Systems Biology in Drug Discovery: Using Predictive Biomedicine to Guide Development Choices for Novel Agents in Cancer

Abstract

This chapter discusses systems-based approaches to translational medicine. This is made possible by the convergent acceleration of several disciplines-computational, post-genomic platform and biomedical sciences-working together to offer a more complete picture of the complexities of disease and disease treatment. Systems-based approaches offer an increasingly less biased set of platforms with which to observe the phenomenal diversity of cancers and conduct experiments in a wider range of more clinically relevant models. This chapter also describes how systems approaches for predictive biomedicine-integrating discovery and clinical data-can be applied to identify novel targets, predictive biomarkers of response to agents in development and targeted use of drug combinations. The objective of these approaches is for new agents, the development of which is guided by predictive biomedicine to improve outcomes for individual cancer patients. Some more recently approved agents, such as sorafenib, intentionally target several members of signaling cascades dysregulated in tumors, taking advantage of the multitargeted nature of many kinase inhibitors. There has also been a phenomenal explosion in the generation of experimental data in the wake of the human genome project. Rapid advances in computational processing power, data storage, modeling, and analysis have become available to make such data interpretable. The convergence of these three trends-targeted therapies in cancer, post-genomic system-wide experimentation platforms, and unprecedented advances in information technology-lays the foundation for the application of systems biology to drug discovery in cancer. © 2010 Elsevier Inc. All rights reserved.