Current
Topics in Medicinal Chemistry
ISSN: 1568-0266
Current Topics
in Medicinal Chemistry
Volume 7, Number 15, 2007
Contents
Melanin-Concentrating Hormone (MCH)
Guest Editor: Andrew J. Souers
Editorial Pp. 1424
Peptide Ligands for the Melanin-Concentrating Hormone
(MCH) Receptor 1 Pp. 1425-1432
Maria A. Bednarek
[Abstract]
Aminoquinoline Melanin-Concentrating Hormone 1-Receptor
(MCH1-R) Antagonists Pp. 1433-1434
Robert J. DeVita
[Abstract]
Melanin Concentrating Hormone Receptor Antagonists
as Antiobesity Agents: From M2 to MCHR-1 Pp. 1440-1454
Mark D. McBriar
[Abstract]
Biphenyl Amides and Isosteres as MCH R1 Antagonists
Pp. 1455-1470
Donald L. Hertzog and David R. Witty
[Abstract]
Lead Optimization Strategies and Tactics Applied to
the Discovery of Melanin Concentrating Hormone Receptor 1
Antagonists Pp. 1471-1488
Philip R. Kym, Andrew S. Judd, John K. Lynch, Rajesh Iyengar,
Anil Vasudevan and Andrew J. Souers
[Abstract]
Computational Medicinal Chemistry
Guest Editor: Hugo O. Villar
Editorial Pp. 1489-1490
Designing Drugs on the Internet? Free Web Tools and
Services Supporting Medicinal Chemistry Pp. 1491-1501
Peter Ertl and Stephen Jelfs
[Abstract]
Complementarity Between Public and Commercial Databases:
New Opportunities in Medicinal Chemistry Informatics
Pp. 1502-1508
Christopher Southan, Péter Várkonyi and
Sorel Muresan
[Abstract]
Computational Techniques in Fragment Based Drug Discovery
Pp. 1509-1513
Hugo O. Villar and Mark R. Hansen
[Abstract]
Shapes of Things: Computer Modeling of Molecular Shape
in Drug Discovery Pp. 1514-1524
Santosh Putta and Paul Beroza
[Abstract]
Mini Review on Molecular Modeling of P-Glycoprotein
(Pgp) Pp. 1525-1529
Sookhee N. Ha, Jerome Hochman and Robert P. Sheridan
[Abstract]
Connecting Small Molecules to Nuclear Receptor Pathways
Pp. 1530-1536
Kristina Hettne, Montserrat Cases, Scott Boyer and Jordi
Mestres
[Abstract]
In-Silico Nanobio-Design. A New Frontier
in Computational Biology Pp. 1537-1540
Raul E. Cachau, Fernando D. Gonzalez-Nilo, Oscar N. Ventura
and Martin J. Fritts
[Abstract]
Molecule
of Month Pp. 1541-1542
Abstracts
[Back to top]
Editorial
Melanin-concentrating hormone (MCH) is a neuropeptide that
plays a role in multiple physiological processes, including
the regulation of feeding behavior and energy balance. Since
the discovery of the first MCH receptor in 1999, referred
to as MCHr1, reports of mice deficient in both the MCH peptide
as well as the receptor have been disclosed. Genetically altered
mice lacking the gene encoding MCH are hypophagic, lean, and
maintain elevated metabolic rates while those lacking the
gene encoding MCHr1 maintain elevated metabolic rates yet
remain lean despite hyperphagia on a normal diet. While some
differences are apparent in the two phenotypes, the possible
result of other functional peptides being derived from the
prepro MCH gene, both results support a role for MCHr1 antagonism
in the treatment of obesity. This and other data have since
fueled an aggressive effort by the pharmaceutical industry
to identify small molecule antagonists that are suitable for
the mentioned indication. Since the first disclosure of Takeda’s
T-226296, which appeared in print in 2002, a large number
of diverse small molecule antagonists has been reported. Many
of these have demonstrated dose dependent weight loss and/or
food intake in various animal models. Interestingly, some
reported compounds cause weight loss while not affecting food
intake, which raises questions regarding the role of increased
energy expenditure. Finally, MCHr1 has been implicated in
behavioral roles, and significant effort has been expended
in deriving utility from MCHr1 antagonists as anti-depressants
and anxiolytics as well. This collection of accounts will
focus on the original indication for MCHr1 antagonists.
