Zhu Meng-Die, Shi Xue-Hui, Wen Hui-Ping, Chen Li-Ming, Fu Dan-Dan, Du Lei, Li Jing, Wan Qian-Qian, Wang Zhi-Gang, Yu Chuanming, Pang Dai-Wen, Liu Shu-Lin
State Key Laboratory of Medicinal Chemical Biology, Frontiers Science Center for New Organic Matter, Tianjin Key Laboratory of Biosensing and Molecular Recognition, Research Center for Analytical Sciences, College of Chemistry, School of Medicine and Frontiers Science Center for Cell Responses, Nankai University, Tianjin 300071, P. R. China.
College of Chemistry and Molecular Sciences, Wuhan University, Wuhan 430072, P. R. China.
ACS Nano. 2024 Dec 31;18(52):35256-35268. doi: 10.1021/acsnano.4c10136. Epub 2024 Dec 18.
The outbreak of emerging acute viral diseases urgently requires the acceleration of specialized antiviral drug development, thus widely adopting phenotypic screening as a strategy for drug repurposing in antiviral research. However, traditional phenotypic screening methods typically require several days of experimental cycles and lack visual confirmation of a drug's ability to inhibit viral infection. Here, we report a robust method that utilizes quantum-dot-based single-virus tracking and machine learning to generate unique single-virus infection fingerprint data from viral trajectories and detect the dynamic changes in viral movement following drug administration. Our findings demonstrated that this approach can successfully identify viral infection patterns at various infection phases and predict antiviral drug efficacy through machine learning within 90 min. This method provides valuable support for assessing the efficacy of antiviral drugs and offers promising applications for responding to future outbreaks of emerging viruses.
新兴急性病毒性疾病的爆发迫切需要加速专门抗病毒药物的研发，因此广泛采用表型筛选作为抗病毒研究中药物重新利用的策略。然而，传统的表型筛选方法通常需要数天的实验周期，并且缺乏对药物抑制病毒感染能力的可视化确认。在此，我们报告了一种强大的方法，该方法利用基于量子点的单病毒追踪和机器学习，从病毒轨迹生成独特的单病毒感染指纹数据，并检测给药后病毒运动的动态变化。我们的研究结果表明，这种方法可以在90分钟内通过机器学习成功识别不同感染阶段的病毒感染模式并预测抗病毒药物疗效。该方法为评估抗病毒药物的疗效提供了有价值的支持，并为应对未来新兴病毒的爆发提供了有前景的应用。
