Results selleck products and discussion The drug cocktail network In this study, we extracted 239 known effective pairwise drug combinations from DCDB. The information of ATC code for each drug was obtained from DrugBank. Based on these datasets, we constructed a drug cocktail network with 215 nodes and 239 edges, where nodes represent the drugs and an edge is connected if two drugs are found in an effective drug combination. Build ing up this network can thus give the readers a visual impression of the relationships between drugs that can form effective combinations. Moreover, the network the ory can be utilized to explore possible combinatorial mechanisms between drugs.

In Figure 1, the size of each node approximates its degree, and the width of each edge approximates the therapeutic similarity between the two drugs linked by the edge, while the grey edges indicate that the two drugs linked by the edge have totally different therapeu tical effects. In addition, we found 102 drugs that have at least two neighbors in the drug cocktail network, which we termed as star drugs hereafter and 91 of which have target protein annotations in DrugBank. Since most of biological networks are scale free net works, we analyzed the topology of the drug cocktail network in order to find out whether it is also a scale free network. The degree distribution of the drug cocktail network is shown in Figure 2. It is evident that the degree distribution follows a power Drug_discovery law distribution, suggesting that it is indeed a scale free network. That is, the fraction P of nodes in the drug cocktail network having x con nections to other nodes can be described as where c 2.

1 and a 1. 9 in this case. As the drug cocktail network shown in Figure 1 is not fully connected, the top 6 largest subnetworks were cho sen for further analysis. We considered the drug cocktail network as the union of these 6 subnetworks hereafter unless stated specifically. In particular, selleck Abiraterone each subnetwork was found to be enriched for one or several therapeutic classes according to the ATC classification system, as shown in Table 1. In other words, the drugs having similar therapeutic effects tend to be clustered together in the drug cocktail network. To test our hypothesis that the drugs in one combina tion tend to have similar therapeutic effects, the drug cocktail network was compared against random combi nation networks. For this purpose, a therapeutic similar ity score was calculated for each drug pair, and the average of all TS scores was used as the TS score for the whole drug cocktail network. The random combina tion networks were generated by randomly shuffling the edges while still preserving the degree for each node in the drug cocktail network. This procedure was repeated for 1,000 times.