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Comprehensive Physiologically Based Pharmacokinetic Model to Assess Drug–Drug Interactions of Phenytoin

Regulatory agencies worldwide expect that clinical pharmacokinetic drug–drug interactions (DDIs) between an investigational new drug and other drugs should be conducted during drug development as part of an adequate assessment of the drug’s safety and efficacy. However, it is neither time nor cost e...

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Autores principales: Rodriguez-Vera, Leyanis, Yin, Xuefen, Almoslem, Mohammed, Romahn, Karolin, Cicali, Brian, Lukacova, Viera, Cristofoletti, Rodrigo, Schmidt, Stephan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10609929/
https://www.ncbi.nlm.nih.gov/pubmed/37896246
http://dx.doi.org/10.3390/pharmaceutics15102486
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author Rodriguez-Vera, Leyanis
Yin, Xuefen
Almoslem, Mohammed
Romahn, Karolin
Cicali, Brian
Lukacova, Viera
Cristofoletti, Rodrigo
Schmidt, Stephan
author_facet Rodriguez-Vera, Leyanis
Yin, Xuefen
Almoslem, Mohammed
Romahn, Karolin
Cicali, Brian
Lukacova, Viera
Cristofoletti, Rodrigo
Schmidt, Stephan
author_sort Rodriguez-Vera, Leyanis
collection PubMed
description Regulatory agencies worldwide expect that clinical pharmacokinetic drug–drug interactions (DDIs) between an investigational new drug and other drugs should be conducted during drug development as part of an adequate assessment of the drug’s safety and efficacy. However, it is neither time nor cost efficient to test all possible DDI scenarios clinically. Phenytoin is classified by the Food and Drug Administration as a strong clinical index inducer of CYP3A4, and a moderate sensitive substrate of CYP2C9. A physiologically based pharmacokinetic (PBPK) platform model was developed using GastroPlus(®) to assess DDIs with phenytoin acting as the victim (CYP2C9, CYP2C19) or perpetrator (CYP3A4). Pharmacokinetic data were obtained from 15 different studies in healthy subjects. The PBPK model of phenytoin explains the contribution of CYP2C9 and CYP2C19 to the formation of 5-(4′-hydroxyphenyl)-5-phenylhydantoin. Furthermore, it accurately recapitulated phenytoin exposure after single and multiple intravenous and oral doses/formulations ranging from 248 to 900 mg, the dose-dependent nonlinearity and the magnitude of the effect of food on phenytoin pharmacokinetics. Once developed and verified, the model was used to characterize and predict phenytoin DDIs with fluconazole, omeprazole and itraconazole, i.e., simulated/observed DDI AUC ratio ranging from 0.89 to 1.25. This study supports the utility of the PBPK approach in informing drug development.
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spelling pubmed-106099292023-10-28 Comprehensive Physiologically Based Pharmacokinetic Model to Assess Drug–Drug Interactions of Phenytoin Rodriguez-Vera, Leyanis Yin, Xuefen Almoslem, Mohammed Romahn, Karolin Cicali, Brian Lukacova, Viera Cristofoletti, Rodrigo Schmidt, Stephan Pharmaceutics Article Regulatory agencies worldwide expect that clinical pharmacokinetic drug–drug interactions (DDIs) between an investigational new drug and other drugs should be conducted during drug development as part of an adequate assessment of the drug’s safety and efficacy. However, it is neither time nor cost efficient to test all possible DDI scenarios clinically. Phenytoin is classified by the Food and Drug Administration as a strong clinical index inducer of CYP3A4, and a moderate sensitive substrate of CYP2C9. A physiologically based pharmacokinetic (PBPK) platform model was developed using GastroPlus(®) to assess DDIs with phenytoin acting as the victim (CYP2C9, CYP2C19) or perpetrator (CYP3A4). Pharmacokinetic data were obtained from 15 different studies in healthy subjects. The PBPK model of phenytoin explains the contribution of CYP2C9 and CYP2C19 to the formation of 5-(4′-hydroxyphenyl)-5-phenylhydantoin. Furthermore, it accurately recapitulated phenytoin exposure after single and multiple intravenous and oral doses/formulations ranging from 248 to 900 mg, the dose-dependent nonlinearity and the magnitude of the effect of food on phenytoin pharmacokinetics. Once developed and verified, the model was used to characterize and predict phenytoin DDIs with fluconazole, omeprazole and itraconazole, i.e., simulated/observed DDI AUC ratio ranging from 0.89 to 1.25. This study supports the utility of the PBPK approach in informing drug development. MDPI 2023-10-18 /pmc/articles/PMC10609929/ /pubmed/37896246 http://dx.doi.org/10.3390/pharmaceutics15102486 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
Rodriguez-Vera, Leyanis
Yin, Xuefen
Almoslem, Mohammed
Romahn, Karolin
Cicali, Brian
Lukacova, Viera
Cristofoletti, Rodrigo
Schmidt, Stephan
Comprehensive Physiologically Based Pharmacokinetic Model to Assess Drug–Drug Interactions of Phenytoin
title Comprehensive Physiologically Based Pharmacokinetic Model to Assess Drug–Drug Interactions of Phenytoin
title_full Comprehensive Physiologically Based Pharmacokinetic Model to Assess Drug–Drug Interactions of Phenytoin
title_fullStr Comprehensive Physiologically Based Pharmacokinetic Model to Assess Drug–Drug Interactions of Phenytoin
title_full_unstemmed Comprehensive Physiologically Based Pharmacokinetic Model to Assess Drug–Drug Interactions of Phenytoin
title_short Comprehensive Physiologically Based Pharmacokinetic Model to Assess Drug–Drug Interactions of Phenytoin
title_sort comprehensive physiologically based pharmacokinetic model to assess drug–drug interactions of phenytoin
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10609929/
https://www.ncbi.nlm.nih.gov/pubmed/37896246
http://dx.doi.org/10.3390/pharmaceutics15102486
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