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Genome Med. 2014 Aug 17;6(7):52. doi: 10.1186/s13073-014-0052-z. eCollection 2014.

Molecularly and clinically related drugs and diseases are enriched in phenotypically similar drug-disease pairs.

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1
Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum M√ľnchen, 85764 Neuherberg, Germany ; German Center for Diabetes Research, 85764 Neuherberg, Germany.

Abstract

BACKGROUND:

The incomplete understanding of disease causes and drug mechanisms of action often leads to ineffective drug therapies or side effects. Therefore, new approaches are needed to improve treatment decisions and to elucidate molecular mechanisms underlying pathologies and unwanted drug effects.

METHODS:

We present here the first analysis of phenotypically related drug-disease pairs. The phenotypic similarity between 4,869 human diseases and 1,667 drugs was evaluated using an ontology-based semantic similarity approach to compare disease symptoms with drug side effects. We assessed and visualized the enrichment over random of clinical and molecular relationships among drug-disease pairs that share phenotypes using lift plots. To determine the associations between drug and disease classes enriched among phenotypically related pairs we employed a network-based approach combined with Fisher's exact test.

RESULTS:

We observed that molecularly and clinically related (for example, indication or contraindication) drugs and diseases are likely to share phenotypes. An analysis of the relations between drug mechanisms of action (MoAs) and disease classes among highly similar pairs revealed known and suspected MoA-disease relationships. Interestingly, we found that contraindications associated with high phenotypic similarity often involve diseases that have been reported as side effects of the drug, probably due to common mechanisms. Based on this, we propose a list of 752 precautions or potential contraindications for 486 drugs.

CONCLUSIONS:

Phenotypic similarity between drugs and diseases facilitates the proposal of contraindications and the mechanistic understanding of diseases and drug side effects.

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