Current
Topics in Medicinal Chemistry
ISSN:1568-0266
Upcoming Articles
The hERG Channel and Risk of Drug-Acquired Cardiac
Arrhythmia: An Overview
Armando A. Lagrutta, Elena S. Trepakova
and Joseph J. Salata
[Abstract]
Ligand Structural Aspects of hERG Channel Blockade
Alex M. Aronov
[Abstract]
Overcoming hERG Affinity in the Discovery of Maraviroc;
A CCR5 Antagonist for the Treatment of HIV
David A. Price, Duncan Armour, Marcel de
Groot, Derek Leishman, Carolyn Napier, Manos Perros, Blanda
L. Stammen and Anthony Wood
[Abstract]
The Impact of IKr Blockade on Medicinal Chemistry
Programs
Ian M. Bell and Mark T. Bilodeau
[Abstract]
Lead Optimization of Melanin Concentrating Hormone
Receptor 1 Antagonists with low hERG Channel Activity
Andrew S. Judd, Andrew J. Souers and Philip R.
Kym
[Abstract]
Pharmacogenomics and Personalized
Use of Drugs
Jing-Fang Wang, Dong-Qing Wei, Kuo-Chen Chou
[Abstract]
Artificial Neural Networks from MATLAB® in Medicinal
Chemistry. Bayesian-Regularized Genetic Neural Networks (BRGNN):
Application to the Prediction of the Antagonistic Activity
Against Human Platelet Thrombin Receptor (PAR-1)
Julio Caballero, and Michael Fernández
[Abstract]
Variable Selection Methods in QSAR: An Overview
Maykel Pérez González, Carmen Terán,
Liane Saíz-Urra and Marta Teijeira
[Abstract]
Applications of 2D Descriptors in Drug Design: A DRAGON
Tale
Aliuska Morales Helguera, Robert D. Combes, and Maykel Pérez
González, M. Natália D.S. Cordeiro
[Abstract]
Drug Candidates from Traditional Chinese Medicines
Jing-Fang Wang, Dong-Qing Wei, and Kuo-Chen Chou
[Abstract]
Current Topics on Software Use in Medicinal Chemistry:
Intellectual Property, Taxes, and Regulatory Issues
Aliuska Duardo-Sánchez, Grace Patlewicz, and Antonio
López-Díaz
[Abstract]
Predicting Antimicrobial Drugs and Targets with the
MARCH-INSIDE Approach
Humberto González-Díaz, Francisco Prado-Prado,
and Florencio M. Ubeira
[Abstract]
Weka Machine Learning for Predicting the Phospholipidosis
Inducing Potential
Ovidiu Ivanciuc
[Abstract]
Abstracts
[Back to top]
The hERG Channel and Risk of Drug-Acquired Cardiac
Arrhythmia: An Overview
Armando A. Lagrutta, Elena S. Trepakova
and Joseph J. Salata
This review summarizes current knowledge of the cardiac rapidly
activating delayed rectifier potassium current (IKr),
and its connection to drug-acquired QT prolongation and the
associated risk of ventricular arrhythmia and fibrillation.
The molecular characterization of hERG as the structural correlate
of IKr and the link between
inherited long QT and the KCNH2 gene (hERG), have facilitated
mechanistic studies of drug-acquired QT prolongation. The
development of high throughput assays to evaluate drug effects
on hERG has provided an avenue to determine structure activity
relations (SAR) within chemical series. More than 10 years
of collective data and structural considerations support the
notion that hERG is an unusually promiscuous target among
potassium channels, but that defining SAR within a chemical
series is a viable strategy to reduce or eliminate hERG activity.
Despite a critical need to minimize drug effects on hERG,
one should always keep in mind that hERG is not the only structural
correlate of QT prolongation, and that QT prolongation is
a sub-optimal biomarker for ventricular arrhythmia and fibrillation.
[Back to top]
Ligand Structural Aspects of hERG Channel Blockade
Alex M. Aronov
Sudden death as a side effect of action of non-antiarrhythmic
drugs is a major pharmacological safety concern facing the
pharmaceutical industry and the health regulatory authorities.
