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Prediction for Plasma Trough Concentration and Optimal Dosing of Imatinib under Multiple Clinical Situations Using Physiologically Based Pharmacokinetic Modeling
[Image: see text] (1) Purpose: This study aimed to develop a physiologically based pharmacokinetic (PBPK) model to predict the trough concentration (C(trough)) of imatinib (IMA) at steady state in patients and to explore the role of free concentration (f(up)), α1-acid glycoprotein (AGP) level, and o...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Chemical Society
2023
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10116519/ https://www.ncbi.nlm.nih.gov/pubmed/37091368 http://dx.doi.org/10.1021/acsomega.2c07967 |
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author | Gao, Dongmei Wang, Guopeng Wu, Honghai Wu, JinHua Zhao, Xiaoang |
author_facet | Gao, Dongmei Wang, Guopeng Wu, Honghai Wu, JinHua Zhao, Xiaoang |
author_sort | Gao, Dongmei |
collection | PubMed |
description | [Image: see text] (1) Purpose: This study aimed to develop a physiologically based pharmacokinetic (PBPK) model to predict the trough concentration (C(trough)) of imatinib (IMA) at steady state in patients and to explore the role of free concentration (f(up)), α1-acid glycoprotein (AGP) level, and organic cation transporter 1 (OCT1) activity/expression in clinical efficacy. (2) Methods: The population PBPK model was built using physicochemical and biochemical properties, metabolizing and transporting kinetics, tissue distribution, and human physiological parameters. (3) Results: The PBPK model successfully predicted the C(trough) of IMA administered alone in chronic phase (CP) and accelerated phase (AP) patients, the C(trough) of IMA co-administered with six modulators, and C(trough) in CP patients with hepatic impairment. Most of the ratios between predicted and observed data are within 0.70–1.30. Additionally, the recommendations for dosing adjustments for IMA have been given under multiple clinical uses. The sensitivity analysis showed that exploring the f(up) and AGP level had a significant influence on the plasma C(trough) of IMA. Meanwhile, the simulations also revealed that OCT1 activity and expression had a significant impact on the intracellular C(trough) of IMA. (4) Conclusion: The current PBPK model can accurately predict the IMA C(trough) and provide appropriate dosing adjustment recommendations in a variety of clinical situations. |
format | Online Article Text |
id | pubmed-10116519 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | American Chemical Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-101165192023-04-21 Prediction for Plasma Trough Concentration and Optimal Dosing of Imatinib under Multiple Clinical Situations Using Physiologically Based Pharmacokinetic Modeling Gao, Dongmei Wang, Guopeng Wu, Honghai Wu, JinHua Zhao, Xiaoang ACS Omega [Image: see text] (1) Purpose: This study aimed to develop a physiologically based pharmacokinetic (PBPK) model to predict the trough concentration (C(trough)) of imatinib (IMA) at steady state in patients and to explore the role of free concentration (f(up)), α1-acid glycoprotein (AGP) level, and organic cation transporter 1 (OCT1) activity/expression in clinical efficacy. (2) Methods: The population PBPK model was built using physicochemical and biochemical properties, metabolizing and transporting kinetics, tissue distribution, and human physiological parameters. (3) Results: The PBPK model successfully predicted the C(trough) of IMA administered alone in chronic phase (CP) and accelerated phase (AP) patients, the C(trough) of IMA co-administered with six modulators, and C(trough) in CP patients with hepatic impairment. Most of the ratios between predicted and observed data are within 0.70–1.30. Additionally, the recommendations for dosing adjustments for IMA have been given under multiple clinical uses. The sensitivity analysis showed that exploring the f(up) and AGP level had a significant influence on the plasma C(trough) of IMA. Meanwhile, the simulations also revealed that OCT1 activity and expression had a significant impact on the intracellular C(trough) of IMA. (4) Conclusion: The current PBPK model can accurately predict the IMA C(trough) and provide appropriate dosing adjustment recommendations in a variety of clinical situations. American Chemical Society 2023-04-03 /pmc/articles/PMC10116519/ /pubmed/37091368 http://dx.doi.org/10.1021/acsomega.2c07967 Text en © 2023 The Authors. Published by American Chemical Society https://creativecommons.org/licenses/by-nc-nd/4.0/Permits non-commercial access and re-use, provided that author attribution and integrity are maintained; but does not permit creation of adaptations or other derivative works (https://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Gao, Dongmei Wang, Guopeng Wu, Honghai Wu, JinHua Zhao, Xiaoang Prediction for Plasma Trough Concentration and Optimal Dosing of Imatinib under Multiple Clinical Situations Using Physiologically Based Pharmacokinetic Modeling |
title | Prediction for
Plasma Trough Concentration and Optimal
Dosing of Imatinib under Multiple Clinical Situations Using Physiologically
Based Pharmacokinetic Modeling |
title_full | Prediction for
Plasma Trough Concentration and Optimal
Dosing of Imatinib under Multiple Clinical Situations Using Physiologically
Based Pharmacokinetic Modeling |
title_fullStr | Prediction for
Plasma Trough Concentration and Optimal
Dosing of Imatinib under Multiple Clinical Situations Using Physiologically
Based Pharmacokinetic Modeling |
title_full_unstemmed | Prediction for
Plasma Trough Concentration and Optimal
Dosing of Imatinib under Multiple Clinical Situations Using Physiologically
Based Pharmacokinetic Modeling |
title_short | Prediction for
Plasma Trough Concentration and Optimal
Dosing of Imatinib under Multiple Clinical Situations Using Physiologically
Based Pharmacokinetic Modeling |
title_sort | prediction for
plasma trough concentration and optimal
dosing of imatinib under multiple clinical situations using physiologically
based pharmacokinetic modeling |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10116519/ https://www.ncbi.nlm.nih.gov/pubmed/37091368 http://dx.doi.org/10.1021/acsomega.2c07967 |
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