyclic

system), or which would take too long analyse by that process (large molecular).

Example

The twist boat conformation of cyclohexane remains as the twist boat when energy

minimization is carried out. ‘Heating’ the molecule by molecular dynamics in Chem3D

produces a variety of different conformations, including the more stable chair conformation.

5.5 Quantum mechanics

Unlike molecular mechanisms the quantum mechanics approach to molecular

modeling does not require the use of parameters similar to those used in molecular

mechanics. It is based on the realization that electrons and all material particles exhibit

wave like properties. This allows the well defined, parameter free, mathematics of wave

motion to be applied to electrons, atomic and molecular structure. The basis of this

calculation is the Schrodinger wave equation, which in its simplest form may be stated as

Hφ = Eφ

In molecular modeling term Eφ represents the total potential and kinetic energy

of all the particles in the structure and H is the Hamiltonium operator acting on the wave

function φ.

The energy of a structure calculated via quantum mechanics can be used in

conformational searches, in the same way that the molecular mechanics energy is used.

Quantum mechanics calculations can also be used for energy minimization. However,

quantum mechanics calculation typically consume a far greater amount of computer

resource than molecular mechanics calculations and are therefore generally limited to small

molecules, where as molecular mechanics can be applied to structures up to the size of

large proteins. Molecular mechanics and quantum mechanics should thus be viewed as

complementary techniques. For instance, conformational energy calculations for a peptide

are best carried out using molecular mechanics. However, molecular mechanics is

generally ineffective for handling conjugated systems, while quantum mechanics, in

calculating electronic structure, takes account of conjugation automatically and is therefore

recommended for optimizing the structure of a small molecule containing conjugated

systems.

The wave function can be used to calculate a range of chemical properties,

which can be in structure activity studies. These include electrostatic potential, electron

density, dipole moment and the energies and positions of frontier orbital. As with the

analysis of a molecular dynamics calculation, molecular graphics is essential for visualizing

these properties. Quantum mechanics calculations are also used frequently to derive atom

centered partial charges (although the term charge itself does not have a strict quantum

mechanical definition). Charges have a wide range of applications in modeling and are

used in the calculation of electrostatic energies in molecular mechanics calculations and in

computing electrostatic potentials.

Quantum mechanical methods are suitable for calculating the following

• Molecular orbital energies and coefficients

• Heat of formation for specific conformations

• Partial atomic charges calculated from molecular orbital coefficients

• Electrostatic potentials

• Dipole moments

• Transition state geometries and energies

• Bond dissociation energies

HYBRID QM/MM

QM. (quantum-mechanical) methods are very powerful however they are

computationally expensive, while the MM (classical or molecular mechanics) methods are

fast but suffer from several limitations (require extensive parameterization; energy

estimates obtained are not very accurate; cannot be used to simulate reactions where

covalent bonds are broken/formed; and are limited in their abilities for providing accurate

details regarding the chemical environment). A new class of method has emerged that

combines the good points of QM (accuracy) are MM (speed) calculations. These methods

are known as mixed or hybrid quantum-mechanical and molecular mechanics methods

(hybrid QM/MM). The methodology for techniques was introduced by Warshel and

coworkers.

CHAPTER - 6

IMPORTANT TECHNIQUES FOR DRUG DESIGN

IMPORTANT TECHNIQUES FOR DRUG DESIGN

To obtain the structural information about molecules necessary for mechanistic design of

drugs, a variety of chemical, physical, and theoretical techniques must be used. Different

techniques provide complementary types of information, which together can be used to

determine how molecules interact.

6.1 X-RAY CRYSTALLOGRAPHY

X-ray crystallography is often the starting point for gathering information from

mechanistic drug design. This technology has the potential to determine total structural

information about a molecule. Furthermore it provides the critically important coordinates

needed for the handling of data by computer modeling systems12. It is the only technique at

present that will give the complete three-dimensional structure in detail at high resolution

including bond distance, angles, stereochemistry and absolute configuration. The use of

such a powerful technique for drug design was recognized over a decade ago .

