Download VI. Tools Used for Systems Biology and Drug Discovery

BME 1450 System Biology Term Paper, Submitted by Anthony Tam, 991717736
1
Applying Systems Biology to Drug Discovery
(October 2003)
A.Tam

Abstract— Systems biology is emerging as an approach to
drug discovery that will help pharmaceutical companies reduce
development time and costs, and produce more effective drugs
with fewer side effects. Systems biology uses an integrated
approach to study and understand the function of biological
systems as they respond to perturbations in their environment
such as the administration of drugs. This paper will discuss the
typical approach to drug discovery, the challenges of discovery,
how systems biology can be used to meet these challenges, the
tools available, applications, and future steps. The intent of this
paper is to show how systems biology can provide a significant
impact to the drug discovery process.
Index Terms— Biochemistry, biological systems, drugs,
modeling.
I. INTRODUCTION
S
ystems biology has generated excitement in the drug
discovery community; however drug companies for the
most part are not pursing this approach. While the study is
generally agreed to be fruitful, the time it will take for the
research to become useful to drug companies is not known.
Leroy Hood, president of the Institute for Systems Biology,
agrees that the role of pharmaceutical businesses is to apply
successful approaches, not to invent new ones. He further
sees the creation of systems biology companies that could
focus on early stage processes of drug target discovery [1].
II. TYPICAL APPROACH TO DRUG DISCOVERY
The primary approach to drug discovery used today involves
screening vast chemical libraries against a small number of
pharmacologically relevant biological targets [1]. This
technique is known as ultra high throughput screening (ultraHTS) and results in hundreds of thousands of screenings of
chemical reagents per week to look for special biological
activities such as reaction with proteins, cells, DNA, or other
biological samples.
The process usually follows
combinatorial chemistry which is a technique that produces a
vast number of chemical reagents at high speed which may
react positively with the target samples [2]. While there has
been some success with this method and specific molecular
Manuscript received October 28, 2003. A. Tam is with the Institute of
Biomaterials and Biomedical Engineering, University of Toronto, Toronto,
Ontario M5S 3G9 CANADA (phone: 416-977-1170; email:
[email protected] ).
targets have been identified, the number of new discoveries
has been limited [2].
III. THE CHALLENGE WITH DRUG DISCOVERY
The challenge with drug discovery is that uncovering the
structural and functional characteristics of a disease is quite
difficult. Biological systems are complex, inherently nonlinear, dynamic, and interactions occur at spatially different
locations in the body. Simultaneous static and temporal data
need to be measured in topographically different regions.
Some biochemical processes take place within milliseconds
whereas others can take hours or days. Also, treatment for
multifactorial diseases, require that multiple targets or
pathways be affected for successful outcomes [3]. In
addition, biological processes involve interaction of different
types of processes such as biochemical networks coupled to
protein transport, chromosome dynamics, cell migration or
morphological changes in tissues [3]. Biological systems are
made up of elements that can have more than one function
and interact selectively and nonlinearly to produce a
coherent behaviour. The human body has hundreds of
functionally specialized cell types that interact differently
with environmental factors, some leading to disease
development or modulating the effects of administered drugs
[4]. Furthermore, drug-metabolizing enzymes in relation to
disease can vary between human populations [4] altering the
effectiveness and toxicity of a drug from one individual to
another. In some cases, cellular transformations of small
molecules do not require enzymes, and these metabonates
can generate toxically or allergenically active species further
complicating the determination of enzyme-controlled
pathways [5], [6].
Biological systems are also robust, maintaining their state
and functions against external and internal perturbations and
adapting to changes in their environment. Robustness is
achieved through feedback and feed-forward mechanisms,
modularity, redundancy and structural stability. Robustness
models can be seen in systems where multiple genes encode
similar proteins, and several networks have complementary
functions. Cancer cells for example use the normal defences
of a patient’s body such as the metabolism of foreign
compounds to render anti-cancer drugs ineffective [7]. The
challenge with robustness is that in some cases many
perturbations need to be done in combination or in a certain
sequence to achieve a desired output.
