Current
Computer-Aided Drug Design
ISSN: 1573-4099
Current Computer-Aided
Drug Design
Volume 1, Number 4, October 2005
Contents
Computational ADME/Tox Modeling: Aiding Understanding and
Enhancing Decision Making in Drug Design Pp. 325
Robert K. Delisle, Jeffery F. Lowrie, Doug W. Hobbs
and David J. Diller
[Abstract]
Computer-Aided Drug Design for Typical and Atypical
Antipsychotic Drugs: A Review of Application of QSAR and Combinatorial
Chemistry Methods - Tools for New Antipsychotics Design
Pp. 347
S. Avram, A.-L. Milac and M.L. Flonta
[Abstract]
In Silico
ADME Prediction: Data Sets and Models Pp. 365
Gonzalo Colmenarejo
[Abstract]
Computational Chemistry, Informatics, and the
Discovery of Vaccines Pp. 377
P. Guan, M. Davies, D.J. Taylor, S. Wan, H.M. McSparron,
S.L. Hemsley, C. Toseland, M.J. Blythe, P.D. Taylor, V. Walshe,
C.K. Hattotuwagama, I.A. Doytchinova, P.V. Coveney, P. Borrow
and D.R. Flower
[Abstract]
A Computational and Experimental Analysis of Ligand
Binding to Type 1 Collagen Pp. 397
J. Vaidyanathan, T.K. Vaidyanathan, N. Ramasubbu and
S. Ravichandran
[Abstract]
The Challenge of Considering Receptor Flexibility
in Ligand Docking and Virtual Screening Pp. 423
Claudio N. Cavasotto, Andrew J.W. Orry and Ruben
A. Abagyan
[Abstract]
Abstracts
[Back to top]
Computational ADME/Tox Modeling: Aiding Understanding
and Enhancing Decision Making in Drug Design
Robert K. Delisle, Jeffery F. Lowrie, Doug W. Hobbs and
David J. Diller
With recent estimates of drug development costs on the order
of $800 million and increased pressure to reduce consumer
drug costs, it is not surprising that the pharmaceutical industry
is keenly interested in reducing the overall expense associated
with drug development. An analysis of the reasons for attrition
during the drug development process found that over half of
all failures can be attributed to problems with human or animal
pharmacokinetics and toxicity. Discovering pharmacokinetics
and toxicity liabilities late within the drug development
process results in wasted resource expenditures. This argues
dramatically for evaluation of these properties as early as
possible, leading to the concept of “Fail Early”.
Computational models provide a low cost, flexible evaluation
of compound properties that can be implemented and used prior
to chemical synthesis thereby creating an alternative philosophy
of “Design for Success”. Here we review the history
and current trends within ADME/Tox modeling and discuss important
issues related to development of computational models. In
addition, we review some of the commercially available tools
to achieve this goal as well as methods developed internally
to address these issues from the design stage through development
and optimization of drug candidates. In particular, we highlight
those features that we feel best exemplify the Design for
Success philosophy.
[Back to top]
Computer-Aided Drug Design for Typical and Atypical
Antipsychotic Drugs: A Review of Application of QSAR and Combinatorial
Chemistry Methods - Tools for New Antipsychotics Design
S. Avram, A.-L. Milac and M.L. Flonta
The central nervous system (CNS) is endowed with very efficient
protection mechanisms. However, the same mechanisms that protect
it, sometimes can be an enemy for therapeutic applications.
In this way, many antipsychotic drugs used, are ineffective
in the treatment of cerebral diseases such as schizophrenia.
Many typical and atypical neuroleptics are very efficient
against the positive symptoms, but not against the negative
symptoms. To reduce the inefficiency of known neuroleptics,
many research efforts have recently focused on the development
of new strategies for new neuroleptics drug design. For this
reason it was necessary to apply very fast and precise techniques,
such as: QSAR (Quantitative Structure-Activity Relationships)
and combinatorial chemistry methods, capable to analyze and
predict the biological activity for these structures, taking
in account the possible changes of the molecular structures.
This review intends to detail recent advances in the field
of structure-activity relationship and combinatorial chemistry
applied to neuroleptics. The antipsychotic activities (log
ED50) of potent neuroleptics as indole derivatives, were correlated
with pharmacokinetic parameters namely: molecular volume (V),
globularity (G), Octanol/water partition coefficient (logP),
solubility(S), dipole moment, polarizability. QSAR studies
of benzothiazepine derivatives as potent neuroleptics are
presented. In addition, the 3D-QSAR methods such as Comparative
Molecular Field Analysis (CoMFA) and Comparative Molecular
Similarity Indices Analysis (CoMSIA) were applied for a set
of dopamine D4 receptor antagonists. The combinatorial chemistry
was used to develop a large chemical library starting from
5-hydroxytryptamine2A receptor antagonists used
as antipsychotics.