To carry out an X-ray crystallographic analysis, material of very high purity is

needed. This material must be carefully crystallized to yield crystals of a suitably high

quality for study. Small molecules can generally be crystallized using standard chemical

techniques13. Macromolecules such as proteins, however, require specialized techniques to

produce suitable crystals. Even with suitable crystals, the solution of a macromolecular

structure is much more difficult than for a small molecule. The larger number of atoms in a

macromolecule makes it hard to attain the high degree of resolution needed. Furthermore,

the instrumentation required is complex, and the data analysis and refinement take

substantial computer time14. Finally, because X-ray crystallography must be carried out with

molecules in the solid phase, the three-dimensional structure obtained may differ from the

molecule in its biologically active state.

Nevertheless, this technology is very important for determining the structure of the drug

(effector), the structure of the drug’s target, and the interaction of the two. It is reasonable to

assume then the future of large molecule crystallography in medical chemistry may well be

of monumental proportions. The reactivity of the receptor certainty lies in the nature of the

environment and position of various amino acid residues15. When the structured knowledge

of the binding capabilities of the active site residues to specify groups on the agonist or

antagonists becomes known, it should lead to proposals for synthesis of very specific

agents with a high probability of biological action. Combined with what is known about

transport of drugs through a Hansch-type analysis, etc., it is feasible that the drugs of the

future will be tailor-made in this fashion16. Certainly, and unfortunately, however, this day is

not as close as one would like. The X-ray technique for large molecules, crystallization

techniques, isolation techniques of biological systems, mechanism studies of active sites

and synthetic talents have not been extremely interwined because of the existing barriers

between vastly different sciences.

Since that time, interdisciplinary scientists have broken down a number of the walls

between the different disciplines. Today it is not unusual to see individuals who can, with

their own hands, synthesize organic heavy-atom derivatives, grow crystals, and solve X-ray

structures of the hardest magnitude, clone genes, and talk rationally, in mechanistic terms,

about substrate specificity. However, the best rational design by modeling from the surface

of known receptors determined from X-ray analysis will not prevent the compound from

bypassing the oxidative enzymes in the liver or deter it from being taken up by fat depots or

serum proteins, or keep it out of the urine, or stop it from having neurotoxicity17. Will we do

any better with the rational design of new agents based on the structural knowledge of the

receptor than with older methods? The score as of this writing is that one drug, Captopril,

has made it to the market place, and a few others appear to be on their way. The hope for

the success of any new agents will rest in the rational design of compounds with sufficient

specificity to circumvent or greater reduce the distribution, toxicity

Crystallography is moving in two directions: 1. macro and 2. mini. The solution of larger and

more complex systems will continue to provide drug designers with atomic details that

promote imaginative approaches to drug design18. The most recent and truly amazing

development in data collection indicates that a whole set of protein data may be acquired in

a second or less using Laue photographs. Such short analysis times may soon provide

structural features at near atomic resolutions of the movements involved in native and

substrate bound proteins. On the opposite end of the kilodalton scale, detailed

crystallographic analyses of the electron charge distribution in small molecules will permit

the assignment of electrostatic potentials to atoms that could aid in the understanding of

drug receptor interactions and how side chains pack in proteins.

The addition to the understanding of packing, with a better understanding of water

interactions in maintaining secondary and tertiary structure, may solve the protein folding

problem19. If that happens, then the nature of any receptor might be deduced from the

genome and X-ray crystallography will take a back seat to the dynamic computational and

spectral methods of analyses of molecules20. Until that day, however, crystallography will

continue to have a dominant role in rational drug design.

6.2 NMR SPECTROSCOPY

The major limitations of X-ray crystallography are the necessity to obtain good

crystals and the fact that three-dimensional data obtained with crystals may not reflect the

molecular structure under biological conditions that involve molecules in solution21. The

best technique for determining structural information on molecules in solution is nuclear

magnetic resonance (NMR) spectroscopy. NMR uses much softer radiation which can

examine molecules in the more mobile liquid phase, so the three-dimensional information

obtained may be more representative of the molecule in its biological environment22.

