Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, 85764 Neuherberg, Germany ; German Center for Diabetes Research, 85764 Neuherberg, Germany.
Genome Med. 2014 Aug 17;6(7):52. doi: 10.1186/s13073-014-0052-z. eCollection 2014.
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.
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.
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.
Phenotypic similarity between drugs and diseases facilitates the proposal of contraindications and the mechanistic understanding of diseases and drug side effects.
对疾病病因和药物作用机制的不完全了解,常常导致药物治疗无效或产生副作用。因此,需要新的方法来改善治疗决策,并阐明疾病和不良药物作用的分子机制。
我们在这里首次分析了表型相关的药物-疾病对。使用基于本体的语义相似性方法评估了 4869 种人类疾病和 1667 种药物之间的表型相似性,以比较疾病症状与药物副作用。我们使用提升图评估和可视化了具有相同表型的药物-疾病对之间临床和分子关系的富集情况。为了确定表型相关对中富集的药物和疾病类别之间的关联,我们采用了一种结合 Fisher 精确检验的基于网络的方法。
我们观察到,分子上和临床上相关的(例如,适应证或禁忌证)药物和疾病可能具有相同的表型。对高度相似对中药物作用机制(MoA)和疾病类别之间关系的分析揭示了已知和可疑的 MoA-疾病关系。有趣的是,我们发现具有高度表型相似性的禁忌证通常涉及到已被报道为药物副作用的疾病,这可能是由于共同的机制。基于此,我们为 486 种药物提出了 752 条预防措施或潜在禁忌证。
药物和疾病之间的表型相似性有助于提出禁忌证,并有助于理解疾病和药物副作用的机制。