Wu Yue, Zhou Yuyuan, Qin Xiaochen, Liu Yaling
Department of Bioengineering, Lehigh University, Bethlehem, Pennsylvania 18015, USA.
Biomicrofluidics. 2021 Nov 9;15(6):061503. doi: 10.1063/5.0062697. eCollection 2021 Dec.
Chemotherapy is one of the most effective cancer treatments. Starting from the discovery of new molecular entities, it usually takes about 10 years and 2 billion U.S. dollars to bring an effective anti-cancer drug from the benchtop to patients. Due to the physiological differences between animal models and humans, more than 90% of drug candidates failed in phase I clinical trials. Thus, a more efficient drug screening system to identify feasible compounds and pre-exclude less promising drug candidates is strongly desired. For their capability to accurately construct tumor models derived from human cells to reproduce pathological and physiological processes, microfluidic tumor chips are reliable platforms for preclinical drug screening, personalized medicine, and fundamental oncology research. This review summarizes the recent progress of the microfluidic tumor chip and highlights tumor vascularization strategies. In addition, promising imaging modalities for enhancing data acquisition and machine learning-based image analysis methods to accurately quantify the dynamics of tumor spheroids are introduced. It is believed that the microfluidic tumor chip will serve as a high-throughput, biomimetic, and multi-sensor integrated system for efficient preclinical drug evaluation in the future.
化疗是最有效的癌症治疗方法之一。从发现新的分子实体开始，将一种有效的抗癌药物从实验室带到患者手中通常需要大约10年时间和20亿美元。由于动物模型与人类之间存在生理差异，超过90%的候选药物在I期临床试验中失败。因此，迫切需要一种更高效的药物筛选系统，以识别可行的化合物并预先排除前景不佳的候选药物。微流控肿瘤芯片能够精确构建源自人类细胞的肿瘤模型，以重现病理和生理过程，是临床前药物筛选、个性化医疗和基础肿瘤学研究的可靠平台。本文综述了微流控肿瘤芯片的最新进展，并重点介绍了肿瘤血管生成策略。此外，还介绍了用于增强数据采集的有前景的成像方式以及基于机器学习的图像分析方法，以准确量化肿瘤球体的动态变化。相信微流控肿瘤芯片未来将成为用于高效临床前药物评估的高通量、仿生和多传感器集成系统。
