Cargando…

Application of Multiple Linear Regression and Artificial Neural Networks for the Prediction of the Packing and Capsule Filling Performance of Coated and Plain Pellets Differing in Density and Size

Plain or coated pellets of different densities 1.45, 2.53, and 3.61 g/cc in two size ranges, small (380–550 μm) and large (700–1200 μm) (stereoscope/image analysis), were prepared according to experimental design using extrusion/spheronization. Multiple linear regression (MLR) and artificial neural...

Descripción completa

Detalles Bibliográficos
Autores principales: Barmpalexis, Panagiotis, Partheniadis, Ioannis, Mitra, Konstantina-Sepfora, Toskas, Miltiadis, Papadopoulou, Labrini, Nikolakakis, Ioannis
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7150777/
https://www.ncbi.nlm.nih.gov/pubmed/32182685
http://dx.doi.org/10.3390/pharmaceutics12030244
_version_ 1783521095750516736
author Barmpalexis, Panagiotis
Partheniadis, Ioannis
Mitra, Konstantina-Sepfora
Toskas, Miltiadis
Papadopoulou, Labrini
Nikolakakis, Ioannis
author_facet Barmpalexis, Panagiotis
Partheniadis, Ioannis
Mitra, Konstantina-Sepfora
Toskas, Miltiadis
Papadopoulou, Labrini
Nikolakakis, Ioannis
author_sort Barmpalexis, Panagiotis
collection PubMed
description Plain or coated pellets of different densities 1.45, 2.53, and 3.61 g/cc in two size ranges, small (380–550 μm) and large (700–1200 μm) (stereoscope/image analysis), were prepared according to experimental design using extrusion/spheronization. Multiple linear regression (MLR) and artificial neural networks (ANNs) were used to predict packing indices and capsule filling performance from the “apparent” pellet density (helium pycnometry). The dynamic packing of the pellets in tapped volumetric glass cylinders was evaluated using Kawakita’s parameter a and the angle of internal flow θ. The capsule filling was evaluated as maximum fill weight (CFW) and fill weight variation (FWV) using a semi-automatic machine that simulated filling with vibrating plate systems. The pellet density influenced the packing parameters a and θ as the main effect and the CFW and FWV as statistical interactions with the coating. The pellet size and coating also displayed interacting effects on CFW, FWV, and θ. After coating, both small and large pellets behaved the same, demonstrating smooth filling and a low fill weight variation. Furthermore, none of the packing indices could predict the fill weight variation for the studied pellets, suggesting that the filling and packing of capsules with free-flowing pellets is influenced by details that were not accounted for in the tapping experiments. A prediction could be made by the application of MLR and ANNs. The former gave good predictions for the bulk/tap densities, θ, CFW, and FWV (R-squared of experimental vs. theoretical data >0.951). A comparison of the fitting models showed that a feed-forward backpropagation ANN model with six hidden units was superior to MLR in generalizing ability and prediction accuracy. The simplification of the ANN via magnitude-based pruning (MBP) and optimal brain damage (OBD), showed good data fitting, and therefore the derived ANN model can be simplified while maintaining predictability. These findings emphasize the importance of pellet density in the overall capsule filling process and the necessity to implement MLR/ANN into the development of pellet capsule filling operations.
format Online
Article
Text
id pubmed-7150777
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-71507772020-04-20 Application of Multiple Linear Regression and Artificial Neural Networks for the Prediction of the Packing and Capsule Filling Performance of Coated and Plain Pellets Differing in Density and Size Barmpalexis, Panagiotis Partheniadis, Ioannis Mitra, Konstantina-Sepfora Toskas, Miltiadis Papadopoulou, Labrini Nikolakakis, Ioannis Pharmaceutics Article Plain or coated pellets of different densities 1.45, 2.53, and 3.61 g/cc in two size ranges, small (380–550 μm) and large (700–1200 μm) (stereoscope/image analysis), were prepared according to experimental design using extrusion/spheronization. Multiple linear regression (MLR) and artificial neural networks (ANNs) were used to predict packing indices and capsule filling performance from the “apparent” pellet density (helium pycnometry). The dynamic packing of the pellets in tapped volumetric glass cylinders was evaluated using Kawakita’s parameter a and the angle of internal flow θ. The capsule filling was evaluated as maximum fill weight (CFW) and fill weight