Current
Pharmaceutical Design
ISSN: 1381-6128
Current Pharmaceutical Design
Volume 13, Number 34, 2007
Contents
Rational Drug Design
Executive Editor: M. Rami Reddy
Editorial Pp. 3453
From Drug Target to Leads-Sketching A Physicochemical
Pathway for Lead Molecule Design In Silico
Pp. 3454-3470
S.A. Shaikh, T. Jain, G. Sandhu, N. Latha and B. Jayaram
[Abstract]
Molecular Dynamics Simulations of Metalloproteinases
Types 2 and 3 Reveal Differences in the Dynamic Behavior of
the S1’ Binding Pocket Pp. 3471-3475
C.A.F. de Oliveira, M. Zissen, J. Mongon and J.A. Mccammon
[Abstract]
The Role and Significance of Unconventional Hydrogen
Bonds in Small Molecule Recognition by Biological Receptors
of Pharmaceutical Relevance Pp. 3476-3493
G. Tóth, S.G. Bowers, A.P. Truong and G. Probst
[Abstract]
Predictive QSAR Modeling Workflow, Model Applicability
Domains, and Virtual Screening Pp. 3494-3504
A. Tropsha and A. Golbraikh
[Abstract]
Computer Aided Drug Design Approaches to Develop Cyclooxygenase
Based Novel Anti-Inflammatory and Anti Cancer Drugs
Pp. 3505-3517
R.N. Reddy, R. Mutyala, P. Aparoy, P. Reddanna and M.R.
Reddy
[Abstract]
Modeling and Informatics in Designing Anti-Diabetic
Agents Pp. 3518-3530
P.V. Bharatam, D.S. Patel, L. Adane, A. Mittal and S.
Sundriyal
[Abstract]
Strategies of Development of Antiviral Agents Directed
Against Influenza Virus Replication Pp. 3531-3542
H.-P. Hsieh and J.T.-A. Hsu
[Abstract]
Abstracts
[Back to top]
Editorial: Rational Drug Design
Discovery of effective and safe drugs traditionally entails
the synthesis and/or screening of large numbers of compounds.
Considering that marketed compounds represent only one compound
out of every 30,000 compounds synthesized, the pharmaceutical
industry has devoted substantial efforts and finances to methodologies
that have the potential to shorten the discovery process.
One of the best strategies is to use “Rational Drug
Design” to shorten the discovery time by dramatically
narrowing the pool of potential drug candidates through calculation
of drug-protein binding interaction energies. Although highly
appealing in theory, in practice the efforts devoted to rational
drug design in the 1980s and 1990s resulted in predictions
that were frequently inaccurate due to the approximations
and assumptions employed in the calculations.
Advances in X-ray crystallography, NMR and extraordinary advances
in available CPU computer power over the past decade have
resulted in more accurate predictions. Greater accuracy is
achieved through the use of higher levels of quantum mechanical
theory to provide a more extensive and accurate set of molecular
mechanics force field parameters for protein residues as well
as small molecules, by inclusion of solvent effects, and by
using computer simulation methods that exhaustively search
conformational space. Improvements in computer simulation
methods such as Molecular Dynamics and Monte Carlo and available
3-dimensional structures of protein-ligand complexes enable
calculation of free energy differences which are important,
since free energy differences are directly related to the
experimental result. This issue on “Rational Drug Design”
covers the recent advances in Computer Aided Drug Design (CADD)
Methods as well as their successful application to a variety
of drug discovery programs.
The first article [1] focuses on various computational aspects
for identification of leads to drug targets in silico.
In addition the article discusses the fundamental issues and
challenges associated with various CADD methods. The second
article [2] describes the dynamical behavior of the binding
pocket S1 in the apo forms of metalloproteinase types
2 and 3 using molecular dynamics simulations. Results from
this study are useful in the design of specific metalloproteinase
inhibitors. The third article [3] summarizes the role of unconventional
hydrogen bonds in the recognition of small molecules by biological
receptors of pharmaceutical relevance. The fourth article
[4] describes predictive QSAR modeling and virtual screening
of small molecule databases for several drug targets. The
fifth article [5] focuses on lead inhibitor optimization strategies
using the free energy perturbation approach and molecular
mechanics methods and evaluates the merits of each method
for predicting relative binding affinities of COX-2 inhibitors.
