Martarelli D, Casettari L, Shalaby K S, Soliman M E, Cespi M, Bonacucina G, Fagioli L, Perinelli D R, Lam J K W, Palmieri G F
Department of Biomolecular Sciences, School of Pharmacy, University of Urbino, Piazza Rinascimento n°6, 61029, Urbino (PU), Italy.
Curr Drug Deliv. 2016;13(4):565-73. doi: 10.2174/1567201812666150608101528.
Efficacy of melatonin in treating sleep disorders has been demonstrated in numerous studies. Being with short half-life, melatonin needs to be formulated in extended-release tablets to prevent the fast drop of its plasma concentration. However, an attempt to mimic melatonin natural plasma levels during night time is challenging.
In this work, Artificial Neural Networks (ANNs) were used to optimize melatonin release from hydrophilic polymer matrices. Twenty-seven different tablet formulations with different amounts of hydroxypropyl methylcellulose, xanthan gum and Carbopol®974P NF were prepared and subjected to drug release studies. Using dissolution test data as inputs for ANN designed by Visual Basic programming language, the ideal number of neurons in the hidden layer was determined trial and error methodology to guarantee the best performance of constructed ANN.
Results showed that the ANN with nine neurons in the hidden layer had the best results. ANN was examined to check its predictability and then used to determine the best formula that can mimic the release of melatonin from a marketed brand using similarity fit factor.
This work shows the possibility of using ANN to optimize the composition of prolonged-release melatonin tablets having dissolution profile desired.
众多研究已证实褪黑素在治疗睡眠障碍方面的疗效。由于褪黑素半衰期短，需要制成缓释片以防止其血浆浓度快速下降。然而，要模拟夜间褪黑素的自然血浆水平具有挑战性。
在这项工作中，人工神经网络（ANNs）被用于优化褪黑素从亲水性聚合物基质中的释放。制备了27种不同的片剂配方，其中羟丙基甲基纤维素、黄原胶和卡波姆®974P NF的含量不同，并进行了药物释放研究。使用溶出度测试数据作为由Visual Basic编程语言设计的ANN的输入，通过试错法确定隐藏层中理想的神经元数量，以确保构建的ANN具有最佳性能。
结果表明，隐藏层有9个神经元的ANN效果最佳。对ANN进行了预测性检验，然后用于确定能够使用相似性拟合因子模拟市售品牌褪黑素释放的最佳配方。
这项工作表明了使用ANN优化具有所需溶出曲线的缓释褪黑素片组成的可能性。
