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
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

22-32

Informations 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.

Auteurs

Rajat Desikan (R)

Clinical Pharmacology Modelling & Simulation, GSK, United Kingdom. Electronic address: rajat.x.desikan@gsk.com.

Massimiliano Germani (M)

Clinical Pharmacology Modelling & Simulation, GSK, Belgium.

Piet H van der Graaf (PH)

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 (M)

Clinical Pharmacology Modelling & Simulation, GSK, United States.

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Classifications MeSH