Systems Biology: A Disruptive Technology : Market Research Report

Systems Biology: A Disruptive Technology

Published by: CHI Insight Pharma Reports

Published: May. 1, 2008 - 156 Pages

Chapter 1
INTRODUCTION: 1.1. Scope and Content of This Report; 1.2. Historical Perspective; 1.3. Defining Systems Biology
Chapter 2
TECHNOLOGICAL ASPECTS OF SYSTEMS BIOLOGY: 2.1. Bioanalytical Technologies; 2.2. Regulatory Mechanisms and Organization; 2.3. Bioinformatics Technologies; 2.4. Summary
Chapter 3
BASIC RESEARCH IN SYSTEMS BIOLOGY: 3.1. Network-Based Models and Simulations; 3.2. Protein Networks; 3.3. An Emerging Paradigm for Viewing Health and Disease
Chapter 4
APPLIED RESEARCH IN SYSTEMS BIOLOGY: 4.1. Impacts of Systems Biology on Specific Disease Areas
Chapter 5
MARKET DYNAMICS: 5.1. Approaches of Small-Company Players X; 5.2. Approaches of Selected Drug Discovery and Development Organizations; 5.3. Systems Biology Deals; 5.4. Insight Pharma Reports Systems Biology Survey: Results and Comments
Chapter 6
CONCLUSIONS AND FUTURE PROSPECTS: 6.1. Challenges for Systems Biology in Drug Discovery; 6.2. Possible Solutions to Advancing Medical and Pharmacological Knowledge via Systems Biology; 6.3. Systems Biology as a Disruptive Technology; 6.4. Future Prospects
Chapter 7
EXPERT INTERVIEWS: David de Graaf, PhD; Director of Systems Biology, Pfizer, Research Technology Center, Cambridge, MA
FIGURES: Figure 1.1. Causation in Living Systems Is a Two-Way Street; Figure 1.2. Experimentally Accessible Levels of Systems Biology; Figure 2.1. The BG Medicine Approach to Commercial Systems Biology; Figure 2.2. Graphic Representation of a BG Medicine Correlation Network Using Proteomic and Metabolomic Data; Figure 2.3. Multicomponent Protein and Metabolite Biomarkers for Distinguishing Among Normal Controls, Alzheimer’s Disease, and Mild Cognitive Impairment; Figure 2.4. ChIP-on-Chip Workflow; Figure 2.5. Cytoscape Graphical User Interface; Figure 2.6. Ariadne Genomics’ Pathway Studio Graphical User Interface; Figure 2.7. Tumor Angiogenesis Network Generated by the Ingenuity Pathways Analysis System; Figure 2.8. Genstruct’s Forward and Reverse Causal Analysis Schema; Figure 3.1. Modeling of Biological Systems at Multiple Levels; Figure 3.2. Important Cellular Networks; Figure 3.3. Human Disease Network/Disease Gene Network; Figure 5.1. Respondents by Sector; Figure 5.2. Respondents by Position; Figure 5.3. Respondents by Stage of Work; Figure 5.4. Use of Systems Biology in R&D Projects; Figure 5.5. Areas of Systems Biology Involvement; Figure 5.6. Companies’ Views Toward Systems Biology; Figure 5.7. Means by Which Systems Biology Effort Is Conducted; Figure 5.8. Vendor Emphasis in Outsourced Systems Biology Efforts; Figure 5.9. Expectations for Fiscal 2008 Systems Biology Budget; Figure 5.10. Expectations for Systems Biology Budget over the Next 3 Years; Figure 5.11. Estimation of 2008 Systems Biology Budget
TABLES: Table 1.1. Growth in Literature Citations Relevant to Systems Biology During the Period 1998 to 2007; Table 5.1. Business Models of Small Systems Biology Companies; Table 5.2. Selected Recent Deals in the Systems Biology Space

Abstract

This report focuses on the current and future applications of Systems Biology in drug discovery, specifically in pinpointing optimal individual targets, and combinations of targets, to overcome metabolic pathway redundancies, leading to efficacious and safe products.

Topics covered include:

Application successes at AstraZeneca, Pfizer, and J&J
Landscape of the Systems Biology marketplace and its future
Implications of innovative predictive modeling and global transcription epigenetics analysis
Review of 18 Systems Biology company business models
How SB will enable pharmacological progress in biologically complex “money” diseases
Projections on the future for Systems Biology in leukemia, Alzheimer’s, and Huntington’s diseases.

Systems biology (SB) is challenging the existing dominant drug discovery approaches and on track to becoming a classic disruptive technology. This report describes examples of SB successes in big pharma and current SB applications as well as the radically new concepts emerging from basic SB research.

The report provides a survey on the origins of SB and the varying definitions in common use and then moves to a review of the current bioanalytical- and bioinformatics-based technologies for making sense of omic’s data through enabling pathway and network analysis. Pathway analysis, cell modeling, and disease modeling technologies today dominate the bioinformatics branch of systems biology. Database-mediated pathway analysis studies, which are particularly popular today, help to discover meaning in global biological data for drug discovery and diagnostics.

As examples, systems biology approaches played a key role in understanding AstraZeneca’s Iressa (gefitinib), liver abnormalities were identified by Pfizer, and Johnson & Johnson identified a kinase inhibitor mechanism. Next, the report provides an overview of the recent explosion of academic SB activity and implications for highly novel approaches to drug discovery and diagnostics not envisioned today. Examples include nanosystems studies to construct a predictive model for transcription control, ChIP-on-chip technology for global transcription factor identification, and methylation-specific polymerase chain reaction (PCR) for global DNA methylation detection as an entry point to epigenetics.

Systems Biology: A Disruptive Technology provides an analysis of the commercial activities of 18 small systems biology companies reviewed in the context of the nature and dynamics of the systems biology market: the business models, deals, scope, and prospects. As examples, commercial databases and software programs from companies such as Ingenuity Systems (Redwood City, CA), GeneGo (St. Joseph, MI), and Ariadne Genomics (Rockville, MD) provide enhanced usability and comprehensiveness. Gen-struct’s Knowledge Assembly platform enables “knowledge-driven systems biology;” Gene Network Sciences’ (Cambridge, MA) REFS (Reverse Engineering and Forward Simulation) systems permit reverse engineering and hypothesis generation from omic data; and Entelos’ (Foster City, CA) PhysioLab biosimulation models, which incorporate both molecular and higher-order disease data, permit construction of “virtual patients.”

Systems Biology: A Disruptive Technology concludes with a discussion and speculation as to the future for SB, supported by interviews with scientists and managers deeply engaged in this space. This analysis explains how and why pharma and diagnostics industries will benefit from advances in SB by leading to highly novel approaches for application to drug discovery and diagnostics discovery and development.

About the Author
Ken Rubenstein, PhD, a biochemist and molecular biologist, received his PhD at the University of Wisconsin and postdoctoral training at the University of Pennsylvania School of Medicine. He was a key innovator and research manager for Syva Company, the diagnostics branch of Syntex Corporation. During his 13 years with Syva, Dr. Rubenstein became vice president, scientific affairs, a function that included strategic planning. Since 1983, he has served as a technology and marketing consultant to biomedical companies and an industry analyst, with more than 40 published studies to his credit.

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Systems Biology: A Disruptive Technology

Table of Contents

Abstract