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Analysis of Host Immunological Response of Adenovirus-Based COVID-19 Vaccines (preprint)
medrxiv; 2021.
Preprint in English | medRxiv | ID: ppzbmed-10.1101.2021.06.24.21259460
ABSTRACT
During the SARS-CoV-2 global pandemic, several vaccines, including mRNA and ade-novirus vector approaches, have received emergency or full approval. However, supply chain logistics have hampered global vaccine delivery, which is impacting mass vaccination strategies. Recent studies have identified different strategies for vaccine dose administration so that supply constraints issues are diminished. These include increasing the time between consecutive doses in a two-dose vaccine regimen and reducing the dosage of the second dose. We consider both of these strategies in a mathematical modeling study of a non-replicating viral vector adenovirus vaccine in this work. We investigate the impact of different prime-boost strategies by quantifying their effects on immunological outcomes based on simple ordinary differential equations. The boost dose is administered either at a standard dose (SD) of 1000 or at a low dose (LD) of 500 or 250 vaccine particles. Simulated Second dose fractionation highlights previously shown dose-dependent features of the immune mechanism. In agreement with clinical characteristics of 175 COVID-19 recovered patients, the model predictions for either SD/SD or SD/LD regimens mainly show that by stretching the prime-boost interval until 18 or 20 weeks, the minimum promoted antibody (Nab) response is comparable with the neutralizing antibody level of COVID-19 recovered patients. The minimum stimulated antibody in SD/SD regimen is identical with the high level of clinical trial data. It is at the same range of the medium-high level of Nab in SD/LD, where the second dose is half or quarter of the standard dose.
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Full text: Available Collection: Preprints Database: medRxiv Main subject: COVID-19 Language: English Year: 2021 Document Type: Preprint

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Full text: Available Collection: Preprints Database: medRxiv Main subject: COVID-19 Language: English Year: 2021 Document Type: Preprint