Department of Organic Chemistry and Technology, Budapest University of Technology and Economics, H-1111 Budapest, Műegyetem rakpart 3.
Department of Organic Chemistry and Technology, Budapest University of Technology and Economics, H-1111 Budapest, Műegyetem rakpart 3.
Int J Pharm. 2021 Mar 15;597:120338. doi: 10.1016/j.ijpharm.2021.120338. Epub 2021 Feb 2.
In this work spectroscopic measurements, process data and Critical Material Attributes (CMAs) are used to predict the in vitro dissolution profile of sustained-release tablets with three machine learning methods, Artificial Neural Networks (ANN), Support Vector Machines (SVM) and Ensemble of Regression Trees (ERT). Beside the effect of matrix polymer content and compression force, the influence of active pharmaceutical ingredient (API) and matrix polymer particle size distribution (PSD) on the drug release rate of sustained tablets is studied. The matrix polymer PSD was found to be a significant factor, thus this factor was included in the dissolution prediction experiments. In order to evaluate the importance of the inclusion of PSD data, models without PSD data were also prepared and the results were compared. In the developed models, the API and hydroxypropyl-methylcellulose (HPMC) content is predicted from near-infrared (NIR) spectra, the compression force is measured by the tablet press and HPMC particle size is measured off-line. The predictions of ANN, SVM and ERT were compared to the measured dissolution profiles of the validation tablets, ANN yielded the most accurate results. In the presented work, data provided by Process Analytical Technology (PAT) sensors is combined with CMAs for the first time to realize the Real-Time Release Testing (RTRT) of tablet dissolution.
在这项工作中，使用光谱测量、过程数据和关键材料属性（CMAs），通过三种机器学习方法（人工神经网络（ANN）、支持向量机（SVM）和回归树集成（ERT））来预测缓释片剂的体外溶解曲线。除了基质聚合物含量和压缩力的影响外，还研究了活性药物成分（API）和基质聚合物粒径分布（PSD）对缓释片剂药物释放速率的影响。发现基质聚合物 PSD 是一个重要因素，因此该因素被纳入溶解预测实验中。为了评估包含 PSD 数据的重要性，还制备了不包含 PSD 数据的模型，并对结果进行了比较。在开发的模型中，从近红外（NIR）光谱预测 API 和羟丙基甲基纤维素（HPMC）的含量，通过压片机测量压缩力，离线测量 HPMC 粒径。ANN、SVM 和 ERT 的预测结果与验证片剂的实测溶解曲线进行了比较，ANN 得到了最准确的结果。在本工作中，首次将过程分析技术（PAT）传感器提供的数据与 CMAs 相结合，实现了片剂溶解的实时释放测试（RTRT）。
