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药物筛选平台和 RPPA。

Drug Screening Platforms and RPPA.

机构信息

Cancer Research UK Edinburgh Centre, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, Scotland, UK.

出版信息

Adv Exp Med Biol. 2019;1188:203-226. doi: 10.1007/978-981-32-9755-5_11.

Abstract

Since its inception as a scalable and cost-effective method for precise quantification of the abundance of multiple protein analytes and post-translational epitopes across large sample sets, reverse phase protein array (RPPA) has been utilized as a drug discovery tool. Key RPPA drug discovery applications include primary screening of abundance or activation state of nominated protein targets, secondary screening for toxicity and selectivity, mechanism-of-action profiling, biomarker discovery, and drug combination discovery. In recent decades, drug discovery strategies have evolved dramatically in response to continual advances in technology platforms supporting high-throughput screening, structure-based drug design, new therapeutic modalities, and increasingly more complex and disease-relevant cell-based and in vivo preclinical models of disease. Advances in biological laboratory capabilities in drug discovery are complemented by significant developments in bioinformatics and computational approaches for integrating large complex datasets. Bioinformatic and computational analysis of integrated molecular, pathway network and phenotypic datasets enhance multiple stages of the drug discovery process and support more informative drug target hypothesis generation and testing. In this chapter we discuss and present examples demonstrating how the latest advances in RPPA complement and integrate with other emerging drug screening platforms to support a new era of more informative and evidence-led drug discovery strategies.

摘要

自反向蛋白质阵列(RPPA)作为一种可扩展且具有成本效益的方法出现以来,它已被用于精确量化大量蛋白质分析物和翻译后表位的丰度,成为一种药物发现工具。RPPA 的主要药物发现应用包括对提名蛋白质靶标的丰度或激活状态进行初步筛选、对毒性和选择性进行二次筛选、作用机制分析、生物标志物发现和药物组合发现。在过去几十年中,药物发现策略发生了巨大变化,以应对支持高通量筛选、基于结构的药物设计、新治疗模式的技术平台的持续进步,以及越来越复杂和与疾病相关的基于细胞和体内临床前疾病模型。药物发现中生物实验室能力的进步,与用于整合大型复杂数据集的生物信息学和计算方法的重大发展相辅相成。整合分子、途径网络和表型数据集的生物信息学和计算分析增强了药物发现过程的多个阶段,并支持更具信息量的药物靶标假设生成和测试。在本章中,我们讨论并展示了如何利用 RPPA 的最新进展来补充和整合其他新兴的药物筛选平台,以支持更具信息量和循证的药物发现策略的新时代。

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