To show the rich context through which the results of Nacher and Schwartz [8
] can be interpreted, we show the power of network approaches for constructing various drug-target and related networks, for predicting new drug targets, and to get around unwanted resistance and side effects of drugs. These tools promise to increase the number of novel drug targets and improve the approval rate of new drugs.
When thinking about the possible network representations of diseases, drugs and drug targets, the elements of the network first have to be defined. For a list of the available databases of drugs and related information, see Table . The next step is to find a general rule determining the elements that are linked in the particular network and the nature (such as weight or directionality) of the links connecting them. Besides the drug-therapy network already mentioned [8
], several other, recently published network-building rule-sets [1
] give additional exciting and novel information on the vast datasets of diseases, drugs and drug targets. A summary of these representations is shown in Figure .
Useful links to therapy, disease, drug and drug-target network data
Figure 1 Overview and possible extensions of therapy, disease, drug and drug-target networks. The ovals represent data types (datasets) that have already been used for network construction (connected with white arrows) or that could be used to construct similar (more ...)
In some of these approaches [1
], the network can be constructed by either linking two drug-target proteins if both bind one or more compounds, or by linking two compounds (drugs) if both have at least one protein as a common target. One result from this analysis is that the average molecular weight of compounds becomes smaller and smaller as we go from preclinical drug candidates to Phase I, II, III and approved drugs. Other physicochemical properties, such as hydrophobicity and the ability to form hydrogen bonds, reduce further the number of drug candidates that can be given orally, which is the method normally desired [7
The analysis of the drug-target network [1
] also reveals further elements of the low-risk behavior of the pharmaceutical industry. The network is particularly enriched in highly targeted proteins, and elements with many neighbors (called hubs) are preferentially connected to each other, forming a so-called 'rich club'. This is a result of the tendency to target an already validated target protein with alternative or follow-up compounds. Experimental drugs act on a greater diversity of target proteins, and show a more diverse localization of the targets than the plasma membrane, which is usually the preferred site of action. So far, however, these efforts have not led to a significant expansion of novel targets, that is, novel classes of protein or cellular compartments [6
An additional approach to deciphering meaningful information for drug development efforts is to link human diseases that have in common at least one gene involved in the development of the disease. This human disease network has also been converted to the other possible network of disease genes, in which two genes are connected if they are associated with the same disorder [15
]. Among human diseases, several types of cancer, such as colon and breast cancer, are hubs that are genetically connected to more than 30 distinct disorders. Disease genes that contribute to a common disorder often have protein products that form larger complexes, are often co-expressed and have similar major functions [15
]. Interestingly, those inheritable disease genes that are not essential occupy a peripheral position in the cellular network. This is in stark contrast to essential genes, which are more central [16
]. By contrast with inheritable disease genes, disease genes associated with somatic mutations, such as somatic cancer genes, have a central position in cellular networks [15
]. When comparing drug-target networks with the related diseases, an ongoing shift of drug development can be observed towards 'novel diseases' with associated genes that were not previous drug targets [6
In the analysis and visual representation of drug-therapy and drug-target networks, the weight of the links (such as the number of drugs binding to both of two linked targets in the drug-target network) is seldom assessed. In addition, these networks have not been thoroughly analyzed by defining their groups, or modules [10
]. Both additions will certainly provide more detailed information on these exciting datasets. Important messages could be drawn from the additional networks shown in Figure . Not only drugs, but also their respective drug targets, can be linked to the various therapies. As an additional, rich source of data, patient records can be analyzed for the diseases diagnosed as well as the drugs prescribed. Patient medication records can be transcribed to a patient-drug target network, which may reveal novel aspects of the phenotype variability of diseases. Yet another set of data lies in the symptoms of patients, which can serve as a basis to construct symptom-disease, symptom-therapy or symptom-drug networks (Figure ).
Drugs may also form a structural network, where two drugs are linked if they contain the same, signature-like chemical segment or feature. Drugs can also be assembled to form a side-effect network, or toxicity network, which may give an overall view of these two key maladies of drug development. As more and more data will be available in the future, patient symptoms can be extended by appropriately selected patient transcriptome, proteome, metabolome, oral microbiome and gut microbiome data. This 'inflation' of drug and drug-related networks is unlikely to solve the current problems of drug design; rather, it may be that the more networks we add, the less clarity and focus we will enjoy. Drug- and disease-related network representations will certainly have their own evolution, however, and it is not yet clear which of them will give the most straightforward, non-obvious visual and analytical information.