Current
Computer-Aided Drug Design
ISSN: 1573-4099
Current Computer-Aided
Drug Design
Volume 4, Number 1, March 2008
Contents
Evolving Paradigms in Drug Design and Discovery
Guest Editor: Shahul H. Nilar
Editorial Pp. 1
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Trends in High-Performance Computing Requirements for
Computer-Aided Drug Design, 2008, 4, 2
George Vacek, Dave Mullally and Knute Christensen
[Abstract] [Purchase
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Changing Paradigms in Drug Discovery: Scientific
Business Intelligence™ and Workflow Solutions,
2008, 4, 13
Shikha Varma-O’Brien, Frank K. Brown, Andrew LeBeau
and Robert D. Brown
[Abstract] [Purchase
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Novel Algorithms for the Identification of Biologically
Informative Chemical Diversity Metrics Pp. 23-34
Bhargav Theertham, Jenna L. Wang, Jianwen Fang and Gerald
H. Lushington
[Abstract] [Purchase
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Novel Rule-Based Method for Multi-Parametric Multi
Objective Decision Support in Lead Optimization Using KEM
Pp. 35-45
Nathalie Jullian and Mohammad Afshar
[Abstract] [Purchase
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PET and SPECT Imaging of Tumor Biology: New Approaches
Towards Oncology Drug Discovery and Development Pp.
46-53
Marcian E. Van Dort, Alnawaz Rehemtulla and Brian D. Ross
[Abstract] [Purchase
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Applications of Computer-Aided Pharmacokinetic
and Pharmacodynamic Methods from Drug Discovery Through Registration
Pp. 54-66
Jennifer Q. Dong, Bin Chen, Megan A. Gibbs, Maurice Emery
and John P. Gibbs
[Abstract] [Purchase
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Computational Strategies to Predict Effect of P
Glycoprotein Transporter Efflux and Minimize its Impact on the
Penetration of Drugs into the Central Nervou System (CNS)
Pp. 67-75
Sanjay Srivastava
[Abstract]
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Abstracts
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Editorial
The cost of bringing a new drug to market has been estimated
to be close to a billion US dollars. With the recent failure
of promising candidates late in the clinical trial stage and
the need to add caution to the use of certain approved drugs,
it has become necessary to review and critique the techniques
currently used in drug discovery, including the area of computational
molecular design.
The theme of this guest issue is “Evolving Paradigms in
Drug Design and Discovery”. Beyond the traditional approaches
to drug design, newer techniques such as novel decision-making
algorithms that help identify new structural candidates and
identify chemical diversity metrics having biological information
encoded are fast becoming an integral part of the mainstream
drug discovery programs.
The need for efficient data mining methods, not only at the
early research stages, but also during clinical trials is paramount
as the decision making processes in bringing a successful drug
into market are dynamic and encompass the results of the various
constituent experiments. This can prove difficult within an
enterprise environment. The incorporation and use of customized
automated workflows is a tool that can address such issues successfully.
There has been a tendency to assume or take for granted the
in-silico workhorses of Computer-Aided Drug Design – the
computers that evaluate the formulas and provide the numerical
results of complicated algorithms to be the successful approach.
High Performance Computing has evolved over the past years in
terms of processor speeds, networking and clustering configurations
and the efficiency of the operating systems. It is important
to review the impact of these advances in the area of molecular
design.
An understanding of efflux transporter mechanisms is fast becoming
an area of active interest in drug design and discovery. A review
on the computational modeling of the P-glycoprotein (Pgp) transporter
using pharmocophoric and quantitative structure-activity relationship
(QSAR) techniques within the context of optimizing the central
nervous system penetration has been included. The evolving trend
of introducing computational pharmacokinetic and pharmacodynamic
techniques early in the drug discovery process necessitates
that the available methodologies are reviewed. Commercially
available software packages and applications in the area of
drug discovery have been discussed in this issue.
The application of techniques in the area of oncology-based
drug design and discovery such as positron emission tomography
(PET) and single photon emission computed tomography (SPECT)
imaging studies in the area of tumor biology has been reviewed.
Such techniques, when incorporated into a drug discovery paradigm,
can reduce the time taken to discover potential liabilities
in the metabolism pathways of drug candidates.
