Department of Organic Chemistry and Technology, Budapest University of Technology and Economics, H-1111 Budapest, Műegyetem rakpart 3, Hungary.
Department of Organic Chemistry and Technology, Budapest University of Technology and Economics, H-1111 Budapest, Műegyetem rakpart 3, Hungary.
Int J Pharm. 2019 Aug 15;567:118464. doi: 10.1016/j.ijpharm.2019.118464. Epub 2019 Jun 25.
This work proposes the application of artificial neural networks (ANN) to non-destructively predict the in vitro dissolution of pharmaceutical tablets from Process Analytical Technology (PAT) data. An extended release tablet formulation was studied, where the dissolution was influenced by the composition of the tablets and the tableting compression force. NIR and Raman spectra of the intact tablets were measured, and the dissolution of the tablets was modeled directly from the spectral data. Partial Least Square (PLS) regression and ANN models were developed for the different spectroscopic measurements individually as well as by combining them together. ANN provided up to 3% lower root mean square error for prediction (RMSEP) than the PLS models, due to its capability of modeling non-linearity between the process parameters and dissolution curves. The ANN model using reflection NIR spectra provided the most accurate predictions with 6.5 and 63 mean f and f values between the computed and measured dissolution curves, respectively. Furthermore, ANN served as a straightforward data fusion method without the need for additional preprocessing steps. The method could significantly advance data processing in the PAT environment, contribute to an enhanced real-time release testing procedure and hence the increased efficacy of dissolution testing.
本工作提出将人工神经网络（ANN）应用于从过程分析技术（PAT）数据中无损预测药物片剂的体外溶出度。研究了一种缓释片剂制剂，其中溶出度受片剂的组成和压片压缩力的影响。测量了完整片剂的近红外（NIR）和拉曼光谱，并直接从光谱数据对片剂的溶出度进行建模。分别为不同的光谱测量值以及将它们组合在一起，开发了偏最小二乘（PLS）回归和 ANN 模型。由于其在过程参数和溶解曲线之间建模非线性的能力，ANN 提供的预测（RMSEP）均方根误差比 PLS 模型低 3%。使用反射 NIR 光谱的 ANN 模型提供了最准确的预测，计算和测量的溶解曲线之间的 f 和 f 值分别为 6.5 和 63。此外，ANN 作为一种直接的数据融合方法，无需额外的预处理步骤。该方法可以显著推进 PAT 环境中的数据处理，有助于增强实时释放测试程序，从而提高溶解测试的效果。
