International Journal of Research in Pharmaceutical and Biomedical Sciences
ISSN: 2229-3701
_________________________________________________________________________Review Paper
Molecular Modeling of Atorvastatin for Cardiovascular Disorder –
A Review
Prashant R. Dhangar1*, Rajesh J. Oswal1, Ashish A. Gawai1 , Rishikesh V. Antre1, Trushal
V. Chorage2 and Amol A. Shirsath2
1Department
of Pharmaceutical Chemistry, JSPM’s Charak College of Pharmacy &
Research, Wagholi, Pune, Maharashtra, India.
2Department
of Pharmacognosy, JSPM’s Charak College of Pharmacy & Research,
Wagholi, Pune, Maharashtra, India.
___________________________________________________________________________
ABSTRACT
Molecular modeling is a general term which covers a wide range of molecular graphics and computational
chemistry techniques used to build, display, manipulate, simulate, and analyze molecular structures, and to
calculate properties of these structures. Atrovastatin is the active ingredient in the most popular prescription
medicine in the world and a member of the statin family of drugs. By lowering cholesterol production in the
body, atrovastatin, lowers the risk of death from cardiovascular disorder, and has several additional benefits.
Atrovastatin is used to treat dyslipidemias, which are disorder characterized by abnormal levels of lipids in the
blood. Specifically, atrovastatin is used along with dietary therapy to decrease elevated serum total cholesterol
and low density lipoprotein cholesterol, apolipoprotein B, and triglyceride concentrations.
Key Words: Molecular modeling, Atrovastatin, CVS disorders, GOLD, HyperChem.
INTRODUCTION
Atorvastatin (INN), marketed under the trade name
Lipitor and several others, is a member of the drug
class known as statins, used for lowering
cholesterol. Atorvastatin inhibits the ratedetermining enzyme located in hepatic tissue that
produces mevalonate, a small molecule used in the
synthesis of cholesterol and other mevalonate
derivatives. This lowers the amount of cholesterol
produced which in turn lowers the total amount of
LDL cholesterol1,2.
Fig. 1: Atorvastatin
________________________________________
*Address for correspondence:
E-mail: jspmpharmacy@gmail.com
Vol. 2 (2) Apr – Jun 2011
With 2006 sales of US$12.9 billion under the brand
name Lipitor, it is the largest selling drug in the
world. Atorvastatin calcium tablets are currently
marketed by Pfizer under the trade name Lipitor, in
tablets (10, 20, 40 or 80 mg) for oral
administration. Tablets are white, elliptical, and
film coated. Pfizer also packages the drug in
combination with other drugs, such as is the case
with its Caduet. Lipitor is a cholesterol-lowering
drug. The drug works by helping to clear harmful
low-density lipoprotein (LDL) cholesterol out of
the blood and by limiting the body's ability to form
new LDL cholesterol Electrical Arrhythmias that
originate in the heart’s upper chambers, the atria
atrial Fibrillation (AF or A Fib) More than 2
million people in the United States have atrial
fibrillation, making it a very common heart rhythm
disorder.3
Mode of action
Cholesterol lowering mechanism
Most circulating cholesterol is manufactured
internally, in amounts of about 1000 mg/day, via
steroid biosynthesis through the HMG-CoA
reductase pathway. Cholesterol, both from dietary
intake and secreted into the duodenum as bile from
the liver, is typically absorbed at a rate of 50% by
the small intestines. The typical diet in the United
States and many other Western countries is
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International Journal of Research in Pharmaceutical and Biomedical Sciences
estimated as adding about 200–300 mg/day to
intestinal intake, an amount much smaller than that
secreted into the intestine in the bile. Thus internal
production is an important factor4,5.
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Direct evidence of the action of statin-based
cholesterol lowering on atherosclerosis was
presented in the ASTEROID trial, which
demonstrated regression of atheroma employing
intravascular ultrasound9,10
Bioinformatics
Bioinformatics is conceptualizing biology in terms
of molecules (in the sense of physical chemistry)
and applying "informatics techniques" (derived
from disciplines such as applied math’s, computer
science and statistics) to understand and organize
the information associated with these molecules, on
a large scale. In short, bioinformatics is a
management information system for molecular
biology and has many practical applications.
Fig. 2: The HMG-CoA reductase pathway
Cholesterol is not water-soluble, and is therefore
carried in the blood in the form of lipoproteins, the
type being determined by the apoprotein, a protein
coating that acts as an emulsifier. The relative
balance between these lipoproteins is determined
by various factors, including genetics, diet, and
insulin resistance. Low density lipoprotein (LDL)
and very low density lipoprotein (VLDL) carry
cholesterol toward tissues and elevated levels of
these lipoproteins are associated with atheroma
formation (fat-containing deposits in the arterial
wall) and cardiovascular disease. High density
lipoprotein, in contrast, carries cholesterol back to
the liver and is associated with protection against
cardiovascular disease.6-8
Statins act by competitively inhibiting HMG-CoA
reductase, the first committed enzyme of the HMGCoA reductase pathway. By reducing intracellular
cholesterol levels, they cause liver cells to make
more LDL receptors, leading to increased clearance
of low-density lipoprotein from the bloodstream.
Vol. 2 (2) Apr – Jun 2011
Fig. 3: Evolution of Bioinformatics
It represents a paradigm shifts during the past
couple of decades have taken much of biology
away from the laboratory bench and have allowed
the integration of other scientific disciplines,
specifically computing. The result is an expansion
of biological research in breadth and depth.
The vertical axis demonstrates how bioinformatics
can aid rational drug design with minimal work in
the wet lab. Starting with a single gene sequence,
we can determine with strong certainty, the protein
sequence. From there, we can determine the
structure using structure prediction techniques.
With geometry calculations, we can further resolve
the protein’s surface and through molecular
simulation determine the force fields surrounding
the molecule. Finally docking algorithms can
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International Journal of Research in Pharmaceutical and Biomedical Sciences
provide predictions of the ligands that will bind on
the protein surface, thus paving the way for the
design of a drug specific to that molecule.
The horizontal axis shows how the influxes of
biological data and advances in computer
technology have broadened the scope of biology.
Initially with a pair of proteins, we can make
comparisons between the between sequences and
structures of evolutionary related proteins. With
more data, algorithms for multiple alignments of
several proteins become necessary. Using multiple
sequences, we can also create phylogenetic trees to
trace the evolutionary development of the proteins
in question. Finally, with the deluge of data we
currently face, we need to construct large databases
to store, view and deconstruct the information.
Alignments now become more complex, requiring
sophisticated scoring schemes and there is enough
data to compile a genome census a genomic
equivalent of a population census – providing
comprehensive statistical accounting of protein
features in genomes.11,12
Applications of Bioinformatics

