Computational Biology, GlaxoSmithKline R&D, King of Prussia, Pennsylvania, USA.
Clin Pharmacol Ther. 2013 Apr;93(4):335-41. doi: 10.1038/clpt.2013.1. Epub 2013 Jan 15.
Traditionally, most drugs have been discovered using phenotypic or target-based screens. Subsequently, their indications are often expanded on the basis of clinical observations, providing additional benefit to patients. This review highlights computational techniques for systematic analysis of transcriptomics (Connectivity Map, CMap), side effects, and genetics (genome-wide association study, GWAS) data to generate new hypotheses for additional indications. We also discuss data domains such as electronic health records (EHRs) and phenotypic screening that we consider promising for novel computational repositioning methods.
传统上，大多数药物都是通过表型或基于靶点的筛选发现的。随后，它们的适应症通常是基于临床观察进行扩展，从而为患者带来额外的益处。这篇综述强调了计算技术在转录组学（连接图谱，CMap）、副作用和遗传学（全基因组关联研究，GWAS）数据分析中的应用，以生成新的假说用于附加适应症。我们还讨论了电子健康记录（EHRs）和表型筛选等数据领域，我们认为这些领域对于新的计算重定位方法具有很大的潜力。
