View
811
Download
20
Category
Preview:
Citation preview
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
Recommended