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Prediction for optimal dosage of pazopanib under various clinical situations using physiologically based pharmacokinetic modeling

This study aimed to apply a physiologically based pharmacokinetic (PBPK) model to predict optimal dosing regimens of pazopanib (PAZ) for safe and effective administration when co-administered with CYP3A4 inhibitors, acid-reducing agents, food, and administered in patients with hepatic impairment. He...

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Autores principales: Wu, Chunnuan, Li, Bole, Meng, Shuai, Qie, Linghui, Zhang, Jie, Wang, Guopeng, Ren, Cong Cong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9510668/
https://www.ncbi.nlm.nih.gov/pubmed/36172188
http://dx.doi.org/10.3389/fphar.2022.963311
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author Wu, Chunnuan
Li, Bole
Meng, Shuai
Qie, Linghui
Zhang, Jie
Wang, Guopeng
Ren, Cong Cong
author_facet Wu, Chunnuan
Li, Bole
Meng, Shuai
Qie, Linghui
Zhang, Jie
Wang, Guopeng
Ren, Cong Cong
author_sort Wu, Chunnuan
collection PubMed
description This study aimed to apply a physiologically based pharmacokinetic (PBPK) model to predict optimal dosing regimens of pazopanib (PAZ) for safe and effective administration when co-administered with CYP3A4 inhibitors, acid-reducing agents, food, and administered in patients with hepatic impairment. Here, we have successfully developed the population PBPK model and the predicted PK variables by this model matched well with the clinically observed data. Most ratios of prediction to observation were between 0.5 and 2.0. Suitable dosage modifications of PAZ have been identified using the PBPK simulations in various situations, i.e., 200 mg once daily (OD) or 100 mg twice daily (BID) when co-administered with the two CYP3A4 inhibitors, 200 mg BID when simultaneously administered with food or 800 mg OD when avoiding food uptake simultaneously. Additionally, the PBPK model also suggested that dosing does not need to be adjusted when co-administered with esomeprazole and administration in patients with wild hepatic impairment. Furthermore, the PBPK model also suggested that PAZ is not recommended to be administered in patients with severe hepatic impairment. In summary, the present PBPK model can determine the optimal dosing adjustment recommendations in multiple clinical uses, which cannot be achieved by only focusing on AUC linear change of PK.
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spelling pubmed-95106682022-09-27 Prediction for optimal dosage of pazopanib under various clinical situations using physiologically based pharmacokinetic modeling Wu, Chunnuan Li, Bole Meng, Shuai Qie, Linghui Zhang, Jie Wang, Guopeng Ren, Cong Cong Front Pharmacol Pharmacology This study aimed to apply a physiologically based pharmacokinetic (PBPK) model to predict optimal dosing regimens of pazopanib (PAZ) for safe and effective administration when co-administered with CYP3A4 inhibitors, acid-reducing agents, food, and administered in patients with hepatic impairment. Here, we have successfully developed the population PBPK model and the predicted PK variables by this model matched well with the clinically observed data. Most ratios of prediction to observation were between 0.5 and 2.0. Suitable dosage modifications of PAZ have been identified using the PBPK simulations in various situations, i.e., 200 mg once daily (OD) or 100 mg twice daily (BID) when co-administered with the two CYP3A4 inhibitors, 200 mg BID when simultaneously administered with food or 800 mg OD when avoiding food uptake simultaneously. Additionally, the PBPK model also suggested that dosing does not need to be adjusted when co-administered with esomeprazole and administration in patients with wild hepatic impairment. Furthermore, the PBPK model also suggested that PAZ is not recommended to be administered in patients with severe hepatic impairment. In summary, the present PBPK model can determine the optimal dosing adjustment recommendations in multiple clinical uses, which cannot be achieved by only focusing on AUC linear change of PK. Frontiers Media S.A. 2022-09-12 /pmc/articles/PMC9510668/ /pubmed/36172188 http://dx.doi.org/10.3389/fphar.2022.963311 Text en Copyright © 2022 Wu, Li, Meng, Qie, Zhang, Wang and Ren. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Pharmacology
Wu, Chunnuan
Li, Bole
Meng, Shuai
Qie, Linghui
Zhang, Jie
Wang, Guopeng
Ren, Cong Cong
Prediction for optimal dosage of pazopanib under various clinical situations using physiologically based pharmacokinetic modeling
title Prediction for optimal dosage of pazopanib under various clinical situations using physiologically based pharmacokinetic modeling
title_full Prediction for optimal dosage of pazopanib under various clinical situations using physiologically based pharmacokinetic modeling
title_fullStr Prediction for optimal dosage of pazopanib under various clinical situations using physiologically based pharmacokinetic modeling
title_full_unstemmed Prediction for optimal dosage of pazopanib under various clinical situations using physiologically based pharmacokinetic modeling
title_short Prediction for optimal dosage of pazopanib under various clinical situations using physiologically based pharmacokinetic modeling
title_sort prediction for optimal dosage of pazopanib under various clinical situations using physiologically based pharmacokinetic modeling
topic Pharmacology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9510668/
https://www.ncbi.nlm.nih.gov/pubmed/36172188
http://dx.doi.org/10.3389/fphar.2022.963311
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