With tens of billions of dollars spent each year on the development of drugs to treat human diseases, and with fewer and fewer applications for investigational new drugs filed each year despite this massive spending, questions now abound on what changes to the drug discovery paradigm can be made to achieve greater success. The high rate of failure of drug candidates in clinical development, where the great majority of these drugs fail due to lack of efficacy, speak directly to the need for more innovative approaches to study the mechanisms of disease and drug discovery. Here we review systems biology approaches that have been devised over the last several years to understand the biology of disease at a more holistic level. By integrating a diversity of data like DNA variation, gene expression, protein-protein interaction, DNA-protein binding, and other types of molecular phenotype data, more comprehensive networks of genes both within and between tissues can be constructed to paint a more complete picture of the molecular processes underlying physiological states associated with disease. These more integrative, systems-level methods lead to networks that are demonstrably predictive, which in turn provides a deeper context within which single genes operate such as those identified from genome-wide association studies or those targeted for therapeutic intervention. The more comprehensive views of disease that result from these methods have the potential to dramatically enhance the way in which novel drug targets are identified and developed, ultimately increasing the probability of success for taking new drugs through clinical development. We highlight a number of the integrative approaches via examples that have resulted not only in the identification of novel genes for diabetes and cardiovascular disease, but in more comprehensive networks as well that describe the context in which the disease genes operate.
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