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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5933_Библиотеки_им_академика_М_И_Перельмана
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List of Contributing Authors
André São Pedro
Federal University of Bahia
Postgraduate Program in Industrial Engineering
Polytechnic School
40210630 Salvador
Bahia
Brazil
Anil K. Sharma*
Department of Biotechnology
Maharishi Markandeshwar University
Mullana-Ambala-133207
E-Mail: anibiotech18@gmail.com
Deepak B. Salunke*
Department of Chemistry
and Centre of Advanced Studies in Chemistry
Panjab University
Chandigarh 160014, India
E-Mail: deepsalunke@gmail.com
Francisco J. B. Mendonça Júnior
Paraíba State University
Campus V
João Pessoa – PB, Brazil
Garima Mathur
Department of Pharmacy
Banasthali University, Banasthali
Rajasthan-304022
India
Luciana Scotti*
Federal University of Paraíba
Campus I
João Pessoa-PB
Brazil
E-mail: luciana.scotti@gmail.com
Madhuri T. Patil
Department of Chemistry
and Centre of Advanced Studies in Chemistry
Panjab University
Chandigarh-160014, India
Manu Sharma*
Department of Pharmaceutical Chemistry
M. M. College of Pharmacy
Maharishi Markandeshwar University
Mullana-Ambala-133207
India
Marcelo S. da Silva
Federal University of Paraíba
Campus I
João Pessoa-PB
Brazil
Marcus T. Scotti
Federal University of Paraíba
Campus IV
58297-000, Rio Tinto-PB
Brazil
Girish Kumar Gupta
Department of Pharmaceutical Chemistry
M. M. College of Pharmacy
Maharishi Markandeshwar University
Mullana-Ambala-133207
India
Komarla Kumarachari Rajasekhar*
Professor, Department of Pharmaceutical
Chemistry
University college of Medicine and Health
Sciences
University of Gondar
Gondar, Amhara
Ethiopia
E-Mail: komarlakrs@gmail.com
Meenakshi Rajpoot
Department of Biotechnology
Maharishi Markandeshwar University
Mullana-Ambala-133207
India
Dr. Prerna Sarup*
Department of Pharmacognosy
M. M. College of Pharmacy
Maharishi Markandeswar University
Mullana-Ambala-133207
India
E-Mail: psarup_pu@hotmail.com
XII List of Contributing Authors
Rajasri Bhattacharyya
Department of Biotechnology
Maharishi Markandeshwar University
Mullana-Ambala-133207
India
Ramesh Kataria*
Department of Chemistry
and Centre of Advanced Studies in Chemistry
Panjab University
Chandigarh-160014, India
E-Mail: rkataria@pu.ac.in
Sarvesh Paliwal
Department of Pharmacy
Banasthali University, Banasthali
Rajasthan-304022
India
Seema Patel*
Bioinformatics and Medical Informatics
Research Center
San Diego State University
92182, San Diego, CA, USA
E-Mail: seemabiotech83@gmail.com
Sreekanth Thota*
National Institute for Science and Technology on
Innovation on Neglected Diseases (INCT/IDN)
Center for Technological Development in Health
(CDTS)
Fundação Oswaldo Cruz – Ministério da Saúde
Av. Brazil 4036 – Prédio da Expansão
8º Andar – Sala 814, Manguinhos
21040-361 – Rio de Janeiro
Brazil
And
Programa de Desenvolvimento de Fármacos
Instituto de Ciências Biomédicas
Universidade Federal do Rio de Janeiro
Rio de Janeiro
E-Mail: stsreekanththota@gmail.com
Subash Chandra Sahoo
Department of Chemistry and Centre of
Advanced Studies in Chemistry
Panjab University
Chandigarh-160014, India
Sumitra Nain*
Department of Pharmacy
Banasthali University, Banasthali
Rajasthan-304022
India
E-Mail: nainsumitra@gmail.com
Sunil Kumar
Department of Chemistry
Sant Longowal Institute of Engineering &
Technology
Longowal District Sangrur
Punjab, India
S.K. Mehta
Department of Chemistry and Centre of
Advanced Studies in Chemistry
Panjab University
Chandigarh, India
Tamara Angelo*
University of Brasília
Department of Pharmacy
Campus Universitário
Darcy Ribeiro
70910900 Brasília
Distrito Federal
Brazil
E-Mail: taosdamasceno@gmail.coml
* Corresponding author
Sreekanth Thota*
1 Overview of chemical drug design
Abstract: Now a days identifying the novel medicines, drug design is the inventive
process. Drug design that relies on the knowledge of the structure-based drug design
is nothing but three-dimensional structure of the biomolecular target. The drug
design approach has already proven as quite useful method in identification of many
successful drugs. Computational approaches have become important tools to accelerate the development of epigenic inhibitors helping in the selection, design and lead
identification of new compounds. The drug discovery research of the compound gives
significant results if computational technology methods compliment in vitro experiments. These compounds may give more favorable ADME and toxicological profiles.
