Department of Pharmaceutical Technology, Aristotle University of Thessaloniki, Thessaloniki, Greece.
Eur J Pharm Biopharm. 2010 Feb;74(2):316-23. doi: 10.1016/j.ejpb.2009.09.011. Epub 2009 Oct 6.
Artificial neural networks (ANNs) were employed in the optimization of a nimodipine zero-order release matrix tablet formulation, and their efficiency was compared to that of multiple linear regression (MLR) on an external validation set. The amounts of PEG-4000, PVP K30, HPMC K100 and HPMC E50LV were used as independent variables following a statistical experimental design, and three dissolution parameters (time at which the 90% of the drug was dissolved, t(90%), percentage of nimodipine released in 2 and 8h, Y(2h), and Y(8h), respectively) were chosen as response variables. It was found that a feed-forward back-propagation ANN with eight hidden units showed better fit for all responses (R(2) of 0.96, 0.90 and 0.98 for t(90%), Y(2h) and Y(8h), respectively) compared to the MLR models (0.92, 0.87 and 0.92 for t(90%), Y(2h) and Y(8h), respectively). The ANN was further simplified by pruning, which preserved only PEG-4000 and HPMC K100 as inputs. Optimal formulations based on ANN and MLR predictions were identified by minimizing the standardized Euclidian distance between measured and theoretical (zero order) release parameters. The estimation of the similarity factor, f(2), confirmed ANNs increased prediction efficiency (81.98 and 79.46 for the original and pruned ANN, respectively, and 76.25 for the MLR).
人工神经网络 (ANNs) 被用于优化尼莫地平零级释放基质片配方，并在外部验证集上与多元线性回归 (MLR) 进行了效率比较。在统计实验设计中，将 PEG-4000、PVP K30、HPMC K100 和 HPMC E50LV 的用量作为自变量，选择三个溶出参数（药物 90%溶解的时间，t(90%)，尼莫地平在 2 和 8 小时内的释放百分比，Y(2h)和 Y(8h)）作为响应变量。结果发现，与 MLR 模型相比（t(90%)、Y(2h)和 Y(8h)的 R(2)分别为 0.92、0.87 和 0.92），具有 8 个隐藏单元的前馈反向传播 ANN 对所有响应的拟合效果更好（t(90%)、Y(2h)和 Y(8h)的 R(2)分别为 0.96、0.90 和 0.98）。通过剪枝进一步简化了 ANN，仅保留 PEG-4000 和 HPMC K100 作为输入。通过最小化实测和理论（零级）释放参数之间的标准化欧几里得距离，根据 ANN 和 MLR 预测确定了最佳配方。相似因子 f(2)的估计证实了 ANNs 提高了预测效率（原始和剪枝 ANN 分别为 81.98 和 79.46，而 MLR 为 76.25）。
