For our purposes here, systems biology means an approach to biology that looks at networks of molecular interactions (including gene products, endogenous small molecules and drugs) and processes these using qualitative graphical models or quantitative mathematical modeling [8
]. Examples of implementations of quantitative methods include Flux Balance Analysis [9
], differential equations [10
], and Petri Nets [11
]. Implementations of qualitative methods include Cytoscape [12
], a graphical network representation, and Genoscape [13
], a network-based knowledge integration extension tool. When the principles of systems biology are extended to medications, we get a network of interactions between drugs and the naturally occurring metabolic and signaling networks (Figure ). These drugs may connect otherwise disconnected and independent sub-networks, and this may cause both expected and unexpected effects. Pharmacological systems biology must combine the biological and chemical characteristics of small and large molecules to develop an understanding of drug action. These protein-drug joint networks provide two opportunities. First, they can provide more detailed descriptions (even signatures) of drug effects, and second, they can provide a framework for the design of novel therapeutic strategies [4
Figure 1 Meta-networks allow novel inferences. Systems approaches allow the generation of networks of genes based on common pathways or common evolutionary history, networks of drugs based on chemical similarity or similarity in biological effects, and networks (more ...)
The intersection of systems biology and chemical biology opens new avenues of research. In particular, there are opportunities to combine data from genomics, three-dimensional structure, large chemical screens, protein-protein interactions, protein-drug binding interactions, and cellular imaging and localization to assemble a high-fidelity model of how and where small molecules interact with cellular components. A harbinger of the opportunities that exist is the work by Apsel et al
], who have integrated chemical biology and systems biology techniques to design drugs that act as dual inhibitors of two families of oncogenes.
The recent work of Xie et al
] is another excellent example of the use of networks combining proteins and drugs. They investigated the reasons for the serious side-effects of torcetrapib, an inhibitor of cholesteryl ester transfer protein (CETP) that was in clinical trials as a preventive treatment for cardiovascular disease. The aim of torcetrapib was to raise the levels of the desirable high-density lipoprotein cholesterol (HDL-C), but torcetrapib turned out to have the side-effect of raising blood pressure, with potentially fatal effects in high-risk patients, and was withdrawn from development in 2006.
Xie et al
] generated off-target binding networks by comparing the structure of ligand-binding sites in all known protein structures. The proteins identified as having similar binding domains were ranked by a normalized docking score and clustered by their structural and functional characteristics into a gene network that includes metabolic and regulation pathways. Using this analysis, the authors identified possible off-targets for torcetrapib even though the binding site of CETP itself is not fully described. Perhaps most interestingly, they incorporated biological pathways into their off-target networks and found a potential explanation for the poorly understood effects of torcetrapib on blood pressure. By combining a simple gene regulation model with the predicted binding affinities to activators and inhibitors of the renin-angiotensin-aldosterone system (RAAS), they showed that torcetrapib caused more severe effects since it has a higher affinity for more RAAS activators.
To validate this approach, the investigators compared the off-target networks for drugs with different side-effect profiles, and show that the networks are quite different and consistent with the different effects of the drugs on blood pressure [7
]. Their method can, however, only use proteins with known structures - a small fraction of the human proteome. As a result, pharmacologists may become fans of high-throughput structural biology!
An alternative approach to discovering off-target effects relies on identifying common chemical features among drugs with the same set of adverse reactions [15
]. This approach links chemical sub-structures to specific toxicities and can be used to determine the potential side-effects of a drug with a novel chemical structure. An implementation of this technique is described by Scheiber et al
] to relate chemical substructures to side-effects and by Campillos et al
] to combine drug chemical similarity to side effect similarity to predict shared drug targets. Recent work by Shoichet and colleagues (Hert et al.
]) in this field uses the similarity ensemble approach with a Bayesian method to build chemoinformatics networks based on chemical similarities between drugs, instead of on structural or sequence similarities between drug targets. Comparisons between the ligand-based network of Hert et al
] and the target-based network of Xie et al
] might provide interesting insights. If the networks' information content is complementary, as opposed to redundant, then a method that utilized both network may outperform either one alone.
Other investigators have taken a complementary approach. Instead of looking for common chemical sub-structures, they focus on common adverse reactions. Scheiber et al
] have incorporated data from a variety of databases and identified drugs with shared toxicities. They then apply an understanding of the molecular pathways underlying these toxicities to predict drug targets. In this way, they can develop data-driven hypotheses about the mechanisms of a particular side-effect. This approach is particularly useful when chemicals with very different structures (not likely to be recognized using measures of chemical similarity) interact with the same biological pathway. The toxicities are effectively used as a proxy for the biological pathways that the drug is involved with.
The success of network-based methods relies heavily on the development and curation of high-quality biological and pharmacological databases. The new high-throughput technologies have provided a huge amount of data on protein-protein and gene-gene interaction networks. The meta-database pathguide.org[18
] currently lists more than 70 such databases that are freely available. However, as Blow points out in a recent review [19
], no one database is complete, and combining datasets will yield more information. The study by Xie et al.
], for example, incorporates data from eight different sources. The availability of these databases will fuel the next generation of chemical systems biology tools and lead to major advances in drug discovery and repositioning. Databases that attempt to integrate these different sources of data are becoming available. One such, STITCH, tries to consolidate knowledge about interactions between proteins and small molecules [20
]. Although undoubtedly useful, these huge databases do raise the issue of false discovery. Incorporating domain knowledge to rank genes by their propensity to cause a modulated drug response may be one way of addressing this issue [21
The ability to predict and even design the effects of new drugs is critical for the future pharmaceutical industry. By integrating biological and chemical knowledge, the pharma cological effects of drugs can be more completely understood and used to create predictive models. Recent work has focused on relating drugs to targets by chemical similarity, target structural similarity and even side-effect similarity. In each case, the results have illustrated the power of thinking about drug responses in the context of a network of interactions, and from a systems perspective.