Systematic identification of proteins that elicit drug side effects

• Published in: Molecular systems biology 2013, Vol.9 (1), p.663-n/a
• Main Authors: Kuhn, Michael, Al Banchaabouchi, Mumna, Campillos, Monica, Jensen, Lars Juhl, Gross, Cornelius, Gavin, Anne‐Claude, Bork, Peer
• Format: Article
• Language: English
• Subjects: Algorithms; Animals; Clinical trials; Clinical Trials as Topic; computational biology; drug targets; Drugs; EMBO10; EMBO24; Experiments; Female; Gene Expression - drug effects; Gene Expression Profiling; Humans; Hyperesthesia - chemically induced; Hyperesthesia - genetics; Hyperesthesia - metabolism; Hyperesthesia - prevention & control; Hypothesis testing; Male; Mice; Oxazolidinones - adverse effects; Pharmacogenetics; Phenols - pharmacology; Predictions; Predictive Value of Tests; Proteins; Receptors, Serotonin - genetics; Receptors, Serotonin - metabolism; Serotonin; Serotonin 5-HT1 Receptor Agonists - adverse effects; Serotonin 5-HT1 Receptor Antagonists - pharmacology; Side effects; Sulfonamides - pharmacology; Target recognition; Tryptamines - adverse effects; Variance analysis
• ISSN: 1744-4292; 1744-4292
• DOI: 10.1038/msb.2013.10

Side effect similarities of drugs have recently been employed to predict new drug targets, and networks of side effects and targets have been used to better understand the mechanism of action of drugs. Here, we report a large‐scale analysis to systematically predict and characterize proteins that cause drug side effects. We integrated phenotypic data obtained during clinical trials with known drug–target relations to identify overrepresented protein–side effect combinations. Using independent data, we confirm that most of these overrepresentations point to proteins which, when perturbed, cause side effects. Of 1428 side effects studied, 732 were predicted to be predominantly caused by individual proteins, at least 137 of them backed by existing pharmacological or phenotypic data. We prove this concept in vivo by confirming our prediction that activation of the serotonin 7 receptor (HTR7) is responsible for hyperesthesia in mice, which, in turn, can be prevented by a drug that selectively inhibits HTR7. Taken together, we show that a large fraction of complex drug side effects are mediated by individual proteins and create a reference for such relations. Protein–side effects associations are identified by integrating drug–target data with side effects information from drug labels. Benchmarking against the literature and validation with an in vivo mouse model shows that these pairs correspond to causal relations. Synopsis Protein–side effects associations are identified by integrating drug–target data with side effects information from drug labels. Benchmarking against the literature and validation with an in vivo mouse model shows that these pairs correspond to causal relations. For more than half of the investigated side effects, we can predict causal proteins. Off‐targets contribute slightly more to the explained side effects than main targets. With the current data, we are most successful in explaining the side effects of drugs that target G protein‐coupled receptors. Activation of HTR7 causes hyperesthesia in mice, explaining a side effect of triptan drugs.