Drug discovery is an empirical process. In spite of the enormous successes over the past 50 years in the discovery and use of therapeutic agents, it is often not clear why some drugs work and others have limited utility, with adverse effects that become apparent only after extensive use. Developing analytical methods that facilitate the discovery of some of the general rules for discovering targets for therapeutic agents, and the effects of drug-target interactions, both beneficial and adverse, would be valuable in moving the drug discovery process forward. For this effort, the field of Systems Biology and network sciences could be useful.
Systems Biology is an emerging interdisciplinary science that integrates biochemistry and cell-biology with genetics and physiology, as well as bioinformatics and computational biology to obtain holistic descriptions of biological systems at the cellular, tissue/organ and organismal levels. Operationally, such descriptions are obtained by tightly combining multivariable experiments and computational modeling to develop global views of dynamics at various scales of organization and across scales. Such integrated operational approaches are made possible due to advancements in experimental techniques, which allow the capture of the state of many cellular components at once. Computational methods and tools have greatly enabled advances in Systems Biology. The dramatic reduction in the cost of hardware, the continuing advances in applied mathematics that contribute to new algorithms, and the rapid pace of new software and database development, as well as the broadband networks that greatly facilitate access to the new databases and software, all contribute to the emergence of Systems Biology as a powerful new discipline. One of the promises Systems Biology brings is our ability to better understand cellular, tissue and organbehavior at the molecular level. This understanding could lead to better drug design, multidrug treatments, side-effect predictions, and rapid drug targeting and development as well as biomarker discovery.
Currently, the most comprehensive knowledge about the functional characteristics of cellular components is qualitative. Hence, graph-theory, a field of mathematics applied to, and developed within, the fields of sociology and computer-science has been used to analyze regulatory networks within cells1,2,3
. Here, cellular components, such as proteins and metabolites, are represented as nodes, and their interactions represented as links. This consideration results in directed or undirected graphs (networks). These networks can be analyzed using different algorithms that provide organizational information about the system from a top-down view. Most commonly, cellular regulatory networks such as cell signaling and gene regulation systems are abstracted to directed networks. These networks, if understood from a global perspective, could, in conjunction with molecular mechanisms, help explain the origins of phenotypic behavior, and explain how this behavior changes in disease states and is restored by drug treatment. The construction of networks may allow us to see how information from initial drug-target interactions affects many components and interactions in regulatory networks within mammalian cells to alter the disease state. To construct such a network, Food and Drug Administration (FDA) approved drugs can be considered nodes and their drug-target interactions as links. At first, this bipartite network of drug-target interactions can be analyzed.
We developed a bipartite network of FDA approved drugs and their targets. We conducted statistical analyses to obtain a description of this network. Analysis of the targets using Gene Ontology indicates that certain functional classes of proteins may be “better” drug targets. This approach is a promising direct method to connect pharmacology and computational graph-theoretical Systems Biology, but it surely has limitations. For example, many drugs share the same therapeutic target but have known differential effects. These may be due to differential distribution within the body or differential interactions with as yet unidentified targets. These would not be captured easily with this approach. We summarize the limitations of graph-theoretical approaches and suggest initial metrics to handle the inherit complexity.