Bayer AG, Chemical and Pharmaceutical Development, Wuppertal, 42117, Germany.
Bayer AG, Engineering and Technology, Leverkusen, 51368, Germany.
Int J Pharm. 2023 Jul 25;642:123109. doi: 10.1016/j.ijpharm.2023.123109. Epub 2023 Jun 7.
Achieving an even coating distribution on tablets during the coating process can be challenging, not to mention the challenges of accurately measuring and quantifying inter-tablet coating variability. Computer simulations using the Discrete Element Method (DEM) provide a viable pathway towards model-predictive design of coating processes. The purpose of this study was to assess their predictivity accounting for both experimental and simulation input uncertainties. To this end, a comprehensive set of coating experiments covering various process scales, process conditions and tablet shapes were conducted. A water-soluble formulation was developed to enable rapid spectroscopic UV/VIS analysis of coating amounts on a large number of tablets. DEM predictions are found to lie within the experimentally inferred confidence intervals in all cases. A mean absolute comparison error of 0.54 % was found between model predictions of coating variability and respective sample point estimates. Among all simulation inputs the parameterization of spray area sizes is considered the most significant source for prediction errors. However, this error was found significantly smaller in magnitude compared to experimental uncertainties at larger process scales underlining the value of DEM in the design of industrial coating processes.
在包衣过程中实现片剂表面均匀的涂层分布具有挑战性，更不用说准确测量和量化片剂间涂层变化的挑战了。使用离散元法 (DEM) 的计算机模拟为模型预测性设计包衣过程提供了可行的途径。本研究的目的是评估它们的预测能力，同时考虑实验和模拟输入的不确定性。为此，进行了一系列全面的包衣实验，涵盖了各种工艺规模、工艺条件和片剂形状。开发了一种水溶性制剂，以实现对大量片剂上涂层量的快速光谱紫外/可见分析。在所有情况下，DEM 预测都落在实验推断的置信区间内。模型预测的涂层变化与相应样本点估计之间的平均绝对比较误差为 0.54%。在所有模拟输入中，喷雾面积大小的参数化被认为是预测误差的最主要来源。然而，在较大的工艺规模下，与实验不确定性相比，该误差的幅度明显较小，这突显了 DEM 在工业包衣过程设计中的价值。