A number of drugs have been withdrawn from the market in recent
years due to cardiovascular toxicity associated with undesirable
blockade of hERG potassium channel. Pharmaceuticals of widely
varying structure have been shown to interact with hERG. Defining
the molecular features that confer hERG inhibitory activity
has therefore become a focus of considerable computational
and statistical modeling efforts. Some of the approaches are
aimed primarily at filtering out potential hERG blockers in
the context of virtual libraries, while others involve understanding
structure-activity relationships governing hERG-drug interactions.
The ability of models to produce structural hypotheses that
can be tested by the project teams has become the key prerequisite
driving their organization-wide adoption.
[Back to top]
Overcoming hERG Affinity in the Discovery of Maraviroc;
A CCR5 Antagonist for the Treatment of HIV
David A. Price, Duncan Armour, Marcel de
Groot, Derek Leishman, Carolyn Napier, Manos Perros, Blanda
L. Stammen and Anthony Wood
Avoiding cardiac liability associated with blockade of
hERG (human ether a go-go) is key for successful drug discovery
and development. This paper describes the work undertaken
in the discovery of a potent CCR5 antagonist, maraviroc
34, for the treatment of HIV. In particular the use
of a pharmacophore model of the hERG channel and a high throughput
binding assay for the hERG channel are described that were
critical to elucidate SAR to overcome hERG liabilities. The
key SAR involves the introduction of polar substituents into
regions of the molecule where it is postulated to undergo
hydrophobic interactions with the ion channel. Within the
CCR5 project there appeared to be no strong correlation between
hERG affinity and physiochemical parameters such as pKa or
lipophilicity. It is believed that chemists could apply these
same strategies early in drug discovery to remove hERG interactions
associated with lead compounds while retaining potency at
the primary target.
[Back to top]
The Impact of IKr Blockade on Medicinal Chemistry
Programs
Ian M. Bell and Mark T. Bilodeau
Inhibition of the cardiac IKr
current leads to prolongation of the QT interval and to a
risk of ventricular arrhythmia. This activity has been observed
for a wide range of small molecules and results from their
binding to the hERG ion channel. The off-target inhibition
of IKr presents a daunting
challenge for virtually all medicinal chemistry programs.
This review article presents case studies and key learnings
across a range of projects at Merck. The article begins with
a review of findings from the original efforts to identify
IKr blockers as antiarrhythmic
therapeutics. A discussion follows of in vitro and
in vivo assays that have been utilized
for the assessment of IKr
inhibition. General SAR rules that have been found to be useful
guides for diminishing hERG activity in lead compounds are
discussed and case studies are presented that illustrate specific
observations. The case studies highlight how the issue of
hERG antagonism was navigated on four distinct medicinal chemistry
programs.
[Back to top]
Lead Optimization of Melanin Concentrating Hormone
Receptor 1 Antagonists with low hERG Channel Activity
Andrew S. Judd, Andrew J. Souers and Philip R.
Kym
The discovery of small molecule melanin concentrating
hormone receptor (MHCr1) antagonists as novel therapeutic
agents has been widely pursued across the pharmaceutical industry.
While multiple chemotypes of small molecule MCHr1 antagonists
have been identified and shown to induce weight loss in rodent
models of obesity, many of these lead compounds have been
found to cross react with the hERG channel. This review describes
efforts that led to the identification of two sub-series of
MCHr1 antagonists with low affinity for the hERG channel.
Ultimately, however, the modifications introduced to thwart
hERG channel activity resulted in lead compounds with sub-optimal
CNS behavior.
[Back to top]
Pharmacogenomics and Personalized Use of Drugs
Jing-Fang Wang, Dong-Qing Wei, Kuo-Chen Chou
As the development of the Human Genome Project (HGP),
the sequencing of whole human genome has been completed, and
a series of human genes have been detected, both of which
result in the naissance of pharmacogenomics. Pharmacogenomics
is the study of how an individual’s genetic inheritance
affects the body’s response to drugs using the information
of human genomics and bioinformatics approaches. It is not
only propitious to the rational use of drugs, but also in
favor for the personalized drug design.