Another advantage of NMR is its ability to examine small molecule-macromolecule

complexes, such as an enzyme inhibitor in the active site of the enzyme. Such information

can be obtained by X-ray crystallography only after co-crystallization or crystal “soaking”

techniques. In addition, NMR can often be used to gather structural information more

rapidly than X-ray crystallography. Consequently, NMR has proved to be a valuable tool in

pharmaceutical research23. In addition to its importance as an analytical method to

elucidate the primary structures of chemically synthesized compounds and isolated natural

products.

NMR can provide information on the three-dimensional structures of small molecules in

solution, high-molecular-weight complexes and the details of the enzyme mechanisms that

can be used to aid in drug design. Some of the recent advances in NMR that have allowed

this information to be obtained include the availability of high magnetic fields improved

software, probe design and electronics, more versatile pulse programmers and perhaps

most importantly, the development of two-dimensional NMR techniques24.

NMR spectroscopy can provide detailed information on the conformational properties of

small molecules in solution, the structure of large molecular complexes and enzyme

reaction mechanisms. It is expected that future developments in NMR and other fields will

contribute to even further progress in the ability of these new developments which are

expected in the near future include

• The availability of large quantities of enzymes and drug receptors through

improved expression systems and cloning technology.

• The availability of isotopically labeled (13C, 15N, 2H) inhibitors, enzymes and

soluble receptors suitable for NMR studies by chemical synthesis and biosynthetic means.

• Improvements in NMR techniques, especially those designed for NMR studies of

large systems

• The availability of increased magnetic-field strengths at a low cost due to the

recently demonstrated improvements in superconducting materials.

These developments should vastly increase our capability to study the three-dimensional

structures of enzyme-bound ligands, enzyme active sites and soluble drug-receptor

complexes. In addition, improvements in solid-state NMR techniques and NMR imaging

should allow structural studies of drugs bound to membrane-bound receptors and the

physiological effects of drugs to be examined, respectively25. Clearly, the future holds even

more exciting prospects for the use of NMR spectroscopy in the rational design of new

pharmaceutical agents.

The disadvantage of NMR is that the data obtained are not as precise or complete as

those from an X-ray structure determination. There is also a limit on the size of molecule

that can be studied with present equipment. Modern high-field NMR spectrometers have

recently been developed that can obtain data on smaller samples and, by the use of two-

dimensional techniques, are able to obtain more precise information about macromolecule

OTHER IMPORTANT CONSIDERATIONS

It has been realized that biological molecules can exist in a variety if different

conformations and depending on the energetics of the molecules and the environmental

conditions, will shift among these conformations. The initial application of molecular

modeling to design drugs generally begins with the use of rigid constructs for structures and

their targets. This concept of molecular behavior is often satisfactory for answering simple

questions, such as whether a drug will fit into the active site of the target. As the questions

about molecular interactions become more complex, however, the concept of molecules in

different dynamic energetic states and configurations becomes much more important.

Sophisticated questions such as what is the most favorable position for a drug in its target’s

active site require more information, based on additional physical parameters, than simply

answering the question, will a molecule fit into a given space.

The flexibility of molecular conformations, both in single molecules and in

molecules interacting with each other, is an important and challenging concept in drug

design. One of the major potentials of computer-aided drug design is the development of

completely new effector compounds for targets. To date, however, this has been very

difficult. A significant reason is our lack of knowledge about the factors that govern

conformational states and flexibility. These concepts and the problems they attempt to

understand and handle are important, since it is in these areas that breakthroughs are still

needed to realize the real potential of computer-aided drug design in predicting new

chemical structures that will interact with the desired targets.

