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Dari A, Pérez Ruixo JJ, Le Gars M, Struyf F, Jacqmin P. Modelling antibody dynamics in humans after different Ad26.COV2.S vaccination schemes. Br J Clin Pharmacol 2024. [PMID: 39327825 DOI: 10.1111/bcp.16251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Revised: 07/12/2024] [Accepted: 07/27/2024] [Indexed: 09/28/2024] Open
Abstract
AIMS To develop a semimechanistic model that describes the kinetic profile and variability of antibody (Ab) concentrations following vaccination with Ad26.COV2.S at different doses and dosing intervals. METHODS Data were collected from participants randomized into 5 clinical trials receiving the Ad26.COV2.S vaccine. The model considered key elements of humoral immune response, dose proportionality and the evolutionary processes of the immune response. Interindividual variability and covariates were explored. RESULTS Fast and slow kinetic phases of Ab and their evolution over time were differentiated. After first and second administrations, Ab concentrations of both phases increased less than dose proportionally, indicating a saturation of B-cell production processes. Ab concentrations produced during the fast kinetic phase increased significantly after the second administration, indicating an underlying evolutive process after antigen exposures. For the slow kinetic phase, a less pronounced increase occurred after the second and third administrations but was relatively higher in subjects who had low concentrations after the first administration. Ab concentrations of the slow phase were higher in females and decreased with age. After multiple administrations, the fast phase had Ab maximum concentrations about 5 times higher than the slow phase. The limiting kinetic factors in the fast and slow phases were the elimination rates of Ab itself and Ab producing cells, respectively. CONCLUSION The model appears suitable to quantitatively describe the inter- and intraindividual kinetics of the immune response and the impact of covariates after multiple administrations of a vaccine.
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Affiliation(s)
- Anna Dari
- Janssen Research & Development, Beerse, Belgium
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Hartmeier PR, Ostrowski SM, Busch EE, Empey KM, Meng WS. Lymphatic distribution considerations for subunit vaccine design and development. Vaccine 2024; 42:2519-2529. [PMID: 38494411 DOI: 10.1016/j.vaccine.2024.03.033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 01/30/2024] [Accepted: 03/13/2024] [Indexed: 03/19/2024]
Abstract
Subunit vaccines are an important platform for controlling current and emerging infectious diseases. The lymph nodes are the primary site generating the humoral response and delivery of antigens to these sites is critical to effective immunization. Indeed, the duration of antigen exposure within the lymph node is correlated with the antibody response. While current licensed vaccines are typically given through the intramuscular route, injecting vaccines subcutaneously allows for direct access to lymphatic vessels and therefore can enhance the transfer of antigen to the lymph nodes. However, protein subunit antigen uptake into the lymph nodes is inefficient, and subunit vaccines require adjuvants to stimulate the initial immune response. Therefore, formulation strategies have been developed to enhance the exposure of subunit proteins and adjuvants to the lymph nodes by increasing lymphatic uptake or prolonging the retention at the injection site. Given that lymph node exposure is a crucial consideration in vaccine design, in depth analyses of the pharmacokinetics of antigens and adjuvants should be the focus of future preclinical and clinical studies. This review will provide an overview of formulation strategies for targeting the lymphatics and prolonging antigen exposure and will discuss pharmacokinetic evaluations which can be applied toward vaccine development.
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Affiliation(s)
- Paul R Hartmeier
- Graduate School of Pharmaceutical Sciences, School of Pharmacy, Duquesne University, Pittsburgh, PA 15282, USA
| | - Sarah M Ostrowski
- Department of Pharmaceutical Sciences, School of Pharmacy, University of Pittsburgh, PA 15213, USA
| | - Emelia E Busch
- Graduate School of Pharmaceutical Sciences, School of Pharmacy, Duquesne University, Pittsburgh, PA 15282, USA
| | - Kerry M Empey
- Center for Clinical Pharmaceutical Sciences, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USA; Department of Pharmacy and Therapeutics, School of Pharmacy, University of Pittsburgh, PA 15213, USA; Department of Immunology, School of Medicine University of Pittsburgh, PA 15213, USA
| | - Wilson S Meng
- Graduate School of Pharmaceutical Sciences, School of Pharmacy, Duquesne University, Pittsburgh, PA 15282, USA; McGowan Institute for Regenerative Medicine, University of Pittsburgh, PA 15219, USA.