[Back to top]
Artificial Neural Networks from MATLAB® in Medicinal
Chemistry. Bayesian-Regularized Genetic Neural Networks (BRGNN):
Application to the Prediction of the Antagonistic Activity
Against Human Platelet Thrombin Receptor (PAR-1)
Julio Caballero, and Michael Fernández
Artificial neural networks (ANNs) have been widely
used for medicinal chemistry modeling. In the last two decades,
too many reports used MATLAB environment as an adequate platform
for programming ANNs. Some of these reports comprise a variety
of applications intended to quantitatively or qualitatively
describe structure-activity relationships. A powerful tool
is obtained when there are combined Bayesian-regularized neural
networks (BRANNs) and genetic algorithm (GA): Bayesian-regularized
genetic neural networks (BRGNNs). BRGNNs can model complicated
relationships between explanatory variables and dependent
variables. Thus, this methodology is regarded as useful tool
for QSAR analysis. In order to demonstrate the use of BRGNNs,
we developed a reliable method for predicting the antagonistic
activity of 5-amino-3-arylisoxazole derivatives against Human
Platelet Thrombin Receptor (PAR-1), using classical 3D-QSAR
methodologies: Comparative Molecular Field Analysis (CoMFA)
and Comparative Molecular Similarity Indices Analysis (CoMSIA).
In addition, 3D vectors generated from the molecular structures
were correlated with antagonistic activities by multivariate
linear regression (MLR) and Bayesian-regularized neural networks
(BRGNNs). All models were trained with 34 compounds, after
which they were evaluated for predictive ability with additional
6 compounds. CoMFA and CoMSIA were unable to describe this
structure-activity relationship, while BRGNN methodology brings
the best results according to validation statistics.
[Back to top]
Variable Selection Methods in QSAR: An Overview
Maykel Pérez González, Carmen Terán,
Liane Saíz-Urra and Marta Teijeira
Variable selection is a procedure used to select
the most important features to obtain as much information
as possible from a reduced amount of features. The selection
stage is crucial. The subsequent design of a quantitative
structure-activity relationship (QSAR) model (regression or
discriminant) would lead to poor performance if little significant
features are selected. In drug design modern era, by the means
of combinatorial chemistry and high throughput screening,
an unprecedented amount of experimental information has been
generated. In addition, many molecular descriptors have been
defined in the last two decays. All this information can be
analyzed by QSAR techniques using adequate statistical procedures.
These techniques and procedures should be fast, automated,
and applicable to large data sets of structurally diverse
compounds. For that reason, the identification of the best
one seems to be a very difficult task in view of the large
variable selection techniques existing nowadays. The intention
of this review is to summarize some of the present knowledge
concerning to variable selection methods applied to some well-known
statistical techniques such as linear regression, PLS, kNN,
Artificial Neural Networks, etc, with the aim to disseminate
the advances of this important stage of the QSAR building
model.
[Back to top]
Applications of 2D Descriptors in Drug Design: A DRAGON
Tale
Aliuska Morales Helguera, Robert D. Combes, and Maykel Pérez
González, M. Natália D.S. Cordeiro
In order to minimize expensive drug failures, is
essential to determine potential activity, toxicity and ADME
problems as early as possible. In view of the large libraries
of compounds now being handled by combinatorial chemistry
and high-throughput screening, identification of potential
drug is advisable even before synthesis using computational
techniques such as QSAR modeling.
A great number of in silico approaches to activity/toxicity
prediction have been described in the literature, using molecular
0D, 1D, 2D and 3D descriptors. Also these descriptors have
been implemented in available computational tools such as
DRAGON, SYBYL and CODESSA for it easy use. However, many of
them only have been used to explain a few prediction problems.
This review attempts to summarize present knowledge related
to the computational biological activity prediction based
in 2D molecular descriptors implemented in the DRAGON software.
These applications rely on new computational techniques such
as virtual combinatorial synthesis, virtual computational
screening or inverse. Several topological molecular descriptors
applications are described, ranging from simple topological
indices to topological indices derived from matrices weighted
with atomic and bond properties. Their advantages, limitations
and its possibilities in drug design are also discussed.
[Back to top]
Drug Candidates from Traditional Chinese Medicines
Jing-Fang Wang, Dong-Qing Wei, and Kuo-Chen Chou
Good progress has been made to modernize traditional
Chinese medicines by obtaining active components from natural
herbs. In this review, some recent works on procuring active
components and modernizing traditional Chinese medicines will
be covered. In addition, some recent works on drug design
using modern drug design tools have been described. With some
well defined targets, the traditional Chinese medicine databases
have been screened so as to identify those compounds for which
the potential as a drug candidate was not known before. Among
these studies, two have been selected as examples to be discussed
in details. First, new anti-HIV candidates have been detected,
namely leucovorin and agaritine derivatives. Subsequently,
GTS-21 is proved to be a good candidate for Alzheimer’s
disease. All these findings may provide useful information
for finding effective drug candidates with lower cost.
