Department of Pharmaceutical Sciences, University at Buffalo, State University of New York, Buffalo, New York, USA.
Clin Pharmacol Ther. 2013 Dec;94(6):651-8. doi: 10.1038/clpt.2013.176. Epub 2013 Sep 11.
Identification of novel targets is a critical first step in the drug discovery and development process. Most diseases such as cancer, metabolic disorders, and neurological disorders are complex, and their pathogenesis involves multiple genetic and environmental factors. Finding a viable drug target-drug combination with high potential for yielding clinical success within the efficacy-toxicity spectrum is extremely challenging. Many examples are now available in which network-based approaches show potential for the identification of novel targets and for the repositioning of established targets. The objective of this article is to highlight network approaches for identifying novel targets with greater chances of gaining approved drugs with maximal efficacy and minimal side effects. Further enhancement of these approaches may emerge from effectively integrating computational systems biology with pharmacodynamic systems analysis. Coupling genomics, proteomics, and metabolomics databases with systems pharmacology modeling may aid in the development of disease-specific networks that can be further used to build confidence in target identification.
鉴定新的靶点是药物发现和开发过程中的关键第一步。大多数疾病,如癌症、代谢紊乱和神经紊乱等,都很复杂,其发病机制涉及多个遗传和环境因素。在疗效-毒性范围内找到一种具有高临床成功潜力的可行药物靶点-药物组合极具挑战性。现在有许多例子表明,基于网络的方法在鉴定新的靶点和重新定位已建立的靶点方面具有潜力。本文的目的是强调网络方法,以确定具有更大获得批准药物的机会,这些药物具有最大的疗效和最小的副作用。通过有效地将计算系统生物学与药效系统分析相结合,这些方法可能会得到进一步增强。将基因组学、蛋白质组学和代谢组学数据库与系统药理学模型相结合,可能有助于开发特定于疾病的网络,进一步用于建立目标鉴定的信心。