Informatics and Analysis, Merck Research Laboratory, West Point, PA 19486, USA.
Curr Top Microbiol Immunol. 2013;363:169-99. doi: 10.1007/82_2012_252.
The pharmaceutical industry is spending increasingly large amounts of money on the discovery and development of novel medicines, but this investment is not adequately paying off in an increased rate of newly approved drugs by the FDA. The post-genomic era has provided a wealth of novel approaches for generating large, high-dimensional genetic and transcriptomic data sets from large cohorts of preclinical species as well as normal and diseased individuals. This systems biology approach to understanding disease-related biology is revolutionizing our understanding of the cellular pathways and gene networks underlying the onset of disease, and the mechanisms of pharmacological treatments that ameliorate disease phenotypes. In this article, we review a number of approaches being used by pharmaceutical and biotechnology companies, e.g., high-throughput DNA genotyping, sequencing, and genome-wide gene expression profiling, to enable drug discovery and development through the identification of new drug targets and biomarkers of disease progression, drug pharmacodynamics, and predictive markers for selecting the patients most likely to respond to therapy.
制药行业在新药的发现和开发上投入了越来越多的资金，但这一投资并没有使 FDA 批准新药的速度得到充分提高。后基因组时代为从大量临床前物种以及正常和患病个体中产生大型高维遗传和转录组数据集提供了丰富的新方法。这种系统生物学方法对于理解与疾病相关的生物学正在彻底改变我们对疾病发生的细胞途径和基因网络的理解，以及改善疾病表型的药物治疗机制。在本文中，我们回顾了制药和生物技术公司正在使用的一些方法，例如高通量 DNA 基因分型、测序和全基因组基因表达谱分析，以通过识别新的药物靶点和疾病进展、药物药效动力学以及预测标记物的生物标志物，从而为药物发现和开发提供支持，这些标记物可用于选择最有可能对治疗有反应的患者。
