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Artificial Neural Network (ANN) Approach to Predict an Optimized pH-Dependent Mesalamine Matrix Tablet

BACKGROUND: Severe bleeding and perforation of the colon and rectum are complications of ulcerative colitis which can be treated by a targeted drug delivery system. PURPOSE: Development of colon-targeted delivery usually involves a complex formulation process and coating steps of pH-sensitive methac...

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Autores principales: Khan, Asad Majeed, Hanif, Muhammad, Bukhari, Nadeem Irfan, Shamim, Rahat, Rasool, Fatima, Rasul, Sumaira, Shafique, Sana
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Dove 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7320029/
https://www.ncbi.nlm.nih.gov/pubmed/32606610
http://dx.doi.org/10.2147/DDDT.S244016
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author Khan, Asad Majeed
Hanif, Muhammad
Bukhari, Nadeem Irfan
Shamim, Rahat
Rasool, Fatima
Rasul, Sumaira
Shafique, Sana
author_facet Khan, Asad Majeed
Hanif, Muhammad
Bukhari, Nadeem Irfan
Shamim, Rahat
Rasool, Fatima
Rasul, Sumaira
Shafique, Sana
author_sort Khan, Asad Majeed
collection PubMed
description BACKGROUND: Severe bleeding and perforation of the colon and rectum are complications of ulcerative colitis which can be treated by a targeted drug delivery system. PURPOSE: Development of colon-targeted delivery usually involves a complex formulation process and coating steps of pH-sensitive methacrylic acid based Eudragit(®). The current work was purposefully designed to develop dicalcium phosphate (DCP) facilitated with Eudragit-S100-based pH-dependent, uncoated mesalamine matrix tablets. MATERIALS AND METHODS: Mesalamine formulations were compressed using wet granulation technique with varying compositions of dicalcium phosphate (DCP) and Eudragit-S100. The developed formulations were characterized for physicochemical and drug release profiles. Infrared studies were carried out to ensure that there was no interaction between active ingredients and excipients. Artificial neural network (ANN) was used for the optimization of final DCP-Eudragit-S100 complex and the experimental data were employed to train a multi-layer perception (MLP) using quick propagation (QP) training algorithm until a satisfactory root mean square error (RMSE) was reached. The ANN-aided optimized formulation was compared with commercially available Masacol(®). RESULTS: Compressed tablets met the desirability criteria in terms of thickness, hardness, weight variation, friability, and content uniformity, ie, 5.34 mm, 7.7 kg/cm(2,) 585±5 mg (%), 0.44%, and 103%, respectively. In-vitro dissolution study of commercially available mesalamine and optimized formulation was carried out and the former showed 100% release at 6 h while the latter released only 12.09% after 2 h and 72.96% after 12 h which was fitted to Weibull release model with b value of 1.3, indicating a complex release mechanism. CONCLUSION: DCP-Eudragit-S100 blend was found explicative for mesalamine release without coating in gastric and colonic regions. This combination may provide a better control of ulcerative colitis.
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spelling pubmed-73200292020-06-29 Artificial Neural Network (ANN) Approach to Predict an Optimized pH-Dependent Mesalamine Matrix Tablet Khan, Asad Majeed Hanif, Muhammad Bukhari, Nadeem Irfan Shamim, Rahat Rasool, Fatima Rasul, Sumaira Shafique, Sana Drug Des Devel Ther Original Research BACKGROUND: Severe bleeding and perforation of the colon and rectum are complications of ulcerative colitis which can be treated by a targeted drug delivery system. PURPOSE: Development of colon-targeted delivery usually involves a complex formulation process and coating steps of pH-sensitive methacrylic acid based Eudragit(®). The current work was purposefully designed to develop dicalcium phosphate (DCP) facilitated with Eudragit-S100-based pH-dependent, uncoated mesalamine matrix tablets. MATERIALS AND METHODS: Mesalamine formulations were compressed using wet granulation technique with varying compositions of dicalcium phosphate (DCP) and Eudragit-S100. The developed formulations were characterized for physicochemical and drug release profiles. Infrared studies were carried out to ensure that there was no interaction between active ingredients and excipients. Artificial neural network (ANN) was used for the optimization of final DCP-Eudragit-S100 complex and the experimental data were employed to train a multi-layer perception (MLP) using quick propagation (QP) training algorithm until a satisfactory root mean square error (RMSE) was reached. The ANN-aided optimized formulation was compared with commercially available Masacol(®). RESULTS: Compressed tablets met the desirability criteria in terms of thickness, hardness, weight variation, friability, and content uniformity, ie, 5.34 mm, 7.7 kg/cm(2,) 585±5 mg (%), 0.44%, and 103%, respectively. In-vitro dissolution study of commercially available mesalamine and optimized formulation was carried out and the former showed 100% release at 6 h while the latter released only 12.09% after 2 h and 72.96% after 12 h which was fitted to Weibull release model with b value of 1.3, indicating a complex release mechanism. CONCLUSION: DCP-Eudragit-S100 blend was found explicative for mesalamine release without coating in gastric and colonic regions. This combination may provide a better control of ulcerative colitis. Dove 2020-06-22 /pmc/articles/PMC7320029/ /pubmed/32606610 http://dx.doi.org/10.2147/DDDT.S244016 Text en © 2020 Khan et al. http://creativecommons.org/licenses/by-nc/3.0/ This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License (http://creativecommons.org/licenses/by-nc/3.0/). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms (https://www.dovepress.com/terms.php).
spellingShingle Original Research
Khan, Asad Majeed
Hanif, Muhammad
Bukhari, Nadeem Irfan
Shamim, Rahat
Rasool, Fatima
Rasul, Sumaira
Shafique, Sana
Artificial Neural Network (ANN) Approach to Predict an Optimized pH-Dependent Mesalamine Matrix Tablet
title Artificial Neural Network (ANN) Approach to Predict an Optimized pH-Dependent Mesalamine Matrix Tablet
title_full Artificial Neural Network (ANN) Approach to Predict an Optimized pH-Dependent Mesalamine Matrix Tablet
title_fullStr Artificial Neural Network (ANN) Approach to Predict an Optimized pH-Dependent Mesalamine Matrix Tablet
title_full_unstemmed Artificial Neural Network (ANN) Approach to Predict an Optimized pH-Dependent Mesalamine Matrix Tablet
title_short Artificial Neural Network (ANN) Approach to Predict an Optimized pH-Dependent Mesalamine Matrix Tablet
title_sort artificial neural network (ann) approach to predict an optimized ph-dependent mesalamine matrix tablet
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7320029/
https://www.ncbi.nlm.nih.gov/pubmed/32606610
http://dx.doi.org/10.2147/DDDT.S244016
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