Biochemical Technologies, Science and Technology Division, Corning, Inc. , Corning, NY 14831 , USA
Expert Opin Drug Discov. 2011 Dec;6(12):1285-98. doi: 10.1517/17460441.2012.642360. Epub 2011 Dec 1.
The need to improve drug research and development productivity continues to drive innovation in pharmacological assays. Technologies that can leverage the advantages of both molecular and phenotypic assays would hold great promise for discovery of new medicines.
This article briefly reviews current label-free platforms for cell-based assays and is primarily focused on fundamental aspects of these assays using dynamic mass redistribution technology as an example. The article also presents strategies for relating label-free profiles to molecular modes of actions of drugs.
Emerging evidence suggests that label-free cellular assays are phenotypic in nature, yet permit molecular mechanistic deconvolution. Together with unique competency in throughput, sensitivity and pathway coverages, label-free cellular assays allow users to screen drugs against endogenous receptors in native cells (including disease relevant primary cells) and determine the molecular modes of action of drug molecules. However, there are challenges for label-free in both basic research and drug discovery: the deconvolution of the cellular and molecular mechanisms for the biosensor signatures of receptor-drug interactions, new methodologies for data analysis and the development of new biosensor technologies. These challenges will need to be met for the wide adoption of these assays in drug discovery.
提高药物研发生产力的需求持续推动药理学分析的创新。能够结合分子和表型分析优势的技术将为新药发现带来巨大希望。
本文简要回顾了目前基于细胞的无标记分析平台，并主要侧重于使用动态质量重分布技术作为示例的这些分析的基本方面。本文还提出了将无标记图谱与药物分子作用模式相关联的策略。
新兴证据表明，无标记细胞分析本质上是表型的，但允许分子机制分解。无标记细胞分析与高通量、灵敏度和途径覆盖范围的独特能力相结合，允许用户在天然细胞（包括与疾病相关的原代细胞）中筛选针对内源性受体的药物，并确定药物分子的分子作用模式。然而，无标记在基础研究和药物发现方面都存在挑战：用于受体-药物相互作用的生物传感器信号的细胞和分子机制的分解、数据分析的新方法以及新的生物传感器技术的开发。这些挑战需要在药物发现中广泛采用这些分析方法。