BME 1450 System Biology Term Paper, Submitted by Anthony Tam, 991717736
IV. SYSTEMS BIOLOGY
To meet some of these challenges and advance the discovery
process, systems biology takes a comprehensive approach by
studying biological function, cellular processes and diseasemediated processes at a systems-level to understand how
complex and dynamic systems work, find underlying causes,
and research options for treatment [3]. This is done by
integrating bioanalytical and computing technologies with
information from genomics (global gene expression analysis
and whole genome functional analysis), proteomics (protein
structure and function), and metabolomics (measurement of
metabolite concentrations and fluxes and secretions in cells
and tissues that have a direct connection to genetic, protein,
and metabolic activity) to incorporate data such as
structurally defined chemical libraries with specific
biological pathway information [4].
Systems biology
integrates massive quantities of complex data generated by
genomic, proteomic and metabolic analyses to understand
phenotypic variation and build comprehensive models of
cellular organization and function. The objective of studying
complex relationships is to use research findings to focus in
on and better define targets with the intent of developing
more effective therapies [3].
Many diseases, such as Alzheimer’s disease are caused by
multi-molecular interactions, involving more than one
alteration in many genes, gene products, and enzymatic
cascades [3]. Instead of looking for single targets, systems
biology looks at a broader range of related biological
structures hopefully leading to the discovery of compounds
that have common structural and functional properties that
can target common mechanisms of action and pathways for
multiple diseases. For example compounds that target the
GPCR family (key facilitators of cellular signalling cascades)
could have dramatic benefits in a broad spectrum of diseases
[3].
Several approaches are used in systems biology. One
approach is in silico modelling which uses known
relationships to create virtual systems. Real biological data
derived from biochemical properties of gene products are
converted to numerical format and plugged into equations
and algorithms to simulate the system. Refinement of the
model is done by perturbing the system to approximate
genetic alteration or the effects of drug administration. The
difficulty with this method is that it uses starting data from
heterogeneous data types including gene expression, protein
function, and metabolic flux. These inter-relationships are
highly dynamic and non-linear among molecular components
in a target organ or tissue. Researchers contributing to
knowledge bases in this area will need to standardize on data
parameters if they intend to collaborate their findings in
meaningful simulations and models [3]. A model network of
known interactions integrated with gene expression profiles
makes it possible to predict and explain why a gene is turned
on or off in a given state of the network. [8].
2
The approach used by the Institute for Systems Biology is to
study system structure dynamics, control methods, and
design methods, as applied to specific areas of biology [8],
[9].
This approach takes comprehensive and precise
measurements of components in the system along with their
functional outputs to create high-resolution simulation
models. The models will then be refined by iterative
processes of hypothesis-driven wet experimentation [3], [7].
Another approach looks for design patterns between
biological systems and the complex organization circuitry
found in technology such as modularity, feedback control,
and convergent evolution in an attempt to reverse engineer
and create a blueprint for the whole system. Convergent
evolution studies the development of similar structures or
biological pathways in unrelated organisms that evolved in
similar environments, such as the wings on insects and birds
[3]. Network theory also plays a large part in systems
biology; the system can be described as an assembly of
nodes that can be regulated. Once the linkages and
interrelationships are determined, you can make predictions
about how you could change development to achieve desired
outcomes by making modifications to inputs or to the
network itself. By understanding the gene regulatory
network that controls development, you can redesign the
network for new properties, and predict the behaviour of a
network under certain perturbations [1]. Many of these
approaches are complementary in studying the system as a
whole.
Two modelling approaches have been employed in systems
biology: Mechanistic or data-driven, and qualitative or
hypothesis-driven [10], [11]. Mechanistic modelling relies
on experimental data from high throughput methods to
model various biological processes. This method has the
potential to link specific regulatory nodes and pathways
within cells and tissues to underlying causes of disease.
