SYSTEMS BIOLOGY: APPLICATIONS IN DRUG DISCOVERY
SYSTEMS BIOLOGY: APPLICATIONS IN DRUG DISCOVERY
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DOI：
10.1002/0471728780.ch4
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发表时间：
2005-01-01
期刊：
影响因子：
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通讯作者：
Nikolskaya, Tatiana
中科院分区：
文献类型：
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作者：
Ekins, Sean;Bugrim, Andrej;Nikolskaya, Tatiana
在过去的几年里，生物学发生了范式转变。使用高通量生物学方法的大规模国际合作已经导致了人类基因组[1，2]，其他几种哺乳动物基因组以及数百种其他基因组的测序和注释。我们不断增长的知识基础的下一个补充将在2到4年内发生，并导致全基因组个体的多态性。基于核糖核酸（RNA）微阵列的表达技术也已经发展成为一种几乎常规的实验室技术，主导蛋白质组学、代谢组学[3]和代谢组学[4]，这些技术也取得了实质性进展，稍后将介绍，政府机构和制药公司都在这些技术上投入了大量资金，部分地基于它们将单独或组合导致药物发现效率的增加的期望。然而，尽管人类生物学取得了前所未有的技术进步和实质性进步，但基因组学迄今未能为药物发现管道做出贡献。事实上，近年来美国食品和药物管理局（FDA）批准的药物数量有所减少，将这些新药推向市场的平均成本超过10亿美元，包括营销成本和10至12年的时间。因此，药物发现的过程是漫长的，最终是复杂的，有几个经济和技术因素导致这种低效率。一个问题是对已经积累的实验数据的利用率很低，这些数据主要来自临床前研究。最近，生物信息学公司推出了强大的数据分析统计解决方案，这些解决方案主要用于数据点聚类，可视化和实验系列之间的比较。然而，仅凭这些统计数据还不足以在疾病机制方面进行有意义的数据挖掘。工业界和学术界的大多数专家都认为，只有基于对人类生物学的基本理解的数据功能分析才能解决这一瓶颈。因此，我们自身生物学的复杂性需要一种全系统的方法来分析基因组和其他分子数据。这将我们带到了系统生物学的最前沿，它专注于复杂生命系统的结构和动力学，并迅速成为人类生物学数据整合和挖掘的领先方法。
The last several years have seen a paradigm shift for biology. Large-scale international collaborations using high-throughput biology methods have resulted in the sequencing and annotation of the human genome [1, 2], several other mammalian genomes, and hundreds of other genomes as well. The next addition to our growing knowledge base will occur over 2 to 4 years and result in the genomewide individual sets of polymorphisms. The ribonucleic acid (RNA) microarray-based expression technology has also evolved into an almost routine laboratory technique dominating proteomics, metabolomics [3], and metabonomics [4], which have also made substantial progress and will be described later.Both government agencies and drug companies have invested heavily in these technologies, partly based on the expectation that they will individually or in combination lead to an increase in efficiency of drug discovery. However, despite the unprecedented technology progression and substantial advancement of human biology, genomics has so far failed to contribute to the drug discovery pipeline. In fact, the number of drugs approved by the Food and Drug Administration (FDA) decreased in recent years, with the average cost of bringing these new drugs to the market exceeding $1 billion including marketing costs and taking between 10 to 12 years [5]. The process of drug discovery is therefore lengthy and ultimately complex with several economic and technology factors contributing to this inefficiency. One problem is the poor utilization of already accumulated experimental data, mostly from preclinical research. Recently, robust statistical solutions for data analysis were introduced by bioinformatics companies, and these have been mainly used for data point clustering, visualization, and comparisons between experimental series. However, such statistics alone are not sufficient for meaningful data mining in the context of disease mechanisms. Most experts in industry and academia agree that only functional analysis of the data based on a fundamental understanding of the human biology can solve this bottleneck. The complexity of our own biology, therefore, requires a systemwide approach to genomic and other molecular data analysis. This brings us to the forefront of systems biology, which focuses on the structure and dynamics of complex life systems and is rapidly becoming a leading approach to the integration and mining of data in human biology.