Heuristic modeling of macromolecule release from PLGA microspheres

Jakub Szlęk,1 Adam Pacławski,1 Raymond Lau,2 Renata Jachowicz,1 Aleksander Mendyk11Department of Pharmaceutical Technology and Biopharmaceutics, Jagiellonian University Medical College, Krakow, Poland; 2School of Chemical and Biomedical Engineering, Nanyang Technological University (NTU), SingaporeA...

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Autores principales:	Szlęk J, Pacławski A, Lau R, Jachowicz R, Mendyk A
Formato:	article
Lenguaje:	EN
Publicado:	Dove Medical Press 2013
Materias:	Medicine (General) R5-920
Acceso en línea:	https://doaj.org/article/2ab3aa2067344326baa546a3d2266d12
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spelling	oai:doaj.org-article:2ab3aa2067344326baa546a3d2266d122021-12-02T01:04:03ZHeuristic modeling of macromolecule release from PLGA microspheres1176-91141178-2013https://doaj.org/article/2ab3aa2067344326baa546a3d2266d122013-12-01T00:00:00Zhttp://www.dovepress.com/heuristic-modeling-of-macromolecule-release-from-plga-microspheres-a15152https://doaj.org/toc/1176-9114https://doaj.org/toc/1178-2013Jakub Szlęk,1 Adam Pacławski,1 Raymond Lau,2 Renata Jachowicz,1 Aleksander Mendyk11Department of Pharmaceutical Technology and Biopharmaceutics, Jagiellonian University Medical College, Krakow, Poland; 2School of Chemical and Biomedical Engineering, Nanyang Technological University (NTU), SingaporeAbstract: Dissolution of protein macromolecules from poly(lactic-co-glycolic acid) (PLGA) particles is a complex process and still not fully understood. As such, there are difficulties in obtaining a predictive model that could be of fundamental significance in design, development, and optimization for medical applications and toxicity evaluation of PLGA-based multiparticulate dosage form. In the present study, two models with comparable goodness of fit were proposed for the prediction of the macromolecule dissolution profile from PLGA micro- and nanoparticles. In both cases, heuristic techniques, such as artificial neural networks (ANNs), feature selection, and genetic programming were employed. Feature selection provided by fscaret package and sensitivity analysis performed by ANNs reduced the original input vector from a total of 300 input variables to 21, 17, 16, and eleven; to achieve a better insight into generalization error, two cut-off points for every method was proposed. The best ANNs model results were obtained by monotone multi-layer perceptron neural network (MON-MLP) networks with a root-mean-square error (RMSE) of 15.4, and the input vector consisted of eleven inputs. The complicated classical equation derived from a database consisting of 17 inputs was able to yield a better generalization error (RMSE) of 14.3. The equation was characterized by four parameters, thus feasible (applicable) to standard nonlinear regression techniques. Heuristic modeling led to the ANN model describing macromolecules release profiles from PLGA microspheres with good predictive efficiency. Moreover genetic programming technique resulted in classical equation with comparable predictability to the ANN model.Keywords: poly(lactic-co-glycolic acid) (PLGA) microparticles, genetic programming, feature selection, artificial neural networks, molecular descriptorsSzlęk JPacławski ALau RJachowicz RMendyk ADove Medical PressarticleMedicine (General)R5-920ENInternational Journal of Nanomedicine, Vol 2013, Iss Issue 1, Pp 4601-4611 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine (General) R5-920
spellingShingle	Medicine (General) R5-920 Szlęk J Pacławski A Lau R Jachowicz R Mendyk A Heuristic modeling of macromolecule release from PLGA microspheres
description	Jakub Szlęk,1 Adam Pacławski,1 Raymond Lau,2 Renata Jachowicz,1 Aleksander Mendyk11Department of Pharmaceutical Technology and Biopharmaceutics, Jagiellonian University Medical College, Krakow, Poland; 2School of Chemical and Biomedical Engineering, Nanyang Technological University (NTU), SingaporeAbstract: Dissolution of protein macromolecules from poly(lactic-co-glycolic acid) (PLGA) particles is a complex process and still not fully understood. As such, there are difficulties in obtaining a predictive model that could be of fundamental significance in design, development, and optimization for medical applications and toxicity evaluation of PLGA-based multiparticulate dosage form. In the present study, two models with comparable goodness of fit were proposed for the prediction of the macromolecule dissolution profile from PLGA micro- and nanoparticles. In both cases, heuristic techniques, such as artificial neural networks (ANNs), feature selection, and genetic programming were employed. Feature selection provided by fscaret package and sensitivity analysis performed by ANNs reduced the original input vector from a total of 300 input variables to 21, 17, 16, and eleven; to achieve a better insight into generalization error, two cut-off points for every method was proposed. The best ANNs model results were obtained by monotone multi-layer perceptron neural network (MON-MLP) networks with a root-mean-square error (RMSE) of 15.4, and the input vector consisted of eleven inputs. The complicated classical equation derived from a database consisting of 17 inputs was able to yield a better generalization error (RMSE) of 14.3. The equation was characterized by four parameters, thus feasible (applicable) to standard nonlinear regression techniques. Heuristic modeling led to the ANN model describing macromolecules release profiles from PLGA microspheres with good predictive efficiency. Moreover genetic programming technique resulted in classical equation with comparable predictability to the ANN model.Keywords: poly(lactic-co-glycolic acid) (PLGA) microparticles, genetic programming, feature selection, artificial neural networks, molecular descriptors
format	article
author	Szlęk J Pacławski A Lau R Jachowicz R Mendyk A
author_facet	Szlęk J Pacławski A Lau R Jachowicz R Mendyk A
author_sort	Szlęk J
title	Heuristic modeling of macromolecule release from PLGA microspheres
title_short	Heuristic modeling of macromolecule release from PLGA microspheres
title_full	Heuristic modeling of macromolecule release from PLGA microspheres
title_fullStr	Heuristic modeling of macromolecule release from PLGA microspheres
title_full_unstemmed	Heuristic modeling of macromolecule release from PLGA microspheres
title_sort	heuristic modeling of macromolecule release from plga microspheres
publisher	Dove Medical Press
publishDate	2013
url	https://doaj.org/article/2ab3aa2067344326baa546a3d2266d12
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Heuristic modeling of macromolecule release from PLGA microspheres

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