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.
Date
2023-09
Journal Title
Journal ISSN
Volume Title
Repository Usage Stats
views
downloads
Citation Stats
Attention Stats
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.
Type
Department
Description
Provenance
Subjects
Citation
Permalink
Published Version (Please cite this version)
Publication Info
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.
This is constructed from limited available data and may be imprecise. To cite this article, please review & use the official citation provided by the journal.
Collections
Scholars@Duke
David Charles Montefiori
Dr. Montefiori is Professor and Director of the Laboratory for HIV and COVID-19 Vaccine Research & Development in the Department of Surgery, Division of Surgical Sciences at Duke University Medical Center. His major research interests are viral immunology and HIV and COVID-19 vaccine development, with a special emphasis on neutralizing antibodies.
Multiple aspects of HIV-1 neutralizing antibodies are studied in his laboratory, including mechanisms of neutralization and escape, epitope diversity among the different genetic subtypes and geographic distributions of the virus, neutralizing epitopes, requirements to elicit protective neutralizing antibodies by vaccination, optimal combinations of neutralizing antibodies for immunoprophylaxis, and novel vaccine designs for HIV-1. Dr. Montefiori also directs large vaccine immune monitoring programs funded by the NIH and the Bill & Melinda Gates Foundation that operate in compliance with Good Clinical Laboratory Practices and has served as a national and international resource for standardized assessments of neutralizing antibody responses in preclinical and clinical trials of candidate HIV vaccines since 1988.
At the onset of the COVID-19 pandemic he turned his attention to SARS-CoV-2, with a special interest in emerging variants and how they might impact transmission, vaccines and immunotherapeutics. His rapid response to emerging SARS-CoV-2 variants of concern provided some of the earliest evidence of the potential risk the variants pose to vaccines. In May 2020, his laboratory was recruited by the US Government to lead the national neutralizing antibody laboratory program for COVID-19 vaccines.
His laboratory utilizes FDA approved validated assay criteria to facilitate regulatory approvals of COVID-19 vaccines. He has published over 750 original research papers that have helped shape the scientific rationale for antibody-based vaccines.
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.
Unless otherwise indicated, scholarly articles published by Duke faculty members are made available here with a CC-BY-NC (Creative Commons Attribution Non-Commercial) license, as enabled by the Duke Open Access Policy. If you wish to use the materials in ways not already permitted under CC-BY-NC, please consult the copyright owner. Other materials are made available here through the author’s grant of a non-exclusive license to make their work openly accessible.