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A Novel Physiologically Based Model of Creatinine Renal Disposition to Integrate Current Knowledge of Systems Parameters and Clinical Observations
Creatinine is the most common clinical biomarker of renal function. As a substrate for renal transporters, its secretion is susceptible to inhibition by drugs, resulting in transient increase in serum creatinine and false impression of damage to kidney. Novel physiologically based models for creatin...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7306622/ https://www.ncbi.nlm.nih.gov/pubmed/32441889 http://dx.doi.org/10.1002/psp4.12509 |
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author | Scotcher, Daniel Arya, Vikram Yang, Xinning Zhao, Ping Zhang, Lei Huang, Shiew‐Mei Rostami‐Hodjegan, Amin Galetin, Aleksandra |
author_facet | Scotcher, Daniel Arya, Vikram Yang, Xinning Zhao, Ping Zhang, Lei Huang, Shiew‐Mei Rostami‐Hodjegan, Amin Galetin, Aleksandra |
author_sort | Scotcher, Daniel |
collection | PubMed |
description | Creatinine is the most common clinical biomarker of renal function. As a substrate for renal transporters, its secretion is susceptible to inhibition by drugs, resulting in transient increase in serum creatinine and false impression of damage to kidney. Novel physiologically based models for creatinine were developed here and (dis)qualified in a stepwise manner until consistency with clinical data. Data from a matrix of studies were integrated, including systems data (common to all models), proteomics‐informed in vitro–in vivo extrapolation of all relevant transporter clearances, exogenous administration of creatinine (to estimate endogenous synthesis rate), and inhibition of different renal transporters (11 perpetrator drugs considered for qualification during creatinine model development and verification on independent data sets). The proteomics‐informed bottom‐up approach resulted in the underprediction of creatinine renal secretion. Subsequently, creatinine‐trimethoprim clinical data were used to inform key model parameters in a reverse translation manner, highlighting best practices and challenges for middle‐out optimization of mechanistic models. |
format | Online Article Text |
id | pubmed-7306622 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-73066222020-06-23 A Novel Physiologically Based Model of Creatinine Renal Disposition to Integrate Current Knowledge of Systems Parameters and Clinical Observations Scotcher, Daniel Arya, Vikram Yang, Xinning Zhao, Ping Zhang, Lei Huang, Shiew‐Mei Rostami‐Hodjegan, Amin Galetin, Aleksandra CPT Pharmacometrics Syst Pharmacol Research Creatinine is the most common clinical biomarker of renal function. As a substrate for renal transporters, its secretion is susceptible to inhibition by drugs, resulting in transient increase in serum creatinine and false impression of damage to kidney. Novel physiologically based models for creatinine were developed here and (dis)qualified in a stepwise manner until consistency with clinical data. Data from a matrix of studies were integrated, including systems data (common to all models), proteomics‐informed in vitro–in vivo extrapolation of all relevant transporter clearances, exogenous administration of creatinine (to estimate endogenous synthesis rate), and inhibition of different renal transporters (11 perpetrator drugs considered for qualification during creatinine model development and verification on independent data sets). The proteomics‐informed bottom‐up approach resulted in the underprediction of creatinine renal secretion. Subsequently, creatinine‐trimethoprim clinical data were used to inform key model parameters in a reverse translation manner, highlighting best practices and challenges for middle‐out optimization of mechanistic models. John Wiley and Sons Inc. 2020-05-22 2020-06 /pmc/articles/PMC7306622/ /pubmed/32441889 http://dx.doi.org/10.1002/psp4.12509 Text en © 2020 The Authors. CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals, Inc. on behalf of the American Society for Clinical Pharmacology and Therapeutics. This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes. |
spellingShingle | Research Scotcher, Daniel Arya, Vikram Yang, Xinning Zhao, Ping Zhang, Lei Huang, Shiew‐Mei Rostami‐Hodjegan, Amin Galetin, Aleksandra A Novel Physiologically Based Model of Creatinine Renal Disposition to Integrate Current Knowledge of Systems Parameters and Clinical Observations |
title | A Novel Physiologically Based Model of Creatinine Renal Disposition to Integrate Current Knowledge of Systems Parameters and Clinical Observations |
title_full | A Novel Physiologically Based Model of Creatinine Renal Disposition to Integrate Current Knowledge of Systems Parameters and Clinical Observations |
title_fullStr | A Novel Physiologically Based Model of Creatinine Renal Disposition to Integrate Current Knowledge of Systems Parameters and Clinical Observations |
title_full_unstemmed | A Novel Physiologically Based Model of Creatinine Renal Disposition to Integrate Current Knowledge of Systems Parameters and Clinical Observations |
title_short | A Novel Physiologically Based Model of Creatinine Renal Disposition to Integrate Current Knowledge of Systems Parameters and Clinical Observations |
title_sort | novel physiologically based model of creatinine renal disposition to integrate current knowledge of systems parameters and clinical observations |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7306622/ https://www.ncbi.nlm.nih.gov/pubmed/32441889 http://dx.doi.org/10.1002/psp4.12509 |
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