ctrlnum article-1104
fullrecord <?xml version="1.0"?> <dc schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"><title lang="en-US">Population-Based Approach to Analyze Sparse Sampling Data in Biopharmaceutics and Pharmacokinetics using Monolix and NONMEM</title><creator>Nugroho, Akhmad Kharis; Department of Pharmaceutics Faculty of Pharmacy Universitas Gadjah Mada Yogyakarta Indonesia</creator><creator>Hakim, Arief Rahman; Department of Pharmacology and Clinical Pharmacy, Faculty of Pharmacy, Universitas Gadjah Mada</creator><creator>Hakim, Lukman; Department of Pharmacology and Clinical Pharmacy, Faculty of Pharmacy, Universitas Gadjah Mada</creator><subject lang="en-US">model, population, sparse sampling data, Monolix, NONMEM</subject><description lang="en-US">Although it has been developed since 1972, the implementation of a population-based modeling approach in Indonesia, particularly to analyze biopharmaceutics and pharmacokinetics data is still very limited. This study was aimed to evaluate the performance of Monolix and NONMEM, two of the popular software packages in a population-based modeling approach, to analyze the limited data (sparse sampling data) of the time profiles of the simulated plasma drug concentration of a theoretical compound. and NONMEM were used to model the limited data (40 data points) as a results of the random selection from the 180 point data of simulated plasma drug concentration (Cp) on 20 subjects at 0.25; 0.5; 0.75; 1; 1.5; 3; 6; 12 and 18 hours after per-oral administration of a 100mg of a theoretical compound. Population values of the absorption rate constant (Ka), the elimination rate constant (Kel) and volume of distribution (Vd) were compared to the average Ka, Kel and Vd obtained by the conventional method (two stage approach) using PKSolver on the Cp data of all subjects. The calculation system of a nonlinear mixed effect model in Monolix and NONMEM, successfully describes the sparse data, based on the visual evaluation of the goodness of fit. Comparison of parameter estimates of population values in Monolix and NONMEM are in the range of 94 to 108% of the real values of the rich data analysed by PKSolver. A population-based modeling can adequately analyze limited or sparse data, demonstrating its capability as an important tool in clinical studies, involving patients.</description><publisher lang="en-US">Faculty of Pharmacy Universitas Gadjah Mada, Yogyakarta, Skip Utara, 55281, Indonesia</publisher><contributor lang="en-US"/><date>2017-12-06</date><type>Journal:Article</type><type>Other:info:eu-repo/semantics/publishedVersion</type><type>Journal:Article</type><type>File:application/pdf</type><identifier>http://indonesianjpharm.farmasi.ugm.ac.id/index.php/3/article/view/1104</identifier><identifier>10.14499/indonesianjpharm28iss4pp205</identifier><source lang="en-US">Indonesian Journal of Pharmacy; Vol 28 No 4, 2017; 205</source><source>2338-9486</source><source>2338-9427</source><language>eng</language><relation>http://indonesianjpharm.farmasi.ugm.ac.id/index.php/3/article/view/1104/853</relation><rights lang="0">Copyright (c) 2017 INDONESIAN JOURNAL OF PHARMACY</rights><rights lang="0">http://creativecommons.org/licenses/by-sa/4.0</rights><recordID>article-1104</recordID></dc>
language eng
format Journal:Article
Journal
Other:info:eu-repo/semantics/publishedVersion
Other
File:application/pdf
File
Journal:eJournal
author Nugroho, Akhmad Kharis; Department of Pharmaceutics Faculty of Pharmacy Universitas Gadjah Mada Yogyakarta Indonesia
Hakim, Arief Rahman; Department of Pharmacology and Clinical Pharmacy, Faculty of Pharmacy, Universitas Gadjah Mada
Hakim, Lukman; Department of Pharmacology and Clinical Pharmacy, Faculty of Pharmacy, Universitas Gadjah Mada
title Population-Based Approach to Analyze Sparse Sampling Data in Biopharmaceutics and Pharmacokinetics using Monolix and NONMEM
publisher Faculty of Pharmacy Universitas Gadjah Mada, Yogyakarta, Skip Utara, 55281, Indonesia
publishDate 2017
topic model
population
sparse sampling data
Monolix
NONMEM
url http://indonesianjpharm.farmasi.ugm.ac.id/index.php/3/article/view/1104
http://indonesianjpharm.farmasi.ugm.ac.id/index.php/3/article/view/1104/853
contents Although it has been developed since 1972, the implementation of a population-based modeling approach in Indonesia, particularly to analyze biopharmaceutics and pharmacokinetics data is still very limited. This study was aimed to evaluate the performance of Monolix and NONMEM, two of the popular software packages in a population-based modeling approach, to analyze the limited data (sparse sampling data) of the time profiles of the simulated plasma drug concentration of a theoretical compound. and NONMEM were used to model the limited data (40 data points) as a results of the random selection from the 180 point data of simulated plasma drug concentration (Cp) on 20 subjects at 0.25; 0.5; 0.75; 1; 1.5; 3; 6; 12 and 18 hours after per-oral administration of a 100mg of a theoretical compound. Population values of the absorption rate constant (Ka), the elimination rate constant (Kel) and volume of distribution (Vd) were compared to the average Ka, Kel and Vd obtained by the conventional method (two stage approach) using PKSolver on the Cp data of all subjects. The calculation system of a nonlinear mixed effect model in Monolix and NONMEM, successfully describes the sparse data, based on the visual evaluation of the goodness of fit. Comparison of parameter estimates of population values in Monolix and NONMEM are in the range of 94 to 108% of the real values of the rich data analysed by PKSolver. A population-based modeling can adequately analyze limited or sparse data, demonstrating its capability as an important tool in clinical studies, involving patients.
id IOS1091.article-1104
institution Universitas Gadjah Mada
institution_id 19
institution_type library:university
library
library Perpustakaan Pusat Universitas Gadjah Mada
library_id 488
collection INDONESIAN JOURNAL OF PHARMACY
repository_id 1091
subject_area Farmasi
city SLEMAN
province DAERAH ISTIMEWA YOGYAKARTA
repoId IOS1091
first_indexed 2017-12-19T19:31:33Z
last_indexed 2018-11-19T22:33:16Z
recordtype dc
merged_child_boolean 1
_version_ 1722525908670087168
score 17.205004