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...

Descripción completa

Detalles Bibliográficos
Autores principales: Zamir, Ammara, Rasool, Muhammad Fawad, Imran, Imran, Saeed, Hamid, Khalid, Sundus, Majeed, Abdul, Rehman, Anees Ur, Ahmad, Tanveer, Alasmari, Fawaz, Alqahtani, Faleh
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2023
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
_version_ 1785091653810257920
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
work_keys_str_mv AT zamirammara physiologicallybasedpharmacokineticmodeltopredictmetoprololdispositioninhealthyanddiseasepopulations
AT rasoolmuhammadfawad physiologicallybasedpharmacokineticmodeltopredictmetoprololdispositioninhealthyanddiseasepopulations
AT imranimran physiologicallybasedpharmacokineticmodeltopredictmetoprololdispositioninhealthyanddiseasepopulations
AT saeedhamid physiologicallybasedpharmacokineticmodeltopredictmetoprololdispositioninhealthyanddiseasepopulations
AT khalidsundus physiologicallybasedpharmacokineticmodeltopredictmetoprololdispositioninhealthyanddiseasepopulations
AT majeedabdul physiologicallybasedpharmacokineticmodeltopredictmetoprololdispositioninhealthyanddiseasepopulations
AT rehmananeesur physiologicallybasedpharmacokineticmodeltopredictmetoprololdispositioninhealthyanddiseasepopulations
AT ahmadtanveer physiologicallybasedpharmacokineticmodeltopredictmetoprololdispositioninhealthyanddiseasepopulations
AT alasmarifawaz physiologicallybasedpharmacokineticmodeltopredictmetoprololdispositioninhealthyanddiseasepopulations
AT alqahtanifaleh physiologicallybasedpharmacokineticmodeltopredictmetoprololdispositioninhealthyanddiseasepopulations