Current practices in drug development have led to therapeutic compounds being approved for widespread use in humans, only to be later withdrawn due to unanticipated toxicity. These occurrences are largely the result of erroneous data generated by in vivo and in vitro preclinical models that do not accurately recapitulate human physiology. Herein, a human primary cell- and stem cell-derived 3D organoid technology is employed to screen a panel of drugs that were recalled from market by the FDA. The platform is comprised of multiple tissue organoid types that remain viable for at least 28 days, in vitro. For many of these compounds, the 3D organoid system was able to demonstrate toxicity. Furthermore, organoids exposed to non-toxic compounds remained viable at clinically relevant doses. Additional experiments were performed on integrated multi-organoid systems containing liver, cardiac, lung, vascular, testis, colon, and brain. These integrated systems proved to maintain viability and expressed functional biomarkers, long-term. Examples are provided that demonstrate how multi-organoid 'body-on-a-chip' systems may be used to model the interdependent metabolism and downstream effects of drugs across multiple tissues in a single platform. Such 3D in vitro systems represent a more physiologically relevant model for drug screening and will likely reduce the cost and failure rate associated with the approval of new drugs.

中文翻译：

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在单个和集成的多有机体单芯片系统中进行药物化合物筛选。

药物开发中的当前实践已导致治疗性化合物被批准在人类中广泛使用，但由于未预料到的毒性而后来被撤回。这些现象主要是由于体内和体外临床前模型产生的错误数据所致，这些数据不能准确地概括人类的生理机能。在此，人类原代细胞和干细胞衍生的3D类器官技术被用来筛选FDA召回的一组药物。该平台由多种组织类器官组成，在体外至少可存活28天。对于许多这些化合物，3D类器官系统能够证明毒性。此外，暴露于无毒化合物的类器官在临床相关剂量下仍然可行。在包含肝脏，心脏，肺，血管，睾丸，结肠和大脑的集成多有机体系统上进行了其他实验。事实证明，这些集成系统可以长期维持活力并表达功能性生物标志物。提供的示例说明了如何在单个平台上使用多有机体“芯片上的身体”系统来建模药物在多个组织中的相互依赖的代谢和下游效应。这样的3D体外系统代表了一种更生理相关的药物筛选模型，并可能降低与新药批准相关的成本和失败率。提供的示例说明了如何在单个平台上使用多有机体“芯片上的身体”系统来建模药物在多个组织中的相互依赖的代谢和下游效应。这样的3D体外系统代表了一种更生理相关的药物筛选模型，并可能降低与新药批准相关的成本和失败率。提供的示例说明了如何在单个平台上使用多有机体“芯片上的身体”系统来建模药物在多个组织中的相互依赖的代谢和下游效应。这样的3D体外系统代表了一种更生理相关的药物筛选模型，并可能降低与新药批准相关的成本和失败率。