Database query tools

Sequence
analysis
and
molecular
Evolution

Genome mapping and comparison

Gene identification

Structure prediction

Drug design and drug target identification

CADD
Drug design is the approach of finding drugs by
design, based on their biological targets. Typically
a drug target is a key molecule involved in a
particular metabolic or signaling pathway that is
specific to a disease condition or pathology, or to
the infectivity or survival of a microbial pathogen.
Computer-assisted drug design (CADD)
Computer-assisted drug design (CADD), also
called
computer-assisted
molecular
design
(CAMD), represents more recent applications of
computers as tools in the drug design process. In
most current applications of CADD, attempts are
made to find a ligand (the putative drug) that will
interact favorably with a receptor that represents
the target site. Binding of ligand to the receptor
may include hydrophobic, electrostatic, and
hydrogen-bonding interactions. In addition,
solvation energies of the ligand and receptor site
also are important because partial to complete
desolvation must occur prior to binding.
This approach to CADD optimizes the fit of a
ligand in a receptor site. However, optimum fit in a
target site does not guarantee that the desired
activity of the drug will be enhanced or that
undesired side effects will be diminished.
Moreover, this approach does not consider the
pharmacokinetics of the drug.
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Fig. 4: Flowchart Representing CADD
Based on the information that is available, one can
apply either, Ligand-based drug design is
applicable when the structure of the receptor site is
unknown, but when a series of compounds have
been identified that exert the activity of interest. To
be used most effectively, one should have
structurally similar compounds with high activity,
with no activity, and with a range of intermediate
activities.
Receptor-based drug design incorporates a number
of molecular modeling techniques, one of which is
docking. Docking allows scoring based on force
fields, which include Vander, Walls and
electrostatic interactions. These results illustrate the
potential for docking programs to search
objectively for ligands than are complementary to
receptor sites.
CADD and Bioinformatics
Bioinformatics was seen as an emerging field with
the potential to significantly improve how drugs are
found, brought to clinical trials and eventually
released to the marketplace. CADD methods are
heavily dependent on bioinformatics tools,
applications and databases. As such, there is
considerable overlap in CADD research and
bioinformatics.
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International Journal of Research in Pharmaceutical and Biomedical Sciences
There are several key areas where bioinformatics
supports CADD research.

