Systems biology in drug discovery and development

Ellen L. Berg
<span title="">2014</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="" style="color: black;">Drug Discovery Today</a> </i> &nbsp;
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more &raquo; ... etary rights. While the advice and information in this book are believed to be true and accurate at the date of going to press, neither the authors nor the editors nor the publisher can accept any legal responsibility for any errors or omissions that may be made. The publisher makes no warranty, express or implied, with respect to the material contained herein. Cover illustration: See Figure 1 of Chapter 13 for more information. Printed on acid-free paper Humana Press is part of Springer Science+Business Media ( v Preface Currently, the drug discovery industry has reached the bottleneck. Adverse drug reactions (ADRs) are one of the leading causes of death and illness in the US. In the mean time, although the industry is spending a tremendous amount of money and time, high-profile drug withdrawals are increasing, with fewer FDA approvals of new drugs. The "one-drugfits-all" model has not been successful. There is an urgent need to change the current drug discovery and development process that has high cost, low efficacy, and high ADRs. It is necessary to develop personalized medicine that treats whole systems and brings the right drug to the right patient with the right dosages. Systems biology emerged with the realization that genes, molecules, tissues, and organs do not work alone but interact with each other in a whole system. Combined with pharmacogenomics studies, systems biology would provide a holistic and thorough understanding of health and medicine. Such understanding would change the emphasis of medicine from diseases to humans and enable the transformation from disease treatment to prevention and health promotion. This book has several features that readers may find helpful to their work. First of all, it focuses on translational methods through applying systems biology approaches directly in drug development and clinical practice. One of the major challenges that needs to be resolved in current bioscience is the translation of basic studies into better clinical outcomes. This book is written in response to this challenge through highlighting the development of translational medicine based on systems biology. We hope that these approaches may help make some breakthroughs and advancement toward the realization of personalized medicine, which is also the second feature of the book. That is, most of the methods and protocols described in the book are geared toward the development of individualized therapeutics. The third feature is that this book provides both practical methods and comprehensive resources that can be used for solving complex problems in medicine. A wide range of approaches are introduced with problem-solving objectives, from theoretical and computational analyses to experimental steps. The fourth feature is that this book integrates the advancement of science with innovative technologies. While the first part of the book describes cutting-edge technologies and methods in the field, the second part illustrates how the technologies can be applied in science for disease understanding and therapeutic discovery. The first part of the book introduces basic and novel concepts, as well as advanced technologies in systems biology for efficient drug discovery and development. Such concepts include proteomics, cell behavior, interactomes, and multi-drug targets. The technologies include computational modeling, Bayesian networks, translational bioinformatics, quantitative proteomics methods, microarrays, and RNA interference (RNAi). These technologies can help us with the identification of biomarker genes and pathways and understanding the interactions among genes, drugs, and diseases. One of the potential results from systems biology studies is the identification of novel drugs tailored for individuals. Concepts such as proteomics, toxicoproteomics, vi Preface epigenetics, and their roles in systematic drug target discovery and clinical trial design are introduced in the first chapter of this book. For instance, current proteomics technologies include two-dimensional gel electrophoresis (2D-GE), mass spectrometry (MS), and protein arrays. Based on proteomic studies, multiple-target approaches are novel ways for the design of drugs against atherosclerosis, cancer, depression, psychosis, and neurodegenerative diseases (see Chapter 2). Novel computational and mathematical modeling are the essential methods for dealing with complex proteomic data and understanding of genetic interaction networks involved in these processes. Quantification approaches are important for the identification of protein biomarker signatures and the study of interactomes. For example, stable isotope labeling by amino acids in cell culture (SILAC) is a quantitative proteomics method (see Chapter 3). It can be combined with high-resolution MS as a potent tool for functional analyses. In combination with RNAi, SILAC can address many of the systems-wide approaches that were previously impossible. Better understanding of intracellular and cellular circuits would contribute to systems biology approaches to drug discovery. Mathematical modeling of molecular modules in a cell can link intracellular molecular machinery and cellular activity to enable the understanding of cell behavior. The cell behavior includes avoidance reaction, escape reaction, conjugation, chemotaxis, cell division, stochastic ball movement, search reaction, and Ca 2+ concentration-dependent movement (see Chapter 4). The elucidation of regulatory networks and pathways from proteomic data reveals how proteins regulate each other, which is important for drug design. Computational methods to the understanding of the functional roles of cellular networks include "static" models, as well as dynamical and stochastic simulations (see Chapter 5). These methods are useful for interpretation of high-throughput interaction data, finding gene expression patterns, and building predictive models. For instance, Gaussian Bayesian network methodology is useful for the analyses of static and dynamic time series data (see Chapter 6). Bayesian network inference methods in the analysis of flow cytometry data can be used to evaluate regulatory network topology. Furthermore, analyses of the large-scale self-regulatory behavior of the cell may help establish comprehensive models of the cell and genes action (see Chapter 7). Such methods for analyzing microarray data include principal component analysis (PCA), clustering, tree building, self-organizing map (SOM), and bootstrapping. Based on these data mining approaches, translational bioinformatics is a powerful method to bridge the gap between systems biology research and clinical practice. Translational bioinformatics would bring novel insights in the identification of biomarkers and systemic interactions. Methods of data integration and data mining can provide decision support for both researchers and clinicians (see Chapter 8). Part II of this book focuses on the application of these methods in the translation of systems biology into understanding of disease states and development of personalized therapeutics. These diseases include cardiovascular disease, cancer, lupus erythematosus, influenza, drug abuse, and brain injury. Most of these diseases have close associations with certain responses such as inflammation and immunological reactions. For example, inflammation is a complex response involved in many diseases including rheumatoid arthritis, asthma, cancer, diabetes, atherosclerosis, Alzheimer's, and obesity. Translational applications of computational simulations applied to inflammation are reviewed in this book, such as agent-based modeling (ABM) and equation-based modeling (EBM) (see Chapter 9). Translational systems biology modeling efforts at various levels, from the systemic level to the cellular level are described.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="">doi:10.1016/j.drudis.2013.10.003</a> <a target="_blank" rel="external noopener" href="">pmid:24120892</a> <a target="_blank" rel="external noopener" href="">fatcat:jynrkxunqzh2tkm32rlv6jj3ri</a> </span>
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