Department of Molecular Pharmacology, Graduate School of Pharmaceutical Sciences, Kyoto University.
Biol Pharm Bull. 2020;43(3):362-365. doi: 10.1248/bpb.b19-00929.
Recent pharmacological studies have been developed based on finding new disease-related genes, accompanied by the production of gene-manipulated disease model animals and high-affinity ligands for the target proteins. However, the emergence of this gene-based strategy in drug development has led to the rapid depletion of drug target molecules. To overcome this, we have attempted to utilize clinical big data to explore a novel and unexpected hypothesis of drug-drug interaction that would lead to drug repositioning. Here, we introduce our data-driven approach in which adverse event self-reports are statistically analyzed and compared in order to find and validate new drug targets. The hypotheses provided by such a data-driven approach will likely impact the style of future drug development and pharmaceutical study.
最近的药理学研究是基于寻找新的与疾病相关的基因而展开的，同时也产生了基因改造的疾病模型动物和针对靶蛋白的高亲和力配体。然而，这种基于基因的药物开发策略的出现导致了药物靶点分子的迅速枯竭。为了克服这一问题，我们试图利用临床大数据来探索一种新颖且出乎意料的药物-药物相互作用假说，从而实现药物的再定位。在这里，我们介绍了一种数据驱动的方法，通过对不良事件的自我报告进行统计分析和比较，以发现和验证新的药物靶点。这种数据驱动方法提供的假设很可能会影响未来药物开发和药物研究的方式。
