Sweet Eric, Yang Brenda, Chen Joshua, Vickerman Reed, Lin Yujui, Long Alison, Jacobs Eric, Wu Tinglin, Mercier Camille, Jew Ryan, Attal Yash, Liu Siyang, Chang Andrew, Lin Liwei
Department of Mechanical Engineering, University of California, Berkeley, CA 94720 USA.
Berkeley Sensor and Actuator Center, Berkeley, CA 94720 USA.
Microsyst Nanoeng. 2020 Nov 2;6:92. doi: 10.1038/s41378-020-00200-7. eCollection 2020.
Microfluidic concentration gradient generators (-CGGs) have been utilized to identify optimal drug compositions through antimicrobial susceptibility testing (AST) for the treatment of antimicrobial-resistant (AMR) infections. Conventional -CGGs fabricated via photolithography-based micromachining processes, however, are fundamentally limited to two-dimensional fluidic routing, such that only two distinct antimicrobial drugs can be tested at once. This work addresses this limitation by employing Multijet-3D-printed microchannel networks capable of fluidic routing in three dimensions to generate symmetric multidrug concentration gradients. The three-fluid gradient generation characteristics of the fabricated 3D -CGG prototype were quantified through both theoretical simulations and experimental validations. Furthermore, the antimicrobial effects of three highly clinically relevant antibiotic drugs, tetracycline, ciprofloxacin, and amikacin, were evaluated via experimental single-antibiotic minimum inhibitory concentration (MIC) and pairwise and three-way antibiotic combination drug screening (CDS) studies against model antibiotic-resistant bacteria. As such, this 3D -CGG platform has great potential to enable expedited combination AST screening for various biomedical and diagnostic applications.
微流控浓度梯度发生器(-CGGs)已被用于通过抗菌药敏试验(AST)来确定最佳药物组合,以治疗耐抗菌药物(AMR)感染。然而,通过基于光刻的微加工工艺制造的传统-CGGs,从根本上局限于二维流体路径,以至于一次只能测试两种不同的抗菌药物。这项工作通过采用能够进行三维流体路径的多喷射3D打印微通道网络来产生对称的多药物浓度梯度,解决了这一局限性。通过理论模拟和实验验证对制造的3D -CGG原型的三流体梯度生成特性进行了量化。此外,通过针对模型耐抗生素细菌的实验性单抗生素最低抑菌浓度(MIC)以及成对和三向抗生素联合药物筛选(CDS)研究,评估了三种高度临床相关的抗生素药物(四环素、环丙沙星和阿米卡星)的抗菌效果。因此,这个3D -CGG平台具有巨大潜力,能够为各种生物医学和诊断应用加快联合AST筛选。