7 APPLICATIONS

It is generally recognized that drug discovery and development are very time and resources

consuming processes. There is an ever growing effort to apply computational power to the

combined chemical and biological space in order to streamline drug discovery, design,

development and optimization. In biomedical arena, computer-aided or in silico design is

being utilized to expedite and facilitate hit identification, hit-to-lead selection, optimize the

absorption, distribution, metabolism, excretion and toxicity profile and avoid safety issues.

Commonly used computational approaches include ligand-based drug design

(pharmacophore, a 3-D spatial arrangement of chemical features essential for biological

activity), structure-based drug design (drug-target docking), and quantitative structure-

activity and quantitative structure-property relationships.

Regulatory agencies as well as pharmaceutical industry are actively involved in

development of computational tools that will improve effectiveness and efficiency of drug

discovery and development process, decrease use of animals, and increase predictability. It

is expected that the power of CADDD will grow as the technology continues to evolve.

Use of computational techniques in drug discovery and development process is rapidly gaining

in popularity, implementation and appreciation. Different terms are being applied to this area,

including computer-aided drug design (CADD), computational drug design, computer-aided

molecular design (CAMD), computer-aided molecular modeling (CAMM), rational drug

design, in silico drug design, computer-aided rational drug design. Term Computer-Aided Drug

Discovery and Development (CADDD) will be employed in this overview of the area to cover the

entire process. Both computational and experimental techniques have important roles in drug

discovery and development and represent complementary approaches. CADDD entails:

1. Use of computing power to streamline drug discovery and development process

2. Leverage of chemical and biological information about ligands and/or targets to identify

and optimize new drugs

3. Design of in silico filters to eliminate compounds with undesirable properties (poor

activity and/or poor Absorption, Distribution, Metabolism, Excretion and Toxicity,

ADMET) and select the most promising candidates.

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. CADDD 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.

Virtual screening is used to discover new drug candidates from different chemical scaffolds

by searching commercial, public, or private 3-dimensional chemical structure databases. It

is intended to reduce the size of chemical space and thereby allow focus on more

promising candidates for lead discovery and optimization. The goal is to enrich set of

molecules with desirable properties (active, drug-like, lead-like) and eliminate compounds

with undesirable properties (inactive, reactive, toxic, poor ADMET/PK). In another words, in

silicomodeling is used to significantly minimize time and resource requirements of chemical

synthesis and biological testing The rapid growth of virtual screening is evidenced by

increase in the number of citations matching keywords “virtual screening” from 4 in 1997 to

302 in 2004. In his 2003 review article, Green of GlaxoSmithKline concluded that: “The

future is bright. The future is virtual”

PriceWaterhouseCoopers Pharma 2005: An Industrial Revolution in R&D report [3] stressed the

reality that pharmaceutical industry needs to find means of improving efficiency and

effectiveness of drug discovery and development in order to sustain itself. This was recently

echoed at the 2006 Drug Discovery Technology Conference in Boston, MA by Dr. Steven Paul,

head of science and technology at Eli Lilly & Co. who stated that the current business model will

become fundamentally untenable unless there is a significant improvement in efficiency and

effectiveness of the process.

The Price Waterhouse Coopers report emphasized growth and value of in silico approaches to

address this issue and projected that in silico methods will become dominant from drug

discovery through marketing. It was suggested that we are in a transitional period where the

roles of primary (laboratory and clinical studies) and secondary (computational) science are in

process of reversal .

Comparison of traditional and virtual screening in terms of expected cost and time

requirements.

Estimates of time and cost of currently bringing a new drug to market vary, but 7–12 years and

$ 1.2 billion are often cited .. Furthermore, five out of 40,000 compounds tested in animals reach

human testing and only one of five compounds reaching clinical studies is approved. This

represents an enormous investment in terms of time, money and human and other resources. It

includes chemical synthesis, purchase, curation, and biological screening of hundreds of

thousands of compounds to identify hits followed by their optimization to generate leads which

requiring further synthesis. In addition, predictability of animal studies in terms of both efficacy

and toxicity is frequently suboptimal. Therefore, new approaches are needed to facilitate,

expedite and streamline drug discovery and development, save time, money and resources,

and as per pharma mantra “fail fast, fail early”. It is estimated that computer modeling and

simulations account for ~ 10% of pharmaceutical R&D expenditure and that they will rise to 20%

by 2016.

