Prinz Jeanette, Vogt Ingo, Adornetto Gianluca, Campillos Mónica
Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, Neuherberg, Germany.
German Center for Diabetes Research, Helmholtz Zentrum München, Neuherberg, Germany.
PLoS Comput Biol. 2016 Sep 27;12(9):e1005111. doi: 10.1371/journal.pcbi.1005111. eCollection 2016 Sep.
The molecular mechanisms that translate drug treatment into beneficial and unwanted effects are largely unknown. We present here a novel approach to detect gene-drug and gene-side effect associations based on the phenotypic similarity of drugs and single gene perturbations in mice that account for the polypharmacological property of drugs. We scored the phenotypic similarity of human side effect profiles of 1,667 small molecules and biologicals to profiles of phenotypic traits of 5,384 mouse genes. The benchmarking with known relationships revealed a strong enrichment of physical and indirect drug-target connections, causative drug target-side effect links as well as gene-drug links involved in pharmacogenetic associations among phenotypically similar gene-drug pairs. The validation by in vitro assays and the experimental verification of an unknown connection between oxandrolone and prokineticin receptor 2 reinforces the ability of this method to provide new molecular insights underlying drug treatment. Thus, this approach may aid in the proposal of novel and personalized treatments.
将药物治疗转化为有益和不良效果的分子机制在很大程度上尚不清楚。我们在此提出一种新方法,基于药物的多药理学特性以及小鼠中单个基因扰动的表型相似性,来检测基因 - 药物和基因 - 副作用关联。我们对1667种小分子和生物制剂的人类副作用谱与5384个小鼠基因的表型特征谱之间的表型相似性进行了评分。与已知关系的基准测试表明,在表型相似的基因 - 药物对中,物理和间接的药物 - 靶点联系、因果性药物靶点 - 副作用联系以及参与药物遗传学关联的基因 - 药物联系都有显著富集。通过体外实验验证以及对氧雄龙和促动力蛋白受体2之间未知联系的实验证实,强化了该方法提供药物治疗潜在新分子见解的能力。因此,这种方法可能有助于提出新的个性化治疗方案。