Seminal work preceding the development of small molecule antagonists
was provided by the identification of receptor selective peptide
agonists and antagonists. These peptides were then used to
delineate a number of functions that were dependent on receptor
activation or antagonism. In the first contribution, Maria
Bednarek, of Merck Research Laboratories, provides a synopsis
of efforts to identify these peptides. Additionally, a description
of the SAR is included, along with the results of several
in vitro and in vivo studies. This paved
the way for small molecule efforts at the same company, and
Robert DeVita describes some of these efforts in the subsequent
manuscript. By first identifying novel 2-aminoquinoline hits
from a high-throughout screen (HTS), DeVita’s group
was able to optimize the hit compounds into potent leads that
were suitable for mechanism of action studies in rats.
A similar hit-to-lead story emerged from the Schering Plough
group, which has published a multitude of high quality manuscripts
describing their efforts in the MCHr1 field. A portion of
their strategy relied on the identification of suitable replacements
for a biphenyl aniline, a moiety with known mutagenic properties.
As described by Mark McBriar, this led to the identification
of a highly potent and novel class of bicyclohexyl ureas.
In order to optimize these compounds for brain penetration
and, ultimately, in vivo efficacy, the Schering group
relied on a medium throughput ex vivo receptor occupancy
assay. This method allowed for the identification of compounds
that delivered a considerable reduction in food intake when
dosed in diet-induced obese mice (DIO), and the authors were
able to clearly demonstrate the correlation between ex
vivo binding and longer term in vivo effects.
In the fourth contribution, Don Hertzog and Dave Whitty from
Glaxo Smithkline provide an overview of their efforts to develop
a thieno[3,2-d]pyrimidinone class of MCHr1 antagonists.
They describe the systematic optimization of an HTS lead,
leading to the identification of a compound class with excellent
receptor affinity as well as brain penetration in mice. Additionally,
members of this class are among the most efficacious of any
MCHR1 antagonists reported, with up to 17% weight loss observed
upon once daily oral dosing in DIO mice.
The final contribution is from Phil Kym and co-workers of
Abbott Laboratories. Their story begins similarly with hit-to-lead
efforts, and then transitions into the description of an aggressive
safety screen against cardiovascular liabilities. Because
of the challenges in addressing cardiovascular safety in the
current environment, especially when considering the at-risk
patient population, the work performed by this lab represents
a highly integrated method for triaging compound classes with
respect to safety early in a discovery effort.
The search for safe and effective MCHr1 antagonists for the
treatment of obesity, along with other indications, continues
to be a tight race within the industry. Compounds from two
different companies are believed to be in the clinic, and
the industry is undoubtedly watching to see the respective
outcomes. Given the lack of a widely used treatment for obesity,
in addition to the exciting prospects of MCHr1 antagonism,
it is clear that this race will continue for some time.
I am grateful to the contributors for putting forth high quality
accounts of the research that occurred in their respective
laboratories. I expect this issue to be quite useful to those
in the field, and believe that it will provide a valuable
set of case-studies for any medicinal chemist.
Dr. Andrew J. Souers
Abbott Laboratories
100 Abbott Park Road
Abbott Park, Il 60064-6099
USA
Email: Andrew.souers@abbott.com
[Back to top]
Peptide Ligands for the Melanin-Concentrating Hormone
(MCH) Receptor 1
Maria A. Bednarek
The melanin-concentrating hormone receptor 1 (MCH-1R) has
been recognized as a receptor which mediates effects of the
endogenous melanin-concentrating hormone (MCH) on appetite
and body weight gain in rodents. In the last several years,
a number of hMCH analogs have been designed which were potent
and selective ligands for hMCH-1R. These peptidic agonists
and antagonists have served as research tools in animal studies
that showed a key role of the MCH-1R in the development of
obesity and proved that MCH-1R antagonism can produce anti-obesity
effects in rodents.