[Back to top]
Current Topics on Software Use in Medicinal Chemistry: Intellectual
Property, Taxes, and Regulatory Issues
Aliuska Duardo-Sánchez, Grace Patlewicz, and Antonio
López-Díaz
In recent times, there has been an increased use of software
and computational models in Medicinal Chemistry, both for
the prediction of effects such as drug-target interactions,
as well as for the development of (Quantitative) Structure-Activity
Relationships ((Q)SAR). Whilst the ultimate goal of Medicinal
Chemistry research is for the discovery of new drug candidates,
a secondary yet important outcome that results is in the creation
of new computational tools. The adoption of computational
tools by medicinal chemists is sadly, and all too often accompanied,
by a lack of understanding of the legal aspects related to
software and model use, that is, the copyright protection
of new medicinal chemistry software and software-mediated
discovered products. This article aims to provide a reference
to the various legal avenues that are available for the protection
of software, and the acceptance and legal treatment of scientific
results and techniques derived from such software. An overview
of relevant international tax issues is also presented. We
have considered cases of patents protecting software, models,
and/or new compounds discovered using methods such as molecular
modeling or QSAR. This paper has been written and compiled
by the authors as a review of current topics and trends on
the legal issues in certain fields of Medicinal Chemistry
and as such is not intended to be exhaustive.
[Back to top]
Predicting Antimicrobial Drugs and Targets with the MARCH-INSIDE
Approach
Humberto González-Díaz, Francisco
Prado-Prado, and Florencio M. Ubeira
The method MARCH-INSIDE (MARkovian
CHemicals IN SIlico
DEsign) is a simple but efficient
computational approach to the study of Quantitative Structure-Activity
Relationships (QSAR) in Medicinal Chemistry. The method uses
the theory of Markov Chains to generate parameters that numerically
describe the chemical structure of drugs and drug targets.
This approach generates two principal types of parameters
Stochastic Topological Indices (sto-TIs) and stochastic 3D-Topographic
Indices (sto-TPGIs). The use of these parameters allows the
rapid collection, annotation, retrieval, comparison and mining
of molecular and macromolecular chemical structures within
large databases. In the work described here, we review and
comment on the several applications of MARCH-INSIDE to the
Medicinal Chemistry of Antimicrobial agents as well as their
molecular targets. First we revised the use of classic sto-TIs
to predict antiparasite compounds for the treatment of Fascioliasis.
Next, we revised the use of chiral sto-TIs (sto-CTIs) to predict
new antibacterial, antiviral and anti-coccidial compounds.
After that, we review multi-target sto-TIs (mt-sto-TIs), which
unifying QSAR models predicting antifungal, antibacterial,
or anti-parasite drugs with multiple targets (microbial species).
We also discussed the uses of mt-sto-TIs to assemble drug-drug
similarity Complex Networks of antimicrobial compounds based
on molecular structure. Last, we review the use of MARCH-INSIDE
to generate macromolecular TIs and TPGIs for proteins or RNA
targets for antimicrobial drugs.
[Back to top]
Weka Machine Learning for Predicting the Phospholipidosis
Inducing Potential
Ovidiu Ivanciuc
The drug discovery and development process is lengthy and
expensive, and bringing a drug to market may take up to 18
years and may cost up to 2 billion $US. The extensive use
of computer-assisted drug design techniques may considerably
increase the chances of finding valuable drug candidates,
thus decreasing the drug discovery time and costs. The most
important computational approach is represented by structure-activity
relationships that can discriminate between sets of chemicals
that are active/inactive towards a certain biological receptor.
An adverse effect of some cationic amphiphilic drugs is phospholipidosis
that manifests as an intracellular accumulation of phospholipids
and formation of concentric lamellar bodies. Here we present
structure-activity relationships (SAR) computed with a wide
variety of machine learning algorithms trained to identify
drugs that have phospholipidosis inducing potential. All SAR
models are developed with the machine learning software Weka,
and include both classical algorithms, such as k-nearest
neighbors and decision trees, as well as recently introduced
methods, such as support vector machines and artificial immune
systems. The best predictions are obtained with support vector
machines, followed by perceptron artificial neural network,
logistic regression, and k-nearest neighbors.
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