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Larivière Y, Garcia-Fogeda I, Zola Matuvanga T, Isekah Osang'ir B, Milolo S, Meta R, Kimbulu P, Robinson C, Katwere M, McLean C, Hens N, Matangila J, Maketa V, Mitashi P, Muhindo-Mavoko H, Van geertruyden JP, Van Damme P. Safety and Immunogenicity of the Heterologous 2-Dose Ad26.ZEBOV, MVA-BN-Filo Vaccine Regimen in Health Care Providers and Frontliners of the Democratic Republic of the Congo. J Infect Dis 2024; 229:1068-1076. [PMID: 37673423 PMCID: PMC11011182 DOI: 10.1093/infdis/jiad350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Accepted: 08/21/2023] [Indexed: 09/08/2023] Open
Abstract
BACKGROUND In response to recent Ebola epidemics, vaccine development against the Zaire ebolavirus (EBOV) has been fast-tracked in the past decade. Health care providers and frontliners working in Ebola-endemic areas are at high risk of contracting and spreading the virus. METHODS This study assessed the safety and immunogenicity of the 2-dose heterologous Ad26.ZEBOV, MVA-BN-Filo vaccine regimen (administered at a 56-day interval) among 699 health care providers and frontliners taking part in a phase 2, monocentric, randomized vaccine trial in Boende, the Democratic Republic of Congo. The first participant was enrolled and vaccinated on 18 December 2019. Serious adverse events were collected up to 6 months after the last received dose. The EBOV glycoprotein FANG ELISA (Filovirus Animal Nonclinical Group enzyme-linked immunosorbent assay) was used to measure the immunoglobulin G-binding antibody response to the EBOV glycoprotein. RESULTS The vaccine regimen was well tolerated with no vaccine-related serious adverse events reported. Twenty-one days after the second dose, an EBOV glycoprotein-specific binding antibody response was observed in 95.2% of participants. CONCLUSIONS The 2-dose vaccine regimen was well tolerated and led to a high antibody response among fully vaccinated health care providers and frontliners in Boende.
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Affiliation(s)
- Ynke Larivière
- Centre for the Evaluation of Vaccination, Vaccine and Infectious Disease Institute, University of Antwerp, Wilrijk
- Global Health Institute, Department of Family Medicine and Population Health, University of Antwerp, Wilrijk
| | - Irene Garcia-Fogeda
- Centre for Health Economics Research and Modelling Infectious Diseases, Vaccine and Infectious Diseases Institute, University of Antwerp, Antwerp, Belgium
| | - Trésor Zola Matuvanga
- Centre for the Evaluation of Vaccination, Vaccine and Infectious Disease Institute, University of Antwerp, Wilrijk
- Global Health Institute, Department of Family Medicine and Population Health, University of Antwerp, Wilrijk
- Tropical Medicine Department, University of Kinshasa, Kinshasa, Democratic Republic of the Congo
| | - Bernard Isekah Osang'ir
- Centre for the Evaluation of Vaccination, Vaccine and Infectious Disease Institute, University of Antwerp, Wilrijk
- Global Health Institute, Department of Family Medicine and Population Health, University of Antwerp, Wilrijk
| | - Solange Milolo
- Tropical Medicine Department, University of Kinshasa, Kinshasa, Democratic Republic of the Congo
| | - Rachel Meta
- Tropical Medicine Department, University of Kinshasa, Kinshasa, Democratic Republic of the Congo
| | - Primo Kimbulu
- Tropical Medicine Department, University of Kinshasa, Kinshasa, Democratic Republic of the Congo
| | | | | | | | - Niel Hens
- Centre for Health Economics Research and Modelling Infectious Diseases, Vaccine and Infectious Diseases Institute, University of Antwerp, Antwerp, Belgium
- Data Science Institute, Interuniversity Institute for Biostatistics and statistical Bioinformatics, UHasselt, Diepenbeek, Belgium
| | - Junior Matangila
- Tropical Medicine Department, University of Kinshasa, Kinshasa, Democratic Republic of the Congo
| | - Vivi Maketa
- Tropical Medicine Department, University of Kinshasa, Kinshasa, Democratic Republic of the Congo
| | - Patrick Mitashi
- Tropical Medicine Department, University of Kinshasa, Kinshasa, Democratic Republic of the Congo
| | - Hypolite Muhindo-Mavoko
- Tropical Medicine Department, University of Kinshasa, Kinshasa, Democratic Republic of the Congo
| | - Jean-Pierre Van geertruyden
- Global Health Institute, Department of Family Medicine and Population Health, University of Antwerp, Wilrijk
| | - Pierre Van Damme
- Centre for the Evaluation of Vaccination, Vaccine and Infectious Disease Institute, University of Antwerp, Wilrijk