variation (FWV) using a semi-automatic machine that simulated filling with vibrating plate systems. The pellet density influenced the packing parameters a and θ as the main effect and the CFW and FWV as statistical interactions with the coating. The pellet size and coating also displayed interacting effects on CFW, FWV, and θ. After coating, both small and large pellets behaved the same, demonstrating smooth filling and a low fill weight variation. Furthermore, none of the packing indices could predict the fill weight variation for the studied pellets, suggesting that the filling and packing of capsules with free-flowing pellets is influenced by details that were not accounted for in the tapping experiments. A prediction could be made by the application of MLR and ANNs. The former gave good predictions for the bulk/tap densities, θ, CFW, and FWV (R-squared of experimental vs. theoretical data >0.951). A comparison of the fitting models showed that a feed-forward backpropagation ANN model with six hidden units was superior to MLR in generalizing ability and prediction accuracy. The simplification of the ANN via magnitude-based pruning (MBP) and optimal brain damage (OBD), showed good data fitting, and therefore the derived ANN model can be simplified while maintaining predictability. These findings emphasize the importance of pellet density in the overall capsule filling process and the necessity to implement MLR/ANN into the development of pellet capsule filling operations. MDPI 2020-03-08 /pmc/articles/PMC7150777/ /pubmed/32182685 http://dx.doi.org/10.3390/pharmaceutics12030244 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Barmpalexis, Panagiotis
Partheniadis, Ioannis
Mitra, Konstantina-Sepfora
Toskas, Miltiadis
Papadopoulou, Labrini
Nikolakakis, Ioannis
Application of Multiple Linear Regression and Artificial Neural Networks for the Prediction of the Packing and Capsule Filling Performance of Coated and Plain Pellets Differing in Density and Size
title Application of Multiple Linear Regression and Artificial Neural Networks for the Prediction of the Packing and Capsule Filling Performance of Coated and Plain Pellets Differing in Density and Size
title_full Application of Multiple Linear Regression and Artificial Neural Networks for the Prediction of the Packing and Capsule Filling Performance of Coated and Plain Pellets Differing in Density and Size
title_fullStr Application of Multiple Linear Regression and Artificial Neural Networks for the Prediction of the Packing and Capsule Filling Performance of Coated and Plain Pellets Differing in Density and Size
title_full_unstemmed Application of Multiple Linear Regression and Artificial Neural Networks for the Prediction of the Packing and Capsule Filling Performance of Coated and Plain Pellets Differing in Density and Size
title_short Application of Multiple Linear Regression and Artificial Neural Networks for the Prediction of the Packing and Capsule Filling Performance of Coated and Plain Pellets Differing in Density and Size
title_sort application of multiple linear regression and artificial neural networks for the prediction of the packing and capsule filling performance of coated and plain pellets differing in density and size
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7150777/
https://www.ncbi.nlm.nih.gov/pubmed/32182685
http://dx.doi.org/10.3390/pharmaceutics12030244
work_keys_str_mv AT barmpalexispanagiotis applicationofmultiplelinearregressionandartificialneuralnetworksforthepredictionofthepackingandcapsulefillingperformanceofcoatedandplainpelletsdifferingindensityandsize
AT partheniadisioannis applicationofmultiplelinearregressionandartificialneuralnetworksforthepredictionofthepackingandcapsulefillingperformanceofcoatedandplainpelletsdifferingindensityandsize
AT mitrakonstantinasepfora applicationofmultiplelinearregressionandartificialneuralnetworksforthepredictionofthepackingandcapsulefillingperformanceofcoatedandplainpelletsdifferingindensityandsize
AT toskasmiltiadis applicationofmultiplelinearregressionandartificialneuralnetworksforthepredictionofthepackingandcapsulefillingperformanceofcoatedandplainpelletsdifferingindensityandsize
AT papadopouloulabrini applicationofmultiplelinearregressionandartificialneuralnetworksforthepredictionofthepackingandcapsulefillingperformanceofcoatedandplainpelletsdifferingindensityandsize
AT nikolakakisioannis applicationofmultiplelinearregressionandartificialneuralnetworksforthepredictionofthepackingandcapsulefillingperformanceofcoatedandplainpelletsdifferingindensityandsize