Role of computational models is to increase prediction based on existing knowledge .

Computational methods are playing increasingly larger and more important role in drug

discovery and development and are believed to offer means of improved efficiency for the

industry they are expected to limit and focus chemical synthesis and biological testing and

thereby greatly decrease traditional resource requirements.

Modern drug discovery and development process including prominent role of computational

modeling.

Computer - aided design and evaluation of Angiotensin-Converting enzyme inhibitors.

• Role of computer-aided molecular modeling in the design of

novel inhibitors of Renin.

• Inhibitors of Dihydrofolate reductase.

• Approaches to Antiviral drug design.

• Conformation biological activity relationships for

receptor- selective, conformationally constrained opioid peptides.

• Design of conformationally restricted cyclopeptides for the

inhibition of cholate uptake of Heepatocytes

CONCLUSIONS

8 CONCLUSIONS

The process of drug discovery and development is a long and difficult one, and the costs of

developing are increasing rapidly. Today it takes appropriately 10years and $100million to

bring a new drug to market. Inspite of the tremendous costs involved, the payoff is also

high, both in dollars and in the improvements made in preventing and controlling human

diseases. The emphasis now is not just on finding new ways to treat human disease, but

also on improving the quality of life of people in general. The use of new computer-based

drug design techniques has the ability to accomplish both of these goals and to improve the

efficiency of the process as well, thus reducing costs.

Mechanism-based drug design tackles medical problems directly. It provides an opportunity

to discover entirely new lead compounds not possible using other techniques for drug

development. Thus it offers the potential for treating diseases that are not currently

controllable by existing drugs. Similarly, these new techniques in drug design can improve

the lead optimization process.

By understanding the physical interaction of a drug and its receptor, one has the means to

improve the potency and selectivity of a drug and thereby reduce its undesirable

interactions with other physiological processes in the body. The quality of life of patients

receiving these newer drugs, which have greater potency and fewer side effects, is

improving. Finally, since the traditional lead optimization process typically requires the

synthesis of hundreds or even thousands of new compounds, it is a time-Consuming and

labor-intensive process. The use of newer computer-based techniques in combination with

techniques in combination with techniques that have been successful in the past provides a

means to greatly reduce the number of new compounds that must be synthesized and

tested and thus speeds up the process of drug discovery.

Future developments will continue to improve the efficiency of all aspects of drug

discovery. Knowledge about the molecular basis of diseases is rapidly expanding on all

fronts and will continue unabated. Molecular biologists will soon be able to provide

quantities of receptor molecules and enzymes that have not yet been available to drug

researchers. these new data, will come improvements in computational techniques and

their ability to predict the conformational state of a small compound and its macro-molecular

receptor.

REFERENCES

1. Propst, C., Modern technologies for the discovery of new pharmaceuticals. In The

World Biotech Report- USA, Vol.2. Online Publications, New York, pp. 283-289.

2. Perun, T., The use of molecular modeling and computer graphic techniques in the

design of new cardiovascular drugs. In The World Biotech Report- USA, Vol.2. Online

publications, New York, pp. 313-320 (1985).

3. Newall, C. Injectable cephalosporin antibiotics: Cephalothin to ceftazidime. In

Medicinal Chemistry- The Role Of Organic Chemistry in Drug Research, S. Roberts and

B.Price, eds. Academic Press, London, pp. 209-226 (1985).

4. Hopfinger, A., Computer-assisted drug design. J. Med. Chem. 28: 1133-1139 (1985).

5. Hol, W., Protein crystallography and computer graphics- toward rational drug design.

Angew. Chem.. Int. Ed. Engl. 25:767-778 (1986).

