Kim Rosa S, Goossens Nicolas, Hoshida Yujin
Division of Liver Diseases, Department of Medicine, Liver Cancer Program, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, USA.
Division of Liver Diseases, Department of Medicine, Liver Cancer Program, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, USA; Division of Gastroenterology and Hepatology, Geneva University Hospital, Geneva, Switzerland.
Expert Rev Precis Med Drug Dev. 2016;1(3):245-253. doi: 10.1080/23808993.2016.1174062. Epub 2016 Apr 28.
Drug development has been a costly and lengthy process with an extremely low success rate and lack of consideration of individual diversity in drug response and toxicity. Over the past decade, an alternative "big data" approach has been expanding at an unprecedented pace based on the development of electronic databases of chemical substances, disease gene/protein targets, functional readouts, and clinical information covering inter-individual genetic variations and toxicities. This paradigm shift has enabled systematic, high-throughput, and accelerated identification of novel drugs or repurposed indications of existing drugs for pathogenic molecular aberrations specifically present in each individual patient. The exploding interest from the information technology and direct-to-consumer genetic testing industries has been further facilitating the use of big data to achieve personalized Precision Medicine. Here we overview currently available resources and discuss future prospects.
药物研发一直是一个成本高昂且耗时漫长的过程,成功率极低,且未考虑药物反应和毒性方面的个体差异。在过去十年中,一种替代性的“大数据”方法以前所未有的速度不断扩展,这基于化学物质、疾病基因/蛋白质靶点、功能读数以及涵盖个体间基因变异和毒性的临床信息的电子数据库的发展。这种范式转变使得能够系统、高通量且加速识别针对每个个体患者特有的致病分子异常的新型药物或现有药物的新用途适应症。信息技术和直接面向消费者的基因检测行业的兴趣激增进一步推动了利用大数据来实现个性化精准医学。在此,我们概述当前可用的资源并讨论未来前景。