This chapter provides an overview of some techniques used in chemical drug design
to date.
1.1 Drug design
1.1.1 Introduction
Drug design, also called simply rational design or rational drug design, is the inventive
ess of identifying novel medicines [1]. Most commonly the drug is an organic small
proc
molecule that inhibits or activates the function of a biomolecule, which is nothing but
a protein; these results are of therapeutic benefit to the patient. Drug design involves
the design of molecules that are complementary in shape and charge to the biomolecular target with which they interact and therefore will bind to. Drug design frequently
relies on computer modeling techniques which are often referred to as computer-aided
drug design [2]. Drug design that relies on the knowledge of the three-dimensional
structure of the biomolecular target is known as structure-based drug design [3].
Ligand design is nothing but design of a molecule that will bind tightly to its
target [4]. The modeling techniques are successful in prediction of binding affinity
of the compound. During clinical phases of drug development, there is more focus
on the drug design process, especially on selection of candidate leads based on their
predicted physicochemical properties and fewer complications of the drugs during
development process so that it can be easily approved in the market as a drug [5]. The
drug discovery research of the compound gives good results if computational technology methods compliment in vitro experiments. These compounds may give more
favorable ADME and toxicological profiles [6].
2 Sreekanth Thota*
1.1.2 Drug targets
Understanding the identity of drug targets that are encoded by the human genome
is of great importanc
allocation of resources within academic and industrial biomedical research. Many of
the potential drug targets are not necessarily disease causing but must by definition
be disease modifying [7]. In a specific disease modifying pathway, small molecules
(receptor agonists, antagonists, inverse agonists, or modulators; enzyme activators or
inhibitors; or ion channel openers or blockers) will be designed to inhibit or enhance
the target function [8]. These small molecules will be designed so that they are complementary to the binding site of target [9]. The major important approach when
considering the designing of small molecule is that they may not affect any other
important “off-target” molecules. If the drug interacts with off-target molecules this
may lead to undesirable side effects [10]. Protein networks or modules are increasingly being studied in the field of network biology using methods from graph theory,
which is a growing field within computer science. Most common drugs are produced
through chemical synthesis, but biopharmaceuticals (biopolymer-based drugs) produced through biological processes are becoming increasingly more common [11]. In
addition, mRNA-based gene silencing technologies may have therapeutic applications [12].
e for the development of new pharmaceutical products and the
1.1.3 Challenges of drug design
Any drug that is taken undergoes a number of chemical reactions in the liver as the
bod
y att
empts to neutralize foreign substances. This set of reactions is well characterized, and a great deal of knowledge exists as to how drugs are modified as the body
eliminates them. Scientists have worked for many years to abolish the limitations of
screening by designing molecules to perform specific therapeutic tasks [13].
The general drug-target scheme suggests that three important basic tasks play a
key role in structure-based rational drug design. First, the identification of an appropriate protein target for a given therapeutic need. Second, determination of the distinguishing structure of the target protein. Finally, designing a structure for a drug which
interacts with the target protein. A number of technical difficulties have slowed down
the work in the area of structure-based drug design [14].
1.2 Rational drug design
Rational drug design is also sometimes called drug design or rational design. 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
1Overview of chemical drug design 3
to the patient. Biomolecules play an essential role in disease progression by either
protein-nucleic acid interactions or protein-protein interactions, which lead to the
alteration of metabolic processes [15–18].
Rational drug design can be broadly classified into two categories:
(a) Development of small molecules with desired properties for targets and biomol-
ecules.
(b) Development of small molecules with predefined properties for targets, whose
cellular functions and their structural information may be known or unknown.
Steps related to these two approaches and evaluations of other properties in
rational drug design are presented in the following figures (Figs. 1.1–1.3).
After identification of a target, then both approaches A and B for development of small
molecules would require examination of several aspects (Fig.1.3). Therefore, rational
drug design would be an integral approach to drug development and drug discovery.