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Kim YA, Mousavi K, Yazdi A, Zwierzyna M, Cardinali M, Fox D, Peel T, Coller J, Aggarwal K, Maruggi G. Computational design of mRNA vaccines. Vaccine 2024; 42:1831-1840. [PMID: 37479613 DOI: 10.1016/j.vaccine.2023.07.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 06/23/2023] [Accepted: 07/10/2023] [Indexed: 07/23/2023]
Abstract
mRNA technology has emerged as a successful vaccine platform that offered a swift response to the COVID-19 pandemic. Accumulating evidence shows that vaccine efficacy, thermostability, and other important properties, are largely impacted by intrinsic properties of the mRNA molecule, such as RNA sequence and structure, both of which can be optimized. Designing mRNA sequence for vaccines presents a combinatorial problem due to an extremely large selection space. For instance, due to the degeneracy of the genetic code, there are over 10632 possible mRNA sequences that could encode the spike protein, the COVID-19 vaccines' target. Moreover, designing different elements of the mRNA sequence simultaneously against multiple objectives such as translational efficiency, reduced reactogenicity, and improved stability requires an efficient and sophisticated optimization strategy. Recently, there has been a growing interest in utilizing computational tools to redesign mRNA sequences to improve vaccine characteristics and expedite discovery timelines. In this review, we explore important biophysical features of mRNA to be considered for vaccine design and discuss how computational approaches can be applied to rapidly design mRNA sequences with desirable characteristics.
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Affiliation(s)
| | | | | | | | | | | | | | - Jeff Coller
- Johns Hopkins University, Baltimore, MD, USA
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Stróż S, Kosiorek P, Stasiak-Barmuta A. The COVID-19 inflammation and high mortality mechanism trigger. Immunogenetics 2024; 76:15-25. [PMID: 38063879 DOI: 10.1007/s00251-023-01326-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Accepted: 11/29/2023] [Indexed: 02/01/2024]
Abstract
The COVID-19 pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) lasted from March 2020 to May 2023, infecting over 689 million and causing 6.9 million deaths globally. SARS-CoV-2 enters human cells via the spike protein binding to ACE2 receptors, leading to viral replication and an exaggerated immune response characterized by a "cytokine storm." This review analyzes the COVID-19 pathogenesis, strains, risk factors for severe disease, and vaccine types and effectiveness. A systematic literature search for 2020-2023 was conducted. Results show the cytokine storm underlies COVID-19 pathogenesis, causing multiorgan damage. Key viral strains include Alpha, Beta, Gamma, Delta, and Omicron, differing in transmissibility, disease severity, and vaccine escape. Risk factors for severe COVID-19 include older age, obesity, and comorbidities. mRNA, viral vector, and inactivated vaccines effectively prevent hospitalization and death, although new variants exhibit some vaccine escape. Ongoing monitoring of emerging strains and vaccine effectiveness is warranted. This review provides updated information on COVID-19 pathogenesis, viral variants, risk factors, and vaccines to inform public health strategies for containment and treatment.
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Affiliation(s)
- Samuel Stróż
- Department of Clinical Immunology, Medical University of Bialystok, 15-089, 1 Jana Kilińskiego Str., Białystok, Poland.
| | - Piotr Kosiorek
- Department of Clinical Immunology, Medical University of Bialystok, 15-089, 1 Jana Kilińskiego Str., Białystok, Poland
- Department of Emergency, Maria Sklodowska-Curie Bialystok Oncology Centre, 15-027, 12 Ogrodowa Str., Białystok, Poland
| | - Anna Stasiak-Barmuta
- Department of Clinical Immunology, Medical University of Bialystok, 15-089, 1 Jana Kilińskiego Str., Białystok, Poland
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Desikan R, Germani M, van der Graaf PH, Magee M. A Quantitative Clinical Pharmacology-Based Framework For Model-Informed Vaccine Development. J Pharm Sci 2024; 113:22-32. [PMID: 37924975 DOI: 10.1016/j.xphs.2023.10.043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Revised: 10/27/2023] [Accepted: 10/28/2023] [Indexed: 11/06/2023]
Abstract
Historically, vaccine development and dose optimization have followed mostly empirical approaches without clinical pharmacology and model-informed approaches playing a major role, in contrast to conventional drug development. This is attributed to the complex cascade of immunobiological mechanisms associated with vaccines and a lack of quantitative frameworks for extracting dose-exposure-efficacy-toxicity relationships. However, the Covid-19 pandemic highlighted the lack of sufficient immunogenicity due to suboptimal vaccine dosing regimens and the need for well-designed, model-informed clinical trials which enhance the probability of selection of optimal vaccine dosing regimens. In this perspective, we attempt to develop a quantitative clinical pharmacology-based approach that integrates vaccine dose-efficacy-toxicity across various stages of vaccine development into a unified framework that we term as model-informed vaccine dose-optimization and development (MIVD). We highlight scenarios where the adoption of MIVD approaches may have a strategic advantage compared to conventional practices for vaccines.
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Affiliation(s)
- Rajat Desikan
- Clinical Pharmacology Modelling & Simulation, GSK, United Kingdom.
| | | | - Piet H van der Graaf
- Certara QSP, Canterbury Innovation Centre, University Road, Canterbury CT2 7FG, United Kingdom; Leiden Academic Centre for Drug Research, Einsteinweg 55, 2333 CC Leiden, Netherlands
| | - Mindy Magee
- Clinical Pharmacology Modelling & Simulation, GSK, United States
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