Current Topics in Medicinal Chemistry
Send Orders for Reprints to reprints@benthamscience.ae
Current Topics in Medicinal Chemistry, 2018, 18, 1745-1754
1745
REVIEW ARTICLE
Systems Biology: A Powerful Tool for Drug Development
Sneha Rai
1
, Utkarsh Raj
2,3
and Pritish Kumar Varadwaj
3,*
1
Division of Biotechnology, Netaji Subhas Institute of Technology, University of Delhi, New Delhi, India;
2
Department
of Biotechnology & Bioinformatics, NIIT University, Neemrana, Rajasthan, India;
3
Department of Bioinformatics &
Applied Sciences, Indian Institute of Information Technology, Allahabad, Uttar Pradesh, India
A R T I C L E H I S T O R Y
Received: June 15, 2018
Revised: August 17, 2018
Accepted: October 07, 2018
DOI:
10.2174/1568026618666181025113226
Abstract: The conventional way of characterizing a disease consists of correlating clinical symptoms
with pathological findings. Although this approach for many years has assisted clinicians in establish-
ing syndromic patterns for pathophenotypes, it has major limitations as it does not consider preclinical
disease states and is unable to individualize medicine. Moreover, the complexity of disease biology is
the major challenge in the development of effective and safe medicines. Therefore, the process of drug
development must consider biological responses in both pathological and physiological conditions.
Consequently, a quantitative and holistic systems biology approach could aid in understanding complex
biological systems by providing an exceptional platform to integrate diverse data types with molecular
as well as pathway information, leading to development of predictive models for complex diseases.
Furthermore, an increase in knowledgebase of proteins, genes, metabolites from high-throughput ex-
perimental data accelerates hypothesis generation and testing in disease models. The systems biology
approach also assists in predicting drug effects, repurposing of existing drugs, identifying new targets,
facilitating development of personalized medicine and improving decision making and success rate of
new drugs in clinical trials.
Keywords: Network models, Dynamical models, Biomarkers, Drug repurposing, Drug combinations, CVD.
1. INTRODUCTION
Over the past several decades there has been numerous
advances in biological science that has led to production of
enormous amount of data relating to genomics, proteomics,
transcriptomics and metabolomics. These techniques aid in
identifying all the genes, proteins and other biological enti-
ties within a biological system. However, the knowledge of
individual molecules within the system is not sufficient to
understand the complexity underlying a biological system.
For better understanding of biosystems, it is important to
study the arrangement of biological entities, interaction be-
tween them and dynamical changes in behavior of these enti-
ties under external factors. Therefore, the concept of systems
biology emerged which consists of computational modeling
of biological systems [1, 2] and hence enable examination of
structure and dynamics of cells, tissues and organisms work-
ing as a system [2].
Despite large amount of investments in the
pharmaceutical sector, there has not been a great increase in
the introduction of successful drugs in the market [3]. The
foremost problem in drug development is the increased attri-
tion rate due to toxicity, development of drug resistance and
inconsistent efficacy of drugs in different individuals due to
*Address correspondence to this author at Department of Bioinformatics &
Applied Sciences, Indian Institute of Information Technology, Allahabad,
Uttar Pradesh, India; E-mail: pritish@iiita.ac.in
varied therapeutic responses [4]. Furthermore, a number of
candidate drugs fail during clinical trials as their mechanism
of action is not completely understood. The reductionist ap-
proaches utilized in medical research by most of the pharma-
ceutical industries provide only limited understanding of
complex diseases like Cancer, Cardiovascular Disease
(CVD) and neurogenerative diseases [5]. These systemic
diseases are regulated by large interconnected signaling net-
works and many of these signaling pathways take part in
pathogenesis of the disease [6, 7]. The feedback mechanisms
and redundancy in these networks hamper the development
of new therapeutics for complex diseases [8, 9]. Therefore,
there is a need for more integrative systemic approach for
complete understanding of disease mechanism and drug re-
sponse as represented in Fig. (1).
The aim of “Systems Biology” is to study physiological
and pathological conditions from the level of regulatory net-
works, signaling pathways, cell, tissue, organs and eventu-
ally the entire organism [10]. Systems biology consists of a
number of approaches and models that aid in studying
biological complexity of various diseases [10]. It provides a
platform to combine a large amount of reductionist data from
genomics, proteomics and/or metabolomics experiments to
generate a network model for studying a disease and devel-
oping new therapeutics for the same. Therefore, this review
focuses on use of systems biology in drug development cov-
ering different approaches (methods) used in systems biol-
ogy and how it assists in network modeling of biological
1873-4294/18 $58.00+.00 © 2018 Bentham Science Publishers