[Back to top]
Aminoquinoline Melanin-Concentrating Hormone 1-Receptor
(MCH1-R) Antagonists
Robert J. DeVita
Structure-activity relationships of a 4-aminoquinoline MCH-1R
antagonist lead series were explored by synthesis of analogs
with modifications at the 2-, 4- and 6-positions of the original
HTS hit. Improvements to the original screening lead were
made by addition of lipophilic groups at the 2-position and
biphenyl, cyclohexyl phenyl and hydrocinnamyl carboxamides
at the 6-position. Viable modifications of the 4-amino group
were limited and did not allow further optimization of the
physical-chemical properties of this class of compounds. Transposition
of the 4-amino group to the 2-position of the quinoline core
structure provided the 2-aminoquinoline lead class with similar
MCH1R binding affinity. A series of 2-aminoquinoline compounds
was prepared and evaluated in MCH-1R binding and functional
antagonist assays. Small dialkyl, methylalkyl, methylcycloalkyl
and cyclic amines along with 3-substituted pyrrolidines were
tolerated at the quinoline 2-position. The in vivo
efficacy of compound A was explored and compared
to that of a related inactive compound B
to determine their effects on food intake and body weight
in rodents. The biological activities of this matched active
–inactive pair provide in vivo proof of concept
in rodents that antagonism of MCH1R by a 2-aminoquinoline
MCH1R antagonist which led to a reduction of food intake in
an acute feeding assay paradigm.
[Back to top]
Melanin Concentrating Hormone Receptor Antagonists
as Antiobesity Agents: From M2 to MCHR-1
Mark D. McBriar
Melanin concentrating hormone (MCH) is a cyclic, nonadecapeptide
expressed in the CNS of all vertebrates that regulates feeding
behavior and energy homeostasis. The MCH-1 receptor (MCH-R1)
has been identified as a key target in MCH regulation, as
small molecule antagonists of MCH-R1 have demonstrated activity
in vivo. Herein, we chronicle our efforts to optimize
a hit identified via high throughput screening of our proprietary
compound library. Several challenges such as selectivity over
other receptors, toxicity of a potential metabolite and determining
receptor occupancy via a medium throughput assay will be reviewed.
[Back to top]
Biphenyl Amides and Isosteres as MCH R1 Antagonists
Donald L. Hertzog and David R. Witty
The pursuit of MCH R1 antagonists for the treatment of obesity
has become an active area of research for many pharmaceutical
companies. The evidence supporting the use of MCH R1 antagonists
for the treatment of obesity is ample, and the recent demonstration
of MCH R1 antagonists’ efficacy in animal models of
obesity has served to augment earlier studies involving MCH
peptide and transgenic animals. We report herein our search
for MCH R1 antagonists from the discovery of a biphenyl amide
by high throughput screening, through the optimization of
the biphenyl amide to a series of constrained aryl-substituted
thienopyrimidinones, and extending the application of the
thienopyrimidinone substructure to other series of MCH R1
antagonists. Importantly, these MCH R1 antagonists have demonstrated
efficacy in animal models of obesity through once-daily oral
administration at low doses.