6. Weiss, R., Scientists study the art of protein folding. Sci. News 28:344-346 (1987).

7. C. L. Propst, Thomas J. Perun Computer-aided drug design, 2-10.

8. Young, R., R. Ozols, and C. Myers, The anthracycline antineoplastic drugs. N. Engl.

J. Med. 305:139-153 (1981).

9. Fischer, E., Einfluss der Configuration auf die Wirkung der Enzyme. Ber Deutsch.

Chem. Ges 27:2984-2993 (1894).

10. Koshland, D., Application of a theory of enzyme specificity to protein synthesis. Proc.

Natl. Acad. Sci. USA 44:98-104 (1958).

11. Illango. Text book of medicinal chemistry, Vol 2, 396-431.

12. Burton, J., Cody, J. Herd and E. Haber, Specific inhibition of rennin by an

angiotensinogen analog: Studies in sodium depletion and rennin-dependent hypertension.

Proc. Natl. Acad. Sci. USA 77:5476-5479 (1980).

13. Chivvis, A. Jr., D. Mackie and N. Selby, playing to win the new game in

pharmaceuticals. Pharm. Exe. 10:12-16 (1987).

14. Oldham, R., Drug development: Who foots the bill. Biotechnology 5:648 (1987).

15. Mannon, J., Developing a new drug: How fast? How safe? Indust. Chem. 8:26-29

(1987).

16. Croog, S., S. Levine, M. Testa, B.Brown, C.Bulpitt, C.Jenkins, G. Kierman and G.

Williams, The effects of antihypertensive therapy on the quality of life, N. Engl. J. Med.

314:1657-1664 (1986).

17. Abraham, D. J, The potential role of single crystal X-ray diffraction in medicinal

chemistry. Intra-sc. Chem. Rep. 8:1-9 (1974).

18. Pauling, L., Lecture presented at the International Congress of X-ray Crystallography

at Stonybrook, N.Y. (Aug. 1973).

19. McPherson, A., preparation and Analysis of protein Crystals. John Wiley and Sons,

New York (1982).

20. Feigelson, R. S., ed., protein crystal growth. In proceedings of the first international

conference on protein crystal growth, Stanford university, Stanford, calif., August 14-16,

1985. North Holland, Amsterdam (1986).

21. Abraham, D. J., and J. Sutcliffe, unpublished results.

22. Zetta, L., and F. Cabassi, 270 MHz H.nuclear magnetic resonance study of met-

enkephalin in solvent mixtures. Conformational transition from dimethylsulphoxiide to water.

Eur .J. Biochem. 122:215-222 (1982).

23. Mirau, P. A., and R. H. Shafer, High resolution proton nuclear magnetic resonance

analysis of conformational properties of biosynthetic actinomycin analogues. Biochemistry

21:2622-2626(1982).

24. Hruby, V. J., and H.I.Mosberg, Conformational and dynamic considerations in

peptide structure-function studies. Peptides 3:329-336 (1982).

25. Kessler, H., Conformation and biological activity of cyclicpeptides. Angrew, Chem. Int

.Ed. Engl. 21:512-523 (1982).

26 I.M. Kapetanovic Chemopreventive Agent Development Research Group, Division of

Cancer Prevention, National Cancer Institute, Bethesda, MD 20892-7322

List of National Seminars & Conferences where the study Abstract

was Presented (Poster Presentation)

1) 3rd Annual Biotechnology Conference For Students organized by

International Institute of Information Technology (I2IT) Pune, During 12 -

13 Nov.2011.

2) National Conference on Frontiers in Biological Sciences’ organized by ‘Veer

Bahadur Singh Purvanchal University, Jaunpur (U.P) during 4-5

Dec.2011.

3) ‘National Seminar on Drug Discovery from Plants: Promises And Challenges’

(DDPC 2012) Organized by ‘School of Life Sciences, S.R.T.M.University,

Nanded during 14 – 15 Feb.2012