Development and evaluation of physiologically based pharmacokinetic drug-disease models for predicting captopril pharmacokinetics in chronic diseases

Abstract The advancement in the processing speeds of computing machines has facilitated the development of complex physiologically based pharmacokinetic (PBPK) models. These PBPK models can incorporate disease-specific data and could be used to predict pharmacokinetics (PK) of administered drugs in...

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Autores principales:	Muhammad F. Rasool, Shazia Ali, Sundus Khalid, Ramsha Khalid, Abdul Majeed, Imran Imran, Hamid Saeed, Muhammad Usman, Mohsin Ali, Amer S. Alali, Abdullah F. AlAsmari, Nemat Ali, Ali Mohammed Asiri, Fawaz Alasmari, Faleh Alqahtani
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/5645c673416f4b848546c5cbfcdc70be
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spelling	oai:doaj.org-article:5645c673416f4b848546c5cbfcdc70be2021-12-02T13:39:29ZDevelopment and evaluation of physiologically based pharmacokinetic drug-disease models for predicting captopril pharmacokinetics in chronic diseases10.1038/s41598-021-88154-22045-2322https://doaj.org/article/5645c673416f4b848546c5cbfcdc70be2021-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-88154-2https://doaj.org/toc/2045-2322Abstract The advancement in the processing speeds of computing machines has facilitated the development of complex physiologically based pharmacokinetic (PBPK) models. These PBPK models can incorporate disease-specific data and could be used to predict pharmacokinetics (PK) of administered drugs in different chronic conditions. The present study aimed to develop and evaluate PBPK drug-disease models for captopril after incorporating relevant pathophysiological changes occurring in adult chronic kidney disease (CKD) and chronic heart failure (CHF) populations. The population-based PBPK simulator Simcyp was used as a modeling and simulation platform. The visual predictive checks and mean observed/predicted ratios (ratio(Obs/pred)) of the PK parameters were used for model evaluation. The developed disease models were successful in predicting captopril PK in all three stages of CKD (mild, moderate, and severe) and CHF, as the observed and predicted PK profiles and the ratio(obs/pred) for the PK parameters were in close agreement. The developed captopril PBPK models can assist in tailoring captopril dosages in patients with different disease severity (CKD and CHF).Muhammad F. RasoolShazia AliSundus KhalidRamsha KhalidAbdul MajeedImran ImranHamid SaeedMuhammad UsmanMohsin AliAmer S. AlaliAbdullah F. AlAsmariNemat AliAli Mohammed AsiriFawaz AlasmariFaleh AlqahtaniNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-16 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Muhammad F. Rasool Shazia Ali Sundus Khalid Ramsha Khalid Abdul Majeed Imran Imran Hamid Saeed Muhammad Usman Mohsin Ali Amer S. Alali Abdullah F. AlAsmari Nemat Ali Ali Mohammed Asiri Fawaz Alasmari Faleh Alqahtani Development and evaluation of physiologically based pharmacokinetic drug-disease models for predicting captopril pharmacokinetics in chronic diseases
description	Abstract The advancement in the processing speeds of computing machines has facilitated the development of complex physiologically based pharmacokinetic (PBPK) models. These PBPK models can incorporate disease-specific data and could be used to predict pharmacokinetics (PK) of administered drugs in different chronic conditions. The present study aimed to develop and evaluate PBPK drug-disease models for captopril after incorporating relevant pathophysiological changes occurring in adult chronic kidney disease (CKD) and chronic heart failure (CHF) populations. The population-based PBPK simulator Simcyp was used as a modeling and simulation platform. The visual predictive checks and mean observed/predicted ratios (ratio(Obs/pred)) of the PK parameters were used for model evaluation. The developed disease models were successful in predicting captopril PK in all three stages of CKD (mild, moderate, and severe) and CHF, as the observed and predicted PK profiles and the ratio(obs/pred) for the PK parameters were in close agreement. The developed captopril PBPK models can assist in tailoring captopril dosages in patients with different disease severity (CKD and CHF).
format	article
author	Muhammad F. Rasool Shazia Ali Sundus Khalid Ramsha Khalid Abdul Majeed Imran Imran Hamid Saeed Muhammad Usman Mohsin Ali Amer S. Alali Abdullah F. AlAsmari Nemat Ali Ali Mohammed Asiri Fawaz Alasmari Faleh Alqahtani
author_facet	Muhammad F. Rasool Shazia Ali Sundus Khalid Ramsha Khalid Abdul Majeed Imran Imran Hamid Saeed Muhammad Usman Mohsin Ali Amer S. Alali Abdullah F. AlAsmari Nemat Ali Ali Mohammed Asiri Fawaz Alasmari Faleh Alqahtani
author_sort	Muhammad F. Rasool
title	Development and evaluation of physiologically based pharmacokinetic drug-disease models for predicting captopril pharmacokinetics in chronic diseases
title_short	Development and evaluation of physiologically based pharmacokinetic drug-disease models for predicting captopril pharmacokinetics in chronic diseases
title_full	Development and evaluation of physiologically based pharmacokinetic drug-disease models for predicting captopril pharmacokinetics in chronic diseases
title_fullStr	Development and evaluation of physiologically based pharmacokinetic drug-disease models for predicting captopril pharmacokinetics in chronic diseases
title_full_unstemmed	Development and evaluation of physiologically based pharmacokinetic drug-disease models for predicting captopril pharmacokinetics in chronic diseases
title_sort	development and evaluation of physiologically based pharmacokinetic drug-disease models for predicting captopril pharmacokinetics in chronic diseases
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/5645c673416f4b848546c5cbfcdc70be
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Development and evaluation of physiologically based pharmacokinetic drug-disease models for predicting captopril pharmacokinetics in chronic diseases

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