. 2023 Jan 19;14(1):331.

doi: 10.1038/s41467-022-35768-3.

Immune correlates analysis of the PREVENT-19 COVID-19 vaccine efficacy clinical trial

Youyi Fong^{1

2}, Yunda Huang^{1

3}, David Benkeser⁴, Lindsay N Carpp¹, Germán Áñez⁵, Wayne Woo⁵, Alice McGarry⁵, Lisa M Dunkle⁵, Iksung Cho⁵, Christopher R Houchens⁶, Karen Martins⁶, Lakshmi Jayashankar⁶, Flora Castellino⁶, Christos J Petropoulos⁷, Andrew Leith⁸, Deanne Haugaard⁸, Bill Webb⁸, Yiwen Lu¹, Chenchen Yu¹, Bhavesh Borate¹, Lars W P van der Laan^{1

9}, Nima S Hejazi^{1

10}, April K Randhawa¹, Michele P Andrasik¹, James G Kublin¹, Julia Hutter¹¹, Maryam Keshtkar-Jahromi¹¹, Tatiana H Beresnev¹¹, Lawrence Corey^{1

12}, Kathleen M Neuzil¹³, Dean Follmann¹⁴, Julie A Ake¹⁵, Cynthia L Gay¹⁶, Karen L Kotloff¹³, Richard A Koup^#¹⁷, Ruben O Donis^#⁶, Peter B Gilbert^#^{18

19

20}; Immune Assays Team; Coronavirus Vaccine Prevention Network (CoVPN)/2019nCoV-301 Principal Investigators and Study Team; United States Government (USG)/CoVPN Biostatistics Team

Affiliations

¹ Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA, USA.
² Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA.
³ Department of Global Health, University of Washington, Seattle, WA, 98195, USA.
⁴ Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA, USA.
⁵ Novavax, Inc., Gaithersburg, MD, USA.
⁶ Biomedical Advanced Research and Development Authority, Washington, DC, USA.
⁷ LabCorp-Monogram Biosciences, South San Francisco, CA, USA.
⁸ Nexelis, Seattle, WA, USA.
⁹ Department of Statistics, University of Washington, Seattle, WA, USA.
¹⁰ Department of Biostatistics, T.H. Chan School of Public Health, Harvard University, Boston, MA, USA.
¹¹ Division of Microbiology and Infectious Diseases, National Institute of Allergy and Infectious Diseases, NIH, Rockville, MD, USA.
¹² Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA.
¹³ Center for Vaccine Development and Global Health, University of Maryland School of Medicine, Baltimore, MD, USA.
¹⁴ Biostatistics Research Branch, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA.
¹⁵ U.S. Military HIV Research Program, Walter Reed Army Institute of Research, Silver Spring, MD, USA.
¹⁶ University of North Carolina School of Medicine, Chapel Hill, NC, USA.
¹⁷ Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA.
¹⁸ Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA, USA. pgilbert@fredhutch.org.
¹⁹ Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA. pgilbert@fredhutch.org.
²⁰ Department of Biostatistics, School of Public Health, University of Washington, Seattle, WA, USA. pgilbert@fredhutch.org.

^# Contributed equally.

PMID: 36658109
PMCID: PMC9851580
DOI: 10.1038/s41467-022-35768-3

Immune correlates analysis of the PREVENT-19 COVID-19 vaccine efficacy clinical trial

Youyi Fong et al. Nat Commun. 2023.

. 2023 Jan 19;14(1):331.

doi: 10.1038/s41467-022-35768-3.

Authors

Affiliations

¹ Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA, USA.
² Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA.
³ Department of Global Health, University of Washington, Seattle, WA, 98195, USA.
⁴ Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA, USA.
⁵ Novavax, Inc., Gaithersburg, MD, USA.
⁶ Biomedical Advanced Research and Development Authority, Washington, DC, USA.
⁷ LabCorp-Monogram Biosciences, South San Francisco, CA, USA.
⁸ Nexelis, Seattle, WA, USA.
⁹ Department of Statistics, University of Washington, Seattle, WA, USA.
¹⁰ Department of Biostatistics, T.H. Chan School of Public Health, Harvard University, Boston, MA, USA.
¹¹ Division of Microbiology and Infectious Diseases, National Institute of Allergy and Infectious Diseases, NIH, Rockville, MD, USA.
¹² Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA.
¹³ Center for Vaccine Development and Global Health, University of Maryland School of Medicine, Baltimore, MD, USA.
¹⁴ Biostatistics Research Branch, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA.
¹⁵ U.S. Military HIV Research Program, Walter Reed Army Institute of Research, Silver Spring, MD, USA.
¹⁶ University of North Carolina School of Medicine, Chapel Hill, NC, USA.
¹⁷ Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA.
¹⁸ Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA, USA. pgilbert@fredhutch.org.
¹⁹ Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA. pgilbert@fredhutch.org.
²⁰ Department of Biostatistics, School of Public Health, University of Washington, Seattle, WA, USA. pgilbert@fredhutch.org.

