Abstract
In pharmacology, it is essential to identify the molecular mechanisms of drug action in order to understand adverse side effects. These adverse side effects have been used to infer whether two drugs share a target protein. However, side-effect similarity of drugs could also be caused by their target proteins being close in a molecular network, which as such could cause similar downstream effects. In this study, we investigated the proportion of side-effect similarities that is due to targets that are close in the network compared to shared drug targets. We found that only a minor fraction of side-effect similarities (5.8 %) are caused by drugs targeting proteins close in the network, compared to side-effect similarities caused by overlapping drug targets (64%). Moreover, these targets that cause similar side effects are more often in a linear part of the network, having two or less interactions, than drug targets in general. Based on the examples, we gained novel insight into the molecular mechanisms of side effects associated with several drug targets. Looking forward, such analyses will be extremely useful in the process of drug development to better understand adverse side effects.
Highlights
As almost 30% of drug candidates fail in clinical stages of drug discovery due to toxicity or concerns about clinical safety [1], an increased understanding of unwanted side effects and drug action is desirable
As there are large variations in number of interactions between proteins in STRING, we developed a normalized score based on the confidence-weighted edges in STRING, that reflects the closeness of drug targets in the protein-protein network
In this study we have shown that the similarity of adverse effects for a number of drugs can uniquely be explained by the common protein subnetwork that they target
Summary
As almost 30% of drug candidates fail in clinical stages of drug discovery due to toxicity or concerns about clinical safety [1], an increased understanding of unwanted side effects and drug action is desirable. Large-scale computational analyses of chemical and biological data have made it possible to construct drug-target networks that can be correlated to physiological responses and adverse effects of drugs and small molecules [2]. Such drug side effects have been predicted from the chemical structure of drugs [3], can be implied if drugs use a similar target or have been used themselves to predict new (off-)targets of drugs [2,4,5]. Integration with the drug-therapy network [11] and the analysis and intentional targeting of the protein interaction network underlying drug targets could expand our current range of drug treatments and reduce drug-induced toxicity [12,13]
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