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Garcia-Fogeda I, Besbassi H, Larivière Y, Ogunjimi B, Abrams S, Hens N. Within-host modeling to measure dynamics of antibody responses after natural infection or vaccination: A systematic review. Vaccine 2023:S0264-410X(23)00422-X. [PMID: 37198016 DOI: 10.1016/j.vaccine.2023.04.030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 04/08/2023] [Accepted: 04/10/2023] [Indexed: 05/19/2023]
Abstract
BACKGROUND Within-host models describe the dynamics of immune cells when encountering a pathogen, and how these dynamics can lead to an individual-specific immune response. This systematic review aims to summarize which within-host methodology has been used to study and quantify antibody kinetics after infection or vaccination. In particular, we focus on data-driven and theory-driven mechanistic models. MATERIALS PubMed and Web of Science databases were used to identify eligible papers published until May 2022. Eligible publications included those studying mathematical models that measure antibody kinetics as the primary outcome (ranging from phenomenological to mechanistic models). RESULTS We identified 78 eligible publications, of which 8 relied on an Ordinary Differential Equations (ODEs)-based modelling approach to describe antibody kinetics after vaccination, and 12 studies used such models in the context of humoral immunity induced by natural infection. Mechanistic modeling studies were summarized in terms of type of study, sample size, measurements collected, antibody half-life, compartments and parameters included, inferential or analytical method, and model selection. CONCLUSIONS Despite the importance of investigating antibody kinetics and underlying mechanisms of (waning of) the humoral immunity, few publications explicitly account for this in a mathematical model. In particular, most research focuses on phenomenological rather than mechanistic models. The limited information on the age groups or other risk factors that might impact antibody kinetics, as well as a lack of experimental or observational data remain important concerns regarding the interpretation of mathematical modeling results. We reviewed the similarities between the kinetics following vaccination and infection, emphasising that it may be worth translating some features from one setting to another. However, we also stress that some biological mechanisms need to be distinguished. We found that data-driven mechanistic models tend to be more simplistic, and theory-driven approaches lack representative data to validate model results.
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Affiliation(s)
- Irene Garcia-Fogeda
- Centre for Health Economics Research and Modelling Infectious Diseases (CHERMID), Vaccine & Infectious Diseases Institute (VAXINFECTIO), University of Antwerp, Antwerp, Belgium.
| | - Hajar Besbassi
- Centre for Health Economics Research and Modelling Infectious Diseases (CHERMID), Vaccine & Infectious Diseases Institute (VAXINFECTIO), University of Antwerp, Antwerp, Belgium
| | - Ynke Larivière
- Global Health Institute (GHI), Family Medicine and Population Health (FAMPOP), University of Antwerp, Antwerp, Belgium; Centre for the Evaluation of Vaccination, Vaccine & Infectious Diseases Institute (VAXINFECTIO), University of Antwerp, Antwerp, Belgium
| | - Benson Ogunjimi
- Centre for Health Economics Research and Modelling Infectious Diseases (CHERMID), Vaccine & Infectious Diseases Institute (VAXINFECTIO), University of Antwerp, Antwerp, Belgium; Antwerp Unit for Data Analysis and Computation in Immunology and Sequencing (AUDACIS), Antwerp, Belgium; Antwerp Center for Translational Immunology and Virology (ACTIV), Vaccine & Infectious Diseases Institute (VAXINFECTIO), University of Antwerp, Antwerp, Belgium; Department of Paediatrics, University Hospital Antwerp, Antwerp, Belgium
| | - Steven Abrams
- Global Health Institute (GHI), Family Medicine and Population Health (FAMPOP), University of Antwerp, Antwerp, Belgium; Data Science Institute (DSI), Interuniversity Institute for Biostatistics and statistical Bioinformatics (I-BioStat), UHasselt, Hasselt, Belgium
| | - Niel Hens
- Centre for Health Economics Research and Modelling Infectious Diseases (CHERMID), Vaccine & Infectious Diseases Institute (VAXINFECTIO), University of Antwerp, Antwerp, Belgium; Data Science Institute (DSI), Interuniversity Institute for Biostatistics and statistical Bioinformatics (I-BioStat), UHasselt, Hasselt, Belgium