However, limitations of analytical technology in signal
detection and the complexity of interactions makes it
difficult to produce complete solutions for many cellular
processes. Hypothesis-driven modelling aims to bridge gaps
in available data to construct logical models of biological
processes to fit known information. This method uses fuzzylogic rule-based approaches to model gene protein and
metabolic networks. Many organizations are developing new
tools with common descriptors for biological systems
modelling. With sufficient amounts of data, including
temporal information, and development of more advanced
computational tools, these approaches may one day be key
technologies in drug discovery [3].
V. GENOMICS, PROTEOMICS, AND METABOLOMICS
Advances in genomics, such as the Human Genome Project,
have provided much information on systemic gene mutations
and gene relationships.
Through large-scale DNA
sequencing, DNA arrays, and genotyping, vast quantities of
data will be available to understand how perturbations to
genetic code affect the system [1].
BME 1450 System Biology Term Paper, Submitted by Anthony Tam, 991717736
While genes contain the genetic blueprints, encoded proteins
are the biomolecules that perform the functions of the cell
such as regulation and replication. Changes in the abundance
and structure of proteins can be correlated with disease [10].
If the connection between proteins and diseases is known,
specific inhibitors, or activators can be used against the
target protein. Developments of high throughput analytical
tools, such as protein biochips will be necessary to identify
and quantify proteins to investigate their interactions and
measure chemical modifications [10].
Metabolomics research can provide information about the
mechanism of drug action, absorption, distribution,
metabolism, excretion, and prediction of drug toxicity [3].
Metabolomics uses genomic and proteomic advances to
understand how perturbations at the gene and protein level
relate to changes in metabolic flux and enzymatic products.
Statistical pattern analysis of genes and protein expression
are used to aid interpretation of metabolic patterns to reveal
functional differences in silent phenotypes. Obtaining
detailed metabolic maps of such critical systems as tissues,
liver, or kidneys, can dramatically accelerate the drug
development process [3].
VI. TOOLS USED FOR SYSTEMS BIOLOGY AND DRUG
DISCOVERY
High throughput DNA assays and advances in bioinformatics
have produced vast amounts of biological data. The
challenge is to use the data to categorize and find
information about relationships to describe living organisms.
Many gene, protein, and small-molecule interaction
databases have been established for these pursuits.
Databases such as the National Center for Biotechnology
Information, Protein Bank, Kyoto Encyclopaedia of Genes
and Genomes, and the Biomolecular Interaction Network
Database store information about modular and static
biochemical states. Many databases are based on Systems
Biology Markup Language (SBML) that enables researchers
to develop machine-executable models rather than humanreadable forms [7]. To integrate information about disjointed
biological events, a new class of analytical and mathematical
tools will need to be developed, and directed research efforts
will be needed to fill in information gaps. A rigorous
selection process and preparation of normal and diseaserelevant clinical samples for comparative analysis is also
crucial to the successful implementation of systems-level
research. These samples will need to be sorted into welldefined phenotypic categories, and characterized by either
disease or perturbation [3].
Bioanalytical techniques for quantitative analysis of
metabolites are nuclear magnetic resonance (NMR), gas
chromatography coupled with mass spectrometry (GC-MS),
and liquid chromatography coupled with mass spectrometry
(LC-MS) [13]. Measurements from these devices are used to
create metabolic profiles that provide the biochemical status
of the system. These profiles can be used to identify
enzymatic steps in metabolically controlled pathways.
3
Bioanalytical measurements must be taken for both static and
functional components of a system, correlating gene
expression with protein function or physiological profiles
[3].
