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Characterization of Correction Factors to Enable Assessment of Clinical Risk from In Vitro CYP3A4 Induction Data and Basic Drug-Drug Interaction Models

BACKGROUND AND OBJECTIVE: Induction of drug-metabolizing enzymes can lead to drug-drug interactions (DDIs); therefore, early assessment is often conducted. Previous reports focused on true positive cytochrome P450 3A (CYP3A) inducers leaving a gap in translation for in vitro inducers which do not ma...

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Autores principales: Ramsden, Diane, Fullenwider, Cody L.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9232448/
https://www.ncbi.nlm.nih.gov/pubmed/35344159
http://dx.doi.org/10.1007/s13318-022-00763-y
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author Ramsden, Diane
Fullenwider, Cody L.
author_facet Ramsden, Diane
Fullenwider, Cody L.
author_sort Ramsden, Diane
collection PubMed
description BACKGROUND AND OBJECTIVE: Induction of drug-metabolizing enzymes can lead to drug-drug interactions (DDIs); therefore, early assessment is often conducted. Previous reports focused on true positive cytochrome P450 3A (CYP3A) inducers leaving a gap in translation for in vitro inducers which do not manifest in clinical induction. The goal herein was to expand the in vitro induction dataset by including true negative clinical inducers to identify a correction factor to basic DDI models, which reduces false positives without impacting false negatives. METHODS: True negative clinical inducers were identified through a literature search, in vitro induction parameters were generated in three human hepatocyte donors, and the performance of basic induction models proposed by regulatory agencies, concentration producing twofold induction (F2), basic static model (R3) and relative induction score (RIS), was used to characterize clinical induction risk. RESULTS: The data demonstrated the importance of correcting for in vitro binding and metabolism to derive induction parameters. The aggregate analysis indicates that the RIS with a positive cut-off of < 0.7-fold area under the curve ratio (AUCR) provides the best quantitative prediction. Additionally, correction factors of ten and two times the unbound peak plasma concentration at steady state (C(max,ss,u)) can be confidently used to identify true positive inducers when referenced against the concentration resulting in twofold increase in messenger ribonucleic acid (mRNA) or using the R3 equation, respectively. CONCLUSIONS: These iterative improvements, which reduce the number of false positives, could aid regulatory recommendations and limit unnecessary clinical explorations into CYP3A induction. GRAPHICAL ABSTRACT: [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s13318-022-00763-y.
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spelling pubmed-92324482022-06-26 Characterization of Correction Factors to Enable Assessment of Clinical Risk from In Vitro CYP3A4 Induction Data and Basic Drug-Drug Interaction Models Ramsden, Diane Fullenwider, Cody L. Eur J Drug Metab Pharmacokinet Original Research Article BACKGROUND AND OBJECTIVE: Induction of drug-metabolizing enzymes can lead to drug-drug interactions (DDIs); therefore, early assessment is often conducted. Previous reports focused on true positive cytochrome P450 3A (CYP3A) inducers leaving a gap in translation for in vitro inducers which do not manifest in clinical induction. The goal herein was to expand the in vitro induction dataset by including true negative clinical inducers to identify a correction factor to basic DDI models, which reduces false positives without impacting false negatives. METHODS: True negative clinical inducers were identified through a literature search, in vitro induction parameters were generated in three human hepatocyte donors, and the performance of basic induction models proposed by regulatory agencies, concentration producing twofold induction (F2), basic static model (R3) and relative induction score (RIS), was used to characterize clinical induction risk. RESULTS: The data demonstrated the importance of correcting for in vitro binding and metabolism to derive induction parameters. The aggregate analysis indicates that the RIS with a positive cut-off of < 0.7-fold area under the curve ratio (AUCR) provides the best quantitative prediction. Additionally, correction factors of ten and two times the unbound peak plasma concentration at steady state (C(max,ss,u)) can be confidently used to identify true positive inducers when referenced against the concentration resulting in twofold increase in messenger ribonucleic acid (mRNA) or using the R3 equation, respectively. CONCLUSIONS: These iterative improvements, which reduce the number of false positives, could aid regulatory recommendations and limit unnecessary clinical explorations into CYP3A induction. GRAPHICAL ABSTRACT: [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s13318-022-00763-y. Springer International Publishing 2022-03-28 2022 /pmc/articles/PMC9232448/ /pubmed/35344159 http://dx.doi.org/10.1007/s13318-022-00763-y Text en © The Author(s) 2022 https://creativecommons.org/licenses/by-nc/4.0/Open AccessThis article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License, which permits any non-commercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) .
spellingShingle Original Research Article
Ramsden, Diane
Fullenwider, Cody L.
Characterization of Correction Factors to Enable Assessment of Clinical Risk from In Vitro CYP3A4 Induction Data and Basic Drug-Drug Interaction Models
title Characterization of Correction Factors to Enable Assessment of Clinical Risk from In Vitro CYP3A4 Induction Data and Basic Drug-Drug Interaction Models
title_full Characterization of Correction Factors to Enable Assessment of Clinical Risk from In Vitro CYP3A4 Induction Data and Basic Drug-Drug Interaction Models
title_fullStr Characterization of Correction Factors to Enable Assessment of Clinical Risk from In Vitro CYP3A4 Induction Data and Basic Drug-Drug Interaction Models
title_full_unstemmed Characterization of Correction Factors to Enable Assessment of Clinical Risk from In Vitro CYP3A4 Induction Data and Basic Drug-Drug Interaction Models
title_short Characterization of Correction Factors to Enable Assessment of Clinical Risk from In Vitro CYP3A4 Induction Data and Basic Drug-Drug Interaction Models
title_sort characterization of correction factors to enable assessment of clinical risk from in vitro cyp3a4 induction data and basic drug-drug interaction models
topic Original Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9232448/
https://www.ncbi.nlm.nih.gov/pubmed/35344159
http://dx.doi.org/10.1007/s13318-022-00763-y
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