Chen Xiaodong, Sadineni Vikram, Maity Mita, Quan Yong, Enterline Matthew, Mantri Rao V
Drug Product Science and Technology, Bristol-Myers Squibb, 1 Squibb Drive, New Brunswick, NJ, 08903, USA.
Department of Chemical Engineering, University of Delaware, Newark, DE, 19716, USA.
AAPS PharmSciTech. 2015 Dec;16(6):1317-26. doi: 10.1208/s12249-015-0318-9. Epub 2015 Mar 20.
Lyophilization is an approach commonly undertaken to formulate drugs that are unstable to be commercialized as ready to use (RTU) solutions. One of the important aspects of commercializing a lyophilized product is to transfer the process parameters that are developed in lab scale lyophilizer to commercial scale without a loss in product quality. This process is often accomplished by costly engineering runs or through an iterative process at the commercial scale. Here, we are highlighting a combination of computational and experimental approach to predict commercial process parameters for the primary drying phase of lyophilization. Heat and mass transfer coefficients are determined experimentally either by manometric temperature measurement (MTM) or sublimation tests and used as inputs for the finite element model (FEM)-based software called PASSAGE, which computes various primary drying parameters such as primary drying time and product temperature. The heat and mass transfer coefficients will vary at different lyophilization scales; hence, we present an approach to use appropriate factors while scaling-up from lab scale to commercial scale. As a result, one can predict commercial scale primary drying time based on these parameters. Additionally, the model-based approach presented in this study provides a process to monitor pharmaceutical product robustness and accidental process deviations during Lyophilization to support commercial supply chain continuity. The approach presented here provides a robust lyophilization scale-up strategy; and because of the simple and minimalistic approach, it will also be less capital intensive path with minimal use of expensive drug substance/active material.
冻干是一种常用于制备不稳定药物的方法，这些药物无法以即用型（RTU）溶液的形式商业化。冻干产品商业化的一个重要方面是将实验室规模冻干机中开发的工艺参数转移到商业规模，同时不损失产品质量。这个过程通常通过成本高昂的工程运行或在商业规模上的迭代过程来完成。在此，我们重点介绍一种计算与实验相结合的方法，用于预测冻干一次干燥阶段的商业工艺参数。通过压力温度测量（MTM）或升华试验实验确定传热系数和传质系数，并将其用作基于有限元模型（FEM）的名为PASSAGE的软件的输入，该软件可计算各种一次干燥参数，如一次干燥时间和产品温度。传热系数和传质系数在不同的冻干规模下会有所不同；因此，我们提出一种在从实验室规模扩大到商业规模时使用适当因子的方法。这样一来，就可以根据这些参数预测商业规模的一次干燥时间。此外，本研究中提出的基于模型的方法提供了一种在冻干过程中监测药品稳健性和意外工艺偏差的过程，以支持商业供应链的连续性。这里提出的方法提供了一种稳健的冻干放大策略；并且由于该方法简单且简约，它也是资本密集度较低的途径，对昂贵药物/活性材料的使用最少。