On the computing resources side, new tools for gathering,
classifying, analyzing, integrating, modelling, simulating,
and ultimately developing treatments will need to be
developed [1]. Accounting tools for cataloguing metabolic
and structural components, and sophisticated statistical
analysis and normalization tools will be needed to integrate
knowledge and data from many sources. One of the
challenges is adapting the tools to accept heterogeneous data
sets, for example signal-based output from spectral intensity
measurements, fluorescence intensity readings, concentration
and digital images [3].
Protein biochips have tremendous potential to speed up
target validation. These chips immobilize proteins on the
chip surface and orientate them to expose biologically active
regions for interaction [10]. Recent developments in low
density protein arrays include the universal protein array
system (UPA) on filter membranes enabling the study of
protein affinities on filters, as well as protein-protein and
protein-ligand interactions. High density protein arrays have
also been developed for the production of cDNA expression,
high-throughput protein expression and large-scale protein
analysis.
These devices employ automated spotting,
immobilization, and measurement of signals for analysis.
One of the major challenges of protein biochips is the supply
of well-characterized proteins and their respective binders
for assay formats [10]. One application of identification
used screening of protein array filters with antibody
fragments to identify disease-specific patterns of antibodyantigen interactions [14]. To study protein-compound
interaction, the compounds need to interact inside the
nucleus with target molecules; protein biochips have the
potential of performing these studies in micro-wells in
solution [10].
VII. APPLICATIONS TO DRUG DISCOVERY
By using a systems-level approach to analyzing differences
between normal and diseased samples, systems biology
could be used to identify new molecular targets or novel uses
for existing molecular targets in connection with diseases.
For example, new relations can be discovered between wellcharacterized proteins and specific diseases, or between
uncharacterized or mutant proteins and diseases.
Understanding how the system responds to perturbations will
provide researchers with a way to discern whether changes in
cellular processes are therapeutic benefits or side effects; this
knowledge would also be useful for regulatory purposes in
the pharmaceutical industry [3].
Desired therapeutic outcomes are often coupled with
unwanted side-effects due to complex relationships in the
signalling cascades of biochemical pathways. Some drugs
are neutralized by feedback systems and therefore treatment
BME 1450 System Biology Term Paper, Submitted by Anthony Tam, 991717736
requires higher doses to be effective; however the sideeffects are also amplified. Advances in signalling research
would allow drug developers to target the appropriate entry
points in the signalling cascade to provide effective therapies
and avoid unnecessary side-effects [3].
Research in
signalling networks is done through comparative analysis of
diseased and normal samples, as well as studies using drug
perturbations. Appropriate selection of therapeutic agents
for parallel perturbation studies would identify differences in
mechanisms and side-effects allowing for development of
more effective drugs [16].
Certain individuals may not respond effectively to certain
drugs, or may have adverse side-effects because diseases can
be caused by differing combinations of genetic and
environmental factors but present the same symptoms – one
drug may only be effective for a fraction of the patients [1].
Leroy Hood believes that systems biology will revolutionize
predictive, preventative, and personalized medicine. It will
be able to predict by identifying variant genes that may
predispose an individual to late-onset diseases, prevent by
designing drugs, or modified proteins or genes to replace
defective genes, and personalize treatment for individuals
[1]. Advances in diagnostic genetic and protein analysis
tools will better classify individuals for appropriate
treatment. At present the cost of sequencing an individual’s
genome is estimated at US$50 million or more, however
Leroy Hood estimates that with advances in the field of
microfluidics, microelectronics, and nanotechnology, this
cost can be brought down to under US$1000 [1].
IX. CONCLUSION
There are many challenges to overcome; however, taking the
systems biology approach to understanding biological
structure and function holds great potential for the drug
discovery process by enabling faster development of more
effective, personalized drugs, with fewer side effects.
REFERENCES
[1]
[2]
[3]
[4]
[5]
[6]
[7]
[8]
[9]
[10]
VIII. FUTURE STEPS
Hood recommends developments in the fields of
microfluidics, microelectronics, and nanotechnology for
integrating, automating and creating high-throughput
production of tools for data production and analysis.
