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首页> 外文期刊>Drug Design, Development and Therapy >Artificial Neural Network (ANN) Approach to Predict an Optimized pH-Dependent Mesalamine Matrix Tablet
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Artificial Neural Network (ANN) Approach to Predict an Optimized pH-Dependent Mesalamine Matrix Tablet

机译：人工神经网络（ANN）方法预测优化的pH依赖性Mesalamine矩阵片剂

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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 Eudragitsup?/sup. 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 Masacolsup?/sup. 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/cmsup2,/sup 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.
机译：背景：结肠和直肠的严重出血和穿孔是溃疡性结肠炎的并发症，其可以通过靶向药物输送系统治疗。目的：结肠靶向递送的发展通常涉及基于pH-敏感的甲基丙烯酸的eudragit 的复合制剂工艺和涂覆步骤。目前的工作被设计为开发与Eudragit-S100基的pH依赖性未涂覆的未涂覆的咪烷基质基质片促进的磷酸二钙（DCP）。材料和方法：使用湿造粒技术压缩梅纳胺制剂，具有不同磷酸二钙（DCP）和Eudragit-S100的改变组合物。开发的制剂的特征在于物理化学和药物释放曲线。进行红外研究以确保活性成分和赋形剂之间没有相互作用。人工神经网络（ANN）用于优化最终DCP-Eudragit-S100复合物，使用实验数据使用快速传播（QP）训练算法培训多层感知（MLP），直到令人满意的根均方误差（RMSE）已达到。将Ann-Aged优化的制剂与市售的Masacol αs-sup>进行比较。结果：压缩片剂在厚度，硬度，重量变化，脆性和含量均匀性方面达到了期望标准，即5.34mm，7.7kg / cm 2， 585±5 mg？（％）分别为0.44％和103％。进行的体外溶解研究进行市售的梅萨明胺和优化的制剂，前者在6小时内显示100％释放，而后者在2小时后仅释放12.09％，并在12小时后释放为72.96％，用B配合到Weibull释放模型。值为1.3，表示复杂的释放机制。结论：发现DCP-EUDRAGIT-S100混合物在胃和结肠区涂层的情况下进行梅纳胺释放的解剖学。这种组合可以更好地控制溃疡性结肠炎。

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