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Physiologically based and population PK modeling in optimizing drug development: A predict–learn–confirm analysis

Physiologically based pharmacokinetic (PBPK) modeling and classical population pharmacokinetic (PK) model‐based simulations are increasingly used to answer various drug development questions. In this study, we propose a methodology to optimize the development of drugs, primarily cleared by the kidne...

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Detalles Bibliográficos
Autores principales: Suri, A, Chapel, S, Lu, C, Venkatakrishnan, K
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5039936/
https://www.ncbi.nlm.nih.gov/pubmed/26031410
http://dx.doi.org/10.1002/cpt.155
Descripción
Sumario:Physiologically based pharmacokinetic (PBPK) modeling and classical population pharmacokinetic (PK) model‐based simulations are increasingly used to answer various drug development questions. In this study, we propose a methodology to optimize the development of drugs, primarily cleared by the kidney, using model‐based approaches to determine the need for a dedicated renal impairment (RI) study. First, the impact of RI on drug exposure is simulated via PBPK modeling and then confirmed using classical population PK modeling of phase 2/3 data. This methodology was successfully evaluated and applied to an investigational agent, orteronel (nonsteroidal, reversible, selective 17,20‐lyase inhibitor). A phase 1 RI study confirmed the accuracy of model‐based predictions. Hence, for drugs eliminated primarily via renal clearance, this modeling approach can enable inclusion of patients with RI in phase 3 trials at appropriate doses, which may be an alternative to a dedicated RI study, or suggest that only a reduced‐size study in severe RI may be sufficient.