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Besbassi H, Garcia-Fogeda I, Quinlivan M, Breuer J, Abrams S, Hens N, Ogunjimi B, Beutels P. Modeling antibody dynamics following herpes zoster indicates that higher varicella-zoster virus viremia generates more VZV-specific antibodies. Front Immunol 2023; 14:1104605. [PMID: 36875105 PMCID: PMC9978810 DOI: 10.3389/fimmu.2023.1104605] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 01/27/2023] [Indexed: 02/18/2023] Open
Abstract
Introduction Studying antibody dynamics following re-exposure to infection and/or vaccination is crucial for a better understanding of fundamental immunological processes, vaccine development, and health policy research. Methods We adopted a nonlinear mixed modeling approach based on ordinary differential equations (ODE) to characterize varicella-zoster virus specific antibody dynamics during and after clinical herpes zoster. Our ODEs models convert underlying immunological processes into mathematical formulations, allowing for testable data analysis. In order to cope with inter- and intra-individual variability, mixed models include population-averaged parameters (fixed effects) and individual-specific parameters (random effects). We explored the use of various ODE-based nonlinear mixed models to describe longitudinally collected markers of immunological response in 61 herpes zoster patients. Results Starting from a general formulation of such models, we study different plausible processes underlying observed antibody titer concentrations over time, including various individual-specific parameters. Among the converged models, the best fitting and most parsimonious model implies that once Varicella-zoster virus (VZV) reactivation is clinically apparent (i.e., Herpes-zoster (HZ) can be diagnosed), short-living and long-living antibody secreting cells (SASC and LASC, respectively) will not expand anymore. Additionally, we investigated the relationship between age and viral load on SASC using a covariate model to gain a deeper understanding of the population's characteristics. Conclusion The results of this study provide crucial and unique insights that can aid in improving our understanding of VZV antibody dynamics and in making more accurate projections regarding the potential impact of vaccines.
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Affiliation(s)
- Hajar Besbassi
- Centre for Health Economics Research and Modeling Infectious Diseases (CHERMID), Vaccine and Infectious Disease Institute (VAXINFECTIO), University of Antwerp, Antwerp, Belgium.,Antwerp Unit for Data Analysis and Computation in Immunology and Sequencing (AUDACIS), University of Antwerp, Antwerp, Belgium.,Antwerp Center for Translational Immunology and Virology (ACTIV), Vaccine and Infectious Disease Institute (VAXINFECTIO), University of Antwerp, Antwerp, Belgium
| | - Irene Garcia-Fogeda
- Centre for Health Economics Research and Modeling Infectious Diseases (CHERMID), Vaccine and Infectious Disease Institute (VAXINFECTIO), University of Antwerp, Antwerp, Belgium
| | - Mark Quinlivan
- Division of Infection and Immunity, University College London, London, United Kingdom
| | - Judy Breuer
- Division of Infection and Immunity, University College London, London, United Kingdom
| | - Steven Abrams
- Global Health Institute (GHI), Family Medicine and Population Health (FAMPOP), University of Antwerp, Antwerp, Belgium.,Data Science Institute (DSI), Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat), UHasselt, Hasselt, Belgium
| | - Niel Hens
- Centre for Health Economics Research and Modeling Infectious Diseases (CHERMID), Vaccine and Infectious Disease Institute (VAXINFECTIO), University of Antwerp, Antwerp, Belgium.,Data Science Institute (DSI), Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat), UHasselt, Hasselt, Belgium
| | - Benson Ogunjimi
- Centre for Health Economics Research and Modeling Infectious Diseases (CHERMID), Vaccine and Infectious Disease Institute (VAXINFECTIO), University of Antwerp, Antwerp, Belgium.,Antwerp Unit for Data Analysis and Computation in Immunology and Sequencing (AUDACIS), University of Antwerp, Antwerp, Belgium.,Antwerp Center for Translational Immunology and Virology (ACTIV), Vaccine and Infectious Disease Institute (VAXINFECTIO), University of Antwerp, Antwerp, Belgium.,Department of Paediatrics, Antwerp University Hospital, Edegem, Belgium
| | - Philippe Beutels
- Centre for Health Economics Research and Modeling Infectious Diseases (CHERMID), Vaccine and Infectious Disease Institute (VAXINFECTIO), University of Antwerp, Antwerp, Belgium
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