Cargando…

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...

Descripción completa

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
_version_ 1782456153169461248
author Suri, A
Chapel, S
Lu, C
Venkatakrishnan, K
author_facet Suri, A
Chapel, S
Lu, C
Venkatakrishnan, K
author_sort Suri, A
collection PubMed
description 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.
format Online
Article
Text
id pubmed-5039936
institution National Center for Biotechnology Information
language English
publishDate 2015
publisher John Wiley and Sons Inc.
record_format MEDLINE/PubMed
spelling pubmed-50399362016-09-30 Physiologically based and population PK modeling in optimizing drug development: A predict–learn–confirm analysis Suri, A Chapel, S Lu, C Venkatakrishnan, K Clin Pharmacol Ther Research 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. John Wiley and Sons Inc. 2015-07-14 2015-09 /pmc/articles/PMC5039936/ /pubmed/26031410 http://dx.doi.org/10.1002/cpt.155 Text en © 2015 The Authors. Clinical Pharmacology & Therapeutics published by Wiley Periodicals, Inc. on behalf of The American Society for Clinical Pharmacology and Therapeutics. This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial‐NoDerivs (http://creativecommons.org/licenses/by-nc-nd/3.0/) License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.
spellingShingle Research
Suri, A
Chapel, S
Lu, C
Venkatakrishnan, K
Physiologically based and population PK modeling in optimizing drug development: A predict–learn–confirm analysis
title Physiologically based and population PK modeling in optimizing drug development: A predict–learn–confirm analysis
title_full Physiologically based and population PK modeling in optimizing drug development: A predict–learn–confirm analysis
title_fullStr Physiologically based and population PK modeling in optimizing drug development: A predict–learn–confirm analysis
title_full_unstemmed Physiologically based and population PK modeling in optimizing drug development: A predict–learn–confirm analysis
title_short Physiologically based and population PK modeling in optimizing drug development: A predict–learn–confirm analysis
title_sort physiologically based and population pk modeling in optimizing drug development: a predict–learn–confirm analysis
topic Research
url 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
work_keys_str_mv AT suria physiologicallybasedandpopulationpkmodelinginoptimizingdrugdevelopmentapredictlearnconfirmanalysis
AT chapels physiologicallybasedandpopulationpkmodelinginoptimizingdrugdevelopmentapredictlearnconfirmanalysis
AT luc physiologicallybasedandpopulationpkmodelinginoptimizingdrugdevelopmentapredictlearnconfirmanalysis
AT venkatakrishnank physiologicallybasedandpopulationpkmodelinginoptimizingdrugdevelopmentapredictlearnconfirmanalysis