Research Center Pharmaceutical Engineering GmbH, Graz, Austria.
Research Center Pharmaceutical Engineering GmbH, Graz, Austria.
Int J Pharm. 2024 Dec 5;666:124796. doi: 10.1016/j.ijpharm.2024.124796. Epub 2024 Oct 2.
In this work, a high-fidelity digital twin was developed to support the design and testing of control strategies for drug product manufacturing via direct compression. The high-fidelity digital twin platform was based on typical pharmaceutical equipment, materials, and direct compression continuous processes. The paper describes in detail the material characterization, the Discrete Element Method (DEM) model and the DEM model parameter calibration approach and provides a comparison of the system's response to the experimental results for stepwise changes in the API concentration at the mixer inlet. A calibration method for a cohesive DEM contact model parameter estimation was introduced. To assure a correct prediction for a wide range of processes, the calibration approach contained four characterization experiments using different stress states and different measurement principles, namely the bulk density test, compression with elastic recovery, the shear cell, and the rotating drum. To demonstrate the sensitivity of the DEM contact parameters to the process response, two powder characterization data sets with different powder flowability were applied. The results showed that the calibration method could differentiate between the different material batches of the same blend and that small-scale material characterization tests could be used to predict the residence time distribution in a continuous manufacturing process.
本工作开发了一个高保真数字孪生体,以支持通过直接压缩进行药物产品制造的控制策略的设计和测试。高保真数字孪生体平台基于典型的制药设备、材料和直接压缩连续工艺。本文详细描述了材料特性、离散元法 (DEM) 模型和 DEM 模型参数校准方法,并提供了系统对混合器入口处 API 浓度逐步变化的实验结果的响应的比较。介绍了一种用于粘性 DEM 接触模型参数估计的校准方法。为了确保在广泛的工艺范围内进行正确的预测,校准方法包含了四个使用不同应力状态和不同测量原理的特性化实验,即堆积密度测试、带弹性恢复的压缩、剪切室和旋转鼓。为了演示 DEM 接触参数对工艺响应的敏感性,使用了两个具有不同可流动性的粉末特性化数据集。结果表明,该校准方法可以区分同一混合物的不同批次的材料,并且可以使用小规模的材料特性化测试来预测连续制造过程中的停留时间分布。