[Back to top]
Lead Optimization Strategies and Tactics Applied to
the Discovery of Melanin Concentrating Hormone Receptor 1
Antagonists
Philip R. Kym, Andrew S. Judd, John K. Lynch, Rajesh Iyengar,
Anil Vasudevan and Andrew J. Souers
The discovery of small molecule melanin concentrating hormone
receptor (MCHr1) antagonists as novel therapeutic agents for
the treatment of obesity has been actively pursued across
the pharmaceutical industry. While multiple chemotypes of
small molecule MCHr1 antagonists have been identified and
shown to deliver weight loss in animal models of obesity,
many of these lead compounds have been found to cross-react
with the hERG channel and/or demonstrate deleterious effects
on cardiovascular hemodynamic parameters. This review describes
an approach to rapidly identifying safer MCHr1 antagonists
by placing assays to assess cardiovascular safety early in
the lead optimization compound prioritization process. Ultimately,
despite putting significant effort toward the discovery of
a MCHr1 antagonist for the treatment of obesity, we were unable
to deliver a candidate compound that attained an acceptable
therapeutic index (TI = 30-100) in our in vivo models.
Our inability to identify a compound with an acceptable therapeutic
index was driven by two primary factors: 1) high levels of
sustained drug exposure in the brain was required to achieve
efficacy; and 2) many small molecule MCHR1 receptor antagonists
suffer from receptor cross-reactivity that leads to cardiovascular
toxicity at low multiples of their therapeutic plasma concentration.
[Back to top]
Editorial
Computational chemistry has become a pervasive tool for the
medicinal chemistry work because the entire process of drug
discovery is becoming without question information intensive.
The challenge for the chemist today is to navigate through
the deluge of information, select compounds for synthesis
and create the most informative series of analogs that can
be made to optimize the chemical series. Even when knowledgeable
scientists may be available to determine the priority of compounds
for synthesis, the decision process could be laborious, particularly
when large datasets have to be considered. Predominantly,
expertise is scarce or unevenly distributed, particularly
at the on-set of new projects.
The way in which this data intensive paradigm for drug discovery
presents a challenge for traditional chemoinformatics has
been given considerable attention [1] and present a significant
opportunity for research [2].
In very broad terms, computational chemistry techniques are
used for modeling or data mining and management. There is
some convergence between the two. In recent past, two areas
in modeling have received the most attention. One is the problem
of virtual screening, which is easy to understand, generates
considerable interest. The other is the modeling of metabolic
and pharmacokinetic processes.
Accurate prediction of binding affinity and binding mode are
crucial in drug design, as decisions as to which compounds
or compound libraries to evaluate next as a lead evolves towards
a drug candidate. Some reviews provide a good overview of
the state of the art in these techniques, indicating their
limitations and applicability [3-7]. The different algorithms
and scoring functions to rank docked compounds have also been
extensively reviewed. [8-10], and docking techniques are generating
a list of successes that can be impressive depending on the
target class [11]. However, efforts to compare the outcomes
of the different techniques are sparse. [12-15].
Another area of modeling that has elicited considerable interest
in the last few years is the prediction of toxicological,
pharmacokinetic or other properties related to preclinical
development. [16-19]. Also metabolism with its multiple implications
for drug-drug interactions receives continuous attention by
the computational teams. [20, 21].
The shift observed in the way in which early drug discovery
is carried out has spurred significant activity in this area.
Predictions are used to tailor libraries and prioritize compounds.
From the calculation of octanol/water partition coefficients
to define pharmacokinetic characteristics to modeling of complex
biological processes predictions are becoming ubiquitous and
means to prioritize directions.
Data management and mining issues are likely to increase in
complexity for the chemist and new generations of tools are
needed to aid in what was done manually in the past. Aligning
experimental and in-silico techniques has been an
on-going goal [22], but systems biology and high content screening
are likely to increase the amount of information handled by
even the simplest of projects [23,24]. and seems the most
effective way to go in some therapeutic indications [25].
The integration of chemical data with bioinformatics is likely
to add yet another dimension to the complexity of the data
sets to be handled [26] and the data to be analyzed [27,28].