Virtual High-Throughput Screening

Sequence Analysis

Homology Modeling

Similarity Searches

Drug Lead Optimization

Physicochemical Modeling

Drug Bioavailability and Bioactivity

CADD and bioinformatics together are a
powerful combination in drug research and
development.
Softwares used
HyperChem
HyperChem is a versatile molecular modeler and
editor and a powerful computational package. It
offers many types of molecular and quantum
mechanics calculations. The following actions can
be performed by HyperChem :
1. Building and Displaying Molecules
2. Optimizing the Structures of Molecules
3. Investigating the Reactivity of Molecules and
Functional Groups
4. Generating and Viewing Orbital and
Electronic Plots
5. Evaluating
Chemical
Pathways
and
Mechanisms
6. Studying the Dynamic Behavior of Molecules
HyperChem Release 7.5 for our work.
Gold (Genetic Optimization for Ligand
Docking)
Gold uses genetic algorithm to provide docking of
flexible ligand and a protein with flexible hydroxyl
groups. Otherwise the protein is considered to be
rigid. This makes it a good choice when the
binding pocket contains amino acids that form
hydrogen bonds with the ligand.
GOLD offers a choice of scoring functions:
GoldScore, ChemScore and User Defined Score.
The solutions are known to have 70-80% accuracy
when tested on complexes extracted from PDB.
GOLD will only produce reliable results if it is
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used properly and correct atom typing for both
protein and ligand is particularly important.
GOLD version 2.1
Open eye software
Methodology
Computer Aided Drug Design Scheme
High resolution X-ray structure of 1CY6 is used to
study the interactions of potential ligands with the
allosteric binding site and design new analogs.
Methods used to design inhibitors ranged from
graphical visualization of the ligand in the binding
site cavity to calculation of relative binding
affinities using molecular mechanics method in
conjunction with the 1M17 approach. Fig. Shows a
typical flowchart employed by drug discovery
groups using different CADD approaches.
This work is focused on the discovery of potential
drug candidates using the CADD methods in
conjunction with X-ray crystallography. The
process begins by generating a working
computational model from crystallographic data.
This step usually entails developing molecular
mechanics parameters for non-standard residues,
assigning the protonation states of histidines, and
orientating carbonyl and amide groups of
asparagine and glutamine amino acid residues
based upon neighboring donor/acceptor groups.
Characterization of the active site is then aided by a
variety of visualization tools.
For example,
calculating the electrostatic potential at different
surface grid points readily identifies hydrophobic
and hydrophilic regions of the active site. The
information gained by graphical analysis of the
active site aids new lead design and optimization of
the lead through analog design. LEAD
GENERATION13,14
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International Journal of Research in Pharmaceutical and Biomedical Sciences
Three methods for discovery of lead compounds
DE NOVO drug design methods
De novo drug design requires the 3-dimensional
structure of the target protein. A few successes are
reported but overall de novo design represents a
goal and not a reality. De novo molecular design
methods have been used to design new structures
by sequentially adding molecular fragments to a
growing structure, by adding functionality to an
appropriately-sized molecular scaffold, or by
adding fragments building toward the center of a
molecule starting from distant sites thought to
interact with the target. These approaches can be
used for generating diverse molecular structures.
Database search methods
In some cases, new lead compounds have been
identified by screening structures found in
databases of known commercial as well as
proprietary chemical databases for particular
structural features using three dimensional structure
of a target protein with known active site. In
addition, database search methods have been
developed that search databases for compounds
that have particular molecular functionality
separated by a specified number of bonds or
distance ranges. More chemically intuitive database
search methods search for chemicals with
particular steric and electrostatic fields.
Combinatorial methods
This method doesn’t require target protein
structure, which is the main requirement for other
two methods. Combinatorial chemistry helps to
create a large library of structures with a great deal
of diversity. A growing number of drug leads are
being generated by combinatorial methods in
combination with high-throughput screening .15
Optimization of lead compounds
Optimization of lead compounds is often a stepwise process using computational methods in
combination with SAR information to determine
areas on the molecule to expand, contract, or
modify. Accordingly, the challenge is, to prioritize
a large diverse set of molecules to a small set of
compounds that have the highest likelihood to bind.
Methods that rapidly and accurately predict
absolute binding affinities represent the long-term
goals. Currently, the methods range from being
able to provide qualitative rank ordering of a large
number of molecules in a relatively short period of
time
that generate quantitatively accurate
predictions of relative binding affinities for
structurally related molecules shows typical
flowchart used for optimization of lead compounds
using CADD methods. A large percentage of the
proposed analogs can usually be eliminated by
evaluating their expected binding affinities based
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on docking graphical analysis, desolvation costs
and conformational analysis.
The remaining analogs are prioritized using one or
all of the following methods, depending on the
availability of computational power, time and
resources:
i) Free Energy Perturbation (FEP) calculations,
which provide accurate predictions, but are
computationally very expensive
ii) Molecular mechanics calculations, which
provide rapid qualitative predictions and
iii) Regression methods that incorporate interaction
variables and ligand properties, which provide
semi-quantitative predictions and are much faster
than FEP calculations. The top scoring compounds
are synthesized and tested for activity. The process
is repeated in an interactive fashion until potential
drug candidates are identified with the desired
biological activity.
Molecular Mechanics Force Field thermodynamic
properties of both small and large molecules. The
AMBER, CHARMM and GROMOS force fields
are used extensively in calculating relative
solvation free energies (SFEs) of small organic
molecules as well as relative binding free energies
of enzyme inhibitors. The functional form of a
typical molecular mechanics force field (MM+) is
as follows:
Etotal Kr(r req)2  K (0)2 
bonds
angles
 Aij
R
i j

