MAIVeSS: streamlined selection of antigenically matched, high-yield viruses for seasonal influenza vaccine production

Cheng Gao, Feng Wen, Minhui Guan, Bijaya Hatuwal, Lei Li, Beatriz Praena, Cynthia Y. Tang, Jieze Zhang, Feng Luo, Hang Xie, Richard Webby, Yizhi Jane Tao and Xiu-Feng Wan ()
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Cheng Gao: University of Missouri
Feng Wen: Mississippi State University
Minhui Guan: University of Missouri
Bijaya Hatuwal: University of Missouri
Lei Li: Georgia State University
Beatriz Praena: University of Missouri
Cynthia Y. Tang: University of Missouri
Jieze Zhang: Rice University
Feng Luo: University School of Computing, Clemson University
Hang Xie: Division of Viral Products, Office of Vaccines Research and Review, Center for Biologics Evaluation and Research, US Food and Drug Administration
Richard Webby: St. Jude Children’s Research Hospital
Yizhi Jane Tao: Rice University
Xiu-Feng Wan: University of Missouri

Nature Communications, 2024, vol. 15, issue 1, 1-15

Abstract: Abstract Vaccines are the main pharmaceutical intervention used against the global public health threat posed by influenza viruses. Timely selection of optimal seed viruses with matched antigenicity between vaccine antigen and circulating viruses and with high yield underscore vaccine efficacy and supply, respectively. Current methods for selecting influenza seed vaccines are labor intensive and time-consuming. Here, we report the Machine-learning Assisted Influenza VaccinE Strain Selection framework, MAIVeSS, that enables streamlined selection of naturally circulating, antigenically matched, and high-yield influenza vaccine strains directly from clinical samples by using molecular signatures of antigenicity and yield to support optimal candidate vaccine virus selection. We apply our framework on publicly available sequences to select A(H1N1)pdm09 vaccine candidates and experimentally confirm that these candidates have optimal antigenicity and growth in cells and eggs. Our framework can potentially reduce the optimal vaccine candidate selection time from months to days and thus facilitate timely supply of seasonal vaccines.

Date: 2024
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DOI: 10.1038/s41467-024-45145-x

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