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Non-compartmental data analysis using SimBiology and MATLAB

MATLAB® is widely used for numerical analysis, modeling, and simulation. One of MATLAB's tools, SimBiology®, is often used for pharmacokinetic, pharmacodynamic model and dynamic systems; however, SimBiology seems to be rarely used for non-compartmental analysis (NCA), and the published official...

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Detalles Bibliográficos
Autores principales: Park, Jin-Sol, Kim, Jung-Ryul
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Korean Society for Clinical Pharmacology and Therapeutics 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6989240/
https://www.ncbi.nlm.nih.gov/pubmed/32055588
http://dx.doi.org/10.12793/tcp.2019.27.3.89
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author Park, Jin-Sol
Kim, Jung-Ryul
author_facet Park, Jin-Sol
Kim, Jung-Ryul
author_sort Park, Jin-Sol
collection PubMed
description MATLAB® is widely used for numerical analysis, modeling, and simulation. One of MATLAB's tools, SimBiology®, is often used for pharmacokinetic, pharmacodynamic model and dynamic systems; however, SimBiology seems to be rarely used for non-compartmental analysis (NCA), and the published official documentation provides a poor description of the analysis algorithm for NCA. Therefore, we conducted NCAs with a hypothetical dataset and some scenarios and compared the results. According to the results of this study, SimBiology estimates parameters using the unweighted linear regression for the terminal slope and linear interpolation method. Moreover, although the documentation describing the actual analysis algorithm used to process non-numeric data is not easily accessible to users, users may introduce numeric data at time zero to perform NCA properly. Using the command window, users can perform analyses more quickly and effectively. If the NCA official documentation were improved, SimBiology might be more widely adopted to perform NCA in clinical pharmacology.
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spelling pubmed-69892402020-02-13 Non-compartmental data analysis using SimBiology and MATLAB Park, Jin-Sol Kim, Jung-Ryul Transl Clin Pharmacol Opinion MATLAB® is widely used for numerical analysis, modeling, and simulation. One of MATLAB's tools, SimBiology®, is often used for pharmacokinetic, pharmacodynamic model and dynamic systems; however, SimBiology seems to be rarely used for non-compartmental analysis (NCA), and the published official documentation provides a poor description of the analysis algorithm for NCA. Therefore, we conducted NCAs with a hypothetical dataset and some scenarios and compared the results. According to the results of this study, SimBiology estimates parameters using the unweighted linear regression for the terminal slope and linear interpolation method. Moreover, although the documentation describing the actual analysis algorithm used to process non-numeric data is not easily accessible to users, users may introduce numeric data at time zero to perform NCA properly. Using the command window, users can perform analyses more quickly and effectively. If the NCA official documentation were improved, SimBiology might be more widely adopted to perform NCA in clinical pharmacology. Korean Society for Clinical Pharmacology and Therapeutics 2019-09 2019-09-30 /pmc/articles/PMC6989240/ /pubmed/32055588 http://dx.doi.org/10.12793/tcp.2019.27.3.89 Text en Copyright © 2019 Jin-Sol Park and Jung-Ryul Kim http://creativecommons.org/licenses/by-nc/3.0/ It is identical to the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/).
spellingShingle Opinion
Park, Jin-Sol
Kim, Jung-Ryul
Non-compartmental data analysis using SimBiology and MATLAB
title Non-compartmental data analysis using SimBiology and MATLAB
title_full Non-compartmental data analysis using SimBiology and MATLAB
title_fullStr Non-compartmental data analysis using SimBiology and MATLAB
title_full_unstemmed Non-compartmental data analysis using SimBiology and MATLAB
title_short Non-compartmental data analysis using SimBiology and MATLAB
title_sort non-compartmental data analysis using simbiology and matlab
topic Opinion
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6989240/
https://www.ncbi.nlm.nih.gov/pubmed/32055588
http://dx.doi.org/10.12793/tcp.2019.27.3.89
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