^# Contributed equally.

PMID: 36658109
PMCID: PMC9851580
DOI: 10.1038/s41467-022-35768-3

Erratum in

Publisher Correction: Immune correlates analysis of the PREVENT-19 COVID-19 vaccine efficacy clinical trial.
Fong Y, Huang Y, Benkeser D, Carpp LN, Áñez G, Woo W, McGarry A, Dunkle LM, Cho I, Houchens CR, Martins K, Jayashankar L, Castellino F, Petropoulos CJ, Leith A, Haugaard D, Webb B, Lu Y, Yu C, Borate B, van der Laan LWP, Hejazi NS, Randhawa AK, Andrasik MP, Kublin JG, Hutter J, Keshtkar-Jahromi M, Beresnev TH, Corey L, Neuzil KM, Follmann D, Ake JA, Gay CL, Kotloff KL, Koup RA, Donis RO, Gilbert PB; Immune Assays Team; Coronavirus Vaccine Prevention Network (CoVPN)/2019nCoV-301 Principal Investigators and Study Team; United States Government (USG)/CoVPN Biostatistics Team. Fong Y, et al. Nat Commun. 2023 Mar 22;14(1):1581. doi: 10.1038/s41467-023-37367-2. Nat Commun. 2023. PMID: 36949083 Free PMC article. No abstract available.

Abstract

In the PREVENT-19 phase 3 trial of the NVX-CoV2373 vaccine (NCT04611802), anti-spike binding IgG concentration (spike IgG), anti-RBD binding IgG concentration (RBD IgG), and pseudovirus 50% neutralizing antibody titer (nAb ID50) measured two weeks post-dose two are assessed as correlates of risk and as correlates of protection against COVID-19. Analyses are conducted in the U.S. cohort of baseline SARS-CoV-2 negative per-protocol participants using a case-cohort design that measures the markers from all 12 vaccine recipient breakthrough COVID-19 cases starting 7 days post antibody measurement and from 639 vaccine recipient non-cases. All markers are inversely associated with COVID-19 risk and directly associated with vaccine efficacy. In vaccine recipients with nAb ID50 titers of 50, 100, and 7230 international units (IU50)/ml, vaccine efficacy estimates are 75.7% (49.8%, 93.2%), 81.7% (66.3%, 93.2%), and 96.8% (88.3%, 99.3%). The results support potential cross-vaccine platform applications of these markers for guiding decisions about vaccine approval and use.

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Conflict of interest statement

All authors have completed the ICMJE uniform disclosure form at www.icmje.org/downloads/coi_disclosure.docx. P.B.G., Y.F., Y.H., D.B., Y.L., C.Y., B.B., L.W.P.v.d.L., A.K.R., M.P.A., J.G.K., L.C., and L.N.C. declare support (in the form of grant payments to their institutions) from the National Institute of Allergy and Infectious Diseases of the National Institutes of Health (NIH) for the submitted work. Y.H. additionally declares contract payments to her institution within the past 36 months from the World Health Organization to conduct statistical analysis work related to COVID-19 vaccines (outside the scope of the current work), as well as direct payment (approved by her institute) within the past 36 months from Worcester HIV Vaccine for participation on a Data Safety Monitoring Board or Advisory Board. L.D., G.A., I.C., W.W., and A.M. are stockholders and employees at Novavax, Inc. C.J.P. declares support from BARDA and the US Centers for Disease Control within the past 36 months, for SARS-CoV-2 nAb testing for vaccine trials and surveillance studies, respectively, and is a shareholder in Labcorp (LH) and in Novavax (NVAX). N.S.H. declares a grant from the National Science Foundation within the past 36 months paid to his institution. K.M.N. declares grants within the past 36 months from Pfizer to her institution to conduct clinical trials of COVID-19 vaccines (but receives no salary support from these grants), as well as grants from the NIH to participate in the overall organization of COVID-19 vaccine trials and for participation in vaccine trials. K.L.K. declares support in the form of grant payments to her institution for the submitted work; serving as the PREVENT-19 Trial co-chair, CoVPN, with salary support from the NIH paid to her institution within the past 36 months; as well as PCR Cepheid and Abbott equipment for volunteer testing from CoVPN within the past 36 months (payments to her institution; will be returned). C.L.G. is a site Principal Investigator and a CoVPN protocol co-chair for the Novavax phase 3 trial. Her institution receives grant funding from NIH that includes salary support for her from NIAID for these activities. All other authors declare no support from any organization for the submitted work, no financial relationships with any organizations that might have an interest in the submitted work in the previous 3 years, and no other relationships or activities that could appear to have influenced the submitted work.

