制药行业在药物开发期间继续面临高研发(R&D)成本和临床化合物的低总体成功率。对可在早期发现中实施的健康或疾病相关和生理人类细胞模型的开发和验证的需求日益增加。从而将未来疗法的损耗转移到发现成本显着降低的程度。在早期药物发现阶段需要进行范式转变(这是漫长而昂贵的),远离简单的细胞模型,这些模型显示无法有效和高效地再现健康或人类疾病相关状态,以引导目标和化合物选择以确保安全,药理学,和功效问题。这篇透视文章涵盖了从靶标识别(ID)和验证到命中/前导发现阶段的早期药物发现的各个阶段,引线优化,和临床前安全性。我们概述了开发时应考虑的关键方面,排位赛,在这些阶段实施复杂的体外模型(CIVMs),因为诸如细胞类型之类的标准(例如,细胞系,原代细胞,干细胞,和组织),平台(例如,球体,支架或水凝胶,类器官,微生理系统,和生物打印),吞吐量,自动化,和单一和多路复用端点将有所不同。这篇文章强调需要充分限定这些eCIVM,使它们适合各种应用(例如,使用背景)药物发现和转化研究。这篇文章结束展望未来,其中组合计算建模的增加,人工智能和机器学习(AI/ML)和CIVM。
The pharmaceutical industry is continuing to face high research and development (R&D) costs and low overall success rates of clinical compounds during drug development. There is an increasing demand for development and validation of healthy or disease-relevant and physiological human cellular models that can be implemented in early-stage discovery, thereby shifting attrition of future therapeutics to a point in discovery at which the costs are significantly lower. There needs to be a paradigm shift in the early drug discovery phase (which is lengthy and costly), away from simplistic cellular models that show an inability to effectively and efficiently reproduce healthy or human disease-relevant states to steer target and compound selection for safety, pharmacology, and efficacy questions. This perspective article covers the various stages of early drug discovery from target identification (ID) and validation to the hit/lead discovery phase, lead optimization, and preclinical safety. We outline key aspects that should be considered when developing, qualifying, and implementing complex in vitro models (CIVMs) during these phases, because criteria such as cell types (e.g., cell lines, primary cells, stem cells, and tissue), platform (e.g., spheroids, scaffolds or hydrogels, organoids, microphysiological systems, and bioprinting), throughput, automation, and single and multiplexing endpoints will vary. The article emphasizes the need to adequately qualify these CIVMs such that they are suitable for various applications (e.g., context of use) of drug discovery and translational research. The article ends looking to the future, in which there is an increase in combining computational modeling, artificial intelligence and machine learning (AI/ML), and CIVMs.