12
ij

Bij
Rij6
Vn
 21cos(n) 
dihedrals

qi q j 
 -----Rij 
(1)
In this equation, the first term is the strain energy
associated with the variation of the bond length r
from its equilibrium value r0, and KR is the force
constant associated with the deformation. The
second term is the energy due to the deformation of
the bond angle θ from its equilibrium value θ 0, with
a force constant of deformation, Kθ. The third term
is the torsional energy due to bond rotation, where
Vn represents the barrier to rotation (γ) about a
bond with a phase angle ηø. A Fourier series
approach to the torsional energy allows rather
accurate simulation of conformational preferences
in simple and complex molecules.
The nonbonded energy of the system is represented
by a 6-12 potential and the Columbic law treats the
electrostatic energy. Rij is the interatomic distance
between a pair of atoms i and j, whereas Aij and Bij
are Leonard Jones parameters, qi and qj are the
atomic charges, and ∑ is the dielectric constant.
Computational details
All molecular mechanics calculations were carried
out with the Hyperchem program using an all atom
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force field and the SPC/E model potential to
describe water interactions. Electrostatic charges
and parameters for the standard residues were taken
from the Hyperchem database. For non-standard
solute atoms, partial charges were obtained by
fitting wave functions calculated with Gaussian94
ab initio 6-31G* basis set level with CHELP .16
DISCUSSION
In this work, the binding modes of the
putative/proposed inhibitors were obtained by
carefully aligning them with the known crystal
structures of inhibitors in the active site of the
1CY6. These inhibitors, which are shown in Fig.
were then evaluated by performing minimization
calculations both in solvent and in complex using
the AMBER force field.
The technical details used for estimating relative
binding affinities using energy components
obtained from minimizations of each inhibitor, both
in solvent as well as in complex phases, were
explained by four stage protocol as described in the
in methodology section.
ISSN: 2229-3701
protein is calculated by performing docking
process. The molecule with minimum binding
energy will have the maximum binding affinity.
The binding free energy is calculated by the
formula Z = Sum of the energy of optimized ligand
devoid of solvation parameters and the energy of
the protein-ligand optimization. The binding free
energy of the designed molecules is obtained by
eliminating the energy of the main molecule i.e.
Atorvastatin. From the results obtained it’s clear
that ligand 3 have the maximum binding affinity.
So this molecule is determined as the best lead
molecules targeting (HMG CoA reductase for
curing cardiovascular disorders) computationally.
The inhibitors 3 with the Substituent Cl identified
as the most suitable analogue in the present study
that needs to be further evaluated in laboratory.
ACKNOWLEDGEMENT
Authors are thankful to Prof. T.J. Sawant, Founder
Secretary of Jaywant Shikshan Prasarak Mandal for
providing necessary facilities.
REFERENCES
CONCLUSION
1.
Comparisons of the calculated binding affinities for
structurally similar Inhibitors to ATORVASTATIN
indicate that the molecular mechanics methods
gave suitable analogues. These results clearly
indicate that before synthesis and biochemical
testing of new analogs, one can use molecular
mechanics based methods for qualitative
assessment of relative binding affinities for
speeding up drug discovery process by eliminating
less potent compounds from synthesis.
One of the most common diseases found among the
world’s population has been in steep rise, which is
called the (cardiovascular disorders), associated
have effected more than one-third of the world’s
population .As these are found to be an associated
one, its being a challenge to the medical field. Drug
designing, one of the hottest topics have found its
new pathway to create a history in the field of
medical science. The lead compound analysis starts
with CADD, assisting to identify and to optimize
the right compound. The technique helps in
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Vol. 2 (2) Apr – Jun 2011
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