Model-Based Optimisation and Control Strategy for the Primary Drying Phase of a Lyophilisation Process

Abstract

The standard operation of a batch freeze-dryer is protocol driven. All freeze-drying phases (i.e., freezing, primary and secondary drying) are programmed sequentially at fixed time points and within each phase critical process parameters (CPPs) are typically kept constant or linearly interpolated between two setpoints. This way of operating batch freeze-dryers is shown to be time consuming and inefficient. A model-based optimisation and real-time control strategy that includes model output uncertainty could help in accelerating the primary drying phase while controlling the risk of failure of the critical quality attributes (CQAs). In each iteration of the real-time control strategy, a design space is computed to select an optimal set of CPPs. The aim of the control strategy is to avoid product structure loss, which occurs when the sublimation interface temperature ( T i ) exceeds the the collapse temperature ( T c ) common during unexpected disturbances, while preventing the choked flow conditions leading to a loss of pressure control. The proposed methodology was experimentally verified when the chamber pressure and shelf fluid system were intentionally subjected to moderate process disturbances. Moreover, the end of the primary drying phase was predicted using both uncertainty analysis and a comparative pressure measurement technique. Both the prediction of T i and end of primary drying were in agreement with the experimental data. Hence, it was confirmed that the proposed real-time control strategy is capable of mitigating the effect of moderate disturbances during batch freeze-drying.

Economic Analysis of a Freeze-Drying Cycle

“…Moreover, even though economic evaluations have been presented in the past [9][10][11], they are often too broad or too specific to be used for making comparisons between different process strategies. Whereas little effort has been made in this field to determine the economic impact of freeze-drying quantitatively, the optimization of the process has usually remained product-or quality-specific [12][13][14][15][16][17][18], with no considerations of possible bottlenecks in the expenses. In this work, we aim to create a model to evaluate the actual economic impact of a freeze-drying cycle, and evaluate the impact of process parameters on processing costs and, finally, the benefits of process optimization.…”

Model-based optimization of the primary drying phase of oral lyophilizates

“…(13) describes the temperature difference over the frozen product layer. This system of equations is already thoroughly described in literature ( De Meyer et al, 2017 ; Leys et al, 2020 ; Mortier et al, 2015 ; Vanbillemont et al, 2020c ). …”

“…For secondary drying, a 1 °C/min ramp towards 25 °C was implemented and maintained for 1 h. The primary drying phase was monitored by inserting a type-K thermocouple in an edge and center population blister and the chamber pressure by Pirani guage ( P c , Pir [Pa]) was also recorded. The endpoint of primary drying was evaluated by the differential pressure measurement of both pressure gauges ( P ratio [−]) ( Vanbillemont et al, 2020c ). All parameters values are listed in Table 2 .…”

“…The conversion of the mechanistic description of a vial system towards more exotic blister forms is in-scope. Dynamic optimization of the primary drying phase in a freeze-drying process is shown to significantly reduce the processing times while keeping a qualitative product ( De Meyer et al, 2019 ; Fissore et al, 2011 ; Vanbillemont et al, 2020c ). When the critical process parameters (CPPs), i.e.…”

Working within the Design Space: Do Our Static Process Characterization Methods Suffice?

“…Most scenarios are limited to individual process steps and do not specify or prove strict stability criteria for given controller implementations. Reinforcement control algorithms with guaranteed stability conditions and MPC concepts under uncertainties must, therefore, be transferred and validated to the requirements in the field of pharmaceutical process engineering [32,43,44]. The system properties also play an essential role in the success of process control and monitoring in the context of dynamic design space.…”