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通过使用表型报告系统、连接性图谱和汇集式CRISPR筛选在癌症药物发现方面的最新进展。

Recent Advances in Cancer Drug Discovery Through the Use of Phenotypic Reporter Systems, Connectivity Mapping, and Pooled CRISPR Screening.

作者信息

Salame Natasha, Fooks Katharine, El-Hachem Nehme, Bikorimana Jean-Pierre, Mercier François E, Rafei Moutih

机构信息

Department of Biomedical Sciences, Université de Montréal, Montreal, QC, Canada.

Lady Davis Institute for Medical Research, Montreal, QC, Canada.

出版信息

Front Pharmacol. 2022 Jun 20;13:852143. doi: 10.3389/fphar.2022.852143. eCollection 2022.

Abstract

Multi-omic approaches offer an unprecedented overview of the development, plasticity, and resistance of cancer. However, the translation from anti-cancer compounds identified to clinically active drugs have a notoriously low success rate. Here, we review how technical advances in cell culture, robotics, computational biology, and development of reporter systems have transformed drug discovery, enabling screening approaches tailored to clinically relevant functional readouts (e.g., bypassing drug resistance). Illustrating with selected examples of "success stories," we describe the process of phenotype-based high-throughput drug screening to target malignant cells or the immune system. Second, we describe computational approaches that link transcriptomic profiling of cancers with existing pharmaceutical compounds to accelerate drug repurposing. Finally, we review how CRISPR-based screening can be applied for the discovery of mechanisms of drug resistance and sensitization. Overall, we explore how the complementary strengths of each of these approaches allow them to transform the paradigm of pre-clinical drug development.

摘要

多组学方法为癌症的发展、可塑性和耐药性提供了前所未有的全面视角。然而,从已鉴定的抗癌化合物转化为临床活性药物的成功率极低,这是出了名的。在此,我们回顾细胞培养、机器人技术、计算生物学和报告系统开发等方面的技术进步如何改变了药物发现过程,使筛选方法能够针对临床相关的功能读数(例如,绕过耐药性)进行定制。通过“成功案例”的选定示例进行说明,我们描述了基于表型的高通量药物筛选过程,以靶向恶性细胞或免疫系统。其次,我们描述了将癌症转录组分析与现有药物化合物相联系以加速药物重新利用的计算方法。最后,我们回顾了基于CRISPR的筛选如何应用于耐药性和致敏机制的发现。总体而言,我们探讨了这些方法各自的互补优势如何使它们能够改变临床前药物开发的模式。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c8e/9250974/d9d78199b097/fphar-13-852143-g001.jpg

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