Guangdong Provincial Key Laboratory of Sensor Technology and Biomedical Instrument School of Biomedical Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518000, China.
Department of Biomedical Engineering, City University of Hong Kong, Kowloon, Hong Kong SAR 999077, China.
Nano Lett. 2023 Feb 22;23(4):1280-1288. doi: 10.1021/acs.nanolett.2c04456. Epub 2023 Jan 31.
Large-scale screening of molecules in organisms requires high-throughput and cost-effective evaluating tools during preclinical development. Here, a novel screening strategy combining hierarchically structured biohybrid triboelectric nanogenerators (HB-TENGs) arrays with computational bioinformatics analysis for high-throughput pharmacological evaluation using is described. Unlike the traditional methods for behavioral monitoring of the animals, which are laborious and costly, HB-TENGs with micropillars are designed to efficiently convert animals' behaviors into friction deformation and result in a contact-separation motion between two triboelectric layers to generate electrical outputs. The triboelectric signals are recorded and extracted to various bioinformation for each screened compound. Moreover, the information-rich electrical readouts are successfully demonstrated to be sufficient to predict a drug's identity by multiple-Gaussian-kernels-based machine learning methods. This proposed strategy can be readily applied to various fields and is especially useful in explorations to accelerate the identification of novel therapeutics.
在临床前开发阶段,大规模筛选生物体内的分子需要高通量且具有成本效益的评估工具。在这里,描述了一种将分层结构的生物混合摩擦纳米发电机 (HB-TENG) 阵列与计算生物信息学分析相结合的新型高通量药理学评估筛选策略。与传统的动物行为监测方法相比,这种方法具有劳动强度大、成本高的特点,而带有微柱的 HB-TENG 则被设计用来有效地将动物的行为转化为摩擦变形,并在两个摩擦电层之间产生接触-分离运动,从而产生电输出。摩擦电信号被记录并提取为每个筛选化合物的各种生物信息。此外,信息丰富的电读数成功地证明足以通过基于多高斯核的机器学习方法来预测药物的身份。该策略可以很容易地应用于各个领域,特别是在加速新型疗法的鉴定方面具有很大的作用。