Merck & Co., Inc., Kenilworth, New Jersey.
Merck & Co., Inc., Kenilworth, New Jersey
Mol Pharmacol. 2021 Apr;99(4):256-265. doi: 10.1124/molpharm.120.000142. Epub 2021 Feb 5.
The high failure rate of drugs in the clinical pipeline is likely in part the result of inadequate preclinical models, particularly those for neurologic disorders and neurodegenerative disease. Such preclinical animal models often suffer from fundamental species differences and rarely recapitulate all facets of neurologic conditions, whereas conventional two-dimensional (2D) in vitro models fail to capture the three-dimensional spatial organization and cell-to-cell interactions of brain tissue that are presumed to be critical to the function of the central nervous system. Recent studies have suggested that stem cell-derived neuronal organoids are more physiologically relevant than 2D neuronal cultures because of their cytoarchitecture, electrophysiological properties, human origin, and gene expression. Hence there is interest in incorporating such physiologically relevant models into compound screening and lead optimization efforts within drug discovery. However, despite their perceived relevance, compared with previously used preclinical models, little is known regarding their predictive value. In fact, some have been wary to broadly adopt organoid technology for drug discovery because of the low-throughput and tedious generation protocols, inherent variability, and lack of compatible moderate-to-high-throughput screening assays. Consequently, microfluidic platforms, specialized bioreactors, and automated assays have been and are being developed to address these deficits. This mini review provides an overview of the gaps to broader implementation of neuronal organoids in a drug discovery setting as well as emerging technologies that may better enable their utilization. SIGNIFICANCE STATEMENT: Neuronal organoid models offer the potential for a more physiological system in which to study neurological diseases, and efforts are being made to employ them not only in mechanistic studies but also in profiling/screening purposes within drug discovery. In addition to exploring the utility of neuronal organoid models within this context, efforts in the field aim to standardize such models for consistency and adaptation to screening platforms for throughput evaluation. This review covers potential impact of and hurdles to implementation.
药物在临床开发中失败率高的部分原因可能是临床前模型不足，尤其是神经疾病和神经退行性疾病的模型。这些临床前动物模型通常存在基本的物种差异，很少能重现神经疾病的所有方面，而传统的二维（2D）体外模型则无法捕捉到大脑组织的三维空间组织和细胞间相互作用，而这些组织被认为对中枢神经系统的功能至关重要。最近的研究表明，由于其细胞结构、电生理特性、人类起源和基因表达，干细胞衍生的神经元类器官比 2D 神经元培养物更具有生理相关性。因此，人们有兴趣将这些生理相关模型纳入化合物筛选和药物发现中的先导化合物优化工作中。然而，尽管它们被认为具有相关性，但与以前使用的临床前模型相比，人们对它们的预测价值知之甚少。事实上，由于高通量和繁琐的生成方案、固有变异性以及缺乏兼容的中高通量筛选测定法，一些人对广泛采用类器官技术用于药物发现持谨慎态度。因此，已经并正在开发微流控平台、专用生物反应器和自动化测定法来解决这些缺陷。这篇综述简要概述了将神经元类器官更广泛地应用于药物发现环境中所存在的差距，以及可能更好地利用它们的新兴技术。
神经元类器官模型为研究神经疾病提供了一个更具生理相关性的系统，人们正在努力将其不仅用于机制研究，还用于药物发现中的分析/筛选目的。除了在这种背景下探索神经元类器官模型的实用性外，该领域的努力还旨在使这些模型标准化，以实现一致性并适应高通量筛选平台的评估。本文综述了实施过程中的潜在影响和障碍。