Figures

**Fig. 1. D35 antibody marker level by COVID-19 outcome status in baseline SARS-CoV-2 negative per-protocol vaccine recipients (U.S. study sites).**
a Anti-spike IgG concentration, b anti-RBD IgG concentration, and c pseudovirus (PsV) neutralization nAb-ID50 titer (n = 12 cases, n = 639 non-cases for each a–c). The violin plots contain interior box plots with upper and lower horizontal edges the 25th and 75th percentiles of antibody level and middle line the 50th percentile, and vertical bars the distance from the 25th (or 75th) percentile of antibody level and the minimum (or maximum) antibody level within the 25th (or 75th) percentile of antibody level minus (or plus) 1.5 times the interquartile range. At both sides of the box, a rotated probability density curve estimated by a kernel density estimator with a default Gaussian kernel is plotted. Frequencies of participants with positive spike IgG/detectable nAb ID50 responses were computed with inverse probability of sampling weighting (reported at the top of the plots as “Rate”). Pos.Cut, Positivity cut-off for spike IgG defined by IgG >10.8424 BAU/ml, the assay positivity cut-off. ULoQ = 6934 BAU/ml for spike IgG. Positivity cut-off for RBD IgG defined by IgG > 30.6 BAU/ml. ULoQ = 9801 BAU/ml for RBD IgG. Seroresponse for ID50 was defined by a detectable value >limit of detection (LOD) (2.612 IU50/ml). ULoQ = 8319.938 IU50/ml for ID50. Cases experienced the primary COVID-19 endpoint starting 7 days post D35 visit through to the data cut (April 19, 2021). Non-cases are sampled into the immunogenicity subcohort with no evidence of SARS-CoV-2 infection (i.e., never tested RT-PCR positive) up to the end of the correlates study period (the data cut-off date April 19, 2021). Source data are provided in Fig. 1 Source Data file. BAU binding antibody unit; D35, Day 35 visit; ID50, 50% inhibitory dilution; nAb neutralizing antibody; RBD receptor binding domain; ULoQ upper limit of quantification.

Fig. 2. Scatterplots of pairs of D35 antibody marker values (spike IgG, RBD IgG, pseudovirus nAb-ID50) for baseline SARS-CoV-2 negative per-protocol vaccine recipients in the immunogenicity subcohort (U.S. study sites).
Source data are provided in Fig. 2 Source Data file. Corr, Spearman rank correlation; D35, Day 35 visit; nAb-ID50, 50% inhibitory dilution neutralizing antibody titer; RBD receptor binding domain.

**Fig. 3. COVID-19 risk by D35 antibody marker level in baseline SARS-CoV-2 negative per-protocol vaccine recipients (U.S. study sites).**
Baseline risk score-adjusted cumulative incidence of COVID-19 by Low, Medium, High tertile of D35 antibody marker level. a, d Anti-spike IgG concentration; b, d anti-RBD IgG concentration; c, d pseudovirus (PsV) nAb-ID50 titer, where in c the estimate for the High tertile is very similar to that for the Medium tertile and hence appears superimposed with the Medium tertile estimate. No p values for hypothesis tests are used because of the small number of COVID-19 breakthrough cases, as specified in the Statistical Analysis Plan. Source data are provided in Fig. 3 Source Data file. D35, Day 35 visit; ID50, 50% inhibitory dilution; nAb neutralizing antibody; RBD receptor binding domain.

**Fig. 4. Analyses of D35 antibody markers as a correlate of risk in baseline SARS-CoV-2 negative per-protocol vaccine recipients (U.S. study sites).**
Baseline risk score-adjusted cumulative incidence of COVID-19 by 59 days post D35 by D35 a anti-spike IgG, b anti-RBD IgG, or c pseudovirus (PsV)-nAb ID50 titer, estimated using a marginalized Cox model. The dotted black lines indicate bootstrap pointwise 95% CIs. The upper and lower horizontal gray lines are the overall cumulative incidence of COVID-19 from 7 to 59 days post D35 in placebo and vaccine recipients, respectively. Curves are plotted over the antibody marker range from the 2.5th percentile to the 97.5th percentile: 60.9 to 6934 BAU/ml for spike IgG (where 6934 is the ULOQ), 89.8 to 9801 BAU/ml for RBD IgG (where 9801 is the ULOQ), and 14.2 to 7230 IU50/ml for PsV-nAb ID50. Baseline risk score-adjusted cumulative incidence of COVID-19 by 59 days post D35 by D35 d, g anti-spike IgG, e, h anti-RBD IgG, or f, i PsV-nAb ID50 titer above a threshold. The blue dots are point estimates at each COVID-19 primary endpoint linearly interpolated by solid black lines; the gray shaded area is pointwise 95% confidence intervals (CIs). The estimates and CIs assume a non-increasing threshold-response function. The upper boundary of the green-shaded area is the estimate of the reverse cumulative distribution function (CDF) of D35 antibody marker level. The vertical red dashed line is the D35 antibody marker threshold above which no COVID-19 endpoints occurred (in the time frame of 7 days post D35 through to the data cut-off date April 19, 2021). g–i D35 antibody marker thresholds, risk estimates, and 95% confidence intervals correspond to the plots in d–f. Source data are provided in Fig. 4 Source Data file. BAU, binding antibody unit; D35, Day 35 visit; ID50, 50% inhibitory dilution; nAb neutralizing antibody; RBD receptor binding domain; ULoQ, upper limit of quantification.

