Dynamic Profiling and Prediction of Antibody Response to Booster Inactivated Vaccines by Microsample-driven Biosensor and Machine Learning

medrxiv(2024)

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摘要
Knowledge on the antibody response to inactivated vaccines in third dose is crucial because it is one of the primary global vaccination programs. This study integrated microsampling with optical biosensors to profile neutralizing antibodies (NAbs) in fifteen vaccinated healthy donors, followed by application of machine learning to predict antibody response at given timepoints. Over a nine-month duration, microsampling and venipuncture were conducted at seven individual timepoints. A refined iteration of fiber optic-biolayer interferometry (FO-BLI) biosensor was designed, enabling rapid multiplexed biosensing of NAbs towards both wild-type and Omicron variants in minutes. Findings revealed a strong correlation (Pearson r of 0.919, specificity of 100%) between wild-type NAbs levels in microsamples and sera. Following the third dose, Sera NAbs levels for wide-type increased by 2.9-fold after seven days and 3.3-fold within a month, subsequently waning and becoming undetectable in three months. Considerable but incomplete escape of the latest omicron subvariants from booster vaccine elicited NAbs was confirmed, although a higher number of binding antibodies (BAbs) was identified by another rapid FO-BLI biosensor in minutes. Significantly, FO-BLI highly correlated with a pseudovirus neutralization assay in identifying neutralizing capacities (Pearson r of 0.983). Additionally, machine learning demonstrated exceptional accuracy in predicting antibody levels with an error of <5% for both NAbs and BAbs across multiple timepoints. Microsample-driven biosensing enables individuals to access their results within hours after self-collection, while precise models could guide personalized vaccination strategies. The technology's innate adaptability positions its potential for effective translation in diseases prevention and vaccines development. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This research was supported by the National Natural Science Foundation of China [Grant No. 82104122], the Research Center for Industries of the Future of Westlake University [Grant No. 210230006022219/001] and the Westlake University [Grant No. 10318A992001]. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: This study was conducted with the ethic approval of both the Sir Run Run Shaw (SRRS) Hospital, School of Medicine Zhejiang University (research 20210706-7) and the Institutional Review Board of Westlake University (20210301BSM001). I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced in the present study are available upon reasonable request to the authors.
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