In summary, it is hoped that this issue will illustrate the
many aspects of various multi-disciplinary inputs that are increasingly
becoming mainstream technologies in bringing a successful drug
into the commercial arena. It is with this focus that this guest
issue of Current Computer-Aided Drug Design reviews the areas
of change in computer hardware, workflow logistics, novel methods
and algorithms in drug design, together with computational pharmacokinetics
and the contributions of imaging techniques in the evolving
drug design, discovery and development processes.
Shahul H. Nilar
(Guest Editor)
Current Computer-Aided Drug Design
Research Investigator/Computational Chemistry
Novartis Institute for Tropical Diseases
10 Biopolis Road, #05-01 Chromos
Singapore 138670
Email: Shahul.Nilar@novartis.com
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Trends in High-Performance Computing Requirements for
Computer-Aided Drug Design
George Vacek, Dave Mullally and Knute Christensen
Computer-aided drug design (CADD) has become a mainstream
component of the drug discovery and development process. High
Performance Computing (HPC) provides the power that allows CADD
researchers to explore more designs in less time, and some of
the greatest improvements in CADD result directly from advances
in HPC. This paper examines some of the more significant trends
in HPC that influence computer-aided drug design (CADD).
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Changing Paradigms in Drug Discovery: Scientific Business Intelligence™
and Workflow Solutions
Shikha Varma-O’Brien, Frank K. Brown, Andrew LeBeau
and Robert D. Brown
Workflow solutions driven by data pipelining are increasingly
becoming popular for accessing, aggregating and analyzing disparate
data to make informed and intelligent decisions. Uses of workflow
technologies which facilitate business intelligence (BI) improve
productivity, decision making and research efficiency. In order
to provide BI in a scientific or clinical based organization,
it is imperative that the application or workflow technology
must be compatible with multiple data types and formats, be
able to analyze the data and make it available throughout the
organization. We term this as Scientific Business Intelligence
(SBI) and discuss how modeling, simulations and informatics
software, integrated with open and standards-based scientific
operating platform (SOP), can deliver scientifically-relevant
BI solutions. We illustrate SBI with several examples encompassing
all levels of users within an organization.
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Novel Algorithms for the Identification of Biologically Informative
Chemical Diversity Metrics
Bhargav Theertham, Jenna L. Wang, Jianwen Fang and Gerald
H. Lushington
Despite great advances in the efficiency of analytical
and synthetic chemistry, time and available starting material
still limit the number of unique compounds that can be practically
synthesized and evaluated as prospective therapeutics. Chemical
diversity analysis (the capacity to identify finite diverse
subsets that reliably represent greater manifolds of drug-like
chemicals) thus remains an important resource in drug discovery.
Despite an unproven track record, chemical diversity has also
been used to posit, from preliminary screen hits, new compounds
with similar or better activity. Identifying diversity metrics
that demonstrably encode bioactivity trends is thus of substantial
potential value for intelligent assembly of targeted screens.
This paper reports novel algorithms designed to simultaneously
reflect chemical similarity or diversity trends and apparent
bioactivity in compound collections. An extensive set of descriptors
are evaluated within large NCI screening data sets according
to bioactivity differentiation capacities, quantified as the
ability to co-localize known active species into bioactive-rich
K-means clusters. One method tested for descriptor selection
orders features according to relative variance across a set
of training compounds, and samples increasingly finer subset
meshes for descriptors whose exclusion from the model induces
drastic drops in relative bioactive colocalization. This yields
metrics with reasonable bioactive enrichment (greater than 50%
of all bioactive compounds collected into clusters or cells
with significantly enriched active/inactive rates) for each
of the four data sets examined herein. A second method replaces
variance by an active/inactive divergence score, achieving comparable
enrichment via a much more efficient search process.
Combinations of the above metrics are tested in 2D rectilinear
diversity models, achieving similarly successful colocalization
statistics, with metrics derived from the active/inactive divergence
score typically outperforming those selected from the variance
criterion and computed from the DiverseSolutions software.
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Novel Rule-Based Method for Multi-Parametric Multi Objective
Decision Support in Lead Optimization Using KEM
Nathalie Jullian and Mohammad Afshar
This paper focuses on the recent development of rule-based
methods and their applications to the drug discovery process.
