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Advanced In Vivo Prediction by Introducing Biphasic Dissolution Data into PBPK Models

Coupling biorelevant in vitro dissolution with in silico physiological-based pharmacokinetic (PBPK) tools represents a promising method to describe and predict the in vivo performance of drug candidates in formulation development including non-passive transport, prodrug activation, and first-pass me...

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Autores principales: Denninger, Alexander, Becker, Tim, Westedt, Ulrich, Wagner, Karl G.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10386266/
https://www.ncbi.nlm.nih.gov/pubmed/37514164
http://dx.doi.org/10.3390/pharmaceutics15071978
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author Denninger, Alexander
Becker, Tim
Westedt, Ulrich
Wagner, Karl G.
author_facet Denninger, Alexander
Becker, Tim
Westedt, Ulrich
Wagner, Karl G.
author_sort Denninger, Alexander
collection PubMed
description Coupling biorelevant in vitro dissolution with in silico physiological-based pharmacokinetic (PBPK) tools represents a promising method to describe and predict the in vivo performance of drug candidates in formulation development including non-passive transport, prodrug activation, and first-pass metabolism. The objective of the present study was to assess the predictability of human pharmacokinetics by using biphasic dissolution results obtained with the previously established BiPHa+ assay and PBPK tools. For six commercial drug products, formulated by different enabling technologies, the respective organic partitioning profiles were processed with two PBPK in silico modeling tools, namely PK-Sim and GastroPlus(®), similar to extended-release dissolution profiles. Thus, a mechanistic dissolution/precipitation model of the assessed drug products was not required. The developed elimination/distribution models were used to simulate the pharmacokinetics of the evaluated drug products and compared with available human data. In essence, an in vitro to in vivo extrapolation (IVIVE) was successfully developed. Organic partitioning profiles obtained from the BiPHa+ dissolution analysis enabled highly accurate predictions of the pharmacokinetic behavior of the investigated drug products. In addition, PBPK models of (pro-)drugs with pronounced first-pass metabolism enabled adjustment of the solely passive diffusion predicting organic partitioning profiles, and increased prediction accuracy further.
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spelling pubmed-103862662023-07-30 Advanced In Vivo Prediction by Introducing Biphasic Dissolution Data into PBPK Models Denninger, Alexander Becker, Tim Westedt, Ulrich Wagner, Karl G. Pharmaceutics Article Coupling biorelevant in vitro dissolution with in silico physiological-based pharmacokinetic (PBPK) tools represents a promising method to describe and predict the in vivo performance of drug candidates in formulation development including non-passive transport, prodrug activation, and first-pass metabolism. The objective of the present study was to assess the predictability of human pharmacokinetics by using biphasic dissolution results obtained with the previously established BiPHa+ assay and PBPK tools. For six commercial drug products, formulated by different enabling technologies, the respective organic partitioning profiles were processed with two PBPK in silico modeling tools, namely PK-Sim and GastroPlus(®), similar to extended-release dissolution profiles. Thus, a mechanistic dissolution/precipitation model of the assessed drug products was not required. The developed elimination/distribution models were used to simulate the pharmacokinetics of the evaluated drug products and compared with available human data. In essence, an in vitro to in vivo extrapolation (IVIVE) was successfully developed. Organic partitioning profiles obtained from the BiPHa+ dissolution analysis enabled highly accurate predictions of the pharmacokinetic behavior of the investigated drug products. In addition, PBPK models of (pro-)drugs with pronounced first-pass metabolism enabled adjustment of the solely passive diffusion predicting organic partitioning profiles, and increased prediction accuracy further. MDPI 2023-07-19 /pmc/articles/PMC10386266/ /pubmed/37514164 http://dx.doi.org/10.3390/pharmaceutics15071978 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Denninger, Alexander
Becker, Tim
Westedt, Ulrich
Wagner, Karl G.
Advanced In Vivo Prediction by Introducing Biphasic Dissolution Data into PBPK Models
title Advanced In Vivo Prediction by Introducing Biphasic Dissolution Data into PBPK Models
title_full Advanced In Vivo Prediction by Introducing Biphasic Dissolution Data into PBPK Models
title_fullStr Advanced In Vivo Prediction by Introducing Biphasic Dissolution Data into PBPK Models
title_full_unstemmed Advanced In Vivo Prediction by Introducing Biphasic Dissolution Data into PBPK Models
title_short Advanced In Vivo Prediction by Introducing Biphasic Dissolution Data into PBPK Models
title_sort advanced in vivo prediction by introducing biphasic dissolution data into pbpk models
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10386266/
https://www.ncbi.nlm.nih.gov/pubmed/37514164
http://dx.doi.org/10.3390/pharmaceutics15071978
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