A Quantitative Clinical Pharmacology-Based Framework For Model-Informed Vaccine Development.
Clinical trial simulation(s)
Clinical trial(s)
Dose-response
Immune response(s)
Immunogenicity
In silico modeling
Interspecies (dose) scaling
Pharmacokinetic/pharmacodynamic (PK/PD) modeling
Toxicity
Vaccine(s)
Journal
Journal of pharmaceutical sciences
ISSN: 1520-6017
Titre abrégé: J Pharm Sci
Pays: United States
ID NLM: 2985195R
Informations de publication
Date de publication:
01 2024
01 2024
Historique:
received:
06
09
2023
revised:
27
10
2023
accepted:
28
10
2023
medline:
25
12
2023
pubmed:
5
11
2023
entrez:
4
11
2023
Statut:
ppublish
Résumé
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.
Identifiants
pubmed: 37924975
pii: S0022-3549(23)00468-9
doi: 10.1016/j.xphs.2023.10.043
pii:
doi:
Substances chimiques
Vaccines
0
Types de publication
Journal Article
Review
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
22-32Informations de copyright
Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.
Déclaration de conflit d'intérêts
Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.