Mortier Séverine Thérèse F C, Van Bockstal Pieter-Jan, Corver Jos, Nopens Ingmar, Gernaey Krist V, De Beer Thomas
BIOMATH, Department of Mathematical Modelling, Statistics and Bioinformatics, Faculty of Bioscience Engineering, Ghent University, Coupure Links 653, 9000 Ghent, Belgium; Laboratory of Pharmaceutical Process Analytical Technology (LPPAT), Department of Pharmaceutical Analysis, Faculty of Pharmaceutical Sciences, Ghent University, Ottergemsesteenweg 460, 9000 Ghent, Belgium.
Laboratory of Pharmaceutical Process Analytical Technology (LPPAT), Department of Pharmaceutical Analysis, Faculty of Pharmaceutical Sciences, Ghent University, Ottergemsesteenweg 460, 9000 Ghent, Belgium.
Eur J Pharm Biopharm. 2016 Jun;103:71-83. doi: 10.1016/j.ejpb.2016.03.015. Epub 2016 Mar 15.
Large molecules, such as biopharmaceuticals, are considered the key driver of growth for the pharmaceutical industry. Freeze-drying is the preferred way to stabilise these products when needed. However, it is an expensive, inefficient, time- and energy-consuming process. During freeze-drying, there are only two main process variables to be set, i.e. the shelf temperature and the chamber pressure, however preferably in a dynamic way. This manuscript focuses on the essential use of uncertainty analysis for the determination and experimental verification of the dynamic primary drying Design Space for pharmaceutical freeze-drying. Traditionally, the chamber pressure and shelf temperature are kept constant during primary drying, leading to less optimal process conditions. In this paper it is demonstrated how a mechanistic model of the primary drying step gives the opportunity to determine the optimal dynamic values for both process variables during processing, resulting in a dynamic Design Space with a well-known risk of failure. This allows running the primary drying process step as time efficient as possible, hereby guaranteeing that the temperature at the sublimation front does not exceed the collapse temperature. The Design Space is the multidimensional combination and interaction of input variables and process parameters leading to the expected product specifications with a controlled (i.e., high) probability. Therefore, inclusion of parameter uncertainty is an essential part in the definition of the Design Space, although it is often neglected. To quantitatively assess the inherent uncertainty on the parameters of the mechanistic model, an uncertainty analysis was performed to establish the borders of the dynamic Design Space, i.e. a time-varying shelf temperature and chamber pressure, associated with a specific risk of failure. A risk of failure acceptance level of 0.01%, i.e. a 'zero-failure' situation, results in an increased primary drying process time compared to the deterministic dynamic Design Space; however, the risk of failure is under control. Experimental verification revealed that only a risk of failure acceptance level of 0.01% yielded a guaranteed zero-defect quality end-product. The computed process settings with a risk of failure acceptance level of 0.01% resulted in a decrease of more than half of the primary drying time in comparison with a regular, conservative cycle with fixed settings.
大分子,如生物制药,被认为是制药行业增长的关键驱动力。在需要时,冷冻干燥是稳定这些产品的首选方法。然而,这是一个昂贵、低效、耗时且耗能的过程。在冷冻干燥过程中,只需设置两个主要过程变量,即搁板温度和腔室压力,不过最好采用动态方式。本手稿重点探讨不确定性分析在确定和实验验证制药冷冻干燥动态主干燥设计空间中的重要应用。传统上,在主干燥过程中腔室压力和搁板温度保持恒定,导致工艺条件不够优化。本文展示了主干燥步骤的机理模型如何提供机会来确定加工过程中两个过程变量的最佳动态值,从而形成具有已知失败风险的动态设计空间。这使得主干燥过程步骤能够尽可能高效地运行,从而保证升华前沿的温度不超过塌陷温度。设计空间是输入变量和过程参数的多维组合与相互作用,以可控(即高)概率导致预期的产品规格。因此,纳入参数不确定性是设计空间定义的重要组成部分,尽管它常常被忽视。为了定量评估机理模型参数的固有不确定性,进行了不确定性分析以确定动态设计空间的边界,即与特定失败风险相关的随时间变化的搁板温度和腔室压力。与确定性动态设计空间相比,0.01%的失败风险接受水平(即“零失败”情况)会导致主干燥过程时间增加;然而,失败风险处于可控范围内。实验验证表明,只有0.01%的失败风险接受水平才能保证最终产品质量零缺陷。与具有固定设置的常规保守循环相比,计算得出的0.01%失败风险接受水平的工艺设置使主干燥时间减少了一半以上。