Figure 1: Approach A
(a) Targets with known Gene Ontology (GO):
Biological Process (BP),
Molecular Functions (MF),
Cellular Compartments (CC),
Protein Domains,
Pathways
interacting networks, and known
(b) 3D structure (by X-ray crystallography or NMR)
High-throughput screening (HTS) of Chemical library Virtual screening (ZINC database)
Lead discovery
Mining NCI database/3D mind for similar structures and data
Modification to enhance binding anity
Figure 3 Evaluation of biological potency/other properties
Fig. 1.1: Display of the number of possible approaches in drug design for known targets.
4 Sreekanth Thota*
Figure 2: Approach
Biological screening/Identification of potent molecules
Global gene expression Analysis
(i) Database design
(ii) Bioinformatics analysis
(iii) Targets Identification
(iv) Validation
Targets with known 3D structure Targets without 3D structure
Homology model building/quality evaluation
Evaluation of binding anity by Docking
Modification of drug for desired property
Biololgical Evaluation
Evaluation of 3D structureFigure 3
Fig. 1.2: Display of the number of possible approaches for unknown targets.
B (for new molecules)
Target identification by
Docking a potent drug
against a library of targets
1.2.1 Structure-guided computer-aided drug design
Structure-guided methods are an integral part of drug development for known 3D
structure of pot
ential drug binding sites, which are the active sites. For a lead discovery, this is the starting point of structure-guided drug design for a known target. Once
the ligand-bound 3D structure is known, a virtual screening of large collections of
chemical compounds, such as ZINC [19], can be performed. Such a screening enables
the identification of potential new drugs by performing docking experiments with
this collection of molecules. To enhance binding and hence to improve binding affinity/specificity, a group of molecules with similar docking scores is generally used for
potency determination; this is High-Throughput Screening (HTS) (Fig.1.1).
Besides the evaluation of potency, binding specificity/affinity as well as other
properties including drug-like properties (pharmacokinetics) such as log P, molar
refractivity, hydrogen bond acceptor and number of hydrogen donors and molecular
weight are also determined (Fig.1.3). These parameters are important molecular properties as formulated by [20] and later developed by [21]. Toxicity predictions of the
drug itself and its metabolic products can also be examined initially by computational
methods; however, these properties should be verified by experimental methods.
1Overview of chemical drug design 5
Figure 3
Examination of
(i) QSAR
(ii) QSPR
(iii) Potency
(iv) Docking score
(v) Multi linear regression analysis.
Reactivity evaluation (examination bio-degradation profile):
(i) Electrophilic,
(ii) Nucleophilic, and
(iii) Radical attack
Improvement of bioavailability
Evaluation of:
(i) In vivo experments
(ii) Gene expression profiling
(a) Bioinformatics analysis
(b) Identification of:
genes responsible for:
i. Toxicity
ii. Drug resistance
iii. Metabolism
iv. Immune suppression
Preclinical evaluation
Fig. 1.3: Display of the essential properties for the improvement of drug-like properties.
The drug design approach has already been proven in that many successful drugs
have been developed and some of them are already in use in the market. Among
the best example we are discussing here the development of imatinib is worth mentioning, which is mainly used for the treatment of certain cancers including chronic
myelogenous (or myeloid) leukemia (CML).
1.3 Ligand-based drug design
Ligand-based drug design is also called indirect drug design and deals with the
information of diverse molecules that bind to the biological target of interest [24].
As an alternative, a quantitative structure-activity relationship (QSAR), which is a
relationship between calculated properties of molecules and their biological activity
determined through an experiment, could also be derived. These QSAR relationships
6 Sreekanth Thota*
sequentially could also be used to predict the activity of latest analogs. In ligandbased drug design, you choose series of molecule that have revealed smart activity
and run them in software like Sybyl 7.1, here you may get the groups such as hydrophobic, stearic and hydrophilic groups that are responsible for action [25].
1.4 Structure-based drug design
Structure-based drug design is also known as direct drug design which relies on
knowledge of the three-dimensional structure of the biological target obtained
through methods such as x-ray crystallography or NMR spectroscopy [26]. 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
techniques may be used to suggest new drug candidates [27].
Structure-based drug design can be divided roughly into three main categories
based on their current methods [28]. (i) The first method (virtual screening) is identification of new ligands for a given receptor. (ii) A second category is de novo design
of new ligands [29–31]. (iii) A third method is the optimization of known ligands by
evaluating proposed analogs within the binding cavity [28].
