Learn–confirm in model‐informed drug development: Assessing an immunogenicity quantitative systems pharmacology platform

Immunogenicity against therapeutic proteins frequently causes attrition owing to its potential impact on pharmacokinetics, pharmacodynamics, efficacy, and safety. Predicting immunogenicity is complex because of its multifactorial drivers, including compound properties, subject characteristics, and t...

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Detalles Bibliográficos
Autores principales: Franssen, Linnea C., Swat, Maciej J., Kierzek, Andrzej M., Rose, Rachel H., van der Graaf, Piet H., Grimm, Hans Peter
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2022
Materias:
Perspectives
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9931426/
https://www.ncbi.nlm.nih.gov/pubmed/36418887
http://dx.doi.org/10.1002/psp4.12887
Descripción
Sumario:Immunogenicity against therapeutic proteins frequently causes attrition owing to its potential impact on pharmacokinetics, pharmacodynamics, efficacy, and safety. Predicting immunogenicity is complex because of its multifactorial drivers, including compound properties, subject characteristics, and treatment parameters. To integrate these, the Immunogenicity Simulator was developed using published, predominantly late‐stage trial data from 15 therapeutic proteins. This single‐blinded evaluation with subject‐level data from 10 further monoclonals assesses the Immunogenicity Simulator's credibility for application during the drug development process.

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