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Artificial Neural Network (ANN) Approach to Predict an Optimized pH-Dependent Mesalamine Matrix Tablet.
Drug Design, Development and Therapy ( IF 4.8 ) Pub Date : 2020-06-22 , DOI: 10.2147/dddt.s244016 Asad Majeed Khan 1, 2 , Muhammad Hanif 1 , Nadeem Irfan Bukhari 3 , Rahat Shamim 3 , Fatima Rasool 3 , Sumaira Rasul 4 , Sana Shafique 5
Drug Design, Development and Therapy ( IF 4.8 ) Pub Date : 2020-06-22 , DOI: 10.2147/dddt.s244016 Asad Majeed Khan 1, 2 , Muhammad Hanif 1 , Nadeem Irfan Bukhari 3 , Rahat Shamim 3 , Fatima Rasool 3 , Sumaira Rasul 4 , Sana Shafique 5
Affiliation
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/cm2, 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.
中文翻译：
预测优化的 pH 依赖性美沙拉嗪骨架片剂的人工神经网络 (ANN) 方法。
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，可以通过靶向给药系统进行治疗。
目的：结肠靶向给药的开发通常涉及复杂的配方过程和基于 pH 敏感的甲基丙烯酸的 Eudragit ®的包衣步骤。目前的工作旨在开发磷酸二钙 (DCP)，该磷酸二钙 (DCP) 由基于 Eudragit-S100 的 pH 依赖性、未包衣的美沙拉嗪骨架片剂促进。
材料和方法：使用具有不同磷酸二钙 (DCP) 和 Eudragit-S100 组成的湿法制粒技术压制美沙拉嗪制剂。所开发的制剂的物理化学和药物释放曲线进行了表征。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) 用于优化最终的 DCP-Eudragit-S100 复合体，并利用实验数据使用快速传播 (QP) 训练算法训练多层感知 (MLP)，直到达到满意的均方根误差(RMSE) 已达到。人工神经网络辅助优化配方与市售的 Masacol ®进行了比较。
结果：压片在厚度、硬度、重量变化、脆碎度和含量均匀度方面均符合合意标准，即分别为 5.34 mm、7.7 kg/cm 2、585 ±5 mg (%)、0.44% 和 103%。对市售的美沙拉嗪和优化配方进行体外溶出度研究，前者在 6 h 时释放 100%，而后者在 2 h 后仅释放 12.09%，在 12 h 后仅释放 72.96%，符合 Weibull 释放模型与 b值为 1.3，表明释放机制复杂。
结论：发现 DCP-Eudragit-S100 混合物对胃和结肠区域无涂层的美沙拉嗪释放具有解释性。这种组合可以更好地控制溃疡性结肠炎。
更新日期：2020-06-30
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/cm2, 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.
中文翻译：
预测优化的 pH 依赖性美沙拉嗪骨架片剂的人工神经网络 (ANN) 方法。
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，可以通过靶向给药系统进行治疗。
目的：结肠靶向给药的开发通常涉及复杂的配方过程和基于 pH 敏感的甲基丙烯酸的 Eudragit ®的包衣步骤。目前的工作旨在开发磷酸二钙 (DCP)，该磷酸二钙 (DCP) 由基于 Eudragit-S100 的 pH 依赖性、未包衣的美沙拉嗪骨架片剂促进。
材料和方法：使用具有不同磷酸二钙 (DCP) 和 Eudragit-S100 组成的湿法制粒技术压制美沙拉嗪制剂。所开发的制剂的物理化学和药物释放曲线进行了表征。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) 用于优化最终的 DCP-Eudragit-S100 复合体，并利用实验数据使用快速传播 (QP) 训练算法训练多层感知 (MLP)，直到达到满意的均方根误差(RMSE) 已达到。人工神经网络辅助优化配方与市售的 Masacol ®进行了比较。
结果：压片在厚度、硬度、重量变化、脆碎度和含量均匀度方面均符合合意标准，即分别为 5.34 mm、7.7 kg/cm 2、585 ±5 mg (%)、0.44% 和 103%。对市售的美沙拉嗪和优化配方进行体外溶出度研究，前者在 6 h 时释放 100%，而后者在 2 h 后仅释放 12.09%，在 12 h 后仅释放 72.96%，符合 Weibull 释放模型与 b值为 1.3，表明释放机制复杂。
结论：发现 DCP-Eudragit-S100 混合物对胃和结肠区域无涂层的美沙拉嗪释放具有解释性。这种组合可以更好地控制溃疡性结肠炎。
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