Stochastic Interventional Vaccine Efficacy and Principal Surrogate Analyses of Antibody Markers as Correlates of Protection against Symptomatic COVID-19 in the COVE mRNA-1273 Trial.
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2023-09
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Abstract
The COVE trial randomized participants to receive two doses of mRNA-1273 vaccine or placebo on Days 1 and 29 (D1, D29). Anti-SARS-CoV-2 Spike IgG binding antibodies (bAbs), anti-receptor binding domain IgG bAbs, 50% inhibitory dilution neutralizing antibody (nAb) titers, and 80% inhibitory dilution nAb titers were measured at D29 and D57. We assessed these markers as correlates of protection (CoPs) against COVID-19 using stochastic interventional vaccine efficacy (SVE) analysis and principal surrogate (PS) analysis, frameworks not used in our previous COVE immune correlates analyses. By SVE analysis, hypothetical shifts of the D57 Spike IgG distribution from a geometric mean concentration (GMC) of 2737 binding antibody units (BAU)/mL (estimated vaccine efficacy (VE): 92.9% (95% CI: 91.7%, 93.9%)) to 274 BAU/mL or to 27,368 BAU/mL resulted in an overall estimated VE of 84.2% (79.0%, 88.1%) and 97.6% (97.4%, 97.7%), respectively. By binary marker PS analysis of Low and High subgroups (cut-point: 2094 BAU/mL), the ignorance interval (IGI) and estimated uncertainty interval (EUI) for VE were [85%, 90%] and (78%, 93%) for Low compared to [95%, 96%] and (92%, 97%) for High. By continuous marker PS analysis, the IGI and 95% EUI for VE at the 2.5th percentile (519.4 BAU/mL) vs. at the 97.5th percentile (9262.9 BAU/mL) of D57 Spike IgG concentration were [92.6%, 93.4%] and (89.2%, 95.7%) vs. [94.3%, 94.6%] and (89.7%, 97.0%). Results were similar for other D29 and D57 markers. Thus, the SVE and PS analyses additionally support all four markers at both time points as CoPs.
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Huang, Ying, Nima S Hejazi, Bryan Blette, Lindsay N Carpp, David Benkeser, David C Montefiori, Adrian B McDermott, Youyi Fong, et al. (2023). Stochastic Interventional Vaccine Efficacy and Principal Surrogate Analyses of Antibody Markers as Correlates of Protection against Symptomatic COVID-19 in the COVE mRNA-1273 Trial. Viruses, 15(10). p. 2029. 10.3390/v15102029 Retrieved from https://hdl.handle.net/10161/33629.
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Scholars@Duke
Marcella Sarzotti-Kelsoe
Ongoing Applied Activities
•I direct a Global Quality Assurance Program, which I developed and pioneered here at Duke University, to oversee compliance with Good Clinical Laboratory Practice Guidelines in three HIV vaccine trial networks (CHAVI, CAVD, Duke HVTN, EQAPOL, Duke VTEU) involving domestic and international laboratory sites.
•I also direct a Global Proficiency Testing Program for laboratories testing for neutralizing antibody function in individuals infected with HIV or vaccinated against HIV. The Program was launched in 2009.
•I provide assistance and oversight for endpoint assay standardization, qualification and validation, as well as for the QSU of the GMP facility at DHVI, which will manufacture HIV vaccine products for first-in-man Phase I trials.
Past Basic Research
•Development of T cell responses in neonates.
•Neonatal T cell receptor Vβ repertoire diversity in the peripheral T cell pool.
•The role of heat shock protein, as a natural adjuvant, at eliciting innate and adaptive immune responses.
•Development of the T cell receptor repertoire in naïve, immunodeficient infants, given bone marrow or thymic transplantation.
•Thymic output, T cell diversity and T cell function in long-term human SCID chimeras.
•Telomere length in T cells from SCID chimeras.
Avi Kenny
Avi Kenny is an Assistant Professor in the Department of Biostatistics and Bioinformatics at Duke University, with a secondary appointment at the Duke Global Health Institute. He holds a PhD in biostatistics from the University of Washington, where he developed statistical methods for immune correlates analysis of vaccine clinical trial data. Prior to this, he worked for five years in Liberia as the Director of Research, Monitoring, and Evaluation at Last Mile Health. His current research interests include statistical methods to handle treatment effect heterogeneity in cluster randomized trials, survival analysis using machine learning tools, evaluation of global health programs, and data quality assurance in low-resource settings.
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