The breadth of the developments in computational techniques
applied to drug discovery is extremely wide and it would not
be possible to cover them in a single issue. The areas we
chose to cover in this issue are only a small fraction of
the innovative computational techniques recently put forward
to aid in drug design. We decided to provide a sampling of
the breadth in computational applications covering some areas
less frequently addressed in review form. We start with coverage
of sources of information for the medicinal chemist. Ertl
and Jelfs provide an overview of tools available on the internet
that can be of use to medicinal chemists, while Southan, Varkonyi
and Muresan contrast public and commercial sources of chemical
information. Villar and Hansen review how computational techniques
are applied to an alternative paradigm for drug discovery,
namely fragment based drug design. Many of the techniques
discussed are also powerful to mine chemical data and manage
it. Putta and Beroza offer an insight into the influence of
shape in drug discovery. The most basic feature of the molecules
can provide simple yet valuable tools in drug discovery. Ha,
Hochman and Sheridan show how molecular modeling efforts,
mostly in homology modeling and QSAR studies, have increased
our understanding of a fundamental ion pump, Pgp. Hettne,
Cases, Boyer and Mestres show how a systems biology type approach
is being used to connect small molecules to biological pathways,
with particular focus to pathways involving members of the
nuclear receptor family. Finally, Cachau, Gonzalez-Nilo, Ventura
and Fritts provide a view of the computational drug design
to come, by analyzing the applications of computational techniques
to nanotechnology issues and how they are likely to affect
medicinal chemistry research. This is a broad sampling to
drive the point that computational techniques are not optional
in drug discovery, but the methods and situations when we
choose to apply them are as varied as the technologies available
for lead discovery and optimization.
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Hugo O. Villar, Ph.D., MBA
ChemApps, an Altoris, Inc. Project
[Back to top]
Designing Drugs on the Internet? Free Web Tools and
Services Supporting Medicinal Chemistry
Peter Ertl and Stephen Jelfs
The drug discovery process is supported by a multitude of
freely available tools on the Internet. This paper summarizes
some of the databases and tools that are of particular interest
to medicinal chemistry. These include numerous data collections
that provide access to valuable chemical data resources, allowing
complex queries of compound structures, associated physicochemical
properties and biological activities to be performed and,
in many cases, providing links to commercial chemical suppliers.
Further applications are available for searching protein-ligand
complexes and identifying important binding interactions that
occur. This is particularly useful for understanding the molecular
recognition of ligands in the lead optimization process. The
Internet also provides access to databases detailing metabolic
pathways and transformations which can provide insight into
disease mechanism, identify new targets entities or the potential
off-target effects of a drug candidate. Furthermore, sophisticated
online cheminformatics tools are available for processing
chemical structures, predicting properties, and generating
2D or 3D structure representations - often required prior
to more advanced analyses. The Internet provides a wealth
of valuable resources that, if fully exploited, can greatly
benefit the drug discovery community. In this paper, we provide
an overview of some of the more important of these and, in
particular, the freely accessible resources that are currently
available.
[Back to top]
Complementarity Between Public and Commercial Databases:
New Opportunities in Medicinal Chemistry Informatics
Christopher Southan, Péter Várkonyi and
Sorel Muresan
The last two years have seen a dramatic expansion in public
cheminformatics, as exemplified by the approximate five-fold
growth of PubChem from over 50 contributing data sources.
Consequently, medicinal chemists who were hitherto limited
to commercial databases now also have access to public sources
that they can download and/or query directly over the Web.
The range of public sources, particularly where they link
out to structured bioinformatic and biological data, already
offer utilities that have no commercial equivalent. This work
reviews compound content comparisons between selected public
and commercial databases that capture bioactive content. We
focused particularly on those that specify relationships between
compounds and their protein targets. Our stringent filtering
produced lower unique compound numbers than those reported
for individual databases and thereby facilitated standardised
comparisons of content. The resultant matrix shows the pairwise
comparison of each database and selected subsets. Overall,
this showed an unexpected degree of non-overlap, thereby emphasising
the complementarity gained from combining public and commercial
sources. This conclusion is supported by a Venn-type analysis
of GVKBIO, WOMBAT (both commercial) and PubChem (public).
