Authors: Khan AM | Muhammad Hanif | Bukhari NI | Rahat Shamim | Muhammad Fawad Rasool | Sanaa Rasul | S. Shafique
Source: SHILAP Revista de lepidopterología (article, 2020-06-22)
Field: Chemistry — Analytical Chemistry

Artificial Neural Network (ANN) Approach to Predict an Optimized pH-Dependent Mesalamine Matrix Tablet Asad Majeed Khan,1,2 Muhammad Hanif,1 Nadeem Irfan Bukhari,3 Rahat Shamim,3 Fatima Rasool,3 Sumaira Rasul,4 Sana Shafique5 1Faculty of Pharmacy, Bahauddin Zakriya University, Multan, Pakistan; 2Lahore Pharmacy College, Lahore Medical and Dental College, Lahore, Pakistan; 3University College of Pharmacy, University of the Punjab, Lahore, Pakistan; 4Institute of Molecular Biology and Biotechnology, Bahauddin Zakriya University, Multan, Pakistan; 5Faculty of Pharmacy, Rippha International University, Lahore, PakistanCorrespondence: Muhammad Hanif; Nadeem Irfan Bukhari Email Muhammad.hanif@bzu.edu.pk; nadeem_irfan@hotmail.comBackground: 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&amp;amp;reg;. 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&amp;amp;reg;.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/cm2, 585&amp;amp;plusmn; 5 mg&amp;amp;nbsp;(%), 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.Keywords: ulcerative colitis, DCP-Eudragit-S100 complex, phosphatidylserine, Weibull release model Artificial neural network ; Matrix (chemical analysis) ; Artificial intelligence ; Computer science ; Biological system ; Materials science ; Composite material ; Biology Analytical Methods in Pharmaceuticals ; Computational Drug Discovery Methods