For a given target, the path for designing new drugs with a
lower attrition rate is based on an effective mining of the
huge amount of experimental in vitro and in vivo
data which has been collected. These data often come in various
formats, from many different areas such as chemistry, biology,
pharmacology, toxicity and extraction of the critical information
is not an easy task.To guide the multi-objective optimization,we
have developed a decision-support system (KEM®),
based on the Galois lattices theory and constraint satisfaction
programming (CSP). After a brief overview of machine learning
applications, we will describe the methodology used in KEM for
data mining and prediction. Two examples of applications in
the drug discovery area will be discussed.
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PET and SPECT Imaging of Tumor Biology: New Approaches Towards
Oncology Drug Discovery and Development
Marcian E. Van Dort, Alnawaz Rehemtulla and Brian D. Ross
Spiraling drug developmental costs and lengthy time-to-market
introduction are two critical challenges facing the pharmaceutical
industry. The clinical trials success rate for oncology drugs
is reported to be 5% as compared to other therapeutic categories
(11%) with most failures often encountered late in the clinical
development process. PET and SPECT nuclear imaging technologies
could play an important role in facilitating the drug development
process improving the speed, efficiency and cost of drug development.
This review will focus on recent studies of PET and SPECT radioligands
in oncology and their application in the investigation of tumor
biology. The use of clinically-validated radioligands as imaging-based
biomarkers in oncology could significantly impact new cancer
therapeutic development.
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Applications of Computer-Aided Pharmacokinetic and Pharmacodynamic
Methods from Drug Discovery Through Registration
Jennifer Q. Dong, Bin Chen, Megan A. Gibbs, Maurice Emery
and John P. Gibbs
Computer-aided pharmacokinetic, pharmacodynamic, and pharmacokinetic/pharmacodynamic
methods are commonly applied to quantify the disposition and
the pharmacological effects of the drug, to explore exposure-response
relationships, and to predict safety and efficacy outcomes.
Use of modeling and simulation throughout the drug development
continuum can support more efficient preclinical and clinical
study design and interpretation. Mechanism-based approaches
where sound biological understanding exists provide meaningful
quantitative comparisons between candidates and are sought to
support science-based decisions. Simulations from these models
allow for scientists to investigate a variety of trial designs
where assumptions are clearly stated. The objectives of this
review article are to describe commercially available PK/PD
software packages and present examples of their application
in drug discovery and development. With industry and regulatory
support, use of exposure response information may optimize the
path to delivery of new medicines to patients. This review is
focused on the most common computer software applications in
discovery through early development (i.e., GastroPlus, Simcyp
Population-based ADME simulator, SAAM II, and WinNonlin), in
development (i.e., NONMEM, ADAPT II, MATLAB, WinBUGS, Trial
Simulator, and Drug Model Explorer), and across the continuum
for data management (i.e., SAS, S-PLUS, and R).
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Computational Strategies to Predict Effect of P Glycoprotein
Transporter Efflux and Minimize its Impact on the Penetration
of Drugs into the Central Nervous System (CNS)
Sanjay Srivastava
Development of a drug involves several aspects, one of which
is an adequate DMPK profile that is related to its absorption,
distribution, metabolism and excretion. The distribution of
the drug to its site of action is partly regulated by several
biological membrane barriers. One such barrier is created
by the brain capillaries of the endothelial cells, also known
as the Blood-Brain-Barrier (BBB). Depending on the therapeutic
action, one may need higher permeation of the drug through
BBB if the site of action is in the CNS, or minimize the entry
through the BBB if this biological target is located in the
periphery. The physicochemical properties of the drug usually
regulate its permeability through the BBB and constitute passive
permeability. However, “non-passive permeation”
may also exist and is affected by other transporter mechanisms
present in the BBB, and may involve both efflux as well as
influx systems. Amongst these, the P-Glycoprotein (Pgp) has
been the most extensively characterized efflux transporter.
The “passive BBB” has been well studied and characterized
by various theoretical groups, but the “non-passive
BBB” (often caused by Pgp, for example) has gained more
attention from computational methodologies in recent years.
This review will provide a brief summary of the computational
strategies that have addressed Pgp efflux inhibition, especially
in the context of optimizing CNS penetration during rational
drug design. The advances in the computational methods that
have modeled the Pgp recognition while addressing non-passive
permeation will be a chief focus, but coverage is also given
to recent and impactful Pgp modeling approaches. These include
computational approaches that analyze data from assays targeting
Pgp in particular or multidrug resistance reversal assays
where Pgp is a chief implicating factor.
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