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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2015
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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 |
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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 |
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