Network-Driven Analysis Methods and their Application to Drug Discovery

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Author(s): Daniel Ziemek (Pfizer Inc., USA)and Christoph Brockel (Pfizer Inc., USA)
Copyright: 2011
Pages: 22
Source title: Handbook of Research on Computational and Systems Biology: Interdisciplinary Applications
Source Author(s)/Editor(s): Limin Angela Liu (Shanghai Jiao Tong University, China), Dongqing Wei (Shanghai Jiao Tong University, China), Yixue Li (Shanghai Jiao Tong University, China)and Huimin Lei (Shanghai Jiao Tong University, China)
DOI: 10.4018/978-1-60960-491-2.ch013

Keywords: Biologically-Inspired Computing / Biotechnology and Bioinformatics / Engineering Science Reference / Medicine & Healthcare

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Abstract

Drug discovery and development face tremendous challenges to find promising intervention points for important diseases. Any therapeutic agent targeting such an intervention point must prove its efficacy and safety in patients. Success rates measured from first studies in human to registration average around 10% only. Over the last decade, massive knowledge on biological systems has been accumulated and genome-scale primary data are produced at an ever increasing rate. In parallel, methods to use that knowledge have matured. This chapter will present some of the problems facing the pharmaceutical industry and elaborate on the current state of network-driven analysis methods. It will focus especially on semi-quantitative methods that are applicable to large-scale data analysis and point out their potential use in many relevant drug discovery challenges.

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Network-Driven Analysis Methods and their Application to Drug Discovery

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