Butcher Eugene C, Berg Ellen L, Kunkel Eric J
Laboratory of Immunology and Vascular Biology, Department of Pathology, Stanford University School of Medicine, Stanford, California 94305-5324, USA.
Nat Biotechnol. 2004 Oct;22(10):1253-9. doi: 10.1038/nbt1017.
The hope of the rapid translation of 'genes to drugs' has foundered on the reality that disease biology is complex, and that drug development must be driven by insights into biological responses. Systems biology aims to describe and to understand the operation of complex biological systems and ultimately to develop predictive models of human disease. Although meaningful molecular level models of human cell and tissue function are a distant goal, systems biology efforts are already influencing drug discovery. Large-scale gene, protein and metabolite measurements ('omics') dramatically accelerate hypothesis generation and testing in disease models. Computer simulations integrating knowledge of organ and system-level responses help prioritize targets and design clinical trials. Automation of complex primary human cell-based assay systems designed to capture emergent properties can now integrate a broad range of disease-relevant human biology into the drug discovery process, informing target and compound validation, lead optimization, and clinical indication selection. These systems biology approaches promise to improve decision making in pharmaceutical development.
“从基因到药物”的快速转化的希望，因疾病生物学的复杂性以及药物开发必须由对生物反应的深入理解所驱动这一现实而破灭。系统生物学旨在描述和理解复杂生物系统的运作，并最终开发人类疾病的预测模型。尽管建立有意义的人类细胞和组织功能分子水平模型仍是一个遥远的目标，但系统生物学的研究已经在影响药物发现。大规模的基因、蛋白质和代谢物测量（“组学”）极大地加速了疾病模型中假设的产生和检验。整合器官和系统水平反应知识的计算机模拟有助于确定靶点的优先级并设计临床试验。旨在捕捉新兴特性的基于原代人类细胞的复杂检测系统的自动化，现在可以将广泛的与疾病相关的人类生物学知识整合到药物发现过程中，为靶点和化合物验证、先导化合物优化以及临床适应症选择提供信息。这些系统生物学方法有望改善药物研发中的决策制定。