The sixth article [6] summarizes use of molecular modeling
and informatics tools for the discovery of anti-diabetic agents.
The last article [7] reviews rational drug design strategies
for development of antiviral agents directed against the influenza
virus replication. Overall the issue provides computational
and medicinal chemists in both academia and industry an extensive
overview of the scope and limitations of CADD methods useful
for rational drug design.
As an Executive Editor of Current Pharmaceutical Design, I
would like to thank all the authors for contributing to this
issue on Rational Drug Design. I would also like to thank
Dr. Mark Erion for his helpful suggestions, encouragement
and support in editing this issue.
References
[1] Shaikh SA, Jain T, Sandhu G, Latha N, Jayaram B. From
drug target to leads-sketching a physicochemical pathway for
lead molecule design in silico. Curr Pham Des 2007;
13(34): 3454-3470.
[2] de Oliveira CAF, Zissen M, Mongon J, Mccammon JA. Molecular
dynamics simulations of metalloproteinases types 2 and 3 reveal
differences in the dynamic behavior of the S1’ binding
pocket. Curr Pham Des 2007; 13(34): 3471-3475.
[3] Tóth G, Bowers SG, Truong AP, Probst G. The role
and significance of unconventional hydrogen bonds in small
molecule recognition by biological receptors of pharmaceutical
relevance. Curr Pham Des 2007; 13(34): 3476-3493.
[4] Tropsha A, Golbraikh A. Predictive QSAR modeling workflow,
model applicability domains, and virtual screening. Curr Pham
Des 2007; 13(34): 3494-3504.
[5] Reddy RN, Mutyala R, Aparoy P, Reddanna P, Reddy MR. Computer
aided drug design approaches to develop cyclooxygenase based
novel anti-inflammatory and anti-cancer drugs. Curr Pham Des
2007; 13(34): 3505-3517.
[6] Bharatam PV, Patel DS, Adane L, Mittal A, Sundriyal S.
Modeling and informatics in designing anti-diabetic agents.
Curr Pham Des 2007; 13(34): 3518-3530.
[7] Hsieh H-P, Hsu JT-A. Strategies of development of antiviral
agents directed against influenza virus replication. Curr
Pham Des 2007; 13(34): 3531-3542.
M. Rami Reddy, Ph .D.
Metabasis Therapeutics, Inc,
11119 N. Torrey Pines Road
La Jolla, CA 92037
USA
[Back to top]
From Drug Target to Leads-Sketching A Physicochemical Pathway
for Lead Molecule Design In Silico
S.A. Shaikh, T. Jain, G. Sandhu, N. Latha and B. Jayaram
The discovery of new pharmaceuticals via computer
modeling is one of the key challenges in modern medicine.
The advent of global networks of genomic, proteomic and metabolomic
endeavors is ushering in an increasing number of novel and
clinically important targets for screening. Computational
methods are anticipated to play a pivotal role in exploiting
the structural and functional information to understand specific
molecular recognition events of the target macromolecule with
candidate hits leading ultimately to the design of improved
leads for the target. In this review, we sketch a system independent,
comprehensive physicochemical pathway for lead molecule design
focusing on the emerging in silico trends and techniques.
We survey strategies for the generation of candidate molecules,
docking them with the target and ranking them based on binding
affinities. We present a molecular level treatment for distinguishing
affinity from specificity of a ligand for a given target.
We also discuss the significant aspects of drug absorption,
distribution, metabolism, excretion and toxicity (ADMET) and
highlight improved protocols required for higher quality and
throughput of in silico methods employed at early
stages of discovery. We present a realization of the various
stages in the pathway proposed with select examples from the
literature and from our own research to demonstrate the way
in which an iterative process of computer design and validation
can aid in developing potent leads. The review thus summarizes
recent advances and presents a viewpoint on improvements envisioned
in the years to come for automated computer aided lead molecule
discovery .