These databases show not only overlap but also unique bioactive
content in each case because of their different strategies
for source selection and data collection.
[Back to top]
Computational Techniques in Fragment Based Drug Discovery
Hugo O. Villar and Mark R. Hansen
Fragment based drug discovery is gaining acceptance as a complement
to other more established techniques to identify leads and
optimize drug candidates. In this review we illustrate areas
where fragment based drug discovery has had an impact and
point to some examples that show how fragment based analysis
is being applied to new arenas.
The traditional uses of computational methods in fragment
based for lead discovery and optimization and for risk assessment
are briefly summarized. The application of fragment analysis
for the definition of bioisosteric replacements are discussed
together with techniques to characterize the diversity of
chemical libraries based on fragment distribution.
[Back to top]
Shapes of Things: Computer Modeling of Molecular Shape
in Drug Discovery
Santosh Putta and Paul Beroza
We review recent advances in computer modeling of molecular
shape in drug discovery. We summarize the ways of representing
shape computationally, discuss the various means of aligning
molecules and shapes, consider the various ways of scoring
similarity of shapes, and describe the ways in which these
shapes can be used to construct molecular descriptors. Finally,
we evaluate the success of these methods to date, suggest
when they are best applied, and provide our recommendations
for the direction of future work.
[Back to top]
Mini Review on Molecular Modeling of P-Glycoprotein
(Pgp)
Sookhee N. Ha, Jerome Hochman and Robert P. Sheridan
Membrane bound P-glycoprotein (Pgp) acts as an active transport
pump. It plays a major role as a cause of multidrug resistance
(MDR) and acts as a component of the blood-brain barrier.
Pgp transports a wide variety of structurally unrelated compound
from the cell interior into the extracellular space. Recent
molecular modeling efforts, mostly in homology modeling and
QSAR studies, have brought some understanding to the interactions
between the protein and the drugs at the atomic level. We
review the recent developments from the point of view of methodology.
[Back to top]
Connecting Small Molecules to Nuclear Receptor Pathways
Kristina Hettne, Montserrat Cases, Scott Boyer and Jordi
Mestres
Many efforts are currently being made to connect small molecules
to target proteins by extracting pharmacological data from
bibliographic sources and storing them in annotated chemical
libraries. Here, small molecules are further connected to
biological pathways, with particular focus to pathways involving
members of the nuclear receptor family. The results bring
to light the relative importance for molecules on gaining
selectivity at the target level, when the target has an intrinsic
promiscuity at the pathway level, and highlight the implications
for drug discovery to address current challenges related to
poor drug efficacy and toxicity. Details on the main limitations
encountered during the molecule-to-target-to-pathway annotation
process are also discussed.
[Back to top]
In-Silico Nanobio-Design. A New Frontier
in Computational Biology
Raul E. Cachau, Fernando D. Gonzalez-Nilo, Oscar N. Ventura
and Martin J. Fritts
Nanobiology is a fast-emerging discipline that brings the
tools of nanotechnology to the biological sciences. The introduction
of new techniques may accelerate the development of highly
specific biomedical treatments, increase their efficiency,
and minimize their side effects. Introducing foreign bodies
into the complex machinery of the human body is, however,
a great and humbling challenge, as past experience has shown.
In order for nanobiology to reach its full potential, we must
devise a means to alter the properties of nanoparticles, as
expressed in the human body, in a predictable manner. Computer-aided
methods are the natural option to speed up the development
of these technologies. Yet, the procedures for annotation
and simulation of nanoparticle properties must be developed
and their limitations understood before computational methods
can be fully exploited. In this review we will compare the
state of development of nanoscale simulations in the biological
sciences to that of the computer-aided drug design efforts
in the past, tracing a historical parallel between both disciplines.
From this comparison, lessons can be learned and bottlenecks
identified, helping to speed up the development of computer-aided
nanobiodevice design tools.
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