Global Sensitivity Analysis as Good Modelling Practices tool for the identification of the most influential process...

Pharmaceutical batch freeze-drying is commonly used to improve the stability of biological therapeutics. The primary drying step is regulated by the dynamic settings of the adaptable process variables, shelf temperature T_s and chamber pressure P_c. Mechanistic modelling of the primary drying step leads to the optimal dynamic combination of these adaptable process variables in function of time. According to Good Modelling Practices, a Global Sensitivity Analysis (GSA) is essential for appropriate model building. In this study, both a regression-based and variance-based GSA were conducted on a validated mechanistic primary drying model to estimate the impact of several model input parameters on two output variables, the product temperature at the sublimation front T_i and the sublimation rate ṁ_sub. T_s was identified as most influential parameter on both T_i and ṁ_sub, followed by P_c and the dried product mass transfer resistance α_Rp for T_i and ṁ_sub, respectively. The GSA findings were experimentally validated for ṁ_sub via a Design of Experiments (DoE) approach. The results indicated that GSA is a very useful tool for the evaluation of the impact of different process variables on the model outcome, leading to essential process knowledge, without the need for time-consuming experiments (e.g., DoE).