[Back to top]
Molecular Dynamics Simulations of Metalloproteinases Types
2 and 3 Reveal Differences in the Dynamic Behavior of the
S1’ Binding Pocket
C.A.F. de Oliveira, M. Zissen, J. Mongon and J.A. Mccammon
Matrix Metalloproteinases (MMPs) are zinc-containing
proteinases that are responsible for the metabolism of extracellular
matrix proteins. Overexpression of MMPs has been associated
with a wide range of pathological diseases such as arthritis,
cancer, multiple sclerosis and Alzheimer’s disease.
The excessive and unregulated activity of Matrix Metalloproteinases
type 2 (MMP-2), also known as gelatinase A, has been identified
in a numbers of cancer metastases. Several MMP inhibitors
(MMPi) have been proposed in the literature aiming to interfere
in the MMPs activity. In this work we performed long MD simulations
in order to study the dynamical behavior of the binding pocket
S1’ in the apo forms of MMP type 2 and 3, and
identify, at the molecular level, the structural properties
relevant for the designing of specific inhibitor of MMP-2.
[Back to top]
The Role and Significance of Unconventional Hydrogen Bonds
in Small Molecule Recognition by Biological Receptors of Pharmaceutical
Relevance
G. Tóth, S.G. Bowers, A.P. Truong and G. Probst
The discovery and optimization of nonbonded interactions,
such as van der Waals interactions, hydrogen bonds, salt bridges
and the hydrophobic effect, between small molecule ligands
and their receptors is one of the main challenges in rational
drug discovery. As the theory of molecular interactions advances
more evidence accumulates that nonbonded interactions, such
as unconventional hydrogen bonds (X-H•••Y
interactions, where X can be either C, N or O atom and Y can
be either an aromatic ring system,O or F atom), contribute
to ligand recognition by biological receptors. This review
provides an overview of unconventional hydrogen bonds between
ligands and their receptors of pharmaceutical relevance by
dissecting their structure activity relationships and 3D structural
elements. Gaining an understanding of the energetic and the
structural properties of unconventional hydrogen bonds in
ligand-receptor interactions leads us to the elucidation of
their practical significance. Ultimately, this enables us
to consciously apply these interactions in hit and lead optimization
in rational structure based drug design.
[Back to top]
Predictive QSAR Modeling Workflow, Model Applicability Domains,
and Virtual Screening
A. Tropsha and A. Golbraikh
Quantitative Structure Activity Relationship (QSAR) modeling
has been traditionally applied as an evaluative approach,
i.e., with the focus on developing retrospective and explanatory
models of existing data. Model extrapolation was considered
if only in hypothetical sense in terms of potential modifications
of known biologically active chemicals that could improve
compounds’ activity. This critical review re-examines
the strategy and the output of the modern QSAR modeling approaches.
We provide examples and arguments suggesting that current
methodologies may afford robust and validated models capable
of accurate prediction of compound properties for molecules
not included in the training sets. We discuss a data-analytical
modeling workflow developed in our laboratory that incorporates
modules for combinatorial QSAR model development (i.e., using
all possible binary combinations of available descriptor sets
and statistical data modeling techniques), rigorous model
validation, and virtual screening of available chemical databases
to identify novel biologically active compounds. Our approach
places particular emphasis on model validation as well as
the need to define model applicability domains in the chemistry
space. We present examples of studies where the application
of rigorously validated QSAR models to virtual screening identified
computational hits that were confirmed by subsequent experimental
investigations. The emerging focus of QSAR modeling on target
property forecasting brings it forward as predictive, as opposed
to evaluative, modeling approach.
[Back to top]
Computer Aided Drug Design Approaches to Develop Cyclooxygenase
Based Novel Anti-Inflammatory and Anti Cancer Drugs
R.N. Reddy, R. Mutyala, P. Aparoy, P. Reddanna and M.R.
Reddy
Cyclooxygenases (COXs), the enzymes involved in the formation
of prostaglandins from polyunsaturated fatty acids such as
arachidonic acid, exist in two forms-the constitutive COX-1
that is cytoprotective and responsible for the production
of prostaglandins and COX-2 which is induced by cytokines,
mitogens and endotoxins in inflammatory cells and responsible
for the increased levels of prostaglandins during inflammation.