[Back to top]
In Silico ADME Prediction: Data Sets and
Models
Gonzalo Colmenarejo
The models available in the literature for the in silico
prediction of ADME (absorption, distribution, metabolism,
excretion) properties, as well as the data sets used to derive
them, are reviewed. Special emphasis is given to describe
the latest and most complete models, with the largest and
most diverse data sets. Models for human intestinal absorption,
oral bioavailability, plasma protein binding, blood-brain
barrier permeation, P-glycoprotein substrates and inhibitors,
and metabolism are reviewed and discussed. An attempt is made
to describe the general picture emerging from each set of
models when possible, as well as the issues remaining to address
in the different areas for future work. These models are an
example of the utility of models and computer simulations
for the prediction of pharmacokinetics.
[Back to top]
Computational Chemistry, Informatics, and the Discovery
of Vaccines
P. Guan, M. Davies, D.J. Taylor, S. Wan, H.M. McSparron,
S.L. Hemsley, C. Toseland, M.J. Blythe, P.D. Taylor, V. Walshe,
C.K. Hattotuwagama, I.A. Doytchinova, P.V. Coveney, P. Borrow
and D.R. Flower
Perhaps the most exciting sub-discipline within Bioinformatics
is the application of informatic methods to immunology. Immunoinformatics,
which combines informatics with computational chemistry, is
facilitating important change within immunology. As it frees
itself from the empirical straight jacket that has characterised
its development, immunoinformatics is helping immunology to
engage fully with the dynamic post-genomic revolution sweeping
through bioscience. Focussing on quantitative aspects, we
will review recent developments within immunoinformatics,
paying particular attention to the following: the development
of functional immunological databases; prediction of the antigen
presentation pathway; predicting the specificity of peptide
Major Histocompatibility Complex (MHC) interactions, using
statistical techniques and atomistic molecular dynamics; and
the grouping of MHC molecules into supertypes.
[Back to top]
A Computational and Experimental Analysis of Ligand
Binding to Type 1 Collagen
J. Vaidyanathan, T.K.Vaidyanathan, N.Ramasubbu and
S. Ravichandran
Type 1 collagen is the primary protein in extracellular
matrix of major tissues. Ligand binding to type 1 collagen
is therefore an important problem of interest in areas such
as adhesive bonding to tissues, mineralization of collagen
scaffolds etc. The triple helical structure of collagen molecule,
and the self-assembly of these molecules into fibrils as well
as the role of water in its conformational state present interesting
challenges in evaluating the binding of ligands to such a
structure. Computer simulation of interactions between collagen
and other molecular entities (e.g., ligands, proteins, mineral
entities etc.) can provide a wealth of information. This paper
reviews the computational methods suitable for applications
to collagen-ligand binding studies and the current literature
on such studies. These methods have been used for indirect
(active analog approach) and direct (manual and automatic
docking) methods of computer binding simulations. In particular,
AutoDock method was extremely valuable to identify the low
energy collagen-ligand complexes, to visualize the hydrogen
bonds between collagen and ligands in their complexes, and
to characterize the docking/binding energy parameters in the
presence of water. Experimental binding assay studies were
also used to characterize the interactions. The results give
valuable information on criteria for formulation design in
practical applications of adhesive bonding to tissues (e.g.,
bonding of dentin prior to filling of cavities to treat caries).
Ongoing current studies also focus on immobilization of charged
protein molecules on type 1 collagen to aid in biomimetic
mineralization of collagen scaffolds.
[Back to top]
The Challenge of Considering Receptor Flexibility
in Ligand Docking and Virtual Screening
Claudio N. Cavasotto, Andrew J.W. Orry and Ruben
A. Abagyan
Computational ligand docking and screening is widely employed
throughout the pharmaceutical industry to speed up the drug
discovery process and identify drug candidates from very large
pools of virtual compound libraries. When a ligand interacts
with a receptor a number of structural changes within the
ligand binding site might occur. It is therefore critical
for these methods to accurately predict, or otherwise take
into account the receptor flexibility upon ligand binding.
This flexibility within the binding pocket explains why a
diverse range of ligand sizes and shapes can sometimes bind
to the same receptor pocket. This observation supersedes the
notion that ligand-receptor interaction is a purely ‘lock
and key’ mechanism. The capability to correctly predict
molecular interactions is critical for computer-aided molecular
design technology. In this review, we discuss biological cases
of receptor flexibility upon ligand binding that can range
from ‘large-scale’ movement of loops to single
‘gate-keeper’ amino acid movements. In addition,
we provide further evidence that rigid receptor docking alone
will more than likely fail in the drug-discovery process.
We then discuss computational methods, which have been developed
to mimic flexibility within the binding pocket and predict
ligand-receptor interactions. Early flexible receptor docking
methods used ‘soft-potential docking’ and rotamer
libraries. More recently methods have focused on constructing
an ensemble of structures generated by a variety of means
including X-ray crystallography, NMR, Monte Carlo sampling,
Normal Modes-based methods and Molecular Dynamics. It is evident
that methods that ignore receptor flexibility can result in
poorly docked solutions and therefore the challenge is to
develop computational methods, which can accurately and efficiently
predict this phenomenon.
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