**Fig. 5. Vaccine efficacy by D35 antibody marker level in baseline SARS-CoV-2 negative per-protocol vaccine recipients (U.S. study sites).**
Curves shown are for D35 a anti-spike IgG concentration, b anti-RBD concentration, or c pseudovirus (PsV)-nAb ID50 titer. The dotted black lines indicate bootstrap pointwise 95% confidence intervals. The green histogram is an estimate of the density of D35 antibody marker level, and the horizontal gray line is the overall vaccine efficacy from 7 to 59 days post D35, with the dotted gray lines indicating the 95% confidence intervals. Analyses adjusted for baseline risk score. Curves are plotted over the antibody marker range from the 2.5th percentile to the 97.5th percentile: 60.9–6934 BAU/ml for anti-spike IgG (where 6934 is the ULOQ), 89.8–9801 BAU/ml for anti-RBD IgG (where 9801 is the ULOQ), and 14.2–7230 IU50/ml for PsV-nAb ID50. Source data are provided in Fig. 5 Source Data file. BAU binding antibody unit; D35, Day 35 visit; ID50, 50% inhibitory dilution; nAb neutralizing antibody; RBD receptor binding domain; ULoQ upper limit of quantification.

Fig. 6. Vaccine efficacy (solid lines) in baseline SARS-CoV-2 negative per-protocol participants by post-vaccination antibody marker level in four randomized, placebo-controlled COVID-19 vaccine efficacy trials.
Vaccine efficacy (VE) was estimated using the marginalized Cox proportional hazards implementation of ref. . Vaccine efficacy estimates are shown by a anti-spike IgG concentration [D57 in COVE, D29 in ENSEMBLE-United States sites (ENSEMBLE-US), D35 in PREVENT-19, D56 in COV002] or by b pseudovirus (PsV)-nAb ID50 titer (D57 in COVE, D29 in ENSEMBLE-US, D35 in PREVENT-19, D56 in COV002). The dashed lines indicate bootstrap point-wise 95% confidence intervals. The follow-up periods for the VE assessment were: COVE (doses D1, D29), 7 to 100 days post D57; ENSEMBLE-US (one dose, D1), 1 to 53 days post D29; PREVENT-19 (doses D0, D21), 7 to 59 days post D35; COV002 (doses D0, D28; 28 days post D28 until the end of the study period). The histograms are an estimate of antibody marker density in baseline SARS-CoV-2 negative per-protocol vaccine recipients. Curves are plotted over the following antibody marker ranges: a COVE: 2.5th percentile to 97.5th percentile of marker, ENSEMBLE-US: 2.5th percentile to 97.5th percentile, PREVENT-19: 2.5th percentile to 97.5th percentile, COV002: 29 to 899 BAU/ml; b COVE: 10 IU50/ml to 97.5th percentile of marker, ENSEMBLE-US: 2.5th percentile to 97.5th percentile, PREVENT-19: 2.5th percentile to 97.5th percentile, COV002: 3 to 140 IU50/ml. Baseline covariates adjusted for were: COVE: baseline risk score, comorbidity status, and Community of color status; ENSEMBLE-US, baseline risk score; PREVENT-19: baseline risk score; COV002: baseline risk score. Source data are provided in Fig. 6 Source Data file. BAU binding antibody unit; D0, Day 0 visit; D1, Day 1 visit; D35, Day 35 visit; D59, Day 59 visit; ID50, 50% inhibitory dilution; IU50 international unit 50; nAb neutralizing antibody; RBD receptor binding domain; ULoQ upper limit of quantification.

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References

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Immune correlates analysis of the PREVENT-19 COVID-19 vaccine efficacy clinical trial

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Immune correlates analysis of the PREVENT-19 COVID-19 vaccine efficacy clinical trial

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