Ochsenbein David R, Billups Matthew, Hong Bingbing, Schäfer Elisabeth, Marchut Alexander J, Lyngberg Olav K
Janssen-Cilag AG, Pharmaceutical Companies of Johnson & Johnson, Switzerland.
Janssen Supply Group, LLC, Pharmaceutical Companies of Johnson & Johnson, United States.
Int J Pharm X. 2019 Aug 12;1:100028. doi: 10.1016/j.ijpx.2019.100028. eCollection 2019 Dec.
This work demonstrates the application of state-of-the-art modeling techniques in pharmaceutical manufacturing for fluid bed granulation at varying scales to successfully predict process conditions and ultimately replace experiments during a technology transfer of five products. We describe a mathematical model able to simulate the time-dependent moisture profile in a fluid bed granulation process. The applicability of this model is then demonstrated by calibrating and validating it over a range of operating conditions, manufacturing scales, and formulations. The inherent capability of the moisture profile to serve as a simple, scale-independent surrogate is shown by the large number of successful scale-ups and transfers. A methodology to use this 'digital twin' to systematically explore the effects of uncertainty inherent in the process input and model parameter spaces and their impact on the process outputs is described. Two case studies exemplifying the utilization of the model in industrial practice to assess process robustness are provided. Lastly, a pathway to leverage model results to establish proven acceptable ranges for individual parameters is outlined.
这项工作展示了先进建模技术在制药生产中不同规模流化床制粒的应用，以成功预测工艺条件，并最终在五种产品的技术转移过程中取代实验。我们描述了一个能够模拟流化床制粒过程中随时间变化的水分分布的数学模型。然后，通过在一系列操作条件、生产规模和配方上对该模型进行校准和验证，证明了其适用性。大量成功的放大和转移表明，水分分布作为一个简单的、与规模无关的替代指标具有内在能力。本文描述了一种使用这种“数字孪生”来系统探索过程输入和模型参数空间中固有不确定性及其对过程输出影响的方法。提供了两个案例研究，例证了该模型在工业实践中用于评估过程稳健性的应用。最后，概述了利用模型结果为各个参数建立经证实的可接受范围的途径。
