Physiologically Based Pharmacokinetic Model To Predict Metoprolol Disposition in Healthy and Disease Populations
[Image: see text] The evolution in the development of drugs has increased the popularity of physiologically based pharmacokinetic (PBPK) models. This study seeks to assess the PK of metoprolol in populations with healthy, chronic kidney disease (CKD), and acute myocardial infarction (AMI) conditions...
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/PMC10433471/ https://www.ncbi.nlm.nih.gov/pubmed/37599939 http://dx.doi.org/10.1021/acsomega.3c02673 |
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author | Zamir, Ammara Rasool, Muhammad Fawad Imran, Imran Saeed, Hamid Khalid, Sundus Majeed, Abdul Rehman, Anees Ur Ahmad, Tanveer Alasmari, Fawaz Alqahtani, Faleh |
author_facet | Zamir, Ammara Rasool, Muhammad Fawad Imran, Imran Saeed, Hamid Khalid, Sundus Majeed, Abdul Rehman, Anees Ur Ahmad, Tanveer Alasmari, Fawaz Alqahtani, Faleh |
author_sort | Zamir, Ammara |
collection | PubMed |
description | [Image: see text] The evolution in the development of drugs has increased the popularity of physiologically based pharmacokinetic (PBPK) models. This study seeks to assess the PK of metoprolol in populations with healthy, chronic kidney disease (CKD), and acute myocardial infarction (AMI) conditions by developing and evaluating PBPK models. An extensive literature review for identifying and selecting plasma concentration vs time profile data and other drug-related parameters was undergone for their integration into the PK-Sim program followed by the development of intravenous, oral, and diseased models. The developed PBPK model of metoprolol was then evaluated using the visual predictive checks, mean observed/predicted ratios (R(obs/pre)), and average fold error for all PK parameters, i.e., the area under the curve (AUC), maximal plasma concentration, and clearance. The model evaluation depicted that none of the PK parameters were out of the allowed range (2-fold error) in the case of the mean R(obs/pre) ratios. The model anticipations were executed to determine the influence of diseases on unbound and total AUC after the application of metoprolol in healthy, moderate, and severe CKD. The dosage reductions were also suggested based on differences in unbound and total AUC in different stages of CKD. The developed PBPK models have successfully elaborated the PK changes of metoprolol occurring in healthy individuals and those with renal and heart diseases (CKD & AMI), which may be fruitful for dose optimization among diseased patients. |
format | Online Article Text |
id | pubmed-10433471 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | American Chemical Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-104334712023-08-18 Physiologically Based Pharmacokinetic Model To Predict Metoprolol Disposition in Healthy and Disease Populations Zamir, Ammara Rasool, Muhammad Fawad Imran, Imran Saeed, Hamid Khalid, Sundus Majeed, Abdul Rehman, Anees Ur Ahmad, Tanveer Alasmari, Fawaz Alqahtani, Faleh ACS Omega [Image: see text] The evolution in the development of drugs has increased the popularity of physiologically based pharmacokinetic (PBPK) models. This study seeks to assess the PK of metoprolol in populations with healthy, chronic kidney disease (CKD), and acute myocardial infarction (AMI) conditions by developing and evaluating PBPK models. An extensive literature review for identifying and selecting plasma concentration vs time profile data and other drug-related parameters was undergone for their integration into the PK-Sim program followed by the development of intravenous, oral, and diseased models. The developed PBPK model of metoprolol was then evaluated using the visual predictive checks, mean observed/predicted ratios (R(obs/pre)), and average fold error for all PK parameters, i.e., the area under the curve (AUC), maximal plasma concentration, and clearance. The model evaluation depicted that none of the PK parameters were out of the allowed range (2-fold error) in the case of the mean R(obs/pre) ratios. The model anticipations were executed to determine the influence of diseases on unbound and total AUC after the application of metoprolol in healthy, moderate, and severe CKD. The dosage reductions were also suggested based on differences in unbound and total AUC in different stages of CKD. The developed PBPK models have successfully elaborated the PK changes of metoprolol occurring in healthy individuals and those with renal and heart diseases (CKD & AMI), which may be fruitful for dose optimization among diseased patients. American Chemical Society 2023-08-03 /pmc/articles/PMC10433471/ /pubmed/37599939 http://dx.doi.org/10.1021/acsomega.3c02673 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 | Zamir, Ammara Rasool, Muhammad Fawad Imran, Imran Saeed, Hamid Khalid, Sundus Majeed, Abdul Rehman, Anees Ur Ahmad, Tanveer Alasmari, Fawaz Alqahtani, Faleh Physiologically Based Pharmacokinetic Model To Predict Metoprolol Disposition in Healthy and Disease Populations |
title | Physiologically
Based Pharmacokinetic
Model To Predict Metoprolol Disposition in Healthy and Disease Populations |
title_full | Physiologically
Based Pharmacokinetic
Model To Predict Metoprolol Disposition in Healthy and Disease Populations |
title_fullStr | Physiologically
Based Pharmacokinetic
Model To Predict Metoprolol Disposition in Healthy and Disease Populations |
title_full_unstemmed | Physiologically
Based Pharmacokinetic
Model To Predict Metoprolol Disposition in Healthy and Disease Populations |
title_short | Physiologically
Based Pharmacokinetic
Model To Predict Metoprolol Disposition in Healthy and Disease Populations |
title_sort | physiologically
based pharmacokinetic
model to predict metoprolol disposition in healthy and disease populations |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10433471/ https://www.ncbi.nlm.nih.gov/pubmed/37599939 http://dx.doi.org/10.1021/acsomega.3c02673 |
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