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
Trends in High-Performance Computing Requirements
for Computer-Aided Drug Design Pp. 2-12
George Vacek, Dave Mullally and Knute Christensen
[Abstract]
Changing Paradigms in Drug Discovery: Scientific
Business Intelligence™ and Workflow Solutions
Pp. 13-22
Shikha Varma-O’Brien, Frank K. Brown, Andrew LeBeau
and Robert D. Brown
[Abstract]
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]
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]
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]
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]
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]
Abstracts
[Back to top]
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
[Back to top]
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).
[Back to top]
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.
[Back to top]
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.
[Back to top]
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.
[Back to top]
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.
[Back to top]
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).
[Back to top]
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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