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Rapid Deployment of Antiviral Drugs Using Single-Virus Tracking and Machine Learning

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    Rapid Deployment of Antiviral Drugs Using Single-Virus Tracking and Machine Learning
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    • Meng-Die Zhu
      Meng-Die Zhu
      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
      More by Meng-Die Zhu
    • Xue-Hui Shi
      Xue-Hui Shi
      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
      More by Xue-Hui Shi
    • Hui-Ping Wen
      Hui-Ping Wen
      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
      More by Hui-Ping Wen
    • Li-Ming Chen
      Li-Ming Chen
      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
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    • Dan-Dan Fu
      Dan-Dan Fu
      College of Chemistry and Molecular Sciences, Wuhan University, Wuhan 430072, P. R. China
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    • Lei Du
      Lei Du
      College of Chemistry and Molecular Sciences, Wuhan University, Wuhan 430072, P. R. China
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    • Jing Li
      Jing Li
      College of Chemistry and Molecular Sciences, Wuhan University, Wuhan 430072, P. R. China
      More by Jing Li
    • Qian-Qian Wan
      Qian-Qian Wan
      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
      More by Qian-Qian Wan
    • Zhi-Gang Wang
      Zhi-Gang Wang
      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
      More by Zhi-Gang Wang
      Orcidhttps://orcid.org/0000-0002-3461-4014
    • Chuanming Yu
      Chuanming Yu
      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
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    • Dai-Wen Pang
      Dai-Wen Pang
      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
      More by Dai-Wen Pang
      Orcidhttps://orcid.org/0000-0002-7017-5725
    • Shu-Lin Liu*
      Shu-Lin Liu
      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
      *Email: [email protected]
      More by Shu-Lin Liu
      Orcidhttps://orcid.org/0000-0002-1043-4238
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    ACS Nano

    Cite this: ACS Nano 2024, 18, 52, 35256–35268
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    https://doi.org/10.1021/acsnano.4c10136
    Published December 18, 2024
    Copyright © 2024 American Chemical Society
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    Abstract

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    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.

    Copyright © 2024 American Chemical Society

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    Supporting Information

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    The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acsnano.4c10136.

    • Characterization of QDs, labeling efficiency evaluation, imaging condition optimization, representative images of viral movement, feature comparison among drug treatment groups, and analysis of drug inhibition on viral transport (PDF)

    • Typical trajectories of phase 2 (AVI)

    • Typical trajectories of phase 1 (AVI)

    • Typical trajectories of phase 0 (AVI)

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    ACS Nano

    Cite this: ACS Nano 2024, 18, 52, 35256–35268
    Click to copy citationCitation copied!
    https://doi.org/10.1021/acsnano.4c10136
    Published December 18, 2024
    Copyright © 2024 American Chemical Society
    Request reuse permissions

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