As a result COX-2 has become the natural target for the development
of anti-inflammatory and anti-cancer drugs. While the conventional
NSAIDs with gastric side effects inhibit both COX-1 and COX-2,
the newly developed drugs for inflammation with no gastric
side effects selectively block the COX-2 enzyme. NSAIDs, nonselective
non-aspirin NSAIDs and COX-2 selective inhibitors, are being
widely used for various arthritis and pain syndromes. Selective
inhibitors of COX-2, however, convey a small but definite
risk of myocardial infarction and stroke; the extent of which
varies depending on the COX-2 specificity. In view of the
gastric side effects of conventional NSAIDs and the recent
market withdrawal of rofecoxib and valdecoxib due to their
adverse cardiovascular side effects there is need to develop
alternative anti-inflammatory agents with reduced gastric
and cardiovascular problems. The present study reviews various
Computer Aided Drug Design (CADD) approaches to develop Cyclooxygenase
based anti-inflammatory and anti-cancer drugs.
[Back to top]
Modeling and Informatics in Designing Anti-Diabetic Agents
P.V. Bharatam, D.S. Patel, L. Adane, A. Mittal and S.
Sundriyal
Diabetes mellitus is a chronic metabolic disorder, characterized
by glucose overproduction and glucose underutilization. Cur-rent
therapy for T2DM includes drugs, like metformin, glitazones,
sulphonyl ureas, etc. Extensive research has been carried
out world wide on molecular targets for T2DM like PPARγ,
PTP1B, DPP-IV, GSK-3, cannabinoid receptor, fructose-bisphosphatases,
β3
adrenoceptor, etc. in the development of newer anti-diabetic
agents. These therapeutic targets are quite important and
most of them are suitable for in silico analysis.
Hence, many molecular modeling and informatics studies like,
molecular docking, pharmacophore mapping, 3D-QSAR, virtual
screening, quantum chemical studies, and pharmacoinformatics
like bioinformatics and chemoinformatics studies have been
performed on the drugs / leads / targets associated with T2DM.
Several of these in silico efforts are exemplary
studies; the methodologies adopted in these studies can be
emulated in many other therapeutic areas. A review of the
rational approaches reported in designing anti-diabetic agents
is presented in this article.
[Back to top]
Strategies of Development of Antiviral Agents Directed Against
Influenza Virus Replication
H.-P. Hsieh and J.T.-A. Hsu
In this review, we will discuss drug design based on
proven and potential anti-influenza drug targets including
viral hemagglu-tinin (HA), neuraminidase (NA), M2 ion channel,
3P polymerase complex, and host factors such as kinases. We
have summarized influenza inhibitors based on their mode of
actions. For instance, included are descriptions of (1) inhibitors
of HA cleavage, such as nafamostat, camostat, gabexate, epsilon-aminocapronic
acid and aprotinin, (2) inhibitors of fusion and entry, such
as benzoquinones and hydroquinones, CL 385319, BMY-27709,
stachyflin, and their analogues, (3) inhibitors of viral RNPs/polymerase/endonuclease,
such as T-705, L-735,822, flutimide and their analogues, (4)
inhibitors of MEK, such as PD 0325901, CI-1040 and ARRY-142886,
and (5) inhibitors of NA such as DANA, FANA, zanamivir, and
oseltamivir, etc. Although amantadine and rimantadine are
not recommended for treating influenza virus infections because
of drug resistance problem, these viral M2 ion channel blockers
established a proof-of-concept that the endocytosis of virion
into host cells can be a valid drug target because M2 protein
is involved in the endocytosis process. The influenza polymerase
complex not only catalyzes RNA polymerization but also encodes
the “cap snatching” activity. After being exported
from the nucleus to the cytoplasm, the newly synthesized vRNPs
are assembled into virions at the plasma membrane. The progeny
virions will then leave the host cells through the action
of NA. The strategies for discovery of small molecule inhibitors
of influenza virus replication based on each particular mechanism
will be discussed. Finally, the lessons learned from the design
of NA inhibitors (NAI) are also included. Many exciting opportunities
await the cadre of virologists, medicinal chemists, and pharmacologists
to design novel influenza drugs with favorable pharmacological
and pharmacokinetic properties to combat this threatening
infectious disease.
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