Chaibva Faith, Burton Michael, Walker Roderick B
Faculty of Pharmacy, Rhodes University, P.O. Box 94, Grahamstown 6140, South Africa.
Department of Mathematics (Pure and Applied), Rhodes University, P.O. Box 94, Grahamstown 6140, South Africa.
Pharmaceutics. 2010 May 6;2(2):182-198. doi: 10.3390/pharmaceutics2020182.
An artificial neural network was used to optimize the release of salbutamol sulfate from hydrophilic matrix formulations. Model formulations to be used for training, testing and validating the neural network were manufactured with the aid of a central composite design with varying the levels of Methocel K100M, xanthan gum, Carbopol 974P and Surelease as the input factors. In vitro dissolution time profiles at six different sampling times were used as target data in training the neural network for formulation optimization. A multi layer perceptron with one hidden layer was constructed using Matlab, and the number of nodes in the hidden layer was optimized by trial and error to develop a model with the best predictive ability. The results revealed that a neural network with nine nodes was optimal for developing and optimizing formulations. Simulations undertaken with the training data revealed that the constructed model was useable. The optimized neural network was used for optimization of formulation with desirable release characteristics and the results indicated that there was agreement between the predicted formulation and the manufactured formulation. This work illustrates the possible utility of artificial neural networks for the optimization of pharmaceutical formulations with desirable performance characteristics.
采用人工神经网络优化硫酸沙丁胺醇从亲水性基质制剂中的释放。借助中心复合设计，以羟丙甲纤维素K100M、黄原胶、卡波姆974P和Surelease的用量作为输入因素，制备用于训练、测试和验证神经网络的模型制剂。在六个不同取样时间的体外溶出时间曲线用作训练神经网络进行制剂优化的目标数据。使用Matlab构建具有一个隐藏层的多层感知器，并通过反复试验优化隐藏层中的节点数，以开发具有最佳预测能力的模型。结果表明，具有九个节点的神经网络对于制剂的开发和优化是最优的。用训练数据进行的模拟表明所构建的模型是可用的。将优化后的神经网络用于具有理想释放特性的制剂优化，结果表明预测制剂与制备的制剂之间具有一致性。这项工作说明了人工神经网络在优化具有理想性能特征的药物制剂方面的潜在用途。
Zotero 插件
