- Open Access
Drug-therapy networks and the prediction of novel drug targets
© BioMed Central Ltd 2008
- Published: 31 July 2008
A recent study in BMC Pharmacology presents a network of drugs and the therapies in which they are used. Network approaches open new ways of predicting novel drug targets and overcoming the cellular robustness that can prevent drugs from working.
- Drug Target
- Cellular Network
- Betweenness Centrality
- Bipartite Network
- Anatomic Therapeutic Chemical Classification
Despite a significant and continuous increase in medical research spending, the number of new drugs approved and new drug targets identified each year has remained almost constant for the past 20–25 years, with about 20 new drugs and about five new targets per year. Lengthy development procedures and the high risk of unexpected side-effects in advanced-stage clinical trials reduce the ability of the drug development process to be innovative. At this rate it will take more than 300 years to double the number of available drugs . However, there are several ways to overcome these burdens. Promising areas of drug design include: wide-range screens of existing drugs, seeking novel applications; combination therapy, that is, the use of several drugs or short DNA oligomers, called aptamers, together [2–4]; and the development of multi-target drugs .
The organization of our rapidly growing knowledge on diseases, disease-related genes, drug targets and their structures, and drugs and their chemical structures gives us another exciting way to discover novel areas of drug development. Several networks have recently been constructed to help drug discovery [1, 6–9]. In the network concept a complex system is perceived as a set of interacting elements bound together by links. Links usually have a weight, which characterizes their strength such as the affinity of binding between the two elements, or the propensity of one element to act on the other. Links can also be directional, when one of the elements has a larger influence on the other than vice versa [10, 11].
In a recent study in BMC Pharmacology, Nacher and Schwartz  compiled a drug-therapy network in which all US-approved drugs and associated human therapies – that is, the therapeutic properties of the drugs involved according to the Anatomic Therapeutic Chemical (ATC) classification – were connected to each other. From a bipartite network of therapies and drugs (in which therapies were connected to drugs, but drugs were not connected to other drugs or therapies to other therapies) they constructed two other networks: a drug network, in which two drugs were connected if they were both involved in at least one common therapy; and a therapy network, in which two therapies were connected if a particular drug was implicated in both therapies. Their analysis  provides the first view of the relationships between therapies as defined by drug-therapy interactions, and it highlights a few key drugs that connect distinct classes of therapy in a few steps.
Both the drug and the therapy networks  proved to be small worlds; that is, distant therapies were separated by an average of less than three chemicals [10, 11]. Highly connected therapies, 'therapy hubs', are likely to be relevant in the therapy network, because this network behaved in a manner close to that of a tree-like network, in which the relative importance of hubs is high. Most drugs (79%) were grouped in clusters connected to a specific therapy. However, a minority of drugs (21%) formed bridges spanning different therapeutic classes; these drugs may have a particular significance.
Nacher and Schwartz  computed several measures of network 'centrality' characterizing the importance of the drugs in the network context of various therapies. They identified a subnetwork of the bridging drugs with high 'betweenness centrality' (drugs that participated in a large number of shortest paths connecting other drugs) in the drug-therapy network; these include scopolamine, morphine, tretionin and magnesium sulfate. For example, tolbutamide and magnesium sulfate defined a key shortest path of two steps between the distant classes of therapy 'insulins and analogs' and 'dermatological preparations'. Apparently unrelated disorders were thus separated by a much lower number of chemicals than might be expected. Most drugs act on one target, but a few drugs act on a large number of targets. Nacher and Schwartz  propose that drugs that have a high betweenness centrality and act on multiple targets may influence multiple metabolic pathways, and they especially highlighted hydroxocobalamin, vitamin B3, vitamin B12, atropine, ophenadrine and procaine as members of this category.
The network approach not only gives us a systematic way to organize our vast databases, but also provides a visual image that can help us to understand the daunting complexity of these systems. However, many networks, such as that of the 1,360 individual chemical substances studied by Nacher and Schwartz , are too big for easy visualization. The ATC classification system used by these authors  gave them the opportunity to construct a hierarchical representation of drug-therapy information, in which we can zoom in from the top layer of 15 anatomical main therapeutic groups, through the 66 therapeutic subgroups (second layer), the 123 pharmacological subgroups (third layer), the 448 chemical subgroups (fourth layer) until reaching the fifth layer of 1,360 individual chemical substances. Where the dataset is not as straightforwardly hierarchical as the ATC classification , network hierarchy can be explored by various other techniques [12–14].