Nanotechnology can be used to sequence individual DNA
molecules, measure RNA and protein concentrations and
protein interactions [1]. He also recommends developing
computational tools for integrating the many different types
of biological information, DNA, RNA, protein, protein
interactions, and the phenotypes that are required by systems
biology approaches.
Further developments include
graphically integrated models that can be converted to
mathematical descriptions of systems [1].
Initiatives are underway for the development of large scale
models, even whole-patient models for specific diseases,
such as obesity and diabetes to predict disease development
and drug discovery [7]. Another futuristic approach is the
use of genetic circuits to control biological processes.
Perhaps a genetic circuit could be devised to sense the level
of a protein when DNA is damaged and switch on other
circuits to compensate [7].
4
[11]
[12]
[13]
[14]
[15]
[16]
S.L. Carnery, “Leroy Hood Expounds the Principles, Practice and
Future of Systems Biology (Interview)”, Drug Discovery Today,
vol. 8, no. 10, pp. 436-438, May 2003
True Force. (2003, October 20) Encyclopedia Robotica [Online]
Available:
http://trueforce.com/encyclopaedia/robot_encyclopaedia-H.htm
E. J. Davidov , J. M. Holland, E. W. Marple, and S. Naylor,
“Advancing drug discovery through systems biology,” Drug
Discovery Today, vol. 8, no. 4 pp. 175-183, February 2003
J.K. Nicholson and I.D. Wilson, “Understanding ‘Global’ Systems
Biology: Metabonomics and Continuum of Metabolism”, Nature,
vol. 2, pp. 668-676, August 2003
M.A. Schwartz, “Chemical aspects of penicillin allergy”, Journal
of Pharmaceutical Sciences, vol. 58, 643–661 (1969).
S.C. Connor, J. Everett, K.R. Jennings, G. Woodnut, and J.K.
Nicholson, “High-resolution 1H NMR spectroscopic studies of the
metabolism and excretion of ampicillin and amoxycillin”, The
Journal of Pharmacy and Pharmacology, vol. 46, 128–134 (1994).
H. Kitano, “Computational Systems Biology”, Nature, vol. 420,
pp. 206-210, November 2002
E. Werner, “Systems biology: the new darling of drug discovery?”,
Drug Discovery Today, vol. 7, no. 18, pp. 947-949, September
2002
H. Kitano, “Systems biology: a brief overview”, Science vol. 295,
pp. 1662–1664 (2002)
A.P. Arkin, “Synthetic cell biology” Current Opinion in
Biotechnology, vol. 12, no. 6, pp. 638–644, 2001, December 2001
R. Somogyi, and L.D. Greller, “The dynamics of molecular
networks: applications to therapeutic discovery” Drug Discovery
Today, vol. 6, no. 24, pp. 1267–1277, December 2001
C.H. Huels, S. Muellner, H.E. Meyer, and D.J. Cahill, “The impact
of protein biochips and microarrays on the drug development
process,” Drug Discovery Today, vol. 7, no. 18 pp. S119-S124,
2002
J.K. Nicholson, J.C. Lindon, E. Holmes, “‘Metabonomics’:
understanding the metabolic responses of living systems to
pathophysiological stimuli via multivariate statistical analysis of
biological NMR spectroscopic data” Xenobiotica, vol. 29, pp.
1181–1189, 1999
H.R. Robinson et al. “Autoantigen microarrays for multiplex
characterization of autoantibody responses”, Nature Medicine, vol.
8, no. 3 pp. 295–301, March 2002
G. Wess, “How to escape the bottleneck of medicinal chemistry”,
Drug Discovery Today, vol. 7, no. 10, pp. 533–535, May 2002
M.J. Marinissen, and J.S. Gutkind, “G-protein-coupled receptors
and signaling networks: emerging paradigms”, Trends in
Pharmacological Sciences, vol. 22, no. 7, pp. 368–376, July 2001