1.4.1 Finding leads
The first step in the structure-based design of new inhibitors is elucidating the threedimensional structur
for a compound that binds to the protein of interest; it often exhibits weak affinity
or is too toxic, too unstable or has other shortcomings, yet it forms a starting point to
develop molecules with improved pharmacological properties [32].
To begin to rival the complexity provided by nature, several groups have turned to
screening techniques aimed at discovering tightly binding ligands from combinatorial
libraries. For example, the phage display method is based on the display of a random
sequence peptide on the surface of a phage. The phage library, typically including
106–108 different peptides, is mixed with the target protein, which is immobilized
on the surface of a plate. Nonbinding phages are washed away while the bound ones
can be used to decipher the sequence of the peptide bound to the target protein [33].
Alternatives are the affinity screening of synthetic peptide [34] and oligonucleotide
[35] peptide-based libraries, chemical libraries [36, 37].
e of a tar
get protein. The next step is to find a lead – the term
1Overview of chemical drug design 7
1.4.2 Optimizing leads
Screening procedures generally come up with leads that are far from perfect. These
molecules then hav
e to be optimized. At this point, the structure of the target protein
in complex with the lead molecule can be extremely useful in suggesting ways to
improve the affinity of the lead for the target [38].
1.4.3 Tools for structure-based drug design
Although quantitative ab initio pr
ediction of bindin
g constants remains a tremendous
challenge [39, 40], a number of qualitative rules for the design of high affinity ligands
can be deduced from the many crystal structures of protein-ligand complexes:
(i) Excellent steric and electronic complementarity to the target biomacromolecule
is required.
(ii) A fair amount of hydrophobic surface should be buried in the complex for tight
binding.
(iii) Chemical stability.
(iv) Sufficient conformational rigidity is essential to ensure that the loss of entropy
upon ligand binding is acceptable.
(v) Sufficient solubility in water for inhibition tests and structural studies.
(vi) Ease of synthesis, including the avoidance of chiral centers and of ‘dead-end
leads’.
1.4.4 Docking algorithms
Many different strategies are currently in use for docking ligands on a target protein
surfac
e: The pr
ogram GRID [41] is an example of the first strategy. Other design programs include AUTODOCK [42], LEGEND [43], and Group Build [44]. Closely related
to GRID is MCSS, where thousands of copies of functional groups are simultaneously but independently positioned optimally on the protein surface by a molecular
dynamics protocol [45]. A typical representative of the second strategy for docking
is the program LUDI [46]. Other programs include CLIX [47], DOCK [48], GRID-like
energy evaluation [49], traditional pharmacophore matching programs like ALADIN
[50], FOUNDATION [51], MACCS-3D [52], ChemDBS-3D [53], CATALYST [54, 55] and
BOXSEARCH [56].
8 Sreekanth Thota*
1.4.5 Three-dimensional ligand databases
For many of the computer programs developed for lead discovery or inhibitor optimization, larg
compounds are required as essential input. The basic source for experimentally determined structures is the Cambridge Structural Database (CSD), containing more than
110,000 organic molecules [57, 58]). All of these contain models of the compounds
obtained by structure-generation programs [59] that convert two-dimensional connection tables into three-dimensional structures. CONCORD [60] is the most popular
of these programs, and has recently been used to convert 5,000,000 organic molecules of the Chemical Abstracts Service Registry file [61].
e collections of three-dimensional structures of low molecular weight
1.5 Pharmacophore-based approaches
Pharmacophore-based approaches describe the background and updated progress
of pharmacophore-based drug design and provide the fundamental approach strategies on both structure-based and ligand-based pharmacophore approaches. Pharmacophore-based drug design processes include (i) pharmacophore modeling and
validation; (ii) pharmacophore-based virtual screening, virtual hit profiling and lead
identification [62].
1.6 Structure-based approaches
The other branch at the first decision point is used when the three-dimensional structure of the enzyme or complex is known. The process typically begins by generating
a working computational model from crystallographic data, but methods to develop
models of the binding site from active ligands are becoming more prevalent [63–66].
1.7 New lead generation
Generation of new lead compounds can be accomplished using de novo design
methods to design new structures [67, 68] by searching databases [69–75] of known
chemicals for particular structural features. De novo molecular design methods may
design structures by sequentially adding or joining molecular fragments to a growing
structure [76–78], by adding functionality to an appropriately sized molecular scaffold, or by evolving complete structures [79–81]. Some de novo design methods have
concentrated on the design of diverse molecular scaffolds [82] or on the development of diverse substituents to place on a single scaffold. Methods of ligand evaluation include graphical visualization of the ligand in the binding site, substitution of
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