Existing segments of drug-target networks may have hidden information on additional drug targets that are not yet included in the network. Extension of existing networks by predicting links and elements is a recent, exciting field of network studies [13, 17, 18]. The identification of novel drug-target candidates can be accomplished by finding missing links in all networks in which drug targets serve as links, for example, in drug [1, 6, 7, 9], therapy  or patient networks joined by common drug targets. Methods for discovering new links can identify new nodes in all bipartite networks, in which any of the nodes of one type can be converted to links joining nodes to the other type. This may give us novel methods to predict, discover, test and extend gene regulatory, metabolic, opinion (recommendation), collaboration (co-authorship), sexual and any other affiliation-type networks.
Robustness is an intrinsic property of cellular networks that enables them to maintain their functions despite various perturbations . Networks of different topology vary by orders of magnitude in their robustness to mutations and noise. Enhanced robustness is a property of only a very small number of all possible network topologies . Cellular networks in both health and disease belong to this extreme minority and show this robust behavior.
Many times when a drug fails or produces side effects, cellular robustness provides most of the explanation. A drug can be ineffective when the robustness of cellular networks of disease-affected cells or parasites compensates for the changes caused by it. By contrast, drug side effects can be the result of hitting an unexpected point of fragility in the affected networks . Robustness analysis is already being used to reveal primary drug targets , and the first methods have also been established to give a quantitative measure of changes in robustness during drug action .
Cellular robustness can be caused by strong links forming negative or positive feedbacks that help the cell to return to the original state or jump to another, respectively; by weak links that provide alternative, redundant pathways; or by a range of other mechanisms [11, 19, 21]. But achieving robustness always has a price. Robust cells have their fragile point, their 'Achilles heel', and cannot be optimized for all other aspects of cellular life, such as proliferation. This gives us chances to conquer or redirect cellular robustness by the application of drugs. We can develop drugs that, for example, find the Achilles heel of the cellular robustness of disease-affected cells or parasites, or that decrease robustness, for example, by inhibiting the effect of weak links [11, 19, 21]. Note that a decrease in robustness makes the cellular network noisier and less predictable, which means that robustness-decreasing drugs will be more difficult to find than conventional drugs using currently available analytical methods that assume an 'equilibrium' network. The development of 'fuzzy', stochastic network analysis [14, 18] and the comparison of network time series may help to overcome this difficulty.
Our current knowledge on cellular networks and their analytical methods has arrived at a time when testing the effects of drug candidates with known cellular targets or target-sets on the robustness of cellular networks is becoming possible. A robustness test, revealing both resistance-related failures and side effects, should, in our opinion, be a mandatory element of standard drug-development protocols. The more we know about tissue- and disease-specific changes in cellular networks and about variations in these changes between individuals, the better we will be able to predict the efficiency of drugs in in silico experiments.
In summary, the recently published drug-therapy  and drug-target networks [1, 6, 7, 9] as well as their potential extensions (Figure 1) provide a powerful and exciting tool for the organization of the expanding drug-development data and give us a global view on major trends and limitations. The advent of combinatorial therapies [2–4] and multi-target drugs  may greatly help us to break or re-direct the robust behavior of the cell. For the knowledge-based design of appropriate drug combinations and multi-target drugs, however, we need novel approaches and techniques to explore the dynamic complexity of cellular networks after multiple perturbations [24, 25].
During the processing of this manuscript Campillos et al.  published a network of 502 drugs and their side effects using these data to predict novel drug targets based on the similarity of side-effect of two chemically dissimilar drugs. From the same data a side-effect network could also be constructed, where two side effects are linked, if a drug exists, which has both. This side-effect network in combination with the link-prediction methods outlined above [13, 17, 18] opens the possibility to predict additional side effects of existing drugs and drug candidates. Network-based side-effect prediction would greatly help the development of better clinical trial protocols, and would uncover additional possible dangers before the large-scale use of novel drugs.
We thank the members of the LINK Group  for their helpful comments. Work in the authors' laboratory was supported by the EU (FP6-518230) and the Hungarian National Science Foundation (OTKA K69105). The funding bodies had no role in the content and submission of this manuscript.
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