The role of translational bioinformatics in drug discovery

被引:28
|
作者
Buchan, Natalie S. [1 ]
Rajpal, Deepak K. [2 ]
Webster, Yue [3 ]
Alatorre, Carlos [3 ]
Gudivada, Ranga Chandra [3 ]
Zheng, Chengyi [3 ]
Sanseau, Philippe [1 ]
Koehler, Jacob [3 ]
机构
[1] GlaxoSmithKline, Computat Biol, Stevenage SG1 2NY, Herts, England
[2] GlaxoSmithKline, Computat Biol, Res Triangle Pk, NC 27709 USA
[3] Eli Lilly & Co, Indianapolis, IN 46285 USA
基金
美国国家卫生研究院;
关键词
CANCER; CLASSIFICATION; REGISTRATION; INFORMATICS; PREDICTION; PROGRESS; GENES;
D O I
10.1016/j.drudis.2011.03.002
中图分类号
R9 [药学];
学科分类号
1007 ;
摘要
The application of translational approaches (e.g. from bed to bench and back) is gaining momentum in the pharmaceutical industry. By utilizing the rapidly increasing volume of data at all phases of drug discovery, translational bioinformatics is poised to address some of the key challenges faced by the industry. Indeed, computational analysis of clinical data and patient records has informed decision-making in multiple aspects of drug discovery and development. Here, we review key examples of translational bioinformatics approaches to emphasize its potential to enhance the quality of drug discovery pipelines, reduce attrition rates and, ultimately, lead to more effective treatments.
引用
收藏
页码:426 / 434
页数:9
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