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使用 3D 微肿瘤模型的可扩展的多重药物组合筛选平台用于精准医疗。

Scalable Multiplexed Drug-Combination Screening Platforms Using 3D Microtumor Model for Precision Medicine.

机构信息

Department of Electrical Engineering and Computer Science, University of Michigan, 1301 Beal Avenue, Ann Arbor, MI, 48109-2122, USA.

University of Michigan Comprehensive Cancer Center, 1500 E. Medical Center Drive, Ann Arbor, MI, 48109, USA.

出版信息

Small. 2018 Oct;14(42):e1703617. doi: 10.1002/smll.201703617. Epub 2018 Sep 21.

Abstract

Cancer heterogeneity is a notorious hallmark of this disease, and it is desirable to tailor effective treatments for each individual patient. Drug combinations have been widely accepted in cancer treatment for better therapeutic efficacy as compared to a single compound. However, experimental complexity and cost grow exponentially with more target compounds under investigation. The primary challenge remains to efficiently perform a large-scale drug combination screening using a small number of patient primary samples for testing. Here, a scalable, easy-to-use, high-throughput drug combination screening scheme is reported, which has the potential of screening all possible pairwise drug combinations for arbitrary number of drugs with multiple logarithmic mixing ratios. A "Christmas tree mixer" structure is introduced to generate a logarithmic concentration mixing ratio between drug pairs, providing a large drug concentration range for screening. A three-layer structure design and special inlets arrangement facilitate simple drug loading process. As a proof of concept, an 8-drug combination chip is implemented, which is capable of screening 172 different treatment conditions over 1032 3D cancer spheroids on a single chip. Using both cancer cell lines and patient-derived cancer cells, effective drug combination screening is demonstrated for precision medicine.

摘要

癌症异质性是该疾病的一个显著特征,因此需要为每个个体患者量身定制有效的治疗方法。与单一化合物相比,药物联合治疗已广泛应用于癌症治疗,以提高治疗效果。然而,随着研究中目标化合物数量的增加,实验的复杂性和成本呈指数级增长。主要挑战仍然是如何使用少量患者原代样本进行高效的大规模药物组合筛选测试。本研究报道了一种可扩展、易于使用、高通量的药物组合筛选方案,该方案具有对任意数量的药物进行所有可能的两两药物组合筛选的潜力,对数混合比可达多个数量级。引入了“圣诞树混合器”结构来生成药物对之间的对数浓度混合比,为筛选提供了较大的药物浓度范围。三层结构设计和特殊入口布置便于简单的药物加载过程。作为概念验证,实施了一个 8 种药物组合芯片,该芯片能够在单个芯片上筛选 1032 个 3D 肿瘤球体的 172 种不同治疗条件。通过使用癌细胞系和患者来源的癌细胞,证明了该方案在精准医学中的有效药物组合筛选。

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