A machine learning model for predicting serum neutralizing activity against Omicron SARS-CoV-2 BA.2 and BA.4/5 sublineages in the general population.

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  • Additional Information
    • Source:
      Publisher: Wiley-Liss Country of Publication: United States NLM ID: 7705876 Publication Model: Print Cited Medium: Internet ISSN: 1096-9071 (Electronic) Linking ISSN: 01466615 NLM ISO Abbreviation: J Med Virol Subsets: MEDLINE
    • Publication Information:
      Publication: New York Ny : Wiley-Liss
      Original Publication: New York, Liss.
    • Subject Terms:
      COVID-19*/prevention & control; Humans ; COVID-19 Vaccines ; Reproducibility of Results ; SARS-CoV-2 ; Seroepidemiologic Studies ; Machine Learning ; Antibodies, Neutralizing ; Antibodies, Viral
    • Abstract:
      Supervised machine learning (ML) methods have been used to predict antibody responses elicited by COVID-19 vaccines in a variety of clinical settings. Here, we explored the reliability of a ML approach to predict the presence of detectable neutralizing antibody responses (NtAb) against Omicron BA.2 and BA.4/5 sublineages in the general population. Anti-SARS-CoV-2 receptor-binding domain (RBD) total antibodies were measured by the Elecsys® Anti-SARS-CoV-2 S assay (Roche Diagnostics) in all participants. NtAbs against Omicron BA.2 and BA4/5 were measured using a SARS-CoV-2 S pseudotyped neutralization assay in 100 randomly selected sera. A ML model was built using the variables of age, vaccination (number of doses) and SARS-CoV-2 infection status. The model was trained in a cohort (TC) comprising 931 participants and validated in an external cohort (VC) including 787 individuals. Receiver operating characteristics analysis indicated that an anti-SARS-CoV-2 RBD total antibody threshold of 2300 BAU/mL best discriminated between participants either exhibiting or not detectable Omicron BA.2 and Omicron BA.4/5-Spike targeted NtAb responses (87% and 84% precision, respectively). The ML model correctly classified 88% (793/901) of participants in the TC: 717/749 (95.7%) of those displaying ≥2300 BAU/mL and 76/152 (50%) of those exhibiting antibody levels <2300 BAU/mL. The model performed better in vaccinated participants, either with or without prior SARS-CoV-2 infection. The overall accuracy of the ML model in the VC was comparable. Our ML model, based upon a few easily collected parameters for predicting neutralizing activity against Omicron BA.2 and BA.4/5 (sub)variants circumvents the need to perform not only neutralization assays, but also anti-S serological tests, thus potentially saving costs in the setting of large seroprevalence studies.
      (© 2023 Wiley Periodicals LLC.)
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    • Contributed Indexing:
      Keywords: BA/4/5 sublineages; Omicron BA.2; SARS-CoV-2; anti-receptor-binding domain antibodies; machine learning; neutralizing antibodies
    • Accession Number:
      0 (COVID-19 Vaccines)
      0 (Antibodies, Neutralizing)
      0 (Antibodies, Viral)
    • Publication Date:
      Date Created: 20230515 Date Completed: 20230517 Latest Revision: 20230623
    • Publication Date:
      20230624
    • Accession Number:
      10.1002/jmv.28739
    • Accession Number:
      37185857